Ldc Graduation CriteriaLDC Graduation Criteria - Calculations Behind

LDC Graduation Criteria

- Calculations Behind

Jin (Lara) Zhou (Ms.)

Research Assistant Intern on LDC Graduation

Poverty Reduction Unit, UNDP Lao PDR

[email protected]

[email protected]

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: [email protected]. OR

[email protected]

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


2009 LDC

Review:510

Inclusion

Threshold:905

Graduation

Threshold:1086


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.


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,


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


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


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


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


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


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


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


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%


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;


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


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


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


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



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


0.95 0.1 0.3343513001

EVI -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


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


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



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



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


(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


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


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


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!!


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



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

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



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


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


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


(Location)

3.1.2 Remoteness the higher this component, the

more vulnerable, the higher EVI.

Use equation I



3.1.3.1 Merchandise Export

Concentration



Use equation I



3.1.3.2 Share of Agriculture,

Forestry, Fishery



Use equation I


(Environment)

3.1.4 Share of Population in Low

Elevated Costal Zone



Use equation I

3.2.1 EVI Shock Index

(Trade Shock)

3.2.1 Instability of Exports the higher this component, the


Use equation I


(Natural Shock)

3.2.2.1 Victims of Natural

Disasters



Use equation I


(Natural Shock)

3.2.2.2 Instability of Agricultural

Production



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!

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