essays on poverty, microfinance and labor economics

ESSAYS ON POVERTY, MICROFINANCE

AND LABOR ECONOMICS

by

SANDARADURA INDUNIL UDAYANGA DE SILVA, B.Sc., M.A.

A DISSERTATION

IN

ECONOMICS

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Masha Rahnama Chairperson of the Committee

Thomas Steinmeier

Robert McComb

Accepted

John Borrelli Dean of the Graduate School

August, 2006

ii

ACKNOWLEDGEMENTS

I would like to extend my gratitude and thanks to my dissertation

committee chair, Dr. Masha Rahnama, for his guidance throughout my work. I also wish

to extend my sincere gratitude to other members of my committee, Dr. Thomas

Steinmeier and Dr. Robert McComb for their helpful comments, discussion and guidance.

Deep appreciation goes to my parents. Without their encouragement, devotion and

sacrifices, my education would not have reached this level.

Finally, I owe a debt of gratitude to Dr. Joseph King, Chairman of the Department

of Economics at Texas Tech University, for providing continuous encouragement during

my Ph.D. studies.

iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS………………………………………………… ii

LIST OF TABLES………………………………………………………... v

LIST OF FIGURES……………………………………………………….. vii

CHAPTER

1. INTRODUCTION………………………………………... …… 1

2. A ROBUST POVERTY PROFILE FOR SRI LANKA IN A MULTIVARIATE FRAMEWORK……………………… 4

2.1 Introduction…………………………………………….. 4

2.2 Data and Methodology………………………………….. 5

2.3 Unconditional Poverty Profile: Cross Tabulations……… 6

2.4 Conditional Poverty Profile: Marginal Effects………….. 14

2.5 Conclusion………………………………………………. 24

3. EVALUATING THE IMPACT OF MICROFINANCE

ON SAVINGS AND INCOME: QUASI-EXPERIMENTAL

APPROACH USING PROPENSITY SCORE MATCHING…… 34

3.1 Introduction………………………………………………. 34

3.2 Microfinance Institutions and Impact Studies……………. 37

3.3 Econometric Methodology and Data……………………... 53

3.4 Results…………………………………………………….. 66

iv

3.5 Conclusion………………………………………………… 71

4. RETURNS TO EDUCATION IN SRI LANKA:

QUANTILE REGRESSION ANALYSIS………………………… 103

4.1 Introduction……………………………………………… 103

4.2 Human Capital Framework, Signaling and the Returns

To Schooling…………………………………………….. 105

4.3 Econometric Methodology………………………………. 111

4.4 Empirical Results………………………………………… 114

4.5 Conclusion……………………………………………….. 118

REFERENCES………………………………………………………. 134

v

LIST OF TABLES

2.1 Unconditional Poverty Profile (cross tabulations)…………………… 26

2.2 Inequality Indices……………………………………………………. 27 2.3 Variable definitions and means……………………………………… 28 2.4 Logit regression estimates…………………………………………… 29 2.5 Quantile and OLS regression estimates…………………………….... 30 3.1 Social Transfers and social expenditure,1999……………………….. 73 3.2 Income Transfer Component………………………………………… 74 3.3 Bottom 20th percentile probit model for the propensity score………. 75 3.4 20th-40th percentile probit model for the propensity score…………… 76 3.5 40th-60th percentile probit model for the propensity score…………… 77 3.6 60th-80th percentile probit model for the propensity score…………… 78 3.7 80th-100th percentile probit model for the propensity score………….. 79 3.8 Matching quality indicators (covariate balancing) for the 20th percentile……………………………………………………. 80 3.9 Matching quality indicators (covariate balancing) for the 20th-40th percentile……………………………………………….. 83 3.10 Matching quality indicators (covariate balancing) for the 40th-60th percentile………………………………………………. 86

vi

LIST OF TABLES 3.11 Matching quality indicators (covariate balancing) for the 60th-80th percentile……………………………………………….. 89 3.12 Matching quality indicators (covariate balancing) for the 80th-100th percentile……………………………………………… 92 3.13 Individuals lost due to common support requirement……………….. 95 3.14 Impact of microfinance on household savings……………………….. 96 3.15 Impact of microfinance on household income………………………… 97 4.1 Cross Country evidence on the Returns to Schooling (year-1995)…… 120 4.2 Quantile and OLS regression estimates (model 1-full sample)……….. 121 4.3 Quantile and OLS regression estimates (model 1-Sinhala)…………… 122 4.4 Quantile and OLS regression estimates (model 1-Tamil)…………….. 123 4.5 Quantile and OLS regression estimates (model 2-full sample)……….. 124 4.6 Quantile and OLS regression estimates (model 2-Sinhala)…………….125 4.7 Quantile and OLS regression estimates (model 2-Tamil)………………126

vii

LIST OF FIGURES

2.1 Cumulative poverty gap curves……………………………………… 31

2.2 Lorenz curves………………………………………………………… 32

2.3 Ordinary Least Squares and Quantile Regression Estimates………… 33

3.1 Histogram of the estimated propensity score (bottom 20th percentile)………………………………………… 98 3.2 Histogram of the estimated propensity

score (20th-40th percentile)……………………………………………. 99

3.3 Histogram of the estimated propensity





score (80th-100th percentile)…………………………………………… 102

4.1 Returns to Schooling in Europe, Men and Women…………………… 127

4.2 OLS and quantile regression estimates (model 1-full sample)……....... 128 4.3 OLS and quantile regression estimates (model 1-full Sinhala)……….. 129 4.4 OLS and quantile regression estimates (model 1-full Tamil)………… 130 4.5 OLS and quantile regression estimates (model 2-full sample)………... 131 4.6 OLS and quantile regression estimates (model 2–Sinhala)…………… 132

viii

4.7 OLS and quantile regression estimates (model 2–Tamil)……………... 133

1

CHAPTER 1

INTRODUCTION

This dissertation presents three essays examining some of the issues concerning

poverty, microfinance and returns to education. The first essay examines the micro-level

determinants and correlates of poverty, and presents a poverty profile for Sri Lanka. This

is the first study that examines the probable determinants and correlates of Sri Lankan

poverty in a multivariate framework employing both logit and quantile regressions. The

most appealing feature of quantile regression is that it does not impose constant

parameters over the entire distribution. The empirical findings are broadly encouraging.

The estimation results show poverty is more acute in rural areas than in urban areas.

Furthermore, since 76% of the population lives in rural areas, the rural shares in the total

composition of poverty are more higher. However, the degree of inequality is much more

greater in urban areas, compared to the rural sector. Therefore priority need to be given

for policy initiatives aimed at reducing poverty in rural areas, while recognizing the need

to tackle urban inequality. Results also indicate that the pay off to smaller families is

higher, and larger families are more likely to be poor. . The study also found evidence to

support the hypothesis of the feminization of poverty. Female-headed households are

significantly worse off compared to male-headed households, especially in poorer

households. Household head's education level also had an instrumental effect in

determining the vulnerability to poverty. Poverty incidence declined monotonically with

years of education. The finding of a strong correlation between poverty and children,

2

suggests that the presence of children need to be considered as a strong indicator

candidate for targeting. The beauty of enacting poverty alleviation programs through

targeting key poverty indicators is that both administrative costs and leakage can be

lowered. These findings indicate the importance of a set of policies which are super pro-

poor, namely increasing school enrollment and achievement, effective family planning

programs to reduce the birth rate and dependency load within households, and granting

priorities for specific cohorts (children, elderly, rural and female headed households) in

targeted interventions.

The second essay applied recent advances in propensity score matching to assess

the impact of microfinance on household per-capita income and savings. Microfinance

for the poor has become a focus of attention in the Sri Lankan development community

over the last several years. The microfinance revolution has built on innovations in

financial intermediation that lowers the cost of risks of lending to poor households. Using

Data from a nationally representative household survey, this paper analyzed the impact of

participating in microfinance on household per-capita income and savings employing a

quasi-experimental approach. Since a baseline survey or randomization are not feasible

options in this case, the study is well suited to matching methods. There are several

attractive features associated with propensity score matching, including the potential to

allow for heterogeneous impacts, while optimally weighting observed characteristics

when constructing a comparison group. The technique is well suited due to its flexible

(non-parametric) nature, not imposing exclusion restrictions or ad hoc assumptions about

the functional form of impacts. The method eliminates selection bias due to observable

differences between program participants and non-participants. Although a rich data set

3

has been used, permitting to match on a wide range of household characteristics, the

likelihood always remains of latent unobserved factors being correlated with program

participation and outcome variables. Results suggests that program participation increases

with household size, being a Sinhalese, living in a rural area, and employed as a casual

worker or self-employed. Overall program participants benefit incidence is indeed pro-

poor. With respect to household per-capita savings, program participation definitely has a

positive impact for all low-income households. Household per-capita savings are

significantly higher on average for participants of microfinance than for observationally

identical non-participants. However, the overall results are rather discouraging for

household per-capita income, since the impact estimates are negative for all estimated

income quintiles. Finally, the principle message that emerges form the study is; there are

quantitatively non-negligible, average gains from microfinance on household savings,

especially for the poor.

The third essay investigates the returns to education in Sri Lankan labor market

using the latest Consumer Finance and Socio-economic Survey. This essay employs the

quantile regression technique for each conditional quantile wage group rather than mean

regression analysis used in most labor market analysis. Quantile regression results

suggest that returns to education are positive and significant across all quantiles.

However, a comparison of wage returns to education between ethnic groups reveals that

returns are higher for Sinhalese workers than for Tamil Workers.

4

CHAPTER 2 A ROBUST POVERTY PROFILE FOR SRI LANKA IN A MULTIVARIATE FRAMEWORK 2.1. Introduction

Notwithstanding its achievements in human development, poverty in Sri Lanka is

still a pervasive phenomenon. According to the World Bank (2002), "Sri Lanka's success

in reducing income poverty is less noteworthy, especially when contrasted with that of

East Asian countries that were at comparable levels of development only a few decades

ago". During the past decade, there has been a renewed sense of urgency for poverty

reduction strategies in Sri Lanka by the government, non-governmental organizations and

international donors. The design of effective poverty reduction strategies requires the

knowledge of who are the poor, where they live and what their socio-economic profile is.

Ideally, policy makers and program designers would like to know- 1) the income

generating activities of the poor (e.g., whether they are self employed, earning wages,

traders, microentrepreneurs, etc.), 2) to what degree do the poor have access to services

and infrastructure (e.g., piped water electricity sanitation facilities, etc.), 3) housing

conditions (e.g., owns a house, lives in a shanty or line room, etc.). The current paper

zeroes on this aspect, with the objective of identifying the poor using a microeconometric

approach. The specific questions addressed in this chapter are: firstly, is poverty more

prevalent among female-headed households than among male headed households?

Typically in developing countries feminization of poverty occurs mainly due to women

5

being relatively less educated and also as a result of discrimination in the labor market.

Grootaert and Braithwaite (1998), finds that female headed households have a higher

probability of being in poverty than their male headed counterparts. On the other hand,

Szekely (1998) found no evidence claiming that female-headed households are more

likely to be in poverty. The second question examined is that whether and what levels of

education contributes positively to higher living standards? Findings of Schultz (1988)

and Psacharapoulous (1985) indicate that there is a positive relationship between

education and higher earnings. The third major question addressed is whether households

in rural and estate (plantation) sectors face a higher probability being in poverty. There is

a vast amount of literature demonstrating that poverty is a predominantly rural

phenomena in developing countries, World Bank (1990). The final key question

examined is whether the occupation of the household head shows a significant

association with the likelihood of being in poverty. More specifically, an examination of

how living standards vary across households in salaried employment, casual wage and

business.

The rest of the chapter is organized as follows. The next section briefly describes the

data and methodology. Section 2.3 presents the results of the unconditional poverty

profile (cross-tabulations) and some sectorial inequality indices. Sections 2.4 presents the

conditional poverty profile based on logit and quantile regression estimates. Section 5

concludes.

2.2 Data and Methodology

The study is based on the latest Sri Lanka Integrated Survey (SLIS),

commissioned by the World Bank in 1999/2000. The survey is nationally representative

6

and consists of 7500 households and a 34,330 individual population. The SLIS is unique

in the sense that it is the first integrated survey that covered the entire island. The survey

collects information on a broad range of topics including demographic characteristics,

household income and expenditure, literacy and education, household amenities and

employment.

The single monetary indicator of household welfare (or living standard) used is

real per-capita consumption1. Although the survey collects information on both

household income and consumption, consumption rather than income is used as the

welfare indicator due to many reasons. Firstly there is a relatively high rate of under-

reporting of income which biases reported household aggregate income. Secondly

consumption captures welfare achievement more precisely then income, since the latter is

a more appropriate measure of welfare opportunity. In other words, consumption is a

better outcome indicator then income. Furthermore, income tends to fluctuate more than

consumption, especially in agrarian economies according to the harvest cycle.

Throughout the chapter, I use four poverty lines; Rs 1206 (national), Rs 1391 (Urban), Rs

1189 (rural), and Rs 1067 (estate), estimated by Siddhisena and Jayathilaka (2004)2.

2.3 Unconditional Poverty Profile: Cross Tabulations

Poverty is frequently considered as the defining characteristic of

underdevelopment and its reduction is the ultimate goal of development policy. To

reduce poverty, policy makers first need to know the incidence, depth and severity of

poverty. Three different poverty measures nested in the Foster-Greer-Thorbecke (FGT)

1 Total household consumption divided by the number of household members. 2 The paper provides a detailed explanation for derivation of poverty lines.

7

class were utilized to capture the different dimensions of poverty. The FGT indices

combines income and the poverty line in to poverty gaps, and aggregate these gaps to

evaluate the extent of poverty. The FGT poverty index can be expressed as;

1 *

0( ; ) [ ( : )]P z z Q p z dpαα = −∫

Incomes censored at the poverty line z, is given by *( : )Q p z . Thus, the poverty gap at

percentile p is *( ; ) ( : )g p z z Q p z= − . When 0α = , the FGT index reduces to the simple

headcount poverty measure. Poverty headcount is the share of population with incomes

falling below the poverty line. Using the poverty lines and per capita consumption levels,

the poverty headcount figures show that 25.2 percent of the Sri Lankan population are in

poverty (Table 2.1). Furthermore the highest incidence of poverty is in the estate sector

followed by the rural and urban sectors. However, looking only at the percentage of

people falling below the poverty may gloss over some vital variation in the depth and

severity of poverty in different sectors. The depth in poverty across the three sectors was

captured using the poverty gap index. The average poverty gap, ( ; 1)P z α = , is the average

extra consumption that would be required to bring each poor household up to the poverty

line. The second column in Table 2.1, presents the normalized average poverty gap

estimates. The national unnormalized average poverty gap (derived from the normalized

average poverty in Table 2.1)3 was Rs.69.95. After extrapolating the poverty gap of the

survey population to that of the nation, the total annual poverty gap in Sri Lanka was

estimated at Rs.315 million. However, in reality this figure will be much higher after

accounting for targeting inefficiencies and administration costs in poverty reduction

programs. Furthermore, in practice closing the total gap solely through income transfers

3 Unnormalized average poverty is equal to the normalized poverty multiplied by the absolute poverty line.

8

is not feasible. A more prudent way is through poverty reduction programs that raise the

income of the poor via income generating activities. Both the headcount index and the

poverty gap violates the transfer principle since they are insensitive to transfers among

the poor. To overcome this shortcoming, the squared poverty gap was used to depict the

severity of poverty. The squared poverty gap, ( ; 2)P z α = , applies more weight on the

poverty gaps of those households whose consumption fall further below the poverty line

and takes in to account the inequality of the poor. According to Table 2.1, the estate

sector has the worst situation, with a poverty incidence of 28 percent and an average

poverty gap of almost 7 percent. The remaining two measures (poverty gap and squared

poverty gap) also indicate that the highest level of poverty is in the estate sector followed

by the rural and urban sectors.

Poverty profile cross tabulations with respect to the characteristics of the

household, housing and access to services are also summarized in Table 2.1. Firstly, it is

important to explore the age and gender dimensions of poverty. Since household

characteristics such as age and gender are easily observable, they serve as important

targeting variables. Results show that the incidence, depth and severity of poverty varies

significantly with respect to the gender of the household head. Female headed households

are six percent more likely to be in poverty compared to male headed households. Since

this analysis is based solely on the headship of, it might be also be reasonable to believe

that the "average welfare" of women is much more lower than men after accounting for

gender wage gaps in the labor marker and/or intra-household distribution of resources4.

The age of the household does not seem to be significant correlate of poverty. In Table

4 An elegant paper by Deaton (1989) on intra-household resource allocation.

9

2.1, increase in the poverty incidence with age is negligible. This fact is proven to a

further extent in the conditional poverty profile (based on logit regression) discussed in

the next section, which reveals that the marginal effect of age on poverty is statistically

insignificant after controlling for other factors such as education and household size.

One of the most significant and extremely pronounced negative correlates of poverty is

the level of education of the household. According to Table 2.1, poverty declines

monotonically with years of education. Households with no schooling has a forty three

percent probability of being in poverty, while a household with tertiary education has

only a five percent chance of being in poverty.

Another important correlate of poverty is the household size. As indicated in Table 2.1,

households consisting of four or more persons being in poverty on average is more than

twice, compared to a household with one or two members. Even after the household size

was disaggregated in to different sub-groups (number of children, women, men and

elders), the size of each group shows a positive relationship with poverty incidence. The

correlation is strongest with the number children, where households with three or more

children have a poverty incidence of more than double the national rate. Furthermore, for

households with children in the age group of 0-6 years have a higher probability of being

in poverty than households with the same number of children aged 7-16 years. Similarly

if one considers two households with the same number of men and women, the one

consisting of men has a lower poverty incidence than the household with women. This

shows again that gender is critical factor with respect to poverty.

As regards employment status, households with salaried employment have the lowest

headcount, compared to the ones in business (including trade and manufacture) and in

10

casual labor (wage). Households engaged in casual labor have thirty eight percent

probability of being below the poverty line, while the salary employed households have a

ten percent probability. Furthermore, results indicate that household heads with

retirement benefits are less likely to be in poverty than ones without.

The poverty profile with respect to housing characteristics and access to services need

to analyzed cautiously, since it reveals only the association between variables and not

casual relationships. According to Table 2.1, houses with electricity as the main source of

lighting has a seventeen percent poverty incidence, while houses using kerosene as the

main source of lighting have a poverty incidence of thirty seven percent. Poverty

incidence is also high for houses which uses firewood or sawdust as fuel for cooking than

houses using gas. Households living in shanties and line rooms have a poverty incidence

twice as much as households in single houses.

An insightful way to depict the incidence, intensity and inequality of poverty is

through cumulative poverty gap (CPG) curves or TIP-curves5 shown in Figure 2.1. The

cumulative poverty gap curve aggregates the average poverty gaps of the bottom p

percentiles of the population and is expressed as:

0

( : ) ( : ) .p

G p z g q z dq= ∫

The poverty gap ( : )g p z , at a given value of p is given by the slope of ( : )G p z . The

average poverty gap equals the cumulative poverty gap at 1p = . Figure 2.1 indicates that

when 1p = , that the unnormalized average poverty gap are Rs.75.11, Rs.70.15 and

Rs.71.5 for the urban, rural and estate sectors, respectively. The percentile at which the

cumulative poverty gap curve becomes horizontal indicates the poverty headcount. For

5 The "Three I's of Poverty" (Jenkins and Lambert 1997).

11

the urban sector, the percentile in which the CPG curve becomes horizontal is 0.207,

implying that 20.7 percent of urban household are in poverty. Similarly, in the rural

sector 25.7 percent and in the estate sector 28.1 percent of the households are in poverty.

Furthermore, the inequality dimension among the poor is captured by the degree of

concavity in the non-horizontal section of the CPG curve. Thus, if income among the

poor were equal or if the poverty gaps were the same the non-horizontal section in Figure

2.1 would be a straight line. According to Figure 2.1, among the bottom 0.25p =

proportion of the population the highest level of inequality is found in the urban sector

followed by the rural and estate sectors.

Household level consumption inequality across sectors was measured using

Lorenz curves and Gini indices. Lorenz curve plots the cumulative percentages of total

consumption against the cumulative percentage of households, starting with the poorest

household. Lorenz curve can be defined as:

01 0

0

( ) 1( ) ( )( )

ppQ q dq

L p Q q dqQ q dq µ

= =∫∫

∫

The numerator is the sum of income of the bottom percentile, p . The denominator

sums the income of all households. When the size of the population is normalized to one,

the denominator can be viewed as the average incomeµ . Figure 2.2 shows the Lorenz

curves for the three sectors (urban, rural and estate). From the graph it is evident that the

highest level of consumption inequality is in the urban sector followed by rural and

estate. In other words the consumption in the estate sector is more egalitarian than in the

rural and urban sector since the poorest people in the estate sector receive a share

superior to that of their equivalents in the rural and urban sectors. Furthermore the Lorenz

12

curve for the estate sector can viewed as having been obtained from rural or urban Lorenz

curves through a series of equalizing Pigou-Dalton transfers6. When 0.5p = the sectorial

estimates for urban, rural and estate are ( ) 0.256L p = , ( ) 0.308L p = and ( ) 0.336L p =

respectively. These values can be interpreted, for instance in the urban sector as the

poorest 50% households holding 25.6% of the total consumption in the total population.

The ratio between the area enclosed by the line of equality and the Lorenz curve

can be summarized by the Gini coefficient. The Gini index can be expressed as:

1

0Gini Index of Inequality = 2 ( ( ))p L p dp−∫

One of this implicit assumptions in the Lorenz curve is that the distance, ( )p L p− , from

the line of perfect equality in consumption is weighted equally across percentiles, p . A

more general version is the class of S-Gini (single parameter Gini) inequality indices

which applies percentile dependent weights to the distance ( )p L p= . The S-Gini

inequality indices can be expressed as:

1

0( ) ( ( )) ( :)I p L p p dpρ κ= −∫

Where, ( : )pκ ρ are percentile dependent weights expressed as:

( 2)( : ) ( 1)(1 )p P ρκ ρ ρ ρ −= − −

Larger the value of ρ , larger will be the weight placed on the inequality of the bottom

percentile (or the poorest people). Therefore larger the value of ρ , greater will be the 6 Dalton transfers principle states that a transfer of income from someone lower in the income distribution to someone higher in the income distribution, holding everyone else's income constant, should increase the numerical value of an inequality index. If vector y′ which is a transformation of the vector y obtained by

a transfer δ from jy to iy , and i jy yδ δ+ > − , then the transfer principle is satisfied

( ) ( )iff I y I y′ ≥ .

13

ethical concern felt for the poor by the social decision maker. Note, when 2ρ = , we have

the standard Gini index which gives equal weight to all percentiles, p . An alternative

inequality measure that explicitly incorporate normative judgments about the social

welfare is the Atkinson index. The Atkinson inequality index is based on an additive

social welfare function7 and is expressed as;

11 (1 ) 10

1

0

( ( ) )1 , 1

( )exp( ln( ( ) )

1 , 1

Q p dpwhen

IQ p dp

when

ε ε

εµε

εµ

− − − ≠ =

− =

∫

∫

( )Q p is the standard of living of the individual whose rank or percentile in the

distribution is p . The parameter ε reflects the strength of society's preference for equality

and is bounded by zero and infinity. When 0ε = an increase in a poor individual's

income has the same effect on social welfare as increasing the income of a rich individual

by the same amount. However, when 0ε > increasing the income of a poor individual is

socially more enviable than increasing the income of a rich individual.

Table 2.2 reports the Gini coefficients (when ρ , is equal to 1.5, 2.0, and 2.5) and

Atkinson measures (whenε , equal to 0.5, 1.0, and 2.0) for the urban, rural and estate

sectors. According to both the Atkinson and the standard Gini index (when ρ =2), the

highest degree of inequality is in the urban sector followed by the rural and estate sector.

7 Social welfare function (W)

1

11 , 1where is the standard of living.1

N

ii

xx

N

ε

εε

−

== ≠−

∑

1

1ln ln , 1N

ii

W xN

ε=

= =∑

14

2.4 Conditional Poverty Profile: Marginal effects

Although, there are numerous studies on the measurement of poverty in Sri Lanka,

literature on the determinants and correlates of poverty in a multivariate framework is

best at scanty. Siddhisena and Jayathilaka (2004) looked at the composition of the poor

according to several demographic and socio-economic characteristics on a one-to-one

basis (bivariate analysis). The primary drawback of unconditional bivariate analysis is

that it often erroneously oversimplifies complex relationships. For an example, if poverty

is higher in rural area, it is not clear if the observed relationship should be attributed to

rural areas per se, or to some factor that is correlated with rural areas such as low

educational attainment. Bivariate unconditional poverty profiles is useful to a certain

extent in the case of geographical targeting, but multivariate conditional poverty profiles

are highly desirable for evaluating proposed policy interventions.

The primary objective of this section is to assess the relative importance of various

correlates of poverty, and where possible attribute causality to them. Conditional poverty

profile is constructed on the basis of a multivariate analysis of poverty correlates. Partial

correlates of poverty are computed using two comparable methodologies. Firstly, a

logistic regression was estimated, with the probability of a household being in poverty as

the dependent variable and a set of economic and demographic variables as correlates.

The response variable is a dummy defines as:

1,0,

Pr 1 ( , )

Pr 0 1 ( , )

if the household is belowthe poverty linePOV

if otherwiseand

POV X F X

POV X F X

β

β

=

= =

= = −

15

Where X is the vector of economic and demographic variables. β is the set of

parameters reflecting the impact of changes in X on the probability. The vector of

economic and demographic variables ( X ) that are hypothesized to determine

consumption and hence poverty can be categorized under, demographic, education,

employment, region and dwelling characteristics. The demographic data include

household size disaggregated by age and sex: number of children 0-6 years, number of

children 7-16 years, number of women 17-60 years, number of men 17-60 years and

number of elderly 60+ years. In regressions, a quadratic term in household size is

included to capture the nonlinearities in the relationship between household size and

living standards. Based on the findings of other developing country studies, (Lanjouw

and Raviallion, 1995; Deaton and Paxton, 1998), the expectation is a positive relationship

between household size and the probability being in poverty (or a negative relationship

between household size and total consumption per capita). The level of educational

attainment was measured on three different levels, based on the assumption that human

capital (as measured by education) contributes negatively to the probability of being in

poverty. The three different levels that was used to measure the maximum level of

education attained by the household head are: primary education (studying in year 1-

passed year 6), secondary education (passed year 7- GCE O/L), and tertiary education

(beyond GCE O/L). In the employment category four variables were used: household

head is engaged in casual labor, household head is in salaried employment, if any

member of the household receives or entitled to receive pension income(EPF or ETF),

and whether the household head is engaged in business(including trade and

manufacturing). Four variables were selected to reflect the housing characteristics and

16

access to services: Ownership of the dwelling tenure, type of housing, main source of

lighting utilized for dwelling and main type of fuel used for cooking. And finally,

regional heterogeneity was controlled by allowing for the sector and province in which

the household resides. Variable definitions and means are provided in Table 2.3.

From the stand point of econometric purity, the set of independent variables used

in this study are fairly generous and the argument for exogenity is stronger especially in a

short time horizon model. As the time horizon gets longer, most of the economic

variables at the household level become endogenous. Except for a few variables (such as

gender and age), all other variables end up being a function of the household welfare

level to some extent. Even though the ideal solution is instrumental variable technique,

reasonable instruments were not available in the survey data. Therefore, special care must

be taken when interpreting coefficients, since the regressions will only return results for

the degree of association or correlation and not for casual relationships. The probability

model is the regression;

0[1 ( )] 1[ ( )] ( ]E POV X F X F X F Xβ β β′ ′ ′= − + =

Based on the logistic distribution,

Pr 11

X

X

ePOV Xe

β

β

′

′= =+

Table 2.4 presents the parameter estimates for the logistic regression. The column, dydx

,

is the marginal effect of a change in a specific element of X on the probability of being

poor. Since almost all the variables (except for age, ageSQR, urbundum, south and

nwest) in the model have estimated parameters significantly different from zero, the

model does point at a sharply defined set of potentially useful targeting variables in the

17

context of policy intervention to alleviate poverty. Firstly, there is a significant positive

and concave relationship between household size and being in poverty. All five variables

measuring the household size, disaggregated by age and sex are positive and highly

significant. In developing countries, due to low savings and underdeveloped social

security systems, fertility rates among the poor are high, since parents receive some

economic support from children once they reach old age. Being consistent with the

bivariate unconditional poverty profile, the number of children in the age group of 0-6

years has the strongest positive correlation with poverty. After controlling for other

factors, age of the household head does not have a significant effect on the probability of

being in poverty. This finding is not surprising, since the unconditional poverty profile

also indicated that age to be weak correlate of poverty. However even after controlling

for other factors, the gender of the household head is a significant correlate of poverty.

According to Table 2.4, the probability of being in poverty declines monotonically

with years of education. All three educational level variables are statistically significant

and have the expected negative association with poverty. Tertiary education has the

largest impact on poverty (followed by secondary and primary), reflecting the fact that

education increases the stock human capital, which in turn increases labor productivity,

earnings and consumption. Turning then to the marginal effects of employment related

variables, the findings are policy-wise imperative. Firstly, household heads working as

casual laborers increases the probability of being in poverty, while working in a salaried

occupation decreases the probability of being in poverty. This fact is not surprising, since

occupations which requires low amount of human capital (casual wage jobs) will be

associated with low earnings and thereby increases the likelihood of being below the

18

poverty line. Furthermore, the results indicate that the probability of being poor is

significantly lower for household heads engaged in business (including trade and

manufacturing). Finally with respect to employment benefits, households with members

receiving or entitled to receive EPF or ETF are less likely to be in poverty than those who

are not receiving or entitled to receive pension income.

The unconditional poverty profile revealed earlier that the highest incidence of poverty

is in the estate sector followed by the rural and urban sectors. According to the

multivariate poverty regression, still living in the rural sector significantly increases the

probability of being in poverty. But, after controlling for other factors, urban and estate

sector dummies turns out as insignificant correlates of poverty. For any permutation of

sector dummies included in the poverty regression, urban and estate sector dummies

remained statistically insignificant all the time, while the rural sector dummy was

positive and significant. The finding suggests two salient features about poverty in Sri

Lanka. Firstly, the high incidence of poverty in the estate sector should not be attributed

to the estate sector per se, but for some other factor[s] (such as low educational

attainment), that might be correlated with the estate sector. Secondly, poverty in Sri

Lanka is certainly a rural phenomenon, that needs to be explained by many other factors,

which deserves continuing attention and scrutiny.

The estimated coefficients the dwelling tenure and type of housing are both statistically

significant and positive. There is a positive correlation between poverty and households

not owning their house, living in a shanty room or line room. The marginal effects are

strongest for the ones living in shanty and line rooms - both in terms of the magnitude of

the coefficient and statistical significance. Ownership of a house is important, especially

19

in rural areas, since it provides the location for a household enterprise and also acts as a

collateral for a loan. The two variables used to capture a household’s access to services

are also statistically significant and have plausible signs. Firstly, there is a negative

correlation between households using electricity (as the main source of lighting) and the

probability of being in poverty. Secondly, households using firewood or sawdust as the

main fuel for cooking has a positive association with poverty. It is important to note here

that the caveat about interpreting the estimated coefficients as partial correlation

coefficients is particularly important, since the direction of causation is most certainly

from poverty to variables related to housing and access to services.

As a robustness check, it is important to note here that the results of the multivariate

poverty regression had corroborated with the findings of the bivariate unconditional

poverty profile in the preceding section. Substantively, the pattern of the partial correlates

of poverty in the poverty regression is entirely consistent with the pattern of correlates

that was revealed by the bivariate poverty profile. All factors which are correlated with

an increase (decrease) in the poverty headcount are correlated with an increase (decrease)

in probability to be poor.

Next, the quantile regression8 approach was utilized to examine the correlates of per

capita consumption at different points on the distribution. The most appealing feature of

quantile regression is that it does not impose constant parameters over the entire

distribution. It assumes the effect of economic and demographic characteristics of the thi

household to differ across the welfare spectrum.

8 Koenker and Basset (1978).

20

The quantile regression model can be expressed as:

,i i iy x τ τβ µ′= +

Where iy is the log of per capita consumption of the thi household, and ix represents the

economic and demographic characteristics of the thi household. By Imposing the

assumption that the thτ − quantile of the error term conditional on the regressors is zero,

,( ( | ) 0)i iQ u xτ τ = , the thτ − conditional quantile of iy with respect to ix can be

expressed as:

( | )i i iQ y x xτ τβ′=

For any τ ∈(0,1), the parameter τβ can be estimated by

{ } { }| |

ˆ arg min | | (1 ) | |k

i i i i

i i i ii i y x i i y x

y x y xτ τ τ

τβ β β

β τ β τ β∈ ′ ′∈ ≥ ∈ ≥

′ ′= − + − − ∑ ∑

R

Note, that when 0.5τ = , we have the special case known as the median regression or the

least absolute deviation (LAD) estimator. Five quantile regressions were estimated at the

10th, 25th, 50th, 75th and 90th quantiles. The standard errors were computed by

bootstrapping with 100 replications. OLS regression was also estimated for the purpose

of comparison. Table 2.5 reports the results of the OLS and quantile regressions.

Firstly, at all quantiles estimated on the conditional expenditure distribution,

household size is inversely related with the standard of living as measured by

consumption. All five variables measuring the household size, disaggregated by age and

gender are highly significant at all estimated quantiles. Furthermore, additional children

have a much larger effect on per capita expenditure than adults. These results reconfirm

the earlier findings of both the unconditional poverty profile and the logistic regression.

21

A more comprehensive manner of presenting the results is in the form of a graph. Figure

2.3 (panel 1-5) shows the development of the coefficients representing household size

over the entire conditional consumption distribution. The estimated coefficient for each

percentile is plotted as a continuous line and its 95%-confidence interval is the shaded

area. The OLS estimate is the dark horizontal line and parallel to it is the 95%-confidence

bands. With the exception of elders, all the other variables reflecting household size tends

to have an increasingly larger impact on consumption as one moves up on the

expenditure distribution. At the two extreme end of the distribution, estimates for these

variables fall outside the confidence interval of the OLS estimate and is quite different

from the OLS estimate.

According to the Table 2.5 age tends to have an extremely weak negative relationship

with the standard living of at all estimated quantiles on the conditional distribution. This

again confirms the findings of the unconditional poverty profile and the logit regression.

However, the gender of the household head has a significant association with the standard

of living . At all quantiles there is a negative relationship between per capita expenditure

and being a female-headed household. But the gender effect on welfare tends to weaken

as one moves up the conditional distribution. Below the median (50-th quantile), being a

female headed household reduces per capita expenditure by at least 4%, but the fall in per

capita expenditure is nearly three times less on the top of the distribution. The

significance of the gender effect begins to fade beyond the median and eventually

becomes insignificant at the 90-th quantile.

Quantile regression results also indicate that households residing in the rural sector are

worse off. Figure 2.3 shows that households in the bottom quantiles and upper quantiles

22

are less sensitive to the rural sector compared to the households in the median quantiles.

This implies that the poorest and the least poorest people experience less of the negative

impact of living in a rural area than the median poor. Also, as Table 2.5 indicates, for the

households on the top of the conditional distribution (90-th quantile), living in the rural

sector has no significant effect on the standard of living.

With respect to education, all three variables indicating the levels of education shows a

positive relationship with per capita consumption. Figure 2.3 reveals that the impact of

primary education does not vary a lot between quantiles and the quantile coefficients do

not differ much from the least-square results. In other words, returns to primary education

are not different for the poor and non-poor. Table 2.5 also shows that the primary

education variable is not significant for the households in the bottom 10-th quantile,

implying that the pay off from primary education to the poorest is not significant for the

poorest is not significant. But secondary and tertiary education significantly increases the

standard of living across all quantiles. Figure 2.3 also shows that the premium on tertiary

education is relatively high for less poor households. It is important to note that at the two

extreme ends of the distribution, quantile regression estimates fall outside of the OLS

estimates. Thus, the conventional least squares confidence interval does a poor job

representing this range for the tertiary education variable.

Turning to the employment related variables, with the exception of the bottom 10-th

quantile, the casual wage coefficient is significant and negative across all other quantiles.

Being consistent with the earlier findings from the unconditional poverty profile and logit

regression, household heads engaged in casual labor are associated with lower per capita

expenditure. However the impact of being in casual labor on per capita is relatively

23

negligible at upper quantiles than at the bottom quantiles. Figure 2.3 also indicates that

the OLS method underestimates the effects of being in casual labor on per capita

consumption on the upper quantiles of the conditional distribution. According to Table

2.5 and Figure 2.3, for the poorer households (quantiles 10 and 25), the marginal effect of

being in a salaried occupation is relatively high, compared to the less poor households.

Results also suggest that households in businesses (including and manufacturing)

experience higher levels of per capita consumption. The coefficient for a household being

engaged in business is highly significant (Table 2.5) and stable across all quantiles

(Figure 2.3). With respect to retirement benefits, not receiving or not being entitled to

receive pension income has a strong negative effect on per capita expenditure for the

households in lower quantiles. The impact of retirement benefits on consumption and its

significance level is relatively less at higher quantiles.

With regard to the four variables (ShantyLineroom, HHnotowninghouse, Firewood,

Electricity) reflecting housing characteristics and access to services, the quantile

regression estimates only return the results of the degree of association with per capita

expenditure and no influence of causation can be made. All four coefficients are

significant across the conditional distribution and have signs consistent with the

unconditional poverty profile. Finally, the interpretations of the casual effects of regional

dummies are somewhat difficult and can only be described as dramatic. The regional

dummies were included primarily for controlling regional heterogeneity.

24

2.5 Conclusion

This chapter investigated the probable determinants and correlates of poverty in

Sri Lanka. It is worth summarizing some of the main results of this chapter. First, poverty

remains more acute in rural areas than in urban areas. Furthermore, since 76% of the

population live in rural areas, the rural shares in the total composition of poverty is more

higher. However, the degree of inequality is much more greater in urban areas, compared

to the rural sector. Therefore priority need to be given for policy initiatives aimed at

reducing poverty in rural areas, while recognizing the need to tackle urban inequality.

Results also indicate that the pay off to smaller families is higher, and larger families

are more likely to be poor. Furthermore, the costs of dependents are significant for all

expenditure groups. An extra child or elderly creates a greater economic burden than an

extra man or woman in household. With regard to the age of the household, the

unconditional bivariate poverty profile indicated age to be a weak correlate of poverty.

Confirming this result, multivariate analysis also found the age of the household head to

be insignificant correlate of poverty, even after controlling for other factors. The study

also found evidence to support the hypothesis of the feminization of poverty. Female-

headed households are significantly worse off compared to male-headed households,

especially in poorer households.

Household head's education level also had an instrumental effect in determining the

vulnerability to poverty. Poverty incidence declined monotonically with years of

education. Furthermore, quantile regression results indicate that the pay off from primary

education to the poorest is not significant. With respect to the labor market, the incidence

and probability of being in poverty is higher for households in casual labor, compared to

25

the ones in salaried employment. Finally, the poor are more likely to live in shanty and

line rooms and to use kerosene and firewood for lighting and cooking.

All of the above suggests the need for increasing school enrolment; supplemental

educational programs and upgrading of schools are sensible components of a poverty

reduction strategy. Clearly, programs of information, micro-credit, marketing, small

business incubators, etc. deserves special attention in the design of national poverty

reduction strategies. Findings regarding the link between welfare and household size,

employment status and the access to services, are invaluable in the realm of indicator

targeting. For an example, finer targeting can be done on the basis of household size and

composition (eg., number of children, number of female members). The finding of a

strong correlation between poverty and children, suggests that the presence of children

need to be considered as a strong indicator candidate for targeting. The beauty of

enacting poverty alleviation programs through targeting key poverty indicators is that

both administrative costs and leakage can be lowered.

In conclusion, these findings indicate the importance of a set of policies which are

super pro-poor, namely increasing school enrollment and achievement, effective family

planning programs to reduce the birth rate and dependency load within households, and

granting priorities for specific cohorts (children, elderly, rural and female headed

households) in targeted interventions.

26

Table 2.1: Unconditional Poverty Profile (cross tabulations) 0α = 1α = 2α = 0α = 1α = 2α =

Sri Lanka 0.252 0.058 0.021 Number of men (17-60 years)

Urban 0.207 0.054 0.020 1 0.235 0.051 0.017

Rural 0.257 0.059 0.021 2 0.252 0.058 0.020

Estate 0.281 0.067 0.025 3 0.308 0.077 0.030

Characteristics of the Head 4 0.347 0.086 0.030

Male 0.242 0.054 0.019 Number of Women (17-60 years)

Female 0.302 0.078 0.031 1 0.237 0.052 0.018

Age ≤29 years 0.224 0.043 0.013 2 0.259 0.059 0.020

Age: 30-59 years 0.248 0.056 0.019 3 0.319 0.077 0.030

Age ≥60 0.270 0.066 0.026 4 0.351 0.091 0.037

Education Number of Elders

No schooling 0.426 0.116 0.048 1 0.263 0.062 0.024

(primary.edu) 0.336 0.079 0.029 2 0.278 0.069 0.025

(secondary.edu) 0.196 0.041 0.013 3 0.269 0.117 0.054

(tertiary.edu) 0.052 0.008 0.003 Employment

Household Size Salary 0.101 0.018 0.007

0-2 0.143 0.038 0.018 Casual Wage 0.376 0.093 0.035

3-4 0.178 0.036 0.012 Business 0.139 0.027 0.009

4+ 0.342 0.082 0.030 Receiving (or entitled) for pension income 0.216 0.052 0.020

Number of Children (0-6 years) Not receiving (or not entitled) for pension income 0.261 0.060 0.022

1 0.296 0.066 0.023 Housing Tenure and Type

2 0.389 0.097 0.036 Owned by household head 0.234 0.052 0.018

3 0.508 0.136 0.048 Not owned by household head 0.312 0.077 0.029

4 0.800 0.234 0.074 Single house 0.241 0.053 0.019

Number of Children (7-16 years) Annexe 0.250 0.117 0.058

1 0.226 0.050 0.017 Shanty or line room 0.550 0.161 0.067

2 0.304 0.068 0.024 Main Source of Lighting

3 0.407 0.097 0.035 Electricity 0.173 0.036 0.012

4 0.444 0.122 0.048 Kerosene 0.366 0.088 0.032

Main Fuel Used for Cooking

Gas 0.022 0.004 0.002 Firewood or sawdust 0.284 0.065 0.023

27

Table 2.2: Inequality Indices

S-Gini Index Atkinson Index

ρ=1.5 ρ=2 ρ=2.5 ε=0.5 ε=1.0 ε=2.0

Urban 0.260 0.375 0.438 0.116 0.213 0.817

Rural 0.197 0.288 0.345 0.072 0.138 0.716

Estate 0.162 0.243 0.296 0.052 0.101 0.222

28

Table 2.3: Variable definitions and means Variable Definition Poor Non-poor Total

LogPCE Logarithm of real per capita consumption 2.9466 3.3233 3.2282

Children (1) Number of children (0-6 years) 0.5896 0.3631 0.4201

Children (2) Number of children (7-16 years) 1.2062 0.8256 0.9216

Men Number of men (17-60 years) 1.4569 1.353 1.3793

Women Number of women (17-60) 1.5711 1.4336 1.4683

Elders Number of persons (60+ years) 0.3882 0.3545 0.3629

HsizeSQR Household size squared 30.9974 21.444 23.853

Age age of household head 49.6345 49.2157 49.3213

AgeSQR Age squared 2660.517 2602.498 2617.128

Dummy Variables

POV Household is below the poverty line 1 0 0.2521

Female Head Household head is female 0.2009 0.1564 0.1676

Rural Household resides in the rural sector 0.8138 0.7471 0.7639

Urban Household resides in the urban sector 0.1132 0.2159 0.1900

Primary.edu Year 1-Year 6 0.4706 0.3138 0.3533

Secondary.edu Year 7-GCE (O/L) 0.3527 0.4862 0.4525

Tertiary.edu Year 12 and above 0.0211 0.1289 0.1017

Salary Household head in salaried employment 0.0645 0.1965 0.1632

Casual Wage Household head works for a casual wage 0.3702 0.2077 0.2486

Business Household head in business (including 0.0851 0.1776 0.1542

trade and manufacture)

NoRetBenifit Household head is not receiving or not 0.8290 0.7896 0.7995

entitled to receive retirement benefits.

ShantyLineR Household lives in shanty or line room

HHnotowningHouse Dwelling unit is not owned by 0.2818 0.2093 0.2276

by household

Firewood Household uses firewood or sawdust as 0.9719 0.8248 0.8619

the main of fuel for cooking

Electricity Main source of lighting is electricity 0.3968 0.6385 0.5775

South Household in Sourthen province 0.1644 0.1103 0.1240

North Household in Northern province 0.0597 0.1269 0.1100

East Household in Eastern province 0.1094 0.1503 0.1400

Uva Household in Uva province 0.0766 0.0743 0.0720

Sabara Household in Sabaragamuwa province 0.1258 0.0750 0.0878

Central Household in Central Province 0.1930 0.1007 0.1240

Nwest Household in North Western province 0.0936 0.0941 0.0940

Ncentral Household in North Central Province 0.0528 0.0731 0.0680

29

Table 2.4: Logit regression estimates Independent Variable Dy/dx (marginal effects) z-value Children (1) 0.126 11.44

Children (2) 0.089 9.55

Men 0.073 7.18

Women 0.082 8.02

Elders 0.089 6.72

HsizeSQR -0.002 -3.70

Age -0.003 -1.38

AgeSQR 0.000 1.52

Female Head 0.068 5.47

Rural 0.105 4.24

Urban 0.031 1.18

Primary.edu -0.035 -2.40

Secondary.edu -0.085 -5.37

Tertiary.edu -0.217 -7.36

Salary -0.068 -3.79

Casual Wage 0.055 4.92

Business -0.123 -8.04

NoRetBenifit 0.059 4.44

ShantyLineR 0.174 -11.12

HHnotowningHouse 0.024 2.18

Electricity -0.113 -11.12

Firewood 0.198 8.73

South 0.031 1.83

North -0.262 -12.11

East -0.095 -5.21

Uva -0.067 -3.17

Sabara 0.043 2.31

Central 0.111 6.38

Nwest -0.022 -1.19

Ncentral -0.132 -5.94

N=7481 Pseudo R²=0.225 Pr>χ²=0.000 Loglikelihood =-3274.5261 Notes

1,1) Dependent variable:

0,if the household is belowthe poverty line

POVif otherwise

=

2) Poverty line = Rs.1206 3) Variable definitions and means are given in Table 2.3.

30

Table 2.5: Quantile and OLS regression estimates (standard errors in parentheses) Quantile Independent Variable 0.1 0.25 0.5 0.75 0.9 OLS

Constant 3.19 (0.065)

3.366 (0.043)

3.473 (0.039)

3.591 (0.040)

3.698 (0.056)

3.431 (0.036)

Children(1) -0.087 (0.009)

-0.095 (0.006)

-0.106 (0.006)

-0.114 (0.007)

-0.113 (0.009)

-0.097 (0.005)

Children(2) -0.065 (0.008)

-0.074 (0.006)

-0.078 (0.005)

-0.085 (0.007)

-0.089 (0.008)

-0.074 (0.004)

Men -0.06 (0.007)

-0.072 (0.006)

-0.075 (0.006)

-0.079 (0.008)

-0.079 (0.010)

-0.069 (0.005)

Women -0.062 (0.009)

-0.073 (0.006)

-0.078 (0.005)

-0.088 (0.007)

-0.086 (0.009)

-0.069 (0.005)

Elders -0.064 (0.010)

-0.070 (0.008)

-0.066 (0.007)

-0.078 (0.010)

-0.066 (0.012)

-0.063 (0.006)

HsizeSQR 0.003 (0.000)

0.004 (0.000)

0.004 (0.000)

0.004 (0.001)

0.004 (0.001)

0.003 (0.000)

Age 0.007 (0.002)

0.006 (0.002)

0.005 (0.001)

0.004 (0.001)

0.005 (0.002)

0.006 (0.001)

AgeSQR 0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

FemaleHead -0.042 (0.009)

-0.04 (0.007)

-0.043 (0.008)

-0.033 (0.009)

-0.015 (0.0012)

-0.037 (0.006)

Rural -0.068 (0.023)

-0.008 (0.016)

-0.057 (0.015)

-0.028 (0.013)

-0.021 (0.029)

-0.056 (0.013)

Urban -0.025 (0.023)

-0.029 (0.016)

0.007 (0.015)

0.042 (0.015)

0.081 (0.030)

0.013 (0.014)

Primary.edu 0.020 (0.015)

0.021 (0.009)

0.025 (0.008)

0.027 (0.009)

0.028 (0.001)

0.031 (0.008)

Secondary.edu 0.068 (0.016)

0.056 (0.011)

0.058 (0.009)

0.062 (0.008)

0.075 (0.015)

0.072 (0.009)

Tertiary.edu 0.142 (0.019)

0.139 (0.013)

0.164 (0.014)

0.181 (0.016)

0.221 (0.021)

0.179 (0.011)

Salary 0.035 (0.110)

0.024 (0.007)

0.012 (0.006)

-0.002 (0.009)

-0.025 (0.012)

0.007 (0.007)

Casual Wage -0.017 (0.010)

-0.026 (0.006)

-0.033 (0.006)

-0.042 (0.007)

-0.069 (0.010)

-0.044 (0.006)

Business 0.080 (0.012)

0.008 (0.009)

0.078 (0.007)

0.078 (0.009)

0.089 (0.015)

0.081 (0.006)

NoRetBenifit -0.037 (0.010)

-0.039 (0.007)

-0.029 (0.007)

-0.016 (0.007)

-0.009 (0.011)

-0.031 (0.006)

ShantyLineR -0.144 (0.027)

-0.119 (0.019)

-0.087 (0.014)

-0.062 (0.014)

-0.059 (0.027)

-0.110 (0.013)

HHnotowningHouse -0.028 (0.009)

-0.022 (0.007)

-0.015 (0.006)

-0.019 (0.006)

-0.037 (0.010)

-0.029 (0.005)

Electricity 0.078 (0.008)

0.066 (0.006)

0.060 (0.005)

0.065 (0.007)

0.059 (0.009)

0.069 (0.005)

Firewood -0.125 (0.013)

-0.138 (0.008)

-0.159 (0.008)

-0.173 (0.011)

-0.180 (0.016)

-0.156 (0.007)

South -0.050 (0.014)

-0.047 (0.009)

-0.022 (0.009)

-0.016 (0.009)

-0.032 (0.014)

-0.025 (0.009)

North 0.116 (0.013)

0.112 (0.011)

0.138 (0.011)

0.144 (0.011)

0.122 (0.014)

0.137 (0.009)

East 0.069 (0.012)

0.037 (0.009)

0.024 (0.008)

0.001 (0.009)

-0.010 (0.017)

0.029 (0.009)

Uva -0.043 (0.016)

0.030 (0.012)

0.019 (0.008)

0.001 (0.012)

-0.024 (0.017)

0.024 (0.010)

Sabara -0.046 (0.015)

-0.055 (0.011)

-0.019 (0.008)

-0.038 (0.013)

-0.043 (0.021)

-0.026 (0.009)

Central -0.109 (0.012)

0.104 (0.011)

-0.064 (0.010)

-0.055 (0.011)

-0.050 (0.019)

-0.070 (0.009)

Nwest -0.004 (0.014)

0.001 (0.009)

0.018 (0.012)

0.014 (0.013)

0.002 (0.019)

0.006 (0.009)

Ncentral 0.096 (0.023)

0.058 (0.010)

0.046 (0.010)

0.036 (0.012)

0.048 (0.024)

0.070 (0.010)

31

Figure 2.1.Cumulative poverty gap curves

32

Figure 2.2 Lorenz curves

33

−0

.10−

0.0

8−

0.0

6−

0.0

4−

0.0

2m

en

0 .2 .4 .6 .8 1Quantile

−0

.10−

0.0

8−

0.0

6−

0.0

4w

om

en

0 .2 .4 .6 .8 1Quantile

−0

.14−

0.1

2−

0.1

0−

0.0

8−

0.0

6ch

ild

ren

1

0 .2 .4 .6 .8 1Quantile

−0

.10−

0.0

8−

0.0

6−

0.0

4−

0.0

2ch

ild

ren

2

0 .2 .4 .6 .8 1Quantile

−0

.10−

0.0

8−

0.0

6−

0.0

4−

0.0

2e

lde

rs

0 .2 .4 .6 .8 1Quantile

0.0

00

.000

.000

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Figure 2.3: Ordinary Least Squares and Quantile Regression Estimates

34

CHAPTER 3

EVALUATING THE IMPACT OF MICROFINANCE ON SAVINGS AND

INCOME: QUASI-EXPERIMENTAL APPROACH USING

PROPENSITY SCORE MATCHING

3.1. Introduction

The microfinance revolution has changed attitudes towards helping the poor and

has provided a large amount of credit, often to very low-income households, usually who

would have been excluded by conventional financial institutions. There is no precise

estimate of the number of microfinance service providers worldwide. According to the

“A worldwide Inventory of Microfinance Institutions. Sustainable banking with the poor”

(World Bank, 1996), 101 microfinance programs were surveyed in 101 developing

countries. The report indicates that there are more than 1000 microfinance institutions,

consisting of commercial banks, savings banks, credit unions, and non-governmental

organization (NGOs). According to the report microfinance is more prevalent in Asia

compared to other regions in the world. Out of the total loans disbursed, 76 percent are in

Asia, 21 percent in Latin America, and 3 percent in Africa. The report further suggests

that total funding mostly comes from external donors. Foreign donors provide 55 percent

in Latin America, 47 percent in Asia, and 39 percent in Africa.

However, still the academic development community is rather more skeptical

about the impact of microfinance programs given the enthusiasm shown for these

programs in donor and policy-making circles. To quote from Zeller and Meyer (2002):

35

"MFI field operations have far surpassed the research capacity to analyze them, so

excitement about the use of microfinance for poverty alleviation is not backed up with

sound facts derived from rigorous research. Given the current state of knowledge, it is

difficult to allocate confidently public resources to microfinance development."

This is a very strong statement of doubt on what proportion of income, savings

and other effects on the beneficiaries of microfinance can be actually attributed to

programs themselves. In spite of the large amount of subsidized resources that is pumped

into microfinance programs, still there is a lack of statistically and scientifically robust

impact assessment of such programs. Statistically robust evaluations have been limited

due to several problems, such as selection bias, non-random program placement,

difficulties in finding instrument variables, and paucity of reliable data. Moreover,

methodological issues such as attribution and fungibility also poses problems. One of the

major obstacles in evaluating microfinance programs is related to the attribution of

specific impacts (effects) to specific causes (microfinance treatments). Fungibility of

loans is referred to as the use of a loan by an individual else than the borrower or use of a

loan for a purpose other than the one for which the loan was issued in the first place.

Recently, an increasing effort is made to measure the impact of microfinance-not just to

demonstrate the effectiveness of microfinance but to develop as well. As the concept of

microfinance expands, it is sensible to ask the big question: does microfinance work at all

as a poverty reduction tool. It is important to note that microfinance impact evaluations

vary immensely in both quality and rigor. Copestake et al. (2001) describes how the

concept of impact evaluation is scrutinized in three different schools of thought:

36

"The first accepts the case for doing a limited number of rigorous studies but argues that

it is a specialized and expensive task. The second trusts more in the ability of

practitioners to interpret and be guided by a mixture of routine monitoring and qualitative

studies, more akin to market research than to academic research. A third view seeks an

intermediate or `middle range' level of assessment: cheap enough to be carried out quite

widely, but sufficiently rigorous to be credible."

In developing countries, access to formal saving instruments for low-income

people is non-universal. In these countries, opening a bank account is associated with

high transaction costs and fees. Usually it is believed that low-income people does not

save. However according to Mansell (1995) and Robinson (1992) if suitable financial

instruments are available to low-income people, they become eager and regular savers.

Unfortunately, no reliable estimates of the exact effect of these instruments on individual

saving behavior exists. The belief that low income people do not save can also be refuted

on the fact that their savings are not necessarily financial assets. It can be anything that

can be used to preserve value and even increase it from the present to the future. For an

example, it might be gold, coins, jewelry, cash and even animals. Rosenzweig and

Wolpin (1993) illustrated the use of bullocks as assets in India. They showed that

bullocks served as investment assets to generate income and also to smooth consumption.

The lack of access to financial services could also lead households to adopt very

inefficient forms of savings, ranging from cash under the mattress to extreme forms such

as children (with the expectation of receiving old age support from children). Thus, all

these adverse savings methods have significant effects on and in the perpetuation of

poverty. Deaton (1990) states at least three reasons for studying savings in developing

37

countries. Firstly, in a microeconomic context, households are large and poor with

income prospects being more uncertain compared to developed countries. Secondly, in a

macroeconomic context, the fiscal system of low-income countries is not well developed

enough to control the level disposable income to stabilize output and employment.

Thirdly postwar literature suggests that savings is at very low levels, and this acts as an

impediment to development.

3.2 Microfinance Institutions and Impact studies

In Sri Lanka the concept of formal microfinance originated in 1906, with the

creation of the first Sanasa society, the beginnings of a movement which was formalized

subsequently in 1911, with the passing of the Cooperative Societies Ordinance. However

if microfinance was to include informal credit providers as well, then very much the

major proportion of micro-loans has been obtained from such informal sources, such as

family, moneylenders, merchants, and produce buyers. Even as recently as 1986/87 a

central bank survey revealed that some three quarters of credit transactions in Sri Lanka

originated in this informal sector (Senanayake 1999). During the last twenty years, a wide

range of institutional sources of microfinance developed, and the presidential commission

on finance and banking encouraged the development of a "pluralistic" approach to rural

finance. According to the Presidential Commission on Financing and Banking:

… provides scope for the involvement of a wide array of financial institutions,

cooperatives, NGOs (nongovernmental organizations) and a range of governmental

and informal agencies, each employing different techniques and strategies based on

their different credit cultures, together with market-based interest rates and adequate

38

support services … (Presidential Commission on Finance and Banking 1992, 122).

According to Attanayake (1997), providers of microfinance services in the rural sector

can be classified under three categories.

(I) regulated financial institutions --- consist of commercial banks, both state

and private, savings and development banks, and the regional development banks

(RDBs).

(ii) cooperatives; and

(iii) "other formal nongovernmental institutions."

The next subsection examines the providers of microfinance in Sri Lanka, using the

above classification.

2.2.1. Regulated Financial Institutions in Microfinance

Commercial banks: The Sri Lankan government had been using both commercial

banks and regional development banks for channeling credit in poverty alleviation

programs. State commercial banks and RDBs disbursed large amounts for microcredit in

Janasaviya program from 1989 to 1994. The Janasaviya Trust Fund (from 1991) and its

successor, the National Development Trust Fund (to end-1997 depended mostly on rural

branch banking networks for disbursing credit. The main government program, Samurdhi

(beginning 1995), relied on two state banks , while the Small Farmers and Landless

Credit (SFLC) project (beginning 1990) employed RDBs for channeling credit. Samurdhi

and the SFLC project will be discussed in sections 2.3 and 4.2 respectively. Apart from

"policy lending", the only commercial bank that was engaged in broad range of

microfinance activities was Hatton National Bank (HNB), the leading private commercial

39

bank in Sri Lanka. In addition to channeling credit in government poverty reduction

programs, HNB has formed an novel microfinance program called Gami Pubuduwa

Upadeshaka (GPU). Originating in 1989, GPU served small and micro-savers and

borrowers in rural and semi-urban areas. To the end of 1998, GPU had advanced SLR

876 million for 27,500 small and micro activities, while 10,900 small projects had an

outstanding amount of SLRs277 million. Furthermore, HNB's collection performance is

remarkable compared to other commercial banks lending in rural areas. HNB has been

very successful in mobilizing rural savings, recognizing the potential for growth within

this sector that accounts for less than 5 percent of the total savings in the formal banking

sector. Even though estimating rural savings involves some difficulties, available data

suggest that in the late 1990s, national and rural savings experienced a growth of 57

percent in constant price terms. During the same period, HNB's rural branches recorded a

nominal increase in savings of greater than 200 percent. Also, deposits within the GPU

"micro" program increased over the same period by 183 percent, at an annual compound

rate of 23.2 percent (Wijesundera 1999). HNB extended their services to rural areas by

adopting innovative approaches to rural banking. Most banking transactions are

processed inside the village rather than at bank branches. Most loans are issued on an

individual basis, while many smaller loans are processed through nongovernmental

organizations (NGOs). The average loan size of HNB's micro-program is around $700-

800 (country's where per capita GNP is around $800). In conclusion, HNB has developed

the capacity to provide convenient financial services to the rural poor and genuine

microcredit clients. From the viewpoint of HNB, this is a profitable strategy of resource

mobilization; at the end of 2000, HNB recorded a deposit/loan ratio of around 2.5, while

40

having an average deposit balance of approximately SLRs10, 500 or $150. Unfortunately,

HNB's is constrained both by the relatively small size of its branch network and

consequent lack of rural infrastructure to expand its microfinance activities in rural areas.

Savings and Development Banks: One of the recent developments was the establishment

of two small specialized banks, Sanasa Development Bank and Pramuka Savings and

Development Bank. Sanasa Development Bank was established in 1997 and was based

on the Sanasa movement of TCCSs, and is has a micro-lending portfolio in its business

operations. It is also be engaged in overseas-funded poverty lending activities operated

by the CBSL. However, the operations of Pramuka Bank is rather different, since it is

urban based and as a result the capability to reach the rural network is very much limited.

The Pramuka Bank is mainly engaged in savings mobilization from urban and corporate

sources.

Regional Development Banks: The RDBs were created in 1997, by amalgamating the

previously operating RRDBs (eight RDBs were to be formed from the seventeen

RRDBs). Several reasons were behind the amalgamation; firstly to be consistent with

provincial boundaries and political decentralization. Secondly, to specialize RDB

management and realize economies of scale. Thirdly, and to increase the CBSL

independence over RDB's operations, since the central bank owned all shares under the

RRDBs system, and their chairmen usually politically appointed after the Monetary

Board nominating board members for approval by the minister of finance. The RDBs

plays an active role in government poverty alleviation lending. Furthermore the RDBs

have the obligation to develop the governments Samurdhi program; "… promoting and

developing … projects of Samurdhi beneficiaries …" within the province (Parliament of

41

Sri Lanka 1997). RDBs are also involved in lending to beneficiaries of the SFLC project,

both directly to groups of the poor and indirectly to NGOs and cooperatives which onlend

to the poor.

3.2.2 Cooperatives in Microfinance

Under cooperatives, there are two types of institutions involved in providing

financial services to the poor: cooperative rural banks (CRBs) and thrift and credit

cooperative societies (TCCs). Both institutions are regulated by the Department of

Cooperatives.

Cooperative rural banks: CRBs and TCCCs are very effective in mobilizing savings, and

account for more than 5.3 million individual savings accounts in the country. They also

accounted for more deposits in total than RRDBs. However they are less noteworthy as

lending institutions, usually having a loan/deposit ratio of less than 40 percent.

According to Attanayake(1997), the performance of CRB loans are not satisfactory,

since 75 percent of the loans have been given non-income-generating purposes, such as

pawning, housing, and consumption. The study notes:

“[They] … have not played an important role in providing credit facilities for their

rural membership to undertake income-generating activities … [and] as in the case of

commercial banks have not been innovative in rural lending. They do not have

adequately trained staff to handle development credit. They also lack an effective

credit supervision and follow-up system to ensure the proper use of credit by the

borrowers and to undertake recovery at field level.”

42

Furthermore, CRB are exposed to a high degree of politicization as a result of being

attached to multi-purpose cooperatives, thus hindering the capacity to adopt a sustainable

lending approach. It is also reasonable to believe that CRBs could become even more

vulnerable, if the institution is expand its lending activities. Even with all the

disadvantages, the potential for effective microcredit is enormous, given the institution's

outreach and infrastructure. Therefore, removal of CRB's from a politicized environment

and rebuilding the institutional capacity are critical in attaining a significant achievement

in rural financial intermediation.

Thrift and credit societies: The thrift and credit cooperative societies in Sri Lanka are

structured according to a federation referred to as the Sanasa movement. According to

Kiriwandeniya (1998), at the end of 1997, the total membership was 786,000 in 8400

TCCs. This indicates the ability of the Sanasa movement to reach a significant proportion

of households in country with a population less than 20 million people. The primary

societies are mainly engaged in mobilizing savings and providing loans for small

business activities and consumption. However, still a larger proportion of its membership

consists of the village elite and the salary employed, despite the movements effort to

include the village poor. The Sanasa movement also serves as a channel for several

microcredit schemes funded by both government and internationally. Sanasa expanded its

activities available to its members by establishing an insurance company in 1992, and

later the Sanasa Development Bank in 1997. The Sanasa Development Bank started with

an initial subscribed capital of $1.43 million, and has established a large number of

"agency banks", while preserving their society status.

43

2.2.3. NGOs in Microfinance

There is a large number of NGOs in Sri Lanka, comprising of small, local, and

with religious or social welfare objectives. Furthermore, there are also community-based

organizations created by government agencies for the purpose of serving as agents in

official programs. The most well-established movement working in the sphere of

microfinance and rural development is the Sarvodaya Economic Enterprises

Development Services (SEEDS). SEEDS was established as a company limited by

guarantee, with the purpose of building a specialized development bank under CBSL

regulation by 2003. This is to be expanded on the base of an existing network of "village

banks," of which some 250 were operating in mid-1999, with a further 2,200 "societies"

(potential village banks) altogether serving some 294,000 people. SEEDS accumulated

savings amounted to SLRs323 million ($4.6 million) in late 2000, an increase of SLRs31

million for the quarter ended 30 June 1999, while loan disbursements were SLRs97

million ($1.4 million) for the quarter of 2000. The repayment rate (amounts paid on

current loans as a proportion of loans currently due) was 93 percent for the quarter, while

portfolio at risk (defined as the proportion of current portfolio more than 12 months

overdue) was 14.5 percent (SEEDS 1999). According to Gunatilaka(1997), SEEDS

acting primarily as a provider of savings should necessarily be considered noteworthy:

"Some of the better-established small savings programs … may … not be accessible

to individual small savers. This is because they prefer to exclude themselves from the

credit and microenterprise components of an on-going program because of the

risks involved … In such cases, the catchment of savers could be widened by making

it possible for such poor … to save regularly through an easily accessible savings

44

program" (Gunatilaka 1997, 32).

The societies and "village banks" are listed as membership institutions under the

Societies Ordinance, 1891. SEEDS adopted a "credit plus" approach of microfinance, by

establishing separate divisions for banking and training operations. This is evident in the

loan portfolio for the June quarter of 1999; borrowers for the quarter amounted to 5,839

(of which 60 percent were women) from a society membership of 294,000 individuals. It

appears that most of the members were participating for access to savings services and

other benefits, rather than credit. The mean loan size was estimated around SLRs16,300

($230) which, when compared to GNP per capita of $800, imply that the bulk of loans

can be categorized under the microcredit range for Sri Lanka.

2.2.4 Samurdhi Poverty Alleviation Program

In 1995 the Sri Lankan government launched Samurdhi poverty alleviation program. The

Samurdhi program is Sri Lanka's largest microfinance and welfare expenditure program

implemented in 22 districts of the island. Currently 51 percent of the total population are

recipients of the program. The program is financed by general revenue and has consumed

about half of the total welfare budget of the economy excluding health and education.

The total cost of the program is estimated to be 4 percent of the government revenue, or

almost 1 percent of GDP (Table 3.1).

The Samurdhi program consists of 3 main components, 1) welfare component-

food stamp, insurance 2) group savings and credit component 3) integrated rural

development. The monthly welfare grant( food stamp) and the insurance scheme falls

under the protect ional component of the program. The protectional component of the

45

program acts as a temporary method of cushioning poverty by the use of an income

transfer. The savings/credit schemes and the integrated rural development element fall

under the promotional component of the program. The promotional element of the

program contains medium and long-term goals for eradicating poverty mainly through

institutional support. An important note to point is that the program is a poverty

alleviation program rather than a social security scheme. The main goals of the Samurdhi

Program are:

1) Broadening opportunities for income enhancement and employment for poor.

2) Integrate youth, woman and other disadvantaged groups in to economic and social

development activities.

3) Developing latent talents and skills of the youth and increasing their marketability.

4) Increase the productiveness of rural assets to generate additional income and

employment opportunities.

The administration of the program is under the ministry of Samurdhi, Youth and

Sports and the department for poor relief. The administration structure of the program

starts from the top national bodies and extends down to village level animators or

development officers. Since the program was launched in 1995, it has recruited

approximately 25000 youths for various administrative tasks. However one of the key

drawbacks of the program is that the administrative costs are very high. Salaries and

wages alone accounted for around 10 percent of the total expenditure in the program.

With respect to the administrative structure, several key points need to be noted. Firstly

from the launch of the program (in 1995) until 2001 administrative jobs/recruits increased

by approximately 25 percent. Most of the increase is as a result of the expansion of the

46

program to the North and East regions of the country. However it is also evident that

most of the recruitment was done to gain political mileage, since there was a 20 percent

increase in the recruitment of development officers in the run-up to the general elections.

Secondly salaries paid to development officers and managers have doubled since the

launch of the program in 1995. Furthermore all positions have been made permanent as a

result of strikes and requests made. Thirdly as a result of politicization of the program,

most of the development officers employed are political appointees. Due to this fact,

several problems arose in the execution of the program in areas where the popularity for

the government is weak. There were occasions when programs and projects were even

sabotaged in such areas.

1) Group-Savings and Credit Component: The main objective behind group savings is to

increase the savings habit of the poor. Under this scheme a group of 5 people contribute a

small amount weekly or fortnightly towards the group fund. The group fund is then either

deposited at a Samurdhi bank for an interest rate or invested in a common project. By

June 2000, the total group savings of the program was Rs. 2264 million. The main

weakness of the scheme are ; 1) most of the members are too poor to save 2) Since the

group is involuntary formed by development officers the members are heterogeneous. As

a result the breakdown rate is high.

2) Rural Infrastructure Development Component: The objective of this scheme is to

increase the stock of rural infrastructure. For small scale projects(gravel a road, re roof a

school) 80 percent of the funds are contributed by the government. Labor contribution by

the members account for the remaining 20 percent. Most of the projects are identified,

designed and implemented by the members with some technical assistance provide by the

47

government. Large-scale projects(irrigation canal, dams, roadways and bridges) are fully

financed by the government. Most often program beneficiaries are hired for these

projects, which as a result provides off-season employment in the villages. Since the

inception of the program, approximately Rs. 2 billion has been spent on nearly 41000

projects. The main weaknesses encountered with project implementation are, 1) ad-hoc

planning 2) poor maintenance 3) haphazard use of land and resources.

3) Welfare Component (food stamps, insurance): The main element of the welfare

component is the consumption grant transfer(food stamp). Monthly coupons are issued to

beneficiaries which can be later used to purchase goods from the local co-operative store.

The face value of a coupon given to a household is either Rs 1000, Rs 500, Rs 250, Rs

200, Rs 100. Firstly households earning less than Rs 1500 a month are identified. The

identified families are then divided in to 4 categories (see table 3.2). Finally the

beneficiaries can claim their entitlements in either cash or kind depending on the category

they belong to. Deductions for savings and insurance are made at the point of collection.

Furthermore, a requirement for beneficiaries is contributing "voluntary" labor. The

amount of labor that should be contributed is based on the size of the grant. For an

example, a beneficiary need to contribute 4-5 man-days towards community development

projects if the recipient's grant is Rs 500. When benefiting household's income exceeds

Rs 2000 and remains as such for 6 continuous months or when a family member finds

employment, the household must exit the program. However most of the time rules and

limits of the exit criteria are difficult to enforce.

48

One of the key problems associated with the food transfer program is related to

sufficiency. The perception of most recipients was that the grant was not adequate

enough to have a significant impact. When a beneficiary receives Rs 1000 or Rs 500 the

real net amount received is only Rs 755 or Rs 375, since deductions for savings and

insurance are made at the point of collection. Moreover, the real value is further reduced

as a result of efficiency issues via the delivery system. One positive aspect of the program

is that it is unlikely to a disincentive to labor supply. The welfare program (Janasaviya)

launched by the former political regime issued out a larger grant but also created a

disincentive to supply labor. The co-operative system is used to deliver grants in the

program. However there are several inefficiencies associated with grant deliveries. Most

of the food stamp recipients are not satisfied with the quality if food that are being issued

to them. Recipients also complain that corrupt practices in terms of weights and measures

are common in the co-operative delivery system. Furthermore a prosperous secondary

market is in existence for the publicly provided goods. Most often the recipients sell their

goods purchased from the co-operative to private traders (at prices below the market

value). The cash they get are in turn is used to purchase goods not provided by the co-

operatives. With respect to targeting the household eligibility threshold was set at

approximately one-third of the national poverty line. However the central bank reported

50 percent of the households in the country are grant recipients. While the poverty rate

was 20 percent. Therefore it can be concluded that many non-poor households receives

grants. Almost all the practical problems in the targeting occurs in the political economic

framework. Most of the targeting is politically biased since the development officers are

political appointees and are subject to political pressure. This explains the fact that why

49

half of the country's population are program beneficiaries when less that a quarter is

below the poverty line. In 1999 a survey was done based on a multistage stratified sample

to judge the distributional effectiveness of the program. In the sample 40 percent of

households reported receiving food stamps. It was revealed that the incidence of being a

transfer recipient is progressive than any of the country's other functioning public transfer

programs. Unfortunately the program did not capture the 36 percent of the households in

the lowest expenditure quintile. It was revealed that the lowest two expenditure quintiles

accounted for 60 percent of the total food stamp budget. While quintiles three, four and

five accounted for the remaining 40 percent. The best way to judge the effectiveness of

the program is to compare it with a similar program in another country. According to

Grosh (1994) across Latin American countries in a targeted transfer program the two

expenditure quintiles accounted for 70 percent of the total budget. In an untargeted

program (primary health care and public education) lowest two quintiles accounted for 60

percent of the total budget. Therefore this suggests that the outcomes of the Samurdhi

program is more similar to an untargeted program even though it is a targeted program

However, according to Grosh (1994) a targeted transfer program has a administrative cost

of around 9 percent of the total program cost, which is therefore comparable to the

Samurdhi program administration cost of nine percent.

Since microfinance is viewed as a "win-win strategy" for poverty alleviation, it is

important to explore the existing evidence from the literature of the impact of

microfinance programs. Evidence at micro-level: In spite of most microfinance

institutions view income and employment generation as the explicit objective, poverty

reduction remains the principal goal. The impact of program participation on income,

50

consumption and net worth of households is generally used as the criteria for judging the

success of poverty reduction. The first microfinance institution to attract international

attention was the Grameen Bank in Bangladesh. As a result being a pioneer in

microfinance, most of the earliest impact evaluations were focused on the Grameen Bank.

The first impact assessment of the Grameen Bank was done by Mahabub Hossain (1988).

Hossain compared the welfare Grameen clients to eligible non-clients in Grameen

villages as well as non clients in non-Grameen villages. His findings indicate that

Grameen members average household income to be 43 percent higher than non-members

in non-Grameen villages and 28 percent higher than eligible non-members in Grameen

villages. Furthermore, he found out that per capita spending on food for Grameen

members to be 8 percent higher than non-members in Grameen villages, and 35 percent

more on food and 32 percent more on clothing than non-members in non-Grameen

villages. The first most sophisticated econometric study on the impact of micro-credit on

poor households was done by Pitt and Khandker (1998). The study was influential in a

econometric context, since it was the first serious attempt to evaluate the impacts of

micro-credit after controlling for selection bias and non-random program placement. The

study was based on data collected by the World Bank and the Bangladesh Institute of

Development Studies(BIDS) in 1991-1992. The survey includes 1978 households who

were either Grameen, Bangladesh Rural Advancement Committee's (BRAC) or

Bangladesh Rural Development Board's Rural Development Project 12 (RD-12), as well

as non-participant households. The results of the study uplifted the confidence of

microfinance after demonstrating highly positive effects on bank members and their

families.

51

The main conclusions of the study are:

♦ Every additional taka lent to a woman adds an additional 0.18 additional taka to

annual household expenditure- an 18 percent return to income from borrowing.

♦ The probability of girl’s school enrolment increases by 1.86 percent for every one

percent increase in credit to Grameen women.

♦ A 10 percent increase in credit to member women increased the arm

circumference of girls by 6 percent.

♦ One percent increase in credit to women increased the height-for-age of girls by

1.16 percent and 1.42 for boys.

Later, Khandker (2005) extended the study by employing panel data (1998/1999

resurvey of World Bank-BIDS data). Khandker (2005) estimates revealed that for each

additional 100 taka of credit to women increased total annual household expenditure by

more than 20 taka. Furthermore, the use of panel data permitted to compare the poverty

rates in 1991/1992 and 1998/1999. Results showed that moderate poverty in all villages

declined by 17 percentage points, and 18 points decline in villages with microfinance,

and a decline of 13 percentage points in non-program villages.

The most unique microfinance evaluation was done by Asian Development Bank

(ADB) economist Brett Coleman(1999). Coleman study involved two microfinance

institutions in Northeast Thailand, the Rural Friends Association and the Foundation for

Integrated Agricultural Management. Cole tackled the selection bias issue in a very

clever manner. In order to determine who in the comparison villages would have chosen

to participate in microfinance programs had they been available, he actually had

52

interested individuals sign up a year in advance- now it is possible to compare borrowers

to people with the same `entrepreneurial spirit' who had not been offered credit. After

controlling for selection bias, findings showed zero impact from microfinance programs

but positive impact using naive estimates. Coleman's technique is highly credible and

provides strong for the need to address selection bias. Later, the follow-up article of the

study-Coleman (2002), disaggregated participation and impact by type of client and

found:

“self-selected program participants are significantly wealthier than nonparticipants even

prior to program intervention, and the wealthiest villagers are almost twice as likely to

participate in the program than the poorer villagers. Moreover, some of the wealthiest

villagers obtain a disproportionate share of program loan volume by virtue of holding

influential positions as village bank committee members. Positive impact is seen largely

in this wealthier group. Impact on rank and file members is significantly smaller than

impact on the wealthy, and is largely insignificant.”

However, Coleman himself recognized why Thailand is too atypical for the

results of such a study to provide evidence for the impact of microfinance in other

countries. He noted that in the villages surveyed, 63 percent of households held the

membership the Bank for Agriculture and Agricultural Cooperatives (BAAC), a state

bank that provides subsidized credit to rural households with significantly larger average

loans than village banks. Coleman claimed that since only 30 percent of BAAC members

in the survey are women, only 19 percent of households (30 percent of 63 percent) in the

survey included women who are BAAC members. Furthermore the fungibility issue of

credit within the household is also not accounted. In essence, 63 percent of surveyed

53

households---male and female members of BAAC--- already had access to a significant

amount of credit than the village banks provide. Coleman observed that the average

household low-interest debt, not including village bank debt, was 31,330 baht16 (9,342 of

which was held by women), while village bank loans only amounted to 7,500 baht.

Coleman wrote: "In such an environment, it should not be surprising that loans of 1,500

to 7,500 baht would have a negligible impact."

3.3 Econometric Methodology and Data

2.3.1. The evaluation problem

The basic problem in treatment evaluation involves the inference of a casual

relationship between the treatment and outcome. In a canonical single treatment setting,

one can observe ( , , ), ,..., ,i i iY D i NΧ and the impact on Y from a hypothetical change in

D, while holding Χ constant. Such inference is the key feature of a potential outcome

model, where the outcome variable of the treated state is compared to the outcome

variable of the untreated state. However, it is impossible to simultaneously observe both

states for any given individual. Thus, the problem is akin to one of missing data, which

can be solved by techniques of casual inference carried out in terms of counterfactuals.

The counterfactual question is: ‘what would have happened to people who participated in

a program (or received treatment) if they had not done so (or else had participated in

another program)’. First, assume the setup of a randomized treatment assignment, where

no one is included in the treatment group because the benefits of the treatment to that

individual would be large, and no one is excluded because the expected benefit is small.

Let the vector of observables be ( , , ; ,..., )i i iY D i NΧ . Where Y is the scalar-value

54

outcome variable, Χ is a vector of observables, and D a binary indicator of treatment (D

takes the value of 1 if the treatment is applied, 0 otherwise). In the potential outcome

framework, one can define ∆ as the difference between the outcome in the treated and

untreated states

1 0Y Y∆ = −

Where, 1Y is the outcome of the treated individual, and 0Y is the outcome of the

untreated individual. It is important to note that ∆ is not directly observable since an

individual cannot be observed in both states. The two key evaluation parameters that will

be used in this study are average treatment effect (ATE) and average treatment effect on

the treated (ATT). ATE is the expected effect of treatment on a randomly drawn person

from the population. ATT is the mean effect of those who actually participate in the

program. The ATE is important when the treatment has universal applicability and it is

useful to consider the hypothetical gain from treatment to a randomly selected individual

of the population. The ATT is important when considering the average gain from

treatment for the treated. In sample analogues, the ATE and ATT can be defined as,

1

1 [ ]N

ii

ATEN =

= ∆∑

1

1 [ | 1]TN

i iiT

ATT DN =

= ∆ =∑

where 1

NT ii

N D=

=∑ . The computation of both ATE and ATT is straight-forward if i∆

can be estimated. The empirical strategy to estimate the average outcomes for the

participants had they in fact not participated (the counterfactual), is through substituting

55

the outcome of untreated individuals for whom the observable characteristics, Χ , match

those of the treated up to some selected degree of closeness.

The treatment evaluation problem can be easily understood by writing the ATT as

1 0ATT=E( |D=1)=E(Y |D=1)-E(Y |D=1)∆

From the above equation, the problem of selection bias is straightforward, since the

second term on the right side- 0E(Y |D=1) ≡ the counterfactual mean of the treated, is not

observable. If the condition 0 0E(Y |D=1)=E(Y |D=0) holds, one can use the non-

participants as the comparison group. But with non-experimental data this condition will

not hold, since the components which determines the participation decision also

determines the outcome variable of interest. Thus, the outcomes of the participants would

differ even in the absence of program participation, leading to a selection bias. It may be

the case that selection bias can be fully accounted for by observables characteristics (such

as age, skill differences, etc.). In this case, selection bias can be eliminated simply by

including the relevant variables in the outcome equation. But in practice, unobservable

characteristics effecting participation can also influence outcomes. For an example, it

may be that highly motivated individuals are more likely to participate in microfinance

programs and are also more likely to have higher income and savings. On the other hand,

program entry may be a function of administrator selection. It is reasonable to believe

that administrators are discriminating between the less and better able, as a basis for

program selection. If administrators are `creamskimming' by selecting the best for the

program, then program effects will be overstated. Equally, if program administrators are

targeting program resources on the least able then program effects may be understated.

56

The ATT can also be expressed as

1 0 0 0[ | 1] [ | 0) ( | 1] [ | 0]E Y D E Y D ATT E Y D E Y D= − = = + = − =

The difference between the left hand side of the equation and the ATT is the self

selection bias. The true parameter ATT is only identified if

0 0[ | 1] [ | 0] 0E Y D E Y D= − = =

The crux of the evaluation problem is that 0[ | 1]E Y D = is unobservable. Practically three

solutions exist: experimental, quasi-experimental, and non-experimental strategies

identification strategies. The experimental approach solves the evaluation problem by

gathering a set of individuals equally eligible and willing to participate and then

randomly dividing in to two groups: the set of individuals who receive the treatment

(treatment group) and a set of individuals who are denied treatment (control group). In

this setting, random assignment serves as a perfect counterfactual, free from the

bothersome self-selection problem, thus ensuring that no bias arises in comparing

observed outcomes for treated and control units. Even though the experimental approach

is the optimal method to estimate project impacts, in practice several problems exist. For

an example, randomization may be unethical due to the denial of benefits (treatment) to

otherwise eligible members, or it can be politically infeasible to provide an intervention

to one group and not another.

The second identification strategy relies on non-experimental evaluation methods.

In non-experimental methods, program participants are compared to non-participants by

controlling statistically for differences between participants and non-participants. The

non-experimental approach is appropriate when it is not possible to randomly select a

control group or identify a suitable comparison group through using techniques such

57

matching (discussed later). The non-experimental method applies statistical and

econometric techniques such as instrumental variable(IV) and Heckman selection

estimators. The primary drawback with the IV technique is due to the extreme difficulty

in finding suitable instruments. To identify the treatment effect, one has to find at least

one regressor which determines program participation but is not itself determined by

factors which affect outomes. Practically, in a survey data set finding such instruments is

a ardent task. Similarly, again for the Heckman selection estimator the most significant

practical obstacle to successful implementation is the identification of suitable

instruments. Additionally, Heckman selection estimates are highly dependent on the

underlying distributional assumptions relating to the unobserved variables. Goldberger

(1983) and Puhani (2000) shows that estimates can surprisingly sensitive if these

assumptions are not met.

The quasi-experimental approach identifies the true parameter ATT by

constructing a comparison group using reflexive or matching techniques. In a reflexive

comparison, the counterfactual is constructed by taking into account the situation of

program participants before the program. The basic idea of reflexive comparison is to

compare the outcome set of a group of individuals after participation in a program with

outcomes of before participation and to view the difference as the difference as the

estimate of ATT. The most commonly used econometric technique in reflexive

comparisons is the difference in differences (DD) estimator. The DD technique estimates

the ATT as the difference between the before-after estimate of participants and before-

after estimates of non-participants. Alternatively, matching involves identifying non-

program participants comparable in essential characteristics to participants, and then

58

compare differences in mean outcomes between these two groups to identify the impact

of the program. The primary econometric technique that will be utilized in this study to

solve the evaluation problem is ‘matching’ or more precisely what's referred to as

‘propensity score matching (PSM)’.

2.3.2. Propensity Score Matching

The essential idea of propensity score matching (PSM) is to match participants

and non-participants on their observable characteristics. Propensity score matching

assumes that selection can be explained purely in terms of observable characteristics and

that any selection on unobservables is trivial and they do not affect outcomes in the

absence of treatment. The mean effect of treatment (participation) can be estimated as the

average difference in outcomes between the treated and non-treated. Both non-

experimental and quasi-experimental approaches share one thing in common: when the

counterfactual mean for the treated- 0[ | 1]E Y D = , is not observed, one has to invoke

`identifying assumptions' to estimate the casual effect of a program on the outcome. The

first identification assumption in propensity score matching is referred to as the

conditional independent assumption (CIA), and is expressed as

0 1, |Y Y D⊥ Χ

It states that outcomes are independent of program participation, after controlling

for the variation in outcomes induced by differences in Χ 9. Under the conditional

independence assumption, the matching process is analogous to constructing an

9 Using Dawid's (1979) notation, ⊥ represents independence.

59

experimental dataset in that, conditional on observed characteristics, the selection process

becomes random.

The second identification assumption is referred to as the overlap or matching

assumption, written as

0 Pr[ 1| ] 1D< = Χ <

This assumption implies that for each value of Χ there are both treated and untreated

individuals. In other words, for each participant there is another non-participant with a

similar Χ . A practical constraint that exists in matching is that when the number of

covariates iΧ increases, the chances of finding a match reduces. However, Rosenbaum

and Rubin (1983) showed that matching on the propensity score ( )p Χ - the probability of

participating in a program, could achieve consistent estimates of the treatment effect the

same way as matching on all covariates.

Proposition 1 (Rosenbaum and Rubin 1983);

(1 )

1, 2 1, 2 1,...

( ) s gn ,( ) Pr( 1| ) ( | ). 0 ( ) 1, ,

( ) (1 ( ))Pr( ,... | ,... )

i i

i

i i i i i i iD D

i iN N i

Let p be the probability of unit i having been as i ed to treatment definedas p D E D Assume that P for all and

p p for the N units inD D D

−

=

ΧΧ ≡ = Χ = Χ < Χ < Χ

Χ − ΧΧ Χ Χ =

1 0 1 0

.{( , ) } | {( , ) } | ( )

N

i i i i i i i i

the sample ThenY Y D Y Y D p⊥ Χ ⇒ ⊥ Χ

∏

Corollary;

1 0

1

{( , ) } | 1 ,

| { ( | 1, ( )) ( | 0, ( )) | 1},

i i i i

D i i i i i i i

If Y Y D and the asumptions of proposition hold then

E E Y D P E Y D P D=

⊥ Χ

∆ = = Χ − = Χ =

60

assuming the expectations are defined. The outer expectation is over the distribution of

( ) | 1i iP DΧ = . The proposition implies that observations with the same propensity score

have the same distribution of the full vector of covariates, iΧ .

When estimating the propensity score, two decisions have to be made. Firstly, the

evaluator needs to choose the appropriate model and the functional form to estimate the

propensity score. In general any discrete model (such as a logit or a probit) can be used to

estimate the propensity score. In a single treatment framework, logit and probit models

gives similar estimates for the probability of participation. After choosing the model, the

evaluator needs to decide the covariates to be included in the model. This is a knife-edge

decision since including too many as well as too few variables might lead to undesirable

consequences. Heckman, Ichimura and Todd (1997) show that that omitting important

variables can seriously increase the bias in the estimate. However there also strong

reasons to avoid over-parameterized models. According to Bryson, Dorset and Purdon

(2002), there are two reasons why over-parameterized models should be avoided. Firstly,

including too many extraneous variables might exacerbate the support problem.

Secondly, although the inclusion of non-relevant variables will not bias the estimates or

make them inconsistent, it can increase the variance. On the other hand, Rubin and

Thomas (1996), strongly criticizes `trimming models' in the name of parsimony. If there

are doubts about whether a variable is related to the outcome or not a proper covariate,

they explicitly advise to include the variable in the propensity score estimation. It is

important to note that the standard regression based method and propensity score

matching differs significantly with regard to the choice of control variables. In a standard

regression, preference is usually given to variables that one can argue are exogenous o

61

outcomes, but in propensity score matching the primary interest is in covariates (not good

predictors) and thus including variables even when they are poor predictors. Analytic

results and simulations of Rubin and Thomas (1996) suggests that variables with weak

predictive ability for outcomes can still help minimize bias in estimating casual effects

with propensity score matching. In essence, the main purpose of the propensity score

estimation is not to predict selection in to treatment but to balance covariates and get

closer to the observationally identical non-participant. Finally it should be noted that with

`too good' data also problems can arise. If ( ) 0P Χ = or ( ) 1P Χ = , this implies that

individuals with such characteristics either always or never participate. Therefore some

random is needed to ensure that individuals can be observed in both states.

After estimating the propensity score, the next decision to be made concerns the

common support region(s). Enforcing the common support region ensures that any

combination of characteristics observed in the participation group can also be observed

among non-participants. The approach referred to as the `minima and maxima' condition

will be used in all estimations in this chapter. The basic idea of this condition is to delete

all participants, whose propensity score is smaller than the minimum and higher than the

maximum in the non-participants. Therefore participants who fall outside the common

support region will be discarded and for these individuals the treatment effect will not be

estimated. When the proportion of lost individuals is small, this poses few problems.

However, if there is a significant reduction in the sample size, then there are doubts about

whether the estimated effect on the remaining individuals can be viewed as a

representative of the full sample.

62

Having enforced the common support region, the next step is to choose the

matching algorithm. The general formula for the matching estimator is

1 0{ 1}

1 [ ( , ) ],M i ji D jT

B Y w i j YN ∈ =

= −∑ ∑

where MB denotes the matching estimator for the bias, 0 ( . ) 1w i j< ≤ , is the set of treated

individuals, and j is an element of the set of matched comparison units. ( , )w i j represents

a weighting function that depends on the specific matching estimator. Results will be

presented for four matching algorithms: nearest-neighbor matching, caliper matching,

radius matching and kernel matching. The nearest-neighbor matching method assigns a

weight equal to one [ ( , ) 1]w i j = , and takes each participant in turn and identifies the non-

participant with the closest propensity score. The nearest neighbor method will be

implemented with replacement, so that a non-participant can be used more than once as a

match. A variant of the nearest-neighbor matching is caliper matching. The caliper

matching method chooses the nearest-neighbor within a caliper of width δ , so that

{ :| ( ) ( ) | }i jj P X P X δ− < where ( )P X is the propensity score. Therefore caliper

matching imposes a form of quality control on the match by setting a tolerance level on

the maximum propensity score distance. Dehejia and Wahba (2002) introduced a variant

of caliper matching which is referred to as radius matching. In radius matching the idea is

to use not only the nearest-neighbor within each caliper but all of the comparison

members (non-participants) within the caliper. The final matching algorithm that will be

used in the study is referred to as kernel matching. Kernel matching uses all the non-

participants for each participant in the matching process. The kernel is a function that

weights the contribution of each non-participant, so that more importance is attached to

63

those non-participants providing a better match. The Gaussian and the Epanechnikov will

be used as weighting functions with kernel matching.

The final step in propensity score matching is to assess the matching quality.

Since the conditioning is not done on all covariates but on the propensity score, one has

to check the ability of the matching procedure to balance the relevant covariates. Three

measures will be used to judge the performance of the match: standardized bias, Pseudo-

R² and the t-Test. The standardized bias for each covariate as suggested by Rosenbaum

and Rubin (1985) is defined as the percentage of the square root of the average sample

variances in both groups. The standardized bias before matching is given by

1 0

1 0

1000.5 ( ( ) ( ))beforeSB

V VΧ −Χ

= ⋅⋅ Χ + Χ

1 0

1 0

100 ,0.5 ( ( ) ( ))

M Mafter

M M

SBV VΧ −Χ

= ⋅⋅ Χ + Χ

where 1 1( )VΧ is the mean (variance) in the treatment group before matching and 0 0( )VΧ

the analogue for the comparison group. 1 1( )M mVΧ and 0 0( )M MVΧ are the corresponding

values for the matched samples.

The basic idea of the Pseudo-R² is to re-estimate the propensity score on the

matched sample, that is only on participants and matched non-participants and compare

the Pseudo-R²'s before and after matching. The Pseudo-R² indicates how well the

regressors X explain the participation probability. After matching there should be no

systematic differences in the distribution of covariates. Furthermore one can also perform

an t-Test to check if there are significant differences in covariate means of treated and

64

comparison units. Before matching differences are expected, after matching the

covariates should be balanced in both groups and hence no significant differences should

exist.

There are two main advantages of propensity score matching over standard linear

regression models. Firstly, propensity score matching does not rely on any functional

form assumptions for the outcome equation. Regression models depend on the form of

relationship (linear, log-linear, etc.) which may be inaccurate and which propensity score

matching avoids. This is especially important when functional form restrictions are not

justified by economic theory. Secondly, propensity score matching does not make the

assumption of constant additive treatment effects across individuals as it is done in

simple regression. In propensity score matching heterogeneous treatment effects are

permitted.

2.3.3. Data

The study is based on the latest Consumer Finances and Socio-Economic Survey

(CFS) 2003/2004 conducted by the Central Bank of Sri Lanka. The CFS was initiated in

1953 and is the eighth multipurpose household survey that helps to capture long-term

changes in the living standards of the country. The foremost achievement of the survey

was its coverage of the Northern and Eastern provinces in the country after a time lag of

20 years. The CFS used the Census 2001 in constructing the distribution of housing units

across sectors and districts within provinces. Unfortunately, the CFS population frame

excluded three districts, Killinochchi, Mannar and Mulaitivu due to the ongoing civil

conflict. The under-coverage due to excluding the three districts is estimated around 2

65

percent of the total estimated housing units in the country. The CFS used a two-stage

stratified sampling procedure for the sample design. The survey is nationally

representative and consists of 11722 households and a 50545 individual population.

The survey collects information on a broad range of topics including demographic

characteristics, household income and expenditure, literacy and education, household

amenities and employment. The variables used in this study can be broadly categorized as

following:

1) Household per-capita income

2) Household per-capita savings

3) Household per-capita expenditure

4) Household size (household size 3-4, household size 5-6, household size ≥ 7)

5) Gender (male, female)

6) Age

7) Ethnicity (Sinhala, Tamil)

8) Sector (urban, rural)

9) Education (primary, secondary, tertiary)

10) Employment sector (regular worker-formal, casual worker-informal, self-

employed)

11) Housing particulars ( household not owning house, living in a single house, living

in a shanty/lineroom, floor type-cement, floor type-clay/mud/earth, main source

of lighting-electricity, main source of lighting-kerosene, energy for cooking-LP

gas, energy for cooking-firewood, source of water-common well)

66

12) Microfinance participant/client (if any member of a household has participated in

a microfinance program or had any financial transaction with a microfinance

institution)

13) Province (central, southern, northern, eastern, north western, north central, Uva,

Sabaragamuwa)

2.4. Results

Results are presented for the individual samples stratified by income quintiles:

bottom 20th percentile, 20th-40th percentile, 40th-60th percentile, 60th-80th percentile, top

20th percentile. Table 3.3 reports the 20th percentile estimates of the probit regression

where the binary outcome takes a value one if the household is a client of a microfinance

institution or a microfinance program. The results are generally unsurprising and reveals

a number of significant covariates of program participation. The probability of a

household participating in microfinance tends to increase with household size, and

decrease with the age. Microfinance participants are also relatively more likely to be

Sinhalese households and live in rural areas. The coefficient for Sinhalese is greater in

both the magnitude and statistical significance relative to the Tamil. With regard to

employment, program participation increases when the household is engaged in casual

labor or self-employment. Self-employed households have a greater propensity in

participation compared to the casual workers, which might be a indication of their better

entrepreneurial skills that is needed to reap the full benefits of microfinance.

Table 3.4 presents the results for the 20th-40th percentile estimates of the probit

regression. Again the most significant covariate of program participation is household

67

size. Clearly, there is a positive and monotonic effect of household size on the probability

of being a microfinance client. Household being a Sinhalese also has a positive impact on

program participation. However, in contrast to the bottom income percentile, the

significance of the estimate for age vanishes. But, still the coefficient for a household

being a casual laborer remains statistically significant. There are also several regional and

housing characteristic dummy variables functioning as important covariates for the

probability of program participation.

Table 3.5 reports the 40th-60th percentile estimates of the probit regression. Being

consistent with the previous two income categories, household size still remains a

positive covariate of program participation and the likelihood of being microfinance

client increases when the household is Sinhalese. According to table 3.5, both the

estimates for a household being a casual laborer and being self-employed increases the

probability of program participation. Furthermore, results suggests that households with

higher per capita expenditure are less likely to be clients of microfinance.

Table 3.6 presents the results for the 60th-80th percentile estimates of the probit

regression. Results indicate that the probability of being a microfinance client decreases

with the household age. Being consistent with the previous three lower percentile

estimates, yet again the likelihood of program participation increases when household

belongs to the Sinhala ethnicity. Results also suggests that casual laborers and self-

employed house are more likely to be participants of microfinance. According to table

3.6, households not owning a house tends to exhibit a lower probability of being a

microfinance participant.

68

Table 3.7 reports the 80th-100th percentile estimates of the probit regression.

Results show that the probability of program participation to decrease with household age

and to increase for households with seven members or more. According to table 3.7, the

probability of Sinhalese households participating in microfinance programs is higher

compared to households belonging to the Tamil ethnic minority. Households in self-

employment, living in houses with cement or clay floors are also more likely to be

microfinance clients. However the likelihood of program participation decreases for

households living in urban areas.

Again it is important to emphasize that all the variables with weak predictive

ability included in the probit regressions can be still helpful to minimize bias in

estimating casual effects in propensity score matching, since the ultimate goal is to not to

predict selection in to treatment but to balance covariates and get closer to the

observationally identical non-participant.

Next, the common support region can be examined by plotting a histogram of the

propensity score. The common support is the region where the propensity score has a

positive density for both treated and non-treated units. Figures 3.1-3.5 gives the

frequency distribution of the propensity scores based on tables 3.3-3.7 for the participants

(treated) and non-participants (untreated) of microfinance. Except for the 80th-100th

percentile histogram (figure 3.5), all other histograms reveals that there is a substantial

region of overlap, and a severe common support problem does not exist. Since the main

purpose is not on the probability estimations (probit estimations) but to match

households, it is encouraging to see that a large fraction of households from both groups

(treated and untreated) gets an estimated probability in the range of 0.2 to 0.6.

69

Unfortunately, for the 80th-100th percentile untreated group is large relative to the treated

group and there is a limited overlap between the two groups. As a result of this limited

overlap, the impact estimates for the 80th-100th percentile will be statistically less robust

relatively.

Tables 3.8-3.12 present the results on covariate balancing. Each cell reports the

average standardized bias of the different covariates before and after matching. It evident

that the differences between the households in the treated and untreated groups are quite

small before matching, and matching removes most of the existing bias for almost all

covariates. A t-test of equality of means in the two samples of participants and non-

participants was also conducted for each covariate. Results indicates that there is no

systematic patterns of significant differences between the covariates in the treatment and

non-treated groups after conditioning on the propensity score. Additionally, after

matching the pseudo- 2R is fairly low implying that the matching procedure is able to

balance the characteristics of the treated and non-treated groups.

Table 3.13 reports the percentage of all deleted treated observations, whose

propensity score is smaller than the minimum and higher than the maximum in the

untreated group. It can be seen that the number of lost individuals is very low, except

when the tolerance level is set very high (when 0.001δ = ). Therefore, in general the

estimated effect on the remaining individuals can be viewed as a representative, since the

proportion of lost individuals is small.

The empirical analysis led in this chapter will focus on two parameters of interest

when estimating treatment effects. First, the impact of microfinance on household per-

capita income and savings who were actually treated- i.e., the average treatment effect on

70

the treated (ATT). Second, what effect microfinance would have on a household drawn

randomly from the population- i.e. the average treatment effect (ATE). If we assume

homogeneous responses to treatment among households, these two effects will be

identical. The ATT and ATE will differ, should the responses be allowed to vary across

households. The first of policy makers concern is to of course to determine whether

microfinance had any impact on household per-capita income and savings. Another

important concern is whether the expansion of microfinance programs is worth

considering. While the ATT provides answers to the question of the impact, ATE is

required to go further and assess the opportunity of expanding microfinance. For

instance, if only individuals with the largest expected gains participate in microfinance,

then ATE will be smaller than ATT. Thus, a generalization of the program may generate

a lower effect than the one indicated by ATT.

Table 3.14 reports the estimated mean impacts on household per capita savings.

The estimates of the average treatment effect on the treated (ATT) and the average

treatment effect (ATE) obtained via propensity score matching, using four matching

algorithms and imposing the ‘minima and maxima’ common support. The results for the

mean impact indicate that program participation significantly increases household per

capita savings for the bottom four quintiles, though the magnitude varies by matching

method. However, these gains are not visible for the households in the 80th-100th

percentile, implying that even without participating in microfinance programs there

would be no difference in savings for the richest quintile. But, it is important to note that

the estimates of the richest quintile (80th-100th percentile) are highly unreliable, due to the

common support problem revealed by the propensity score histogram (figure 3.5). Table

71

3.15 reports the estimated mean impacts on household per capita income. Even though

there are sizable gains in household per capita savings for program participants, this is

not evident for household per capita income. Results suggest that there is no impact on

household per capita income across all quintiles.

3.5 Conclusion

Microfinance for the poor has become a focus of attention in the Sri Lankan

development community over the last several years. The microfinance revolution has

built on innovations in financial intermediation that lowers the cost of risks of lending to

poor households. Replications of the movement flagship, the Grameen Bank of

Bangladesh, have now expanded universally. To date, there has been no comprehensive

investigation of their impact on Sri Lankan household income and savings. Using Data

from a nationally representative household survey, this chapter analyzed the impact of

participating in microfinance on household per-capita income and savings employing a

quasi-experimental approach. The study applied recent advances in propensity score

matching methods to assess the impact microfinance on household income and savings.

Since a baseline survey or randomization are not feasible options in this case, the study is

well suited to matching methods. There are several attractive features associated with

propensity score matching, including the potential to allow for heterogeneous impacts,

while optimally weighting observed characteristics when constructing a comparison

group. The technique is well suited due to its flexible (non-parametric) nature, not

imposing exclusion restrictions or ad hoc assumptions about the functional form of

impacts. The method eliminates selection bias due to observable differences between

72

program participants and non-participants. Although a rich data set has been used,

permitting to match on a wide range of household characteristics, the likelihood always

remains of latent unobserved factors being correlated with program participation and

outcome variables. Results suggests that program participation increases with household

size, being a Sinhalese, living in a rural area, and employed as a casual worker or self-

employed. Overall program participants benefit incidence is indeed pro-poor. With

respect to household per-capita savings, program participation definitely has a positive

impact for all low-income households. Household per-capita savings are significantly

higher on average for participants of microfinance than for observationally identical non-

participants. However, the overall results are rather discouraging for household per-capita

income, since the impact estimates are negative for all estimated income quintiles.

Finally, the principle message that emerges form the study is; there are quantitatively

non-negligible, average gains from microfinance on household savings, especially for the

poor.

73

Table 3.1: Social Transfers and social expenditure,1999 Social transfers Sri Lanka Percent of Percent of

Rupees (million) the sub-total the total

Samurdhi 8145 58.20 12.05

Fertilizer subsidy 1451 10.37 2.15

Refugees 2661 19.01 3.94

Other 1739 12.42 2.57

Sub-total 13,996 100.00 20.70

Other social expenditures

Pensions 20,723 30.65

Education 22,231 32.89

Health 10,651 15.76

Total 67,601 100

74

Table 3.2: Income Transfer Component Category of family Amount received Distribution of coupons Families earning less Rs. 1000 1) Rs. 400-only to purchase than Rs. 500 and food items having more than 5 2) Rs. 375-to buy goods or members to encash 3) Rs. 25- insurance premium 4) Rs.200- compulsory saving Families with only Rs. 100 Only for the purchase of food 1 member Families with 2 members Rs. 200 Only for the purchase of food Others Rs. 500 1) Rs. 200- only to purchase food items 2) Rs. 175- to buy goods or to encash 3) Rs. 25- insurance premium 4) Rs. 100- compulsory savings Janasaviya recipients Rs. 250 Can either buy goods or encash

75

Table 3.3: Bottom 20th percentile probit model for the propensity score Variable Coefficient Standard Error Age -0.0072* 0.0022 Female 0.0130 0.0866 Married 0.0390 0.0873 Household size 3-4 0.3200* 0.0760 Household size 5-6 0.5533* 0.0923 Household size ≥ 7 0.5074* 0.1646 Sinhala 0.4563* 0.1250 Tamil 0.2720 0.1477 Primary education 0.0233 0.0780 Secondary education -0.0058 0.0836 Tertiary education 0.1010 0.1973 Regular employee -0.0044 0.1622 Casual employee 0.2050** 0.0885 Self-employed 0.2764* 0.0860 Urban sector 0.4456** 0.2119 Rural sector 0.5409** 0.1710 Household expenditure -4.4100 2.8000 Household not owning house -0.1116 0.1144 Lives in a single house 0.3917 0.2202 Lives in a slum/shanty/lineroom 0.3551 0.2563 Floor type- clay/mud/earth 0.0204 0.2787 Floor type-cement -0.0500 0.2760 Main source of lighting- electricity -0.4440 0.2327 Main source of lighting- kerosene -0.3452 0.2314 Energy for cooking- LP gas -0.2634 0.3950 Energy for cooking- firewood 0.0949 0.2720 Source of water- common well 0.0114 0.0595 Central province 0.2025 0.1222 Southern province 0.5200* 0.1210 Northern province -0.2272 0.2342 Eastern province -0.0768 0.1435 North western province 0.2237 0.1282 North central province 0.0246 0.1396 Uva province 0.2390 0.1290 Sabaragamuwa province 0.3268* 0.1205 Constant -1.5600* 0.5822

Number of observations = 2346 2 (35) 288.46LR chi = 2 0.0921Pseudo R = 2 0.0000prob chi> =

1) Bold indicates 10% significance level. 2) ** indicates 5% significance level. 3) * indicates 1% significance level.

76

Table 3.4: 20th-40th percentile probit model for the propensity score

Variable Coefficient Standard Error

Age -0.0017 0.0022 Female 0.1148 0.0952 Married 0.0420 0.0998 Household size 3-4 0.3066* 0.0966 Household size 5-6 0.4746* 0.1040 Household size ≥ 7 0.5233* 0.1433 Sinhala 0.4101* 0.1176 Tamil 0.1237 0.1493 Primary education -0.0538 0.0969 Secondary education -0.1347 0.0980 Tertiary education -0.1972 0.1653 Regular employee 0.1395 0.1314 Casual employee 0.1945 0.1014 Self-employed 0.1438 0.1010 Urban sector 0.1396 0.2007 Rural sector 0.2428 0.1697 Household expenditure -2.6400 2.4900 Household not owning house 0.0749 0.1113 Lives in a single house 0.1823 0.1842 Lives in a slum/shanty/lineroom -0.2090 0.2182 Floor type- clay/mud/earth 0.6070** 0.2681 Floor type-cement 0.4668 0.2629 Main source of lighting- electricity 0.0448 0.1744 Main source of lighting- kerosene 0.0091 0.1763 Energy for cooking- LP gas -0.2382 0.3150 Energy for cooking- firewood 0.0931 0.2373 Source of water- common well 0.0568 0.0605 Central province -0.1179 0.1046 Southern province 0.2375** 0.1010 Northern province 0.3606 0.2075 Eastern province -0.0803 0.1361 North western province 0.2783* 0.1021 North central province 0.0375 0.1220 Uva province -0.2050 0.1214 Sabaragamuwa province 0.0589 0.1040 Constant -1.8470* 0.5093


1)Bold indicates 10% significance level. 2)** indicates 5% significance level. 3) * indicates 1% significance level.

77

Table 3.5: 40th-60th percentile probit model for the propensity score Variable Coefficient Standard Error Age -0.0030 0.0024 Female 0.0200 0.0988 Married 0.0435 0.1064 Household size 3-4 0.1384 0.1130 Household size 5-6 0.2303 0.1180 Household size ≥ 7 0.2761 0.1483 Sinhala 0.3754* 0.1256 Tamil 0.1630 0.1681 Primary education 0.0251 0.1238 Secondary education -0.0519 0.1228 Tertiary education -0.2448 0.1577 Regular employee 0.0031 0.1174 Casual employee 0.3144* 0.1067 Self-employed 0.2691* 0.1020 Urban sector 0.1025 0.2361 Rural sector 0.2538 0.2273 Household expenditure 4.0400** 2.0100 Household not owning house 0.0337 0.1200 Lives in a single house 0.1167 0.1777 Lives in a slum/shanty/lineroom -0.1310 0.2479 Floor type- clay/mud/earth 0.1900 0.2140 Floor type-cement 0.0678 0.2018 Main source of lighting- electricity -0.0298 0.1897 Main source of lighting- kerosene 0.1415 0.1953 Energy for cooking- LP gas -0.1678 0.2021 Energy for cooking- firewood 0.1014 0.1760 Source of water- common well 0.0701 0.0658 Central province -0.0510 0.1007 Southern province 0.5240* 0.0890 Northern province -0.2410 0.2218 Eastern province 0.1897 0.1496 North western province 0.5160* 0.0941 North central province 0.0918 0.1295 Uva province 0.0647 0.1341 Sabaragamuwa province 0.2510** 0.1040 Constant -1.5070* 0.4960



78

Table 3.6: 60th-80th percentile probit model for the propensity score Variable Coefficient Standard Error Age -0.004 0.0024 Female 0.0489 0.1004 Married 0.0856 0.1122 Household size 3-4 0.0605 0.1350 Household size 5-6 0.1135 0.1391 Household size ≥ 7 0.1271 0.1585 Sinhala 0.6440* 0.1333 Tamil 0.2379 0.1933 Primary education 0.1781 0.1663 Secondary education 0.1424 0.1628 Tertiary education 0.0230 0.1780 Regular employee 0.1587 0.1087 Casual employee 0.2330** 0.1102 Self-employed 0.2437** 0.1010 Urban sector -0.3357 0.2512 Rural sector -0.1485 0.2455 Household expenditure -5.8400 9.1200 Household not owning house -0.3016** 0.1295 Lives in a single house -0.0361 0.1541 Lives in a slum/shanty/lineroom 0.0997 0.2583 Floor type- clay/mud/earth 0.3068 0.1886 Floor type-cement 0.1645 0.1594 Main source of lighting- electricity 0.0253 0.2416 Main source of lighting- kerosene 0.2571 0.2534 Energy for cooking- LP gas -0.1377 0.1816 Energy for cooking- firewood 0.1230 0.1721 Source of water- common well 0.0900 0.0793 Central province -0.0426 0.1026 Southern province 0.4667** 0.0900 Northern province -0.2266 0.2638 Eastern province 0.2042 0.1681 North western province 0.4763* 0.0915 North central province -0.1475 0.1283 Uva province 0.0112 0.1450 Sabaragamuwa province 0.1735 0.1090 Constant -1.5480* 0.5315



79

Table 3.7: 80th-100th percentile probit model for the propensity score Variable Coefficient Standard Error Age -0.0073* 0.0026 Female -0.0846 0.1125 Married 0.0303 0.1320 Household size 3-4 0.0479 0.1485 Household size 5-6 0.1567 0.1505 Household size ≥ 7 0.2891 0.1654 Sinhala 0.8650* 0.1705 Tamil 0.3912 0.2287 Primary education 0.2080 0.2359 Secondary education 0.1169 0.2303 Tertiary education -0.0490 0.2389 Regular employee 0.0721 0.0987 Casual employee 0.2968** 0.1160 Self-employed -0.0101 0.0983 Urban sector -0.6509 0.3592 Rural sector -0.4700 0.3570 Household expenditure 3.3900 3.3000 Household not owning house -0.2006 0.1312 Lives in a single house 0.0115 0.1367 Lives in a slum/shanty/lineroom -0.5230 0.4249 Floor type- clay/mud/earth 0.4039** 0.1937 Floor type-cement 0.2512** 0.1050 Main source of lighting- electricity 0.0949 0.2550 Main source of lighting- kerosene 0.2888 0.2860 Energy for cooking- LP gas -0.1400 0.2272 Energy for cooking- firewood 0.1309 0.2277 Source of water- common well 0.0322 0.1089 Central province -0.1470 0.1225 Southern province 0.5799* 0.1084 Northern province -0.0225 0.2870 Eastern province 0.2050 0.1808 North western province 0.4065* 0.0957 North central province -0.1909 0.1428 Uva province 0.1496 0.1759 Sabaragamuwa province 0.1950 0.1316 Constant -1.3960** 0.6222

Number of observations = 2344 2 (35) 278.73LR chi = 2 0.1089Pseudo R =


80

Table 3.8: Matching quality indicators (covariate balancing) for the 20th percentile Mean standardized %reduction t-test Variable Sample Treated Untreated Bias% In |bias| t p>|t| age Unmatched 46.086 49.888 -26.8 -6.23 0 Matched 46.117 46.638 -3.7 86.3 -0.73 0.468 female Unmatched 0.19362 0.26583 -17.2 -4.01 0 Matched 0.19426 0.17439 4.7 72.5 0.97 0.33 married Unmatched 0.81298 0.74182 17.2 4 0 Matched 0.81236 0.82119 -2.1 87.6 -0.43 0.664 Household size 3-4 Unmatched 0.52145 0.46625 11.1 2.61 0.009 Matched 0.52318 0.5287 -1.1 90 -0.21 0.834 Household size 5-6 Unmatched 0.25633 0.17467 19.9 4.78 0 Matched 0.25386 0.24945 1.1 94.6 0.19 0.847 Household size ≥ 7 Unmatched 0.0374 0.03271 2.6 0.61 0.544 Matched 0.03753 0.03753 0 100 0 1 Sinhala Unmatched 0.85149 0.67919 41.5 9.51 0 Matched 0.85099 0.87748 -6.4 84.6 -1.47 0.141 Tamil Unmatched 0.10231 0.21851 -32.1 -7.32 0 Matched 0.10265 0.08389 5.2 83.9 1.23 0.22 Primary education Unmatched 0.42024 0.42171 -0.3 -0.07 0.944 Matched 0.42053 0.45585 -7.2 -2302.9 -1.35 0.176 Secondary education Unmatched 0.38724 0.36395 4.8 1.14 0.256 Matched 0.38631 0.3819 0.9 81 0.17 0.863 Tertiary education Unmatched 0.0242 0.02296 0.8 0.19 0.847 Matched 0.02428 0.01987 2.9 -256.7 0.57 0.568 Regular employee Unmatched 0.033 0.0675 -15.8 -3.61 0 Matched 0.03311 0.03753 -2 87.2 -0.45 0.649 Casual employee Unmatched 0.42024 0.36534 11.3 2.66 0.008 Matched 0.42053 0.39735 4.8 57.8 0.9 0.37 Self-employed Unmatched 0.41804 0.3222 19.9 4.73 0 Matched 0.41722 0.43377 -3.4 82.7 -0.64 0.524 Urban sector Unmatched 0.033 0.06541 -15 -3.43 0.001 Matched 0.03311 0.02649 3.1 79.6 0.74 0.459

81

Table 3.8 continued Mean standardized %reduction t-test Variable Sample Treated control Bias % In |bias| t p>|t|

rural sector Unmatched 0.92409 0.8142 33 7.49 0 Matched 0.92384 0.92936 -1.7 95 -0.4 0.687 Household expenditure Unmatched 7662.2 8273.3 -5.8 -1.31 0.19 Matched 7664 7382 2.7 53.9 0.73 0.465 Household not owning house Unmatched 0.08141 0.18163 -30 -6.82 0 Matched 0.08168 0.07395 2.3 92.3 0.55 0.583 Lives in a single house Unmatched 0.94169 0.85943 27.7 6.29 0 Matched 0.9415 0.93488 2.2 91.9 0.52 0.601 Lives in a slum/shanty/lineroom Unmatched 0.0484 0.11065 -23.1 -5.25 0 Matched 0.04857 0.05298 -1.6 92.9 -0.38 0.702 Floor type- clay/mud/earth Unmatched 0.43234 0.34586 17.8 4.22 0 Matched 0.43157 0.40177 6.1 65.5 1.15 0.25 Floor type-cement Unmatched 0.55776 0.6444 -17.8 -4.21 0 Matched 0.5585 0.57616 -3.6 79.6 -0.68 0.498 Main source of lighting- electricity Unmatched 0.41034 0.49408 -16.9 -3.98 0 Matched 0.4117 0.43488 -4.7 72.3 -0.89 0.372 Main source of lighting- kerosene Unmatched 0.56876 0.49548 14.7 3.47 0.001 Matched 0.56954 0.55188 3.5 75.9 0.68 0.499 Energy for cooking- LP gas Unmatched 0.0055 0.01461 -9.1 -2.06 0.04 Matched 0.00552 0.00331 2.2 75.8 0.63 0.527 Energy for cooking- firewood Unmatched 0.9879 0.96381 15.7 3.52 0 Matched 0.98786 0.99338 -3.6 77.1 -1.09 0.276 Source of water- common well Unmatched 0.38614 0.34029 9.5 2.26 0.024 Matched 0.38631 0.38852 -0.5 95.2 -0.09 0.931 Central province Unmatched 0.14741 0.16562 -5 -1.18 0.24 Matched 0.1479 0.16998 -6.1 -21.2 -1.15 0.251 Southern province Unmatched 0.17492 0.10369 20.7 5 0 Matched 0.17439 0.16446 2.9 86.1 0.5 0.615

82

Table 3.8 continued

Variable Sample Treated control Bias % In |bias| t p>|t| Northern province Unmatched 0.0154 0.04036 -15.2 -3.42 0.001 Matched 0.01545 0.00773 4.7 69 1.37 0.17 Eastern province Unmatched 0.09021 0.15031 -18.5 -4.27 0 Matched 0.09051 0.07837 3.7 79.8 0.83 0.406 North western province Unmatched 0.11441 0.10647 2.5 0.6 0.549 Matched 0.11479 0.12804 -4.2 -66.8 -0.77 0.441 North central province Unmatched 0.07811 0.07933 -0.5 -0.11 0.915 Matched 0.07837 0.09272 -5.3 -1072.2 -0.98 0.329 Uva province Unmatched 0.13641 0.10786 8.7 2.08 0.037 Matched 0.13687 0.11921 5.4 38.1 1.01 0.315 Sabaragamuwa province Unmatched 0.17382 0.13848 9.7 2.32 0.02 Matched 0.17219 0.17108 0.3 96.9 0.06 0.956

Pseudo-2R

Unmatched 0.092 Matched 0.012

83

Table 3.9: Matching quality indicators (covariate balancing) for the 20th-40 percentile Variable Mean standardized %reduction t-test Sample Treated Control Bias% In |bias| t p>|t| age Unmatched 45.708 46.124 -3.3 -0.77 0.439 Matched 45.708 45.535 1.4 58.5 0.28 0.781 female Unmatched 0.16087 0.17066 -2.6 -0.63 0.529 Matched 0.16087 0.18371 -6.1 -133.4 -1.21 0.227 married Unmatched 0.87488 0.85629 5.4 1.3 0.194 Matched 0.87488 0.85799 4.9 9.2 0.99 0.321 Household size 3-4 Unmatched 0.50348 0.51347 -2 -0.48 0.632 Matched 0.50348 0.51043 -1.4 30.5 -0.28 0.781 Household size 5-6 Unmatched 0.34657 0.29042 12.1 2.9 0.004 Matched 0.34657 0.33664 2.1 82.3 0.42 0.676 Household size ≥ 7 Unmatched 0.07249 0.06362 3.5 0.85 0.397 Matched 0.07249 0.07547 -1.2 66.4 -0.23 0.82 Sinhala Unmatched 0.84806 0.69237 37.6 8.87 0 Matched 0.84806 0.84608 0.5 98.7 0.11 0.912 Tamil Unmatched 0.0993 0.21781 -32.9 -7.71 0 Matched 0.0993 0.1003 -0.3 99.2 -0.07 0.947 Primary education Unmatched 0.37239 0.36228 2.1 0.5 0.615 Matched 0.37239 0.37537 -0.6 70.6 -0.12 0.902 Secondary education Unmatched 0.48163 0.49551 -2.8 -0.67 0.506 Matched 0.48163 0.47071 2.2 21.3 0.44 0.662 Tertiary education Unmatched 0.03674 0.04491 -4.1 -0.98 0.326 Matched 0.03674 0.03178 2.5 39.2 0.55 0.585 Regular employee Unmatched 0.07646 0.12201 -15.3 -3.61 0 Matched 0.07646 0.10526 -9.7 36.8 -2.01 0.045 Casual employee Unmatched 0.46773 0.40344 13 3.12 0.002 Matched 0.46773 0.47071 -0.6 95.4 -0.12 0.905 Self-employed Unmatched 0.36544 0.34431 4.4 1.06 0.29 Matched 0.36544 0.33664 6 -36.3 1.21 0.228 Urban sector Unmatched 0.04469 0.07111 -11.3 -2.67 0.008 Matched 0.04469 0.04767 -1.3 88.7 -0.28 0.777 rural sector Unmatched 0.91063 0.81063 29.2 6.84 0

84

Table 3.9 continued

Variable Mean standardized %reduction t-test Sample Treated control Bias% In |bias| t p>|t| Household expenditure Unmatched 10831 11236 -3.7 -0.89 0.371 Matched 10831 10505 3 19.4 0.62 0.538 Household not owning house Unmatched 0.09037 0.16392 -22.2 -5.23 0 Matched 0.09037 0.1003 -3 86.5 -0.68 0.499 Lives in a single house Unmatched 0.94935 0.86228 30.1 7.02 0 Matched 0.94935 0.94141 2.7 90.9 0.7 0.485 Lives in a slum/shanty/lineroom Unmatched 0.03277 0.10329 -28.3 -6.55 0 Matched 0.03277 0.0427 -4 85.9 -1.04 0.298 Floor type- clay/mud/earth Unmatched 0.28004 0.21482 15.2 3.65 0 Matched 0.28004 0.26415 3.7 75.6 0.71 0.476 Floor type-cement Unmatched 0.71301 0.76796 -12.6 -3.02 0.003 Matched 0.71301 0.73188 -4.3 65.7 -0.84 0.4 Main source of lighting- electricity Unmatched 0.57895 0.63174 -10.8 -2.59 0.01 Matched 0.57895 0.59285 -2.8 73.7 -0.56 0.573 Main source of lighting- kerosene Unmatched 0.39523 0.34356 10.7 2.57 0.01 Matched 0.39523 0.38332 2.5 76.9 0.49 0.625 Energy for cooking- LP gas Unmatched 0.00993 0.02695 -12.7 -2.94 0.003 Matched 0.00993 0.00596 3 76.7 0.89 0.371 Energy for cooking- firewood Unmatched 0.98014 0.95135 15.9 3.7 0 Matched 0.98014 0.9861 -3.3 79.3 -0.92 0.355 Source of water- common well Unmatched 0.36445 0.29566 14.7 3.53 0 Matched 0.36445 0.37041 -1.3 91.3 -0.25 0.805 Central province Unmatched 0.12115 0.18413 -17.6 -4.16 0 Matched 0.12115 0.11619 1.4 92.1 0.31 0.759 Southern province Unmatched 0.18173 0.11452 19 4.61 0 Matched 0.18173 0.16882 3.6 80.8 0.68 0.497 Northern province Unmatched 0.01986 0.04491 -14.2 -3.31 0.001 Matched 0.01986 0.02085 -0.6 96 -0.14 0.888

85

Table 3.9 continued Variable Mean standardized %reduction t-test Sample Treated control Bias% In |bias| t p>|t| North western province Unmatched 0.1857 0.11751 19.1 4.64 0 Matched 0.1857 0.19662 -3.1 84 -0.56 0.579 North central province Unmatched 0.0864 0.06886 6.6 1.58 0.114 Matched 0.0864 0.08937 -1.1 83 -0.21 0.833 Uva province Unmatched 0.07051 0.10105 -10.9 -2.59 0.01 Matched 0.07051 0.07051 0 100 0 1 Sabaragamuwa province Unmatched 0.13406 0.11826 4.8 1.14 0.253 Matched 0.13406 0.13505 -0.3 93.7 -0.06 0.954

Pseudo-2R


86

Table 3.10: Matching quality indicators (covariate balancing) for the 40th-60th percentile Mean standardized %reduction t-test Variable Sample Treated Control Bias% In |bias| t p>|t| age Unmatched 46.056 46.537 -3.9 -0.92 0.359 Matched 46.17 46.219 -0.4 89.9 -0.08 0.939 female Unmatched 0.13982 0.17655 -10.1 -2.34 0.019 Matched 0.14172 0.13265 2.5 75.3 0.5 0.618 married Unmatched 0.89374 0.87241 6.6 1.55 0.122 Matched 0.89229 0.89796 -1.8 73.4 -0.35 0.726 Household size 3-4 Unmatched 0.47204 0.49655 -4.9 -1.15 0.249 Matched 0.47619 0.45692 3.9 21.4 0.73 0.465 Household size 5-6 Unmatched 0.38479 0.33517 10.3 2.44 0.015 Matched 0.38435 0.36168 4.7 54.3 0.89 0.375 Household size ≥ 7 Unmatched 0.08725 0.07379 4.9 1.17 0.241 Matched 0.08277 0.10317 -7.5 -51.7 -1.33 0.184 Sinhala Unmatched 0.88591 0.77034 31 7.06 0 Matched 0.88435 0.87868 1.5 95.1 0.33 0.74 Tamil Unmatched 0.06488 0.13655 -24 -5.44 0 Matched 0.06576 0.06236 1.1 95.3 0.26 0.793 Primary education Unmatched 0.32662 0.26759 12.9 3.06 0.002 Matched 0.32313 0.36054 -8.2 36.6 -1.49 0.135 Secondary education Unmatched 0.54586 0.56897 -4.7 -1.09 0.274 Matched 0.55215 0.52721 5 -8 0.95 0.344 Tertiary education Unmatched 0.06152 0.10414 -15.5 -3.55 0 Matched 0.06236 0.05329 3.3 78.7 0.74 0.462 Regular employee Unmatched 0.11633 0.19586 -22 -5.06 0 Matched 0.11791 0.10884 2.5 88.6 0.54 0.588 Casual employee Unmatched 0.39374 0.31241 17.1 4.04 0 Matched 0.38662 0.35147 7.4 56.8 1.38 0.168 Self-employed Unmatched 0.40828 0.35379 11.2 2.65 0.008 Matched 0.4127 0.44785 -7.2 35.5 -1.34 0.179 Urban sector Unmatched 0.06711 0.13724 -23.3 -5.29 0 Matched 0.06803 0.0771 -3 87.1 -0.66 0.508 rural sector Unmatched 0.91387 0.81241 29.8 6.76 0

87

Table 3.10 continued Mean standardized %reduction t-test Variable Sample Treated Control Bias % In |bias| t p>|t| Household expenditure Unmatched 14940 16653 -11.3 -2.54 0.011 Matched 14985 15724 -4.9 56.9 -1.13 0.261 Household not owning house Unmatched 0.06264 0.11379 -18.1 -4.13 0 Matched 0.06349 0.06349 0 100 0 1 Lives in a single house Unmatched 0.96421 0.91172 21.9 4.92 0 Matched 0.96372 0.95692 2.8 87 0.66 0.51 Lives in a slum/shanty/lineroom Unmatched 0.01678 0.04828 -17.8 -3.97 0 Matched 0.01701 0.01814 -0.6 96.4 -0.16 0.87 Floor type- clay/mud/earth Unmatched 0.18456 0.11586 19.3 4.65 0 Matched 0.17347 0.178 -1.3 93.4 -0.23 0.822 Floor type-cement Unmatched 0.79866 0.86207 -16.9 -4.06 0 Matched 0.80952 0.80272 1.8 89.3 0.33 0.745 Main source of lighting- electricity Unmatched 0.71477 0.81034 -22.6 -5.4 0 Matched 0.72449 0.69955 5.9 73.9 1.04 0.297 Main source of lighting- kerosene Unmatched 0.26063 0.17034 22.1 5.29 0 Matched 0.25057 0.28231 -7.8 64.8 -1.36 0.174 Energy for cooking- LP gas Unmatched 0.03579 0.09379 -23.7 -5.32 0 Matched 0.03628 0.04082 -1.9 92.2 -0.45 0.656 Energy for cooking- firewood Unmatched 0.94519 0.85931 29.2 6.57 0 Matched 0.94444 0.94898 -1.5 94.7 -0.38 0.702 Source of water- common well Unmatched 0.29418 0.22207 16.5 3.93 0 Matched 0.29138 0.29932 -1.8 89 -0.33 0.742 Central province Unmatched 0.09955 0.15448 -16.5 -3.81 0 Matched 0.10091 0.11338 -3.8 77.3 -0.76 0.446 Southern province Unmatched 0.22371 0.12138 27.3 6.62 0 Matched 0.21882 0.21315 1.5 94.5 0.26 0.794 Northern province Unmatched 0.0179 0.04828 -17 -3.81 0 Matched 0.01814 0.02154 -1.9 88.8 -0.46 0.644

88

Table 3.10. continued. Mean standardized %reduction t-test Variable Sample Treated Control Bias % In |bias| t p>|t| North western province Unmatched 0.19239 0.10207 25.7 6.24 0 Matched 0.18934 0.19728 -2.3 91.2 -0.38 0.704 North central province Unmatched 0.05481 0.05586 -0.5 -0.11 0.914 Matched 0.05556 0.03968 6.9 -1408.5 1.41 0.158 Uva province Unmatched 0.05145 0.05655 -2.3 -0.53 0.598 Matched 0.05215 0.04649 2.5 -11.2 0.5 0.62 Sabaragamuwa province Unmatched 0.11409 0.0931 6.9 1.64 0.102 Matched 0.11565 0.14512 -9.7 -40.4 -1.66 0.098

Pseudo-2R


89

Table 3.11: Matching quality indicators (covariate balancing) for 60th-80th percentile

Mean standardized %reduction t-test Variable Sample Treated Control Bias% In |bias| t p>|t| age Unmatched 47.207 48.216 -8.3 -1.85 0.064 Matched 47.251 47.531 -2.3 72.3 -0.42 0.676 female Unmatched 0.13733 0.15496 -5 -1.12 0.264 Matched 0.13673 0.12869 2.3 54.4 0.42 0.676 married Unmatched 0.90533 0.88833 5.6 1.25 0.213 Matched 0.90483 0.90617 -0.4 92.1 -0.08 0.936 Household size 3-4 Unmatched 0.44133 0.46989 -5.7 -1.29 0.196 Matched 0.44236 0.38874 10.8 -87.8 1.92 0.055 Household size 5-6 Unmatched 0.396 0.35006 9.5 2.16 0.031 Matched 0.3941 0.40885 -3.1 67.9 -0.53 0.596 Household size ≥ 7 Unmatched 0.12 0.11606 1.2 0.28 0.782 Matched 0.12064 0.15952 -12 -886.7 -1.98 0.048 Sinhala Unmatched 0.916 0.79548 34.8 7.4 0 Matched 0.91555 0.92091 -1.5 95.6 -0.34 0.73 Tamil Unmatched 0.04667 0.10916 -23.5 -4.98 0 Matched 0.04692 0.04021 2.5 89.3 0.58 0.563 Primary education Unmatched 0.23867 0.20075 9.2 2.09 0.036 Matched 0.23727 0.24263 -1.3 85.9 -0.22 0.825 Secondary education Unmatched 0.58933 0.57779 2.3 0.53 0.597 Matched 0.58981 0.60054 -2.2 7.1 -0.39 0.7 Tertiary education Unmatched 0.14133 0.18444 -11.7 -2.59 0.01 Matched 0.14209 0.13941 0.7 93.8 0.14 0.892 Regular employee Unmatched 0.22533 0.27415 -11.3 -2.52 0.012 Matched 0.22654 0.22788 -0.3 97.3 -0.06 0.955 Casual employee Unmatched 0.24933 0.2064 10.2 2.34 0.019 Matched 0.24665 0.21046 8.6 15.7 1.52 0.129 Self-employed Unmatched 0.43333 0.38331 10.2 2.31 0.021 Matched 0.43432 0.48257 -9.8 3.5 -1.71 0.088 Urban sector Unmatched 0.08667 0.1744 -26.3 -5.64 0 Matched 0.08713 0.09651 -2.8 89.3 -0.57 0.567

90

Table 3.11. continued. Mean standardized %reduction t-test Variable Sample Treated Bias % Bias % In |bias| t p>|t| rural sector Unmatched 0.88933 0.79235 26.7 5.78 0 Matched 0.88874 0.88606 0.7 97.2 0.15 0.881 Household expenditure Unmatched 23305 24988 -4.4 -0.89 0.375 Matched 23344 23619 -0.7 83.7 -0.2 0.839 Household not owning house Unmatched 0.05067 0.101 -19.1 -4.09 0 Matched 0.05094 0.03753 5.1 73.4 1.15 0.25 Lives in a single house Unmatched 0.95067 0.91656 13.7 2.97 0.003 Matched 0.9504 0.96113 -4.3 68.6 -0.92 0.358 Lives in a slum/shanty/lineroom Unmatched 0.02133 0.02949 -5.2 -1.14 0.255 Matched 0.02145 0.01475 4.3 17.8 0.89 0.375 Floor type- clay/mud/earth Unmatched 0.104 0.06148 15.5 3.65 0 Matched 0.0992 0.09786 0.5 96.8 0.08 0.937 Floor type-cement Unmatched 0.86933 0.89523 -8 -1.85 0.065 Matched 0.87399 0.88204 -2.5 68.9 -0.43 0.665 Main source of lighting- electricity Unmatched 0.83733 0.90715 -21 -4.96 0 Matched 0.84182 0.85255 -3.2 84.6 -0.53 0.599 Main source of lighting- kerosene Unmatched 0.148 0.0803 21.4 5.08 0 Matched 0.14343 0.13807 1.7 92.1 0.27 0.786 Energy for cooking- LP gas Unmatched 0.11867 0.23149 -30 -6.48 0 Matched 0.1193 0.13673 -4.6 84.6 -0.92 0.358 Energy for cooking- firewood Unmatched 0.85867 0.72334 33.7 7.3 0 Matched 0.85791 0.84853 2.3 93.1 0.47 0.64 Source of water- common well Unmatched 0.19067 0.14053 13.5 3.12 0.002 Matched 0.18767 0.19437 -1.8 86.6 -0.3 0.764 Central province Unmatched 0.08933 0.12986 -13 -2.85 0.004 Matched 0.08981 0.07373 5.2 60.3 1.04 0.301 Southern province Unmatched 0.192 0.09975 26.3 6.26 0 Matched 0.19035 0.19571 -1.5 94.2 -0.24 0.811

91

Table 3.11. Continued. Mean standardized %reduction t-test Variable Sample Treated Bias % Bias % In |bias| t p>|t| Eastern province Unmatched 0.03333 0.05332 -9.8 -2.14 0.033 Matched 0.03351 0.03351 0 100 0 1 North western province Unmatched 0.19733 0.10289 26.7 6.33 0 Matched 0.19571 0.17828 4.9 81.5 0.79 0.431 North central province Unmatched 0.05467 0.06399 -3.9 -0.88 0.379 Matched 0.05496 0.06971 -6.2 -58.2 -1.08 0.282 Uva province Unmatched 0.04133 0.04391 -1.3 -0.29 0.774 Matched 0.04155 0.0429 -0.7 48.1 -0.12 0.906 Sabaragamuwa province Unmatched 0.096 0.07403 7.9 1.82 0.069 Matched 0.09651 0.09786 -0.5 93.9 -0.08 0.936

Pseudo-2R


92

Table 3.12: Matching quality indicators (covariate balancing) for the 80th-100 percentile Mean standardized %reduction t-test Variable Sample Treated Bias % Bias% In |bias| t p>|t| age Unmatched 49.014 50.049 -8.5 -1.75 0.081 Matched 49.025 49.033 -0.1 99.3 -0.01 0.993 female Unmatched 0.11775 0.15123 -9.8 -1.96 0.05 Matched 0.11818 0.08727 9.1 7.7 1.57 0.117 married Unmatched 0.91304 0.9029 3.5 0.71 0.477 Matched 0.91273 0.92545 -4.4 -25.5 -0.72 0.473 Household size 3-4 Unmatched 0.37862 0.44029 -12.6 -2.56 0.01 Matched 0.38 0.40727 -5.6 55.8 -0.86 0.39 Household size 5-6 Unmatched 0.4058 0.36998 7.4 1.52 0.129 Matched 0.40727 0.38727 4.1 44.2 0.63 0.529 Household size ≥ 7 Unmatched 0.17572 0.12333 14.7 3.15 0.002 Matched 0.17273 0.16909 1 93.1 0.15 0.882 Sinhala Unmatched 0.94022 0.81417 39.1 7.21 0 Matched 0.94 0.94545 -1.7 95.7 -0.36 0.718 Tamil Unmatched 0.03623 0.0904 -22.4 -4.17 0 Matched 0.03636 0.03636 0 100 0 1 Primary education Unmatched 0.17754 0.11272 18.5 4 0 Matched 0.17455 0.20545 -8.8 52.3 -1.21 0.225 Secondary education Unmatched 0.55254 0.51674 7.2 1.47 0.141 Matched 0.55455 0.51273 8.4 -16.8 1.29 0.197 Tertiary education Unmatched 0.25181 0.35156 -21.8 -4.38 0 Matched 0.25273 0.25091 0.4 98.2 0.06 0.949 Regular employee Unmatched 0.31341 0.36105 -10.1 -2.05 0.04 Matched 0.31455 0.27091 9.2 8.4 1.48 0.14 Casual employee Unmatched 0.18478 0.10882 21.6 4.71 0 Matched 0.18182 0.21273 -8.8 59.3 -1.2 0.232 Self-employed Unmatched 0.37681 0.35379 4.8 0.99 0.325 Matched 0.37818 0.40727 -6 -26.4 -0.92 0.359

93

Table 3.12 continued. Mean standardized %reduction t-test Variable Sample Treated Variable Bias % In |bias| t p>|t| Urban sector Unmatched 0.13587 0.29855 -40.2 -7.71 0 Matched 0.13636 0.15273 -4 89.9 -0.72 0.474 rural sector Unmatched 0.85326 0.6942 38.7 7.46 0 Matched 0.85273 0.83818 3.5 90.9 0.62 0.536 Household expenditure Unmatched 47187 51170 -4.5 -0.88 0.381 Matched 47281 42732 5.2 -14.2 0.99 0.32 Household not owning house Unmatched 0.0471 0.09821 -19.8 -3.75 0 Matched 0.04727 0.04727 0 100 0 1 Lives in a single house Unmatched 0.95471 0.89732 22 4.15 0 Matched 0.95455 0.94727 2.8 87.3 0.52 0.605 Lives in a slum/shanty/lineroom Unmatched 0.00362 0.01004 -7.8 -1.43 0.152 Matched 0.00364 0.00364 0 100 0 1 Floor type- clay/mud/earth Unmatched 0.06341 0.02734 17.4 4 0 Matched 0.06 0.07273 -6.1 64.7 -0.79 0.431 Floor type-cement Unmatched 0.86413 0.80301 16.5 3.26 0.001 Matched 0.86727 0.83818 7.8 52.4 1.26 0.206 Main source of lighting- electricity Unmatched 0.88768 0.94922 -22.6 -5.14 0 Matched 0.89091 0.87091 7.3 67.5 0.95 0.342 Main source of lighting- kerosene Unmatched 0.09601 0.04018 22.3 5.13 0 Matched 0.09273 0.10364 -4.4 80.5 -0.56 0.573 Energy for cooking- LP gas Unmatched 0.25362 0.48828 -50 -9.93 0 Matched 0.25455 0.24182 2.7 94.6 0.45 0.65 Energy for cooking- firewood Unmatched 0.73188 0.48605 52 10.37 0 Matched 0.73091 0.75091 -4.2 91.9 -0.7 0.483 Source of water- common well Unmatched 0.11957 0.07533 14.9 3.25 0.001 Matched 0.11818 0.11636 0.6 95.9 0.09 0.931 Central province Unmatched 0.06159 0.09542 -12.6 -2.46 0.014 Matched 0.06182 0.05636 2 83.9 0.36 0.722

94

Table 3.12 continued. Mean standardized %reduction t-test Variable Sample Treated Variable Bias% In |bias| t p>|t| Northern province Unmatched 0.01268 0.0279 -10.8 -2.03 0.042 Matched 0.01273 0.02182 -6.5 40.3 -1.07 0.283 Eastern province Unmatched 0.03442 0.04967 -7.6 -1.49 0.135 Matched 0.03455 0.03636 -0.9 88.1 -0.15 0.88 North western province Unmatched 0.19384 0.09208 29.4 6.58 0 Matched 0.19273 0.18909 1 96.4 0.14 0.887 North central province Unmatched 0.05435 0.05357 0.3 0.07 0.944 Matched 0.05455 0.06 -2.4 -602.5 -0.36 0.718 Uva province Unmatched 0.03623 0.02734 5.1 1.08 0.28 Matched 0.03636 0.05091 -8.3 -63.7 -1.1 0.273 Sabaragamuwa province Unmatched 0.07246 0.05134 8.8 1.88 0.06 Matched 0.07273 0.07091 0.8 91.4 0.11 0.914

Pseudo-2R


95

Table 3.13: Individuals lost due to common support requirement (%) Matching Algorithm

0-20th

percentile

20th-40th percentile




NN 0.255 0.59 0.59 1.27 1.74 NN (caliper) δ=0.01 0.68 0.72 0.63 1.57 1.91 δ=0.001 15.68 8.83 9.85 11.51 20.26 Radius δ=0.01 0.68 0.72 0.63 1.57 1.91 δ=0.001 15.68 8.83 9.85 11.51 20.26 Kernel Epanechnikov (bw=0.1)

0.255 0.59 0.59 1.27 1.74

Epanechnikov (bw=0.2)

0.255 0.59 0.59 1.27 1.74

Gaussian (bw=0.1)

0.255 0.59 0.59 1.27 1.74

Gaussian (bw=0.2)

0.255 0.59 0.59 1.27 1.74

96

Table 3.14: Impact of microfinance on household savings Matching Algorithm

0-20th

percentile ATT ATE

20th-40th percentile ATT ATE




NN 293.09 57.60 393.67 423.17 60.98 69.62 678.71 54.74 -1616.28 -1387.18 NN (caliper) δ=0.01 293.49 56.40 393.73 423.24 60.98 62.95 678.71 54.49 -1617.07 -1389.55 δ=0.001 287.05 70.82 429.06 344.64 72.05 -40.27 861.58 403.52 -291.68 -1538.20 Radius δ=0.01 251.03 313.88 297.52 431.54 252.24 507.07 286.79 113.73 -1329.95 -1628.91 δ=0.001 371.42 333.58 427.05 426.34 214.44 45.48 857.09 704.32 -1063.78 -1893.89 Kernel Epanechnikov (bw=0.1)

215.26 261.43 298.05 391.71 216.10 347.12 242.88 142.03 -1325.03 -1755.08


158.06 210.17 244.77 303.28 168.78 140.34 195.75 194.47 -1449.91 -1812.34

Gaussian (bw=0.1)

148.13 192.93 236.60 296.97 153.3 116.52 195.14 202.00 -1469.92 -18176.38

Gaussian (bw=0.2)

37.73 58.06 186.37 231.80 2.90 -67.91 137.67 194.00 -1723.51 -1920.53

97

Table 3.15: Impact of microfinance on household income Matching Algorithm

0-20th

percentile ATT ATE





NN -20.87 -11.70 -9.84 14.07 -154.95 -65.52 -100.70 71.95 -951.69 -1047.76 NN (caliper) δ=0.01 -21.71 -12.50 -8.33 14.75 -154.95 -67.12 -100.70 68.21 -936.33 -1043.14 δ=0.001 -16.62 -19.95 -4.92 9.81 -175.1 -81.35 -101.56 80.54 -985.76 -877.35 Radius δ=0.01 4.85 -8.91 -18.94 -28.06 -39.35 -34.78 -58.47 -82.05 -911.85 -1276.17 δ=0.001 -12.12 -14.81 -16.86 -12.05 -90.09 -68.90 -27.72 12.44 -1152.63 -1016.00 Kernel Epanechnikov (bw=0.1)

-13.48 -19.04 -25.64 -33.65 -62.34 -48.72 -82.59 -97.25 -1060.08 -1541.74


-44.37 -45.96 -63.93 -61.54 -90.88 -104.84 -132.17 -148.64 -1397.12 -1853.59

Gaussian (bw=0.1)

-50.74 -51.48 -68.86 -67.93 -96.79 -112.34 -142.39 -159.24 -1471.09 -1893.93

Gaussian (bw=0.2)

-113.91 -111.53 -123.33 -123.47 -161.48 -180.82 -226.94 -233.12 -2181.90 -2346.53

98

020

4060

80F

requ

ency

0 .2 .4 .6 .8Estimated p(x)

Treated

020

4060

80F

requ

ency


Untreated

Figure 3.1: Histogram of the estimated propensity score (bottom 20th percentile)

99

020

4060

8010

0F

requ

ency


Treated

020

4060

8010

0F

requ

ency


Untreated

Figure 3.2: Histogram of the estimated propensity score (20th-40th percentile)

100

050

100

Fre

quen

cy


Treated

050

100

Fre

quen

cy


Untreated


101

050

100

150

Fre

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Treated

050

100

150

Fre

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Untreated


102

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103

CHAPTER 4

RETURNS TO EDUCATION IN SRI LANKA:

QUANTILE REGRESSION ANALYSIS

4.1. Introduction

Two centuries ago Adam Smith stated that the capital stock of a country is partly

made out of

“…..the acquired and useful abilities of all the inhabitants or members of the

society. The acquisition of such talents, by the maintenance of the acquirer during

his education, study, or apprenticeship, always costing a real expense, which is a

capital fixed and realized, as it were, in his person. Those talents, as they make a

part of his fortune, so do they likewise of that of the society to which he belongs.

The improved dexterity of a workman may be considered in the same light as a

machine or instrument of trade which facilitates and abridges labor, and which,

though it costs a certain expense, repays that expense with a profit.” ( The Wealth

of Nations, 19776, book 2, chapter 1).

Unfortunately, Smith’s words were quickly forgotten, and the concept of capital was

defined narrowly in terms of nonhuman elements in production. But, about 50 years ago

the concept of human capital reemerged like a new finding, giving rise to range of

research topics. Several inquiries were made in relation to human capital, such as; why

invest in education and training? What are the returns to the individual and to the

economy? How much should be spent on education, and who should bear the expenses?

104

Analyzing the changes in skill differentials across cohorts (genders, ethnicities,

sectors of work, etc.) are imperative for skill formation, school enrolment and training

efforts. Most of the wage distribution studies are concerned with estimating the

unexplained wage gap for groups of people. Unexplained wage gaps arise when two

individuals possessing equal abilities and skills are employed in comparable jobs, but

exhibit differences in earnings. These wage gaps arising from wage and employment

discrimination leads to a reduction in earnings and total product. Therefore policies that

help to eliminate discrimination in labor markets are able to raise both the earnings of the

discriminated group (generally more vulnerable to poverty than the dominant group) and

the total product.

Previous studies rarely dealt with the returns to schooling by race and gender.

Duraisamy (2000) estimated the returns to schooling by gender and location for India.

The main findings were that the private rate of return to schooling increases as the level

of education increases up to the secondary level. According to the study, returns to

women education exceed that to men at the secondary level. Rural-urban comparison of

returns reveal that there are higher returns to education in rural than in urban areas for

primary and secondary levels, but returns for college education are higher for individuals

in urban areas. Chase (1998) investigates how returns to education in the Czech Republic

and Slovakia changed after the collapse communism in these countries. According to the

study, there was a significant increase in returns to schooling with the transition to non-

communist countries. Results indicated that returns to schooling rose 2.4% to 5.2% for

Czech men between 1984 and 1993. Furthermore, results suggested the returns to

schooling for men increases more with regime change. Taber (2001) estimates a dynamic

105

programming selection model to examine the return to schooling in the U.S. during the

1980s. The study finds a large increase in the gap between returns to high school and

college during the decade. According to Taber (2001), the increase in college earnings

premium is due to the rise in the relative demand for high ability workers and not due to

an increase in demand for skills accrued in college (which is revealed by a significant

increase in the variance of wage residuals). Barrow and Rouse (2005) used the U.S.

Decennial Census and the National Longitudinal Surveys to examine whether returns to

schooling differ by race and ethnicity. The study found little evidence of differences in

returns to schooling across racial and ethnic groups, even after controlling for ability and

measurement biases. According to Barrow and Rouse (2005), there is no evidence that

returns to schooling are lesser for African-American or Hispanics compared to non-

minorities.

The principle objective of this chapter is to analyze the returns education in the

Sri Lankan labor market by ethnicity employing the quantile regression technique for

each conditional quantile wage group rather than mean regression analysis used in most

labor market analysis. Quantile regression results suggest that returns to education are

positive and significant across all quantiles. However, a comparison of wage returns to

education between ethnic groups reveals that returns are higher for Sinhalese workers

than for Tamil Workers.

4.2. Human Capital Framework, Signaling and the Returns to Schooling

The analysis of returns for education is based on the concept of human capital

which was first introduced by Gary Becker, Jacob Mincer and Theodore Schultz. The

106

human capital approach views education as an investment of current resources with the

expectation of future returns. According to Becker (1964), the amount of education (s) is

undertaken to maximize the expected present value of future income until the retirement

date (T), after accounting for the costs of education (c). Thus, the condition is given by:

11

1 (1 )

T ss s

s st s

W W W Cr

−−

−=

−= +

+∑

Where sr is referred to as the internal rate of return. Internal rate of return can be

interpreted as the discount rate that equates the present value of benefits to the present

value of costs. Optimal rule is to invest in the ths year of education if sr i> , where i is

the market interest rate. Investment in schooling takes place until the marginal cost of

education is equal to the marginal return to education. An individual will prefer more

education if the internal rate of return is greater than the market interest rate. It can also

be seen that individuals with higher discount rates prefer more value current income than

future income, and hence will prefer less schooling. The net benefits of schooling are also

lower when the direct costs of schooling increases. The level education will also rise if

the wage gap between the educated and the uneducated increases or if more income

received while being in school.

In the theory of human capital, the empirical estimation of the earnings function

takes the form:

2log i i i iw edu exp expβ ϕ δ γ µ= Χ + + + +

Where iw is the hourly wage, edu years of schooling, exp is the years of experience and

iΧ represents set of other variables such gender, sector of work, etc. Experience is

107

included with a quadratic term to account for the concave earnings pattern. The above

earnings function is commonly referred to as the Mincerian earnings specification.

Mincer (1974) analyzed the returns to education using 1960 census data and estimated

that returns to education to be 10% and returns to experience as 8%. The Mincerian

specification is also used in many other studies including, gender and racial

discrimination, effectiveness of labor training programs and in the estimation of returns

to beauty. In the Mincerian specification, schooling is identified as exogenous even when

education is an endogenous variable. Furthermore, the error term captures the

unobservable factors (such as ability), and some of these factors affect schooling leading

to a correlation between the schooling and the error term. One solution to the problem is

to include a proxy for ability in the earnings function. Some studies have also used twins

to analyze differences in earnings and education on the assumption that the unobserved

factors are additive and common among twins. There are two effects when differences in

ability are introduced to the earnings function. Firstly, the internal rate of return will

increase for the more able individuals since they are capable of converting schooling into

human more efficiently. Secondly, the possibility also exists for a reduction in the

internal rate of return when the ability to succeed in school is positively correlated with

the ability to increase earnings in the labor market (high opportunity costs education).

The optimal amount of schooling will also differ between individuals for reasons other

than differences in ability. For an example, marginal rates of substitution may differ

between individuals resulting in different discounts across individuals. Discount rates

might vary for several other reasons such as differences in access to funds or different

tastes and preferences for schooling. However if ability levels are the same for all

108

individuals then the outcome is straightforward, where individuals with low discount

rates choosing more schooling. In an empirical analysis, the estimated returns to

education may be influenced by sample selection. Since low productive individuals are

most of the time non-participants, there is very good chance of OLS estimates being bias.

If the selected sample of workers truncates the lower part of the wage distribution then

the outcome would be to inflate the mean of the distribution. As a result of OLS going

through the mean of the sample, the estimates will be affected by the truncation of data.

For an example, if the participation of women is less compared to men then the returns to

education for women would be biased downwards. But, since the median is not affected

by truncation, estimates based on median regressions will not encounter this problem.

Furthermore, returns to education for individuals in the lower segment of the wage

distribution may not be the same as the returns to education for individuals in the upper

segment of the wage distribution. If this is the case, then an alternative technique to OLS

is the use of quantile regressions. Quantile regressions permit to estimate the returns to

education at various points the wage distribution.

Table 4.1 presents the rate of return to education for several countries using the

International Social Survey Program (ISSP) data. The estimates are based on OLS

regressions, after controlling only for age and union status. Thus it is reasonable to

believe that these returns to fall if additional variables (such as experience) are included

in the model. Results indicate that Great Britain, Northern Ireland and the republic of

Ireland are experiencing high returns to education relative to other countries. Figure 4.1

gives some cross-country rates of return to education broken down by gender. Estimates

109

indicates that for countries such as UK, Ireland, Germany, Greece and Italy experience a

significant variation in the returns to education between men and women.

Since the supply of educated workers have increased in the last two decades

(especially in OECD countries), there are doubts that the supply of educated workers

have exceeded the demand for these workers, thus leading to an over-education in the

workforce. Over-education implies that skills workers possess are more than what is

required for the job. Under this situation, workers will experience lower returns to

education. Therefore in order to obtain a correct picture one has to break up an

individuals total years of schooling in to required years and surplus years of education.

Over-education can be measured in several ways such as directly posing a question on

workers of whether they are over-educated. Alternatively, one can take the difference

between the actual years of education and the education required by the employer on

chapter. Three hypotheses concerning the over-education in the United Kingdom

graduate labor market was tested by Dolton and Vignoles (2000). Firstly, the study

concluded that the return to surplus years of education quite different from the return to

required years of education. Secondly, the study revealed that return to surplus years of

education is the same for all different degree classes. For an example, an over-educated

worker with first-class degree has the same return as an over-educated worker with a

second-class. Final conclusion of the study was that returns to surplus education are the

same across private and public sectors.

Spence (1973) developed an alternative view of the labor market based on job

market signaling. According to the signaling model, employers have limited and

uncertain information about potential workers and they use education as a sorting devise

110

to find the potential productivity of a worker. According to Spence (1973), it is difficult

for employees to determine the productivity of a new worker until the worker has been

inside the firm for some time. Therefore, when a firm evaluates job candidates it has to

foretell how productive workers will be after training and the likelihood they are going to

stay with the firm after training. In this instance, firms use the level and type of schooling

to predict the productiveness of workers. In the model schooling acts as a signal only if

two conditions are met. Firstly, the costs incurred to an individual must be inversely

related to the value a firm places on a worker. The underlying idea is that the cost of

schooling will be high for slow learners, and slow learners are most likely to be slow

learners on the job too. This condition ensures that slow and unproductive workers have a

relatively less incentive of devoting their time and investing in school. Secondly, there

should not be any less expensive or more accurate signals available to the firm when

evaluating job candidates. For an example test scores might be a cheap alternative years

of education for measuring cognitive skills. But test scores are less important when trying

to ascertain attitudinal characteristics compared to other methods such as interviews and

references. In the signaling model, the estimates returns to education will not only

produce the productivity enhancing effect of schooling but also the underlying ability that

education signals. Even after controlling for education, if schooling still shows a strong

effect on earning then the difference can be interpreted as signaling effect of education.

Alternatively, one can estimate the returns to education for the self-employed where

education does not act as a signal since individuals know their own skills and have no

incentive to signal themselves by obtaining more schooling. The main drawback with

self-employed individuals is that they possess specific (and usually unobservable)

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characteristics that makes them self-employed so self-employment is not random. The

main problem with ability measures is that they should not be contaminated with

education. Furthermore, ability measures need to capture the aptitude to make money and

increase earnings rather than measuring ability in an intelligence quotient (IQ) aspect.

4.3. Econometric Methodology and Data

The standard model used to returns to education is based on the human capital

earnings function developed by Mincer (1974). The first human capital wage equation to

be estimated has the form:

Model 1: 0 0 0 1ln exp expi i i iw edu sqrα β δ δ ε= + + + +

Where ln iw is the natural log of the hourly wage, edu are years of schooling, exp are

years of experience and exp sqr is the square of the level of experience. It is important to

note that model-1 is based on some restrictive assumptions. Firstly, the equation assumes

that workers have equal abilities and face equal opportunities. Secondly, the coefficient

for edu is an estimate of the impact of schooling on wages, and it is not an internal rate

of return on investment. If it is to be an internal rate if return, still it would be a private

one, since model disregards any subsidizing of schooling and also overlook any positive

or negative externalities associated with schooling.

Next, a model that disaggregates the years of schooling in to education levels was

estimated in order to capture differences in returns to education between education levels.

Generally, it is observed that returns to an additional year of education are not identical

across levels of schooling. The model is based on three levels of the Sri Lankan school

112

system; primary, secondary and tertiary education. Thus, the empirical estimation takes

form;

Model-2: 0 0 1 2 0 1ln sec exp expi i i i i i iw primary ondary tertiary sqrα β β β δ δ ε= + + + + + +

The estimates on the splines are interpreted in an identical manner as an estimate of a

continuous education variable (as in model-1).

An appropriate empirical strategy to estimate the above earnings models across

different points on the conditional wage distribution is through the quantile regression

method (Koenker and Basset, 1978). Quantile regression analysis is an attractive

estimation method to overcome various shortcomings of OLS analysis. OLS estimates the

effect of education on the mean of the conditional wage distribution, and assumes that

possible differences in terms of the impact of education along the conditional wage

distribution are unimportant. In contrast to OLS, quantile regression technique assumes

the effect of education of the thi individual to differ across the earnings spectrum.

The quantile regression model can be expressed as:

,ln i i iw x τ τβ µ′= +

Where ln iw is the natural log of the hourly wage of the thi individual, and ix represents

the demographic and employment related characteristics of the thi individual. By

Imposing the assumption that the thτ − quantile of the error term conditional on the

regressors is zero, ,( ( | ) 0)i iQ u xτ τ = , the thτ − conditional quantile of ln iw with respect

to ix can be expressed as:

(ln | )i i iQ w x xτ τβ′=

For any ε ∈(0,1), the parameter τβ can be estimated by

113

{ } { }| |

ˆ arg min | | (1 ) | |k

i i i i

i i i ii i y x i i y x

y x y xτ τ τ

τβ β β

β τ β τ β∈ ′ ′∈ ≥ ∈ ≥

′ ′= − + − − ∑ ∑

R

Note, that when 0.5τ = , we have the special case known as the median regression or the

least absolute deviation (LAD) estimator. Five quantile regressions were estimated at the

10th, 25th, 50th, 75th and 90th quantiles. The standard errors were computed by

bootstrapping with 100 replications. OLS regression was also estimated for the purpose

of comparison.

The study is based on the latest Consumer Finances and Socio-Economic Survey

(CFS) 2003/2004 conducted by the Central Bank of Sri Lanka. The CFS was initiated in

1953 and is the eighth multipurpose household survey that helps to capture long-term

changes in the living standards of the country. The foremost achievement of the survey

was its coverage of the Northern and Eastern provinces in the country after a time lag of

20 years. The CFS used the Census 2001 in constructing the distribution of housing units

across sectors and districts within provinces. Unfortunately, the CFS population frame

excluded three districts, Killinochchi, Mannar and Mulaitivu due to the ongoing civil

conflict. The under-coverage due to excluding the three districts is estimated around 2

percent of the total estimated housing units in the country. The CFS used a two-stage

stratified sampling procedure for the sample design. The survey is nationally

representative and consists of 11722 households and a 50545 individual population.

The survey collects information on a broad range of topics including demographic

characteristics, household income and expenditure, literacy and education, household

amenities and employment. The survey collects information on earnings and hours of

work, thus making it possible to obtain hourly wage rates. The working sample in this

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study contains workers who worked at least 30 hours per week and were not self-

employed. Self-employed workers were omitted due to the difficulty in separation of

income in to returns to labor and returns to capital.

4.4. Empirical Results

Table 4.2 presents the results of the full sample (both ethnic groups) estimates for

model-1. OLS estimates of all variables are statistically significant, and returns to

education are positive. Quantile regression results also suggest that returns to education

are positive and significant across all quantiles. However, the returns to education vary

significantly across all estimated quantiles. Figure 4.2 shows the development of the

coefficients over the entire wage distribution. The estimated coefficient for each quantile

is plotted as a continuous line and its 95% confidence interval is the shaded area. The

OLS estimate is the dark horizontal line and parallel to it is the 95% confidence band.

Results suggests that whereas the average return to education is of 1.9%, the return at the

bottom 10th quantile is 0.63% and the return at the top 90th quantile reaches 4.6%.

Returns to experience are also positive and significant across all quantiles, and shows a

slight upward trend when moving from the lower quantiles to upper quantiles. The

average return to experience is 1.6%, the return at the bottom 10th quantile is 1.1% and

the return at the top 90th quantile is 1.9%. Thus, the returns to experience exhibit less

variation across quantiles compared to returns to education (as seen clearly in figure 4.2).

Table 4.3 reports the estimated returns to education and experience in model-1 for

the Sinhala ethnic group. According to Table 4.3 all the estimated coefficients are

statistically significant, and returns to education and experience are positive for all

115

quantiles for the Sinhala ethnic group. OLS estimates indicate that the average return to

education for Sinhalese workers to be 2.4%. Across all quantiles, returns to education for

the Sinhalese ethnic group tend to increase as one move up the conditional wage

distribution. At the bottom 10th quantile, returns to education is 0.8% while workers in

the upper 90th quantile exhibit a return of 4.8%. However, returns to experience tends to

be relatively more uniform across quantiles for the Sinhala ethnic group. The average

return to experience as indicated by OLS is 1.8%, while the bottom and top quantiles

shows a return of 1.4% and 2.1% respectively.


the Tamil ethnic minority. Results indicate that returns to education in the 10th quantile

are not statistically significant for the Tamil ethnic minority. Table 4.4 also suggest that

both the statistical significance and the magnitude of returns to education to increase

systematically moving up the wage distribution. The average return to education for the

Tamil ethnic minority in model-1 is 0.8%, while the returns at the bottom 10th and 90th

quantiles are 0.3% and 3.5% respectively. According to Table 4.4 returns to education are

positive for the along the entire wage distribution, even though there is a slight variation

in the statistical significance across quantiles.

Model-1 results indicate that returns to education are substantially different for

Sinhalese and Tamil workers across quantiles. Firstly, for both ethnic groups returns to

education tend to increase when moving from the bottom to the top part of the

conditional wage distribution. Furthermore, Sinhalese workers have higher returns

education than for Tamil workers especially in the lower quantiles of the distribution, but

the difference in returns tends to be lesser in higher quantiles. The returns to education

116

also seem more homogeneous across the lower quantiles for Tamil workers than for

Sinhalese workers. Similarly, the returns for experience are also higher and much more

statistically significant for the Sinhalese workers than for Tamil workers.

Table 4.5 reports the OLS returns as well as conditional returns at five

representative quantiles: 0.10, 0.25, 0.5, 0.75, and 0.9, for model-2. The estimated results

are for the full sample consisting both Sinhalese and Tamil ethnic groups. OLS estimates

reveal that all the estimated coefficients are statistically significant at the 1% level.

However according to the quantile regression estimates, the primary education coefficient

is significant only at the 50th quantile. At all quantiles estimated on the conditional wage

distribution, primary education is positively related with the hourly wage rate. Figure 4.5

shows the development of the coefficient representing primary education over the entire

conditional wage distribution. In general, it is clear that the returns for primary education

do not vary a lot along the wage distribution. The returns to secondary education are

constant until the 80th quantile, while an upward trend is shown for the upper 90th

quantile. Unsurprisingly, results indicate that tertiary education to have very strong effect

on the hourly wage, conformed by both OLS and quantile regression estimates. Returns

to tertiary education are especially higher at the upper 90th quantile. With respect to work

experience, returns are statistically significant at all quantiles but the magnitude is less

compared to schooling.


the Sinhala ethnic group. For all estimated quantiles the statistical significance of primary

education is low compared to secondary and tertiary education. As to be expected,

tertiary education has the greatest effect on earnings followed by secondary and primary

117

education across all quantiles. With respect to experience, the returns are statistically

significant and tend to be uniform across the entire wage distribution.


the Tamil ethnic minority. Both OLS and quantile estimates suggest that primary

education is not statistically significant across all quantiles for the Tamil workers.

Furthermore, coefficient for the secondary education becomes statistically significant

beyond the 25th quantile. The estimate for tertiary education is statistically significant and

systematically increases as we move from the lowest to the highest quantile.

Model-2 results indicate that returns to education at different levels are

significantly different for Sinhalese and Tamil workers across quantiles. The main

distinction between the two ethnic groups is that the returns to secondary education are

slightly higher for Sinhalese workers for all estimated quantiles. Furthermore, there is

upward trend in secondary education returns around the center of the wage distribution

for Tamil workers, where as the returns to secondary education are generally uniform for

the Sinhalese. However, the returns to tertiary education are greater for Tamil workers

from the 50th quantile upwards. Figures 4.6 - 4.7 shows the development of all estimated

coefficients over the entire wage conditional wage distribution for the two ethnic sub-

samples. For the sub-sample of Tamil workers it evident that the returns to tertiary

education differs significantly between the lower and upper quantiles. Furthermore, at the

extreme upper end of the distribution, the estimate of tertiary education falls outside the

confidence interval of the OLS estimate and is quite different from the OLS estimate.

118

4.5. Conclusion

Any extensive program of macroeconomic stabilization and rapid liberalization of

trade in a developing economy are likely to generate crucial and widespread

consequences for the labor market. In the latter half of the 1980s Sri Lanka experienced

an increased openness of the economy leading to significant exportation of labor

intensive products. Sri Lanka having a comparative advantage in labor-intensive sectors

requiring unskilled labor would suggest an increase in demand for low-unskilled rather

than for skilled labor. In view of such economic changes, it certainly would be interesting

to observe the returns to education in the Sri Lankan labor market. This chapter

undertakes an empirical examination of returns to education in the Sri Lankan labor

market using the latest Consumer Finance and Socio-Economic Survey. The study

employs the quantile regression method for each conditional quantile wage group, rather

than the mean regression analysis used in most labor market analysis. The quantile

regression technique fits hyperplanes through out the conditional wage and is ideal for

characterizing the entire wage distribution. The standard Mincerian wage equation was

estimated for the full sample of male workers and separately for the two ethnic groups in

Sri Lanka. Quantile regression results suggest that average returns to education for both

ethnic groups differs significantly from the returns at the two extreme ends of the wage

distribution. In general the returns to education are positive for both groups, but the

returns are higher for Sinhalese workers than for Tamil. An increasing trend in returns to

education is evident for both ethnic groups when moving up wage distribution. Sinhalese

workers experience higher returns to education than for Tamil especially at the bottom of

the wage distribution, but the difference becomes less at the upper part of the distribution.

119

Estimated results with spline in years of education suggest that returns to secondary

education are higher for Sinhalese workers, but the returns to tertiary education are

greater for Tamil workers at the upper part of the wage distribution. Findings indicate

that returns to experience are also higher for Sinhalese workers than for Tamil workers.

120

Table 4.1: Cross Country evidence on the Returns to Schooling (year-1995)

Source: Harmon, Oosterbeek and Walker (2000). Note: Standard Errors in italics.

121

Table 4.2: Quantile and OLS regression estimates (model 1-full sample)

coefficient

Bootstrap standard error t-value

P>|t|

q10 edu 0.006319 0.001139 5.550 0.000 exp 0.011061 0.002136 5.180 0.000 expsqr -0.000340 5.660005 -5.960 0.000 consant 1.064279 0.023498 45.29 0.000 q25 edu 0.011317 0.001593 7.100 0.000 exp 0.017371 0.001683 10.32 0.000 expsqr -0.000490 4.980005 -9.780 0.000 constant 1.154758 0.019620 58.86 0.000 q50 edu 0.025822 0.001799 14.35 0.000 exp 0.019514 0.001388 14.06 0.000 expsqr -0.000470 4.520005 -10.39 0.000 constant 1.172323 0.020842 56.25 0.000 q75 edu 0.035738 0.001537 23.25 0.000 exp 0.019776 0.002179 9.080 0.000 expsqr -0.00040 7.210005 -5.560 0.000 constant 1.232041 0.019220 64.10 0.000 q90 edu 0.046208 0.001819 25.41 0.000 exp 0.019536 0.002074 9.420 0.000 expsqr -0.000300 6.180005 -4.890 0.000 constant 1.298972 0.022178 58.57 0.000 OLS edu 0.019337 0.001111 17.40 0.000 exp 0.016851 0.001321 12.76 0.000 expsqr -0.000380 3.680005 -10.29 0.000 constant 1.246002 0.014925 83.48 0.000

122

Table 4.3: Quantile and OLS regression estimates (model 1-Sinhala)

coefficient


P>|t|

q10 edu 0.008043 0.002173 3.700 0.000 exp 0.014276 0.003162 4.520 0.000 expsqr -0.000460 9.390005 -4.880 0.000 _cons 1.068700 0.029267 36.52 0.000 q25 edu 0.015461 0.002322 6.660 0.000 exp 0.019509 0.001942 10.04 0.000 expsqr -0.000540 6.180005 -8.690 0.000 _cons 1.127452 0.027466 41.05 0.000 q50 edu 0.030930 0.002308 12.99 0.000 exp 0.021360 0.001585 13.48 0.000 expsqr -0.000480 0.000054 -8.940 0.000 _cons 1.128302 0.026159 43.13 0.000 q75 edu 0.038618 0.002080 18.56 0.000 exp 0.020451 0.002173 9.410 0.000 expsqr -0.000380 6.590005 -5.780 0.000 _cons 1.203375 0.023665 50.85 0.000 q90 edu 0.048383 0.002029 23.84 0.000 exp 0.021267 0.002091 10.17 0.000 expsqr -0.000330 6.250005 -5.260 0.000 _cons 1.270688 0.024245 52.41 0.000 OLS edu 0.023714 0.001355 17.50 0.000 exp 0.018623 0.001497 12.44 0.000 expsqr -0.000410 4.230005 -9.690 0.000 _cons 1.209412 0.017709 68.29 0.000

123

Table 4.4: Quantile and OLS regression estimates (model 1-Tamil)

coefficient


P>|t|

q10 edu 0.002938 0.003189 0.920 0.357 exp 0.005744 0.004570 1.260 0.209 expsqr -0.000110 0.000108 -1.010 0.311 constant 1.006615 0.054497 18.47 0.000 q25 edu 0.003880 0.001845 2.100 0.036 exp 0.007455 0.003504 2.130 0.034 expsqr -0.000180 8.290005 -2.200 0.028 constant 1.140844 0.037250 30.63 0.000 q50 edu 0.008106 0.003252 2.490 0.013 exp 0.007632 0.004017 1.900 0.058 expsqr -0.000200 9.560005 -2.060 0.039 constant 1.275534 0.051122 24.95 0.000 q75 edu 0.021085 0.005574 3.780 0.000 exp 0.013077 0.004414 2.960 0.003 expsqr -0.000300 0.000131 -2.270 0.024 constant 1.302319 0.058955 22.09 0.000 q90 edu 0.034554 0.006039 5.720 0.000 exp 0.011229 0.006323 1.780 0.076 expsqr -0.000100 0.000198 -0.520 0.605 constant 1.347976 0.063310 21.29 0.000 OLS edu 0.008564 0.002080 4.120 0.000 exp 0.008709 0.003030 2.870 0.004 expsqr -0.000170 0.000078 -2.240 0.025 constant 1.272718 0.031635 40.23 0.000

124

Table 4.5: Quantile and OLS regression estimates (model 2-full sample)

Coefficient

Bootstrap Standard Error. t-value

P>|t|

q10 primary 0.048946 0.050912 0.960 0.336 secondary 0.205679 0.049757 4.130 0.000 tertiary 0.443819 0.049191 9.020 0.000 exp 0.018826 0.002060 9.140 0.000 expsqr -0.000420 6.230005 -6.720 0.000 constant 0.859709 0.049047 17.53 0.000 q25 primary 0.052829 0.033157 1.590 0.111 secondary 0.208309 0.031062 6.710 0.000 tertiary 0.430995 0.030209 14.27 0.000 exp 0.018944 0.001414 13.40 0.000 expsqr -0.000430 5.040005 -8.630 0.000 constant 1.038040 0.030342 34.21 0.000 q50 primary 0.052460 0.018224 2.880 0.004 secondary 0.227014 0.016367 13.87 0.000 tertiary 0.461729 0.017562 26.29 0.000 exp 0.020518 0.001243 16.51 0.000 expsqr -0.000450 3.490005 -12.90 0.000 constant 1.167785 0.016027 72.86 0.000 q75 primary 0.035789 0.022852 1.570 0.117 secondary 0.213596 0.021513 9.930 0.000 tertiary 0.479334 0.023509 20.39 0.000 exp 0.018359 0.002441 7.520 0.000 expsqr -0.000350 0.000078 -4.450 0.000 constant 1.342088 0.023643 56.76 0.000 q90 primary 0.038085 0.031410 1.210 0.225 secondary 0.246998 0.028517 8.660 0.000 tertiary 0.577881 0.031498 18.35 0.000 exp 0.021994 0.002351 9.360 0.000 expsqr -0.000370 7.030005 -5.290 0.000 constant 1.454727 0.028247 51.50 0.000 OLS primary 0.056244 0.020290 2.770 0.006 secondary 0.239790 0.019035 12.60 0.000 tertiary 0.497390 0.020172 24.66 0.000 exp 0.019650 0.001200 16.28 0.000 expsqr -0.000390 0.000030 -11.68 0.000 constant 1.154330 0.020340 56.73 0.000

125

Table 4.6: Quantile and OLS regression estimates (model 2-Sinhala)

Coefficient


P>|t|

q10 primary 0.094427 0.0542914 1.740 0.082 secondary 0.269427 0.0546298 4.930 0.000 tertiary 0.494878 0.0555607 8.910 0.000 exp 0.021972 0.0027689 7.940 0.000 expsqr -0.000510 0.0000887 -5.780 0.000 constant 0.806024 0.0564095 14.29 0.000 q25 primary 0.081863 0.0317356 2.580 0.010 secondary 0.243767 0.0307653 7.920 0.000 tertiary 0.456219 0.0319011 14.30 0.000 exp 0.020349 0.0015455 13.17 0.000 expsqr -0.000470 0.0000512 -9.120 0.000 constant 1.009352 0.0309662 32.60 0.000 q50 primary 0.040893 0.0342083 1.200 0.232 secondary 0.212035 0.0342209 6.200 0.000 tertiary 0.434629 0.0364486 11.92 0.000 exp 0.022127 0.0013746 16.10 0.000 expsqr -0.000490 0.0000419 -11.58 0.000 constant 1.184314 0.0339347 34.90 0.000 q75 primary 0.042947 0.0294690 1.460 0.145 secondary 0.212639 0.0304455 6.980 0.000 tertiary 0.464373 0.0330742 14.04 0.000 exp 0.020530 0.0023610 8.700 0.000 expsqr -0.000410 0.0000796 -5.130 0.000 constant 1.339696 0.0329567 40.65 0.000 q90 primary 0.015132 0.0488720 0.310 0.757 secondary 0.226079 0.0497276 4.550 0.000 tertiary 0.542847 0.0529322 10.26 0.000 exp 0.023075 0.0021202 10.88 0.000 expsqr -0.000380 0.0000587 -6.550 0.000 constant 1.472891 0.0482806 30.51 0.000 OLS primary 0.061440 0.0264700 2.320 0.020 secondary 0.249500 0.0247800 10.07 0.000 tertiary 0.499660 0.0257500 19.40 0.000 exp 0.020880 0.0013850 15.07 0.000 expsqr -0.000410 0.0000300 -10.70 0.000 constant 1.147780 0.0262000 43.81 0.000

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Table 4.7: Quantile and OLS regression estimates (model 2-Tamil)

Coefficient

BootstrapStandard error t-value

P>|t|

q10 primary 0.049911 0.064335 0.780 0.438 secondary 0.144278 0.069195 2.090 0.037 tertiary 0.435165 0.111695 3.900 0.000 exp 0.012022 0.004306 2.790 0.005 expsqr -0.000220 0.000104 -2.070 0.038 constant 0.880291 0.081788 10.76 0.000 q25 primary -0.024360 0.045348 -0.540 0.591 secondary 0.080193 0.049733 1.610 0.107 tertiary 0.365616 0.065839 5.550 0.000 exp 0.007349 0.003440 2.140 0.033 expsqr -0.000160 9.730005 -1.650 0.100 constant 1.135356 0.046479 24.43 0.000 q50 primary -0.003780 0.025997 -0.150 0.884 secondary 0.134977 0.029174 4.630 0.000 tertiary 0.452884 0.056329 8.040 0.000 exp 0.012599 0.002366 5.320 0.000 expsqr -0.000250 6.550005 -3.840 0.000 constant 1.206258 0.024628 48.98 0.000 q75 primary -0.005400 0.048577 -0.110 0.911 secondary 0.177777 0.048616 3.660 0.000 tertiary 0.538524 0.073098 7.370 0.000 exp 0.014200 0.004617 3.080 0.002 expsqr -0.000270 0.000127 -2.170 0.031 constant 1.328006 0.055418 23.96 0.000 q90 primary 0.006410 0.052708 0.120 0.903 secondary 0.186644 0.055268 3.380 0.001 tertiary 0.755078 0.124952 6.040 0.000 exp 0.008360 0.005918 1.410 0.158 expsqr -0.000110 0.000174 -0.610 0.544 constant 1.507489 0.056284 26.78 0.000 OLS primary 0.026551 0.033083 0.800 0.422 secondary 0.178914 0.032569 5.490 0.000 tertiary 0.508884 0.041317 12.32 0.000 exp 0.013254 0.002708 4.900 0.000 expsqr -0.000230 6.930005 -3.340 0.001 constant 1.162422 0.035884 32.39 0.000

127

Source: Harmon, Oosterbeek and Walker (2000) Figure 4.1 Returns to Schooling in Europe, Men and Women

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Figure 4.2 OLS and quantile regression estimates (model 1-full sample)

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Figure 4.3 OLS and quantile regression estimates (model 1-full Sinhala)

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Figure 4.4 OLS and quantile regression estimates (model 1-full Tamil)

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Figure 4.5 OLS and quantile regression estimates (model 2-full sample)

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Figure 4.6 OLS and quantile regression estimates (model 2–Sinhala)

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Figure 4.7 OLS and quantile regression estimates (model 2–Tamil)

134

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