Declining Absolute Poverty in Nepal:
Analysis of Contributing Factors

A Research Paper Submitted to
Graduate School of Public Policy,
The University of Tokyo

Ram Kumar Timalsina
Student ID: 51-138211
Masters of Public Policy/International Program

Academic Advisor:

Professor Dr. Masahiro Kawai

20th May, 2014

Contents

Abstract.............................................................................................................................................. 1
1. Introduction ................................................................................................................................... 1
2. Review of Literature ...................................................................................................................... 5
3. Empirical Evidence: Relationship between absolute poverty trend and its dynamics .................. 6
3.1. Data ......................................................................................................................................... 6
3.2. Methodology/Statistical Procedure /Model ........................................................................... 7
3.3. Empirical Results and Discussions .......................................................................................... 8
3.3.1. Level effects on poverty head count ratio ........................................................................... 9
3.3.1.1 Variables with negative effect to poverty head count ratio.......................................... 9
3.3.1.2. Variables with positive effect to poverty head count ratio .......................................... 9
3.3.1.3. Variables whose effects are undetermined in terms of poverty head count ratio ...... 9
3.3.1.4. Regional, MDGs, Natural Disaster’s effect on level of poverty head count ratio....... 10
3.3.2. Annualized change effects on poverty head count ratio ................................................... 10
3.3.2.1. Variables which have negative effects on annualized changes in PHR ...................... 10
3.3.2.2. Variables with positive effects on annualized changes in PHR................................... 10
3.3.2.3. Variables whose effects on annualized changes in PHR are undetermined .............. 10
3.4. Simplified estimation equation......................................................................................... 11
3.5 Graphical presentation from the South Asia region .............................................................. 12
4. A case of Nepal ............................................................................................................................ 13
4.1. Estimation of the level of absolute poverty in Nepal ........................................................... 13
4.2. Key contributors that set declining trend of absolute poverty in Nepal .............................. 14
5. Conclusion and policy implication ............................................................................................... 15
References ....................................................................................................................................... 17
Appendix -1 ...................................................................................................................................... 19
Appendix – 2 .................................................................................................................................... 20
Appendix – 3 .................................................................................................................................... 21
Appendix – 4 .................................................................................................................................... 27
Appendix – 5 .................................................................................................................................... 28
Appendix-6 ....................................................................................................................................... 29
Appendix-7 ....................................................................................................................................... 30
Appendix-8 ....................................................................................................................................... 31ii

Abstract

Characterized as a least developed country of South Asia, Nepal has been facing absolute
poverty, a socioeconomic problem, throughout the country for the last several decades.
Though the absolute poverty rate is still high at 23.8% in 2014, its trend is declining despite
the challenging political situation of the country. Why is absolute poverty declining as atrend in Nepal despite the political instability in the past decades? It is needless to say thatpoverty is a multidimensional phenomenon and multiple factors affect its intensity andtrend. This research paper attempts to analyze multiple contributing factors of declining
poverty in Nepal by utilizing secondary data.

1. Introduction

Despite economic, institutional and technological progress in most developing countries,
extreme poverty is still widespread around the world. According to World Bank Poverty
Indicators, 1,011 million people in more than 145 counties lived on less than $1.25 a day(measured at PPP) in 2011. Though the number of affected people remains alarmingly high,absolute poverty in the world has been declining over time. In 1990, 36.4% of the world
population was under the poverty line and the poverty ratio dropped to 14.55% in 2011.
Nevertheless, the proportion of extremely poor people was not evenly distributed aroundthe world; it varied from region to region and country to country.
In terms of extreme poverty concentration, South Asia (SA) is the second largest region of
the world, following Sub-Sahara Africa. The SA region includes eight SAARC member
countries (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri
Lanka). The World Bank reports that more than 1.6 billion people lived in this region in2003 and 24.5 percentage of these people lived under extreme poverty (below $1.25 perday) in 2011. Regardless of this fact, the head count ratio of absolute poverty has declined
over the years while the total population of the region has risen in number. While the headcount ratio of absolute poverty was at 54% of total population in 1990, the ratio declined bymore than half to 24.5% in year 2011. Table 1 summarizes the poverty trend of the Worldand South Asia.

1

Table 1: Absolute poverty trend in terms of head count ratios: World and South Asia
Geography
World
South Asia
Nepal

Head count ratio at $1.25 per day PPP
Year 1990
Year 2011
36.4%
14.55%
54.1%
24.5%
68.0% (1996)
23.7%

Source: http://povertydata.worldbank.org/poverty/home/

All of the South Asian countries have been facing, in some way or the other, a commonsocial economic problem of absolute poverty for the last several decades. Moresignificantly, along with the world poverty trend, the proportion of extreme poverty of South
Asian countries has been declining with different pace. Figure 1 depicts the trend of poverty
headcount ratios in South Asian countries. It indicates that Bangladesh, India, Nepal andPakistan had relatively high poverty ratios (below $1.25 per day) at around or above 50%
in the mid-1990s. In particular, Nepal had the highest absolute poverty ratio at 68%, whereas
Sri Lanka had the lowest absolute poverty ratio at 16.3%. In addition, the figure shows thatevery country in South Asia has made significant progress in reducing absolute poverty overthe last 15 years.
Figure 1: Trend of Poverty Head Count Ratio in South Asian Countries
80
70

Head Count Ratios in %

60
50
40
30
20
10
0

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Bangladesh

60.9

58.6

50.5

Bhutan
India
Nepal

24
49.4

10.2
41.6

68

Pakistan
Srilanka

43.3
32.7

53.1
48.1

29.1

16.3

35.9

23.7
22.6

14

Source: http://povertydata.worldbank.org/poverty/home/ accessed on 3/30/2015.

2

17.2
7

12.7
4.1

The slope of the poverty ratio for each country in the figure indicates how fast poverty
reduction has taken place. The rate of poverty reduction in Bangladesh and India seems tohave been slow while that of Nepal has been fast, especially after 2003, compared to other
South Asian countries. For example, Bangladesh had a lower extreme poverty ratio (60%)
than Nepal (68%) in 1996, but the situation reversed itself over time. That is, in year 2010,
Bangladesh had a higher extreme poverty ratio (43.3%) than Nepal (23.7%). This fact
supports the argument that Nepal has made significant progress on absolute poverty
reduction over the last decades.
Nepal has made significant progress on absolute poverty reduction despite the adverse
political situation the country has faced in the past decades. It has witnessed many politicalchanges during the last twenty years, i.e., the occurrence of armed conflict for 10 years, thedeclaration of a republic state, and the failure of first constitutional assembly. More
importantly, the country is still in political transition. In spite of political flux in Nepal, thehead count ratio of absolute poverty has been declining across the country, both in urban
and in rural areas. The Nepal Life Standard Survey (NLSS), conducted in different time
periods, shows that Nepal has been able to reduce absolute poverty by 16.6% points in termsof head count ratios over the last 15 years. During the same period, urban area poverty has
been reduced by 12 % points whereas rural area poverty reduced by 15.9% points. Figure 2
shows that absolute poverty in urban areas, rural areas and nationally has declined as a trend.

Headcount ratio

Figure 2: Trend of Poverty by National Standard
50
45
40
35
30
25
20
15
10
5
0

Nepal

Urban

Rural

1995/96

41.8

21.6

43.3

2003/04

30.9

9.6

34.6

2010/11

25.2

15.5

27.4

1995/96

Area

2003/04

2010/11

Source: Derived from Central Bureau of Statistics (2014), Nepal, Kathmandu.

3In addition, the government expects a continuously declining trend of poverty in Nepal. The
Nepal Planning Commission (2013) reported that Nepal was likely to meet MDG indicators
related to extreme poverty reduction, with the head count ratio targeted to reach 21% by
2015, and there has been a supportive environment for it. The Central Bureau of Statistics(CBS) estimated that the absolute-poverty head count ratio reached 23.85% in 2013 1.
Given this scenario, this paper aims to address the research question of why absolute poverty
has been declining as a trend in Nepal despite the political instability in the past decades.
As poverty is a multidimensional phenomenon, multiple factors affect its intensity and trend.
This research paper attempts to analyze various economic and non-economic factors thatmight have contributed to declination of poverty head count ratio over the time in Nepal.
For this purpose, poverty influencing multiple variables that have apparently negative or
positive trend in Nepal have been chosen and analyzed. Such variables includes, but notlimited to gross domestic product (GDP) per capita, personal remittance received, foreign
direct investment (FDI), livestock production index, agriculture and service sector share in
GDP, electricity power consumption per capita, public expenditure on education sector etc.
Following the introduction section, this paper is organized in three parts; a review ofliterature, analysis and conclusion. After a brief literature review in poverty, this paper
utilizes econometric analysis to find out the variables that have significant impact on thelevel of poverty as well as an annualized change in the poverty head count ratio. Thenestimating equations for the level of poverty and a change in the poverty headcount ratio
will be established. After that the estimated equations will be utilized to test the Nepalesecase. Finally, the concluding section provides some policy implications based on thefindings of this research.

1

Government of Nepal (2013). Nepal Millennium Development Goals Progress Report 2013.
Kathmandu. Nepal Planning Commission and United Nations Country Team of Nepal (p.9).

4

2. Review of Literature

Elimination of absolute poverty, the situation of being unable or only barely able to meetthe subsistence essentials of food, clothing, and shelter, is at the core of all development

problems and the principal objective of development policy of the world. Though countries
with sustained growth have been able to reduce absolute poverty, growth does not guarantee
poverty reduction. However, faster growth probably supports poverty reduction by
improved incomes, education, and health. With pro-poor policy designed to promote
inclusive economic growth and modern sector expansion, economic growth results in lower
poverty (Todaro, 2014).
Furthermore, OECD (2012) identifies eight domains of empowerment within three spheres:the economic (markets, decent employment and productive assets); the political (politicalrepresentation and collective action); and the social (human capabilities, critical awarenessand inclusion) which can promote pro-poor development i.e. less absolute poverty.
Whereas ADB (2012) considers infrastructure development as the backbone of
economic/overall progress, which in turn raises household incomes and reduces poverty. Itemphasizes the availability of transport, electricity, safe water and sanitation, and other key
facilities such as schools and hospitals, which has a tremendous impact on improving thequality of life of households, especially poor ones. It also recognizes the right mix andsynergy of the soft and hard components of infrastructure for inclusive growth and poverty
reduction.
However, many researchers and institution are conducting research to understand the impact
of various economic and non-economic variables on absolute poverty reduction. In this
scenario, Acosta, Fajnzylber, & Lopez (2007) found that regardless of the counterfactual
used remittances appear to lower poverty levels in most recipient countries in Latin
America. Anyanwu & Erhijakpor (2010) found that international remittances –as a share of
remittances in a country’s GDP – reduce the level, depth, and severity of poverty in Africa.
Similarly, another study conducted by Lokshin, Bontch, Mikhail and Glinskaya(2007) indicated that one-fifth of poverty reduction in Nepal occurring between 1995 and2004 could be attributed to higher levels of work-related migration and remittances senthome.
In other case, Jung & Thorbecke (2003) got a result of the simulation experiments that a welltargeted pattern of education expenditure can be effective for poverty alleviation. Likewise,5

Neumark and Wascher (2002) found that minimum wages tend to boost the incomes of poorfamilies that remain below the poverty line. And there are also studies on the impact of good
governance/bad governance, participation, globalization, and inclusiveness on poverty.
The above brief overview on the poverty-related literature reflects the complexities andvastness of the subject matter. It also indicates current efforts that are being made tounderstand poverty and its determining factors. However, there seems a lack of research thatmeasures/predicts the level of poverty and a change in poverty on the basis of independent
variables.

3. Empirical Evidence: Relationship between absolute poverty
trend and its dynamics

3.1. Data

The study considers the variables that have obvious positive or negative trends in Nepal
during last decades. Such variables include various indicators available in the World Bank

website. The poverty head count ratio at US$ 1.25 and the share of agriculture production
to GDP are declining over time. In contrast, GDP per capita, personal remittances, the shareof service production to GDP, foreign direct investment (FDI), livestock production relative
to GDP, electricity power consumption per capita, and public expenditure on education asa ratio of GDP are trending up.
Based on these variables a new dataset is created for econometric analysis. GDP index iscreated as base year of 2005 from GDP data. Livestock production index (LSPI) to GDP
index is created by utilizing LSPI and GDP data. Likewise, FDI/GDP, personal
remittance/GDP are also created by using particular variable and GDP data.
Since poverty head count ratios for a country are not reported every year, annualizedchanges in particular variables are derived from the original level values. For example, inthe case of Nepal, the poverty ratios are reported only for 1996, 2003 and 2010. An
annualized change in the poverty ratio can be obtained as the difference between the poverty
ratio in 2010 and in 2003 divided by 7, or as the difference between the poverty ratio in
2003 and in 1996 divided by 7. So for Nepal there are three observations for the level ofpoverty and only two observations for the change in the level of poverty. General formulaused for deriving new variables are presented in appendix 1.

6

Regional dummies are defined using the World Bank’s regional classification. Countries
under the World Bank classification are enlisted in Appendix 2. The MDG dummy is createdon the basis of time when the MDG was formally introduced. The natural disaster dummyis also introduce when a natural disaster caused damage above 0.1% of GDP.
Cross-country unstructured data for aforementioned variables are collected from the WorldBank website, and disaster-related data are collected from the Centre for Research on the
Epidemiology of Disaster (CRED)’s website (www.emdat.be). Though there are many
poverty observations in the World Bank data, the availability of data for all variables limits
the number of countries for analysis to 58 at most and the time period of 1984 to 2011. Allof the developing countries and their data used for analysis are tabulated in appendix 3.

3.2. Methodology/Statistical Procedure /Model

Two different equations are estimated to explain the poverty head count ratio of thedeveloping world by utilizing cross-country, time-series data. The first equation (Eq.1)
which is used for estimating the level of poverty head count ratio is written as:
PHR(i,t) = α0 + α1*(GDP_PC(i,t)) + α2*(Rem_GDP(i,t)) + α3*(FDI_GDP(i,t))+ α4*(Edu_GDP(i,t)) +α5*(Agri_GDP(i,t)) + α6*(Service_GDP(i,t)) + α7*(LSPI_GDPI(i,t)) + α8*(EPC(i,t)) +α9*(SA_dummy (i,t)) + α10*(EAP_Dummy(i,t)) + α11*(SSA_Dummy(i,t)) +α12*(LAC_Dummy(i,t)) + α13*(MDG_Dummy(i,t)) + α14*(Dis_Dummy(i,t)) + μ(i,t)……………………………………………………………………………………………( Eq. 1)

The second equation (Eq.2) which is used for estimate the annualized change in the poverty
head count ratio is written as:
ΔPHR(i,t)= β0 + β1*(ΔGDP_PC(i,t)) + β2*(ΔRem_GDP(i,t)) + β3*(ΔFDI_GDP(i,t))+β4*(ΔEdu_GDP(i,t)) + β5*(ΔAgri_GDP(i,t)) + β6*(ΔService_GDP(i,t)) ++β8*(ΔEPC(i,t))+β9*(SA_dummy(i,t))+
β7*(ΔLSPI_GDPI(i,t))β10*(EAP_Dummy(i,t)) + β11*(SSA_Dummy(i,t)) + β12*(LAC_Dummy(i,t)) ++μ(i,t)
β13*(MDG_Dummy(i,t))……………………………………………………………………………………….(Eq. 2)

Where,
i = countryt = yearα0 = fixed effect to the level of poverty headcount ratio/ constant parameter
α1, α2, α3, α4, α5, α6, α7, α8, α9, α10, α11, α12, α13, α14, α15 = coefficient for adjacent variables
β0 = fixed effect to the annualized change in poverty head count ration/ constantparameterβ1, β2, β3, β4, β5, β6, β7, β8, β9, β10, β11, β12, β13, β14, β15 = coefficient for adjacent variables
Δ = Annualized change
PHR = Poverty head count ratio
GDP_PC = Gross domestic product per capita
Rem_GDP = Remittances to Gross Domestic Product (GDP) ratio
FDI_GDP = Foreign direct investment (FDI) to GDP ratio
Edu_GDP = Public expenditure on educational to GDP ratio
Agri_GDP = Agriculture sector share to GDP

7

Service_GDP = Service sector share to GDP
LSPI_GDPI = Livestock production index to gross domestic product index (base year
2005)
EPC = Electricity power consumption in kWh per capita
SA_Dummy = Regional dummy for South Asian countries
EAP_Dummy = Regional dummy for East Asian and Pacific countries
SSA_Dummy = Regional dummy for Sub-Sahara African countries
LAC_Dummy = Regional dummy for Latin America and Caribbean countries
MDG_Dummy = Dummy to reflect existence of Millennium Development Goals
Dis_Dummy = Natural disaster dummy
μ= error term

3.3. Empirical Results and Discussions

Table 2 summarizes the results obtained by the least square estimation of the absolute
poverty level (Eq.1) and the annualized change in absolute poverty (Eq.2). More details arepresented in appendix 4 and appendix 5.
Table 2: Summary of Empirical Analysis
Absolute poverty level estimation (Eq. 1)

Annualized change in absolute poverty estimation (Eq. 2)

Variable/Parameter

Coefficient

Prob.

Variable/Parameter

Coefficient

Prob.

α0

8.241418

0.2036β0

0.196099

0.7729

GDP_PC-0.001322

0.0017***Δ GDP_PC

0.000533

0.3948

REM_GDP-0.236517

0.0126**

Δ REM_GDP-0.291665

0.0585*

FDI_GDP0.088956

0.5375Δ FDI_GDP-0.118268

0.1615

EDU_GDP-1.484671

0.0003***

Δ EDU_GDP-0.664903

0.3773

AGRI_GDP

0.528328

0.0000***Δ AGRI_GDP

0.037069

0.8733SERVICE_GDP

0.084725

0.2853

Δ SERVICE_GDP-0.185593

0.2889

LSPI_GDPI-0.724567

0.6264

Δ LSPI_GDPI

5.3479780.0266**EPC-0.000666

0.4356

Δ EPC

0.001097

0.7031

SA_DUMMY

18.10979

0.0000***

SA_DUMMY-1.597142

0.1860

EAP_DUMMY

6.498677

0.0045***

EAP_DUMMY-0.880844

0.3307

SSA_DUMMY

22.13517

0.0000***

SSA_DUMMY-0.657968

0.5853

LAC_DUMMY

4.024089

0.0368**

LAC_DUMMY-0.418341

0.4982

MDG_DUMMY-1.689187

0.2317

MDG_DUMMY-0.663528

0.2662

DIS_DUMMY

2.735205

0.0370**Adjusted R2

0.620933

Adjusted R2

0.064717

Standard Error

9.257285

Standard Error

3.636213

No. of observations

312

No. of observations

212

No.

58

No.

47

of

includedcountryofcountry

included* 10% level of statistical significance, ** 5% level of statistical significance, *** 1% level of statistical significance.

8

Table 2 indicates that there exist three types of relationships between the poverty head countratio and other variables under consideration; positive, negative and relationshipundetermined with current data. The relationships observed in this study are notcontradictory to past studies.

3.3.1. Level effects on poverty head count ratio

3.3.1.1 Variables with negative effect to poverty head count ratio
GDP per capita, the remittances to GDP ratio and the public expenditure on education to

GDP ratio are found to have negative effect on level of absolute poverty. This study showsthat a high level of GDP per capita leads to lower absolute poverty but very slightly. In otherwords, higher GDP per capita has some trickle-down effect on the poverty head count ratio.
However, because of the existence of inequality of wealth distribution in the economy, thepoverty head count ratio could not go down as rapidly as the rise of GDP per capita.
Similarly, it is revealed in this study that a high level of the remittances to GDP ratio has amoderate negative effect on the poverty head count ratio. Likewise, public expenditure toeducation sector as a ratio of GDP strongly affects the level of absolute poverty. An one
percentage point increase in public expenditure on education to GDP lead to more than one
and half percentage point reduction in the level of poverty head count ratio. A possible
reason for this is that education has multiple positive effects on productivity, labor market,
and public health.
3.3.1.2. Variables with positive effect to poverty head count ratio
The agricultural sector share to GDP ratio has a positive impact on the poverty head countratio. This indicator reflects the economic structure of the country, that is, a high agriculturalshare to GDP means low manufacturing and services activities in the economy. It isobserved globally that poverty is high in the agricultural economy than in a manufacturing
once because agriculture tends to have lower productivity and vice versa. Moreover,agriculture is the main activity for rural people, which would cause concentration of poverty
in rural areas. The results of this study indicate that a country with a high agricultural share
in GDP tends to have a high level of poverty.
3.3.1.3. Variables whose effects are undetermined in terms of poverty head countratio
In this analysis, there is no statistical evidence for some variables to show impact of thesevariables on the level of poverty head count ratio. Such variables include foreign direct9investment (FDI) to GDP ratio, service sector share to GDP, livestock production index to
GDP index, and electric power consumption per capita.
3.3.1.4. Regional, MDGs, Natural Disaster’s effect on level of poverty head count ratio
Table 2 also shows that the level of poverty head count ratio varies from one region toanother region of the world. The poverty head count ratio for the Sub-Sahara region is thehighest. It is about 22% higher than the world poverty head count ratio. Following the SubSahara region, the South Asia region seems to have high poverty ratios; 18% point higher
than the world average poverty head count ratio, about 10% point higher than in East Asiaand the Pacific, and around 6% higher than in Latin America and the Caribbean.
However, the estimation results do not provide sufficient evidence to support the claim thatthe adoption of the millennium development goals (MDGs) is effective in reducing the levelof poverty in the world's developing countries.
The study shows that countries with natural disasters are more vulnerable to absolute
poverty risk. The econometric analysis shows that natural disasters cause more than 2
percentage point rise in the absolute poverty level of the country.

3.3.2. Annualized change effects on poverty head count ratio

3.3.2.1. Variables which have negative effects on annualized changes in PHR
The annualized change in the remittance to GDP ratio is found to have negative effect on
the annualized change in the poverty head count ratio. With the confidence level of 10%, anincrease in the remittance to GDP ratio by 1 percentage point lowers the poverty head countratio by about 0.3 percentage point annually.
3.3.2.2. Variables with positive effects on annualized changes in PHR
The empirical results reveal that the annualized change in the ratio of livestock production

to GDP has positive impact on the annualized change in the poverty head count ratio. Like
the agricultural sector, livestock production tends to be an important economic activity indeveloping countries whose productivity tends to be low. An increase in the change inlivestock production (as a ratio of GDP), therefore, can lead to an increase in the change inabsolute poverty. One may note that potentially there could be reverse causality.
3.3.2.3. Variables whose effects on annualized changes in PHR are undetermined
The results do not provide sufficient statistical evidence to establish significant impacts ofother variables on the annualized change in the poverty head count ratio. These othervariables include: GDP per capita, the FDI to GDP ratio, the ODA to GDP ratio, the public
10expenditure on education to GDP ratio, the agricultural share to GDP, the service sector
share to GDP and electricity power consumption (EPC) in kWh per capita.
Similarly, there is no sufficient statistical evidence for the impact of regional dummies on
the annualized change in the poverty head count ratio. In addition, there is a lack of evidence
that the adoption of MDGs has affected the annualized change in the poverty head countratio.
3.4. Simplified estimation equation
This section provides simplified estimation results by modifying the poverty level
estimation, i.e., equation 1. Essentially, we eliminate variables that have shown statisticallyinsignificant coefficients (variables with high prob. value in left side of table 2) from theequation. Such variables are the FDP to GDP ratio, the service sector contribution to GDP
ratio, livestock production index to gross domestic production index, and the MDG dummy.
The third equation (Eq.3), a miniature form of equation 1, which can be used for estimating
the level of poverty head count ratio for the entire sample is written as:
PHR(i,t) = λ0 + λ1*(GDP_PC(i,t)) + λ2*(Rem_GDP(i,t)) + λ3*(Edu_GDP(i,t)) + λ4*(Agri_GDP(i,t)) + λ(i,t))+
λ6*(EAP_Dummy(i,t))+λ7*(SSA_Dummy(i,t))+5*(SA_dummy
λ8*(LAC_Dummy(i,t))+ λ9*(Dis_Dummy(i,t)) + μ(i,t) …………….…………………… (Eq.3)
Where,
i = countryt = year
λ 0 = fixed effect to level of poverty headcount ratio/ constant parameter
λ 1, λ 2, λ 3, λ 4, λ 5, λ 6, λ 7, λ 8, λ 9 = coefficient for adjacent variables
PHR = Poverty head count ratio
GDP_PC = Gross domestic product per capita
Rem_GDP = Remittance to Gross Domestic Product (GDP) ratio
Edu_GDP = Public expenditure on educational sector to GDP ratio
Agri_GDP = Agriculture sector share to GDP
SA_Dummy = Regional dummy for South Asian countries
EAP_Dummy = Regional dummy for East Asian and Pacific countries
SSA_Dummy = Regional dummy for Sub-Sahara African countries
LAC_Dummy = Regional dummy for Latin America and Caribbean countries
Dis_Dummy = Natural disaster dummy
μ= error term

Table 3: Summary of simplified absolute poverty level estimation (Eq.3)
Variable/Parameter

Coefficient

Prob.

λ0

9.882506

0.0007*

GDP_PC-0.001464

0.0000*

REM_GDP-0.212006

0.0121**

11

EDU_GDP

-1.399404

0.0003*

AGRI_GDP

0.494536

0.0000*

SA_DUMMY

19.44642

0.0000*

EAP_DUMMY

6.946508

0.0005*

SSA_DUMMY

22.86515

0.0000*LAC_DUMMY

5.402026

0.0002*

DIS_DUMMY

2.748353

0.0338**2

Adjusted R

0.622056

Standard Error

9.243565

No. of observations

312

No. of country included58* 1% level of statistical significance, ** 5% level of statistical significance

Table 3 summarizes the results obtained by the least square estimation of absolute poverty
level (Eq.3) after eliminating statistically insignificant coefficients. More details arepresented in appendix-6. It points toward a negative relation between poverty and key
economic variables. GDP per capita, remittances, and public expenditure on education havenegative and statistically significant impact on the poverty head count ratio. In contrast, theagricultural sector contribution to GDP has positive and statistically significant impact onpoverty. This suggests that the higher the dependency on agricultural activities more people
live under absolute poverty.
Table 3 also shows estimated level of poverty head count ratio for the world and different
region of the world. It indicate at least 9% people of the developing countries are absolutely
poor. The poverty head count ratio for the Sub-Sahara region is the highest. It is about 22%
higher than the world poverty head count ratio. Following the Sub-Sahara region, the South
Asia region seems to have high poverty ratios; 19% point higher than the world averagepoverty head count ratio. In contrast, it is about 7% and 5.4% for East Asian and the Pacificcountries and Latin America and the Caribbean countries respectively. And, in case thecountry tends to suffer from natural disaster, the poverty level rises nearly by 2.7%.

3.5 Graphical presentation from the South Asia region

The reported relationship between the poverty head count ratio and its determining

variables (GDP per capita, personal remittances to GDP, public expenditure on education toGDP, and the agricultural share to GDP) is supported by the scatter diagrams drawn using
data for South Asian countries presented in appendix-7. In Figure 3, the regression lines
clearly depict an inverse relationship between the poverty head count ratio and GDP per
12capita, the personal remittance to GDP ratio and the public expenditure on education to GDP
ratio. On the other hand, it shows a positive relationship between the agricultural share to
GDP and the poverty head count ratio.

GDP Per Capita in US$

4,000

3,000

2,000

1,000

0
0

20

40

60

80

Public Expenditure in Education as % of GDP

Figure 3: Graphical presentation using data from South Asia
6

5

4

3

2

1

Poverty Head Count Ratio in %

40

60

80

Poverty Head Count Ratio in %70
Agricuture share to GDP in %25
Personal Remittance as % of GDP

20

0

20

15

10

5

0

60
50
40
30
20
10
0

0

20

40

60

80

Poverty Head Count Ratio in %

20

0

40

60

80

Poverty Head Count Ration in %4. A case of Nepal

4.1. Estimation of the level of absolute poverty in Nepal

The econometric results for equation 3 allow us to estimate the level of absolute poverty in
Nepal for the years when official estimates are not available. Using the estimatedcoefficients and the actual values of GDP per capita, personal remittances to GDP ratio,
public expenditure to GDP ratio and agriculture share to GDP ratio, the poverty head countof Nepal during the period 1998 to 2010 (sufficient data are available only for this period)13

is estimated. Figure 4 summaries the result of estimation for Nepal under two scenarios.
Details of calculation are shown in appendix-8.
Since Nepal is vulnerable to natural disasters and each year faces human and non-humanlosses due to disasters, the high value scenario includes natural disaster values in addition
to two standard error. In contrast, the low value scenario is based on the fitted estimation
less two standard error. Figure 4 summaries the trend of estimated values of high and low
levels of poverty for Nepal. It is clear from the figure that the World Bank poverty headcount ratio estimation lies in between the high and low values calculated using equation 3.
It also validates analysis performed so far.
Figure 4: Estimated poverty vs. World Bank PHR observation
70.00

Poverty Head count ration in %

60.00
50.00
40.00
30.00
20.00
10.00
0.00

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year
Est. Low PHR (-2 S.E.)

Est. High PHR (+ 2 S.E. and Dis_ dummy)

world bank estimated poverty

4.2. Key contributors that set declining trend of absolute poverty in Nepal

Figure 5 depicts the key determinants of the levels of absolute poverty in Nepal for 2003
and 2010 and identifies the major contributors to the declining trend of poverty in Nepal.

The calculation in the figure is based on the estimation results of equation 3 and the actual
Nepalese data. The poverty ratio in Nepal declined by 29.4% during year 2003 to 2010.
Since the coefficient for public expenditure on education is strongly negative and the actualchange in expenditure on education was positive in Nepal, it was the major contributor to14

poverty reduction in Nepal causing the poverty ratio to decline by 2.3% points between 2003
and 2010. Personal remittances received as a ratio of GDP are found to be the second major
contributor that induced a decline in poverty by 2.0% points between the two years. It isalso observed that the economic structural change (Agri_GDP) and economic growth (GDPper capita) contributed to poverty reduction in Nepal by 0.5% point each.
Figure 5: Analysis of major contributors to poverty reduction in Nepal

Natural disaster dummySouth Asia dummy
Agricultural output (% of GDP)
Education expenditure (% of GDP)
Remittances (% of GDP)
GDP per capita
Constant-10-5Year 2010

0

5

10

15

20

Year 2003

5. Conclusion and policy implication

Despite the continuous political turmoil in Nepal, the level of absolute poverty has been
declining over a few decades. In this context, the current study is carried out with an aim tounderstand the possible variables that have contributed to a trend decline in absolute poverty
in Nepal. Multiple relevant variables, which have obvious trends during the last decades in
Nepal, are selected for multiple regression analysis. It reveals that GDP per capita, personal
remittances, public expenditure to education sector and economic structure of country(indicated by agriculture share to GDP) are reliable predictors for the poverty head countratio.
After finding the predictors of absolute poverty, the relationship is depicted with graphicalpresentation generated by South Asian data. The econometric results clearly support thepreviously found relationship between the poverty headcount ratio and other key economic
variables. Then, the poverty headcount ratio for Nepal is estimated using the simplified
15

regression estimation equation. The World Bank's poverty data of Nepal are found to becontained within the range of estimated high and low values. Moreover, public expenditure
on education and personal remittances received are found to be major contributors topoverty reduction in Nepal. Nonetheless, these factors explain only part of the large decline
in the poverty reduction between 2003 and 2010 (29.4%), which has yet to be fully explainedin future research.
So, it can be concluded that economic growth, income from foreign employment, a rise in
the education level and a change in economic structure in Nepal which are reflected byincreased GDP per capita, rising personal remittance to GDP, increasing public expenditure
on education sector and a declining agricultural share to GDP have all played significant
roles for reducing absolute poverty in Nepal. The role of education and income throughforeign employment is found very high compared to other determinants. This finding may
provide public policy makers with some useful, practical implications, especially fordesigning and implementing poverty alleviation policy, programs and projects in developing
countries.
Since the study is limited to indicators published by the World Bank, it is mainly focusedon a few economic factors. However, there are other variables as well that may significantly
influence the level of poverty head count ratio but they are omitted in this study. Suchvariables include governance effectiveness, political system, extent of social inclusion,
gender equality, availability of micro-finance etc. Another limitation of the study is thatcountries and observation data are randomly selected. Because of gaps of data availability,
the number of observations is limited to 312 while the number of countries included is only
58. While some countries have many observations, other countries have only one
observation. Future research should therefore concentrate on including other relevant
variables with consideration to time lag effects, increasing the sample size and balancing
the number of observations among countries.

16References

Acosta, P. A., Fajnzylber, P., & Lopez, H. (2007). The impact of remittances on poverty
and human capital: evidence from Latin American household surveys. World Bank Policy
Research Working Paper, (4247).
Anyanwu, J. C., & Erhijakpor, A. E. (2010). Do International Remittances Affect Poverty
in Africa?*. African Development Review, 22(1), 51-91.
Central Bureau of Statistics (2014). Nepal in figures 2013. Kathmandu.
Cervantes-Godoy, D., & Dewbre, J. (2010). Economic importance of agriculture forpoverty reduction.
DFID (2013). Regional Dimensions of Poverty and Vulnerability in Nepal. Discussion
Paper.
Donaldson, J. A. (2008). Growth is good for whom, when, how?: Economic growth andpoverty reduction in exceptional cases. World Development, 36(11), 2127-2143.
doi:10.1016/j.worlddev.2007.10.020
El-Komi, MS (2010) Poverty:. Alleviation through microfinance and implications on
education
GoN and UNDP (2014). Nepal Human Development Report: Beyond Geography
Unlocking Human Potential. Kathmandu. GoN and UNDP.
Government of Nepal (2011). Nepal Living Standard Survey. CBS. Kathmandu.
Government of Nepal (2013). Nepal Millennium Development Goals Progress Report2013. Kathmandu. Nepal Planning Commission and United Nations Country Team of
Nepal (p.9).
Government of Nepal (2013). Small Area Estimates of Poverty, 2011. Kathmandu. CBS.
http://sapkotac.blogspot.jp/2011/08/major-findings-of-nepal-living-standard.html
http://www.emdat.be/advanced_search/index.html
Jung, H. S., & Thorbecke, E. (2003). The impact of public education expenditure on
human capital, growth, and poverty in Tanzania and Zambia: a general equilibrium
approach. Journal of Policy Modeling, 25(8), 701-725.
Lokshin, Michael; Bontch-Osmolovski, Mikhail; Glinskaya, Elena. 2007. Work-Related
Migration and Poverty Reduction in Nepal. World Bank, Washington, DC. © World
Bank. https://openknowledge.worldbank.org/handle/10986/7123 License: CC BY 3.0
Unported.
17

Morrisson, C., & Organisation for Economic Co-operation and Development.
Development Centre. (2002). Education and health expenditure and poverty reduction ineast africa: Madagascar and tanzania. Paris: Development Centre of the Organisation for
Economic Co-operation and Development. doi:10.1787/9789264177499-en
Neumark, D. and Wascher, W. (2002), Do Minimum Wages Fight Poverty?. Economic
Inquiry, 40: 315–333. doi: 10.1093/ei/40.3.315
NPC & UN (2013). Nepal Millennium Development Goals Progress Report 2013.
Kathmandu.
OECD (2012), Poverty Reduction and Pro-Poor Growth: The Role of Empowerment,
OECD Publishing, Paris.
DOI: http://dx.doi.org/10.1787/9789264168350-en
Organisation for Economic Co-operation and Development, & Brooks, J. (2012).
Agricultural policies for poverty reduction. Paris: OECD.
Organisation for Economic Co-operation and Development, & OECD (2001) Poverty
reduction Paris:.... Organisation for Economic Co-operation and Development doi:
10.1787 / 9789264194779-en
Pritchard, B., Rammohan, A., Sekher, M., Parasuraman, S., & Choithani, C. (2013). To
Feed India, to Feed the World: Livelihoods, Entitlements and Capabilities. Routledge.
Tebaldi, E., & Mohan, R. (2010) Institutions and poverty The Journal of Development
Studies, 46 (6), 1047-1066 doi:... 10.1080 / 00220380903012730
The World Bank Group (2014). World Bank Group Strategy October 2013. Washington
DC. The World Bank Group.
Todaro, M. P., & Smith, S. C. (2009). Economic development (10th ed.). Harlow:
Addison-Wesley.

18Appendix -1

Formula used for deriving new variables and data
1.
2.
3.
4.
5.
6.
7.
8.
9.

𝑅𝑅𝑒𝑒𝑒𝑒_𝐺𝐺𝐺𝐺𝐺𝐺 =𝑂𝑂𝑂𝑂𝑂𝑂_𝐺𝐺𝐺𝐺𝐺𝐺 =𝐹𝐹𝐹𝐹𝐹𝐹_𝐺𝐺𝐺𝐺𝐺𝐺 =𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 =𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅(𝑖𝑖,𝑡𝑡)
𝐺𝐺𝐺𝐺𝐺𝐺𝑂𝑂𝑂𝑂𝑂𝑂(𝑖𝑖,𝑡𝑡)

𝐺𝐺𝐺𝐺𝐺𝐺𝐹𝐹𝐹𝐹𝐹𝐹(𝑖𝑖,𝑡𝑡)

𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)∗ 100

𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)
𝑃𝑃𝑃𝑃𝑃𝑃(𝑖𝑖,𝑡𝑡)−𝑃𝑃𝑃𝑃𝑃𝑃(𝑖𝑖,𝑡𝑡−𝑘𝑘)∆𝐺𝐺𝐺𝐺𝐺𝐺_𝑃𝑃𝑃𝑃 =𝑘𝑘
𝐺𝐺𝐺𝐺𝐺𝐺_𝑃𝑃𝑃𝑃(𝑖𝑖,𝑡𝑡)−𝐺𝐺𝐺𝐺𝐺𝐺_𝑃𝑃𝑃𝑃(𝑖𝑖,𝑡𝑡−𝑘𝑘)∆𝑂𝑂𝑂𝑂𝑂𝑂_𝐺𝐺𝐺𝐺𝐺𝐺 =∆𝐹𝐹𝐹𝐹𝐹𝐹_𝐺𝐺𝐺𝐺𝐺𝐺 =𝑘𝑘
𝑂𝑂𝑂𝑂𝑂𝑂_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)−𝑂𝑂𝑂𝑂𝑂𝑂_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡−𝑘𝑘)

𝑘𝑘
𝐹𝐹𝐹𝐹𝐹𝐹_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)−𝐹𝐹𝐹𝐹𝐹𝐹_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡−𝑘𝑘)

10. ∆𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐺𝐺𝐺𝐺𝐺𝐺 =𝑘𝑘
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)−𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡−𝑘𝑘)

11. ∆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆_𝐺𝐺𝐺𝐺𝐺𝐺 =12. ∆𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 =13. ∆𝐸𝐸𝐸𝐸𝐸𝐸_𝐺𝐺𝐺𝐺𝐺𝐺 =14. ∆𝐸𝐸𝐸𝐸𝐸𝐸 =∗ 100

𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,2005)
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿(𝑖𝑖,𝑡𝑡)

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 =∆𝑃𝑃𝑃𝑃𝑃𝑃 =

∗ 100

∗ 100

𝑘𝑘
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)−𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡−𝑘𝑘)

𝑘𝑘
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡−𝑘𝑘)

𝑘𝑘
𝐸𝐸𝐸𝐸𝐸𝐸_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡)−𝐸𝐸𝐸𝐸𝐸𝐸_𝐺𝐺𝐺𝐺𝐺𝐺(𝑖𝑖,𝑡𝑡−𝑘𝑘)

𝐸𝐸𝑃𝑃𝐶𝐶(𝑖𝑖,𝑡𝑡)−𝐸𝐸𝐸𝐸𝐸𝐸(𝑖𝑖,𝑡𝑡−𝑘𝑘)

Where,𝑘𝑘

𝑘𝑘

𝑖𝑖 = Country

𝑡𝑡 = Year of observation

𝑘𝑘 = interval between two nearest poverty observation∆ = Annual change

Rem_GDP is Personal Remittance received as percentage of GDP

ODA_GDP is ODA received as percentage of GDP
FDI_GDP is Foreign direct investment (FDI) received as percentage of GDP
Agri_GDP is Share of Agriculture sector as percentage of GDP
Service_GDP is Share of Service sector as percentage of GDP
EDU_GDP is public expenditure on educational sector as percentage of GDP
GDPI is Gross domestic index as of base year 2005
LSPI_GDPI is ratio of Livestock production index (LSPI) to GDPI
GDP_PC is Gross domestic product (GDP) per capita
EPC is Electricity power consumption in kWh per capita

19Appendix – 2

List of regional developing countries according to the World Bank classification
S.No.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48

Sub-Sahara Africa
AngolaBenin
Botswana
Burkina Faso

Latin America andCarrebianArgentina
BelizeBolivia
BrazilBurundiCameroon
Cabo VerdeCentral African
Republic
Chad

Colombia
Costa Rica
Cuba

Comoros

EcuadorCongo, Dem. Rep.

El Salvador

Congo, Rep
Côte d'Ivoire
Eritrea

GrenadaGuatemala
GuyanaEthiopia
Gabon
Gambia, The
Ghana
GuineaGuinea-Bissau
Kenya
LesothoLiberia

Haiti
Honduras
Jamaica
MexicoNicaragua
PanamaParaguay
Peru
St. Lucia
St. Vincent and the
Grenadines
Suriname
Venezuela, RB

Madagascar
MalawiMali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
RwandaSão Tomé andPrincipe
Senegal
Seychelles
Sierra Leone
SomaliaSouth Africa
South Sudan
Sudan
Swaziland
Tanzania
Togo
UgandaZambiaZimbabwe

Dominica
Dominican Republic

RegionMiddle East andEurope andNorth Africa
Central Asia
Algeria
Albania
Djibouti
ArmeniaEgypt, Arab Rep.
Azerbaijan
Iran, Islamic Rep.
BelarusBosnia andIraq
Herzegovina
JordanBulgaria
LebanonGeorgia

East-Asia PacificSouth Asia

American Samoa
Cambodia
China
Fiji

Afghanistan
Bangladesh
BhutanIndia

Indonesia
Kiribati
Korea, Dem. Rep.

Maldives
Nepal
Pakistan
Sri Lanka

Libya
MoroccoSyrian Arab
Republic

Kazakhstan
KosovoLao PDR
Malaysia

Kyrgyz Republic

TunisiaWest Bank andGaza
Yemen, Rep.

Macedonia, FYRMarshall Islands
Micronesia, Fed.
Sts.

Moldova
Montenegro
Romania
SerbiaTajikistan
TurkeyTurkmenistan
UkraineUzbekistan

Mongolia
Myanmar
Palau
Papua New
GuineaPhilippines
Samoa
Solomon Islands
Thailand
Timor-Leste
Tonga
TuvaluVanuatu
Vietnam

Source: http://data.worldbank.org/about/country-and-lending-groups

20Appendix – 3

Appendix -3 is presented in a different file

21

22

23

24

25

26

Appendix – 4

Eview summary on impact of various variables on the level of poverty head count ratio.

Dependent Variable: PHR
Method: Least Squares
Date: 05/12/15 Time: 11:15
Sample: 1 312
Included observations: 312
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C
GDP_PCREM_GDP
FDI_GDPEDU_GDPAGRI_GDPSERVICE_GDP
LSPI_GDPI
EPC_PCSA_DUMMY
EAP_DUMMY
SSA_DUMMY
LAC_DUMMY
MDG_DUMMYDIS_DUMMY

8.241418-0.001322-0.236517
0.088956-1.484671
0.528328
0.084725-0.724567-0.000666
18.10979
6.498677
22.13517
4.024089-1.689187
2.735205

6.468346
0.000417
0.094199
0.144116
0.406225
0.103073
0.079152
1.486703
0.000853
2.770802
2.272826
2.393243
1.918262
1.409580
1.305371

1.274115-3.171655-2.510816
0.617250-3.654798
5.125775
1.070412-0.487365-0.780737
6.535937
2.859293
9.249028
2.097778-1.198362
2.095346

0.20360.00170.0126
0.53750.00030.00000.28530.62640.43560.00000.00450.00000.03680.23170.0370R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.637997
0.620933
9.257285
25452.11-1129.351
37.38820
0.000000

Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat27

13.29157
15.03576
7.335582
7.515534
7.407503
1.130458

Appendix – 5

Eview summary on impact of various variables on the annual change of poverty head count ratio.

Dependent Variable: PHR_O
Method: Least Squares
Date: 05/12/15 Time: 11:24
Sample: 1 212
Included observations: 212
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

SA_DUMMY
EAP_DUMMY
SSA_DUMMY
LAC_DUMMY
MDG_DUMMY

0.196099
0.000533-0.291665-0.118268-0.664903
0.037069-0.185593
5.3479780.001097-1.597142-0.880844-0.657968-0.418341-0.663528

0.678509
0.000625
0.153253
0.084153
0.751478
0.2321790.174533
2.394470
0.002873
1.203348
0.903376
1.203931
0.616530
0.595132

0.289015
0.852809-1.903159-1.405396-0.884793
0.159657-1.063370
2.233470
0.381717-1.327249-0.975058-0.546516-0.678540-1.114925

0.77290.39480.05850.16150.37730.87330.28890.02660.70310.18600.33070.58530.49820.2662R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.122341
0.064717
3.636213
2617.965-567.2530
2.123083
0.014356

Δ GDP_PC
Δ REM_GDP
Δ FDI_GDPΔ EDU_GDP
Δ AGRI_GDP

Δ SERVICE_GDP
Δ LSPI_GDPIΔ EPC_PC

Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat28

-1.167818
3.759913
5.483519
5.705180
5.573109
2.017013

Appendix-6

Eview summary on Equation 3 output
Dependent Variable: PHR
Method: Least Squares
Date: 05/19/15 Time: 22:01
Sample: 1 312
Included observations: 312
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C
GDP_PCREM_GDPEDU_GDPAGRI_GDP
SA_DUMMY
EAP_DUMMY
SSA_DUMMY
LAC_DUMMYDIS_DUMMY

9.882506-0.001464-0.212006-1.399404
0.494536
19.44642
6.946508
22.86515
5.402026
2.748353

2.873833
0.000322
0.084013
0.379246
0.084314
2.444129
1.967989
2.252327
1.455466
1.288776

3.438789-4.549976-2.523494-3.689967
5.865433
7.956380
3.529750
10.15179
3.711545
2.132529

0.00070.00000.01210.00030.00000.00000.00050.00000.00020.0338R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.632993
0.622056
9.243565
25803.94-1131.492
57.87469
0.000000

Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat29

13.29157
15.03576
7.317259
7.437227
7.365206
1.122699

Appendix-7

South Asian data on poverty head count ratio and other variables
Year

Country

PHR

GDP Per Capita

Personal
Remittance to
GDPRem_GDP

1984

Bangladesh

69.55

214.2618275

2.54572094

32.29348801

1.253229976

1985

Bangladesh

64.35

229.2263543

2.324832321

32.76831115

1.227669954

1988

Bangladesh

71.61

251.0324273

2.978390717

31.05157408

1991

Bangladesh

70.22

281.5987649

2.485236519

30.3652012

1995

Bangladesh

60.91

316.5085869

3.167296077

26.38431849

2000

Bangladesh

58.59

355.9734341

4.17513405

25.51408614

2005

Bangladesh

50.47

421.1233114

7.701680706

20.14203289

2010

Bangladesh

43.25

762.8037395

9.786415941

17.81049791

2003

Bhutan23.96

1009.155197

2007

Bhutan10.22

1760.602629

0.245218583

19.23212392

2012

Bhutan2.38

2458.395828

0.994944834

16.97730249

1987

India

53.59

347.809584

0.938767542

29.1824395

1993

India

49.4

308.5347869

1.239572826

28.67697144

2004

India

41.64

649.7106479

2.598496525

19.02855235

3.294759989

2009

India

32.68

1147.238685

3.603698947

17.73663649

3.210760117

2011

India

23.63

1509.238884

3.391129274

18.37071014

3.853090048

1998

Maldives

25.59

2061.649102

0.453622732

9.54817464

2004

Maldives

1.48

3677.220875

0.269624168

6.131505938

1984

Nepal

78.15

163.5953534

1995

Nepal

67.97

213.7791254

1.29110016

41.75849381

2003

Nepal

53.13

258.1178555

12.18031773

37.53912996

3.11435008

2010

Nepal

23.74

595.7716261

21.68849013

36.52847708

4.716989994

1987

Pakistan

66.46

329.306304

6.538015844

26.24987555

2.977459908

1990

Pakistan

64.71

360.1594114

5.014360479

25.97877804

2.51680994

1996

Pakistan

48.14

486.7648298

2.027869305

25.48283815

2.810570002

1998

Pakistan

29.05

453.4948067

1.88448809

27.31006367

2001

Pakistan

35.87

492.3816981

2.020474727

24.09488168

2004

Pakistan

22.59

631.4978143

4.026423701

22.18350653

1.948410034

2005

Pakistan

22.58

693.1766897

3.90860075

21.46540471

2.254359961

2007

Pakistan

17.15

929.5874437

3.936064446

23.0582756

2.635270119

2010

Pakistan

12.74

1023.195756

5.469457999

24.29211203

2.286870003

1985

Sri Lanka

19.96

377.3804426

4.878383476

27.68926889

2.576139927

1990

Sri Lanka

15.01

472.0864633

4.989396385

26.31935695

2.410929918

1995

Sri Lanka

16.32

718.4438443

6.208499204

23.01049482

2.957600117

2002

Sri Lanka

13.95

903.8964049

7.654263846

14.27932253

2006

Sri Lanka

7.04

1423.477215

7.665258184

11.33628023

2009

Sri Lanka

4.11

2057.113672

7.931686828

12.6919713

Agriculture
share to GDP

Public expenditureon Education toGDP

1.477239966

2.384419918

25.18818182

5.316989899

60.99340852

30

2.055900097

Appendix-8

Table of values of PHR estimation to Nepal
Year

Gdp_PC

Rem
_GDP

Edu_
GDP

Agri_
GDP

SA_Du
mmy

Disaster
Dummy

Est.
PHR for
Nepal

Est.
with +
2 S.E.

Est.
Low
PHR (2 S.E.)

Est. HighPHR (+ 2
S.E. andDis_
dummy)

1998

218.99

1.39

2.89

39.91

19.45

2.75

44.40

62.89

25.91

65.64

1999

221.84

1.66

2.89

41.29

19.45

2.75

45.02

63.51

26.54

66.26

2000

236.98

2.03

2.98

40.82

19.45

2.75

44.58

63.06

26.09

65.81

2001

253.94

2.45

3.71

37.64

19.45

2.75

41.86

60.35

23.38

63.10

2002

251.04

11.21

3.15

38.59

19.45

2.75

41.26

59.74

22.77

62.49

2003

258.12

12.18

3.11

37.54

19.45

2.75

40.57

59.06

22.09

61.81

2004

291.87

11.31

3.17

37.17

19.45

2.75

40.45

58.94

21.96

61.68

2005

321.46

14.91

3.36

36.35

19.45

2.75

38.97

57.45

20.48

60.20

2006

352.80

16.07

3.61

34.64

19.45

2.75

37.49

55.98

19.00

58.73

2007

397.90

16.79

3.52

33.56

19.45

2.75

36.86

55.35

18.37

58.10

2008

477.93

21.74

3.81

32.73

19.45

2.75

34.88

53.37

16.39

56.12

2009

485.96

23.14

4.66

34.03

19.45

2.75

34.02

52.50

15.53

55.25

2010

595.77

21.69

4.72

36.53

19.45

2.75

35.32

53.81

16.84

56.56

world bankestimatedpoverty

53.13

23.74

Estimated PHR for Nepal = 9.882506 -0.001464*GDP Per Capita -0.212006*Personal Rem. to GDP -1.399404*Publicexp. On edu. + 0.494536*Agri. Share to GDP+SA Dummy+ Disaster Dummy+/- Standard Error (based on equation 3)
Disaster Dummy = 2.748353
Standard Error = 9.243565

31