Ldc Graduation CriteriaLDC Graduation Criteria - Calculations Behind
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LDC Graduation Criteria
- Calculations Behind
Jin (Lara) Zhou (Ms.)
Research Assistant Intern on LDC Graduation
Poverty Reduction Unit, UNDP Lao PDR
Jin.lara.zhou@undp.org
jz2417@columbia.edu
Acknowledgement
LDC Identification
LDC Graduation Criteria Factsheet
Sub-indicators
1. GNI per capita
GNI PC Graduation Threshold
1. GNI per capita
2. HAI
Introduction
Methodology
Sub-indicators
HAI Summary
HAI Graduation Threshold 2012
HAI Change over time 2009 & 2006
2.1 Percentage of population undernourished
2.2 Under-five mortality rate
2.3 Gross secondary enrollment rate
2.4 Adult Literacy Rate
3. EVI
Introduction
Methodology
Exposure Index sub-indicators
Exposure Index Summary
3.1.1 Population(Size)
3.1.2 Remoteness(Location)
3.1.3.1 Merchandise Export Concentration(Economic Structure)
3.1.3.2 Share of Agriculture Forestry Fishery (Economic Structure)
3.1.4 Share of Population in Low Elevated Costal Zone (Environment)
Shock Index sub-indicators
Shock Index Summary
EVI summary
EVI Graduation Threshold 2012
EVI Change over time 2009 & 2006
3.2.1 Instability of Exports(Trade Shock)
3.2.2.1 Victims of Natural Disasters(Natural Shock)
3.2.2.2 Instability of Agricultural Production(Natural Shock)
Summary
Summary of Equation choice
Useful Reference
Data Sources
Table of Contents
Acknowledgement
This material has been prepared by Jin Zhou, research
assistant intern on LDC graduation roadmap in UNDPs Poverty Reduction Unit in Lao PDR. Special
acknowledgement should be given to Mr. Matthias
Bruckner, who is the Economics Affairs Officer at UNCDP Secretariat for his guidance and generous help
on clarifying concepts, methodologies and calculations
of all the indicators.
Any comments should be addressed to the author by e-mail: jin.lara.zhou@undp.org. OR
jz2417@columbia.edu
LDC Identification
Qualitatively
Low Income
Severe Structural
Impediments to
sustainable
development
GNI Per capita
Human Assets
Index (HAI)
Economic
Vulnerability Index
(EVI)
Quantitatively
One-Page Snap Shot of all Indicators
MDG 2/3MDG 2/3
MDG 1MDG 1
MDG 4/5MDG 4/5 MDG 2/3MDG 2/3
MDG 8MDG 8
MDG 1MDG 1
MDG 7MDG 7
1.GNI per capita
UN CDP draws data directly from World Bank:
GNI per capita in current US dollars, Atlas Method
Source
http://databank.worldbank.org/ddp/home.do
http://data.un.org
1.GNI per capita
How does World Bank Calculate:
GNI per capita in current US dollars using the Atlas
Method
* World Banks Atlas Method
Purpose:
To reduce the impact of short-term fluctuations in
exchange rates in the cross-country comparison of
national income.
1. GNI Per Capita-Graduation threshold
Graduation threshold:
20% higher above the inclusion threshold
Inclusion threshold: 3-year average falls under WBs low-income countries category
Note:
If a country can achieve a level of GNI per capita that is at least twice the graduation threshold, the country is eligible for graduation even if it doesnt meet either one of the two other criteria (EVI or HAI)
Change over time-GNI 2012
Graduation
Threshold:1190
Inclusion
Threshold:992
2012 LDC
Review:913.3
Change over time-GNI 2009
2009 LDC
Review:510
Inclusion
Threshold:905
Graduation
Threshold:1086
Change over time-GNI 2006
2006 LDC
Review: 350
Inclusion
Threshold:749
Graduation
Threshold:900
2.HAI - Introduction
Measures human capital (Health + Education):
2.HAI - Methodology
Calculation: max-min procedure
Original data are converted into indices ranging from 0 to 100, based on minimum and maximum values in a set of reference countries. What does this mean?
I = [(V-min)/(max-min)] x 100 or II = [(max-V)/(max-min)] x 100
V=observed value for a certain indicator
I=100-II, the index ranges from 0 to 100
(Summary on Equation choice)
Reference Group:
All LDCs and those whose three-year average GNI per capita income is less than 20% higher than low income threshold determined by WB. Basically its LDCs and lower income non-LDCs.
2.HAI - Methodology (contd)
Note:
The max & min are not the largest and smallest values in the reference group distribution. The bounds are based on values of all developing countries, not just the reference group. But in some cases, largest or smallest values are actually used as bounds.
Purpose:
Eliminate the effect of extreme outliers in the distribution
Practice:
The bounds will replace the actual country data in the calculation of the index concerned. For example: (Population)
Min boundary = 0.15 million, Max boundary = 100 million
Countries whose population is fewer than 0.15 million have their value of population replaced by 0.15 million.
Countries whose population is larger than 100 million have their value of population replaced by 100 million.
2.1 Percentage of Population Undernourished
Definition: (FAO) People whose food intake is less than their minimum requirements. Average min energy requirement per person is 1800 kcal per day.
Exact requirements is determined by a persons age, body size, activity level and
physiological conditions such as illness, infection, pregnancy and lactation.
Example: 2012 Review
Undernourishment the lower the better, use equation II = [(max-V)/(max-min)] x 100
Index for undernourishment = [ (65-22)/(65-5))] *100 =71.7
UNCDP draws data from FAO Food Security Statistics or UN
Database http://www.fao.org/economic/ess/ess-fs/fs-data/ess-
fadata/en/ , http://data.un.org/
Upper Bound(Max) Lower Bound(Min) Lao PDR
65.00 5.00 22.00
2.2 Under-five Mortality
Definition:
(UN) Probability per 1,000 that a newborn baby will die before reaching age five.
Example: 2012 Review
Under-five Mortality the lower the better, use equation II = [(max-V)/(max-min)] x 100
Index for under-five mortality= [ (175-57)/(175-10))] *100 =71.5
Data Source: WB databank, UN DESA Population Prospects Database http://databank.worldbank.org/ddp/home.dohttp://esa.un.org/unpd/wpp/Excel-Data/mortality.htm http://data.un.org,
Upper Bound(Max) Lower Bound(Min) Lao PDR
175.00 10.00 57.00
2.3 Gross Secondary Enrollment Rate
Definition: (WB, UNESCO)Number of pupils enrolled in secondary schools
regardless of age/population in the theoretical age group for the same level of education
Example: 2012 Review
Secondary enrollment the higher the better, use equation I = [(V-min)/(max-min)] x 100
Index for Secondary Enrollment = [(44.7-10)/(100-10)]*100=38.5
Data Source: UNESCO Institute for Statistics, WB Databank, http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx http://databank.worldbank.org/ddp/home.do
Upper Bound(Max) Lower Bound(Min) Lao PDR
100.00 10.00 44.70
http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx
Folder Education
Table 5: Enrollment Ratios by ISCED levels
Gross enrollment ratio, Secondary, all programs, total.
2.4 Adult Literacy Rate
Definition: (UNESCO) Literate people aged 15 or above as a percentage of total population of this age group, literacy is defined as If he/she can read and write,
with understanding, a simple statement related to his/her daily life
Example: 2012 Review
Literacy the higher the better, use equation I = [(V-min)/(max-min)] x 100
Index for Literacy = [(72.7-25)/(100-25)]*100 = 63.6
Data Source: UNESCO Institute for Statistics, WB Databank http://www.uis.unesco.org, http://databank.worldbank.org/ddp/home.do
Upper Bound(Max) Lower Bound(Min) Lao PDR
100.00 25.00 72.7
http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx
Folder Literacy and Educational Attainment
National Adult Literacy Rates (15+)
The most recent data available during 2005-2010
2. HAI Final Index Calculation
Undernourishment Under-Five
Mortality
Secondary
Enrollment
Adult Literacy
71.7 71.5 38.5 63.6
HAI for 2012 = * (71.7+71.5+38.5+63.6) = 61.4
2. HAI Graduation Threshold 2012 Graduation Threshold:
10% above the inclusion threshold
Inclusion threshold: third quartile of the distribution comprising all countries
in the reference group. (those who score in the lowest 75% are all included)
2006 Review-
54.05
2006 Review-
54.05
2012 Review-
61.4
2012 Review-
61.4
Inclusion
threshold: 60
2012 LDC
Review: 61.4
Graduation
threshold:66
Change over time-HAI 2009
Inclusion
threshold: 60
2009 LDC
Review: 62.3
Graduation
threshold:66
Change over time-HAI 2006
Inclusion
threshold: 58
2006 LDC
Review: 54
Graduation
threshold:64
3.EVI - Introduction
Reflects the risk posed to a countrys development by exogenous shocks,
the lower the better
Exposure to shock (exposure index) the higher, the lower EVI, the better
Magnitude of the shock (shock index) the lower, the lower EVI, the better
3.EVI -Methodology
Same as HAI
Max-min procedure
3.1.1 Population (Size -Exposure Index)
Rationale: Larger countries are less exposed to shocks. They often have a more diversified economy owing to the presence of economies of scale supported by a relatively large domestic market, thus more resilient towards economic shocks. Additionally, they are also less exposed to natural shocks as in small countries often the whole country is affected by one natural shock. (the larger the population, the less exposure)
Measurement: logarithm of mid-year (July 1) Population, converted into an Index using the max-min Procedure.
Example: 2012 Review (in millions)
The larger the population, the more resilient, less exposure, use equation II = [(max-V)/(max-min)] x 100
Sub-index for Size= [(Log100 Log6.29)/(Log100-Log0.15)]*100 =42.5
Data source: UN DESA Population Prospects Databasehttp://esa.un.org/unpd/wpp/Sorting-Tables/tab-sorting_population.htm, http://data.un.org
EVI -Exposure Index
Upper Bound (Max) Lower Bound(Min) Lao PDR
100.00 0.15 6.29
3.1.2 Remoteness (Location Exposure Index)
Rationale: Countries situated far from major world markets face high transportation costs and limits the possibility for economic diversification.
(the more remote, the less capable to respond to shocks, the less resilience, the
more exposure)
Calculation: trade-weighted minimum average distance for a country to reach 50 % of the world markets.
Data source:
1. Market share of each country in the world markets
UN Statistics National Accounts Main Aggregates Database
http://unstats.un.org/unsd/snaama/
2. Bilateral Physical Distance between Lao and other countries
Centre d'Etudes Prospectives et d'Informations Internationales (CEPII)
http://www.cepii.fr/anglaisgraph/bdd/distances.htm (Use Variable discap in
data series: dist_cepii.xls)
Bilateral Physical distance is calculated as distance between capital cities or major
agglomerations
EVI -Exposure Index
3.1.2 Remoteness (Location Exposure Index)
Step1: Calculate market share of each country
Source: United Nations National Accounts Main Aggregates Database
Step 1.1: Compute 3-year average trading volume (Import + export) of each partner
Step 1.2: Compute market share for each country
Example:
Get the Data: http://unstats.un.org/unsd/snaama/
GDP by expenditures, in current prices - US Dollars
exports of goods and services + imports of goods and services
EVI -Exposure Index
Compute three
year average
3.1.2 Remoteness (Location Exposure Index)
Step 1.1: Compute 3-year Avg. trading volume (Import + export) of each country
3-year Avg. Trading Volume = 0.5 * (3-year Avg. Imports + 3-year avg. Exports)
Lao Trading Volume 2008-2010 = 0.5 * (1667667105.2+ 2203723238.7) = 1935695171.9
Step 1.2: Compute market share for each country
Market share of country A = Avg. 3-year trading volume of country A/ Avg. 3-year World Volume
Lao market share = 1935695171.9 / 17946936110365.9 = 0.01%
Thailand market share = 195568771849.2/ 17946936110365.9 =1.09%
Lao PDR Thailand World Volume
avg. exports 2008-2010 1667667105.2 205323069949.1 .. 18136978716389.3
avg. imports 2008-2010 2203723238.7 185814473749.3 .. 17756893504342.6
Avg. trading volume 2008-2010 1935695171.9 195568771849.2 .. 17946936110365.9
EVI -Exposure Index
3.1.2 Remoteness (Location Exposure Index)
Bilateral Physical
Distance
Market Share of Each
Country in the World
Market
EVI -Exposure Index
Lao Thailand China (use 20% instead of 30%)
= 8703.752*(50%-47.42%)
=224.15
Where can I get the distance data?
UN CDP uses variable dist in
data series dist_cepii.xls from CEPII
Source:
http://www.cepii.fr/anglaisgraph/b
dd/distances.htm
UN CDP uses variable dist in
data series dist_cepii.xls from CEPII
Source:
http://www.cepii.fr/anglaisgraph/b
dd/distances.htm
Calculated in Step 1
Using UN SNA database:
http://unstats.un.org/unsd
/snaama/
Calculated in Step 1
Using UN SNA database:
http://unstats.un.org/unsd
/snaama/
Where can I get the market
share data?
Minimum Average
Distance to reach 50%
of the World Market
Real World Example
Lao PDR in 2012
Review
EVI -Exposure Index
= Sum(E2:E111)/50%
3.1.2 Remoteness (Location Exposure Index)
Step 2.3 Logarithm transformation, then converted to Index using Max-Min Procedure
diis the minimum average distance of country I;
Dmin,
Dmax
is the smallest/largest minimum average distance of all 130
countries included in the calculation of the indicator; and
riis the remoteness value of country I;
Example: 2012 Review
ri= 100 * [ln(4792)-ln(1885)]/[ln(10388.4) ln(1885)] = 54.66
EVI -Exposure Index
Di
(Lao PDR) Dmin,
(Tunisia) Dmax
(Tonga)
4792 1885 10388.4
3.1.2 Remoteness (Location Exposure Index)
Step2.4 Adjustment for Landlocked Country
ri* = 0.85*ri + 0.15* lldci
ri* is the adjusted remoteness value of country I;
lldciis a dummy variable whose value is 100 for landlocked countries and 0 for
other countries
Laos adjusted ri* = 0.85*54.66 + 0.15* 100=61.47
15% is a constant coefficient CDP chose to apply to the distance, the reason being that
landlocked countries usually face higher barriers to trade and often confront relatively
higher transport costs for a given distance. Relying on a number of empirical studies of
the transport costs to or from landlocked countries, an adjustment coefficient of 15%
was chosen and applied to the distance.
EVI -Exposure Index
3.1.2 Remoteness (Location Exposure Index)
Step2.5 (Max Min Procedure, Example of 2012 Review)
Adjusted Minimum Avg. Distance Index to reach 50% of world market
The higher the remoteness index, the less resilient, the higher the exposure to shock.
Use Equation I = [(V-min)/(max-min)] x 100
Remoteness Index = [(61.47-10)/(90-10)]*100 =64.3
Upper Bound(Max) Lower Bound(Min) Lao PDR
90 10 61.47
EVI -Exposure Index
3.1.3.1 Merchandise Export Concentration
(Economic Structure Exposure Index)
Rationale: Reflects the exposure to trade shocks resulting from a concentrated
export structure. (The more concentrated, the less resilient, the more exposure to
shocks)
Data Source: UN CDP draws data directly from UNCTAD
http://unctadstat.unctad.org/.
Access:
International trade
Trade Indicators
Concentration and Diversification indices of merchandise exports and
imports by country
Concentration Index
EVI -Exposure Index
Step 1: Calculate 3-year Avg.
=(0.366+0.309+0.318)/3
=0.3343513001
3.1.3.1 Merchandise Export Concentration
(Economic Structure Exposure Index)
Step 2: Convert by using Max-Min Procedure (Example of 2012 Review)
The higher the merchandise export concentration, the less resilient to exogenous
shocks, the higher EVI, use equation I = [(V-min)/(max-min)] x 100
Index for Export Concentration = [(0.3343513001 0.1)/(0.95-0.1)]*100 = 27.57
Upper Bound(Max) Lower Bound(Min) Lao PDR
0.95 0.1 0.3343513001
EVI -Exposure Index
3.1.3.1 Merchandise Export Concentration
(Economic Structure Exposure Index)
Reference - How UNDCTAD does the calculation: Use the Herfindahl-Hirschmann indices derived from applying the following
formula to the product categories of the Standard International Trade
Classification (SITC) at the three-digit level
EVI -Exposure Index
Notes: Service is excluded from export due to data constraints.
3.1.3.2 Share of Agriculture Forestry Fishery
(Economic Structure Exposure Index)
Rationale: Reflects the exposure of countries caused by their economic structure
because AFF are particularly subject to natural and economic shocks; the higher,
the less resilient, the more exposure to shocks
Calculation: CDP draws the data of Share of gross value added in the Agriculture, Forestry, and Fishery sectors in GDP (%) directly from UN SNA
database, and then converted to index using max-min procedure.
Example:
Get the Data: http://unstats.un.org/unsd/snaama
Value-added by Economic Activity, at current price US dollars
Use Variable Agriculture, hunting, forestry, fishing (ISIC A-B) for year 2008-2010
EVI -Exposure Index
Step1: Compute AFF percentage
2008: 1588395821.05462/ 5041728866.4513 = 31.5%
2009: 1703906334.92745/ 5389768132.34538 = 31.61%
2010: 1986718614.70404/ 6214534762.18563 = 31.97%
Step 2: Compute three-year average:
= 1/3 (31.5% + 31.61%+31.97%) = 31.7%
Step1: Compute AFF percentage
2008: 1588395821.05462/ 5041728866.4513 = 31.5%
2009: 1703906334.92745/ 5389768132.34538 = 31.61%
2010: 1986718614.70404/ 6214534762.18563 = 31.97%
Step 2: Compute three-year average:
= 1/3 (31.5% + 31.61%+31.97%) = 31.7% EVI -Exposure Index
Step 3: Example of 2012 Review
The higher the share of AFF, the less resilient, the higher EVI, use equation I = [(V-
min)/(max-min)] x 100
Index for AFF = [(31.7-1)/(60-1)]*100 = 52.0
Upper Bound(Max) Lower Bound(Min) Lao PDR
60.00 1.00 31.7
EVI -Exposure Index
3.1.3.2 Share of Agriculture Forestry Fishery
(Economic Structure)
3.1.4 Share of Population in Low Elevated Costal
Zone (Environment Exposure Index )
Rationale: Reflects vulnerability to coastal impacts such as sea level rise, storm surges associated with climate change. (the higher, the less resilient, the more exposure to shocks)
Definition: Low elevated coastal zone is defined as an area contiguous to the coast below 10 meters of elevation
Data Source: Columbia University, Center for International Earth Science Information Network
http://sedac.ciesin.columbia.edu/gpw/lecz.jsp
Download the excel, use variable G00PT_lecz for total population in LECZ and variableG00PT_ctry for total population.
Note: They only updated these data until 2000 ? .
Calculation: Use the data from Columbia University and convert by using max-min procedure
Notes: This is a newly introduced index from 2012 onwards. But this index doesnt really affect Lao PDR because Lao PDR is landlocked and doesnt have costal zones. NEW!! Index on Share of population in LECZ for Lao=0
EVI -Exposure Index
3.1 Exposure Index - Summary
Population Population SizeSize
LocationLocation Remoteness Remoteness
Economic
Structure
Economic
Structure
Merchandise Export ConcentrationMerchandise Export Concentration
Share of Agriculture, Forestry, FisheryShare of Agriculture, Forestry, Fishery
Environ-
ment
Environ-
ment
Share of population in Low Elevated
Costal Zone (LECZ)
Share of population in Low Elevated
Costal Zone (LECZ)
EVI -Exposure Index
3.2.1 Instability of Exports
(Trade Shock - Shock Index)
Rationale: Reflects the instability of export earnings, or the capacity of a country to import goods and services from current export earnings
Calculation: Standard error of the regression of deflated export earnings on their past values as well as on a trend variable.
EVI -Shock Index
3.2.1 Instability of Exports
(Trade Shock - Shock Index)
Step 1: Obtain Data on :
1. Exports of Goods and Services (in Current US Dollar)
UN Statistics National Accounts Main Aggregates Database http://unstats.un.org/unsd/snaama/
Data selection Basic data selection
Select Country (Laos), Select Series (GDP by Expenditure, at current prices-US Dollars), Select Year (1991-2010)
2. Import Unit Values,
IMF International Financial Statistics (IFS) http://www.imf.org/external/data.htm and http://data.un.org Import Unit Values are unfortunately not available for a sufficient number of
countries. Therefore, UNCDP uses import unit values for Emerging and developing countries, not for each specific country. Lao doesnt have data available for import unit values at this point.
Currently, IMF IFS database only has this data from 2008-2010 for free. One has to order from IMF for a full set of data. However, this data could also be accessed from UN database although its only updated to 2009.
EVI -Shock Index
3.2.1 Instability of Exports
(Trade Shock - Shock Index)
Step 2: Compute Deflated Export EarningsXt = Value of Exports of Goods and Services/ Import Unit Values
Deflated export earnings can be understood as the purchasing power of exports.
Step 3: Build the Regression
EVI -Shock Index
Using STATA Import Data
Open Data Editor in the menu, click on start Data Editor (Edit)
Past the data into the window
EVI -Shock Index
Analyze the Data(time-series):
tsset year * to tell STATA which variable you want to use for defining time in
time-series data analysis*
Generate defl_exp_earning=export/importunitvalues *generate the new variable
deflated export earning*
generate Logdefl_exp_earning=Log(defl_exp_earning) * Log transformation of
deflated export earning*
Generate trend=_n *generate a time trend variable*
Browse *Browse to see how the data looks now*
What you will see in STATA command window:
What you will see in STATA data browsing window:
Run the regression and read the result
Regress Logdefl_exp_earning L. Logdefl_exp_earning trend, robust
*1st order autoregression of the log of the deflated export earning on its past values and
the trend variable. *
*L. Logdefl_exp_earning is the first lag of the variable Logdefl_exp_earning *
*Robust means regression with robust standard error*
Standard Error of
the Regression
3.2.1 Instability of Exports
(Trade Shock - Shock Index) Step 3: Convert by using Max-Min Procedure(Example of 2012 Review)
UNCDP multiply the original standard error data by 100 to increase
readability.
The higher the instability of exports, the larger the shock, use equation I =
[(V-min)/(max-min)] x 100 , SE of Lao = 10.2 in this case
Index for Instability of Exports = [(10.2-5)/(35-5)]*100=17.3
Upper Bound(Max) Lower Bound(Min) Lao PDR
35.00 5.00 10.2
3.2.2.1 Victims of Natural Disasters (Natural
Shock Shock Index)
Rationale: Reflects vulnerability to natural shocks, in particular the human impact of natural disasters associated with these shocks. (The larger, the bigger
the shock)
Definition: Victims are defined as people killed or affected (i.e., people requiring immediate food, water, shelter, sanitation or medical assistance). It covers
weather and climate-related disasters (such as floods, landslides, storms, droughts
and extreme temperatures) as well as geo-physical disasters (such as earthquakes
or volcanoes).
Calculation: (Example of 2012Review)
Average of the annual share of population killed or affected by a natural disaster
EVI -Shock Index
3.2.2.1 Victims of Natural Disasters (Natural
Shock Shock Index)
Step 1: Obtain data on:
1. People killed or affected by natural disaster: Emergency Disasters Data Base (EM-DAT) of the WHO Collaborating Centre for Research on
the Epidemiology of Disasters (CRED) http://www.emdat.be/
Choose advanced search
In step 1, select Laos under location, select years under timeframe rather than period (1991-2010 for 2012 review), select Climatalogical, Geophysical, Hydrophysical, and Meteorological as disastrous groups under disaster
In step 2, Choose the last option. 1st round select Total number of Deaths by Disaster date and Country. 2nd round select Total number of Total Affected by Disaster date and Country.
Note: For those years that dont have data recorded, it doesnt mean data is missing. It means that theres no victims or deaths. So we will have to add those years back when calculating the average.
2. Total Population: UN DESA Population Prospects Database, http://esa.un.org/unpd/wpp/index.htm
Left hand side: Table in excel format Total population both sexes Estimates
EVI -Shock Index
Step 2: Compute the average of the annual share of
population killed or affected by a natural disaster
EVI -Shock Index
3.2.2.1 Victims of Natural Disasters (Natural
Shock Shock Index)
Step 3: Converted into index number using max-min procedure
The higher the percentage, the larger the shock index, use equation I = [(V-min)/(max-
min)] x 100
Index for Victims = [(ln4.296-ln0.005)/(ln10-ln0.005)]*100 = 88.9
Note: The victims indicator replaces the indicator homelessness caused by
natural disasters, which was used in the 2006 and 2009 reviews and did not cover
the impacts of droughts and extreme temperatures. NEW!!
Upper Bound(Max) Lower Bound(Min) Lao PDR
10 0.005 4.296
EVI -Shock Index
3.2.2.2 Instability of Agricultural Production
(Natural Shock Shock Index)
Rationale: Reflects the vulnerability of countries to natural shocks, in particular impacts of droughts and disturbances in rainfall patterns. (The higher, the larger
the shock)
Calculation: Standard error of the regression of total agricultural production in real terms on its past values as well as on a trend variable.
EVI -Shock Index
3.2.2.2 Instability of Agricultural Production
(Natural Shock Shock Index)
Step1: Getting data from FAO (2012 Review Example)
UNCDP uses Agricultural Production Indices to measure total agricultural production
*What is FAOs Agricultural Production Indices?
Access: http://faostat.fao.org/site/612/default.aspx#ancor
FAO STATS Production Production Indices
Net Production Index Number,
Agriculture (PIN) + Total,
Year 1990-2009
(For 2012 Review, UNCDP accessed FAOs database on 23 August 2011 and by that time the 2010 data hadnt been updated to FAOs website yet. FAO recently updated the database to extend data to 2010 and may contain some data revisions on 2009 data values. )
EVI -Shock Index
EVI -Shock Index
3.2.2.2 Instability of Agricultural Production
(Natural Shock Shock Index)
EVI -Shock Index
Using STATA Import Data:
Click Data Editor in the menu, click start data editor (Edit)
paste the data into the window.
EVI -Shock Index
Analyze the Data(time-series):
tsset year * to tell STATA which variable you want to use for defining time in
time-series data analysis*
generate LogFAO=Log(Faoindice) * Log transformation of FAOs agricultural
Indices*
Generate trend=_n *generate a time trend variable*
Browse *Browse to see how the data looks now*
What you will see in STATA command window:
EVI -Shock Index
What you will see in STATA data browsing window:
EVI -Shock Index
Run the regression and read the result
Regress LogFao L.LaoFao trend, robust
*1st order autoregression of the log of the FAOs agricultural indices on its past values
and the trend variable. *
*L.logFao is the first lag of the variable LogFao*
*Robust means regression with robust standard error*
Standard Error of
the Regression
EVI -Shock Index
3.2.2.2 Instability of Agricultural Production
(Natural Shock Shock Index)
Step 3: Convert into index by using max-min procedure (Example of 2012 Review)
UNCDP multiply the original standard error data by 100 to increase readability.
The higher the standard error, the more volatile is agriculture production, the larger the
shock, use equation I= [(V-min)/(max-min)] x 100 , SE of Lao = 6.35 in this case
Index for Instability of Agriculture = [(6.35-1.5)/(20-1.5)]*100 = 26.2
Upper Bound(Max) Lower Bound(Min) Lao PDR
20.00 1.50 6.35
EVI -Shock Index
* Whats FAO Agriculture Production Indices?
The FAO indices of agricultural production show the relative level of the aggregate volume
of agricultural production for each year in comparison with the base period 2004-
2006.
1. Relative: base period 2004-2006
2. Volume: shows the aggregate volume of production each year.
3. Price-weighted: Total quantities of different agricultural commodities produced
are price-weighted, using 2004-2006 average international commodity prices
4. Deduction of seed and feed: Seed and Feed are not included in the total
quantities of agricultural commodities. The resulting aggregate represents,
therefore, disposable production for any use except as seed and feed.
Equation:
EVI -Shock Index
3.2 Shock Index - Summary
Trade ShockTrade Shock
Natural
Shock
Natural
Shock
Instability of
Exports
Instability of
Exports
Victims of Natural DisasterVictims of Natural Disaster
Instability of Agricultural ProductionInstability of Agricultural Production
EVI -Shock Index
3. EVI Final Index Calculation
Shock Index Exposure Index
37.4 36.7
EVI for 2006 = 1/2 * (37.4+36.7) = 27.1 (in 2012)
3. EVI Graduation Threshold 2012 Graduation Threshold:
10% above the inclusion threshold
Inclusion threshold: first quartile of the distribution comprising all countries in the
reference group. (those who score in the highest75% are all included)
2012 LDC
Review: 37.1
Inclusion
threshold: 36
Graduation
threshold: 32
Change over time-EVI 2009
Inclusion
threshold: 42
2009 LDC
Review:59.9
Graduation
threshold:38
Change over time-EVI 2006
2006 LDC
Review:57.9
Inclusion
threshold: 42
Graduation
threshold:38
Summary -1
GNI Per capita: Absolute Value
HAI Index: Relative Composite Indices
EVI Index: Relative Composite Indices
Summary - 2
Reference Group for Computing HAI & EVI:
All LDCs and those whose three-year GNI per capita
income is less than 20% higher than low income threshold
determined by WB
Note: Any country that has a population larger than 75 million is not included in LDC except those already on the list
before 1991 and those whose population becomes larger
than 75 million after joining the category
2. HAI 2.1 Percentage of population
undernourished
the higher this component, the
lower HAI
Use equation II
2. HAI 2.2 Under-five mortality rate the higher this component, the
lower HAI
Use equation II
2. HAI 2.3 Gross secondary enrollment
rate
the higher this component, the
higher HAI.
Use equation I
2. HAI 2.4 Adult Literacy Rate the higher this component, the
higher HAI
Use equation I
3.1.1 EVI-Exposure Index
(Size)
3.1.1 Population the higher this component, the
less vulnerable, the lower EVI.
Use equation II
3.1.2 EVI-Exposure Index
(Location)
3.1.2 Remoteness the higher this component, the
more vulnerable, the higher EVI.
Use equation I
3.1.3 EVI-Exposure Index
(Economic Structure)
3.1.3.1 Merchandise Export
Concentration
the higher this component, the
more vulnerable, the higher EVI.
Use equation I
3.1.3 EVI-Exposure Index
(Economic Structure)
3.1.3.2 Share of Agriculture,
Forestry, Fishery
the higher this component, the
more vulnerable, the higher EVI.
Use equation I
3.1.4 EVI-Exposure Index
(Environment)
3.1.4 Share of Population in Low
Elevated Costal Zone
the higher this component, the
more vulnerable, the higher EVI.
Use equation I
3.2.1 EVI Shock Index
(Trade Shock)
3.2.1 Instability of Exports the higher this component, the
more vulnerable, the higher EVI.
Use equation I
3.2.2 EVI Shock Index
(Natural Shock)
3.2.2.1 Victims of Natural
Disasters
the higher this component, the
more vulnerable, the higher EVI.
Use equation I
3.2.2 EVI Shock Index
(Natural Shock)
3.2.2.2 Instability of Agricultural
Production
the higher this component, the
more vulnerable, the higher EVI.
Use equation I
Summary of Equation Choice
Note
It is notable that to get the exact same number as UNCDP publishes for its triennial LDC review, one has to
Know the upper bound and lower bound set by UNCDP for each triennial review, this can be obtained from the LDC triennial review data (excel format) published by UNCDP once it finishes the triennial review http://www.un.org/en/development/desa/policy/cdp/ldc/ldc_data.shtml
Visit the according database at the same time UNCDP visited (databases such as FAO are frequently updated for modification of data quality). This information is usually published in the footnote of the LDC triennial review data (excel format). One can get from the same link above.
Useful Reference
2008 CDP Handbook on LDC (Detailed Explanations of Methodologies)at
http://www.un.org/esa/analysis/devplan/cdppublications/2008cdphandbook.pdf
2012 Addendum to CDP Handbook on LDC (Latest revision on EVI Index)at
http://www.un.org/en/development/desa/policy/cdp/cdp_publications/cdp_handb
ook_addendum_jun2012.pdf
UN Stats Planet (Latest LDC Review Data in 2012) at
http://www.un.org/en/development/desa/policy/cdp/ldc/sp/ldc_data/web/StatPlan
et.html
LDC Data Retrieval (Previous LDC Review Data in 2006 and 2009) at
http://www.un.org/en/development/desa/policy/cdp/ldc/ldc_data.shtml
UN DESA LDC Information Page at
http://www.un.org/en/development/desa/policy/cdp/ldc_info.shtml
Data Source
World Bank Data: http://data.worldbank.org/
UN Data: http://data.un.org/
UNESCO Institute for Statistics: http://www.uis.unesco.org
FAO Food Security Statistics: http://www.fao.org/economic/ess/ess-fs/en/
FAO Agricultural Production Indices: http://faostat.fao.org/site/612/default.aspx#ancor
UN DESA, World Population Prospects Database: http://esa.un.org/unpd/wpp/index.htm
UN STATS, National Accounts Main Aggregates Database: http://unstats.un.org/unsd/snaama/
UNCTAD database: http://unctadstat.unctad.org/.
Columbia University, CIESIN database http://sedac.ciesin.columbia.edu/gpw/lecz.jsp.
CEPII Database on geographic distance http://www.cepii.fr/anglaisgraph/bdd/distances.htm
WHO Emergency Disaster Database: http://www.emdat.be/,
Thank you!
Kaup Jai Lai-Lai!
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