This paper was first presented to the Working Party on Agricultural Policy and Markets, 17-20 May 2010. Reference: TAD/CA/APM/WP(2010)23. Global Forum on Agriculture 29-30 November 2010 Policies for Agricultural Development, Poverty Reduction and Food Security OECD Headquarters, Paris Economic Importance of Agriculture for Sustainable Development and Poverty Reduction: The Case Study of Ethiopia Xinshen Diao, IFPRI, [email protected]
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Economic Importance of Agriculture for Sustainable ... TABLE OF CONTENTS ECONOMIC IMPORTANCE OF AGRICULTURE FOR SUSTAINABLE DEVELOPMENT AND POVERTY REDUCTION: THE CASE STUDY OF ETHIOPIA
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This paper was first presented to the Working Party on Agricultural Policy and Markets, 17-20 May 2010. Reference: TAD/CA/APM/WP(2010)23.
Global Forum on Agriculture
29-30 November 2010
Policies for Agricultural Development, Poverty Reduction
and Food Security
OECD Headquarters, Paris
Economic Importance of Agriculture for Sustainable Development and Poverty Reduction: The Case Study of Ethiopia Xinshen Diao, IFPRI, [email protected]
ECONOMIC IMPORTANCE OF AGRICULTURE FOR SUSTAINABLE DEVELOPMENT AND
POVERTY REDUCTION: THE CASE STUDY OF ETHIOPIA .................................................................. 5
1. Introduction .............................................................................................................................................. 5 2. An overview of Ethiopian agricultural policy .......................................................................................... 6 3. Agricultural performance, food security and poverty .............................................................................. 8
3.1. Cereal production and productivity ................................................................................................. 10 3.2. Agriculture and poverty reduction .................................................................................................. 15
4. Agricultural-non-agricultural growth linkages in the Ethiopian economy ............................................ 20 4.1. Why agricultural growth linkages matter? ...................................................................................... 20 4.2. Measuring agricultural growth linkages in Ethiopia – a fixed price input-output model ................ 22 4.3. Results of an Ethiopia fixed price input-output model .................................................................... 25 4.4. Growth linkages in the Ethiopian economy – an economy-wide multimarket model .................... 34
5. Achieving agricultural growth ............................................................................................................... 46 Irrigation ................................................................................................................................................. 46 Adoption of improved seed .................................................................................................................... 47 Promoting modern technology in livestock production ......................................................................... 48 Halving the poverty: markets and non-agriculture matter ...................................................................... 49
APPENDIX A ............................................................................................................................................... 54
A1. An illustration of the fixed price input-output models .................................................................... 54 A2. The fixed price, semi-input-output (SIO) models ........................................................................... 55 A3. The Ethiopia economy-wide multimarket (EMM) model ............................................................... 55
Table 1. Cereal production in 2003/04 and 2007/08 .................................................................................. 11 Table 2. Cereal growth and growth contribution between 2003/04 and 2007/08 ...................................... 12 Table 3. Share of cereal areas and cereal yields by technology ................................................................. 14 Table 4. Yields in on-farm field trials vs. farmers' yield (tonne/ha) .......................................................... 15 Table 5. Poverty incidence and inequality ................................................................................................. 16 Table 6. National poverty rate by different daily 2005 USD PPP poverty line ......................................... 16 Table 7. Poverty rate by sector of employment of household head and livelihood ................................... 17 Table 8. Poverty rate by administrative region .......................................................................................... 18 Table 9. Agricultural and other income sources across four regions for two percentile household groups19 Table 10. Agro-ecological conditions across four regions for two percentile household groups .............. 20 Table 11. Agricultural growth linkages: international evidence ................................................................ 22
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Table 12. Impact on Growth due to one unit of increase in selected sectors' output ................................. 27 Table 13. Impact on Income due to one unit of increase in sectors' output ............................................... 30 Table 14. Importance of services in measuring multiplier effect............................................................... 32 Table 15. Population and poverty rates in the three areas .......................................................................... 37 Table 16. Land size and cereal output per household in the three areas .................................................... 37 Table 17. Cereal yield and input use in the three areas .............................................................................. 38 Table 18. Agricultural and non-agricultural growth rate in the simulations .............................................. 39 Table 19. Agricultural growth is more pro-poor ........................................................................................ 44 Table A 1. The structure of the 2006/07 Ethiopian Social Accounting Matrix (SAM) ............................. 61 Table A 2. Income distribution in SAM .................................................................................................... 61 Table A 3. Household consumption spending patterns in Ethiopia ........................................................... 62 Table A 4. Composition of demand and supply by sector in SAM ........................................................... 63 Table A 5. Sensitivity test result – gains in GDP ....................................................................................... 64 Table A 6. Sensitivity test result – gains in total household income ......................................................... 65 Table A 7. Sensitivity test result - income ratio of rural poor household (rural poor household income in
SAM is 1) ................................................................................................................................................... 66 Table A 8. Sensitivity test result - income ratio of rural non-poor household (rural non-poor household
income in SAM is 1) .................................................................................................................................. 67 Table A 9. Agricultural commodities included in the economy-wide, multi-market model ..................... 68
Figures
Figure 1. Agricultural GDP annual growth rate (%) .................................................................................... 9 Figure 2. Agricultural GDP per capita (2000 constant USD) .................................................................... 10 Figure 3. Total cereal area according to the use of modern input (000 hectares) ...................................... 13 Figure 4. Food deficit, food balanced, and food surplus areas .................................................................. 36 Figure 5. GDP growth multipliers in staple and export agricultural growth scenarios .............................. 40 Figure 6. GDP growth multipliers in agriculture-led and non-agricultural-led growth scenarios ............. 41 Figure 7. National poverty rate (%) in agriculture-led and non-agricultural-led growth scenarios ........... 44 Figure 8. Comparison of effect of agricultural subsector growth on poverty reduction in the food deficit
and food surplus areas ................................................................................................................................ 45
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ECONOMIC IMPORTANCE OF AGRICULTURE FOR SUSTAINABLE DEVELOPMENT AND
POVERTY REDUCTION: THE CASE STUDY OF ETHIOPIA1
1. Introduction
1. It has been more than two and half decades since the government of Ethiopia had formally
adopted Agriculture Development Led Industrialization (ADLI) as its development strategy in 1994. The
main goal of this strategy is to attain fast and broad-based development within the agricultural sector and to
make this sector's development to power broad economic growth. ADLI had been further rationalized as
the basis of the poverty reduction program subsequently adopted by the government in 2002 (MoFED,
2002), a program is officially known as Sustainable Development and Poverty Reduction
Program (SDPRP).
2. With 85% of the population living in the rural areas and depending on agriculture for livelihood,
there is no doubt for the economic importance of the agricultural sector for sustainable development and
poverty reduction in Ethiopia. The agricultural sector accounts for more than 40% of national GDP, 90%
of exports, and provides basic needs and income to more than 90% of the poor. A better performed
agricultural sector has provided growth to the overall economy, improved the food security and reduced
poverty in the recent years.
3. However, debate on the potential for the agricultural sector to lead industrialization and economic
transformation has been lasted for many years in the country, though the debate is often more political and
not well served by rigorous empirical evidence. As the second largest country in Africa and with extreme
high population growth, one of the key questions dominating the policy debate is the doubt for the future
development of agriculture given the increasingly small plots which farmers must earn their living. The
doubt also relates to the role of agriculture in the poverty reduction. While the majority of population lives
in the rural and most poor are the rural poor, there is a question about how much growth in agriculture that
can lead further and significant poverty reduction.
4. Even among those who believe the importance of agriculture in development and poverty
reduction, the debate exists on what kind agricultural growth should be pursued. Should the government
promote large-scale agriculture that is more advantage in adopting modern technology and hence more
productive and competitive? Or should the government focus on the growth of smallholder agriculture
from which a majority of rural population can get benefit? Among the agricultural subsectors, should
considerable specific policy supports or interventions focus on export-led agriculture that may get quick
outcome targeting niche markets, or emphasize the staple crop and livestock sectors that can bring a broad-
based growth for the country?
1. This report is the first draft of the Ethiopia country case study of an OECD project "The Economic
Importance of Agriculture for Sustainable Development and Poverty Reduction." The work should be
considered as work in progress. The principal authors accept responsibility for any errors. The authors are:
Xinshen Diao, Alemayehu Seyoum Taffesse, Bingxin Yu, and Alejandro Nin Pratt, International Food
Policy Research Institute (IFPRI).
6
5. Against this policy background, the objective of this report is to contribute to such debates by
focusing on the role of agriculture‘s future growth in economic transformation and poverty reduction. The
evaluation of such role will be conducted in a broad economic context, and the linkages of agriculture with
the other economic sectors and the possible differential contribution of agricultural growth at sub-sector
levels to the poverty reduction are quantitatively measured. In the following section (Section 2), we first
provide an overview of the agricultural policy evolutions in the last three decades in Ethiopia. The policy
outcome in terms of agricultural growth performance and poverty reduction are assessed in Section 3. In
Section 4 we provide a quantitative measure of agricultural linkages in the economy and assess the role of
future agricultural growth in further poverty reduction. Section 5 provides an assessment on some key
intervention areas that will promote agricultural growth, while the role of the government and policy
implications are concluded the report.
2. An overview of Ethiopian agricultural policy
6. While Ethiopia has been witnessed three major political regime changes in the recent history, the
importance of agriculture has been recognized by each government in this period. However, different
policies pursued by the different regimes have resulted in very different outcomes in agricultural and rural
development, particularly between the last two regimes in the past 35 years. In this period, the Derg regime
(1975-1991) has been characterized as an agrarian socialist regime with widespread government controls in
all economic spheres including agriculture. After overthrowing the imperial regime of Haile Selassie, the
Derg announced an agrarian reform program to declare all rural land to be the property of the state,
together with the nationalization of almost all other assets in the industrial and services sectors such as
manufacturing factories, financial institutions, big hotels and many residential buildings. While the
agrarian reform had prohibited all tenancy relations and provided a large number of rural households with
equal access to cultivation land according to their needs, the restriction on plot size per family, the
prohibition of hired agricultural labour, the intensification of collectivization, the establishment of large-
scale state farms, and a series of other anti-market and state-controlled economic instruments had not only
significantly negatively affected the incentives of farmers but also distorted the market mechanism in
guiding land allocation and promoting productivity improvement. While central planning types of
development strategies had identified agriculture as an engine of growth and targeted the improvement of
food security through agricultural productivity, most growth targets became just a piece of paper and had
never been able to achieve. Ethiopia suffered the worst famine on record in 1984 and the country's
economy was in the dismal state at the end of Derg Regime.
7. Bad policies and brutal political repression during the Derg period generated disastrous economic
outcomes and led to civil conflict. As a consequence the Derg regime collapsed in 1991 and the Ethiopian
People‘s Revolutionary Democratic Front (EPRDF) assumed power. The years that followed witnessed a
radical shift in overall government policy. Both the Transitional government (1991-94) and the EPRDF
government that followed initiated extensive economic reforms including significant market liberalization
and a structural adjustment program. Tariffs have been cut, quota constraints relaxed, licensing procedures
Local seed & no fertilizer Fertilizer & local seed Fertilizer & improved seed
Source: Author’s calculation using CSA data (various years).
31. The government‘s fertilizer promotion policy focuses on the four main cereal crops, which are
relatively more responsive to fertilizer and have higher producer prices. By focusing on these four crops in
Table 1, it indicates that, 55% of the four crops‘ areas were applied by modern inputs, primarily fertilizer,
in 2003/04, while the per cent has increase to 65% in 2007/08. In these four years, total harvest areas of
these four crops increased by 28%. In the meantime, the areas without use of modern input did not change
and stabilize around 2.3 million hectares.
32. However, the yield difference between with and without modern input use is modest for all the
four crops. Except for wheat, the average yield for the areas with modern input use is less than 15% higher
than the average yield for the areas without any modern input. In the case of wheat, the yield gap is 30% in
2003/04, while it falls to 19% in 2007/08. Many factors affect fertilizer efficiency and in their 2007 study,
Byerlee et al. (2007) concluded the following major factors that affect the results of the intensification
program that the government has been promoted:
Low technical efficiency in the use of fertilizer due to the application of standard packages to
very diverse and risky environments, and the timeliness and quality of input supply.
Poor performance of the extension service resulting from limited human capital, competing
responsibilities, and entrenched routines and behaviours among extension agents.
Shortcomings in seed quality and timeliness of seed delivery.
Promotion of regionally inefficient allocation of fertilizer as promotion of fertilizer use tied to
credit programs is fed by government targets for fertilizer consumption at the local, regional and
national levels.
Input distribution tied to credit that limits the opportunity for the emergence of private sector
retailers.
Generation of an unlevel playing field in the rural finance sector by the guaranteed loan program
with below-market interest rates, undermining efforts to set up alternative institutions, branches
of commercial banks, or independent financial cooperatives.
14
Table 3. Share of cereal areas and cereal yields by technology
Share in each crop's total areas Average yield (tonnes/hectare)
2003 2004 2005 2006 2003 2004 2005 2006
Maize
Fertilizer with local seed 11.8 13.9 19.7 16.4
1.98 1.82 2.36 2.26
Fertilizer with improved seed 23.4 17.7 17.7 21.6
2.12 2.04 2.80 2.53
Improved seed without fertilizer 1.3 1.1 0.9 0.6
2.22 1.68 2.21 2.49
Without fertilizer & improved seed 63.6 67.3 61.7 61.5
1.78 1.68 2.10 2.14
Average 1.65 1.51 1.81 1.88
Teff
Fertilizer with local seed 45.2 47.2 54.4 53.5
0.87 1.06 1.06 1.22
Fertilizer with improved seed 0.3 0.5 0.5 0.6
0.95 0.99 0.99 1.24
Improved seed without fertilizer 0.3 0.2 0.1 0.1
0.67 0.61 0.90 1.47
Without fertilizer & improved seed 54.2 52.1 45.0 45.7
0.82 0.89 0.97 1.16
Average 0.45 0.47 0.44 0.54
Wheat
Fertilizer with local seed 50.1 50.4 60.6 53.8
1.62 1.81 1.83 1.69
Fertilizer with improved seed 3.7 3.4 2.6 2.0
1.54 1.79 1.85 1.59
Improved seed without fertilizer 0.9 0.6 0.5 0.9
1.27 1.22 1.41 1.49
Without fertilizer & improved seed 45.3 45.6 36.4 43.3
1.22 1.31 1.38 1.47
Average 0.62 0.67 0.56 0.68
Barley
Fertilizer with local seed 25.9 25.6 27.3 26.6
1.24 1.29 1.52 1.50
Fertilizer with improved seed 0.1 0.3 0.1 0.2
1.17 1.16 1.44 1.86
Improved seed without fertilizer 0.2 0.3 0.0 0.0
0.90 1.06 1.10 1.39
Without fertilizer & improved seed 73.8 73.8 72.5 73.1
1.09 1.04 1.19 1.38
Average 0.81 0.77 0.86 1.01 Source: Author’s calculation using part of CSA data (various years).
33. While all these factors have resulted in the low response of cereal yield to the use of fertilizer,
increased use of fertilizer without use of high-yield seed varieties seems to be the most important factor.
Table 3 shows cereal area allocation for the different technologies and the yield for the four cereal crops
under different technologies. Except for maize, the combined use of fertilizer and improved seed is applied
to 0.3% - 0.6% of harvested teff areas and 2.0 - 3.7% of wheat area. Only in the case of maize, the share is
18% - 23% high. Moreover, when a rapid increase in fertilizer use occurred in the last four years, growth in
the areas with the combination of fertilizer and improved seed has become stagnant. While the low yield
response to the combined use of fertilizer and improved seed in the case of teff and wheat seems to indicate
that the so-called improved seed is not really high yield varieties, a further assessment is necessary for
fully understanding farmer‘s behaviour as well as the constraints for promoting such combined technology
in Ethiopia. To show the potential of doubling Ethiopia cereal production by improving yields, we draw
from the World Bank (2006) to display what are the reachable yields for cereal crops in Table 4.
15
Table 4. Yields in on-farm field trials vs. farmers' yield (tonne/ha)
NAEIP* (1995-1999) SG2000** (1993-1999) Current farm
yield
2000-04
improved traditional improved traditional
Maize 4.73 1.57 4.60 1.57 1.82
Teff 1.43 0.85 1.62 0.64 0.82
Wheat 2.93 1.17 2.31 0.95 1.31
Barley 2.15 1.00 1.05
Sorghum 2.79 1.12 2.08 0.92 1.21 * NAEIP is the National Agricultural Extension Intervention Program. ** SG2000 is Sasakawa Global 2000 Program. Source: World Bank 2006.
34. Agricultural sector performance over the recent five years is also indicative of the new direction
of the country's development strategy (i.e. PASDEP), which indicates an evolution of strategy toward
market-driven diversification and commercialization, and increasing exports with a greater focus on private
sector investment. Following the opening of incentives for private investment in flower industry, a total of
over 100 investors have invested in the industry and flower exports increased to nearly USD 13 million in
2005. Other investments in high value products and supply chains are emerging, including the export of
green beans to Europe, the emergence of a contractual supply chain for the UK based bean industry, and
the supply of quality milk and poultry to urban centres. Several of these emerging industries involve out-
grower and/or contract arrangements with small farmers, often linked with an emerging indigenous
entrepreneurial class of farmers and agribusiness. Exports of oilseeds and pulses, two traditional cash crops
grown by many farmers, have also experienced impressive growth, increasing their value by a factor of ten
between 1997 and 2006 and demonstrating the increasing competitiveness of these sectors through area
specialization and the uptake of new technologies. While coffee is still the most important export crop in
Ethiopia, the combined exports of other crops and leather has passed the coffee in export value in the
recent years.
3.2. Agriculture and poverty reduction
35. Given that 85% of Ethiopians live in the rural area and more than 90% of the poor are in the
rural, agricultural growth direct transfers into poverty reduction if the growth is significant. While income
generation role of agricultural growth is important for poverty reduction, as many poor in rural Ethiopia
either mainly produce for own consumption need or are the net buyers of cereals, the direct consumption
effect of agricultural growth is equally important in poverty reduction in the country. While the country has
conducted agricultural production sample survey almost in each year, the national representative household
surveys in income, consumption and expenditure (HICES), which can provide a poverty measure, have
been conducted only in each five years in the recent 15 years. Moreover, the HICES is only available
publicly for the two runs of 1995/96 and 1999/2000, which have been widely used in poverty assessment
in the literature. The most recent HICES was conducted in 2004/05, but the survey data is not available for
analysis, besides the poverty rate reported by the government.
36. Table 5 first reports the government's official poverty assessment based on the three runs of
HICES and the poverty measure is based on the country's own standard, which is not necessary to be the
same as international standard measured by daily income. Instead, the country's own poverty measure
standard considers the minimum consumption level to meet the basic need of food and other spending and
also considers the different consumption expenditure needs (e.g., for housing) between the rural and urban.
For comparison, we also present the national poverty rate measured according to the World Bank's
16
standards in Table 6, in which the poverty line of USD 1.25 in 2005 PPP dollar per day is commonly used
as an international standard for poverty measure across countries.
37. While the level of national poverty rate for the same year differs between national and the
international measures, the trends of change in the poverty rate are similar. Poverty has declined over time,
and it declines more according to the measure using the international standard. Moreover, the poverty
declined more in both absolute level and per cent change between 1999/2000 and 2004/05, and this is true
for both measures. The third observation is that while poverty is falling, the country seems to be unlikely to
achieve the MDG1 of halving its 1990's poverty rate by 2015, if the poverty reduction trend, which is
calculated according to the country's own poverty measure, will be similar in the next 10 years after
2004/05 as that in the previous years between 1995 and 2005.
Table 5. Poverty incidence and inequality
Poverty rate (%) % Change in incidences
1995/96 1999/00 2004/05 1999/00 over
1995/96
2004/05 over
1995/96
2004/05
over
1999/00
Headcount index
National 45.5 44.2 38.7 -2.9 -14.9 -12.4
Rural 47.5 45.4 39.3 -4.4 -17.3 -13.4
Urban 33.2 36.9 35.1 11.1 5.7 -4.9
Gini coefficient (consumption)
National 0.29 0.28 0.30
Rural 0.27 0.26 0.26
Urban 0.34 0.38 0.44 Source: MOFED 2006.
Table 6. National poverty rate by different daily 2005 USD PPP poverty line
Poverty rate (%) Gini
USD 1.00 USD 1.25 USD 2.00
1981/82 48.6 66.2 89.9 0.32
1995/96 44.9 60.5 84.6 0.40
1999/00 36.3 55.6 86.4 0.30
2004/05 21.7 39.0 77.5 0.30 Source: World Bank PovCal Net.
38. Table 5 also reports the poverty rate for the rural and urban households separately. After
considering different expenditure patterns between the rural and urban households caused by location
factors, the rural poverty rate is still higher than the urban poverty rate in each survey year reported in
Table 5. However, the reduction in poverty rate is more rapidly in the rural than in the urban, indicating the
direct effect of agricultural growth on the poverty reduction. The urban poverty has in fact risen in the
period between the first and third survey years, although it fells slightly between the second and third
surveys. Because of this, the rural and urban poverty rate has converged in the recent years.
39. Gini coefficient that is used to measure inequality is included in both Tables 5 and 6. While Gini
coefficient has slightly increased in Table 5 according the country's own consumption-based assessment,
this coefficient fells significantly in Table 6 between 1995/00 and 2004/05 using the income measured by
the international standard. The slight increase in inequality reported in Table 5 is the result of significant
17
increase in the value of urban Gini coefficient. The urban Gini, which starts with a value higher than the
rural Gini in 1995/96, has increased from 0.34 to 0.44, a 10 percentage points increase, in the period of
10 years. In the same period, the rural Gini has actually fallen slightly, and has been at a low level of 0.27
to 0.26.
40. Significant declines in the rural poverty rate and low and relatively stable rural Gini coefficients
in Ethiopia seem to indicate that agricultural growth in the recent years has benefited the poor and growth
outcome has been shared by a majority of the rural population. Moreover, such growth outcome on poverty
and income distribution seems to be stronger in the first five years of the 21th century than in the previous
years. However, as the data of the recent HICES survey of 2004/05 is not available for our study, we have
to draw from the exiting analysis on the previous HICES surveys to further understand the relationship
between agriculture and poverty reduction in the rest of this section. In 2005, the World Bank published a
detail assessment on the role of agriculture in the well-being and poverty in Ethiopia (World Bank, 2005).
The relevant findings of this report are synthesized here to conclude this section.
Table 7. Poverty rate by sector of employment of household head and livelihood
Poverty rate (%)
Consumption per adult
equivalent Population share
1995/96 1999/00 1995/96 1999/00 1995/96 1999/00
By sector of employment of household head
Agriculture 40 38 1 592 1 600 85 84
Industry 32 43 1 980 1 707 1 6
Services 28 35 2 201 2 113 14 10
By type of livelihood
Mainly agriculture 41 38
Mainly cash crop producers 29 26
Coffee producers 42 40
Chat producers 19 33
Tea producers 41 24 Source: Tables 1.9 and 1.10 in the World Bank (2005).
41. Authors of the World Bank Report (2005) calculate the poverty incidence by sector of
employment of household head and by rural livelihood. As indicated in Table 7, poverty incidence among
households employed in non-agriculture is substantially lower than those employed in the agricultural
sector in the first run of HICES survey (1995/96). The poverty gap became significantly smaller between
these two types of employment and the poverty rate of a group of households whose heads are employed in
the industrial sector is actually higher than the poverty rate for agriculturally employed households. While
the poverty rate of the group households whose heads were employed in the service sector is still lower
than the poverty rate of agriculturally employed households in the second run of the survey, the gap has
become much smaller than that in the first run. Moreover, poverty rate increases in the non-agriculturally
employed household groups and it increases more in the industrially employed household group. This
finding is consistent with the change in the poverty rate by rural and urban household groups, which
further confirms the positive role of agricultural growth to rural poverty reduction.
42. In the second panel of Table 7, the poverty distribution is displayed by different types of rural
livelihood. The poverty rate for the group of households whose main livelihood activity is the engagement
of agriculture is similar as the poverty rate for the group of households whose heads employed in the non-
agricultural sector. However, if we only focus on the rural households whose main livelihood activities are
18
cash crop production, the poverty rate is significantly lower than the whole agricultural group in both runs
of the surveys. Among the cash crop producers, the households mainly involving in the production of
coffee, the most important export crop of Ethiopia, actually are similar poor as the agricultural household
group as a whole. The poverty rate for coffee growers is actually higher than for the agricultural producers
as a group in the second run of the survey (1999/00) due to the declined world coffee price. However, the
poverty rate for coffee growers has to be read in caution as coffee production areas are quite concentrated
in Ethiopia and the comparison between coffee and non-coffee households should make more sense
considering these areas only.
43. Geographically, poverty is widespread in Ethiopia, while the majority of the poor live in the four
large regions (Tigray, Amhara, Oromiya and SNNP) and Addis Ababa. Together, these regions account for
85% of population of the country (World Bank, 2005). The World Bank Report shows that the highest
rates of poverty among the major regions are found in Tigray and SNNP. Authors of the report calculate
their own regional poverty rate and two poverty lines are considered in the calculation. The two large
regions with the high poverty rate are characterized by lower than average arable land per capita, which
underscores the role of land scarcity in determining poverty.
Table 8. Poverty rate by administrative region
Lower poverty rate Upper poverty rate
1995/96 1999/00 1995/96 1999/00
Tigray 45 49 66 69
Afar 20 43 26 63
Amhara 45 36 65 55
Oromiya 28 32 46 52
Somali 8 15 18 33
Benishangul-Gumuz 49 54 72 71
SNNP 49 48 67 65
Gambela 35 66 48 79
Harari 25 29 43 47
Addis Ababa 34 41 50 57
Dire Dawa 47 49 65 68
National 38 38 57 57 Source: Table 1.12 in the World Bank (2005) and national poverty rate is from Table 1.2 in the World Bank (2005).
44. Regional disaggregation in poverty also shows that poverty rose between the two runs of surveys
in 9 of 10 regions and declined only in Amhara under the lower poverty rate assessment and slightly
declined in additions in the other two regions under the upper poverty rate assessment. It has to point out
that the poverty rate in Table 8 is from the World Bank report's authors own calculation based the same
data of HICES from which the official poverty rate is derived. According to them, both lower and upper
poverty rates for the country as a whole did not change between the two runs of HICES, which explains
why poverty rate rose in most regions and declined in few.
45. Focusing on the four large regions that account for 85% of total population in Ethiopia, the
World Bank report further assesses the relationship between livelihood and level of income. Table 9 is
drawn from the findings of the report. By considering only two percentile groups of population in each of
the four regions, it shows that for the poor one-percent of population (the 25th percentile), the level of real
expenditure is only equivalent to 63% of regional average for the three regions and only 48% for Amhara.
On the other hand, for the non-poor one-percent of population (the 75th percentile), the level of real
19
expenditure is 1.85 - 1.88 times of the expenditure level for the 25th percentile of population in the three
regions and 2.53 times in Amhara.
Table 9. Agricultural and other income sources across four regions for two percentile household groups
Tigray Amhara Oromiya SNNP
Real expenditure per adult equivalent in Addis 1995 price
Mean 1 181 1 981 1 396 1 550
25th percentile 740 956 874 984
75th percentile 1 389 2 416 1 647 1 817
Share of income from agriculture (%)
Mean 67 75 73 71
25th percentile 53 71 63 59
75th percentile 95 96 96 92
Share of income from other sources (%)
Mean 18 11 12 13
25th percentile 4 3 2 3
75th percentile 24 14 15 18 Source: Drawn from Table 4.7 in the World Bank (2005).
46. While there are many factors that associate to the income gaps between the poor and non-poor,
we focus only on the sources of income in Table 9. As indicated in the table, for the poor percentile group,
share of agricultural income is consistently lower than that for the regions as a whole and for the rich
percentile group across the four regions. It shows that for the rich percentile group, share of income from
agriculture is more than 95% for the three regions and 92% for SNNP. On the other hand, for the poor
percentile group, the share is below 60% for the two regions, and 63% and 71%, respectively, for Oromiya
and Amhara. The poor not only have lower share of agricultural income, but also few income generation
opportunities from other livelihood sources. This seems to indicate that many poor Ethiopian households,
particularly the extremely poor ones, could not earn enough income to meet their basic expenditures, and
supports of food and other types of aids are important component of their basic consumption. This finding
also indicates the positive relationship between agriculture as the main source of income and rural
households‘ position in income distribution ranking, and hence, the important role of agriculture even for
the relative non-poor rural households.
47. Similar as most African countries, Ethiopia is characterized as rainfall-dominated agriculture and
irrigation covers less than three per cent of agricultural crop areas. Understanding the relationship between
agro-ecological conditions and household income is helpful for identifying a group of policies that are
more location specific and targeted. Table 10 reports three important agro-ecological condition factors for
the four large regions and these factors are defined at the woreda level. As indicated in the table, the poor
group of households (25th percentile) lives in relatively low altitude areas, while the non-poor group
(75th percentile) lives in highland in all the four regions. While long run average rainfall varies across the
four regions, the poor seem to live in the areas with less rain than the areas where the non-poor live. On the
other hand, the variation of rain is larger in the areas the non-poor live than in the areas the poor live.
Agro-ecological condition information compiled in Table 10 indicates that the absolute disadvantage in
agricultural natural production condition partially characterizes the areas where the poor live, which is at
least the case for the one per cent of the poor (25th percentile) population considered in Table 9. While
agricultural potential in such areas is not necessary high and hence these areas can only pay limited role in
national wide agricultural growth, to promote the technology that aims at improving such disadvantage
20
condition in these areas such as land management and other farming practice is important for poverty
reduction. This policy issue will be further discussed in the following sections.
Table 10. Agro-ecological conditions across four regions for two percentile household groups
Tigray Amhara Oromiya SNNP
Mean altitude (m)
Mean 1 912 2 097 1 928 1 818
25th percentile 1 685 1 856 1 705 1 549
75th percentile 2 127 2 359 2 167 2 051
Long-run average rainfall (mm)
Mean 687 1 041 1 121 1 235
25th percentile 512 836 746 1 090
75th percentile 902 1 169 1 441 1 453
Long-run coefficient of variation of rain
Mean 0.28 0.26 0.23 0.20
25th percentile 0.21 0.17 0.16 0.15
75th percentile 0.33 0.33 0.28 0.23 Source: Drawn from Table 4.8 in the World Bank (2005).
4. Agricultural-non-agricultural growth linkages in the Ethiopian economy
4.1. Why agricultural growth linkages matter?3
48. The economic importance of agriculture for development has been quantitatively measured in the
literature and the linkage effect is often used in such measure. As agriculture grows, it stimulates series of
economic linkages with the rest of the economy. The resulting demand linkages fall into two broad
categories: production linkages, and consumption linkages.4
49. Production linkages include backward linkages – the input demands by farmers for farm
equipment, pumps, fuel, fertilizer and repair services – as well as forward linkages from agriculture to non-
farm processors of agricultural raw materials. In prosperous agricultural zones, these linkages prove
substantial as pump suppliers, input dealers, grain traders, processing industries and transporters emerge to
supply agricultural inputs and process and distribute farm output. Empirical work on these relationships
has focused on measurement of input-output coefficients to establish the strength of the forward and
backward supply linkages.
50. Consumption linkages include spending by farm families on locally produced consumer goods
and services. Early work in Green Revolution India indicated that higher-income small farmers spent about
half of their incremental farm income on non-farm goods and services as well as another third on
perishable agricultural commodities such as milk, fruit and vegetables (Mellor and Lele 1971). Thus,
consumption linkages from growing farm income can induce sizable second rounds of rural growth via
3. Although the discussion below focuses explicitly on agricultural growth and linkages thereof, the approach
and concepts therein apply to growth in other sectors as well.
4. It is important to emphasise that this study, and almost all studies of its kind, focus on demand linkages
described in the following paragraphs. Aside from demand linkages, there are other inter-sectoral linkages
in an economy. Briefly, these operate via saving and investment (private and public), labour flows, and
transfers including taxes.
21
increased consumer demand for non-agricultural goods and services as well as perishable, high value farm
commodities such as milk, meat and vegetables. In places like India, where many non-farm goods and
services are produced by labour-intensive methods, the spending multipliers not only accelerate growth but
also enhance the equity of agriculture led growth.
51. Following an initial spurt in farm productivity and incomes, production and consumption
linkages together induce second rounds of demand-led growth. Empirical evidence from around the
developing world suggests that a USD 1 increase in agricultural income will generate an additional
USD 0.30 to USD 0.80 income in rural non-farm economy. Linkages are even higher when consumption of
urban-produced products is included. In Africa and Asia, consumption linkages typically account for over
80% total spending linkages.
52. This evidence contrasts with Hirschman‘s claim of feeble agricultural growth linkages
(Hirschman, 1958). Where did Hirschman go wrong? He underestimated agricultural growth linkages in
two very fundamental ways. As Johnston and Kilby (1975) originally pointed out, agricultural technology
changed during the green revolution. The new high yielding varieties demanded pumps, sprayers, fertilizer,
cement, construction labour, and repair facilities from non-agricultural firms, thus generating substantial
backward linkages. Furthermore, considerable milling, processing and distribution of agricultural produce
took place in rural areas, thus generating important forward production linkages as well. The new
Summary of agricultural linkage effect assessed by a SIO model
82. While the application of a SIO model cannot explicitly assess the source of growth, given the
constraints in land and other resources faced by the Ethiopian economy, it is obvious that technological
change and hence productivity must be the main source of sustainable growth. Alternative growth options
have significant differences in the direction and extent of growth linkages. Growth in agriculture produces
stronger linkages with the rest of economy than growth in non-agriculture. The potential benefits of
stimulating growth in agricultural production (albeit differentiated by products) are thus substantial.
Nevertheless, the size of this potential as well as the extent of its realization depends on a parallel
expansion in non-agricultural sectors (particularly in those associated with growing input or consumption
demand). Stronger linkage effect of agricultural growth is not only due to its use of primary input, but also
consumption linkages. The study shows that at Ethiopia's current development stage, consumption linkages
are more important than the second-run production linkages, which are high among the manufacturing
sectors. Consumption linkages are particularly high for growth led by the agricultural sectors, indicating
that the importance of agriculture to the overall economic growth is not only due to its size in the economy,
but also due the stage of development in which, at current, a majority of population in Ethiopia is poor who
consume most goods and services (including non-agricultural goods and services) produced locally.
83. When households can be disaggregated according to different income levels and rural and urban
location in a SAM, the SIO model can be used to assess income distribution effect of growth. While
ignoring price effect is a critical shortcoming of a SIO model, the analysis does provide us meaningful
message about the complicated relationship between growth and income distribution. Staples led growth
has shown to benefit more the rural households, particularly the rural poor households, while export crop
led growth seems to benefit rural non-poor households disproportionally. When growth is led by the
manufacturing sectors, urban households are major beneficial. Income distribution is worse and income
gap between the rural and urban households increases under such growth options. While the SIO model
cannot explicitly measure the poverty impact of alternative growth options, it implicitly indicates the most
effectiveness of staple led growth in poverty reduction.
84. It should note that the SIO model results depend on the assumption of constrained or
unconstrained supply elasticity among various non-agricultural sectors. By varying the set of non-
agricultural sectors assumed to be supply unconstrained show different results. In order to realize the
important role of services in measuring linkages effect, we altered the assumption about unconstrained
service supply in an additional scenario and assumed that trade, transport, financial and other business
services are constrained in their response to the growth in the studied agricultural and non-agricultural
subsectors. To do it we find that while the extent of linkages effect is lowered in all cases, growth induced
by the non-agricultural sector is constrained more by the service sectors. Barriers to growth due to
constraints faced by the service sector should be paid more attention in a development strategy in which
either agriculture or manufacturing is emphasized.
85. It has to point out that with globalization, growth linkages seem to be weakening in the
manufacturing sector, as export-oriented manufacturing does not need to depend on domestic markets for
input supply and output demand (reference for this argument). While it is true that many developing
countries want to pursue such growth path, following the success in China and other Asian countries'
experience, the current Ethiopian economy actually shows unutilized capacity among many manufacturing
sectors, even at the development level where manufacturing accounts for a small share of the economy.
The industrial survey shows that the main reason for unutilized factory capacity is due to domestic demand
constraint. When such firms cannot produce goods that are for domestic market, can they produce for
exports which often requires much better quality and more competitive in price? Moreover, globalization is
a two-way thing. When domestic firms have opportunities to export their products using imported goods as
input materials, they are also facing stronger competitions from imported goods they produce in their own
34
country's market. We observe that in many African countries' domestic markets have become increasingly
dependent on imported manufacturing goods and de-industrialization has occurred among some
manufacturing sectors. Obviously, competitiveness in export and domestic markets has become more or
less as a same thing. Without increasing domestic market demand and improving competition in domestic
market, it is unlikely to expect Ethiopia to jump into international market for manufacturing exports.
Production linkages effect analyzed in this study, thus, is still highly relevant to the current Ethiopian
economy.
86. While the SIO model is a useful tool for linkage analysis, its rigidity in prices is obvious an
improper assumption given that linkages are primarily through the interactions between supply and
demand in the domestic market. In order to overcome such shortcomings as well as the arbitrary
assumptions of constrained and unconstrained supply elasticity, an economy-wide model is developed and
applied to further assess the growth linkages and their impact on income and poverty reduction. In this
economy-wide model, not only domestic prices are endogenous and all economic sectors can respond to
changes in prices with different levels of elasticity, the economy is further disaggregated geographically.
Results from both models are compared and the complementary roles of the two models are also discussed
at the end of this section.
4.4. Growth linkages in the Ethiopian economy – an economy-wide multimarket model
87. Ethiopia is an open economy. However, as a land-locked country with high transportation and
other marketing costs, partly explained by considerable geographic distances and an inadequate road
network, prevent world market prices from automatically translating into domestic prices. As a
consequence, many commodity prices, especially those of agricultural products, are actually determined by
supply and demand conditions in the domestic market. For these reasons, it is necessary to take into
account for the interaction of prices and growth (the price effect) in analyzing agriculture-non-agriculture
linkages. Accordingly, in this sub-section we develop an economy-wide multimarket (EMM) model in
which prices of most agricultural and some non-agricultural products are endogenous variables. We apply
this EMM model to Ethiopian economy to further assess the importance of the agricultural sector in growth
and poverty reduction.
Why an EMM model?
88. It is possible to develop a CGE model based on the same SAM used in the SIO model analysis
discussed in the previous chapter. Indeed, the CGE approach is more preferable for economy wide analysis
and more comparable with above SIO analysis when the same SAM is used for both models. However, as
shown in the above analysis, the current SAM is for the national economy and there are only four
aggregate households at the national level. Given the current Ethiopian economy is still dominated by
agriculture and more than 80% of population live in the rural areas, special attention must be paid to the
structure of agriculture in the linkage analysis. It is well known that agricultural production systems are
typically characterized by the interactions between human behaviours and natural environment. With
heterogeneity in agro-ecological, social and economic conditions, agriculture in Ethiopia is highly
diversified. To analyze the economic linkages in the country, it is necessary to understand the role of
different agricultural subsectors at different locations of the country. Moreover, agricultural development is
constrained by market opportunities and conditions of market access provide different such opportunities
to different locations in the country.
89. To take into consideration of geographic factors such as agro-ecological conditions, population
distribution, production and market locations and connections and in order to better present the agricultural
sector and rural economy in the linkage analysis, we have developed a highly disaggregated EMM model
for Ethiopia. Most multimarket models focus on particular subsectors of agriculture or segments of
35
economies. The model developed for this study focuses on agriculture but puts the agricultural sector in an
economy-wide context, such that the model can be used for the economy-wide linkage analysis. The
original EMM model was developed and applied for the food security analysis in Ethiopia (Diao and Nin
Pratt, 2007). This model is extended and modified for this study in order to be consistent with the non-
agricultural economy described by the SAM discussed above, while detail agricultural sectors are still kept
as before.
90. Specifically, there are 32 agricultural commodities or commodity groups (see Table B1 in the
Appendix B for a list of agricultural commodities/sectors included in the model) in the EMM model. In
contrast with the SAM that represents the national economy, both agricultural production and consumption
in the EMM model are further disaggregated into sub-national regions in order to capture the geographic
heterogeneity of sectors and households. Limited by the data, the model captures totally 56 administrative
zones and all agricultural supply and demand functions are defined at the zonal level. Detail description of
the EMM model can be found in Appendix A, while a number of key results concerning impact on growth
and poverty are reported as following.
Three Ethiopias - areas of food deficit, food balanced, and food surplus
91. With highly spatially disaggregated information, Ethiopia can be examined according to sources
of domestic food availability, resulting in a division of Ethiopia into three categories: areas of food deficit,
food balanced, and food surplus (Figure 4). Based on data from Ethiopia‘s 2001/02 Agricultural Census,
woredas in which the average cereal equivalent output per rural household is 20% below the national
average fall into the food deficit area, those with output between 80% and 120% of the national average
form the food balanced area, and those with output 20% or more above than national average constitute the
food surplus area.11
11. The study includes 460 woredas. Cereal output equivalents were used to represent food availability.
Equivalents include cereals, pulses, oil crops, and root crops, and account for over 60% of household food
consumption in the urban and 70% in rural area. The conversion ratio for crops other than cereals was
based on their calorie content (see the FAOSTAT web site).
36
Figure 4. Food deficit, food balanced, and food surplus areas
Source: Constructed by authors based on Democratic Republic of Ethiopia (2002).
92. Almost 30 million of Ethiopians live in the food deficit area, where the annual food availability
averages only about 530 kilograms per household, even in a good year.12
This represents half the national
average (Table 15). In contrast, food availability per household in the food surplus averages
1 800 kilograms, which is 70% above the national average. The high proportion of cereals and other staple
crops in the food availability calculation (more than 70% of rural household food consumption) is
indicative of extremely low food availability and alarming food insecurity, in turn a reflection of very low
income levels per capita and a very high rate of poverty. Compared with the rural poverty rate of 46%
nationwide,13
the poverty rate in the food deficit area is 60%; in the food surplus area it is less than 40%.
Fifty per cent of the rural poor now live in the food deficit area; that area, however, only accounts for 37%
of the total rural population.
93. A major constraint to meeting food demand for the majority of rural households in the food
deficit area is extremely small farmland area. National farm size, including permanent and temporal crops,
averages about one hectare. In the food deficit area, however, farm size averages only 0.57 hectare
compared with 1.38 hectares in the food surplus area (Table 15). Of the 184 woredas constituting the food
deficit area, per household farmland is less than 0.4 hectares in half of them, and less than 0.3 hectares in
one-third of them. Cereal production yields are also lower than the national average, further eroding food
security in these areas. The average cereal yield in the food deficit area is about one metric tonne per
hectare, 20% below the national average and 30% below yields in the food surplus area (Table 16). Even
taking other staple crops into account, a significant yield gap in staple crop production still exists between
the food deficit and food surplus areas.
12. The calculation is based on the data for 2001/02, which is a good harvest year for most areas in the
country.
13. The poverty rate used in this study is consistent with data from HICES 1999/2000 (CSA 2000a).
The three areas are based on woreda-level ratios of cereal equivalent output per household to the national average:
Food deficit area—ratio of less than 0.8 Food balanced area—ratio of between 0.8 and 1.2 Food surplus area—ratio of greater than 1.2
37
Table 15. Population and poverty rates in the three areas
Indicator Food deficit areaa
Food balanced areab
Food surplus areac
National level
Total population 25.6 22.1 22.3 70.0
Rural 21.9 19.7 17.2 58.9
Urban 3.7 2.4 5.0 11.1
Share of population
Rural 37.3 33.4 29.3 100.0
Urban 33.0 21.7 45.3 100.0
Share of poor people
Rural 49.1 25.8 25.1 100.0
Urban 20.3 29.1 50.6 100.0
Poverty rate
Rural 60.5 35.4 39.0 45.8
Urban 22.6 49.2 41.0 37.0
Source: Calculated by authors from Federal Democratic Republic of Ethiopia (2002). aWoredas with cereal equivalent output per rural household at levels 20% below the national average.
bWoredas with cereal equivalent output per rural household at levels of 80%–120% of national average.
cWoredas with cereal equivalent output per rural household at levels 20% higher than the national average.
Table 16. Land size and cereal output per household in the three areas
Woreda-level rural household average Food
deficit area
Food
balanced area
Food
surplus area
National
level
Cereal land holding (hectares per household) 0.41 0.74 1.07 0.70
Farmland holding (hectares per household) 0.57 0.94 1.38 0.90
Cereal output (kilograms per household) 418 883 1 579 904
Source: Calculated by authors from Federal Democratic Republic of Ethiopia (2002).
95. Certain agro ecological conditions, such as soil moisture, affect the feasibility and efficiency of
fertilizer use. Using the growth period as an indicator of agro climatic conditions, woredas were spatially
grouped according to two agricultural domains: high agricultural potential with a maximum growth period
of more than six months, and low agricultural potential with a maximum growth period of less than six
months. Surprisingly, 70% of woredas and 80% of rural households in the food deficit area were classified
as having high agricultural potential; this compared with 90% of both woredas and rural households in the
food surplus area. There is no significant difference in the ratio of fertilized cereal area to total area in the
two domains within the food deficit or food surplus areas. An econometric test further proves that
differences in the agricultural potential cannot explain the difference in fertilizer use or the cereal yield gap
between these areas.
96. Given the absence of household-level data, further analysis of factors affecting production
decisions by farmers, including input use, were not possible.14
Nevertheless, findings from woreda-level
data indicate a significant yield gap and, thus, potential for improving land productivity in those areas
dealing with severe food insecurity. We now turn to the EMM model analysis and the above spatial
patterns of Ethiopian agriculture are captured in the analysis.
97. A large number of previous studies have concluded that agriculture, especially food crops, have
strong growth linkages and multiplier effects; that is, increased agricultural (or food crop) production
would generate a disproportionately large increase in the country‘s total GDP, through increased demand
for inputs, and more importantly, through increased consumption demand as a result of higher agricultural
incomes.15
As the SIO model of the previous chapter, the EMM model is first used to derive sector-level
growth multipliers, deriving from total factor productivity (TFP) shocks in corresponding agricultural sub-
sectors.
98. Prior to the comparative analysis of agriculture-non-agriculture growth linkages, the EMM model
is employed to assess a business-as-usual scenario (also known as the ―baseline‖) in which the economy is
assumed to grow following its current trajectory through 2015 (and 2004 is the base-year used in the
model). The business-as-usual growth path is based on average agricultural and non-agricultural growth
trends for 1995–2004, during which time about 70% and 50% of the increase in total crop production and
cereal production, respectively, resulted from area expansion. Over the same period, the cereal production
14. Obtained Agricultural Census data were aggregated to the woreda level.
15. See Bell and Hazell (1980) for an early methodological discussion of alternative multiplier models used in
growth linkage analysis, and the discussion of Haggblade, Hammer, and Hazell (1991) on the improvement
in the multiplier models with limited price endogeneity.
39
growth rate was 2.9% per year – 0.4% higher than the 2.5% population growth rate – and the growth rates
of total staple crop and cereal yields were about 0.8% and 1.5% per year, respectively. Under the business-
as-usual scenario to 2015, and based on livestock production growth of 4.1% per year and non-agricultural
growth of 5.3% per year, GDP is projected to increase at 4.5% per year, and AgGDP at 3.7% per year.
99. On this basis, the livelihood of the majority of rural Ethiopians will not get significantly
improved by 2015. The national poverty rate will fall to 32.1% by 2015, from the high 2003 level of
44.4%. Given 2.5% yearly population growth during 2003–15, the decline in the number of people living
below the poverty line will only occur in the urban areas, while the number of the poor in the rural areas is
estimated to increase by 83 thousand by 2015.
Staples production has stronger growth linkages over time than export-oriented production
100. It is thus clear from the business-as-usual scenario without additional growth in both agriculture
and non-agriculture, it will be impossible for the country to meet the first MDG of halving the poverty rate
by 2015. On the other hand, achieving the objectives of halving poverty requires a greater understanding of
which sub-sectors can best induce the economy-wide growth and cut poverty faster. Hence, this section
focuses on an evaluation of two broad agricultural sub-sectors in terms of the country‘s growth and poverty
reduction strategy. The two sub-sectors are staples (cereals, root crops, pulses, oilseeds and livestock) and
exportables (coffee, selected fruits and vegetables, cotton, chat, sesame seed, sugar, and other horticultural
products). Specifically, these sub-sectors‘ contribution is assessed by exogenously increasing the
productivity growth rate of one sub-sector, while maintaining the growth of the others at their baseline
levels.
Table 18. Agricultural and non-agricultural growth rate in the simulations
Growth Rate
Base-
run
Staple crop led
growth
Export crop led
growth
Agriculture led
growth
Non-agriculture
led growth
GDP growth rate 4.5 5.5 5.5 5.5 5.5
Ag GDP growth rate 3.7 4.1 5.7 4.5 4.2
NonAg GDP growth rate 5.3 6.8 5.4 6.4 6.7
Total staple crop and livestock growth rate 3.1 4.9 3.0 4.3 3.2
Cereal output growth rate 2.9 4.5 2.9 4.2 3.0
Livestock output growth rate 4.1 6.4 4.0 5.2 4.2
Total high value crop growth rate 3.1 2.7 15.6 7.7 3.0
Traditional export crop growth rate 4.0 3.7 15.0 8.0 4.0
Non-traditional exports growth rate 8.0 4.8 31.2 18.3 6.4 Note: 'A sector-led growth' is defined by an exogenous productivity shock imposed in this sector, which endogenously induces growth in the other sector. For example, in the column called 'staple crop led growth', exogenous shock in sector's productivity is imposed on cereal and livestock production, while differences in the other sectors' growth rate in this scenario from that in the base-run are the endogenous results through linkages effect. Source: Authors calculation from the EMM model results.
101. With more than 80% of AgGDP and 40% of GDP, staples represent the largest agricultural sub-
sector in terms of value-added. In contrast, the export subsector constitutes quite small shares accounting
for about 10% of AgGDP and 5% of total GDP. Thus, the simulated additional annual growth for cereals‘
productivity was first determined, at 1.5%, which implies 2.2% additional annual growth in livestock. In
total, additional 1.8% of annual growth rate is obtained for the aggregated staple food sector (staple crops
and livestock). With such growth rate in the staple sector, total GDP will grow at 5.5% (partly through
strong linkage effects on the non-agricultural sector that will be discussed later). In order to produce the
same 5.5% of GDP growth rate, the agricultural exports sector needs to grow at 15.6%, with additional
12.5% of annual growth compared with the base-run (Table 18).
40
102. To make the impacts more clearly comparable growth multipliers are used. The multipliers are
defined as the total increase in real GDP divided by the increase in the shocked sector‘s total output, both
measured at the initial (base-year) level of prices. The resulting multipliers derived using an economy wide
and endogenous price models are in general relatively smaller than the standard fixed-price multipliers.16
Our model‘s simulation results show that the staple sector‘s growth multipliers are consistently greater
than one and increase overtime (Figure 5). These results imply that one unit (not one per cent) increase in
staple production will generate more than one unit of increase in total GDP. Moreover, such growth
linkages become stronger over time. For example, one unit of increase in staple production can have 1.03 –
1.12 units of increase in total GDP in the first five years in the simulation, while the same one unit of
increase in staple production will generate 1.29 units of GDP by 2015. On the other hand, the linkages
from agricultural export sector to total GDP is strong only in the initial five years, while the linkages
become weaker overtime, and the growth multipliers fall to below one by 2015.
Figure 5. GDP growth multipliers in staple and export agricultural growth scenarios
Both with 5.5% GDP annual growth
0.9
1.0
1.1
1.2
1.3
1.4
1.5
2004 2006 2008 2010 2012 2014
One-unit staple output One-unit export ag ouput
Source: Authors calculation from the EMM model results.
103. Different from the discussion in the previous section about the SIO model results in which the
linkages effect on the level of total GDP is decomposed into production and consumption linkages, in the
EMM model that is dynamic and runs for a period of 10 years, we focus on the linkages effect on the GDP
growth rate that can be decomposed into growth rate in the agricultural and non-agricultural sector. The
model result shows that the strong growth linkage effect in the economy induced by the staples growth is
mainly due to growth in the non-agricultural sector in response to the growth in the staples. In the scenario
in which exogenous productivity growth is only assumed for the staple sector, the non-agricultural GDP‘s
average annual growth rises to 6.8%, from 5.3% in the base-run (Table 18, third row, first and second
columns). The additional 1.5 percentage points of annual non-agricultural GDP growth is endogenously
induced by growth in staple sector, as there is no additional exogenous growth shock imposed on the non-
agricultural sector in this scenario. On the other hand, if additional economy wide growth is triggered by
additional growth in the agricultural export sector, at the same level of GDP growth, the annual growth rate
in the non-agricultural sector rises only to 5.4% (Table 18, third row, first and third column), with
additional 0.1 percentage points of annual growth compared with the base-run.
16. See Dorosh and Haggblade (2003) for a comparison of CGE and fixed-price multipliers for several