REVIEW OF DATA AND ANALYTICAL INITIATIVES ON … Text_Updated Nov 28... · ANALYTICAL INITIATIVES ON AGRICULTURAL PUBLIC EXPENDITURES An IFPRI/PIM-sponsored study, in close collaboration

November 28, 2013

REVIEW OF DATA AND

ANALYTICAL INITIATIVES

ON AGRICULTURAL PUBLIC

EXPENDITURES

An IFPRI/PIM-sponsored study, in close collaboration

with: FAO, IDB, IMF, OECD, World Bank

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P U B L I C E X P E N D I T U R E S

Table of Contents 1

ACKNOWLEDGEMENTS ................................................................................................................ iv

ACRONYMS AND ABBREVIATIONS ............................................................................................ v

EXECUTIVE SUMMARY ................................................................................................................. vi

1. INTRODUCTION ................................................................................................................................ 1

1.1 Context and Rationale for Review ................................................................................................ 1

1.2 Objectives, Scope/Criteria, and Approach .................................................................................... 3

2. OVERVIEW OF DATA AND ANALYTICAL INITIATIVES.......................................................... 4

2.1 Summary of Selected Past and Emerging Initiatives .................................................................... 4

2.2 List and Typology of On-going Initiatives Covered ..................................................................... 7

2.3 Summary Descriptive Features (See Annex A for further details) ............................................. 10

3. ASSESSMENT AND COMPARISON OF THE INITIATIVES (see Annex B for details) ............ 25

3.1 Assessment Criteria and Approach ............................................................................................. 25

3.2 Objectives and Unique Features/Value-added ............................................................................ 25

3.3 Scope of Coverage ...................................................................................................................... 29

3.4 Key Methodological Aspects ...................................................................................................... 32

3.5 Public Accessibility Aspects ....................................................................................................... 39

3.6 Strategies/Mechanisms to Link Data Users with Suppliers ........................................................ 41

3.7 Main Issues and Challenges ....................................................................................................... 43

3.8 Sustainability Aspects ................................................................................................................. 46

3.9 Linkages to and Collaboration with Other Data and Analytical Initiatives ................................ 48

3.10 Demand/User Perspectives on Database and Analytical Challenges and Strategies .................. 51

4. CONCLUSIONS AND STRATEGIC OPTIONS ............................................................................. 54

4.1 Main Conclusions ....................................................................................................................... 54

4.2 Strategic Options ......................................................................................................................... 61

BIBLIOGRAPHY ............................................................................................................................ 688

APPENDIX ...................................................................................................................................... 711

1 The report has been prepared by the following IFPRI Consultant study team: Dr. Richard Anson (Principal

Investigator); Tsegaye Assayew (Research Analyst); and Dr. Eduardo Zegarra (Research Analyst). Dr. Tewodaj

Mogues, IFPRI Research Fellow, is the Project Manager, and provided substantive guidance and inputs at all stages

of the work. The acknowledgment page cites the valuable inputs and feedback on an earlier draft from the focal

teams/persons for the data and analytical initiatives covered. The authors are responsible for errors and omissions.

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List of Figures:

Figure 1: Framework of Data and Analytical Initiatives for Supporting Enhanced Agricultural

Expenditure Analyses, Budgetary Cycle, and Outcome and Impacts ............................................ 9

Figure 2: Initiatives within a Typology .......................................................................................... 9 Figure 3: Number of countries covered by each initiatives under Typology A. ........................... 30 Figure 4: Comparability of Agriculture Expenditure Statistics Across four Databases. ............ 344 Figure 5: Trend of Variation in Agriculture Expenditure Statistics across the Four Data

Initiatives....................................................................................................................................... 35

Figure 6: BOOST: Linkages between Organizational Location, Economic Expenditure Type

and functions. .............................................................................................................................. 388 Figure 7: BOOST: Example of How BOOST Can Help Compare Sectoral Budgetary Allocation

Changes (in Good and in the Bad Times – Example of Bulgaria). ............................................... 38

Figure 8: Strategic Options: Strengthening “Backward and Forward Linkages” ....................... 611

List of Tables:

Table 2. 1: List of Key Databases and Analytical Initiatives ........................................................ 7

Table 2. 2: Typology of Data and Analytical Initiatives on Agriculture Public Expenditures ....... 8

Table 3. 1: Table Used as Questionnaire for Country Level Ag. Expenditure Data: Mauritius) . 33

Table 3. 2: Proportion of GDP from basket used for MPS estimations in PSE/LAC initiative. .. 37 No table of figures entries found.

ANNEXES (AS SEPARATE FILES)

A) Tables with Key Descriptive and Assessment Information for each Data and Analytical

Initiative

B) Tables with Summary Comparisons of Data and Analytical Initiatives by Theme and Type

C) Summary Excerpts from Relevant Data Documentation Files

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ACKNOWLEDGEMENTS

The preparation of this final report benefitted immensely by the collaboration and contributions

from focal persons/teams for each of the organizations which are carrying out data and analytical

initiatives on agricultural public expenditures in developing countries, and which were reviewed in this

exercise. Their inputs to and comments/suggestions on earlier drafts are greatly appreciated. The study

team takes responsibility for any errors and/or omissions.

CABRI (Collaborative Africa Budget Reform Initiative):

Nana Boateng and Anke Braumann

CEPAL:

Jose Arroyo and Diana Ramirez

FAO:

Statistics Division (ESS) FAOSTAT: Sangita Dubey, Carlota Fabi, and Erdgin Mane;

SOFA/Agricultural Development Economics Division (ESA) FAO Studies:

State of Food and Agriculture team: Sarah Lowder, Jacob Skoet, & Keith Wiebe (also Deputy Director);

MAFAP: Jean Baliée, Christian Derlagen, and Alban MasAparisi;

IFPRI:

Overall Strategic Aspects: Karen Brooks and Xinshen Diao

ASTI: Nienke Beintema

SPEED: Bingxin Yu

ReSAKSS: Sam Benin and Godfrey Bahiigwa

IMF:

Sage de Clerck

Gary Jones

InterAmerican Development Bank:

Overall Strategic Aspects: Hector Malarin

PSE for LAC:

Paul Trapido

Carmen Fernandez

OECD:

TAD - PSEs and Other Indicators:

Jonathan Brooks

Carmel Cahill

Dalila Cervantes-Godoy

Joanna Ilici-Komorowska

Andrzej Kwiecinski

CRS: Yasmin Ahmad

Overall Strategic Aspects on ODA: Bill Nicol

World Bank:

BOOST: Leif Jensen

RePEAA: Yurie Tanimichi-Hoberg

SNAPE for SSA: Stephen Mink

WDIs: Sup Lee

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ACRONYMS AND ABBREVIATIONS

AGPE Agriculture Public Expenditure

AGPEA Agricultural Public Expenditure Analysis

APCAS Asia and Pacific Commission on Agricultural Statistics

API Application Programming Interface

ASTI Agricultural Science and Technology indicators

CAADP Comprehensive Africa Agricultural Development Programme

CABRI Collaborative Africa Budget Reform Initiative

CEPAL Comision Economica para America Latina (Economic Commission for Latin America)

COFOG Classification of the Functions of Government

CRS Creditor Reporting System

DAC (OECD’s) Development Assistance Committee

DP Development Partner

EDDI Enhanced Data Dissemination Initiative

FAO Food and Agricultural Organization of the United Nations

FAOSTAT FAO Statistics Database

GEA Government Expenditures for Agriculture (component of FAOSTAT)

GFS Government Finance Statistics

IDB Inter-American Development Bank

IFPRI International Food Policy Research Institute

LAC Latin American Countries

MAFAP Monitoring African Food and Agricultural Policies

OECD Organization for Economic Co-operation and Development

OEE OECD and Emerging Economies

OOF Other Official Flows

ODA Official Development Assistance

PIM Policies, Institutions and Markets

PSE Producer Support Estimate

SNA System of National Accounts

ReSAKSS Regional Strategic Analysis and Knowledge Support System

RUTA Rural Unit for Technical Assistance

SNA System of National Accounts

SPEED Statistics of Public Expenditure for Economic Development

SSA SubSahara Africa

STA (IMF’s) Statistics Department

WB World Bank

WDI World Development Indicator

WP-STAT Working Party on Development Finance Statistics

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EXECUTIVE SUMMARY

CONTEXT

One of the most important instruments developing and transition countries possess to achieve the

transformation of their economies through meeting key development outcomes and impacts, especially

where the agricultural sector plays a key role, is efficient and effective agricultural public expenditures,

coupled with a conducive policy environment and dynamic private investments. However, developing

countries face considerable challenges in possessing sound and adequate databases and management

information systems, including robust field data. This would enable appropriate types of evidenced-based

agricultural public expenditure analysis which could facilitate expenditure priority setting and allocations.

Recently (June 2013), the OECD/IFPRI organized a workshop on “Shared Approaches to

Measuring the Agricultural Policy Environment”. The workshop brought together managers, researchers

and practitioners from numerous international development agencies to discuss specific aspects of the

measurement and related database issues and opportunities for greater collaboration. One of the topics

which were discussed involved finding practical ways to address many of the challenges involving

agricultural public expenditure data and related analytical initiatives which could contribute to enhanced

measurement of the agricultural policy environment. In the context of the agricultural public

expenditures part of the workshop, it was recognized that it may require several exercises/steps toward the

creation and maintenance of a community of practice of analysts located both in developing and

developed countries who are concerned with accurately and systematically measuring the quantity and

quality of public spending for agriculture. Accordingly, the present review focuses on one aspect of this

broader process and which is centered on agricultural public expenditures --- while steps would be taken

to address other key components of the agricultural policy measurement agenda.

FRAMEWORK FOR REVIEW

Objectives, Scope/Criteria, and Approach

Considering the above context, the present study aims to review relevant data collection and

analytical initiatives/activities that are focused on, or are inclusive of, agricultural public expenditure data

and studies on developing and transition countries. In addition to stocktaking of such initiatives, this

review endeavors to carryout a comparison of relevant features and identifies differences and similarities,

identify possible avenues for greater collaboration and complementarity, including the use of selected

empirical examples arising from the comparative review. The outcome of this review is to contribute to

the improved coordination and sharing of data on public resource allocations to agriculture in developing

countries, and to the improved communication and exchange on measurement approaches and

methodologies in compiling such data and conducting analytical studies which could contribute to

enhanced budgetary outcomes and impacts of agricultural public expenditures.

The review will be comprised of 3 major components:

a summary of key features on each initiative;

a comparison of the initiatives according to major types, covering strategic aspects, and giving

special attention to the methodological features; and

the main conclusions and recommended strategic options, especially which can promote

enhanced inter-agency collaboration in working together to address relevant challenges.

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One or both of the following main criteria, among others, are used to identify the data and

analytic initiatives to be reviewed:

ongoing “major” databases involving agricultural public expenditures (AgPEs) in at least ten

developing countries, and which include diversity in terms of sponsoring organizations and

regional coverage, and a major global database which has the potential for including AgPE data;

on-going programs/projects which are supporting analytical studies and tools, and which also are

devoting attention to addressing data-related issues and strategies which can enhance the role of

AgPE analysis to better support evidenced-based decisions on expenditure priorities, allocations,

and management; and,

Based on the above criteria, fourteen on-going data and analytical initiatives/programs have been

identified for this review:

1) AGPE for LAC: Agricultural Public Expenditures for LAC

2) ASTI: Agricultural Science and Technology Indicators

3) BOOST: Making Expenditure Data Available for Analysis

4) CRS: Creditor Reporting System of ODA Resource Flows

5) FAOSTAT: Investment dataset (GEA & ODA)

6) GFS: Government Financial Statistics

7) MAFAP: Monitoring African Food and Agricultural Policies

8) PSE-OEE: Producer Support Estimates/PSE and Related Indicators for Agricultural Support (for

OECD & Emerging Economies)

9) PSE-LAC: Producer Support Estimates for Latin America & Caribbean/LAC

10) RePEAA: Resources for Public Expenditure Analysis in Agriculture

11) ReSAKSS: Regional Strategic Analysis and Knowledge Support System for Sub-Sahara Africa

(SSA)

12) SPEED: Statistics of Public Expenditure for Economic Development

13) SNAPE: Strengthening National Agricultural Public Expenditures SSA

14) WDIs: World Development Indicators

The study team worked out a detailed road map of activities, giving emphasis to engaging the relevant

focal teams/persons for each of the fourteen initiatives. Each of them was interviewed (most of them in

person), to compile key information and deeper discussion of key achievements, emerging challenges and

next steps for their enhancement.2 All of them showed keen interest and provided valuable inputs and

advice throughout the exercise.

Based on the main common orientations and features of the above initiatives, the study team derived

a typology of data and analytical initiatives (Types A – E) which was used to guide the comparative

review of diverse initiatives. Figure1 illustrates the inter-relationship between the various data and

analytical initiatives, and how they can enhance the budgetary cycle and contribute to enhanced budgetary

outcomes and impacts for the agriculture sector, if effectively managed and coordinated at the country

level.

2 Other past and emerging data and analytical initiatives are also reviewed for inputs to deriving both lessons

learned and opportunities for enhanced inter-agency collaboration, although with less or no detailed coverage in this

document, given their nature of existence.

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Figure1: Framework of Data and Analytical Initiatives for Supporting Enhanced Agricultural

Expenditure Analyses, Budgetary Cycle, and Outcome and Impacts

MAIN CONCLUSIONS

The conclusions are synthesized according to six strategic crosscutting areas, covering the following

points (with further details in Chapters 3 and 4):

(1) Main Features and Emerging Patterns:

(a) Variation and Rationale of Objectives: the fourteen data and analytical initiatives

comprise five different types and reflect a wide variety of objectives, unique origins, and

diverse users.

(b) Variation in Scope and Disaggregation: The database initiatives witnessed differences

in the scope in terms of sectors covered, level of disaggregation, frequency of data and

their updated databases and analytical studies, and countries, regions and years covered.

Data gaps, especially the limited disaggregation of AGPE data according the main

functions, pose considerable constraints to analysts and policy-makers (who represent

the “demand”) in terms of not providing adequate information for better budgetary

allocations and accountability;

(c) Geographical Coverage and Focus: While most of the initiatives have a global

coverage, there are two initiatives which focus on SSA; there are two initiatives which

give special focus on the LAC countries. Other regions seems be somewhat neglected,

aside from the global data initiatives;

(d) Methodological Aspects: The initiatives exhibit a variety of important methodological

differences, and to a lesser extent, similarities, including:

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narrow and wider definitions of the “agricultural sector”, which has important

implications for data compilation, comparisons and interventions (e.g., MAFAP

taking a wider definition of the agricultural sector;

the PSE expenditure classification system considers support to non-agricultural

sectors which can impact agricultural development, while measuring support to

producers or to various categories of “agricultural” more generally;

the PSE methodology was pioneered by the OECD, used by PSE for LAC and

by MAFAP. The LAC PSE database developed by IDB follows the OECD PSE

Manual for preparation and calculation of the PSE’s to insure compatibility with

OECD country PSE estimates while MAFAP has made relevant adaptations of

the PSE methodology to the African context (further discussed below);

various AgPE analytical initiatives have developed different methodological

toolkits, although there is high level of convergence of key concepts and tools.

There seems to be growing exchange and communications among focal persons

of these initiatives to enhance harmonization of key concepts and tools,

especially where it involves many of the same stakeholders (e.g., SNAPE and

MAFAP coordinating their AGPE-supported studies in SSA, and collaboration

to address common AGPE data constraints);

different instruments and approaches to compile standardized AgPE and PSE

data. The PSEs for OEE compile country level data, to comply with a well-

defined methodology. ReSAKSS compiles AgPE data from existing sources,

while filling key gaps at the country level; GFS and FAOSTAT-GEA use

questionnaires for nearly 200 countries, and face challenges to fill gaps and

limited level of disaggregation; there are legitimate concerns on the reliability of

the underlying data, which is another dimension which needs to be addressed;

most of the initiatives have prepared user documentation and guide manuals and

standards, although the newer initiatives are still preparing the complete

methodological documentation (e.g., PSE for LAC; AGPE for LAC);

(e) Public Accessibility: These two aspects are closely inter-related, where greater public

accessibility tends to encourage expanded use, although there are other factors

influencing usage. Most of the initiatives are publicly available, although some of them

have limited public accessibility for various reasons, and which is another issue to be

addressed (generally at the country level, and using benchmarking as an incentive for

countries to provide public accessibility to public expenditure data);

(f) Linkages between Users and Suppliers: Generally all of the initiatives show a clear

awareness and varied strategies to promote stronger usage by its target stakeholders,

although there appear to be varied levels of effort and effectiveness.

(2) Innovative Aspects and Improvements: Some of the initiatives demonstrate innovative

features, especially in terms of methodological and dissemination aspects which can provide

positive lessons for other initiatives.

(3) Complementarities and Synergies Between Initiatives: Several initiatives demonstrate that

there are emerging complementarities and synergies of varying degrees between data and

analytical initiatives which suggest the potential for being further stimulated.

(a) Figure1 illustrates the important complementarities between the different types of data

and analytical initiatives, which if well-coordinated, managed and integrated in the

budgetary cycle of developing countries, offer the potential for enhanced budgetary

outcomes and impacts from the agricultural public expenditures (coupled with other

appropriate policy reforms);

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(b) All of the initiatives which were reviewed are both users and/or suppliers of agricultural

expenditure data, and to a lesser extent, of the PSE indicators (at least for the countries

being covered). The study team was able to identify variable levels of collaboration and

inter-dependence across databases and analytical studies in terms of sharing resources,

data collection methodologies, and dissemination strategies.

(4) Emerging Challenges: The review of the initiatives have highlighted a wide range of

challenges, especially for each of them to achieve their stated institutional or program

objectives. From an analytical and policy maker perspective, the high level of aggregation of

AgPE data poses a serious constraint to assess AgPE allocation issues, given that different

spending has variable effects on agricultural performance. The report highlights some of the

more important common challenges, whose severity varies according to country. These

challenges adversely affects the ability of analysts to carry out sound AGPE studies,

calculation of robust PSEs, and also to conduct cross-country comparisons, as inputs for

better budgetary allocation priority-setting and decisions.

(5) Demand Aspects and Some Implications: An important aspect of addressing the underlying

incentive issues highlighted above which constrains the continued persistence of inadequate

disaggregation and full use of existing AgPE data is connected to better understanding the

demand aspects for enhanced data and analytical programs to support enhanced AgPE

analysis. There seems to be greater attention on the “supply” side, and insufficient attention

to addressing the demand aspects (especially by policy makers and directors of budget at the

country level). The review endeavors to unpack and highlight some these demand aspects

which arose during the course of conducting this review, reflecting perspectives and inputs

from various actors. There are three aspects which were reviewed:

(a) Response to Demand from Key Actors

(b) Expenditure Information/Reporting Systems

(c) Internalization of the Demand Aspects

(6) Nature and Extent of Intra and Inter-Agency Collaboration: This review identified the general

nature and type of intra and inter-agency collaboration in carrying out the fourteen data and

analytical initiatives. Overall, the focal teams from the various initiatives demonstrated a

variety of positive actions to promote enhanced collaboration, within their own organization

and increasingly with other relevant agencies. Notwithstanding these positive actions, the

report highlight potential scope for enhancing the effectiveness and results of more strategic

and systematic inter-agency collaboration.

STRATEGIC OPTIONS

Based on the results from the review and the above crosscutting conclusions, this review has

identified six strategic options which offer the potential for addressing some of the more critical

constraints. These strategic options are intended to better inform and facilitate discussion and consensus

among target audiences on the most appropriate options to pursue. This target audience includes the

practitioners who participated in the Agricultural Policy Measurement workshop held in June 2013, and

other practitioners who have participated in this review exercise and expressed keen interest get engaged

in a broader and deeper discussion with other practitioners and decision-makers. The overarching strategy

warrants an integrated and sequenced approach to reaching consensus on the main strategy elements and

supporting action plan(s) which can address the main issues highlighted in this report.

The proposed six strategy options comprise a suggested framework (covering both supply and

demand aspects) which could contribute to enhancing the role and effectiveness of data and analytical

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initiatives for achieving enhanced budgetary outcomes and impacts of existing and increased AgPEs. The

substantive details and rationale which underpin these strategic elements are highlighted Chapter 3 and

the above section on conclusions.

1) Strategic Options for Enhanced AgPE Databases

There are eight AgPE databases which are being implemented. While the focal teams of each of

them are carrying sound improvements to better meet their institutional objectives, it could be useful for

each team to consider the following aspects, customized to each data initiative:

a) review the COFOG guidelines with respect to the agriculture sector, including a review and updated

definition of the “agricultural sector”, which can serve as a clear international standard for developing

countries;

b) explore the benefits of an enhanced integrated global AgPE database, which will seek to generate

more systematically AgPE data disaggregated beyond level 3 of COFOG, in a phased manner, with

global coverage and annual updating; at the same time, there is a need to consider the reliability and

credibility issues of the underlying expenditure data, and to encourage governments to take appropriate

steps to ensure reliability and accessibility;

c) encourage the relevant databases to update as needed their methodological user manuals which should

be made accessible to the public to encourage and greater and effective use of the AGPE database;

d) One possible way to curve the consequences of data inconsistency brought about by the difference in

methodology, in particular, is to put in place a more transparent and flexible system in order to better

track public resources allocation enabling customized and consistent aggregation, promote open data

access and provide information in a timely manner;

e) Enhance the public accessibility to the AGPE databases through ensuring user-friendly and efficient

websites and tools, and ensuring easy subscription and free of charge.

2) Enhanced Analytical Programs to be Driven by Ministries of Finance and Greater Focus on

Expenditure Efficiencies and Outcomes (for AgPEs and PSE and other Indicators).

a) Most of the AGPE analytical studies are funded by an external project. While the analytical initiatives

have sought to “involve” Ministries of Finance, it would appear that different approaches are needed to

secure their stronger ownership, toward the aim of getting them to institutionalize AgPEs (and a “liter”

version) as a requirement for enhancing the budgetary process;

b) The program of AgPEs funded and supported by SNAPE is coming to a close by the end of June,

2014. There was a recent workshop which shared emerging conclusions and good practices which can

provide an important inputs for a possible follow-up phase;

c) MAFAP has been successfully completed, and there are steps being take to formulate a second and

scaled-up phase, to be launched in mid-2014. It might be useful for the MAFAP preparation team to

convene a team of diverse peer reviewers to provide independent review and constructive feedback during

the formulation and launch phase of MAFAP (in line with their past consultative approach);

d) The stakeholder feedback on the ReSAKSS agenda, including a recently Continental-wide workshop

(Nov., 2013), is providing valuable inputs for enabling ReSAKSS to prioritize its portfolio of activities

involving strengthening the AGPE database for SSA, capacity development at various levels, the

strengthening of its network of regional and country level nodes, and the prioritization of analytical

studies, which would include AgPEs;

e) The Trade and Agriculture team of the OECD has formulated an agenda for enhancing its PSE and

related indicators agenda, which includes an increase in the number of developing countries which will

require assistance to carry out the required country assessment reports;

f) The PSE for LAC initiative is making important progress in completing the scope of PSE estimates for

eventually all LAC countries, with updated data to the latest year possible (2012), based on the OECD

methodology. Continued support is warranted to enable the full rollout of this initiative to the LACs.

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3) Capacity Development Strategies

a) Many of the key issues and challenges highlighted in this report reflect the need to strengthen

institutional and technical capacities at various levels. There is a need to:

Assess the adequacy of the resources provided (in most cases, it has not been sufficient);

for each initiative to revisit the identification of priority and results-focused capacity

development requirements, and to prepare sound proposals to strengthen capacities;

include strategies for strengthening the demand for improved and more disaggregated AgPE data

by key country level actors.

b) There is an urgent need to formulate a strategy and phased plan for improving the Ministry of Finance

information and reporting systems and processes, coupled with stronger country demand for AgPEs as

part of the budgetary process, such that these “drivers” would lead to a stronger internal demand for

disaggregated AGPE data;

c) A complementary strategy could be oriented to researchers and practitioners who may use data more

intensively if proper incentives are present. It would be of high value to have competitive funds for

AGPE analysis using the available data, and seeking for potential interaction among databases and

approaches.

4) Intra-Agency and Inter-Agency Collaboration

All of the above strategic options will require some form of closer and more effective intra and

inter-agency coordination, to seek consensus on many of the proposals (and other proposals not covered

here), to provide technical guidance as a good practice group of AgPE and PSE practitioners, and to help

mobilize additional resources to support the priority interventions which comprise a “global public good”

in AgPE. There are other major themes, such as climate change, which have benefitted from an inter-

agency community of practice to help foster enhanced collaboration and sharing of good practices, and

possible joint initiatives. Accordingly, it is proposed that:

a) each agency covered in this review considers participating in a proposed “community of practice”

working group for AGPE, drawing on relevant staff members within the organization (combination of

senior and junior staff);

b) the various agencies covered in this review (6), the initiatives (14), in addition to several other relevant

entities (such as RUTA for Central America, ECLAC for South America, CABRI for SSA), should seek

to establish an inter-agency AgPE practice working group, with the focal person from each entity and

initiative being the representative for such a global working group. This working group can also foster

enhanced systematic and institutionalized collaboration among related initiatives;

c) Given that there is special focus on the AGPE requirements of SSA, there is also the option of there

being an AgPE sub-working group for SSA, to focus on specific agenda for SSA.

5) Demand Aspects

There are several components to strengthen the demand aspects, as discussed in the section on

conclusions, including:

a) supporting capacity development activities of key decision makers and technical analysts at various

levels, which will increase the demand for improved AgPE data base (disaggregated expenditures), for

improved and periodic AgPE and PSE analytical studies which can enhance agricultural policies, policy

change measurement, and expenditure priorities, based on comparative returns;

b) supporting the strengthening of expenditure reporting systems cited above will contribute to stronger

demand by key actors for more disaggregated expenditure data and analysis;

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c) promoting the internationalization of increased demand for enhanced AGPE databases (of

disaggregated data, using a consistent/standard classification system), for periodic AgPE and PSE

analytical studies, and effective dissemination to ensure the results are effectively utilized; and

d) For all of the DAI initiatives, there is scope for expanding the ownership and use of the data, results

and tools arising from these initiatives at the country implementation level, on the part of key decision-

makers, including Ministries of Finance, Parliament (e.g., agriculture and budget committees).

6) Sustainability Strategies

The above section highlighted that, aside from the initiatives which are part of the work plans of

international organizations, one of the major concerns was that many of the initiatives are project-and

donor-dependent funded, hence, this casts doubt on countries being able to sustain the improvements

which are introduced. It is of paramount importance for each data initiative to devise an explicit

sustainability strategy regarding the continuation of demand-driven public expenditure data and analytical

work (AgPEs and PSEs) in order to meet their short-term and long-term objectives. At the same time,

there is a need to recognize that the DAIs (which are cross-country in nature) are largely international

“public goods”, which warrant funding in the spirit of the CGIAR system. Various options are explored to

promote enhanced sustainability.

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1. INTRODUCTION

1.1 Context and Rationale for Review

One of the most important instruments developing and transition countries possess to

achieve the transformation of their economies through achieving key development outcomes and

impacts, especially where the agricultural sector plays a key role, is efficient and effective

agricultural public expenditures, coupled with a conducive policy environment and dynamic

private investments. 3 There is a growing recognition among practitioners and policy makers that

public investment in agriculture is a key determinant of productivity growth and essential to

meeting the demands on and strategic role of the sector. 4 In order to ensure much needed

improvements in resource allocation decisions and complementary policy decisions, it is vital for

these countries and development partners to possess sound and adequate databases which would

enable appropriate types of evidenced-based agricultural public expenditure analysis. This also

needs to be complemented by other types of analyses and interventions to enhance the policy

environment and stimulate private sector investment flows to the sector. While recognizing the

importance of private sector investment flows and of non-agricultural public expenditures, this

review focuses on agricultural public expenditures, which is a key component of common interest

to development partners and policy makers in developing countries.

There is no single dataset which would enable a comprehensive and reliable assessment

of trends in investment to or resource flows to agriculture, including datasets on agricultural

public expenditures. 5 With regards to public expenditures, the data challenges include a number

3 A growing number of empirical studies show that public spending in agriculture has a strong and positive effect on

growth and poverty reduction efforts. Some of the relevant synthesis reports include (with detailed AgPERs

providing the foundation, with references contained in the synthesis reports): Impact of Public Investments in and

for Agriculture: Synthesis of Existing Evidence (IFPRI Discussion AgPER, 2012); Production, Productivity, and

Public Investment East Asian Agriculture. by F. Fan and Brzeska, IFPRI Discussion AgPER, 2010; Public

Investment, Growth and Rural Poverty (synthesizes the evidence from IFPRI research), by S. Fan and Rao (IFPRI

Discussion Note, 2008); Public Expenditures, Growth and Poverty: Lessons from Developing Countries. S. Fan.

Washington, DC: The Johns Hopkins University Press, 2008. The detailed country-level research AgPERs provide

the foundation for these reviews, and their detailed references are cited in the reviews.

For example, Fan, Mogues, and Benin (2009), show that for each unit of local currency public spending on the

agricultural sector, on average, 10 local currency units are returned in terms of increased agricultural productivity or

income across several African countries. See Mogues 2012 and the references therein for further information on the

nexus between public spending - agricultural growth - poverty reduction. A useful synthesis of good practices and

practitioner toolkit are: How do We Improve Public Expenditure in Agriculture? (World Bank/DFIP Partnership,

March 2011); and Practioners’ Toolkit for Agricultural Public Expenditure Analysis (World Bank/DFID

Partnership, March 2011).

4 See Cramon-Taubadel S von, Anríquez G, Haen H de, Nivyevskiy O. 2009. Investment in Developing Countries’

Food and Agriculture: Assessing Capital Stocks and their Impact on Productivity. Expert Meeting on How to Feed

the World in 2050. Rome: Food and Agriculture Organization of the United Nations. It is also noted that private

sector investments are the single most important source of investments in the agricultural sector, and therefore,

important to ensure that public investments generate potential synergies from private investments (see reference

below for further details).

5 There are two excellent background papers contributing to the preparation of the State of Food and Agriculture

Report (2012, FAO), which address these issues in greater depth. They include: Lowder, S., Carisma, B. & Skoet, J.

2012. Who invests in Agriculture and How Much? An Empirical Review of the Relative Size of Various

Investments in Agriculture in low- and middle-income Countries. ESA Working Paper No.12–09, Rome, FAO; and



of underlying and systemic weaknesses which also make it difficult to make sound comparisons

across time and countries, as well as comparisons across different data sets. The major data

challenges include, among others:

Varying definition of the “agricultural sector and sub-sector”, resulting in

varying country level classification systems for agricultural public expenditure

data. Although there are a large number of countries which have adopted the

Classification of Functions of Government (COFOG) system, 6 there is variable

application of this classification system;

Many countries draw on administrative classification to comprise agricultural

spending; however, in practice it is not just Ministries of Agriculture and related

agencies which spend on agriculture. Ministries of Land Planning, Rural

Development and others often are responsible for expenditures in some key

agricultural services or commodities. In addition, there are complementary

expenditures in rural areas which are important for agriculture like infrastructure

and social investment. This varies from country-to-country, and needs to be

considered to ensure transparency, accuracy and comparability of expenditures

based on standardized and comparable functions, such as COFOG;

Availability of varying levels of disaggregation of agricultural public expenditure

data, with most data available at a high level of aggregation (say, to level 2 of

COFOG --- the aggregate of “Agriculture, Forestry and Fisheries”) and at the

national level;

Extent to which sub national (e.g., state and local government) and donor grant

funding (e.g., often off-budget) agricultural public expenditures are captured in

aggregate country figures;

Varying time periods of available data;

Varying coverage of countries;

While various organizations have identified these data lacunae and challenges, thus far there does

not exist a systematic overview of the various data and analytic initiatives, how they can better

complement each other, what are the strategic gaps and possible avenues for inter-agency collaboration.

Some of the on-going data and analytical initiatives are endeavoring to contribute toward the construction

of different types of databases for enabling enhanced and more disaggregated agricultural expenditures

and analytical studies which could contribute toward better expenditure priorities and allocation

decisions. However, there are varying levels of harmonization, coordination, complementarity and

sustainability among these initiatives, given their underlying varying objectives. Accordingly, these

constraints and increased awareness of these challenges suggest the potential for exploring synergies and

complementarities among the totality of data and analytic initiatives.

Recently, the OECD – IFPRI/PIM (CGIAR program on Policies, Institutions and Markets)

organized a workshop on “Shared Approaches to Measuring the Agricultural Policy Environment”, 7 and

brought together managers, researchers and practitioners from numerous international development

agencies to discuss specific aspects of the issues and opportunities for greater collaboration. One of the

topics which were discussed involved finding practical ways to address many of the above-mentioned

Lowder, S. & Carisma, B. 2011. Financial Resource Flows to Agriculture: A Review of Data on Government

Spending, Official Development Assistance and Foreign Direct Investment. ESA Working Paper No. 11–18, Rome,

FAO.. 2011).

6 See Appendix 1 for the details of the COFOG classification of activities in the agricultural sector.

7 The conference was held in Paris, on June 24 and 25, 2013.



issues involving agricultural public expenditure data and related analytical initiatives which could

contribute to sound measurement of the agricultural policy environment. In the context of the agricultural

public expenditures part of the workshop, it was recognized that it may require several exercises/steps

toward the creation and maintenance of a community of practice of analysts located both in developing

and developed countries who are concerned with accurately and systematically measuring the quantity

and quality of public spending for agriculture. Accordingly, the present review focuses on one aspect of

this broader process and which is centered on agricultural public expenditures --- while steps would be

taken to address other key components of the agricultural policy measurement agenda. There is an

intention of the key actors from the participating international agencies to hold follow up

forums/platforms to address the emerging results of several parallel reviews and discussion arising from

the Paris workshop. 8

1.2 Objectives, Scope/Criteria, and Approach

Considering the above context, the present study aims to review relevant data collection and

analytical initiatives/activities that are focused on, or are inclusive of, agricultural public expenditure data

and studies on developing and transition countries. In addition to a stocktaking of such initiatives, this

review endeavors to carryout a comparison of relevant features and identifies differences and similarities,

identify possible avenues for greater collaboration and complementarity. The outcome of this review is to

contribute to the improved coordination and sharing of data on public resource allocations to agriculture

in developing countries, and to the improved communication and exchange on measurement approaches

and methodologies in compiling such data and conducting analytical studies.

The review will be comprised of 3 major components:

a summary of basic features on each initiative;

a comparison of the initiatives according to major types, covering strategic aspects, and giving

special attention to the methodological features; and

the main conclusions and emerging recommendations, especially which can promote enhanced

inter-agency collaboration.

The criteria which has been used to identify the data and analytic initiatives to be reviewed

include either one or both of the following main objectives, among others:

comprise an ongoing “major” databases involving agricultural public expenditures (AgPEs) in at

least ten developing countries, and which include diversity in terms of sponsoring organizations

and regional coverage; there was also consideration to including global data base which cover

public expenditure indicators/data, and have the potential to include AgPEs; and

on-going analytical studies and tools, including an active website, which also are devoting

attention to addressing data-related issues and strategies which can enhance the role of AGPE

analysis to better support evidenced-based decisions on expenditure priorities, allocations, and

management.

The approach involves the following sequential steps:

identifying the relevant past, on-going and emerging data and analytical initiatives which comply

with the above criteria;

developing a template table to focus compilation of relevant information for each initiative

(primarily on-going, since there is limited information on past and emerging initiatives);

8 For example in December, 2013, there is a Global Forum on Agricultural, which will include a technical sub-event

to review the progress of follow-actions of the June conference, such as the current review (and other follow-up

actions).



completing the template table for each initiative, based on available information and interviews

with the relevant focal persons, as well as obtaining their feedback/validation and further inputs

for each draft template table;

carrying out a comparative assessment of the various initiatives, based on a typology of initiatives

to ensure comparability, while also utilizing relevant empirical examples to illustrate similarities

and differences;

generating feedback/inputs from “strategic” users of AGPE analysis (e.g., Ministry of Finance

officials from selected developing countries, selected researchers on AGPE, specialists in

agricultural public expenditure analyses, and other international development practitioners);

deriving relevant conclusions and recommendations to foster enhanced relevance, effectiveness,

collaboration and complementarity among on-going and emerging/prospective initiatives.

2. OVERVIEW OF DATA AND ANALYTICAL INITIATIVES

In this section of the document, we first start by providing a summary of the different features of

certain selected past or emerging similar initiatives, in Section 2.1. We, then, provide a new typology

(categorization) of the selected fourteen initiatives based on their similarity in objectives and structures,

under Section 2.2. Finally, we present a summary of the different features of each of the fourteen

initiatives under consideration, in Sections 2.3. (Annex A of the document provides further details on

each of the initiatives.)

2.1 Summary of Selected Past and Emerging Initiatives

Taking into account the above criteria, there are three past initiatives and one “emerging” initiative,

which are highlighted below, based on limited available information

Past Initiatives (databases and analytical studies on AGPE/PSEs)

Expenditure Tracking of the Maputo Declaration target of each country allocating at least 10% of

total public expenditures for the “agricultural sector” (from about 2003 – 2009), FAO;

Regional database (1986-2001) for LAC countries using agricultural and rural development

expenditures, FAO-LAC;

Institutional Strengthening of Agriculture in Belize, Central America and Dominic Republic,

IDB;

A Growing Opportunity: Measuring Investments in African Agriculture, ONE Data Report.

1) Expenditure Tracking of the Maputo Declaration, FAO

Maputo Declaration (2003) refers to the formal commitment by the heads of State of SSA

countries to allocate at least 10% of their public budgets to the agricultural sector, together with a target

of 6% agricultural growth rate per year. In about 2004, FAO established a tracking system to monitor the

progress toward these key policy targets. This work supported by a World Bank-supported Institutional

Development Fund (IDF) Grant. The primary beneficiary was intended to be the NEPAD Secretariat and

member state Ministries of Agriculture. They were to benefit through the setting up of a method for

common and consistent measurement of public expenditure in national agricultural sectors. This would

enable consolidation of information and tracking at the continental level by the Secretariat, to enable

monitoring of progress on CAADP commitments. 9The completion report of this initiative included the

9 One of the main outputs of this activity included the following report: “National Compliance with 2003 AU-

Maputu Declaration to Allocate at least 10% of National Budget to Agriculture Development: 2007 Draft Survey

Report (by NEPAD Secretariat- Agriculture Unit, with FAO Support), August, 2008.



following overall conclusion: 10

“The overall outcome is rated as marginally unsatisfactory. Although the development objective remained

relevant, and the activity components were largely completed, the capacity did not get developed to

sustainably repeat survey-based tracking of country-level public expenditure on agriculture. When the

IDF activity and account closed, there were no further rounds of the AETS. Portions of the tools and

methodology developed under the IDF have subsequently proven useful to other initiatives, including the

separate ATOR (by ReSAKSS) that presents trends on public expenditure, and this is the basis for

attenuating the unsatisfactory rating to “marginally”.

The IDF activity completion memorandum highlighted the following substantive lesson: “On

sustainability, closer attention was needed in the final six months of the project activities to completion of

a continuation plan that was consistent with resources likely to be available. Closer supervision on the

part of the Bank, and better focus by the Recipient would have led to a better transition plan and

communication with member countries on the approach for continued monitoring of public expenditure

performance.”

2) Regional database 1986-2001, FAO-LAC In 2004-2005 the Latin American and Caribbean Regional Office of FAO based in Chile

developed and initiative to measure and analyze agricultural and rural development public expenditures in

18 selected LAC countries. The initiative commanded country studies to gather data on public

expenditures for years 1986-2001. The data was put together in a unique database which was made

available to a group of researchers for regional analyses. Later on, these studies were published in a

collective volume presenting the main findings and policy recommendations (Soto Barquero et al, 2006).

The collected data considered three main categories: (i) production promotion; (ii) rural

infrastructure; (iii) rural social investment. Item (i) was the closest to agriculture-related expenditures,

and the other two were ample and contributing to a supporting environment. However, large variation in

classification criteria was observed across countries in these categories, especially in social investment.

The constructed database also included some macroeconomic variables like sector and global GDP,

agricultural and rural population and employment, among others. Analytical studies used these and

additional variables to assess the impacts and relationships of agricultural and rural expenditure on

growth, poverty reduction and factor productivity using alternative conceptual models.

The initiative did not publish a methodological document explaining how the data was collected

and organized. Specific country reports were not made available to researchers so they could not evaluate

comparability and reliability of the data across specific countries. Data from federal countries was

particularly problematic. This undermined the potential additional use of the data, and the initiative did

not have further impacts on policy analysis and decision making after the volume publication in 2006.

Currently there are no activities related to this initiative at the FAO LAC regional office.

3) Institutional Strengthening of Agriculture in Belize, Central America and Dominic Republic, IDB

This was a technical assistance project (from 2009 to 2011) funded by the Inter-American Development

Bank (IDB), and was comprised of two components: two AgPEs (for Honduras, Costa Rica, and Belize);

and estimates of PSEs and related indicators for five Central American countries (which provided inputs

for the AGPE for LAC). 11

The AGPE studies applied a standard methodology (drawing on the AGPE

10

See Implementation Completion and Results Memorandum (IDF Grant: IDF-NEPAD/DBSA: Public

Expenditure Tracking in Agriculture, prepared by the World Bank, 2010. 11 In spanish, the TA name was: “Mejora lnstitucional Agrícola en Belice, Centroamérica, Panamá y la República Dominicana.

RUTA (Rural Unit for Technical Assistance) executed the TA Project, mainly in the form of contracting 5 separate country-level

studies on agricultural public expenditure analysis and one Producer Support Estimates for Dominican Republic, Guatemala and



toolkit cited above), customized according to the requirements of each country, including a section on

enhanced budgetary management processes and mechanisms, based on a systematic assessment of the

main constraints in each stage of the budgetary cycle. The studies developed a planning, budgeting,

implementation and governance cycle framework for enhanced budgetary management. The conduct of

the country level AgPEs encountered significant challenges in compiling reliable and comprehensive

agricultural public expenditure data to a level of disaggregation below level 3 (COFOG), especially

considering multiple implementing agencies. The PSE estimates were based on applying the OECD

methodology for PSEs and related indicators.

The studies identified data constraint issues and recommendations for enhanced AGPE databases,

and recommended institutionalizing a process whereby Ministries of Agriculture would carry out

periodic “light” budgetary assessments of past performance as a key input for formulating a sectoral

medium term and annual expenditure budgets, within which an annual budget would be drawn up. The

PSE estimates provided key results which were used for policy discussions with each of the

Governments, although the desired country ownership was not achieved for several reasons. At the end

of Project, there was an independent assessment of the project’s objectives, including lessons learned. 12

4) ONE Report

In late 2012/early 2013, ONE carried out a major review of progress of African governments’

efforts to invest in their own agricultural development.13

It is included because it highlights an important

initiative by a key stakeholder promoting sustainable development, and also highlights some of the data

constraints faced by all empirical studies. ONE looked at the 19 African countries with vetted, signed

national agriculture investment plans, developed through CAADP. For each of these countries, the ONE

team looked at progress on their commitments to reduce poverty, to spend 10% of national expenditures

on agriculture, to implement national plans, and to include citizens in decision-making. ONE analyzed

available public budget expenditure and allocation statements from individual countries and surveyed

Agriculture Ministries with the opportunity for feedback and verification. The ONE team developed a

methodology note which provides further details. Unfortunately, because there is no standard system for

reporting data, information must rely upon documented assumptions, sources and caveats.

The report continued to assess donors’ delivery of their L’Aquila commitments, and evaluated the

quantity and quality of their agriculture assistance. In addition, this year the report hones in on the first

Rome Principle of country ownership. For donors, the ONE team looked at four different indicators of

country ownership of national agriculture plans, from inclusion of non-state actors to donor support for

these plans. For African governments, the ONE team looked at whether budgetary and programme

information is available to citizens and whether a country’s national agriculture plan includes a structure

for the participation of non-state actors. The team also included case studies from Benin, Ghana, Kenya

and Tanzania to help illustrate the concept of country ownership and its impact on the CAADP national

process. Finally, given that this year is a turning point for both African and donor governments, ONE

offered some targeted recommendations on how to improve commitments to agriculture and food security

moving forward.

Honduras (based on the OECD methodology).

12 The report is in Spanish and internal for use by RUTA and the IDB (“Informe de Evaluacion del Proyecto: “Mejora

lnstitucional Agrícola en Belice, Centroamérica, Panamá y la República Dominicana” (consultant report prepared for RUTA,

2012).

13 ONE. A Growing Opportunity: Measuring Investments in African Agriculture (Data Report, March, 2013). It should be noted

that the review covered only national expenditures, and did not consider DP funding/expenditures, as part of total public

expenditures. Most initiatives monitor total public expenditures --- national + donor contributions.



Emerging Initiative in SSA

NEPAD/National Planning and Coordination Agency (NPCA) aims to establish and maintain a

database of key agricultural indicators which are connected with key output, outcome and impact

indicators, in support of meeting the strategic objectives of the Comprehensive African Agriculture

Development Programme (CAADP). The intention is to use the emerging CAADP results framework as a

guide for prioritizing the types of indicators which would be included in the database, giving special

attention to tracking the agricultural expenditure allocations and actuals. Overtime, it would aim to track

disaggregated expenditure data, especially if AGPE databases can be improved at the continental,

regional and country levels. This provides an opportunity for strengthening the on-going partnerships

with other key actors which are managing some of the current AGPE databases (e.g., SPEED, ReSAKSS,

FAOSTAT-GEA). As the proposal crystallizes in the next few months, it is timely for inter-agency

collaboration to support this proposal by NPCA on an important policy target.

2.2 List and Typology of On-going Initiatives Covered

Based on the above criteria, fourteen on-going data and analytical initiatives/programs have been

identified for this review. See Table 2.1 for further details.

Table 2. 1: List of Key Databases and Analytical Initiatives

NAME OF DATA/ANALYTICAL

INITIATIVE

HOSTING OR

MANAGING

ORGANIZATION

TYPE/FOCUS

(DCs = developing

countries; EEs=

emerging economies)

1) AGPE for LAC: Agricultural Public Expenditures

for LAC

UN/EC

Commission for

LAC (ECLAC)

Sub-Regional

Database/Central

America and Mexico

2) ASTI: Agricultural Science and Technology

Indicators

IFPRI Database and Analytical

(DCs)

3) BOOST: Making Expenditure Data Available for

Analysis (DCs and EEs)

World Bank Analytical Tool for

Public Expenditures

4) CRS: Creditor Reporting System of ODA Flows OECD Database (DCs and EEs)

5) FAOSTAT: Investment datasets

(focuses on two components: GEA and ODA)

FAO Database (DCs and EEs) sectoral:Investment Dataset

6) GFS: Government Financial Statistics IMF Global Database

7) MAFAP: Monitoring African Food and

Agricultural Policies

FAO Sub-Regional Database

and Analytical/in SSA)

8) PSE-OEE: Producer Support Estimates/PSE and

Related Indicators for Agricultural Support (for OECD

& Emerging Economies)

OECD Database and Analytical

(OECD and 5 emerging

economies)

9) PSE-LAC: Producer Support Estimates for Latin

America & Caribbean/LAC

IDB Regional Database/LAC

10) RePEAA: Resources for Public Expenditure

Analysis in Agriculture

World

Bank

Analytical Resources

(DCs and EEs)

11) ReSAKSS: Regional Strategic Analysis and

Knowledge Support System for Sub-Sahara Africa

IFPRI

(as “host”)

Database and Analytical

(in SSA)

12) SPEED: Statistics of Public Expenditure for

Economic Development

IFPRI Database (multi-sectoral)

(DCs and EEs)

13) SNAPE: Strengthening National Agricultural

Public Expenditures SSA

World

Bank

Regional Analytical/Agr.

PERs (SSA)

14) WDIs: World Development Indicators World Bank Database/multi-sectoral



Based on the main common orientations and features of the above initiatives, Table 2.2 summarizes the

typology of data and analytical initiatives which will be used to guide the comparative review of diverse

initiatives. It should be recognized that each initiative has specific and unique objectives, and hence this

needs to be well understood in the comparisons and conclusions. Accordingly, this review is not an

assessment par se of each initiative; rather the review endeavors to focus on highlighting key information

and relevant comparisons to enable practitioners (both suppliers and users) a better appreciation of each

initiative and to foster enhanced inter-agency collaboration which can respond better to user requirements

(researchers and policy makers).

Table 2. 2: Typology of Data and Analytical Initiatives on Agriculture Public Expenditures

Initiative

Databases

Covering

Public

Expenditures

and APEs

Analytical

Studies for

APEs *

Databases for

PSEs and

Related

Indicators*

Databases for

Overseas

Development

Assistance

(ODA) Flows

Software Data

and Analytic

Tool for Public

Expenditures

(A) (B) (C) (D) (E)

1. APE for LAC 2. ASTI

3. BOOST

4. CRS

5. ODA

6. GFS

7. MAFAP

8. PSE-OEE

9. PSE-LAC

10. RePEAA

11. ReSAKSS

12. SPEED

13. SNAPE

14. WDIs * It is recognized that some of the data base initiatives also support some analytical studies (e.g., ASTI). But inclusion in Type B

requires that the analytical studies be comprehensive in methodology and scope.

Figure 1 illustrates the inter-relationship between the various data and analytical initiatives, and

how they can enhance the budgetary cycle and contribute to enhanced budgetary outcomes and impacts

for the agriculture sector, if effectively managed and coordinated at the country level. This reflects a

results chain, whereby strong databases are the foundation of sound analyses which can help underpin

better budgetary allocations, which, in turn, feed into the databases,. Regions such as SSA, which are

implementing a strategic framework for the agricultural sector (CAADP) at the three levels (continental,



Figure 1: Framework of Data and Analytical Initiatives for Supporting Enhanced Agricultural

Expenditure Analyses, Budgetary Cycle, and Outcome and Impacts

regional and country), can generate synergies in the effective and complementary utilization of the

various types of database and analytical initiatives in AGPE. The following sections highlight some of

these key features and opportunities. Figure 2 provides another perspective of the initiatives and their

inter-relationships.

Figure 2: Initiatives within a Typology



2.3 Summary Descriptive Features (See Annex A for further details)

(1) Type A: Databases for Public Expenditures and Agricultural Public Expenditures

(a) Agricultural Public Expenditures for LAC

The UN Economic Commission for Latin America and the Caribbean (ECLAC) established and

manages a dataset on Agriculture Public Expenditures (AGPE) for its northern sub-region (Central

America, Caribbean and Mexico). There are only two indicators in the database (known as AGPE for

LAC): total public expenditure and agricultural public expenditure. These are part of the System of

Agricultural Information (SIAGRO--Sistema de Información Agropecuaria) which was developed by the

Unit of Agricultural Development (Unidad de Desarrollo Agrícola) of ECLAC´s Mexico sub-regional

office. It was developed in response to information needs from the public and private sectors from the

northern sub-region of LAC countries, complementing other regional and international existing databases

like FAOSTAT or World Bank WDI.

Data is compiled from official published sources, such as publications from Central Banks or Finance

Ministries. No questionnaires are sent specifically to governments. Currently SIAGRO covers Central

America countries (CAFTA+ Dominican Republic/DR), two Caribbean countries (Haiti and Cuba) and

Mexico. The main goals of SIAGRO are: (i) to generate national and regional diagnosis about the

evolution of the agricultural sector; (ii) to support in the formulation of agricultural development policies;

(iii) to give inputs for the preparation of specific socio-economic research in the agricultural sector; (iv) to

help in the decision making of the public and private sectors.

Some unique and value-added features are that the AGPE for LAC database has a long time series

1980-2010, which makes it attractive for comparative research and analysis with other long-term

macroeconomic variables, like GDP, productivity or credit.

The main limitations are its limited geographical coverage ---among 10 counties covered, AGPE data

for Cuba and Haiti are not covered currently --- and it does not have any disaggregation for types of

agricultural expenditures or levels of government. In addition, it does not have a comparable and

standard method for data collection as it is based only on official publications from the countries covered.

(b) Agricultural Science and Technology Indicators (ASTI) Initiative

The Agricultural Science and Technology Indicators (ASTI) initiative is managed by IFPRI and

compiles, analyzes, and disseminates data on institutional developments, investments, and capacity in

agricultural research and development (R&D) in low- and middle-income countries with the objectives of

assisting R&D managers and policymakers in improved policy formulation and decision-making at

country, regional, and international levels. The origin of ASTI traces back to 1981, when two of the

Consultative Group on International Agricultural Research (CGIAR) member institutes—the International

Food Policy Research Institute (IFPRI) and the former International Service for National Agricultural

Research (ISNAR)—initiated a joint venture on agricultural R&D data indicators, and published the best

available data from secondary sources for an ad hoc group of national agricultural research systems.

ISNAR continued this work during the next two decades, involving various institutional survey rounds to

collect primary data for various countries and the developing world and linking this data with secondary

data sources as well as S&T indicators for OECD countries. Since mid-1990s IFPRI and ISNAR Later on

(in 2001), IFPRI and ISNAR collaborated together again in collecting agricultural R&D indicators, which

led to the official establishment of the ASTI initiative in 2001. The initiative, to this end, has identified



four objectives in fulfilling its mission: (i) provide up-to-date, high-quality datasets on agricultural R&D;

(ii) conduct further analysis of agricultural R&D datasets; (iii) communicate data results and analysis for

policy formulation and advocacy; and (iv) build capacity for data collection and analysis.

ASTI has limited itself to measuring inputs into agricultural R&D rather than expanding to include

indicators on the multiple dimensions of the agricultural innovation process, but is currently piloting the

collection of output/performance indicators. It is, however, piloting an agricultural innovation system

framework for use as an analytical tool at the sector and commodity level. ASTI groups “performers” of

agricultural R&D into two sector categories (public sector and private sector) and five four institutional

categories (Government, Higher education, Nonprofit, Business, Public enterprises). The level of data

disaggregation also varies according to the indicators. The ASTI initiative, currently, compiles data from

about 87 developing countries in Sub-Saharan Africa (SSA), the Asia–Pacific (APC), Latin America and

the Caribbean (LAC), and the West Asia and North Africa (WANA) through national institutional survey

rounds, which capture primary data of hundreds of agencies involved in agricultural R&D. surveys. Time-

series data are collected for three main indicators: “research investments spending,” “research funding

sources,” and “research staff totals.” Benchmark data are collected for other indicators such “researcher

staff by degree, gender, age”, “support staff”, and “research focus by commodity and theme”.

ASTI collects and process the data on agricultural R&D going back to the early 1970s using

internationally accepted definitions and statistical procedures for compiling R&D statistics developed by

the Organization for Economic Co-operation and Development (OECD) and United Nations Educational,

Scientific and Cultural Organization (UNESCO).

(c) FAOSTAT: Government Expenditure on Agriculture Component

As part of the global effort in creating a world free from hunger and malnutrition, the Statistics

Division of FAO (FAOSTAT) develops a Global Investment Dataset (GID) comprised of four main

components: Credit to Agriculture; Government Expenditure on Agriculture and Rural Development

(GEA); Official Development Assistance to Agriculture; and Foreign Direct Investment in Agriculture.

The collection and dissemination of the GEA data, in particular, aims at providing researchers,

policymakers (in-country, and supporting analysts) and development partners relevant data to facilitate

the assessment of governments’ role in and contribution to agriculture, rural development and

environmental protection services, in a manner which enables comparability across countries and

harmonization with other international organizations compiling relevant datasets.

There are two important anchors which underpin the FAOSTAT GID’s addition of the Investment

Domain: the FAO charter which calls on FAO to compile basic statistical information on agricultural and

food security, and whereby the Investment Domain has gained status of being a “core” part of FAO’s

statistical information for cross-country monitoring and comparisons; and the Maputo Declaration by the

Heads of State of Sub-Saharan African Countries (in 2003), calling on each Government to allocate at

least 10% of total public expenditures to the agricultural sector.

In 2004, FAO started an initiative to track the progress in allocating public expenditures to the

agricultural sector for SSA, as part of supporting the Maputo Declaration. This initiative contributed to

triggering the inclusion of the GEA in the Investment domain. Under the umbrella of the Global Strategy

to Improve Agricultural and Rural Statistics, GEA was adopted by the United Nations Statistical

Commission in February 2010. In 2012, the GEA database was launched tracking expenditure on

agriculture and rural Development since 2001 through 2011. FAOSTAT used a questionnaire based on

the IMF’s Government Finance Statistics Manual methodology (GFSM 2001) , in particular, the Function

of Government (COFOG, Level 3). This approach was to help ensure comparable data that are aligned



with international statistical standards. Reporting countries and focal persons (within the Ministry of

Finance, Agriculture, or Central Statistics Bureau) are requested to complete the annual FAO

questionnaire, which has additional disaggregation (of expenditure into its recurrent and capital

components) and includes supplemental / cross reference data) introduced by FAO beyond the COFOG

classification. The database, currently, publicly avails annual data for 134 countries across 33 (FAO’s)

geographic (and economic) regions.

The main methodological features of the FAOSTAT GEA include:

The first (2012) FAO’s Government Expenditure on Agriculture (GEA) questionnaire is mainly

developed based on the IMF’s Government Finance Statistics Manual, 2001 (GFSM 2001)

methodology, in particular Table 7: Outlays by Function of Government, in an effort to ensure

comparable data that are aligned with international standards;

The questionnaire is designed to collect key data series for tracking the allocation of

government expenditures to agriculture and rural development and related metadata, requesting

a General Government (and its subsectors) time series for the period 2001 to 2012 (and each

year thereafter, according to an established cycle of compilation, adjustment and release. The

questionnaire is distributed to reporting organs of member countries in about September of each

year, such that it follows about 2 months after the IMF/GFS questionnaire is issued to help

ensure consistency with the GFS data, although at a higher level of aggregation. (Release of the

published data of compiled data is normally at mid-year of the following year);

The revised questionnaire on government expenditure on agriculture (2013) and related

functions by source of funds also includes Supplemental / Cross reference data, on further

disaggregation of outlays on subsidies and grants;

While FAOSTAT team reviews the data sent by each country, there are limitations to which

FAOSTAT team can validate and adjust the data, and fill data gaps. The FAOSTAT team is

devising cost-effective ways to help ensure the data from the countries is complete, timely and

accurate.

(d) Government Financial Statistics/GFS:

The GFS system compiles detailed annual statistical data on revenue, expense, transactions in assets

and liabilities, and stocks of assets and liabilities of general government and its subsectors as reported by

the IMF member countries. The IMF originated the GFS data collection system in 1976 to support

internationally comparable country level fiscal reporting. In the system, provision is made for three levels

of government: central; state, provincial, or regional; and local. It covers all aggregate fiscal data, and

includes major subsectors and functions, in accordance with the Classification of Functions of

Government. (COFOG) to Levels 1 and 2. The system also records two types of flows: transactions and

other economic flows.

The GFS is the most internationally recognized source of data for policymakers and analysts to

examine specific areas of government operations (taxation, grants, subsidies, etc.), developments in the

financial operations, financial position, liquidity situation of the general government or the public sector

as well as the socio-economic objectives of fiscal policy in a consistent and systematic manner. About

130 member countries report to the GFS with varying degrees of institutional and transactions coverage

across the globe (gradually increasing). The electronic database contains COFOG time series data for

approximately 80 countries since 1972 to current. The data can also found in print, online or in CD-ROM

formats.

The main methodological references/features include:



GFS Manual (2001), which provides the economic and statistical reporting principles to be used

in compiling the statistics and guidelines for the presentation of fiscal statistics within an

analytic framework that includes appropriate balancing items.

Public Sector Debt Statistics: Guide for Compilers and Users (2013) -- is the first global guide

on public sector debt statistics prepared under the joint responsibility of nine organizations,

through the mechanism of the Inter-Agency Task Force on Finance Statistics (TFFS). It

provides guidance on (1) concepts, definitions, and classifications of public sector debt

statistics, (2) the sources and techniques for compiling these data, and (3) some analytical tools

that may be used to analyze these statistics

GFS: Compilation Guide for Developing Countries, which represents a new approach by the

IMF’s Statistics Department (STA) to assist developing countries to compile GFS in

accordance with the guidelines of the GFSM 2001. 14

The guideline was stimulated by the

recent economic and financial crises, which highlighted the need for more and better data to

monitor and evaluate economic developments.

Preparation of the GFSM2013 is underway. It is expected to be released by early 2014. One of

the main features the new manual expected to include better alignment with the 2008 System of

National Accounts.

(e) MAFAP: Monitoring African Food and Agriculture Policies

FAO has established a sustainable system for monitoring the impact of food and agricultural policies

(known as MAFAP) in response to the declining agricultural investment and serious food crisis observed

in the continent, in particular, for decades. Although decision makers recognize that appropriate policies

and adequate public spending are critical for closing this gap, evidence to support decision-making is

often limited in Africa. MAFAP, therefore, emerged as a response to this recurrent crisis and growing

data demand to monitor key agricultural policies and expenditures focusing on ten SSA countries:

Burkina Faso, Ghana, Mali, Kenya, Tanzania, Uganda, Ethiopia, Malawi, Mozambique and Nigeria.

The MAFAP Database, in particular, aims at providing access to data and common indicators to African

policymakers and their development partners on public expenditure on food, agriculture and rural

development. (The database also compiles data on market incentives and disincentives for key

commodities, using the OECD methodology for measuring price distortions and will be discussed with

Type B initiatives below.) The initiative’s first phase (2010 – 2013) has been successfully completed,

compiling data from 2005 to 2011. There are steps being taken to prepare a second and expanded phase

(2014 -2019).

With regard to data compilation and dissemination of methodological aspects, MAFAP endeavored to

follow common quantitative methodology and indicators in order to increase transparency and to enable

comparison of agricultural policies across countries. With respect to the public expenditure database, it

provides high level of disaggregation of agricultural expenditure data although the actual level,

availability and reliability of data varied according to country.

The following synthesizes the methodological concepts, based on an explicit classification of

agricultural public expenditures, which reflects the OECD method for estimating PSEs and other

indicators:

14

For further details see: http://www.imf.org/external/pubs/ft/gfs/manual/compil.pdf) (refers to “Compilation Guide

for Developing Countries)



The main variables capture all public expenditures that are undertaken in support of food and

agriculture sector development(from national budget, either central, regional, or any ministry) and

external aid (provided by international organizations or NGOs);

the main focus is on food and agriculture sector, and expenditures in support of forestry and

fishery sectors are also captured;

It also captures all public expenditures in rural areas, such as rural infrastructure, rural education,

rural health;

Expenditure measures generate explicit or implicit monetary transfers to supported individuals or

groups, according to two categories of expenditures:

o agricultural specific expenditures (or transfers to agricultural agents or sector as a whole, in

two further categories: payment to agents individually (producers and consumers) in the

agro-food sector, as a proxy for “private” goods; and general sector support to agro-food

sector agents, collectively), as a proxy for “public” goods;

o agricultural supportive expenditures – public expenditures that are not specific to

agriculture, but have strong influence on agriculture sector development, such as rural

education, health and infrastructure;

Expenditure measures are considered and classified according to the way in which they are

implemented, and not on the basis of their objectives or economic impacts.

The detailed classification of support follows OECD’s principle of classifying policies according

their economic characteristics (the way they are implemented). The particular expenditure categories, are

designed to reflect the types of policies which are applied in African countries, and reflect the experience

of FAO working on public expenditures in developing countries. Furthermore, the classification system

attempts to distinguish policies providing private goods, as opposed to public goods, given different

effects (e.g., decoupling issues were not relevant in the SSA context).

MAFAP seeks to develop a set of measures of value to policymakers in African countries. The

three core types of policy indicators are: (i) measures of explicit policy incentives and disincentives and

market development gap in key agricultural value chains; (ii) measures of budgetary expenditures, and

(iii) measures of policy coherence. MAFAP is based on the OECD PSE approach to measure support to

the food and agricultural sector. The methodology has been adjusted for application in African countries,

but remains complementary with the OECD´s PSE indicators.

(f) ReSAKSS: Regional Strategic Analysis and Knowledge Support System (for SSA)

The ReSAKSS database, facilitated by IFPRI, in partnership with the Africa-based CGIAR centers,

the NEPAD Planning and Coordinating Agency (NPCA), the African Union Commission (AUC), and the

Regional Economic Communities (RECs), tracks agricultural performance in support of the

implementation of the CAADP agenda. The database covers only Africa, disaggregated according to five

regions (Africa-wide, Eastern and Central Africa, Southern Africa, West Africa, North Africa) with a

focus on Sub-Sahara Africa (SSA).

ReSAKSS is developing several databases – notably, involving the tracking of the policy target

arising from the Maputo Declaration – of agriculture public expenditures as a share of total public

expenditures. Its latest Annual Trends and Outlook Report (ATOR) for 2012 presents available

agricultural public expenditure data (since 1980s), at various levels (continental, regional and country

levels), including disaggregation of public expenditures below level 3 of COFOG for selected countries,

namely Congo, Rep. CAR, Congo, D. R., S. T. & Principe, Burundi, Chad, Djibouti, Seychelles, Uganda,

Madagascar, Tanzania, Mauritania, Namibia, Malawi, Zambia, Lesotho, Swaziland, Senegal, Togo, Cote

d'Ivoire, Sierra Leone, Mali.



There is a recognition of the usefulness of generating disaggregated expenditures which can be

compared across countries to help underpin better resource allocation decisions. Accordingly, the

ReSAKSS team, working with and through the regional and country-level nodes, plans to expand the

number of countries for which it will generate disaggregated agricultural expenditures.

The ATOR for 2012 illustrates the type of methodology that is used by ReSAKSS to compile the

required data (using multiple sources/per item):

1) obtained from the SPEED database total expenditures from 1980 onwards;

2) compiled data on the share of PAE in total expenditure based on available data from all the

different sources, using more recent sources where there are competing sources for any data point.

These two were used to obtain the amount of PAE by multiplying the total amount with the shares.

For missing observations in the total expenditure and PAE, the missing values were estimated using

extrapolations based on annual average growth rates that were estimated with the observed data.

These were then used to fill in missing observations in the shares of PAE. To remove the influence of

inflation over time and to more reliably compare expenditures across countries, total expenditures and

PAE were converted into constant 2005 purchasing power parity (2005 PPP$) using PPP conversion

factors from the World Development Indicators (WDI) (World Bank 2013).

Moreover,:

The Guide to the ReSAKSS website is a clear and useful tool to encourage users to use the

available data;

The ReSAKSS 2010 Data Notes are being updated

The ATORs for 2010 and 2012 provide useful public agricultural expenditure data, including

disaggregation of data (subsectoral level) for various countries

The ATOR for 2012 illustrates the type of methodology that is used by ReSAKSS to compile

the required data (using multiple sources/per item):

(g) SPEED: Statistics of Public Expenditure for Economic Development

IFPRI’s SPEED initiative aims at creating the most comprehensive and publicly available public

expenditure information to researchers, policymakers, donors, and the broader development community to

support a variety of economic and policy applications, a better understanding of the linkages between

public expenditures and development; and enhanced insights for promoting overall poverty reduction

strategies and other key economic development objectives.

Stimulated by IFPRI’s research focus on and increasing policymaker demand for clearer

assessments of public expenditure-outcome-impact linkages, the SPEED team at IFPRI started

compilation of data in the early 2000s. The database currently covers 147 countries: 113 developing

countries and 34 developed countries. In close collaboration with other international data initiatives and

different stakeholders, the team continued to draw on available expenditure data from relevant

international agencies such as the IMF’s GFS, and domestic/country-level sources and in July 2010, the

public expenditure database was expanded and formally launched as “SPEED”. In addition to collecting

relevant data from IMF’s GFS and Statistical Appendices and Selected Issues, World Bank’s Public

Expenditure Reviews and various national publications, the SPEED team also uses partners to compile

expenditure data at the country level (e.g., especially in SSA where the ReSAKSS network has regional

and country nodes which facilitate data collection of closing data gaps).

Currently, the SPEED database complies and provides publicly available public expenditure

information on 147 developing countries, in six regions across the globe, and eight sectors: agriculture,



education, health, defense, social protection, mining, transportation and communication, and total

expenditure for the period 1980-2010. While SPEED provides aggregate expenditure data at COFOG

Level-2 disaggregation (for agriculture, for forestry and for fisheries), it provides data at COFOG Level-1

for other key sectors (including health and education). The database also avails data at sub-national level

for some countries, but more work is needed to be incorporated in SPEED, or presented in a separate

project.

The main methodological features include:

A systematic data collection process is used to compile the SPEED Database from multiple

data sources (published and in-country, especially in SSA, drawing on various on-going

initiatives for generating expenditure data and analysis (CAADP/ReSAKSS in SSA). SPEED

does not use a questionnaire;

Certain adjustments were made to the data for various factors including currency

redenomination in order for the figures to be consistent and comparable over time;

Imputations are also estimated for missing values in the database;

Three methods are used to calculate the imputation: Average, linear trend and 5 year average

growth rate.

Desk work to collect local level data from national sources that’s available via internet, forms

one of the supplementary methods for filling data gaps

(h) World Development Indicators

WDI is the World Bank's flagship statistical database and establishes the benchmark against which

development progress is measured. WDI is a high quality, and internationally comparable statistics about

global development, compiled from officially-recognized international sources. It presents the most

current and accurate global development data available, and includes national, regional and global

estimates. The database contains more than 1,200 time series indicators for 214 economies and more than

30 country groups, with data for many indicators going back more than 50 years. This includes indicators

on public expenditures, although not on agricultural public expenditures.

The objective of WDI is to provide data for policymakers, development specialists, students, and

the public, so that they may use the data to reduce poverty and solve the world’s most pressing

development challenges. WDI aims to provide relevant, high-quality, internationally comparable

statistics about development and the quality of people’s lives around the globe.

The data involving public expenditures is part of public finance indicators, which includes

aggregate public expenditure indicators (and some selected sectors, but excluding agriculture and rural

development). One of the eighteen major thematic areas covered by separate databases refers to

Agriculture and Rural Development, which has a database of 22 indicators on key aspects of the

agricultural sector for all countries. However, at this time, none of these indicators include reference to

agricultural public expenditures. The WDI team has 4 criteria for determining the priority indicators for

inclusion in its expanding database (drawing from existing databases): availability, coverage and

timeliness, and “demand” from users. This review has triggered some discussion within the WDI team to

determine whether to add agricultural public expenditures as one of the “core” indicators for ARD.

The main methodological aspects for WDI database include the following features:

The WDI dataset contains data that generally obtained from official sources, although some

adjustments are made in the balance of payments to account for fiscal/calendar-year

differences. An attempt is made to present data that are consistent in definition, timing and



methods. Even so, updates and revisions over time may introduce discrepancies from one

edition to the next.

National accounts and balance of payment, which are the main sources of WDI, data come

from two sources: current reports gathered by the Bank's country management units and data

obtained from official sources.

Following statistical practice, the World Bank has adopted the following terminology in line

with the 1993 System of National Accounts (SNA). The changes are:

i. Gross national product (GNP) to Gross national income (GNI);

ii. GNP per capita to GNI per capita;

iii. Private consumption to Household final consumption expenditure;

iv. General government consumption to General government final consumption expenditure;

and,

v. Gross domestic investment to Gross capital formation

Aggregates are based on the World Bank’s regional and income classification of economies.

(The World Bank’s main criterion for classifying economies is gross national income (GNI)

per capita.)

Growth rates are calculated as annual averages and represented as percentages. Except where

noted, growth rates of values are computed from constant price series. Three principal

methods are used to calculate growth rates: least squares, exponential endpoint, and

geometric endpoint.

The Bank ensures the data work and products are of the highest quality by using standards,

methodologies, sources, definitions, and classifications that are internationally accepted.

General Data Dissemination System (GDDS) is a framework for assessing national statistical

systems and promoting improved dissemination and effectiveness that has been developed by

the International Monetary Fund (IMF), in close collaboration with the World Bank.

Data Quality Assessment Framework (DQAF) has been developed by the IMF, in

collaboration with the World Bank, as a methodology for assessing data quality that brings

together best practices and internationally accepted concepts and definitions in statistics,

including those of the United Nations Fundamental Principles of Official Statistics and the

GDDS.

(2) Type B: Analytical Studies for Agricultural Public Expenditures

(a) Monitoring African Food and Agriculture Policies (MAFAP)

As part of FAO’s mandate to focus on food security, working with national partners, FAO has

established for the first time a sustainable system for monitoring the impact of food and agricultural

policies in Africa (known as MAFAP). MAFAP is implemented by FAO, in collaboration with OECD

and national partners in the participating ten target countries of SSA (Ministries of Agriculture,

Researchers/academia, as well as Ministries of Finance).

The objective of MAFAP is to supply African policymakers and their development partners with

solid evidence on the impacts of policies and investments affecting agriculture and food security, and

enabling the comparison of results across countries and over time (for the target countries). MAFAP also

emphasizes participatory processes to ensure ownership, sustainability and enhanced capacity of the

target countries.

The MAFAP database aims at providing access to data and common indicators on:

price incentives and disincentives for key commodities, using the OECD methodology for

measuring market price support; and

public expenditure on food, agriculture and rural development.



In response to the global food price crisis, the increasing attention to supporting the

implementation of the CAADP agenda for SSA and a renewed focus on the importance of agricultural

policy in creating enabling environments for agricultural development, MAFAP was established and

launched/implemented by FAO in 2010, for an initial phase (2010 – 2013). This phase has been

successfully completed and there are steps being taken to prepare a second and expanded phase (2014 -

2019).

MAFAP endeavored to follow common quantitative methodology and indicators in order to

increase transparency and to enable comparison of agricultural policy effects across countries, over time

and between different commodities and commodity groups (exports, imports, food security crops)..

Having objective evidence strengthens the quality of decisions made by policy-makers, and empowers

people affected by policies, and farmers in particular, in their interaction with governments and donors.

Further details on the MAFAP methodology are outlined above, which also has helped to underpin

MAFAP’s solid work on APE and its country level commodities studies.

(b) Resources for Public Expenditure Analysis in Agriculture (RePEAA)

This initiative was developed by the Agriculture and Rural Development (ARD) of the World

Bank, as part a partnership with and funding from the Department for International Development (DFID)

on Public Expenditure in Agriculture,

The objective is to compile/generate and make available various resources on:

- available tools and methods that may be used in analyzing public expenditures in the agriculture

sector;

- many published studies that have analyzed expenditures in the agricultural sector.

While this analytic initiative does not involve the construction of a database on agricultural public

expenditures, it provides a comprehensive source of valuable resources/references and toolkit for

supporting evidenced-based agricultural public expenditure analysis, and thereby contributing to

enhanced resource allocations and supporting policy decisions. Its references include tools for better

understanding AgPE data, and tools for policymakers on how to classify and organize AgPE data?

The active phase of this initiative which was completed in 2011 generated and compiled various

resources, including a comprehensive website which is currently active. 15

The current phase is continued

through making available the resources in the WB-sponsored website, and providing advisory assistance

and ad-hoc training sessions to teams within the World Bank which are conducting agricultural public

expenditure analysis. This web site provides a comprehensive resource base for supporting Agriculture

Public Expenditure Reviews (AgPERs).

The main methodological resource generated by this initiative was the Practitioners’ Toolkit for

Agricultural Public Expenditure Analysis,16

complemented by a large array of other resources on specific

methodologies and country-level expenditure reports, which illustrate diverse practices. Many of the

other resources included in the website address various methodological issues and practices in carrying

agric. expenditure analysis, including addressing different aspects of data issues and challenges.

15

See: http://www.worldbank.org/AgPER 16

See: Practitioners’ Toolkit for Agricultural Public Expenditure Analysis (World Bank, 2011).



(c) Regional Strategic Analysis and Knowledge Support System (ReSAKSS)

The Regional Strategic Analysis and Knowledge Support System (ReSAKSS) is an Africa-wide

network of regional nodes supporting implementation of the Comprehensive Africa Agriculture

Development Programme (CAADP). ReSAKSS offers high-quality analyses and knowledge products to

improve policymaking, track progress, document success, and derive lessons for the enhanced

implementation of the CAADP agenda and other agricultural and rural development policies and

programs in Africa.

ReSAKSS is facilitated by the International Food Policy Research Institute (IFPRI) in partnership

with the Africa-based CGIAR centers, the NEPAD Planning and Coordinating Agency (NPCA), the

African Union Commission (AUC), and the Regional Economic Communities (RECs).

ReSAKSS has four main inter-related objectives:

i. To respond to the growing demand for credible information and analysis during the design and

implementation of agricultural-led development strategies, especially in support of the CAADP

agenda;

ii. ReSAKSS nodes are being established (in a few countries, with more being added in 2013 and 2014)

to provide: timely access to relevant information on agricultural investment options and priorities;

benchmarks and best practices; statistical information to monitor progress in achieving key targets

(hence the emerging data base on agricultural expenditures, covered under Type A); and other

relevant technical information to help guide and inform the CAADP implementation process;

iii. The goal is to: promote enhanced evidence-based articulation of investment priorities and associated

decision making; improve awareness of the role of agriculture for development in Africa; and to fill

knowledge gaps, promote dialogue, and facilitate the benchmarking and review processes associated

with the CAADP agenda; and

iv. To strengthen national systems and capacities to implement the CAADP agenda at the country level.

ReSAKSS is currently in its second phase (2011 – 2015), following a successful first phase (2007 –

2010).

The main methodological features are summarized above, which covers both the database and

analytical aspects.

(d) Strengthening National Agricultural Public Expenditures in SSA (SNAPE)

The overall purpose of SNAPE in SSA is to contribute toward improving the impact of scarce

public resources spent by Sub-Saharan African governments on agricultural sector development activities,

hence improving the welfare of rural ( predominantly poor) populations.

There are two operational objectives in providing two levels of analytical support to national

teams of selected SSA countries working on agriculture sector expenditure programming: (a) to support

the conduct of a basic agriculture sector public expenditure review (AGPER) in countries where such a

review has not been undertaken recently; and (b) support countries to carry out specialized agricultural

public expenditure analyses in situations where an adequate understanding of the nature and magnitude of

public expenditures in the agriculture sector already exists (e.g, thematic and subsectoral, and generally

more rigorous type of analyses). Also, analysis is being undertaken to clarify agricultural public

expenditure links to aggregate sector outcomes (although in practice, there has been less emphasis on this

broader component).



A secondary objective is to enhance capacity at the country level to conduct and manage these

and periodic agricultural public expenditure reviews, to contribute to enhanced decisions on resource

allocations priorities. The initiative provides training modules on various topics of AGPE and which are

targeted to analysts in SSA countries.

The two types of Ag PE studies being supported (basic and specialized, covering a total of 17

countries in SSA) have involved varying degrees of disaggregation in analysis, endeavoring to rely on

existing expenditure data, which in practice, many of the teams for the basic diagnostic had to completely

construct and integrate off-budget expenditure data, and other considerable data cleaning and

manipulation, as a basis for meaningful analysis (e.g., consistent time series of actual expenditures). To

varying degrees, each study is giving serious attention to addressing the data requirements, issues and

strategies to address the data requirements and reliability in a more systemic manner.

All of the country-level studies provide some rich examples of the types of data-related issues

emerging from specific expenditure studies. The findings are generating some recommendations on

strategies to address the systemic data issues in SSA, and the need for “lite” approaches to sustainable and

periodic expenditure analysis which can have applicable messages for other regions.

The Program has been funded by the Bill & Melinda Gates Foundation (BMGF), and under

implementation since 2010 and is expected to be completed by the end of June, 2014. Given high

country-level demand for this type of agricultural expenditure analysis, there are discussions underway

for possible extension of the program to provide support in other countries, and possibly also to address

more systematically and in collaboration with others some of the emerging underlying data-related issues.

The Agricultural Public Expenditure study initiative has developed training modules on the

various types of AgPE analysis envisioned to be supported, which includes methodological aspects in

supporting the formulation and conduct of such analyses. The suggested methodologies build on the

Practitioners’ Toolkit for Agricultural Public Expenditure Analysis. The application of these principles

and tools are being adapted and carried out by each country AgPE study to fit the specific country level

requirements (in line with the specific TOR). The dissemination workshops are sharing results and

address comparability issues across country studies (including relevant AgPE data issues) (for example,

the recent workshop held in Tanzania, in 2013).

(3) Type C: Databases for Producer Support Estimates and Related Indicators

(a) Monitoring African Food and Agricultural Policies (MAFAP)

MAFAP initiative adapted the PSE-OECD methodology to the African context. One of the main

features is its treatment of the Market Price Support (MPS) analysis, complementing it with value-chain

analysis which enables the measurement of "development gap" indicators related to market failures and

externalities, which is of crucial importance for developing countries. The initiative also gives special

attention to foreign aid, both through government and directly aimed to NGOs or farmers

organizations/private sector. The MAFAP methodology also distinguishes budgeted and allocated

transfers, which enables the measurement of allocation efficiency (or effectiveness) in government

expenditures --- actual expenditures versus intended goals.

A key innovation of the MAFAP initiative is that it allows for a fruitful interaction between MPS

or policy impacts on prices and AGPE analysis, since “development gaps” identify public good problems

which can be tackled with GSSE expenditures in infrastructure, roads and market improvement. This

feature is of particular interest for AGPE methodologies and interactions with policy analysis.



(b) PSEs and Related Indicators for Supporting the Agriculture Sector (for OECD and Emerging

Economies)

The Producer Support Estimate (PSE) approach followed by OECD has two main objectives: (i)

To monitor agricultural policies in the OECD countries and key emerging countries, with a focus on

measuring aggregate policy transfers – including budgetary transfers – to the agricultural sector; (ii) To

provide inputs for agricultural policy impact analyses, including the use of a policy evaluation model

(PEM), which is using a dataset covering agricultural public expenditures;

The OECD PSE framework and related indicators are based on a conceptual model of transfers

among farmers, consumers and taxpayers in the economy in order to measure incentives/disincentives for

the agriculture sector and assessing their underlying factors. Accordingly, the PSE methodology seeks to

integrate agriculture public expenditures (APE) as one of the important policy tools in which governments

seek to generate incentives (support) to agriculture, but in a general context in which other incentives and

disincentives are created by policies and real market operations.

Two main PSE indicators are: i. Producer Support Estimate (PSE) and ii. Total Support Estimate

(TSE) to agriculture. The first measures the transfers to (or from) producers from (to) the rest of the

economy, including Market Price Support (MPS) and government transfers and subsidies which go

directly to individual producers. The second indicator measures the total value of support received by

agriculture, including government expenditure in non-specific general services in support of agricultural

sector (GSSE). Part of the support in the PSE is APE, and all GSSE involves APE, therefore there is an

important relationship between PSE/TSEE and APE analyses. The composition of APE among NPA-

related items and GSSE is similar to identifying interventions in “private” and “public” goods in

agriculture. Also, the proportion of GSSE in total TSE comprises an important indicator of the extent of

public good investments in total support to agriculture.

This approach, applied dynamically, enables policy analysts and policy makers to assess changes

in agriculture policies in an integrated and systematic way. The method is currently used by 39 OECD

members (EU, with 27 countries, is considered one unit). Also, currently there is PSE monitoring of

seven emerging economies (Brazil, China, Indonesia, Kazakhstan, Russia, South Africa and Ukraine).

An increased number of developing emerging economies is envisioned to be included in OECD.

Colombia and Vietnam are currently undergoing country level agricultural policy studies and estimates of

PSEs and related indicators.

A general approach of the OECD approach has been to collecting data from multiple sources in

order to address a specific analytical need. PSE/TSE measurement does not requires development of data

systems in any way, but rather collect information on expenditures in each of the categories identified in

the methodology, from whatever source contains that information. When OECD undertakes a country

review, the task is basically a sort of detective exercise, finding out what the policies are and who has

information on the associated public expenditures.

Another important caveat about OECD method in relation to APE is that the budgetary element in

PSE/TSE seeks purely to meet the needs of the method. If a budgetary expenditure such as an export

subsidy creates a price gap then it is not included on the expenditure side as that would be double

counting. This prevents the approach of compiling an exhaustive system of public accounts that contain

all expenditures fitting, say, a COFOG definition of agriculture.



(c) PSEs for LAC countries-

The Inter American Development Bank (IDB) has been devoting increased attention to tracking

and promoting sound agricultural policies in order to enhance the impacts of providing increased

agricultural investments to its member countries. As part of this broader effort, over the last several

years, IDB commissioned studies in different LAC countries to assess and measure Producer Support

Estimates (PSE), including General Services Support Expenditure (GSSE), using standard OECD

methodology.

After country studies were completed, IDB has generated country-level databases on PSE

estimates, plus the data from OECD members Mexico, Chile and Brazil. An important part of the

calculations is related to public expenditures in the agricultural sector, both through direct or indirect

payments to individual farmers (in the PSE) and/or financing public goods via general services (GSSE).

The IDB has applied the PSE in 18 countries to date. These include Argentina, Belize, Bolivia, Colombia,

Costa Rica, Dominican Republic, Ecuador, Guatemala, Haiti, Honduras, Jamaica, Nicaragua, El Salvador,

Panama, Paraguay, Peru, Uruguay and Suriname. In 2013 13 countries plus OECD-3 were stored on a

beta web-site testing environment for a pilot use, located in an external server. The LAC PSE design and

data was tested with the participation of a broad experts and potential users including policy makers,

researchers and other beneficiaries. Countries included in the preliminary data base were: Argentina,

Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Jamaica, Mexico, Nicaragua, Peru and

El Salvador. Eight remaining countries, under revision, will be uploaded for the public release of the data

base in December 2013 or in 2014 including Suriname, Dominican Republic, Paraguay, Guatemala,

Uruguay, Belize, Panama and Haiti.

This initiative started in about 2006. It is currently in a pilot phase, and is expected to be

publically accessible in 2014. In the near term, IDB also intends expanding the estimation of PSEs and

related indicators to more LAC countries, while extending coverage of years (currently, it runs from 2006

to 2010, for most countries).

Regarding the methodological aspects, the initiative has a strong commitment to applying the

original OECD methodology as close and rigorously as possible, in order to assure comparability.

Following OECD approach, this effort seek the best available information at each country in order to fill

up the main categories of PSE and collective GSSE expenditures related to agriculture. Nevertheless, the

IDB has been developing expansion for regional needs. In that sense, in 2012 a valuable approach to

understanding how public policy, the private sector and institutional arrangements are related and create

obstacles to competitiveness were studied in Central America and the Caribbean by combining PSE and

Value Chain Analysis (VCA). IDB also has developed a framework to use the PSE framework to address

issues in agriculture and climate change, considering a) mitigation of emission, b) adaptation and c)

vulnerability by commodity and region. The extension of the PSE methodology to address issues in

climate change and agriculture will be discussed in a symposium in early 2014.

Increased IDB administrative and external sources are being sought to expand the PSE

estimations for LAC countries, which could also enable increased efforts to increase capacities at the

country level to sustain the updating of estimates and to respond to the demand for PSE estimates from

country authorities.



(4) Type D: Databases for Overseas Development Flows

(a) Creditor Reporting System

The OECD’s Creditor Reporting System (CRS) data initiative provides data on three aspects: (i)

Official development assistance (ODA); (ii) Official export credits (Private loans and credits under

official guarantee or insurance) and (iii) Other official flows. The objective of the CRS database is to

facilitate the study of sectoral and geographical distribution of aid and other official flows. The CRS aid

activity database, in particular, comprises data on ODA activities in recipient developing countries that

enables analyses on where aid goes, what purposes it serves and what policies it aims to implement, on a

comparable basis for all Development Assistance Committee (DAC) member countries and non-DAC

members and foundations.

Since its establishment in 1967 jointly by the OECD and the World Bank -- with the aim of

supplying the participants with a regular flow of data on indebtedness and capital flows -- the CRS has

evolved to respond to changing needs over the years. The database provides data on developing countries

or territories eligible to receive ODA covering the period from 1973 to 2012. Data consist of approved

commitments and disbursements, focusing on financial data but some descriptive information on each

project are also made available. The CRS sector classification, in general, contains five broad categories:

Social infrastructure and services, Economic infrastructure and services; Production; Multi-sector/cross-

cutting; and Non-sector allocable.

The CRS is an ongoing data initiative and forms a core function of the OECD’s DAC, since its

members provide about 95% of development assistance to developing countries. The OECD Creditor

Reporting System (CRS), which records ODA and Other Official Flows (OOF) at the project level, is

currently the most comprehensive and only source, when considering the allocation of assistance to

agriculture as well as other relevant sectors by recipient country and region. Other international agencies,

such as FAO, build on the CRS database (see below). Major methodological features of the CRS data

compilation strategy includes:

Reporting institutions report according to a set of directives that have been agreed on by DAC;

There is also a reporting format known as CRS++ that contains all data items needed for

reporting to the DAC aggregates. The data items are based on definitions and statistical

classifications established by the Working Party on Development Finance Statistics (WP-

STAT) and endorsed by the DAC. All ODA commitments and disbursements – both bilateral

and multilateral ODA – are reported in CRS++ at the activity-level, applying the agreed

classifications of aid by sector (and subsector), type of aid (e.g. budget support, project-type

intervention, technical assistance), type of finance (e.g. grant, loan and their terms, equity),

policy objective (e.g. gender equality, climate change mitigation and adaptation), channel of

delivery (e.g. NGO, PPP, UN agency, IFI, other multilateral institution), tied/untied aid status.

A number of integrity or reliability checks within the CRS are designed to help reporters avoid

inconsistencies. Members are encouraged to implement these integrity checks in their systems.

They are invited to review their reports using the Checklist prior to sending them to the

Secretariat.

The DAC Secretariat assesses the quality of aid activity data each year by verifying both the

coverage (completeness) of each donor’s reporting and the conformity of reporting with definitions (so as

to ensure the comparability of data between donors). Prior to any statistical analysis, users are advised to

examine the “coverage ratios” available on the website.



(b) FAOSTAT’s: Official Development Assistance (ODA) to Agriculture

Further to the summary above, FAOSTAT’s investment domain database is comprised of various

components, of which ODA to agriculture in broad sense is one component, hence this initiative is

covered under two types (A and D).

Given that most of the donors report their aid activities to the OECD, FAO has decided to move

towards harvesting ODA data from the CRS, with a specific focus on agriculture. FAO Statistics

Division, in consultation with OECD, is developing a comprehensive CRS-based dataset that supports

analysis of the destination of these flows worldwide and allows investigating the role of such investment-

financing source in developing countries. This approach will reduce duplication of efforts across

international organizations, and allow for greater specialization of statistical activities in areas of

comparative advantage.

(5) Type E: BOOST: Analytical and Database Tool for Supporting Public Expenditure

Databases/Analysis

The World Bank has been the entity which has carried out the largest number of aggregate and

sectoral public expenditure reviews in the developing world. Given that Governments are intensifying

their efforts to rationalize public expenditures, given fiscal constraints, and in the light of data constraints

as a constant challenges in these reviews, the Bank sought and developed a new software tool

(“BOOST”) to help enhance public sector performance, especially through compiling/organizing and

analyzing different aspects of public expenditures (aggregate and sectoral levels). BOOST collects and

compiles detailed data on public expenditures from national treasury systems, makes data available in a

format ready for granular analysis, and presents it in a simple user-friendly format.

BOOST has two main objectives:

- it is designed to help researchers, government officials to help improve public expenditure

management decisions, by examining trends in allocations of public resources, analyzing potential sources

of inefficiencies, and informing how governments can better finance the delivery of enhanced public

services. In countries where the BOOST database is public available, the BOOST enables civil society

and researchers to take informed views and positions on public expenditure allocations, efficiency and

effectiveness.

- BOOST aims at improving the quality of public expenditure analysis, dialogue, decisions, and

monitoring.

BOOST is at its infant stage. It was launched in 2008 and country engagements have been

undertaken in some 42-45 countries, of which 25 BOOSTs already delivered. Some 10-14 BOOSTs are

expected to become public available on a new Open Budget Portal which the Bank plans to launch later in

FY14. BOOST has been generally used to support country level public expenditure reviews, covering all

major sectors. More recently, there has been a growing application of BOOST to key sectors (thus far,

education and health), as part of in-depth sectoral expenditure reviews, and with interest from colleagues

in the agriculture sector articulated.

In summary, the main methodological aspects include:

World Bank works with Ministry of Finance to collect and compile data on all public

expenditures (budgeted and executed) in a given Country or Federal State (i.e., all expenditures

recorded by the country’s treasury system);



The BOOST team (consists of World Bank staff/consultant/Govt. counterparts) prepares a

comprehensive easy-to-use database covering expenditures across all sectors, all levels of

government and multiple years; and

The World Bank and Government counterparts use the database to develop new ways of

analyzing public expenditures and provide better policy advice across a variety of sectors. The

BOOST team is developing an online platform that will allow users to analyze public expenditure

data through web-based PivotTable and mapping interfaces to make the analysis of public

expenditures easy and accessible for a non-technical audience, as well as to get citizens and

policymakers thinking about the linkages between spending and results.

3. ASSESSMENT AND COMPARISON OF THE INITIATIVES

3.1 Assessment Criteria and Approach

The section above has summarized the main features for each of the data and analytic initiatives

being reviewed in this exercise. This chapter will focus on a comparative review, based on the following

criteria/approach:

according to the type of initiative, based on the typology framework outlined in Chapter 2, to

ensure comparability, while also recognizing that some inter-type comparisons may also provide

relevant insights;

focus on the following strategic aspects which could foster enhanced data and analytical quality

and inter-agency collaboration

o Objectives and unique features/value-added

o Scope of Coverage (in terms of period, frequency, level of disaggregation)

o Key Methodological Aspects (including comparison on key aspects, such as classification

system (where relevant), data compilation methodologies and documentation, approach

to making data adjustments, metadata documentation, other)

o Public Accessibility

o Strategies/mechanisms to link data users with suppliers

o Main issues and challenges

o Sustainability aspects

o Linkages to and Collaboration with Other Data and Analytic Initiatives

3.2 Objectives and Unique Features/Value-added


Although the central objective of each of the eight initiatives’ database is providing a wide range of

socio-economic data, which include public expenditure on agriculture and related activities, for

diverse stakeholders, there are certain attributes -- depth and breadth -- which differentiate one

databases’ objective from the other. In summary:

a) Four of the databases (ASTI, FAOSTAT- GEA, MAFAP , and ReSAKSS) aim in compiling and

disseminating highly disaggregated data that can be used to facilitate the assessment of

governments’ role in and contribution to agriculture and other activities that affect the agriculture

sector enabling comparability among countries and harmonization with other international

organizations compiling similar datasets;



b) Two of them (GFS and SPEED) endeavors to building and making available a comprehensive

and wide range of (general) public expenditure information to researchers, policymakers, donors,

and the broader development community;

c) Two of the databases (AGPE-LAC and WDI) aim in providing a wide range of relevant, high-

quality, internationally comparable statistics about development and the quality of people’s lives

for policymakers, development specialists, students, and the public, so that they may use the data

to reduce poverty and solve the world’s most pressing development;

d) Some of the databases are designed to complement the information obtained from the others by:

e) providing further disaggregation of information (e.g. FAOSTAT: GEA enhances and provides

additional “value-added” to the data already collected by IMF according to COFOG through the

disaggregation of IMF aggregates pertaining to government expenditure on agriculture (to level 3,

recurrent and capital expenditures); MAFAP adopts a broad definition of the agricultural sector,

and endeavors to use FAOSTAT data where possible, while compiling more disaggregated data

from country-level sources based on an expenditure classification system for the agricultural

public expenditure

f) tracking specific projects (e.g ReSAKSS tracks the progress of CAADP);

g) Compiling scattered publication of PE statistics into one platform (e.g. SPEED is the largest

publicly available public expenditure database. It incorporates local data from a large selection of

countries);

h) Providing primary data through institutional survey rounds that are not captured elsewhere, (e.g.

ASTI compiles information on inputs into agricultural R&D, rather than outputs or outcomes;

although the program is currently piloting the latter in a number of countries);

i) ASTI’s central objective, for example, is limited only to providing as much disaggregated data as

possible on the R&D strand of public agricultural expenditure. This makes it difficult for parallel

cross-referencing of aggregate information (such as overall share of agricultural public

expenditure out of general public expenditure, which ASTI does not compile, but an important

indicator in the assessment of public expenditure on the agricultural sector) across different data

sources. The same is true with other data initiatives, which provide certain aggregate information

on AGPE but not further disaggregation of relevant information on the performance of the

agriculture sector, in general;

j) This calls for further efforts to putting an integrated system in place that can draw relevant

agricultural public expenditure information from all data sources in one platform and ensure

complementarity of objectives of the data sources and two-way flow of readily available and

reliable information -- from aggregate to more disaggregated information and the other way

round.


As can be noted, there are both differences and similarities in their objectives, where each initiative

exhibits noteworthy value-added features:

all of the four initiatives focus on carrying out and disseminating analytical studies on agricultural

public expenditures;

three of the four initiatives focus on SSA countries, reflecting high priority on supporting the

implementation of the CAADP agenda through enhanced agricultural expenditure levels,

priorities/composition and management;

all studies endeavor to focus on generating and disseminating evidenced-based outcome and,

where possible, impact-level results;

although tracking of the Maputo targets is done by nearly all initiatives, ReSAKSS unique feature

is its explicit mandate to do it;

AgPER and RePEAA’s unique features include their strong capacity building materials.



MAFAP’s uniqueness lies in the level of disaggregation in expenditure analysis, as well as its

focus on country-level institutionalization;

all initiatives emphasize participatory approaches and strong in-country ownership, with a

concerted orientation to developing/promoting in-country capacities to sustain these type of

studies and expenditure “agenda” over time (although RePEAA takes a broader and global

dissemination/capacity development approach, with a focus on practitioners from development

organizations);

each initiative exhibits distinct unique features which distinguish it from other initiatives, and

therefore complement each other, although not an intentional strategy (e.g., MAFAP adopts a

broad definition of the agricultural sector, and executed by FAO; ReSAKSS focuses on

supporting the tracking and implementation of CAADP, and has a unique linkage with IFPRI;

SNAPE focuses on supporting basic agricultural PE analysis, supported by training modules, and

executed by the World Bank; RePEAA has a global coverage, developed a comprehensive

toolkit, and developed a comprehensive website with valuable resources, including global good

practice documents on country-level agricultural expenditure analysis).


The comparative assessment of the objectives and value-added of the PSE-related initiatives

highlights the following main findings:

a) These three initiatives share the goal of generating relevant data for agricultural policy analysis,

with a focus on measuring price distortions and AGPE structure and efficiencies. The use of

OECD methodology is clearly key for having a sound and consistent conceptual model in which

AGPE and other supports are classified according to policy considerations (the way in which

incentives are generated);

b) MAFAP was more clearly related to policy monitoring in the SubSahara African (or developing

countries context, more generally), so it shows important adaptations and expansions of OECD

approach in order to meet country needs (e.g.,value-chain analysis, key additional disaggregation,

expanded agriculture-related and supportive expenses).

c) The PSE for LAC initiative originated in a different policy context and requirements than the

PSEs for OECD countries, while still endeavoring to serve as a tool for enhanced policy

monitoring and measurement of a key sector of the LAC countries. It has tended to follow as

closely as possible the OECD PSE method, as comparability is considered a key element. IDB

PSE was generated as a result of work initiated in 2003 as part of its support to the agricultural

sector negotiations of the Central American Free Trade Agreement, and subsequently in other

regions in Latin America. The IDB has since used the PSE analysis to support policy dialog in

many countries in LAC to date, and its’ use for this purpose is established in the IDB Sector

Framework on Agricultural; and

d) Natural Resources Management document, approved by the Board of Directors.

(http://www.IDB.org/en/publications/publication-

etail,7101.html?id=69354%20&dcLanguage=es&dcType=All ;

e) It is therefore expected that the PSE database will contribute over time to a more to a better

understanding and basis for policy dialog on the objectives of food security, trade integration,

rural poverty reduction and competitiveness and adaptation to climate change. Some countries

http://www.iadb.org/en/publications/publication-etail,7101.html?id=69354%20&dcLanguage=es&dcType=All

http://www.iadb.org/en/publications/publication-etail,7101.html?id=69354%20&dcLanguage=es&dcType=All



like Haiti and Suriname have already committed to integrate this policy tool into their agricultural

information systems.

(4) Type D: Databases for ODA Flows

The common dominant feature of the two initiatives’ objective in this category is compiling and

providing a set of readily available data on ODA and Other Official Flows that enable analysis on where

aid goes, what purposes it serves and what policies it aims to implement, on a comparable basis. The

ODA (FAOSTAT) database, in particular, tracks flows of ODA and Other Official Flows worldwide in

order to show whether external assistance to agriculture as well as other relevant sectors distribution is

aligned with need or concentrated in a small number of countries. To this end, it compiles more detailed

data than the OECD’s CRS information on some agricultural activities/purposes and it refers to both

narrow and broad definitions of agriculture. Moreover, it augments the CRS data by continuing to collect

and maintain data for the activities related to agriculture provided by non-CRS reporters included in the

FAO broad definition of agriculture.

Currently, FAOSTAT is in the process of enhancing its database on external flows, and once the

relevant information is available (during 2013), the relevant aspects need to be considered, especially with

regards to the complementarities between the CRS and FAOSTAT/EFF databases. Some particular

attributes of the FAOSTAT ODA database includes that it has relatively broader coverage of:

Donors: more data/info on contributions, priorities, etc.

Purpose codes (primary and secondary activities in agriculture)

Definitions and concepts of narrow and broad of agriculture

Types of aid flows (e.g., concessional and non‐concessional)

(5) Type E: BOOST: Public Expenditure Analytical and Database Tool

BOOST is a unique initiative which is included in this review because it offers a promising and

innovative software tool which can integrate the generation of an agricultural public expenditure data base

(with sector disaggregated data) with facilitating expenditure analytical studies at the macro and sectoral

levels. Since BOOST is unique among other initiatives, and therefore there are not other comparable

initiatives, the following sections summarize the main features of BOOST. Where linkages can be made

with other initiatives, this will be highlighted below.

BOOST has the following objectives:

BOOST is designed to help researchers, government officials and ordinary citizens to examine trends

in allocations of public resources, analyze potential sources of inefficiencies, and inform how

governments can better finance the delivery of enhanced public services.

BOOST aims at improving the quality of public expenditure analysis, dialogue, decisions, and

monitoring.

The value-added features include:

BOOST is a new approach to collecting, combining, analyzing and sharing public expenditure data

aimed at improving the quality of public expenditure analysis by linking spending to results ;

A platform for expenditure dialog for client countries and WB engagement with clients; and

BOOST can provide information on how money is spent and by whom it is spent. Standard tables

showing trends in spending - broken down by the economic, functional, or administrative

classification of the budget (or any combination of each) - can be generated in a matter of hours

instead of weeks or months.



3.3 Scope of Coverage


The comparison of the scope of the eight databases under consideration reflects the following

common similarities and differences:

a) Although all of the databases contain certain information on public expenditure on agriculture,

the scope (sectors covered, level of disaggregation, updates and frequency of data, countries

and years considered) and depth of the information they provide varies to a higher extent.

While four of the databases (AGPE-LAC, GFS, SPEED and WDI) cover a wide range of data

besides expenditure information, four of the initiatives (ASTI, FAOSTAT- GEA, MAFAP and

ReSAKSS) focus only on expenditure in agriculture, rural development and related activities;

b) Three of the databases (GFS, SPEED and WDI) provide public expenditure data on agriculture

only up to the level of COFOG Level 2 classification, if not less (e.g., WDI17

). The rest (AGPE-

LAC, ASTI, GEA-FAOSTAT, MAFAP and ReSAKSS) go further and provide highly

disaggregated data beyond COFOG Level 2;

c) Five of the databases (FAOSTAT/GEA, ReSAKSS, ASTI, AGPE for LAC and WDI) provide a

wide range of non-expenditure data on different aspects of agriculture and food security. The

two of them (GFS and SPEED) do not cover such information. ASTI generates a wide range of

non-expenditure data including research staff, research focus, and funding sources

disaggregated at different levels and categories, generally, does not cover non-expenditure

information unless it is an important contextual matter which affects total expenditures in

support of food and agriculture sector. Databases such as ASTI should also consider including

relevant agricultural expenditure indicators, such as PAE as a share of total government

expenditure, which are pertinent to the analyses of allocation of resources in the agricultural

sector;

d) The countries or territories, thereby the regions, covered by the databases also varies across

initiatives. (See Figure 3, for the number of countries covered by each initiative in the group.)

Given the objectives of the initiatives and the organizational structures of the organizations that

host the initiatives, while half of them (WDI: 188; SPEED: 147; GEA: 134; GFS: 130) cover

more than 100 countries across the globe, the rest half covers less countries focusing only on

certain target regions – developing countries where agriculture plays a central role in their

economies;

e) Three of the long standing databases, except that of FAOSTAT’s GAE, for example,

collect/provide data going back to the 1960s, whereas, the other (relatively young initiatives)

collect/provide data since the early 2000s, except ASTI and ReSAKSS which go back to 1980

(ASTI also dates back to the 1960s for some countries);

f) All of the initiatives in this group, in general, compile/provide data at annual frequency;

g) The WDI database is included in this category only because it also supplies aggregate public

expenditure information for a large number of countries, and over time. There is potential scope

for WDI to include more disaggregated agricultural expenditure data (compiled from other

relevant and reliable sources);

h) The disparities in the scope, coverage and depth of information across the data sources have a

limiting effect on the effort of researchers and data users in their search for a comprehensive

datasets to conduct their studies. The data compilation work alone requires an inefficient use of

professional time hunting (searching) for a complete and relevant set of data from the dispersed



and fragmented sources. (We have faced this challenge even at the time of conducting this

review);

i) It is important to consolidate progress on annual data, before proceeding to compile data on a

shorter frequency (such as seasonal price fluctuations, expenditure outturns per quarter);

j) Initiatives should strive to cover as much countries and regions possible (while recognizing the

resource constraints faced by all of the initiatives);

k) Data initiatives also have to consider compiling budget information in addition to the

expenditure data they collect as it has paramount importance in the evaluation of commitment

of countries to live up to their promises in enhancing the agriculture sector.

Figure 3: Number of countries covered by each initiative under Typology A.


As can be observed, there both similarities and unique differences in the scope of each initiative,

including:

a) three of the four initiatives focus exclusively on SSA, and with MAFAP initiative focused on ten

target countries in SSA;

b) all initiatives endeavored to compile and analyze disaggregated expenditure data, to the extent

possible, with MAFAP and ReSAKSS making more progress in compiling and analyzing

disaggregated expenditure data for selected countries (and some of the same ones); many of the

country-level studies supported by SNAPE achieved varying levels of disaggregated expenditure

analysis, due to data constraints; and

c) There has been limited overlap of country-level agricultural expenditure studies involving some

of the same countries (e.g., MAFAP and the SNAPE studies have involved the same following

four countries --- Burkina Faso, Malawi, Mozambique and Nigeria; ReSAKSS (and its regional

nodes) covering most of the SSA countries. Where this overlap has occurred, the studies and

tracking activities have involved engaging and coordinating with many of the same “core”

country-level counterparts, and there have been some differences in methodological approaches.

There have not been major differences, and the outputs are generally well received by the main



counterparts (from the Ministry of Agriculture), and therefore, it appears that the studies have

been complementary to each other in these countries.


The comparative assessment of the scope of coverage of the PSE-related initiatives highlights the

following main findings:

a) Before 2006, the PSE for LAC database only has three OECD members (Brazil, Chile and

Mexico)18

. The IDB has applied the PSE in 18 countries to date. Some countries are under

revision, and the estimations will be uploaded for the public release of the data base in December

2013 or in 2014. Most of the calculations started in 2006 until 2010/11/12, and updates are being

considering annually. There are plans for building historical series for all countries in the

database for the past decade to increase the potential use of the database for time series and cross

country comparisons; and

b) MAFAP is focused on 10 SAA countries, with more intensive data collection and analysis in six

of them. The time span for PSE data is currently 2005-2011. There are plans for building

historical series with less aggregation, using 2005 as base year (based on CRS database of ODA

which starts that year).


CRS Aid Activity database is currently the most comprehensive when considering the allocation

of assistance to agriculture, as well as other relevant sectors, by recipient country and region at the

activity level. The dataset covers the flows recorded in both commitment and disbursement basis from

1973 to present, although the progressive improvement in donors’ reporting since the 90s should be taken

into consideration during the analyses.“CRS data provide only a partial estimate of ODA to agriculture

because they do not include all donors; many multilaterals are excluded as are emerging donors such as

China. There is a need for a more comprehensive dataset describing development assistance to

agriculture,” (Lowder and Carisma, 2011, p. 29).

(5) Type E: BOOST: Public Expenditure Software and Database Tool

The content of each BOOST is country-specific. By requesting raw data at the most

disaggregated sector level available in a country’s treasury/expenditure system, the resulting BOOST

database takes advantage of the full breadth and depth of the country’s budget classification system and

corresponding data. The data on expenditures, organized using all of the country’s budget classification

codes, is then compiled in one database that covers all sectors, all spending units, and all types of

expenditures recorded in the treasury system.

Nonetheless, all BOOSTs (referring to when the BOOST analytics are applied to a particular

country/sector) have some common features and contain expenditure information on the approved budget,

revised budget, and actual expenditure amounts.

The types of expenditure amounts are broken down by:

government level (central or local);

18 IDB database takes official OECD data for the three OECD members, Brazil, Chile and Mexico, therefore no duplication and

discrepancies may occur.



administrative unit (typically a ministry, department, agency, university, hospital, or school);

sub-national spending unit (such as districts, municipalities, towns and villages);

economic classification (wages, goods and services, capital expenses, etc.);

functional classification (sector and sub-sector);

program classification (if the country uses program based budgeting);

financing source (budget revenue, domestic or foreign borrowing, etc.); and

other classification

3.4 Key Methodological Aspects


In summary, the methodologies applied by each data initiative in compiling and disseminating data

exhibit the following similarities, and to a lesser extent, some differences:

a) Most initiatives develop user guide manuals and useful links to support and to encourage users

to understand the technical details of the methodologies, and to use the available data easily,

while some do not have this documentation available (e.g., AGPE for LAC). ASTI, for

instance, provides all relevant resources, such as operational and work plans, user guild manual,

background information of the project, etc., to the public without restriction. The GFS and

WDI, in particular, provide a thorough list of standards and codes (which includes the General

Data Dissemination System (GDDS), Special Data Dissemination Standard (SDDS)), manuals

and guilds, on their websites. In MAFAP, some capacity development/user guide material

is already online and a full capacity development package will be available at the start

of phase II (preparations ongoing). AgPE for LAC, GEA-FAOSTAT, MAFAP, ReSAKSS

and SPEED should also endeavor to publicly provide such relevant resources together with

their databases online;

b) While most of the initiatives use a questionnaire to collect data (see Table 3.1 which illustrates

the enhanced questionnaire used by FAO to collect AGPE data, with greater disaggregation

than the GFS), some initiatives (SPEED and AgPE for LAC) do not use a questionnaire; rather

they use different (secondary) sources of data. A systematic data collection process is used to

compile the SPEED Database from multiple data sources;

c) The timing of sending questionnaires for reporting countries and institutions or the data

collection effort, in general, should be carried out cautiously/wisely as it may have a critical

implication in gathering quality and comparable data at a reasonable cost of resources. Certain

data initiatives try to insure consistency of data collection by rearranging the timing for sending

questionnaires to reporting countries in accordance with other initiatives. The lesson from

FAOSTAT’s GEA data initiative worth consideration. FAOSTAT’s issues the questionnaire on

government expenditures to agriculture, rural development and related activities in about

September of each year, such that it follows about 2 months after the GFS questionnaire is

issued by IMF, to help ensure consistency with the GFS data;



Table 3. 1: Table Used as Questionnaire for Country Level Ag. Expenditure Data/ex.: Mauritius)

Note: Table was taken from a presentation made by FAO (Inter-Agency Collaboration in Fiscal

Statistics, 2013).

d) All initiatives have endeavored to compile time series agricultural expenditure data, primarily

at national level, with some databases attempting to compile sub-national expenditure data

(e.g., ReSAKSS);

e) The data initiatives also make different adjustments to the data they compile to adjust for

various factors including currency redenomination, inflation, etc. (e.g., GFS, FAOSTAT/GEA,

SPEED, ReSAKSS). This may suggest the need for closer collaboration across initiatives to

ensure consistency, complementarities and enhanced usage by analysts and policy-makers;

f) There is also a growing trend toward enhanced collaboration to ensure improved data quality

and consistency across initiatives. For example, Data Quality Assessment Framework (DQAF)

has been developed by the IMF, in collaboration with the World Bank, as a methodology for

assessing data quality that brings together best practices and internationally accepted concepts

and definitions in statistics. Such collaboration should be inclusive such that other data

initiatives who rely (or supply) for data to (on) these initiatives would be benefitted;

g) Although all data initiatives attempt to follow internationally accepted definitions and statistical

procedures to compile their data, we observe variations in the approaches to defining the

agricultural sector. Whereas some of the databases use a COFOG definition of the agricultural

sector (e.g., GFS, FAOSTAT/GEA, SNAPE), others (e.g., supported by MAFAP) have used a



broader definition of the agricultural sector (which also was influenced by the expenditure

classifications for estimating PSEs in accordance with the OECD methodology). This, coupled

with the other methodological divides mentioned above, resulted in data variation and

inconsistency across the different data sources;

h) As Lowder and Carisma (2011) noted, comparisons across the different datasets capturing

AGPE remain problematic because of the persisting variation in sector definition (whether it

includes forestry and fisheries, agro-processing, rural infrastructure, etc), usage of indicator

(e.g. levels, shares, intensities, per capita, etc.), adjustments made, time period and countries

covered. We reviewed four international datasets (GEA-FAOSTAT, GFS, ReSAKSS and

SPEED) to assess comparability in magnitude and trends in government spending on

agriculture, forestry and fishing, and other activities that may affect the agricultural sector.

These datasets represent the most current and comprehensive data available on resource flows

to agriculture;

i) The evidence we obtain from the four data sources, focusing on eight countries (Egypt,

Ethiopia, Ghana, Kenya, Lesotho, Mauritius, Tunisia, Uganda, Zambia) where we can find data

on the share of PAE out of the total public expenditure from these databases, reinforces the

following patterns: ReSAKSS database seems to have higher estimates; SPEED tends to

understate the share of PAE information; FAOSTAT-GEA (also shown as GES in the figure),

and GFS, on the other hand, tend to have greater convergence with their datasets, reflecting that

the FAOSTAT GAE database endeavors to work within the aggregates estimated by the GFS

(See Figure 4 for further details on the comparisons). The variation in information contained

among the four databases show minor change from 2006 to 2007 for these selected countries

except for Zambia, where the variation between SPEED and RESAKSS statistics, for example,

grows by 8% from 2006 to 2007 (see Figure 4).

Figure 4: Comparability of Agriculture Expenditure Statistics Across four Databases.

Sources: Authors’ computation based on data obtained GEA-FAOSTAT, GFS, ReSAKSS and SPEED databases.

05

1015

20

% s

hare

Egypt Ethiopia Kenya Lesotho Mauritius Tunisia Uganda Zambia

Share of Public Agricultural Expenditure (2006)

FAOSTAT_GES GFS

ReSAKSS SPEED

05

1015

20

% s

hare

Egypt Ethiopia Kenya Lesotho Mauritius Tunisia Uganda Zambia

Share of Public Agricultural Expenditure (2007)

FAOSTAT_GES GFS

ReSAKSS SPEED



Figure 5: Trend of Variation in Agriculture Expenditure Statistics across the Four Data Initiatives

Sources: Authors’ computation based on data obtained GEA-FAOSTAT, GFS, ReSAKSS and SPEED

databases.

(j) It has been well recognized that the aggregate government expenditure statistics provided by the

most referred and internationally recognized databases (such as the GFS and WDI), and even the

relatively young databases such as SPEED, has reduced importance to assess the allocation of

resources within agricultural expenditure. To this end, different researchers are proposing a more

transparent and flexible system in order to better track public resources allocation enabling

customized and consistent aggregation, promote open data access and provide information in a

timely manner to support evidence-based policymaking process. One of the possible way to meet

this objective is to develop a well-defined expenditure coding system, for example, by adopting the

chart of accounts, which organizes spending data according to numeric, alphabetic or a combination

of both codes, together with a flexible aggregation model or program;

(k) In Kenya, for e.g., the chart of accounts for the government expenditure is organized according

to a numerical coding system. The first 2-digit code represents the highest level of government

administration category, e.g., ministries or other ministry level government agencies. The following

3-digit code forms the second level of government administrative category, while the last 3-digits

seems to represent the programs or units within a department. For example, the code 10.103.260

represents the Farmers Training Center (a Unit), where the first 2-digit (10) stands for Ministry of

Agriculture and the first 3-digit (103) represents the Facilitation and Supply of Agriculture

Extension Service Department.

Such coding system allows more flexibility to aggregate data in addition to easing explicit mapping

relationship between the countries government finance statistics system and COFOG or any other

aggregation of classification. This makes sure that the aggregate data is no longer a black-box that

is unlikely to be inconsistent across countries and hence difficult for comparison.

-,8.0

-,6.0

-,4.0

-,2.0

,0.0

,2.0

,4.0

,6.0

,8.0

SPE

ED

-ReS

AK

SS

SPE

ED

-GE

A (

06

)

SPE

ED

-GF

S

ReS

AK

SS-

GE

A

ReS

AK

SS-

GF

S

GE

A-G

FS

Variation between 2006 and 2007

(% C

han

ge)

Nature of variation in share of PAE information across databases from 2006 to 2007

Egypt

Ethiopia

Kenya

Lesotho

Mauritius

Tunisia

Uganda

Zambia




The methodologies applied by each analytical initiative exhibit the following similarities, and to a

lesser extent, some differences:

a) All initiatives have endeavored to compile and analyze agricultural expenditure data, primarily

time series data at primarily at the national level, with some studies attempting to compile sub-

national expenditure data (e.g., RESAKSS);

b) There have been some variations in the approaches to defining the agricultural sector, with some

of the studies using a COFOG definition of the agricultural sector (e.g., supported by SNAPE),

whereas others (supported by MAFAP) have used a broader definition of the agricultural sector;

these latter one was influenced by the expenditure classifications for estimating PSEs in

accordance with the OECD methodology);

c) All initiatives applied “basic” type of descriptive analysis, analyzing historical trends and

composition (by major subsectors and capital/recurrent), and institutional budgetary management

processes according to the budgetary cycle, with resulting recommendations to improve

agricultural expenditure efficiencies;

d) In practice there have limited specialized expenditure analyses/results which have assessed the

quantitative impacts of agricultural expenditures on key target variables, such as agricultural

GDP, incomes and poverty reduction. This reflects data, capacity and resource constraints.

ReSAKSS country level and cross-country studies, together with IFPRI-sponsored research

studies, have tended to carry out more rigorous empirical studies, with an emphasis on assessing

expenditure-outcome linkages, and using quantitative methods to determine public investment

priorities and public investment requirements.19

This kind of quantitative analysis, which would

be useful to carry out periodically, illustrates the rationale for improving the expenditure database

and in-country analytical capacities. Since all of the analytical initiatives tend to include a

capacity building dimension, it would be important for future data and analytic initiatives to

ensure close coordination and complementarity of efforts, responding to the demands of country-

level decision-makers.


The comparative assessment of the methodological aspects of the PSE-related initiatives highlights

the following main findings, including some important methodological caveats:

a) Product basket for MPS estimations. In the PSE OECD methodology a basket of agricultural

goods must be selected to carry out PSE calculations (specifically for the Market Producer

Support-MPS estimation). This basket has to be representative of sectoral output, representing at

least 70% of the value of production, according to the OECD manual. Table 3.2 shows the

average values of this proportion in both databases. In LAC non OECD countries the rule of 70%

has also been adopted, although the exception is Honduras (57%). New updates and historical

revision for Central American countries will address this exception. OECD countries have

consistently values above 70% for the value of the basket20

. In the MAFAP initiative, the rule of

19

For example, several country level studies have been carried out by ReSAKSS researchers, since 2008 (e.g., S.

Kanyarukiga and B. Yu, “Agricultural Growth and Investment Options for Poverty Reduction in Rwanda

(ReSAKSS Working PAgPER No. 21, 2008). An on-going analytical study which is compiling available public

expenditure data for helping to assess updated public investment requirements and targets at the country level and

which will involve collaboration with ReSAKSS (and the regional and country-level nodes) refers to: “Analyzing

CAADP Targets and Agricultural Public Investment Areas “, concept note prepared by ESARO-IFPRI for AUC

grant, March, 2013). 20

It should be said that LAC countries tend to have a more diversified agriculture, with a production base which is

more diverse than the agricultures of industrialized countries and so it is more complicated to achieve the 70% of the



70% has been adopted and selected baskets generally surpass that proportion of agricultural GDP.

In contrast to LAC countries, African agriculture is not as diverse, so the target of 70% is more

feasible with a manageable number of commodities.

Table 3. 2: Proportion of GDP from basket used for MPS estimations in PSE/LAC initiative.

b) Links between MPS estimations and Public Expenditure Analysis. Both PSE for OECD

economies and PSE for LAC countries use a basic model for the law of one price for the MPS

estimation. It has the problem of not identifying how market failures and imperfections distort

incentives and disincentives to agriculture and the way in which these are received by agents.

The MAFAP proposal is to distinguish in the MPS estimation the "market development gap”,

which measures the extent at which market imperfections contribute to the price gap at different

parts of the value-chain. This approach allows for a very interesting interplay between MPS and

AGPE analysis, enabling one to observe if governments are orienting public resources to tackle

these imperfections, which generally affect severely farmers' income and agricultural

development. This seems to be a very promising avenue for supporting the efforts in LAC

countries through using an “expanded” OECD approach, as introduced and used by MAFAP;

c) The role of External Assistance and AGPE analysis. An important feature of MAFAP is that it

explicitly tackles the issue of external assistance (or ODA) and its impact on budgeted and non-

budgeted support to agriculture in the African context. This issue could be of particular

importance for some LAC countries, for instance in Central America and Caribbean countries in

which external assistance continues to be important. MAFAP uses the Creditor Reporting

System (CRS) from OECD to get and classify external assistance and integrate it in the AGPE

analysis, but for non-CRS donors data collection is more complicated;

GDP requirement using a manageable number of commodities for the estimations (no more than 10 or 12, for

instance).

% GDP % GDP

Argentina 73 Brazil 79

Bolivia 63 Chile 76

Colombia 69 Mexico 67

Costa Rica 40 AVG 74

Eduador 80

El Salvador 60 Canada 84

Honduras 57 European Union 73

Jamaica 65 United States 74

Nicaragua 79 AVG 77

Peru 69

AVG 65



d) Other expenditures. MAFAP has included separate modules for measuring public expenditures

which are not agriculture-specific, but important for food and agriculture sector. This approach is

very important and useful for a better assessment of government policies and their impact on

rural-based agriculture. Expenses on education, health and roads in rural areas are important

decisions which affect agricultural and rural development in the medium and long terms.

Managing these expenditures in a separate module allows one to ensure comparability of public

expenditures with traditional GSSE measurements;

e) Other useful disaggregation for AGPE analysis. An important disaggregation is between

allocated and actually expended resources, which allows to measure "expenditure capacity"; this

is a commonly notion of efficiency used in many countries for assessing how policy goals are

being addressed, using the budget. Equally important is to register administrative costs (which

are not considered in the OECD method) as a separate entity, which allows comparison and also

constructing indicators on the importance of these costs in agricultural expenditure. Finally,

MAFAP also allows for including Fishery and Forestry expenditure data, but separated in

independent modules. This flexibility is useful, while preserving the core method, expanding it to

useful areas for policy purposes.


FAO harvests ODA directly from the CRS, developed and maintained by the OECD. In

consultation with OECD, FAO is developing a dataset that supports analysis of the destination of these

flows world-wide and allows investigating the role of such investment financing source in developing

countries. Data compiled to the CRS are collected by CRS++, a reporting format, which consists of a

number of integrity or reliability checks within the CRS and is designed to help reporters avoid

inconsistencies.


As highlighted in the previous sections, the BOOST team at the World Bank prepares a comprehensive

easy-to-use database covering expenditures across all sectors, all levels of government and multiple years.

Figure 6 illustrates the three core dimensions of BOOST, in terms of: administrative/organizational

location of expenditure agencies; functional/programmatic areas covered; and economic classification.

This same methodology can be applied to a specific sector, such as agriculture. See Annex A, Table 3

(item 1 (o)) for further details on the methodological aspects used for BOOST.21

Figure 6: BOOST: Linkages between Organizational Location, Economic Expenditure Type and functions.

Figure 7: BOOST:

21

For further details/links on the methodological aspects and country examples, see

http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPUBLICSECTORANDGOVERNANCE/0,,content

MDK:23150652~pagePK:148956~piPK:216618~theSitePK:286305,00.html (BOOST and Government and Public

Sector Management home page.); http://www.opendata.go.ke/ (Kenya’s BOOST, a country example applying

BOOST);

http://data.gov.md/data/?did=107 (Moldova’s BOOST, another country example).

http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPUBLICSECTORANDGOVERNANCE/0,,contentMDK:23150652~pagePK:148956~piPK:216618~theSitePK:286305,00.html


http://data.gov.md/data/?did=107



Example of How BOOST Can Help Compare Sectoral Budgetary Allocation Changes (in Good and in the

Bad Times – Example of Bulgaria).

The steps involved in engaging with and delivering to Government the BOOST methodology and

country-level package (developed jointly with them) include the following (also applicable if working

with the Ministry of Agriculture):

Final draft version of the database, for clearance of the Government;

A User Manual, and technical supporting documentation on how the database was constructed;

Training sessions on how to construct, maintain and update the database;

Joint training – Policy dialog where Bank and government explore the database, in support of

expenditure analytics; and

Standard Tables for Bank’ PER or for the client, supporting, as example, the annual budget

preparation

Thus far, of the 20 countries which have applied BOOST, they have been applied at the country level,

including agriculture as one of the sectors. The BOOST methodology can be applied to the agriculture

sector (or other sectors).

3.5 Public Accessibility Aspects


In summary, the public accessibility aspects highlight the following main patterns and conclusions:

a) Nowadays access to data is one of the most pressing development issues in the development

arena. Information gather by any organ has no or little use unless citizens have access to the

data at an affordable price. To this end, except the AGPE-LAC and GFS databases, all are

publicly and freely accessible either in the form of printout, CD-ROM, Data-Catalog, or online.

While certain data can be retrieved from the IMF’s databank on GFS, it is, in general, available



by subscription with a fee. On the other hand, the AGPE-LAC database system is still being

developed, and there is limited accessibility to the database (on request).

b) In support of enhanced transparency and usage, IMF should endeavor to make their data on

public expenditure, in particular, publicly available for free.

c) Currently, the AGPE-LAC database system is still being developed, and there is limited

accessibility to the database (on request), and there is public accessibility to the documentation

(until after the launch stage)

d) Allowing citizens to obtain data by principle alone is nothing by itself unless data initiatives

employ innovative data presentation and extraction techniques to allow easy and optimal usage

of data by their clients. Most of the young databases, in particular SPEED, should endeavor to

employ innovative data presentation and extraction techniques to allow easy and optimal use of

their datasets by users. ASTI and GEA-FAOSTAT, for example, can include a feature that

allows selection of all countries at a sign click to avoid a separate selection of each and every

country, which is unwise and time inefficient. ASTI has also to make sure users can extract data

in a friendly spreadsheet so that they can be able to do further analyses with the data.

e) The initiatives have also to devise different strategies (such as through distribution of published

datasets on different events where potential users but who has not the luxury of internet and IT

facilities may participate, though advertisement about their database) so that their data can be

easily and readily consumed by the large public, researchers and policy makers at different

levels of national authorities (e.g. the Ministries of Agriculture). (It is evident that most

government offices in the developing countries have not at all (or poor) Internet access. Hence,

availing published data for these people worth consideration.)


In summary, the comparative assessment reveals the following public accessibility aspects:

a) all analytical initiatives endeavor to make publicly available the final report, once it is cleared by

the relevant Government. It appears that the time required to secure Government clearance varies

considerably according to Government and initiative;

b) all of the four initiatives have a sound and comprehensive website, which are all publically

accessible. ReSAKSS has a website with more updated resources on a broad range of agricultural

development topics vis-à-vis the other initiatives;

c) only the World Bank’s RePEAA website requires a sign-in password for accessing the various

training materials, which is accessible only to World Bank staff members.


The comparative assessment of the public accessibility of the PSE-related initiatives highlights

the following main findings:

a) The PSE for LAC initiative is starting a phase of dissemination which must attract interest from

policy makers and researchers.. MAFAP seems to be transiting in that direction, which is also a feature

of the long standing PSE approach promoted by the OECD;

b) It is important to have databases with solid country assessment reports which are key for policy

analysis and guidance. This practice is taken from the original OECD method and has been improved by

MAFAP, and to a lesser extent by the PSE for LAC;

c) Methodological documents are key for attracting researchers and policy makers to the use of

these data, so efforts in that direction in MAFAP and PSE for LAC are crucial for improving the

relationship with users;



d) The use of webpages and websites are very important for the three initiatives, allowing rapid

access by users. In all cases, however, users cannot access the original databases, which are of particular

importance for comparative policy analysis. It is always difficult to download data case-by-case.


a) Both CRS and FAOSTAT provide their databases on ODA activities to the public without any

charge. Currently (at the time of this draft report preparation), the FAOSTAT ODA data is

working to make publicly available their database, which is expected to go live on November,

2011;

b) The CRS does not share other information such as the export credit to the public. OECD has,

therefore, to broaden the coverage of datasets it publicly available;

c) The two initiatives have also to devise different strategies (such as through distribution of

published datasets on different events where potential users but who has not the luxury of

internet and IT facilities may participate, though advertisement about their database) so that

their data can be easily and readily consumed by the large public, researchers and policy

makers at different levels of national authorities (e.g. the Ministries of Agriculture). (It is

evident that most government offices in the developing countries have not at all (or poor)

internet access. Hence, availing such published data for these people worth consideration.


No expenditure data generated using BOOST is disclosed without governments' consent. At the same

time, making such data publicly available signals a government’s strong commitment to enhanced

transparency. By early 2014, the World Bank expects to launch an Open Budget Portal of available

BOOST databases. The first batch of countries includes 14 BOOST databases.

3.6 Strategies/Mechanisms to Link Data Users with Suppliers


In summary, the strategies/mechanisms the eight data initiatives follow to link data users with

suppliers highlights the following main patterns and conclusions:

2. In line with making data accessible to users for all of the initiatives, in general, exert different

efforts to link data users with producers by:

promoting expanded usage of their database through enhanced communication strategies

and a variety of channels, such as by organizing and participating in country and regional

workshops. The aim of these diverse mechanisms is to create awareness about their

database, actively disseminating their data, and encouraging greater use of their database by

target audiences/users; using innovative and effective approaches (such as API, Bulk

download, Query tool, Mobile app and web portal system receiving questions and

suggestions from the public) and keeping an updated, comprehensive and user-friendly

database;

fostering expanded collaboration with strategic groups, international agencies, regional

development banks, donors, and other partners; and;

offering technical assistance and financial support for statistical development; and,

providing training and seminars to target groups/users.



3. At the same time, there appears to be some differences in approaches and style, which reflect

some of the unique features of each initiative and the specific stakeholders which have been

targeted by each initiative. While the matured (long standing) data initiatives such as the IMF

and WDI focus on continuing making enhancements to their databases based on improved data

accessibility policies, the young data initiatives employ versatile strategies including organizing

and participating in workshops and disseminate their datasets and through developing

comprehensive website and extensive publications to ensure awareness and optimal use of their

databases.


The following comparative patterns on linkages emerge from the above initiatives:

a) All of the initiatives include specific and common mechanisms and processes to link

suppliers of the analytical studies with users, mainly through report dissemination workshops

and user friendly websites;

b) Most of the initiatives have targeted both users and suppliers in their dissemination

workshops, including key actors/decision-makers from sectoral Ministries (Agriculture, other

relevant ones) and central Ministries, most notably, Ministry of Finance;

c) There are some relatively minor differences in approaches and styles to fostering linkages

among users and suppliers, which reflect some of the unique features of each initiative, the

sponsoring organization(s), and the specific stakeholders which have been targeted by each

initiative;

d) It appears that all initiatives have had limited systematic follow-up to the large number of

workshops convened, which suggests there is scope for enhancing the linkages in a more

institutionalized and continuous manner, especially according to the budgetary cycle of the

relevant countries. Such enhancements, to the extent they actively engage policy-makers

(especially from the Ministry of Finance), can contribute to institutionalizing processes for

identifying and designing more policy-relevant agricultural expenditure studies, and helping

to create stronger incentives to strengthen expenditure data systems.


The comparative assessment of the strategies/mechanisms to link data users with suppliers of the

PSE-related initiatives highlights the following main findings:

a) PSE for OECD and emerging economies method of measuring support to agriculture has a long

trajectory of almost three decades and has been tested and analyzed by many researchers and policy

makers. Therefore, it has a strong methodological basis with easy public access to official manuals and

documentation. In this case, there was limited previous discussion with governments or policy

makers/researchers about the more specific goals and needs to be addressed and to engage them actively

in the methodology from the outset, but there was a clear commitment to apply the OECD method as

closely and rigorously as possible in order to take advantage of comparability across countries and

regions; and

b) MAFAP had substantive upfront discussions and orientation with key country-level actors on

goals and methods before applying the OECD method to African countries. This has enabled MAFAP to

secure ownership and to introduce important adaptations and innovations which are useful and attractive

for policy makers and researchers. This participatory approach can be fruitfully taken up by the IDB

initiative in its next phase, now that its pilot phase is being completed, thereby involving policy makers

and researchers in the required adaptation and expansion to LAC agriculture realities.




Both data initiative use different strategies to promote the optimal usage of their databases.

a) FAOSTAT conveys workshops with key practitioners and suppliers of expenditure data to

inform and seek further inputs on the Investment Dataset initiative, and inputs to improve its

design, implementation aspects, usefulness, and is seeking ways to strengthen the linkages

among the four components of the Investment database (Credit to Agriculture, Government

Expenditure on Agriculture and Rural Development, Official Development Assistance to

Agriculture, Foreign Direct Investment in Agriculture), as well as to strengthen users and

with suppliers.

b) The OECD endeavors to link CRS data users with suppliers though improving its website,

providing an on-line tutorial, organizing regional and national training workshops and

collaborating with other initiatives. OECD is working to establish a CRS Learning Center

which is facilitating the links between users and suppliers of the CRS database.


BOOST is using various modalities to promote its use by key decision-makers. It is still in its early stages

of application, with 21 countries having applied it for their expenditure reviews (as of end June, 2013).

The participatory approach followed has included, orientation, training and dissemination workshops

involving a large group of stakeholders, including key policy and budget decision-makers. A web-based

application has been developed that allows citizens easily access to public expenditure data. There is a

demonstration video of the BOOST Excel Pivot Table on the BOOST’s home website.22

Twelve

countries have agreed to make their BOOST data available, in support of public expenditure debates and

discussions with civil society organizations in the countries. Currently, four countries – Kenya, Moldova,

Paraguay and Togo have published BOOST data sets on country portals. Annex 3, Table 3 includes an

appendix table which lists the 21 countries which have delivered BOOST datasets/analyses, the countries

which have made their BOOST publicly available, and the countries which are in the pipeline.

3.7 Main Issues and Challenges


The scrutiny of different attributes of each and every data initiative under consideration highlights

the following main conclusions regarding the challenges to further enhancing their databases and

making available quality and timely data for their users:

a) It is vivid that compiling, organizing and disseminating sensitive information such as

government expenditure of diversified countries, found at different level of socio-economic,

political and technological development, is confronted with serious challenges and obstacles.

The data initiatives under review, nevertheless, exert efforts to provide as quality data as

possible withstanding all these challenges. The most pressing challenges the emerging and

young data initiatives under review encounters includes, among others:

constraints in resources and capacities, lack of incentives and commitment of countries

(including the crucial Ministries of Finance) to compile and disseminate relevant data at the

desired level of disaggregation, coverage and frequency;

22 http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPUBLICSECTORANDGOVERNANCE/0,,contentMDK:231506

52~pagePK:148956~piPK:216618~theSitePK:286305,00.html.





lack of awareness among senior government officials on the importance of internationally

comparable data;

uneven quality of underlying source data, including accounting systems, across reporting

countries;

fragmented sources of expenditure data, which sometimes are not reconciled;

difficulty in measuring some indicators (e.g., R&D outputs are notoriously difficult to

measure at the national level and over time);

inadequate and unsustainable funding strategy to implement and sustain the databases of

the initiatives, since many of them are project-based (e.g. ASTI, ReSAKSS, SPEED);

b) All of the data initiatives highlight the challenges of obtaining reliable and disaggregated

agricultural expenditure data at regular bases. This suggest the need for a global coordinated

effort in addressing the problem;

c) These challenges calls for the need for a global coordinated effort and efficient use of

resources. There is a recognition that compiling and disseminating agricultural expenditure data

require continued external funding, while at the same time the need to find improved modalities

for strengthening the active engagement of key agencies and actors in Government (especially

Finance and Agriculture) structures to address data gaps and ensure a timely flow of data series.

The need to enhance the relevance and user-friendliness of databases at national levels (e.g.,

ASTI has been less successful in reaching national-level stakeholders, for a variety of reasons;


The above initiatives highlight the following similarities and differences involving future challenges:

a) All of the analytical initiatives highlight the challenges of obtaining reliable and disaggregated

agricultural expenditure data to underpin their evidenced-based expenditure studies;

b) While none of the initiatives have addressed frontally the data constraints, several of them are in

the process of developing strategies and actions to address the issues in a next phase (e.g.,

MAFAP, SNAPE, ReSAKSS). This suggest the need for a coordinated approach;

c) All of the analytical initiatives were carried out by external consultants, with concerted efforts

to engage key local counterparts (from MOAs). This reflects the capacity constraints at the

country level, and highlights the challenges to increase the demand, capacities and incentives at

the country level to carry out periodic analytical studies (“lite” approach) to help underpin the

budgetary planning and resource allocation decisions (as part of the budgetary cycle);

d) There is growing recognition of the vital role of the Ministry of Finance to get more actively

engaged in promoting and funding analytical studies to help underpin resource allocations

proposals and decisions (e.g., SNAPE);

e) There is a recognition that the agricultural expenditure studies, especially specialized ones,

require continued external funding, while at the same time the need to find improved modalities

for strengthening the active engagement of key agencies and actors in Government (especially

Finance and Agriculture) in the design and carrying out of such studies (e.g., SNAPE’s efforts

to promote a “lite” approach to the expenditure studies which could support the budgetary

planning cycle, which was done successfully in Tanzania);

f) There is the need for external agencies to coordinate closely in the design and conduct of

analytical studies where they involve the same countries to avoid duplication of efforts and to

foster complementarity and synergies, where there are distinct unique objectives (e.g.,

ReSAKSS to coordinate closely with NEPAD/NPCA; MAFAP and SNAPE held a workshop in

June 2013, to exchange lessons learned);



g) External agencies will need to play an important role in supporting analytical studies in

agricultural expenditure work, especially in sharing and promoting the application of sound

methodologies, good practices and cross-country comparisons and lessons, and building local

in-country capacities. At the same, all of the initiatives face challenges in mobilizing the

resources to sustain such efforts.

(3) Type C: Databases for Producer Support Estimates/PSEs and Related Indicators

The comparative assessment of the issues and challenges of coverage of the PSE-related initiatives

highlights the following main findings:

a) The three initiatives face different challenges according to their level of maturity. The PSE for

OECD economies is a long standing system which seeks to expand the number of countries

(especially important emerging economies) in order to be more useful for policy discussions at

multilateral levels, such as the WTO;

b) The PSE for LAC initiative has been effective in opening a dialog on the structure of support

for agriculture in the region, and the need to increase expenditure on General Services, while

addressing high levels of Market Price Support, which create distortions to producers and

increase costs for basic foods, affecting primarily low income consumers. As the use of the PSE

analysis is expanded in the region, it is expected that more effort will be made to use

complementary kinds of tools, such as Value Chain Analysis, and to strengthen the reliability of

governments’ data collection on production, trade and prices to improve the capacity for

agricultural policy analysis in the region;

c) MAFAP is in a crucial stage in which it requires to show its potential, especially in terms of the

interplay between PSE and AGPE analysis, and to advance in the acceptance and use of the

analysis by governments and policy makers in Africa so it can be institutionalized and

supported also by expanded resources in its next phase.


The following issues are highlighted as challenges for both data initiatives under review:

a) Constraints in resources and capacities, lack of incentives and commitment of countries to

compile and disseminate relevant data at the desired level of disaggregation, coverage and

regular basis;

b) There are some donors which do not report their aid data through the CRS system. Only one of

the top ten bilateral donors to Burkina Faso, China Taipei, reports to Aid Management

Platform. The Chinese Taipei does report its aggregate DAC statistics to the DAC Secretariat,

and not the activity level data.


The main issues/challenges being faced and addressed to varying degrees by the BOOST team (in the

World Bank, working together with country-level counterparts), include:

a) BOOST tool and database does not address underlying data quality issues, and rather tends to

compile existing data, which can be of varying levels of quality and reliability; these issues

would need to be addressed as a separate exercise/process, whereby BOOST often facilitates this



process; at the same time, BOOST has developed various quality standards (see website for

further details);

b) Since the BOOST is applied/adapted to each country situation, it is intended more for application

on a country level basis, rather than to compare expenditure analysis/results across countries; to

the extent there is a common budget classification system/format used for different countries, this

will facilitate comparability across countries;

c) BOOST is in the early stages of scaling-up, and efforts to ensure sustainability are prioritized.

This includes training and capacity building activities to government officials to maintain and

sustain the data sets..

3.8 Sustainability Aspects


The following sustainability patterns and strategic options emerge from the above 8 data initiatives:

a) Ensuring the sustainability of the data initiatives should be at the core of governments’,

development partners’ and other stakeholders’ tasks and priorities who aspire to see a long-term

positive impact on the development of the agriculture sector in the developing economies, in

particular. As a consequence of the number of challenges the data initiatives face, as outlined in

section (6) above, the young and project-funded initiatives, in particular, have to design and put in

place a sound sustainability strategies;

b) Currently half (four) of the eight data initiatives (ASTI, MAFAP, ReSAKSS and SPEED) in

this category are project-funded, and do not have clear sustainability strategies for their

continuation, except for helping to build initial capacities at the country level (in the case of

ReSAKSS, building capacities at the sub-regional/Regional Economic Commissions and

Continental levels (NEPAD). The other four data initiatives are well established and included in

the core part of the permanent programs of the hosting organizations and have no sustainability

issues, at least, in the short-run;

c) Three of the project-funded data initiatives plan to have follow-up phases to continue and to

expand their on-going activities, including supporting public expenditure analytical studies in

expanded target countries. These initiatives also are focused on SSA countries, and aim to

support the implementation of the CAADP agenda;

d) A close observation of the origin of the four project-funded databases reveals that, except in the

case of SPEED, they all were initiated in response to strong data needs by other emerging

regional and global initiatives. While SPEED was established by internal demand from the

IFPRI’s research focus on and increasing policymaker demand for clearer assessments of public

expenditure-outcome-impact linkages, the ReSAKSS database and its structural and

organizational evolution at the continental, regional and country levels, on the other hand, was

stimulated in response to the implementation requirements of CAADP. The success of CAADP

will, therefore, influence the success of the ReSAKSS database and the entire initiative one way

or the other;

e) The survival of the four project-funded data initiatives still depends on the generous funding of

donors. They need to design a strategy to secure sustainable funding to meet their short-term

and long-term objectives. They have to prove themselves as important sources of information

to different stakeholders to guarantee their sustainability. The ASTI data initiative could be a

good example for this. It was at an ad hoc level at IFPRI until it proves significant

achievements and evolves to a sustainable system of up-to-date data compilation and analysis

in agricultural R&D since 2001, thanks to the generous support of donors;



f) It will be important for each data initiative to devise an explicit “exit”/sustainability strategy

regarding the continuation of demand-driven public expenditure data, preferably as part of the

budgetary cycle at the country level and explicit capacity development strategies/interventions

with established organizations at the country, regional and continental levels;


The following sustainability patterns and strategic options emerge from the above 4 analytical

initiatives:

a) Currently three of the four initiatives are project-funded (MAFAP, ReSAKSS and SNAPE),

and do not have clear exit and sustainability strategies for their continuation, except for

helping to build initial capacities at the country level (and in the case of ReSAKSS, building

capacities at the sub-regional/Regional Economic Commissions and Continental levels

(NEPAD);

b) Three of the four initiatives plan to have follow-up phases to continue and to expand their on-

going activities, including supporting public expenditure analytical studies in expanded target

countries. These initiatives also are focused on SSA countries, and aim to support the

implementation of the CAADP agenda;

c) As the analytical programs move into their expanded phase, there is scope for closer

collaboration and coordination of efforts among the various agencies (FAO, ReSAKSS/IFPRI

and WB), especially with regards to strengthening the disaggregated agricultural public

expenditure data bases which would be of common benefit;

d) It will be important for each initiative to devise an explicit “exit”/sustainability strategy

regarding the continuation of demand-driven public expenditure studies, preferably as part of

the budgetary cycle at the country level and explicit capacity development

strategies/interventions with established organizations at the country, regional and continental

levels; and

e) It is understood that as development partners (DPs) carry out their public expenditure reviews

to help underpin their future assistance strategies, the DPs can build on the relevant public

expenditure work stimulated by the above (and other) analytical initiatives.


The comparative assessment of the sustainability of the PSE-related initiatives highlights the

following main findings:

a) Sustainability of the PSE methodology will increase in LAC if it is more linked to the demand

which has emerged from the key government entities (Finance, Planning, Agriculture) involved in

agriculture policy for better analytical tools to monitor their performance and make comparisons

across time and in a regional context; and

b) MAFAP may face the highest challenges for sustainability as it is a relatively high cost

initiative23

and its dependence on external financial support. It is very important that this

initiative finds clear support from African governments which see it as a useful tool for policy

analysis and decision making at country and regional level. In the IDB case the challenge is to

23

It is estimated an initial average cost of about US750, 000 per country, although the cost is expected to be

lowered in the next phase, given the first phase had to invest in vital developmental costs to develop the

methodologies and modalities for operation.



get support for the initiative from policy makers and researchers so governments start to see it as

important for policy making which can be replicated and used.


Both data initiatives are ongoing data compilation efforts and part of the core functions of their

respective host organizations. Their foundation is well established on strong data demand from

within their organization and other stakeholders outside their organizations. Consequently, there is

no immediate treat or concern on their sustainability.


With the increased successful use of BOOST to support the World Bank’s operational programs,

this tool is in the process of becoming institutionalized within World Bank operations. Thus far, there are

early signs that counterpart governments appear to be adopting BOOST as their own tool to support

enhanced public expenditure analysis and budgetary allocations decisions. Moldova, Paraguay, Kenya

and Togo can be mentioned as good examples. There is still no instance where BOOST has been used

exclusively to support agricultural public expenditure analyses, hence its sustainability for the sector is

still untested.

It is suggested that practitioners in carrying out public expenditure analysis may wish to adapt the

BOOST software to support public expenditure reviews, while at the same time taking steps to transfer

this tool to country-level counterparts. Given the active engagement in SSA, it would be appear that one

of the above initiatives may wish to include BOOST as one of its data-gathering and analytic tools, to test

the value-added of applying this tool for the agricultural sector (there has been reported successful

applications in the health and education sectors).

3.9 Linkages to and Collaboration with Other Data and Analytical Initiatives


The comparison of the eight database initiatives highlights the following conclusions:

a) One of the panaceas for the multifaceted challenges, sustainability issues, provision of reliable

and comprehensive datasets at regular periods, and efficient use of resources is enhancing linkage

and collaboration between and within data initiative. Since all of the initiatives under

consideration are both users and/or suppliers of agricultural expenditure data, there are reasonable

and variable levels of collaboration and inter-dependence across their databases in terms of

sharing resources, data collection and dissemination methodologies, and channels of

dissemination, among others. At the same time, there is scope for further enhancing more direct

and proactive collaboration to improve efficiencies and completeness among the databases, while

recognizing the specific objectives of each initiative. For example:

FAOSTAT’s government expenditure on agriculture database heavily depends and builds on

the format of the standard IMF’s Government Finance Statistics Manual;

SPEED has varying links with several data expenditure initiatives (e.g., GFS, ReSAKSS,

FAOSTAT, especially as SPEED endeavors to disaggregate agriculture expenditure data

below level 2 of COFOG);



MAFAP collaboration with the World Bank’s strengthening of Ag. PE Analysis in SSA,

ReSAKSS, AU/NEPAD for monitoring CAADP implementation, especially the budget

allocation target; and

IMF/STA, in collaboration with the World Bank and the OECD, has established an online

database with access to public sector debt statistics; ASTI’s, in particular, relies on a

“network” approach by establishing and cultivating partnerships at national, regional and

international levels for data collection. It establishes “collaborative agreements” with national

research centers

b) Despite the encouraging efforts by some of the data initiatives, there is still a room for further

enhancement of more direct and proactive collaboration to improve efficiencies and completeness

among the databases, while recognizing the specific objectives of each initiative. We have, for

example, shown in the Comparative Analyses part of the methodologies that while ReSAKSS

exaggerates information, SPEED understates the share of PAE information although there is close

collaboration between the two initiatives and among other initiatives where they both draw the

AGPE information. The prevailing data inconsistencies, incompleteness, and difference in the

depth and coverage across the databases of the different initiatives dictate the need for further

collaboration and optimal harnessing of resources.


The above initiatives demonstrate the following main patterns of similarities and differences in

collaboration linkages:

a) All of the analytical initiatives have endeavored to collaborate with at least one other initiative,

although limited in scope (e.g., informal discussion of data bases; workshop to exchange relevant

challenges and lessons, such as SNAPE and MAFAP identifying strategies for addressing the data

constraint issues). It appears that much of this collaboration takes place on an informal basis, on

the initiative of specific individuals, rather than an institutionalized approach, with some

exceptions (e.g., MAFAP has reached out to the World Bank for systematic collaboration). There

are positive signs that this is gradually changing, whereby each institution is seeking to formalize

greater collaboration where there are common interests in enhancing expenditure data bases and

the carrying out of relevant and demand-driven expenditure studies, while fostering greater

ownership and leadership by the host countries. Accordingly, the key actors from most of the

initiatives recognize that it is preferable that country-level actors (especially Ministries of Finance

and Agriculture) take a more proactive lead in fostering and ensuring coordination and

collaboration among external actors in carrying out expenditure analyses (e.g., trying to avoid

multiple expenditure studies, preferably supported as joint efforts, led by Government or regional

bodies, such as NEPAD);

b) There is expressed interest and commitment by major external agencies (FAO, OECD, WB,

IFPRI, IDB) to strengthen coordination and collaboration mechanisms to improve the scope and

reliability of agricultural expenditure databases, especially to help ensure more disaggregated

expenditure data (below COFOG level 3). It is widely recognized that these improvements are a

“global public good” which would enable enhanced and more in-depth country and cross-country

expenditure analyses, and thereby contribute to enhanced expenditure priorities and allocations.

There seems to be relatively less of a commitment/orientation to jointly carrying out agricultural

expenditure analysis, although there is a clear recognition of the need to better coordinate and to

engage key stakeholders at the country level. At the same time, a positive example of joint work



was IFPRI’s (and ReSAKSS’) analytical contributions on agricultural public expenditure analyses

to FAO’s SOFA for 2012, which devoted strong attention to agricultural public and private

investment messages;

c) All of the initiatives (MAFAP, ReSAKSS, SNAPE) are taking concrete steps to enhance the

commitment and capacities of the countries to improve their expenditure databases and to

undertake expenditure analyses (“lite” version/approach) as part of strengthening their budgetary

planning processes and allocation priorities (e.g., organizing toolkit training and study results

dissemination workshops);

d) One initiative (RePEAA) has given impetus to the launching of a regional initiative within the

same WB organization (SNAPE covering selected countries in SSA);

e) Strengthening the linkages among these analytical initiatives along the lines outlined above could

facilitate sound exit and sustainability strategies for demand-driven analytical activities (ref.

section above on sustainability);

f) The analytical initiatives are quite strong in SSA countries, but not in other regions. As

agricultural public expenditures still play an important role in these other regions in contributing

to macro and sectoral objectives, it would be useful for development partners to consider

promoting and supporting in a coordinated manner a program of demand-driven analytical

expenditure studies in these regions, and integrated in the budgetary cycles of the respective

countries.


The comparative assessment of the linkages to and collaboration with other data initiatives of the

PSE-related initiatives highlights the following main findings:

a) There are clear beneficial opportunities for collaboration among these three initiatives. In the

case of LAC countries, IDB initiative can be highly enriched using some of the adaptations and

expansions of MAFAP to the OECD method in the African context. For example, it would be

important to include for lAC countries the expenditure on agricultural support sectors (rural

health and education, national and regional roads) in a more systematic way. It should be

mentioned also that in LAC countries there is also important heterogeneity, i.e. Central America,

Caribbean, Andean, South Cone, MERCOSUR, Federal countries like Mexico, Argentina and

Brazil. Furthermore, FAOSTAT initiative, classified as type A in this report, will also be an

important reference for collaboration, assuring consistency and comparability in aggregate AGPE

data across time, countries and regions; and

b) It is very important that these three initiatives may be coordinated in order to improve quality and

coverage of data. As these share the same methodological framework it is very useful and

beneficial to share difficulties and strategies for tackling these.


Both initiatives highlighted the following similarities with regard to linkages to and collaboration

with other data initiatives:



a) Both data initiatives have collaboration with each other, whereby OECD provided technical

support from the outset; and the Eva’s data methodology and analysis is consistent with

OECD’s principles of classification of purpose codes;

b) FAO is also augmenting the Creditor Reporting System (CRS) data generated by OECD, by

providing more disaggregated data for the agricultural sector; and

c) Both should extend further collaboration and harmonization of ODA data for agriculture

collected by OECD with other donors and initiatives (such as the World Bank’s Aid Flows

database, Development Gateway Aid Data) to ensure consistent reporting of aid data.


BOOST exhibits the following collaboration linkages (which can be transferable to the agricultural

sector, if and when applied)

a) BOOST is being increasingly utilized and integrated as a tool for supporting the conduct of public

expenditure reviews in different regions of the World Bank, and then transferred to participating

Governments;

b) Thus far, BOOST has not been used as a tool for supporting a separate/specialized agricultural

expenditure analysis. Nevertheless, BOOST is being used increasingly for supporting expenditure

reviews in the health and education sectors, and gaining enthusiastic adoption by country

counterparts. See Annex A, Table 3/Appendix 1 for a list of the 21 countries which have applied

the BOOST tool, countries which have made the data publicly available, and those in the pipeline.

3.10 Demand/User Perspectives on Database and Analytical Challenges and Strategies

There are three main groups of “users” who play an important role in demand (or not demanding)

enhanced AGPE data bases and analytical studies: policy makers (e.g., including Ministers and Directors

of Budget/Planning of Ministries of Finance, Agriculture, and other key entities); development partner

managers, and AGPE analysts. It is useful to get a deeper understanding of the nature of “demand” by

these three groups, which can influence the incentives and push to generate more reliable and

disaggregated AGPE data.

First, the feedback obtained during the course of this review suggests that the real need for the

country to have more reliable and disaggregated AGPE are not sufficiently internalized by key actors in

Government (especially from the Ministry of Finance). There is a strong tendency by Ministries of

Finance to focus on tracking expenditure rates (actuals vs. allocations), rather than focusing on outcomes

and “value for money”, which would require disaggregated data. Also, public expenditure reviews are

generally driven by external donors, and the Ministries of Finance (and Ministry of Agriculture) are not

carrying them out as part of their internal budget process. Also, oversight committees, such as Parliament

committees, are not requiring more disaggregated analysis to address value-for-money issues.

Focal persons managing the data and analytical initiatives reviewed in this exercise appear to be

aware of these aspects. Many of the workshops which have been convened to disseminate the findings

from the AGPE and PSE analytical studies have invited key actors from the Ministry of Finance, and

other relevant Ministries, to review the findings and policy implications (e.g., ReSAKSS, MAFAP,

SNAPE, PSE for OEEs). However, it appears that attending workshops is not sufficient, and the



persisting data problems highlighted in this review suggest the need for more systematic follow-up and

in-depth discussions to trigger stronger demand, and therefore specific and appropriate actions. Various

practitioners interviewed during this review emphasized the importance of finding effective approaches

and processes to pose the “right questions” and engaging key actors in these discussions and follow up.

Second, there is growing evidence that DP managers are seeking AGPE data and analysis to help

underpin and justify their assistance strategies. Many of them are willing to spend their administrative

resources to support various types of AgPEs, and other relevant analytical work, in partnership with other

DPs.

Third, there are a growing number of AGPE specialists, given the demand for their analytical

skills. They can play an important role in articulating the rationale for better AGPE, including their own

professional incentives for better data. Accordingly, the use of AGPE data for policy purposes is

increasingly important in developing countries. During last three decades, AGPE has varied widely in

many developing countries, 24

as other priorities have occupied the policy agenda, especially expansion in

social sectors. Ministries of Agriculture and agricultural programs in many countries have seen their

budgets shrink, often well beyond what an equilibrated approach would suggest (i.e. looking at the

economic weight of agriculture, which is between 7 and 40 % for most developing countries, whereas

sectors receive less than 2% of budgets, see footnote). This situation seems to be generalized in many

countries and requires rethinking the role of AGPE for agriculture development, for which AGPE data

collection and analysis is crucial.

A first important issue for AGPE data in developing countries is the issue of general importance

and impacts on agricultural growth. Long term aggregated data is of key importance for assessing the

long term impacts and relationship between AGPE and agriculture growth. Given the heterogeneity of

developing countries, this type of analysis must be carried out among categories of countries with

structural similarities (e.g., Caribbean, Central America, Andean, South Cone for LAC countries, etc.),

and large federal countries like Mexico, Argentina and Brazil may need a special treatment. It is clear

that AGPE data is more complex in federal countries, so this is an additional reason for separate analysis.

Having elasticities of agriculture growth/AGPE in developing countries can be an important tool for

convincing Ministry of Finance authorities of the importance of investing more in agriculture at the

aggregate level, especially when its share in total budget is well below the sector economic importance as

seems to be the norm currently across many developing countries. Therefore, having a solid database

with AGPE long-term data (combined with other agricultural and macroeconomic variables) is of clear

usefulness for many developing countries to have estimations of aggregate importance of this type of

expenditure. This appears as an important need to be fulfilled for developing countries in the short term.

Going deeper in this line, AGPE data will be more useful for developing countries as some key

levels of disaggregation are considered for wider efforts of data collection and analysis. A first and basic

distinction in AGPE, which is easy to record and follow, is the relationship between allocated and

expended budget. This ratio is used for a quick assessment of efficiency in government action, and may

be compared with other sectors within the country and with other countries in AGPE databases. This

information is a high importance for most Finance Ministries which use this indicator as an important

measure of sector absorptive capacity, an important consideration for allocating budgets.

Other important distinctions in AGPE which are important for policy purposes in developing

countries are to be able to distinguish between current and capital expenditure. This distinction is

24

In the SIAGRO database of AGPE for LAC countries in Central America and Mexico, the share of agriculture

expenditure in total budgets was and average of 9.4% in the 1980s, of 4.8% in the 1990s, and only of 2.6% in the

2000's. In 2010 and 2011 the share even declined more, to 1.9%.



complicated in the overall budget as where to put the line is often difficult. An initial assessment of

current versus capital expenditure in agriculture expenditures in some representative developing countries

during last decade may be useful to see the state of the variable. It is clear that this distinction is of great

importance for policy analysis, as the economic impacts of these are very different. In this issue the main

problem is the treatment of large projects financed by external credit, which are generally considered as

capital investment, whereas important shares go to recurrent expenditures. This discussion may also

allow to assess what are levels of recurrent expenditure which are required for sustaining effective

agriculture services (like extension, sanitary services, technical assistance, training), which have

deteriorated in the midst of budget decline in many developing countries.

The OECD/PSE type of disaggregation of AGPE, although highly demanding, seems one of the

more promising for policy analysis in many developing countries as it allows looking a three instruments:

price protection, investment in private and in public goods in the agriculture sector. This basic distinction

is key for assessing how these tri-dimensional structure may distinctively impact on agriculture growth

and other agriculture-related variables in the short and long run, and also suggest policy reorientations for

achieving better results. Investing in this type of data would be of high return for most developing

countries in which agriculture policies have lost presence in the general policy decisions.

Budget accounting in many developing countries follows the COFOG system and the charts of

account which are harmonized by international institutions. The main challenge for AGPE analysis in

this case is how to account for expenditures which are agriculture-related but are made by other ministries

or sectors, especially when functional lines are overlapping (like having three ministries working with

farmers). The key point here is to have clarity about what agriculture-related expenditure means. The

core is to have production-related expenditures well defined and covered across sectors, and after that, to

be able to identify some agriculture-support activities which may also be considered. However, it is key

to distinguish expenditures which go to supporting farmers in their main economic activity from those

which go to improving or keeping their economic environments. This separation is of importance for a

proper policy discussion in developing countries.

Some recent trends in the management of public budgets in developing countries is the use of

"results-based budgeting" This budget practice links diverse types of expenditures to some specific

outcome or set of outcomes, which will attend some specific need or service to the population. AGPE

data may be also organized by this methodology in countries using this approach, and in this case it will

be important to generate data that allows to assess the impacts of these expenditures not only on the

products but also in broader development outcomes which are pursued. In the agriculture sector this will

allow to see an "expanded" expenditure for achieving development goals, which highlights synergies

among different sectors for getting better results in agriculture.

Another key distinction of importance for developing countries is to have sub-national level expenditures

for analytical purposes. In this case, it is worthwhile to have the disaggregation level in order to link

AGPE data with other development variables which are measured at the same level, like regional growth,

regional poverty reduction or/and change in productivity. If those complementary variables are not

measured at that level it is of little use the disaggregated AGPE data. Having regional data may be very

important for policy purposes, within countries a differentiated effectiveness of AGPE by regions may

help to design better agricultural policies.



4. CONCLUSIONS AND STRATEGIC OPTIONS

4.1 Main Conclusions

This review exercise, in close collaboration with focal persons from diverse hosting

organizations, has endeavored to identify and highlight important aspects of fourteen diverse and

complementary data and analytical initiatives. To facilitate the comparative review, it has grouped and

analyzed the initiatives according to five types of initiatives: data bases for public expenditures;

analytical studies of AgPEs; PSEs and related indicators; databases for ODA flows; and software data and

analytical tool. The conclusions are synthesized below according to six strategic cross-cutting areas (with

further details outlined in Chapter 3 and Annexes A and B, according to each type).

(1) Summary of Main Features and Emerging Patterns of the Fourteen Initiatives:

(a) Variation and Rationale of Objectives: the fourteen data and analytical initiatives comprise

five different types and reflect a wide variety of objectives, unique origins, and diverse users.

Some of the initiatives respond to specific policy requirements (e.g., the PSE for OEE is an

instrument which complies with OECD member requirements; ReSAKSS tracks a major

policy target of agricultural public expenditures in SSA, in addition to contributing to other

broader analytical and capacity building objectives). Other initiatives are driven by fulfilling

the mandates of various organizations (GFS for IMF; FAOSTAT for FAO), and/or objectives

to promote enhanced AGPE analysis and expenditure allocations and management (SNAPE,

ReSAKSS, MAFAP). Accordingly, the review has demonstrated that each initiative has its

specific features, undergoes continuous improvements (to varying degrees) in response to a

dynamic context and endeavors to support the perceived priority needs of their generally

multiple stakeholders. All of these aspects tend to justify their role and enhanced

continuation, provided they are being responsive to changing needs and opportunities

(discussed further below);

(b) Variation in Scope and Disaggregation: The database initiatives witnessed difference in the

scope in terms of sectors covered, level of disaggregation, frequency of data and their

updated data bases and analytical studies, and countries, regions and years covered. For

example. Two of the databases (GFS, SPEED) provide public expenditure data on

agriculture only up to the level of COFOG Level 2 classification. MAFAP has defined the

agricultural sector more broadly, and has endeavored to generate more disaggregated data,

but this has required considerable efforts for a small number of countries in SSA. ASTI

focuses its data base on a strategic subsector of agricultural research and development. The

countries covered by the databases vary across the data initiatives. Most of them, including

the long standing GFS database, are not able to generate complete information for a number

of reasons (discussed below), even at COFOG Level 2 or 3. These data gaps, especially the

limited disaggregation of AGPE data according to the main functions, pose considerable

constraints to analysts and policy-makers in terms of not providing adequate information for

better budgetary allocations and accountability;

(c) Geographical Coverage and Focus: While most of the initiatives have a global coverage,

there are two initiatives which focus on SSA (e.g., ReSAKSS and MAFAP); there are two

initiatives which give special focus on the LAC countries (e.g., AGPE for LAC; and PSE for

LAC). Other regions seems be somewhat neglected, aside from the global data initiatives;

(d) Methodological Aspects: The initiatives exhibit a variety of important methodological

differences, and to a lesser extent, similarities, including:



narrow and wider definitions of the “agricultural sector”, which has important

implications for data compilation and interventions (e.g., MAFAP taking a wider

definition of the agricultural sector; FAOSTAT restricting itself to the COFOG

definition);

the MAFAP extension to the PSEs expenditure classification system considers support

to non-agricultural sectors which can impact agricultural development (like rural

education, health and roads);

the PSE methodology was pioneered by the OECD, used by PSE for LAC countries and

by MAFAP for SSA, where MAFAP has made relevant adaptations of the PSE

methodology to the African context (further discussed below);

various AGPE analytical initiatives have developed different methodological toolkits,

although there is high level of convergence of key concepts and tools. There seems to be

growing exchange and communications among focal persons of these initiatives to

enhance harmonization of key concepts and tools, especially where it involves many of

the same stakeholders (e.g., SNAPE and MAFAP coordinating their AGPE-supported

studies in SSA, and collaboration to address common AGPE data constraints);

different instruments and approaches to compile standardized AGPE and PSE data. The

PSEs for OE economies compile country level data, to comply with a well-defined

methodology. ReSAKSS compiles AGPE data from existing sources, while filling key

gaps at the country level; GFS and FAOSTAT-GEA use questionnaires for nearly 200

countries, and face challenges to fill gaps and limited level of disaggregation;

most of the initiatives have prepared user documentation and guild manuals and

standards, although the newer initiatives are still preparing the complete methodological

documentation (e.g., PSE for LAC; AGPE for LAC);

(e) Public Accessibility: Two aspects are closely inter-related ---, where greater public

accessibility tends to encourage expanded use, although there are other factors influencing

usage. Most of the initiatives are publicly available, although some of them are limited for

various reasons (e.g., AGPE for LAC has limited accessibility; PSE for LAC is still in the

development stages, before being fully launched; PSE for OEE has a comprehensive data

base of PSE and other indicators easily accessible on its website). Some of the databases

require subscription and a user fee, which may limited wider usage (e.g., GFS), while

recognizing some of the arguments in favor of a user fee;

(f) Linkages between Users and Suppliers: Generally all of the initiatives show a clear

awareness and varied strategies to promote stronger usage by its target stakeholders, although

there appear to be varied levels of effort and effectiveness. For example, most of the

initiatives use various ways to promote expanded usage of their database and analytical

outputs through their websites (the most common method), enhanced communication

strategies and a variety of channels, such as by organizing and participating in country and

regional workshops (including “piggybacking” on other major events), trainings and

seminars;

(2) Innovative Aspects and Improvements: Some of the initiatives demonstrate innovative

features, especially in terms of methodological and dissemination aspects which can provide

positive lessons for other initiatives. For example:

a) The MAFAP initiative in Africa is a good example of a solid methodological design applied

to a specific reality with important challenges. MAFAP used the well established PSE

approach developed by OECD, with appropriate adaptation to the African context: measuring



MPS type of "development gap" indicators due to market failures and relating it to public

investment or disinvestment on public goods which may ameliorate these market failures. For

example, if the development gap indicator points out to severe market distortions due to lack

of post-harvest infrastructure, and GSSE shows none or very little investment in these key

investments, policy makers can reorient resources in order to reduce the negative impact of

this market failure. The LAC experience so far led by IDB has made important progress in

applying the PSE method to address consistency in collection and analysis of data and allow

comparability over time within country and across the region.

b) Data compiled by the CRS initiative are collected by CRS++, a reporting format, which

consists of a number of integrity or reliability checks within the CRS. This tool is designed to

help reporters avoid inconsistencies. The continued methodological inter-dependence and

collaboration between the two data initiatives (FAOSTAT-EEA and CRS) is vital to insure

comparable and reliable data on ODA and OOF;

c) CRS is devising an innovative dissemination strategy through it CRS Training Center. In

addition, CRS is improving its website, providing an on-line tutorial, organizing regional and

national trainings, workshops and collaborating with other initiatives, which are some of the

strategies the CRS data initiatives employ to establish the links between users and suppliers

of the CRS;

d) Most of the data and analytical initiatives demonstrated on-going efforts to further improve

their methodologies and relevance of their outputs for their targeted stakeholders, and also to

strengthen their complementarity with other initiatives. For example, at this time, CRS is

making enhancements to their user manual. FAOSTAT-EAA is also reviewing various ways

to improve the scope and user of its database. ASTI and SPEED have developed various

enhancements in terms of scope of country coverage and approaches to filling data gaps and

more effective use of its data (e.g., for example, ASTI is giving stronger attention for greater

usage of information at the national level through an enhanced dissemination strategy).

MAFAP convened a workshop with stakeholders to distill key lessons as inputs for a

proposed next phase. SNAPE coordinator is taking proactive role to address the underlying

reasons for the data challenges which most of the AGPE country study team faced, which

could be addressed in a possible next phase of the AGPE-supported studies.

(3) Complementarities and Synergies between and within Initiatives: Several initiatives

demonstrate that there are emerging complementarities and synergies of varying degrees

between data and analytical initiatives which suggest the potential for being further

stimulated.

a) Figure 1 illustrates the important complementarities between the different types of data and

analytical initiatives, which if well-coordinated and integrated in the budgetary cycle of

developing countries (by each initiative and also between DAI initiatives, where relevant),

offer the potential for enhanced budgetary outcomes and impacts from the agricultural public

expenditures (coupled with other appropriate policy reforms). The greater integration of the

relevant initiative to enhance the actual budgetary planning and implementation cycle will

enhance the value-added of the initiative(s).

b) All of the initiatives which were reviewed are both users and/or suppliers of agricultural

expenditure data, and to a lesser extent, of the PSE indicators (at least for the countries being

covered). The study team was able to identify variable levels of collaboration and inter-

dependence across databases and analytical studies in terms of sharing resources, data

collection methodologies, and dissemination strategies. For example:

FAOSTAT’s government expenditure on agriculture database heavily depends and builds

on the format of the internationally agreed standards for compiling and reporting fiscal

statistics as outlined in the IMF’s Government Finance Statistics Manual;



SPEED has varying links with several data expenditure initiatives (e.g., GFS, ReSAKSS,

FAOSTAT-GEA), especially as SPEED endeavors to disaggregate agriculture

expenditure data below level 2 of COFOG). Its strong collaboration with ReSAKSS in

SSA (given that ReSAKSS plays a role in monitoring expenditure data in SSA, and

supports SPEED in terms of data gaps and validation) can be mention as an example.

MAFAP collaboration with the SNAPE in exchanging methodological approaches and

country results, and addressing common country-level data challenges in SSA;

ReSAKSS, AU/NEPAD are working together to track CAADP implementation,

especially the budget allocation target of 10%);

FAO-ODA database draws most of its information from the comprehensive CRS

database and is currently reviewing various aspects for further improvements and

complementarities, expected later in 2013.

c) PSE analysis shows high synergy with APE analysis, especially for policy purposes and

helping to ensure an efficient use of total public resources, combined with sound agricultural

policies. PSE incorporates most APEs as an important part of measuring support for

agriculture in the from of “private” and “public goods”. Contrasting this support with other

policy-induced market price distortions allows the analyst to monitor the most important

policy instruments for the agricultural sector of countries or regions. Thus a PSE-APE

approach allows going from database type to analytical type of initiatives, which are more

powerful for policy making and implementation. Recent innovations in the PSE estimation,

such as those applied by MAFAP, suggest a key relationship between PSE and APE analyses,

as the distortions of development gaps may be also addressed by agriculture public

expenditures, for instance. Some specific issues required by APE analysis can be

incorporated into the PSE analysis without altering the basic method, like disaggregating of

allocated versus expended budget; giving special attention to ODA data, and measuring

administrative costs. These variables are important from the APE perspective and enrich the

policy analysis. Also, both enhanced PSE and APE analysis require more disaggregated

public expenditure data, based on common and clear definition and classification system;

d) The MAFAP initiative in Africa is a good example of a solid methodological design applied

to a specific reality with important challenges. The PSE for LAC experience has made

important progress in applying the PSE method to address consistency in collection and

analysis of data and allow comparability over time within country and across the region; and

e) APE data can be improved basically in term of better classification and a better use of

definitions in specific situations (for instance, how to classify sanitary services expenditures

between private services in PSE or collective services in GSSE). Challenges for improving

PSE data in the MPS component are more daunting as these require knowledge of market

functioning and the extent of market failures and market distortions.

(4) Emerging Challenges: The review of the initiatives have highlighted a wide range of

challenges, especially for each of them to achieve their stated institutional or program

objectives. Either from an analytical or policy maker perspective, the high level of

aggregation of AGPE data poses a serious constraint to assess AGPE allocation issues, given

that different spending has variable effects on agricultural performance (see further

discussion below). The following section highlights some of the more important common

challenges, whose severity varies according to country. These challenges adversely affects

the ability of analysts to carry out sound AGPE studies, calculation of robust PSEs, and also

to conduct cross-country comparisons, as inputs for better budgetary allocation priority-

setting and decisions (with further details presented in Chapter 3 and Annexes A and B):



a) resources and capacity constraints (at various levels, including Ministry of Finance,

Ministry of Agriculture, National Statistical Offices);

b) unclear definition of the “agricultural sector”, coupled with multiple ministries involved in

the agriculture sector, which also complicates the scope of applying the COFOG definition at

various levels;

c) lack of incentives and commitment of countries (including the crucial Ministries of

Finance, Agriculture, etc.) to compile and disseminate relevant data at the desired level of

disaggregation, coverage and frequency;

d) uneven quality of underlying source data, including accounting systems, sub national data,

off-budget expenditures (e.g., development partner grant funds), and across reporting

countries;

e) fragmented sources of expenditure data, which sometimes are not reconciled (especially

between allocations and actual expenditures);

f) with regards to the two ODA databases, there is the challenge of getting reporting countries

and donors to follow a common classification system of ODA flows;

g) deficient coding systems and capacities to facilitate classification and aggregation of

AGPE data in a systematic manner, as part of a bigger challenge --- deficient Ministry of

Finance information systems and reporting, which, if effectively addressed, can also support

more effective budgetary planning and execution processes (for example, along the lines

illustrated in Figure 1);

h) There are important sustainability challenges faced by most initiatives. Currently almost

half (six) of the fourteen data and analytical initiatives (ASTI, BOOST, MAFAP, ReSAKSS,

SPEED and SNAPE) are project-funded, and do not have clear sustainability strategies for

their continuation, except for helping to build initial capacities at the country level (in the

case of ReSAKSS, building capacities at the sub-regional/Regional Economic Commissions

and Continental levels (NEPAD). The other eight data initiatives are well established and are

included in the core part of the permanent programs of the hosting organizations, but also

face varying degrees of challenges to mobilizing sufficient resources to manage the databases

to the desired standard, as well as needed improvements.

(5) Demand Aspects and Some Implications: An important aspect of addressing the underlying

disincentive issues highlighted above, which constrains availability of more disaggregated

data and full use of existing AGPE data, is connected to better understanding the demand

aspects for enhanced data and analytical programs to support enhanced AGPE analysis. There

seems to be greater attention on the “supply” side, and insufficient attention to addressing the

demand aspects. The following points unpack and highlight some these demand aspects

which arose during the course of conducting this review, reflecting perspectives and inputs

from various actors (representing Ministry of Finance, AGPE researchers, development

practitioners from various organizations).



a) Response to Demand from Key Users: It is vital that the data and analytical initiatives

(which are proxies for the “supply” side) are responding to and promoting the demand from

policy makers (especially from Ministries of Finance and Parliamentarians), development

partner managers who influence aid allocations. The teams which manage all of the

initiatives appeared to be generally aware of the importance of devising appropriate and

effective strategies/mechanisms/interventions to better link the suppliers with the users (with

the latter referring to Government officials who influence resource allocation decisions. The

most common approach used by most of the initiatives was to develop and disseminate

information through relevant websites, and to ensure they are user friendly. Not all websites

were found to be fully satisfactory. (For example, two of the initiatives – ASTI and FAO-

GEA -- can introduce a simple feature that would allow selection of all countries at a single

click to avoid a separate selection of each country, which is time consuming; ensuring that

data can be extracted in a proper spreadsheet.) There is also scope for diversifying the

approaches to strengthen ownership and to reach the key actors which could help contribute

to a deeper understanding of why better and more disaggregated expenditure data is needed

for the country’s decision-makers, in order to affect expenditure planning and implementation

(and not driven only by a questionnaire asking for more detailed expenditure data);

b) Expenditure Information/Reporting Systems: Among AgPE practitioners there is a growing

recognition that some of the above mentioned constraints are related to weak expenditure

information and reporting systems typically driven and managed by the Ministry of Finance.

Some of the underlying reasons include:

Lack of adequate understanding by key Ministry of Finance actors why disaggregated

AgPE data (and others sectors, too) is important for their own management and

accountability requirements (aside from the accounting aspects). Accordingly, this

entrenched orientation and patterns contribute to a lack of Ministries of Finance not

demanding such disaggregated data and supporting analysis from the sectoral ministries

and the Government reporting system. At the same time, most Directors of Budget (of

Ministries of Finance) would recognize that this data can be used to better underpin MOA

budget proposals. Accordingly, there is a need to intensify the increased awareness of

Ministries of Finance of the value-added of greater disaggregated data (and using the

agricultural sector as an “entry” point, since they would need to be convinced of the need

for covering other sectors too).

Off-budget development aid, not captured by the Ministry of Finance;

Weak capacities of budget data management staff

c) Internalization of the Demand Aspects: Some of the key aspects highlighted in Chapter 3

(Section J) regarding the rationale for better AGPE and PSE data and related analysis to help

underpin budget monitoring and formulation (medium term and annual basis) are not

sufficiently internalized by key actors in Government (especially from the Ministry of

Finance, which would be both a supplier and user of the disaggregated data). 25

There is a

strong tendency by Ministries of Finance to focus on tracking expenditure rates (actuals vs.

25 Some of the key points outlined in this subsection were shared by Nana Boateng (staff member for the CABRI). For example,

the need to convey clear messages (in non-technical language) to key decision-makers that long term disaggregated AGPE data is

of key importance for assessing: the long term impacts and relationship between AGPE and agriculture growth, overall growth,

and poverty reduction; expenditure priorities in terms of relative returns to different types of AGPE spending; for tracking

differences between budgetary allocations and actual spending in terms of the composition of spending and spending capacities

of implementing agencies; finding a better balance between recurrent and capital spending for different types of public services

and goods; how different AGPE instruments (measured through PSEs and detailed classification) can impact important policy

variables; to enable outcome-based budgeting, to enhance incentives for improved design and implementation of AgPEs;

strategies and interventions to reduce regional disparities.



allocations), rather than focusing on outcomes and “value for money”, which would require

disaggregated data. Also, public expenditure reviews are generally driven by external donors,

and the Ministries of Finance (and Ministry of Agriculture) are not carrying them out as part

of their internal budget process. The main issue here is how to institutionalize an internal

demand for APE analysis in which local authorities and local users are the demanders, and

the supply is also provided by local actors with support from external sources. Also,

oversight committees, such as Parliament committees, are not requiring more disaggregated

analysis to address value-for-money issues. Focal persons managing the data and analytical

initiatives reviewed in this exercise appear to be aware of these aspects and patterns. Many of

the AGPE workshops, generally externally driven, which have been convened to disseminate

the findings from the AGPE and PSE analytical studies have invited key actors from the

Ministry of Finance, and other relevant Ministries, to review the findings and policy

implications (e.g., ReSAKSS, MAFAP, SNAPE, PSE for OEEs). However, it appears that

attending workshops is not sufficient, and the persisting data problems highlighted in this

review suggest the need for more systematic follow-up and in-depth discussions to trigger

specific and appropriate actions. Various practitioners interviewed during this review

emphasized the importance of finding effective approaches and processes to pose the “right

questions” and engaging key actors in these discussions and follow up.

(6) Nature and Extent of Intra and Inter-Agency Collaboration: This review identified the general

nature and type of intra and inter-agency collaboration in carrying out the fourteen data and

analytical initiatives. Overall, the focal teams from the various initiatives demonstrated a

variety of positive actions to promote enhanced collaboration, within their own organization

and with other relevant agencies (see item (2) above which highlights positive

complementarities, Chapter 3, and Annexes A and B for further details and examples).

Notwithstanding these positive actions, the following points highlight potential scope for

enhancing the effectiveness and results of more strategic and systematic collaboration:

First, the nature and extent of collaboration and enhanced quality standards varied. Most of

the collaboration occurred as a result of individual initiative, rather than being mandated or

institutionalized. The notable exception was the OECD which has various internal rigorous

processes and mechanisms across its different departments for reviewing and validating PSE

methodological issues, estimates and policy implications. While the IDB has been working

on the PSEs for LAC for some years, it was only recently that it reached out to the OECD for

technical exchange and advice, especially now that the OECD will be including four LAC

countries in its PSE estimates and dialogue.

Second, there does not exist any type of technical working group among the various agencies

to review common concerns regarding methodological issues and developments, work plans

which could include inter-agency collaboration, and proposals to address some of the

underlying issues highlighted in this report. Even within the same institution, the exchange of

ideas and practices tends to be the results of individual initiatives, rather than being facilitated

by intra-agency working groups (for example, in the World Bank, there was some informal

discussion of establishing a public expenditure working group to discuss good practices and

strategic issues of broader interest to both macroeconomists and sector economists).

Third, the Workshop convened by the OECD and IFPRI/PIM highlighted some of these

issues and the need for more systematic inter-agency collaboration in addressing data base

issues for enhanced policy measurement and tracking. This review was triggered by some of

the next steps highlighted in this conference.



4.2 Strategic Options

Based on the results from the review and the above cross-cutting conclusions, this review has

identified six strategic options which offer the potential for addressing some of the more critical

constraints. These strategic options are intended to better inform and facilitate discussion and consensus

among target audiences on the most appropriate options to pursue. This target audience includes the

practitioners who participated in the Agricultural Policy Measurement workshop held in June 2013, and

other practitioners who have participated in this review exercise and expressed keen interest to get a copy

of the report and to get engaged in a broader and deeper discussion with other practitioners and decision-

makers. The overarching strategy warrants an integrated and sequenced approach to reaching consensus

on the main strategy elements and supporting action plan(s) which can address the main issues

highlighted in this report.

The proposed six strategy options comprise a suggested framework (covering both supply and

demand aspects) which could contribute to enhancing the role and effectiveness of data and analytical

initiatives for achieving enhanced budgetary outcomes and impacts of existing and increased AgPEs (in

line with the underlying logic of Figure 1). The substantive details and rationale which underpin these

strategic elements are highlighted Chapter 3 and the above section on conclusions. It should be noted that

the strategic options cover specific recommendations for individual DAIs covered in this review, and

some of the recommendations have broader implications for the DAI community as a whole.

Figure 8 below illustrates various strategic options amongst key institutional actors and involving

supply and demand aspects and backward and forward linkages. Some of the strategic options which the

data and analytical initiatives and the following sections address include:

Strengthening country-level expenditure reporting systems—What ongoing support is already

being provided? How can it be improved? Strengthening agriculture-specific versus general

reporting systems?

Analytical capacity support for ‘frontline’ users of country-level data; and

Building demand for cross-country databases on the part of country-level policy analysts and

decision makers

Figure 8: Strategic Options: Strengthening “Backward and Forward Linkages”



1) Strategic Options for Enhanced AgPE Data Bases

There are eight AGPE databases which are being implemented. While the focal teams of each of

them are carrying sound improvements to better meet their institutional objectives, it could be useful for

each team to consider the following aspects, customized to each data initiative:

a) review the COFOG guidelines with respect to the agriculture sector, including a review and updated

definition of the “agricultural sector”, considering both a narrow and wide definition, which can serve as a

clear international standard for developing countries. It would be important to draw on relevant technical

discussions, for example, which have taken place as part of the implementation of the CAADP agenda for

SSA. It is envisaged that this review may results in refinements of the existing classification of functions

for the agricultural sector, rather than substantial changes;

b) explore the benefits of an enhanced integrated global AGPE database, which will seek to generate

more systematically AGPE data disaggregated beyond level 3 of COFOG, in a phased manner, with

global coverage and annual updating. FAOSTAT-GEA would seem to offer a good foundation to build

upon, given that it has a wide coverage of countries (134 countries), and is consistent with and

complementary to the GFS framework, which is well established and accepted by developing countries.

This enhancement should build on on-going improvements being carried out by FAOSTAT, including the

recent introduction of the breakdown of capital and recurrent expenditures for level 3 AGPE data. It is

recognized that FAOSTAT would need to be supported with additional resources to support the phased

implementation of this enhancement, which should include other well identified parallel supporting

actions to take place at the country level (e.g., actions to promote stronger demand by key actors at the

country level, especially the Ministry of Finance, well defined expenditure coding systems, enhanced

expenditure information and reporting systems in the Ministry of Finance; enhanced budgetary proposals

by Ministry of Agriculture, which should be underpinned with better expenditure analysis of past

performance and relative returns from different spending subsectors/functions);

c) encourage the relevant data bases to update as needed their methodological user manuals which should

be made accessible to the public to encourage and greater and effective use of the AgPE database. Some

of the databases are currently completing their updating (e.g., SPEED, ASTI, GFS, FAOSTAT, AGPE for

LAC);

d) One possible way to curve the consequences of data inconsistency brought about by the difference in

methodology, in particular, is to put in place a more transparent and flexible system in order to better

track public resources allocation enabling customized and consistent aggregation, promote open data

access and provide information in a timely manner. This system may include developing a well-defined

expenditure coding system by adopting the chart of accounts, which organizes spending data according to

numeric, alphabetic or a combination of both codes, together with a flexible aggregation model or

program. (As discussed in the note by Diao and Yu, IFPRI, 2013). Such an enhanced coding system could

allow more flexibility to aggregate data, in addition to easing explicit mapping relationship between the

countries government finance statistics system and COFOG or any other aggregation classification

becomes explicit. This makes sure that the aggregate data is no longer a black-box that is unlikely to be

consistent across countries and hence difficult for comparison (Diao and Yu, 2013). It would appear that

these improvements would be more relevant for more detailed data at the country level, as opposed to

cross-country dataset (since they are so aggregated that the lack of codes is not the problem).

e) Enhance the public accessibility to the AGPE databases through ensuring user-friendly and efficient

websites and tools (with built-in tutorials), and ensuring easy subscription and free of charge (and

assumes that the data bases have adequate and sustainable funding) (for example, currently, the GFS

requires a paid subscription to access their data base).



2) Enhanced Analytical Programs to be Driven by Ministries of Finance and Greater Focus on

Expenditure Efficiencies and Outcomes (for AgPEs and PSE and other Indicators).26

a) Most of the AGPE analytical studies are funded by an external project. While the analytical initiatives

have sought to “involve” Ministries of Finance, it would appear that different approaches are needed to

secure their stronger ownership, toward the aim of getting them to institutionalize AgPEs (and other

sectoral reviews) as a requirement for the budgetary process. The growing trend toward program-based

budgeting may provide an opportunity to strengthen the focus toward results and AgPEs as key inputs,

which would demand more disaggregated. Therefore, there is a need for each of these analytical

initiatives to develop an explicit “engagement” strategy from the outset, supported by well-designed

capacity building and “exit” strategies to enhance the chances that the relevant countries (led by

Ministries of Finance) continue to carry out periodic and well-designed AGPE studies (preferably as an

input to their medium term and annual expenditure proposals). While some of these analytical initiatives

included a training program (e.g., MAFAP, SNAPE), it would appear that larger efforts and resources are

needed to reach a “critical mass” of government ownership and capacity for the countries to sustain

appropriately designed studies. There is also a need to devise “lite” methodologies which are not costly

and which are more realistic with existing and likely analytical in-country capacities. The AGPE toolkit

used and promoted by SNAPE can be further disseminated to support the exit and sustainability strategy,

including a more active program of “training-the-trainers”, using existing regional and country level

institutions (e.g., SSA has 8 RECs which could potentially play a more active role; there are regional

research and/or training institutes with existing capacities in different regions, which can be identified and

engaged in these tools);

b) As a specific example, the program of AgPEs funded and supported by SNAPE is coming to a close

by the end of June, 2014. The implementation experience demonstrated a high level of country demand

for these AgPEs, together with the training support provided (which was insufficient, due to limited

resources). There are plans to formulate a second phase proposal. It would be useful for the proposal to

reflect the relevant lessons learned from the current phase, including the training component, the country

level studies (basic and specialized AgPE studies, and data constraints, lessons and strategies). 27

c) MAFAP has been successfully completed, and there are steps being take to formulate a second and

scaled-up phase, to be launched in mid-2014. Similar to SNAPE, MAFAP included an integrated

approach, providing capacity development coupled, with AGPE and PSE analytical studies. In mid-2013,

the MAFAP team convened a workshop with stakeholders to review the main lessons learned. These

lessons are being incorporated in the design formulation stage of the next stage. MAFAP has developed

various methodological notes for the AGPE, PSE/price distortion analysis and policy alignment studies.

While there was a relatively high cost to carry out each country level program of activities, 28

the next

phase will seek to lower these costs, while it embarks on a larger number of countries, including SSA and

26

The following section uses specific DAIs covered in this review as examples. 27

For example, SNAPE and ReSAKSS practitioners/team members collaborated in preparing a discussion note

which highlighted some of the main AGPE data difficulties and some “observations” on strategic responses to these

data issues (January, 2013). 28

Costs per country are estimated at around US$ 500,000 per country for a five-year period. During phase I, the

costs per country were approximately US$ 350,000 for a four-year period, on average. A significant share of the

MAFAP budget also covered development of the methodology as well as global tasks at the secretariat level--

outreach, database development, development of capacity development material, formulation of methodology

guidelines, etc-- which cannot be allocated to individual countries but are to be considered contributions to a global

public good produced by FAO.



other regions. It might be useful for the MAFAP preparation team to convene a team of diverse peer

reviewers to provide independent review and constructive feedback during the formulation and launch

phase of MAFAP. This can help add value to building on the relevant experiences and lessons from the

first phase.

d) ReSAKSS is making significant contributions to the tracking of the CAADP agenda, at three levels, as

well as to carrying out analytical studies involving AgPEs in numerous SSA countries. The ATOR for

2012 included some important empirical contributions to the tracking of the expenditure target of 10% of

total budgetary allocations. It would be useful for the ReSAKSS team to update its work plan to ensure it

reflects some of the more relevant recommendations arising from this review, as well as incorporating an

action plan to support recent initiatives, including support to the implementation of the CAADP results

frameworks (at continental, regional and country levels). In November, 2013, ReSAKSS convened a

continental workshop to review the ReSAKSS achievements and priority work program. The stakeholder

feedback provides valuable inputs for enabling ReSAKSS to prioritize its portfolio of activities involving

strengthening the AGPE database for SSA, capacity development at various levels, the strengthening of

its network of regional and country level nodes, and the prioritization of analytical studies, which would

include AgPEs. This work plan could also strengthen its partnerships with key actors, especially

including AU, NEPAD and the RECs.

e) The Trade and Agriculture team of the OECD has formulated an agenda for enhancing its PSE and

related indicators agenda, which includes an increase in the number of developing countries which will

require assistance to carry out the required country assessment reports, PSE estimates (e.g., Columbia,

Vietnam, others). This agenda includes enhancing its expenditure classification system, the GSSE

expenditures.

f) The PSE for LAC initiative is making important progress in completing the scope of PSE estimates for

eventually all LAC countries, with updated data to the latest year possible (2012), based on the OECD

methodology. IDB is planning to conduct regional specific workshops. Regional entities such as CARDI,

RUTA, CIAT, are very interested in supporting the dissemination of the PSE studies and database. IDB

has been conducting national workshops and PSE presentation to the governments (Ministries of Finance

and Agriculture) and public agencies at national level each year. IDB is seeking additional resources to

support these needed improvements.

3) Capacity Development Strategies

a) Many of the key issues and challenges highlighted in this report reflect the need to strengthen

institutional and technical capacities at various levels involving: the construction and management of

enhanced AGPE databases; carrying out analytical studies using sound tools of AGPE and PSE analyses;

using effective approaches to dissemination of the results, coupled with implementation support, on a

demand basis. While most of the data and analytical initiatives include a portion of capacity development

resources, there is a need to:

Assess the adequacy of the resources provided (in most cases, it has not been sufficient);

for each initiative to revisit the identification of priority and results-focused capacity

development requirements, and to prepare sound proposals to strengthen capacities.

include strategies for strengthening the demand for improved and more disaggregated AGPE

data by key country level actors, including the promotion of requiring periodic AGPE reviews as

inputs for the budgetary process (see above). This would complement the proposed supply side

enhancements cited under (1) (e.g., the proposed expansion of the FAOSTAT-GAE activities

for generating more disaggregated data, below COFOG Level 3);



b) There is an urgent need to formulate a strategy and phased plan for improving the Ministry of Finance

information and reporting systems and processes, coupled with stronger country demand for AgPEs as

part of the budgetary process (see below), such that these “drivers” would lead to a stronger internal

demand for disaggregated AGPE data. These latter aspects could include: adding information coding

capacity to Ministry of Finance budget systems; training approaches/modules for training budget data

management staff, to handle an improved well defined expenditure coding system, with a flexible

aggregation model or program which will allow public expenditure data analysis to be routinely

conducted in developing countries;

c) A complementary strategy could be oriented to researchers and practitioners who may use data more

intensively if proper incentives are present. It would be of high value to have competitive funds for

AGPE analysis using the available data, and seeking for potential interaction among databases and

approaches. This may be an important source of future improvements in data collection, organization and

collaboration.

4) Intra-Agency and Inter-Agency Collaboration

The above conclusions section highlighted some of the current constraints on the ad-hoc nature and extent

of intra-and inter-agency collaboration to address the various challenges identified in this review. Also,

all of the above strategic options will require some form of closer and more effective intra and inter-

agency coordination, to seek consensus on many of the proposals (and other proposals not covered here),

to provide technical guidance as a good practice group of AGPE and PSE practitioners, and to help

mobilize additional resources to support the priority interventions which comprise a “global public good”

in AGPE. There are other major themes, such as climate change, which have benefitted from inter-

agency community of practice to help foster enhanced collaboration and sharing of good practices, and

possible joint efforts. Accordingly, it is proposed that:

a) each agency covered in this review consider participating in a proposed “community of practice”

working group for AGPE, drawing on relevant staff members within the organization (combination of

senior and junior staff). The participating organizations should seek to select a “focal person” for this

working group, which in turn should formulate an agreed role and framework work plan; 29

b) the various agencies covered in this review (6), the initiatives (14), in addition to several other relevant

entities (such as RUTA for Central America, ECLAC for South America, CABRI for SSA), should seek

to establish an inter-agency AGPE practice working group, with the focal person from each entity and

initiative being the representative for such a global working group. The main mandate/role would be to:

(i) review and exchange information on on-going and proposed initiatives in AGPE and PSE data and

analytical work; (ii) to review and provide practitioner feedback on important methodological proposals

on AGPE and PSE database and analytical aspects; and (iii) to support priority initiatives with global

significance. The coordination could be conducted on a rotating basis. It is understood that most of the

exchanges would take place virtually, and periodically (say, once every two years), there could be a

workshop to review substantive matters and proposals. One item on the agenda would be review specific

proposals to enhance the AGPE global public good, and to reach consensus on some priority initiatives to

be supported by this inter-agency working group (e.g., such as the above proposal to expand the scope of

29

Possible first step outputs from the coordination of the group could include: (i) Better and clearer data

documentation across DAIs; (ii) More easily accessible data (perhaps through joint website linking to datasets and

studies in respective organisations); (iii) Easily digestible comparison of commonalities and differences in the

methodologies of the DAIs. In addition, given the active engagement of various DAIs in SSA, it could also be

useful to consider a SSA sub-group.



FAOSTAT-GEA, for promoting enhanced disaggregated AGPE data, coupled with other parallel actions

cited above, especially to promote sustainable “incentives” at the country level to pursue enhanced AGPE

data bases and analytical studies). This working group can also foster more systematic and

institutionalized collaboration among related initiatives, given that thus far, the collaboration has tended

to be “ad-hoc”. 30

c) Given that there is special focus on the AGPE requirements of SSA, there is also the option of there

being an AGPE sub-working group for SSA, to focus on specific agenda for SSA. The results of this (and

maybe other) sub-groups (e.g, organized according to “type”) could feed into the agenda and periodic

exchanges of the above global working group.

5) Strengthening Demand Aspects

There are several components to strengthen the demand aspects, as discussed in the section on

conclusions, including:

a) supporting capacity development activities of key decision makers and technical analysts at various

levels, which will increase the demand for improved AGPE data base (disaggregated expenditures), for

improved and periodic AGPE and PSE analytical studies which can enhance agricultural policies, policy

change measurement, and expenditure priorities, based on comparative returns. While such capacity

development can be provided in SSA, given the various ongoing programs, it is less clear how this

capacity development will be achieved in other regions. Similar AGPE study programs need to be

promoted in other regions, which can include capacity development funds and TA activities;

b) supporting the strengthening of expenditure reporting systems cited above will contribute to stronger

demand by key actors for more disaggregated expenditure data and analysis. These aspects need to be

concretized and phased, in different regions, building on on-going initiatives. For example, to the extent

the proposed expanded support for FAOSTAT-GEA is launched, this could serve as an instrument for

promoting this strengthening at the country level (perhaps in collaboration and coordination with the

GFS, which includes periodic training);

c) promoting the internationalization of increased demand for enhanced AGPE databases (of

disaggregated data, using a consistent/standard classification system), for periodic AGPE and PSE

analytical studies, and effective dissemination to ensure the results are effectively utilized. It would be

important to link this strategy to supporting the budgetary planning and implementation cycle, while

requiring Ministries of Agriculture to underpin their budgetary submission with an expenditure

assessment of the previous year(s), to be done periodically. The AGPE “mini-reviews” should give

greater attention to assessing expenditure efficiencies and “value for money”, rather than the traditional

focus on expenditure outturns. 31

d) For all of the DAI initiatives, there is scope for expanding the ownership and use of the data,

results and tools arising from these initiatives at the country implementation level, on the part of key

decision-makers, including Ministries of Finance, Parliament (e.g., agriculture and budget committees).

This expanded demand/use, effectively cultivated, can enhance the value-added of DAIs for better

30

There have been some recent examples where such a practitioner working group can play a productive role (e.g.,

various working groups have been formed to address climate change issues). Further follow up will be made to

draw on some specific lessons, of what worked well and what did not work well. 31

For example, the CABRI team is endearing to promote similar type of incentives for stronger internal demand on

the part of Ministries of Finance to require Ministries of Agriculture to compile and submit more detailed budgetary

proposals (as communicated to R. Anson, by Nana Boateng (CABRI staff member, in early October, 2013).



expenditure planning and implementation. It would be useful if each of the DAIs devise a dissemination

and utilization strategy, with an aim of integrating it as part of the budgetary cycle.

6) Sustainability Strategies

The above section highlighted that, aside from the initiatives which are part of the work plans of

international organizations (e.g., GFS, PSE for OEE, FAOSTAT), one of the major concerns was that

many of the initiatives are project-and donor-dependent funded, hence, this casts doubt on countries being

able to sustain the improvements which are introduced. 32

It is of paramount importance for each data

initiative to devise an explicit sustainability strategy regarding the continuation of demand-driven public

expenditure data and analytical work (AgPEs and PSEs) in order to meet their short-term and long-term

objectives. At the same time, there is a need to recognize that the DAIs (which are cross-country in

nature) are largely international “public goods”, which warrant funding in the spirit of the CGIAR system.

One option at the country level is to strengthen the data systems as part of supporting the budgetary cycle

at the country level and explicit capacity development strategies/interventions with established

organizations at the country, regional and continental levels. Given the priority being accorded to

accelerating agricultural growth in SSA as part of the CAADP agenda, it would appear there is scope for

exploring this option for SSA.33

32

It should be noted BOOST, MAFAP, SNAPE (and CABRI/Value for Money in Agriculture) are all funded by

BMGF, which has played a crucial catalyst role in moving forward this agenda of enhanced and results-focused

agricultural public expenditures.

33

For example, there is a major workshop of development/analytical practitioners in addressing AgPE agenda,

sponsored by ReSAKSS, in Dakar (November 12 and 13, 2013). Perhaps some of the follow up actions can

consider an appropriate initiative which would help address some of these sustainability issues.



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APPENDIX

Classification of Functions of Government (Functional Classification of Expenditures)

Code Description

Relation to

agriculture-related

functions

01 General public services Not ag. f.

02 Defense Not ag. f.

03 Public order and safety Not ag. f.

04 Economic Affairs Encompasses ag. f.

041 General economic affairs Encompasses ag. f.

042 Agriculture, forestry, fishing and hunting Ag. f

0421 Agriculture Ag. f - Administration of agricultural affairs and services; conservation,

reclamation, or expansion of arable land; agrarian reform and

land settlement; supervision and regulation of the agricultural

industry

- Construction or operation of flood control, irrigation, and drainage

systems, including grants, loans, or subsidies for such works

- Operation or support of programs or schemes to stabilize or

improve farm prices and farm incomes; operation or support of

extension services or veterinary services to farmers, pest control

services, crop inspection services, and crop grading services

- Production and dissemination of general information, technical

documentation, and statistics on agricultural affairs and services

- Compensation, grants, loans, or subsidies to farmers in connection

with agricultural activities, including payments for restricting or

encouraging output of a particular crop or for allowing land to

remain uncultivated

0422 Forestry Ag. f - Administration of forestry affairs and services; conservation,

extension, and rationalized exploitation of forest reserves;

supervision and regulation of forest operations and issuance of

tree-felling licenses

- Operation or support of reforestation work, pest and disease

control, forest fire-fighting and fire-prevention services, and

extension services to forest operators


documentation, and statistics on forestry affairs and services

- Grants, loans, or subsidies to support commercial forest activities

0423 Fishing and hunting Ag. f - Administration of fishing and hunting affairs and services;

protection, propagation, and rationalized exploitation of fish and

wildlife stocks; supervision and regulation of freshwater fishing,

coastal fishing, ocean fishing, fish farming, wildlife hunting, and

issuance of fishing and hunting licenses

- Operation or support of fish hatcheries, extension services,

stocking, or culling activities


documentation, and statistics on fishing and hunting affairs and

services

- Grants, loans, or subsidies to support commercial fishing and

hunting activities, including the construction or operation of fish

hatcheries

043 Fuel and energy Not ag. f.



Code Description

Relation to

agriculture-related

functions

044 Mining, manufacturing, and construction Not ag. f.

045 Transport Not ag. f.

046 Communication Not ag. f.

047 Other industries Not ag. f.

048 R&D economic affairs Encompasses ag. f.

0481 R&D General economic, commercial, and labor affairs Not ag. f.

0482 R&D in agriculture, forestry, fishing, and hunting* Ag. f.

- Administration and operation of government agencies engaged in

applied research and experimental development related to

agriculture, forestry, fishing, and hunting

- Grants, loans, or subsidies to support applied research and

experimental development related to agriculture, forestry, fishing,

and hunting undertaken by nongovernment bodies, such as

research institutes and universities

- Excludes basic research, which is classified under “General Public

Services”

0483 R&D Fuel and energy Not ag. f.

0484 R&D Mining, manufacturing, and construction Not ag. f.

0485 R&D Transport Not ag. f.

0486 R&D Communication Not ag. f.

0487 R&D Other industries Not ag. f.

049 Other economic affairs Not ag. f.

05 Environmental protection Encompasses ag. f.

051 Waste management Not ag. f.

052 Waste water management Not ag. f.

053 Pollution abatement Not ag. f.

054 Protection of biodiversity and landscape Ag. f.

0540 Protection of biodiversity and landscape Ag. f. - Administration, supervision, inspection, operation or support of

activities relating to the protection of biodiversity and landscape;

- Grants, loans or subsidies to support activities relating to the

protection of biodiversity and landscape.

055 R&D Environmental protection Not ag. f.

056 Environmental protection n.e.c. Not ag. f.

06 Housing and community amenities Not ag. f.

07 Health Not ag. f.

08 Recreation, culture, and religion Not ag. f.

09 Education Not ag. f.

10 Social protection Not ag. f.

Source: Adapted from IMF (2001). Note: ‘Ag. f.’ = Agriculture function.

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