Discussion Paper No. 805

IMPACTS OF FORCED MARRIAGESIN CAMBODIAUNDER THE POL POT REGIME
Katsuo Kogure
The 13th ISER-Moriguchi Prize (2010)
Awarded Paper

March 2011

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Impacts of Forced Marriages in Cambodia
under the Pol Pot Regime ∗Katsuo Kogure†
University of Tsukuba
February 2011

Abstract
This paper investigates effects of forced marriages in the Pol Pot regime on
educational investments for their children. Specifically, we focus on the difference ineducational investments between the couples who faced different social environments
under the Pol Pot regime. Using the complete set of retrospective census microdata,
we estimate the average treatment effects on the treated with inverse probability
weighting estimation. Our estimation results suggest that forced marriages had
negative impacts on child education. In particular, a harsher social environment
had larger negative impacts on child education.
JEL Classifications: N45, O43, P36.
Keywords: child education, forced marriage, the Pol Pot regime.

∗

I am grateful to my main advisor Atsushi Yoshida for his valuable guidance, comments and support
throughout this project. I am also grateful to my advisors Yoko Kijima and Ryo Nakajima for theirvaluable guidance and comments at various stages. I am also grateful to Niu Bing, Mamoru Kaneko andKoichi Ushijima for many valuable discussions. I am also grateful to Tomoki Fujii, Hidehiko Ichimura,
Hisahiro Naito, Yoshito Takasaki and seminar participants at Hitotsubashi University, Kanazawa University, Nihon University, Osaka University, University of Tsukuba, University of Tokyo, and Kansai Labor
Workshop for helpful comments and suggestions. Special thanks also go to H.E. San Sy Than and Hang
Lina for providing and assisting with 1998 Census microdata. The paper does not reflect the views of
the National Institute of Statistics, Ministry of Planning, Cambodia. The previous version of this paper
was awarded the 13th Moriguchi Prize by the Institute of Social and Economic Research (ISER) at Osaka
University.
†
Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai,
Tsukuba, Ibaraki 305-8573, JAPAN. Email: kkogure@sk.tsukuba.ac.jp1

1

Introduction

We conduct an econometric study of effects of forced marriages in the Pol Pot regime onchild education after its collapse. The Pol Pot regime ruled Cambodia from 1975 to 1979 in
a form of primitive communism and affected severely all the citizens in their socio-economic
and cultural lives. The Pol Pot regime totally destroyed the former politico-socio-economic
systems and forced all the citizens to change into their lives following the new system until
January 1979. We are interested in effects of the Pol Pot regime on general socio-economic
behavior. In this paper, we in particular choose the forced marriage system employed in
the regime and estimate its effects on child education afterward. In this introduction, we
first give a brief summary of historical background relevant for our study. We then explain
our econometric study and finally consider implications derived from our results.
Forced marriages were broadly implemented in Cambodia under the Pol Pot regime(e.g. Dy 2007). The forced marriage system was quite different from conventional marriage
system. While parents traditionally choose a marriage partner for their son or daughter,
they, in most cases, were not allowed to be involved in any decision under the new system.
Instead, marriage was designated by “Angkar” which claimed to be everyone’s motherand father, and each couple could not refuse the order from Angkar. 1 Nevertheless, nostatistical data about forced marriages are available.
Our evaluation approach is to investigate the differences in socio-economic behavior
between the households formed under the Pol Pot regime and those formed after the
Pol Pot regime. We then examine the possible effects of the forced marriage system onsocio-economic behavior.
In this evaluation, we focus on the difference in educational investments between the
couples from urban areas and those from rural areas. Under the Pol Pot regime, they werecalled new and base people and faced very different social environments. Urban people(new people) were regarded as enemies of society and were persecuted by the Khmer
Rouge. They were forced to migrate from urban to rural areas and were given no politicalrights. Many of them were victimized into genocide. On the other hand, rural people(base people) were regarded as innocent and given the privileged rights to build a nation.
We also examine the differences in the effects of the forced marriage system on socioeconomic behavior among different regions. During the Pol Pot regime, different leaders
were assigned from the central committee, and ruled each administrative zone. Historical
documents report that the degree of harshness was different in each zone. For example,
people in Southwest zone were treated severely, while those in East zone were treated less1

Angkar, which literally means organization, did not exist in reality. However, people were instructed
to believe, obey and respect Angkar.

2harshly (e.g. Kiernan 1996).
The evaluation method that we employ is the econometric method of program evaluation based on the potential outcomes framework (e.g. Rubin 1974; Holland 1986). The
parameter that we are interested in is the average treatment effects on the treated (ATT).
This parameter addresses the question of what households formed under the Pol Pot regime
would experience had they be formed after the Pol Pot regime. Using the complete set of
retrospective 1998 census microdata, we employ the inverse probability weighting (IPW)estimation to identify the ATT parameter. In the first step, we estimate logit models
by maximum likelihood estimation and obtain the estimated propensity scores. In thesecond step, we conduct weighted least squares estimation using these propensity scoresas weights.
Our estimation results show the differences in educational investment between thehouseholds formed under the Pol Pot regime and those formed after the Pol Pot regime.
We find the investment in child education was lower for the households formed under the
Pol Pot regime than for those formed after the Pol Pot regime. This finding suggests thatforced marriages in the Pol Pot regime had negative impacts on child education after its
collapse.
Our estimation results also reveal the differences between new and base people. Whilethe investment in child education was lower for the households formed under the Pol Pot
regime than for those formed after the Pol Pot regime for both new and base people, muchlarger effects are found for the new people.
Our estimation results also show the differences among different administrative zones
under the Pol Pot regime. We find that the investment in child education was lower forthe households in West, Central, and in particular Southwest zone formed under the Pol
Pot regime than for those formed after the Pol Pot regime in these zones. On the other
hand, less difference can be seen in Northwest, East, and North zone.
These findings suggest that the differences in social environments under the Pol Pot
regime led to different impacts on child education. During the Pol Pot regime, new andbase people were treated differently. In particular, new people were persecuted. Historical
documents report the degree of differences in control among different administrative zones.
For example, Southwest zone was a special zone during the Pol Pot regime and people inSouthwest zone were harshly controlled by the Khmer Rouge (e.g. Kiernan 1996; Vannak
2003). The Khmer Rouge in East zone was less brutal than those in other zones (e.g.
Kiernan 1996). The estimation results with these historical evidences then suggest that aharsher social environment had more negative impacts on child education.
Our results provide the implication that a rule with strong political power such as3

dictatorship may trigger the emergence of difference in culture. In this case, social environments may play an important role in creating cultural differences, which may bepersistent and have different effects on socio-economic behavior.
The remainder of the paper is structured as follows. Section 2 provides historical background. Section 3 explains data and development of samples. Section 4 describes thecharacteristics of the samples with descriptive statistics and graphs. Section 5 presents
econometric method. Section 6 provides estimation results. Section 7 provides the interpretations of our findings. Section 8 concludes.

2

Historical Background and Data

2.1

Historical Background

The Communist Party of Kampuchea, which is well known as the Khmer Rouge, led by
Pol Pot won the civil war against the Khmer Republic regime led by Marshal Lon Nol
on 17th in April, 1975. The Khmer Rouge then established the state of the Democratic
Kampuchea in 1976 and governed the country with strong political power until 7th inJanuary, 1979. In this section, we first explain how the Khmer Rouge gained politicalpower during the Vietnam War. We then describe social experiments implemented under
the Khmer Rouge regime and finally introduce the marriage system under the Khmer
Rouge regime.
A. Rise of the Khmer Rouge in Cambodia, 1970 - 1975
Political situation in Cambodia was complex and unstable from 1970 to 1975. Cambodia
was gradually influenced by the Vietnam War. During the Vietnam war, Prince Sihanouk
permitted North Vietnam to construct the Ho Chi Minh Trail for providing material
supplies from North to South, while he had taken a neutral stance for diplomatic policy.
Consequently, a relationship with the United States deteriorated.
In March 1970, Marshal Lon Nol and his associates who were pro-United States successfully seized power in a coup when Prince Sihanouk was outside the country. They
deposed Prince Sihanouk from head of state and established the Khmer Republic, which
was backed by the United States. As a result, the United States invaded into Cambodia
to cut the Ho Chi Minh Trail and expanded the campaign of secret bombing in Cambodia.
During this period, the Khmer Rouge gradually gained political power. The Khmer
Rouge attracted more people who did not want to support the Lon Nol government andwanted to restore Prince Sihanouk to power. Prince Sihanouk who went into exile in China
4also encouraged people to join the Khmer Rouge to overthrow the Lon Nol government.
Consequently, the number of Khmer Rouge soldiers increased from about 3,000 in 1970
to over 40,000 in 1973 and about 85 percent of Cambodian territory was in hands of the
Khmer Rouge by early 1973. Finally, the Khmer Rouge won the five-year civil war against
the Lon Nol government on 17th in April, 1975 (cf., Dy 2007).
B. Social Revolution under the Khmer Rouge Regime, 1975 - 1979
The Khmer Rouge established the state of the Democratic Kampuchea in 1976 and governed the country with strong political power until 7th in January, 1979. During thisperiod, the Khmer Rouge implemented the radical institutional changes which were influenced by primitive communism of Mao Zedong, Marx and Lenin. The forced marriage
system studied in this paper is one of them. Examples of social revolution are forced migration, forced labor, genocides, and abolition of private property rights, market function,
normal schooling, religious practice, traditional Khmer culture.
The Khmer Rouge classified people into the two groups: base and new people. Thebase people who spent their whole lives in rural area before the Pol Pot regime wereregarded as innocent to the regime. 2 Under the regime, they were given the privileged
rights to play a role in accomplishing the revolution. On the other hand, the new people
who were from urban area were regarded as enemies of new society and were persecuted
under the regime. They were given no political rights and their political powers werecompletely reduced during the Pol Pot regime. Many intellectual citizens in this groupwere victimized to genocide. 3
The Khmer Rouge divided the country into six geographical zones to manage the country effectively. The Khmer Rouge leaders were assigned to each administrative zone andimplemented social revolution following orders from the central committee. The details
about administrative zones in the regime were presented in Appendix.
The Khmer Rouge regime did not give political power to Prince Sihanouk. To securepolitical power, the Khmer Rouge invited Prince Sihanouk who was in North Korea tocome back to Cambodia in July 1975 and forced him to resign as head of states. This
resulted in the end of nearly two thousand year lasting monarchy system in Cambodia.
The Khmer Rouge then kept Prince Sihanouk isolated in the Royal Palace in Phnom Penh
not to allow him to leave the country just before the collapse of the Khmer Rouge regime.
2

Rural people who were evacuees during the civil war were regarded as new people in the Pol Pot era.
Most of them were not allow to go back to their homeland during the regime. They are not included in
the samples of this study.
3
See e.g. Kiernan (1996)

5

C. Marriage under the Khmer Rouge Regime, 1975 - 1979
Marriage under the Khmer Rouge regime was different from traditional one. Althoughparents traditionally choose a marriage partner for their son or daughter, they, in mostcases, were not allowed to involve in any decision-making under the new system. Instead,Angkar which was everyone’s mother and father decided marriage partners. People were
not allowed to refuse the order from Angkar to get married. In an extreme case, theirrefusal could have resulted in punishment or death. A role of marriage under the regime
was not to form family units, but to produce pure children who could serve the revolutionof the new regime.

2.2

Data

We employ the complete set of microdata from General Population Census of Cambodia
in 1998. We focus on child education for a first child and children aged 6-14 as household
outcomes. Because of our sampling design, which is discussed in the next section, we
separately develop outcome measures for these two groups. Outcome measures for a firstchild are (1) an indicator variable for graduating from primary school and (2) an indicatorvariable for having never attended school, while those for children aged 6-14 are (3) anindicator variable for attending school at the time of census period and (4) an indicatorvariable for having never attended school. The detailed definitions of variables used inthis paper are presented in appendix.

3

Development of Samples

3.1

Treatment and Control Households

While census data contain the information on basic demographic and socio-economic characteristics of households and individuals, they do not include the information on whenhouseholds were formed. Consequently, we can never observe which households wereformed under the Pol Pot regime. Instead, we specify a part of the households which werehighly likely to be formed under the Pol Pot regime by focusing on mother’s age and thetime of having a first child.
We illustrate this with Table 1. Panel (A) shows the age table for children aged 15-21,
while that for women aged 33-39 is presented in Panel (B). The tables contain the agesof these people at each year from 1971-83. Our target population is the households withmother aged 34-38 and a first child aged 19-20 as of 1998. We assume they were formed6under the Pol Pot regime. Recall that the Pol Pot regime started in April 1975 and endedin January 1979. Mother aged 34-38 were 11-15 years old in 1975. They appear to be
too young to get married before the Pol Pot regime. If they gave a birth to a first childin 1978-1979, they were highly likely to get married under the Pol Pot regime. We call aset of these households a treatment group. We then divide the treatment group into twogroups based on the time of having a first child. We call a group of the households witha first child aged 20 “Treatment 1” and that with a first child aged 19 “Treatment 2”.
We next specify control households in the same way by focusing on mother’s age andthe time of having a first child. We select the households with mother aged 34-38 and afirst child aged 16-17 as control households. We assume they were formed after the Pol Pot
regime. We don’t employ the households with a first child aged 18 as control households,
because some of them might be formed under the Pol Pot regime. We develop two control
groups based on the time of having a first child. We call a group of the households witha first child aged 17 “Control 1” and that with a first child aged 16 “Control 2”. 4 Using
these two control samples, we evaluate Treatment 1 and Treatment 2.

3.2

Definition of Base and New People

We classify households into base and new people based on mother’s status during the Pol
Pot regime. A household is defined as base people if mother’s birth place is neither aprovincial town nor Phnom Penh and the birth place of a first child is same as that ofmother. On the other hand, a household is defined as new people if mother’s birth place
is a provincial town or Phnom Penh.

3.3

Sampling

Using the complete set of census microdata, we implement a sampling to develop both
treatment and control groups. The detailed procedures are summarized in Table 2. Thereare 2,188,177 households in census data. First, we restrict the households with a firstchild aged 16, 17, 19, and 20, which results in 351,071 households. We then impose
ten conditions to reduce unobservable factors affecting outcomes. This is shown in (2)through (11). Next two conditions address limitations of census data. One limitation isthat they do not collect the information on when households were formed. As a result,remarried households formed after the Pol Pot regime might be included in the treatmentgroups. Condition (12) is imposed to reduce those households from our samples. Another
4

There is still concern that the households formed under the Pol Pot might be included in the control
households. To address this, we prepare for two different control groups and obtain reliable estimates.

7

limitation is that census data do not contain the information of the household members
who lived elsewhere at the time of census. This might happen when children had got
married and formed a new independent household. This threatens the validity of our
indicator to identify a first child in a household. Condition (13) is imposed to make it aright indicator of a first child. We obtain two treatment and control groups. The numberof households in Treatment 1 and 2 are 1,230 and 3,993, while those in Control 1 and
2 are 14,661 and 15,761. We finally develop nine samples with different combinations oftreatment and control groups for our evaluation.

3.4

Attrition Problem

There is concerned about attrition problem for the samples developed in the previous
section. Because women who were forced to marry under the Pol Pot regime were nolonger to stay together after the collapse of the regime, they may be more likely to divorce
as of 1998. To explore this, the proportions of mother’s marital status by different groups
and samples are presented in Table 3. The proportion of mother’s marital status for basepeople is shown in panel (A), while that for new people is provided in panel (B). Although
the proportion of married households is 86.4 percent for new people in Treatment 1, thatfor the rest of the samples exceed 90 percent. This suggests that most of the households
still stay together after it took about 20 years since the Pol Pot regime ended in 1979.
5
Because attrition problem is not serious, we use only “married” households for ourevaluation.

4

Characteristics of the Samples

In this section, we describe the characteristics of the samples. First, we present thedescriptive statistics of the samples. Second, we provide graphical analyses on outcomes.
Finally, we show the ArcGIS maps on spatial distributions of the samples.

4.1

Descriptive Statistics

Table 4 provides the descriptive statistics by different groups and samples. The tableconsists of the three panels: (A) Household Characteristics, (B) Characteristics of A FirstChild, and (C) Characteristics of Children Aged 6-14.

5

One possible reason is that sex imbalance due to the Pol Pot regime gives less incentive for women
to divorce.

8

Households. Panel (A) provides the means and standard deviations about household
characteristics. Descriptive statistics for base people are shown in column (1) and (2) for
treatment groups and in column (3) and (4) for control groups. Treatment 1 contains
more female headed households than other three groups. Mother’s age is similar between
treatment and control groups. There are slightly differences in father’s age, mother’s
education, and father’s education between treatment and control groups. Father’s age,mother’s education, and father’s education are larger for treatment groups than for control
groups. The proportion of whether father is new people is similar between treatment andcontrol groups.
Descriptive statistics for new people are shown in column (5) and (6) for treatmentgroups and in column (7) and (8) for control groups. Treatment groups contain more female headed households than control groups. There are also differences between treatmentgroups. The proportion of female headed households is larger for Treatment 1 than for
Treatment 2. Mother’s age is similar between treatment and control groups. Father’s age
is larger for treatment groups than for control groups. There are differences in mother’s
education, father’s education, and father’s class is new between treatment 1 and otherthree groups. Mother’s and father’s education are lower for Treatment 1 than for Treatment 2 and control groups. The proportion of whether father is new people is larger for
Treatment 1 than for Treatment 2 and control groups.
A First Child. Panel (B) presents the means and standard deviations about thecharacteristics of a first child. Descriptive statistics for a first child of base people areshown in column (1) and (2) for treatment groups and in column (3) and (4) for control
groups. There are no variations in age of a first child for each group due to our sampling
design. The proportion of female is smaller for treatment groups than for control groups.
The table also shows the difference in outcomes between Treatment 1 and other threegroups. The first child in Treatment 1 is less likely to graduate from primary school thanthat in other three groups. In addition, the proportion of a first child who has neverattended school is larger for Treatment 1 than for Treatment 2 and control groups.
Descriptive statistics for a first child of new people are shown in column (5) and (6)for treatment groups and in column (7) and (8) for control groups. The proportion of
female is smaller for treatment groups than for control groups. The proportion of a firstchild who graduated from primary school is smaller for Treatment 1 than for Treatment
2 and two control groups. The proportion of a first child who has never attended school
is larger for Treatment 1 than for Treatment 2 and control groups. While these findings
are similar to those for a first child of base people, it is found that there are much larger
differences in educational investments for a first child of new people between Treatment9

1 and two control groups. The descriptive statistics on outcomes in panel (B) show that
there are differences in educational investment for a first child between Treatment 1 andcontrol groups, but not between Treatment 2 and two control groups. While this can beseen for both base and new people, larger differences are found for new people.
Children aged 6-14. Panel (C) presents the means and standard deviations about
the characteristics of children aged 6-14. Descriptive statistics for children of base people
are shown in column (1) and (2) for treatment groups and in column (3) and (4) for
control groups. Average of child age and proportion of female is close between treatment
and control groups. The table shows the difference in outcomes between Treatment 1 andother three groups. The proportion of children currently attending school is smaller for
Treatment 1 than for Treatment 2 and two control groups. In addition, the proportionof children who have never attended school is larger for Treatment 1 than for other threegroups. These findings are consistent with those for a first child.
Descriptive statistics for children aged 6-14 of new people are shown in column (5) and(6) for treatment groups and in column (7) and (8) for control groups. There are not clear
differences in average of child age and proportion of female between treatment and control
groups. There are differences in educational outcomes between treatment and control
groups. The proportion of children aged 6-14 who are currently attending school is smaller
for treatment groups, in particular for Treatment 1, than for control groups. In addition,
the proportion of children aged 6-14 who have never attended school is larger for treatmentgroups, particularly for Treatment 1, than for control groups. The descriptive statistics
on outcomes in panel (C) show that there are differences in educational investment forchildren aged 6-14 between Treatment 1 and control groups, but not between Treatment 2
and control groups for base people. On the other hand, they also show that an educational
investment for children aged 6-14 of new people is lower for treatment groups, in particular
for Treatment 1, than for control groups.

4.2

Graphical Analysis

Figure 1 provides educational attainments of children by different groups. We provide
three types of figures that show the differences in child education between treatment andcontrol groups.
Educational Attainment of a First Child. Figure (1a-1) and (1b-1) provide the
distributions of educational attainment of a first child for base and new people, respectively. It is found that there are slightly differences in the locations of distributions between
treatment and control groups, which suggests that there are difference in educational investment for a first child between these two groups. Educational attainment of a first10

child is lower for treatment groups, in particular for Treatment 1, than for control groups.
This is found for both base and new people. On the other hand, there are differences in
educational attainment of a first child between Control 1 and Control 2.
Educational Attainment of Children Aged 6-14. Figure (1a-2) and (1b-2) plot
the estimated proportions of children aged 6-14 who are currently attending school bydifferent samples and groups. They are constructed using Epanechnikov kernel function
to nonparametrically regress the indicator variable of currently attending school or noton the child age variable. Both figures show that there are differences in educational
investment between treatment and control groups. The proportion of children who are
currently attending school is smaller for treatment groups, in particular for Treatment
1, than for control groups. While this can be seen for both base and new people, larger
difference is found for new people. While we find a difference in educational attainmentsof a first child between control groups, there is no difference in those of children aged 6-14
between these two groups.
Figure (1a-3) and (1b-3) plot the estimated proportion of children aged 6-14 who havenever attended school by different samples and groups. They are developed by employing
the same methods mentioned above. Both figures show that the proportion of children
who have never attended school is larger for treatment groups, particularly for Treatment
1, than for control groups. In particular, larger difference is found for new people.

4.3

Spatial Distributions of the Samples

Figure 2 and 3 provide the spatial distributions of the households for base and new people,
respectively. There are two objectives to plot the samples onto maps. One objective is to
grasp how the samples are spatially distributed within a country. Another objective is tocheck whether the geographical distributions of control samples are well overlapped withthose of treatment samples. This is important in developing counter factual outcomes fortreatment groups. 6
Figure 2 and 3 show that the samples of base people are widely distributed throughout
the country, while those of new people are distributed in limited areas. They also show thatthe geographical distributions of control samples are well matched with those of treatment
samples for both base and new people.

6

In program evaluation literature on job training programme, geographical mismatch between treatment and control groups is major source of bias (Heckman, Ichimura and Todd 1997, Heckman, Ichimura,
Smith and Todd 1998). Geographical match between treatment and control groups are also important inour context, because educational environments are different among regions.

115

Econometric Evaluation

The evaluation method that we employ is an econometric method of program evaluation
based on the potential outcomes framework (e.g. Rubin 1974; Holland 1986). In thissection, we first define a parameter and then discuss the econometric method as well as
the assumptions employed for identification.

5.1

Parameter of Interest and Assumptions

The parameter that we are interested in is the average treatment effects on the treated(ATT), which addresses the question of what households formed under the Pol Pot regime
would experience had they got married under the conventional marriage system. Despitemuch attention and concern for a long time, it is still unknown how the Pol Pot regime
has affected the well-beings of survivors after the collapse of the regime. 7 Thus, the ATTparameter is the most appropriate parameter in our context.
We define the ATT parameter as follows. Let (Yj (1), Yj (0)) denote the potential outcomes of household j with and without treatment, where treatment means that household
j was formed under the Pol Pot regime. Let Dj = 1 if household j was formed under the
Pol Pot regime and let Xj denote a vector of conditioning variables of household j. The
ATT for our target population is defined as
E[Yj (1) − Yj (0) | Xj , Dj = 1].

(1)

Because we focus on child education as household outcomes, we suppose that Xj canbe divided into two components, Xj = (Tij , Zj ), where Tij determines outcomes of child iin household j and Zj influence the treatment status for the households formed under the
Pol Pot regime. The ATT in (1) is then redefined as
E[Yij (1) − Yij (0) | Tij , Zj , Dj = 1].

(2)

To identify the ATT parameter, we make three key assumptions. The first is conditional
independence assumption, which is first introduced by Rosenbaum and Rubin (1983),
Assumption 5.1 (Conditional Independence Assumption)
Yij (0) ⊥ Dj | Tij , Zj .
7

There are scientific evidences in the field of psychiatry that Cambodian survivors suffers from longterm post-traumatic stress disorder (PTSD) after the collapse of the Pol Pot regime, suggesting that the
Pol Pot regime might have negative effects on their economic outcomes (e.g. Mollica et al. 1993, Mollica
et al. 2002, Marshall et al. 2005).

12

This assumption can be formally written as
P r(Dj | Yij (0), Tij , Zj ) = P r(Dj | Tij , Zj )= P r(Dj | Zj ).
We next make the second assumption about the joint distribution of the treatment
and covariates:
Assumption 5.2 (Overlap)
P r(Dj = 1 | Zj ) < 1 for all pj ∈ supp(Pj (Zj )).
Let z1j denote an indicator of household j for having a first child. To make it possible
to compare between the households with a first child born under the Pol Pot regime andthose born after the regime, we assume that the following exclusion restriction is hold:
Assumption 5.3 (Exclusion Restriction)
Y (Dj , z1j ) = Y (Dj , z1j ) for Dj = 0, 1 and z1j = 1.

5.2

Estimation Method

We employ inverse probability weighting (IPW) estimation to estimate the ATT parameter. This method combines propensity score estimation and regression adjustment withweighting. Specifically, we first estimate logit models by maximum likelihood estimation:0

exp(Zj γ)
P r(Dj = 1 | Zj ) =.
0
1 + exp(Zj γ)(3)

We then estimate weighted least squares models using the propensity scores obtainedin the first step as weights:
00

Yij = β0 + τAT T · Dj + β1 Tij + β2 (Tij − T 1 ) · Dj + ²ij ,where weights are


1
λij =
 p̂(Zj )1−p̂(Zj )(4)

if Dj = 1,
if Dj = 0,

and T 1 is the sample average of T for the subsample of treated households. 8
IPW estimation is appropriate in our context. Our samples are not randomly assigned
between treatment and control groups. To address this, we use the estimated propensity scores which are obtained in the first step to compare the households with similar
8

See Hirano and Imbens (2001).

13

characteristics between treatment and control groups. We also need adjustment between
children with different characteristics. Despite the unit of our study is a household, we
focus on child education as household outcomes. Weighted least square regression in thesecond step then makes it possible to control for covariates of children. 9
This method is called “double robust estimation”, which is discussed by Robins andRotnitzky (1995), Scharfstein, Rotnitzky and Robins (1999) and Wooldridge (2007). 10
In our context, our missspecification for propensity score estimation does not affect theconsistency of the ATT parameters in the second step, as long as weighted least squares
models are correctly specified. Busso, DiNardo and McCrary (2009) provide the detailed
discussion about the relationship between matching and double robust estimation.

6

Estimation Results

In this section, we report estimation results. We first present the results of propensity score
estimations. We then provide the estimation results of weighted least squares models forbase and new people. We finally show the heterogeneous results among base people indifferent administrative zones.

6.1

Results of Propensity Score

Table 5 provides the estimation results of logit models for base people. Parent’s characteristics and indicator variables for each administrative zone during the Pol Pot regime areused as covariates. These variables are important factors affecting the status of whether
households were formed under the Pol Pot regime, because a way of implementing theforced marriage system might be different among administrative zones. The estimation
results show that the households in treatment groups are more likely to contain older
mother and father. The coefficients of parent’s education and dummies for administrative
zones, such as West, Southwest, East, Central, and North zone, are statistically significant
in some samples.
Table 6 provides the estimation results of logit models for new people. The estimation
results show that the households in treatment groups are more likely to contain older
9

Our estimation approach is also related to sharp regression discontinuity (SRD) design. Our sample
construction exploits the discontinuity in political institutions or distribution of political power. However,unlike a standard SRD design, the assignment mechanism is not random even close at the discontinuity
point. The ATT parameter of our weighted least squares estimation can be interpreted as the SRD
estimand which addresses selection problem with matching.
10
This estimation approach is often used in the epidemiology literature (e.g. Lunceford and Davidian
2004; Austin and Mamdani 2006).

14

mother and father. The coefficients of dummies for administrative zones, including West,Southwest, North zone, and Kratie, are statistically significant in some samples. The
estimated propensity scores are then used as weights in the second step.
Figure 4 and 5 provide the distributions of estimated propensity score by different
samples and groups. While the shapes of distributions between treatment and control
groups are different in most of the samples, the distributions of propensity scores forcontrol samples are well overlapped to those for treatment groups in each sample, whichsuggests there is adequate support between treatment and control samples. We use a 5
percent trimming rule to secure the overlaps between treatment and control groups. 11

6.2

Estimation Results on Base and New People

Table 7 reports the results of inverse probability weighting estimation of equation (4) bydifferent samples and groups. The dependent variables for a first child are the indicatorvariables for graduating from primary school and for having never attended school, while
those for children aged 6-14 are the indicator variables for currently attending school andfor having never attended school. The control variables employed in these estimations
are child age, square of child age, female dummy, dummy for a female headed household,mother’s and father’s age, mother’s and father’s education, and regional dummies. Childage and square of child age are only used for children aged 6-14. The propensity scoresobtained in equation (3) are used as weights.
Panel (A) provides the estimates for base people. Let us first focus on the results of afirst child, which is shown in panel (A-1). Column (1) through (3) report the estimated
ATT for a first child of the households in both Treatment 1 and 2. Recall that thehouseholds in Treatment 1 had a first child born in 1978, while those in Treatment 2 hada first child born in 1979. The estimation results show that marriages under the Pol Pot
regime lowered the proportion of a first child who has graduated from primary school by
1.8-4.2 percent. On the other hand, they also show that marriages under the Pol Pot
regime raised the proportion of a first child who has never attended school by 2.0-2.3
percent.
Panel (A-2) reports the estimated ATT for the children aged 6-14 of the households inTreatment 1 and 2. The results show that marriages under the Pol Pot regime lowered the
proportion of children who are currently attending school by 1.4-2.9 percent, while thoseraised the proportion of children who have never attended school by 1.8-3.2 percent. All
the coefficients are statistically different from zero at conventional significance levels.
11

See Heckman et al (1997).

15

Let us now move on to the results for the children of new people in Treatment 1 and
2, which is shown in column (1) through (3) in Panel (B). It is found that marriages
under the Pol Pot regime had larger negative impacts on child education for new people
than that for base people. The estimation results show that marriages under the Pol Pot
regime decreased the proportion of attending school by 2.1-4.7 percent, though statisticalsignificance can be seen only for Sample 1-2. They also show that marriages under the
regime raised the proportion of having never attended school by 6.0-6.7 percent. The
results in panel (B-2) suggest that marriages under the Pol Pot regime had larger negative
impacts on child education for new people than that for base people. They decreased
the proportion of attending school by 4.7-5.2 percent, while they increased that of having
never attending school by 4.8-5.7 percent. All the coefficients are statistically significant.
We evaluate Treatment 1 and 2 separately to see heterogeneous impacts. The results
are presented in column (4) through (6) for Treatment 1 and column (7) through (9) for
Treatment 2. They reveal that marriage under the Pol Pot regime had larger impacts onchild education for Treatment 1 than that for Treatment 2. The estimation results forbase people in panel (A-1) show that marriages under the regime lowered the proportionof a first child who has graduated from primary school by 5.5-7.2 percent for Treatment 1
and by 0.6-3.0 percent for Treatment 2. They also show that marriages under the regime
raised the proportion of a first child who has never attended school by 5.8-6.4 percent
for Treatment 1, while none of the coefficients of ATT parameters for Treatment 2 arestatistically significant. The estimation results for children aged 6-14 in panel (A-2) showthat the marriage under the Pol Pot regime lowered the school participation rate by 3.34.4 percent for Treatment 1 and by 0.9-2.3 percent for Treatment 2. They also present
that marriages under the regime raised the proportion of having never attending schoolby 3.9-4.9 percent for Treatment 1 and by 1.2-2.6 percent for Treatment 2.
The estimated ATT are also different between Treatment 1 and 2 for new people. The
results in panel (B-1) show that marriages under the Pol Pot regime lowered the proportionof a first child who has graduated from primary school by 7.6-11.9 percent for Treatment
1, while none of the ATT is statistically significant for Treatment 2. They also show thatmarriages under the regime raised the proportion of a first child who has never attended
school by 13.8-17.2 percent for Treatment 1, while none of the coefficients is statisticallysignificant for Treatment 2. The estimation results for children aged 6-14 in panel (B-2)
show that marriages under the Pol Pot regime decreased the school participation rate by
9.7-10.7 percent for Treatment 1 and by 3.8-4.0 percent for Treatment 2. They also showthat marriages under the regime increased the proportion of having never attending schoolby 9.8-11.1 percent for Treatment 1 and by 4.0-4.5 percent for Treatment 2.

16

Overall, the results in Table 7 indicate that marriages under the Pol Pot regime had
negative impacts on child education for both base and new people. In particular, muchlarger effects are found for children of new people.
In the next section, we explore the heterogeneous impacts among different administrative zones under the Khmer Rouge regime. We in particular focus on base people
and develop the subsamples for Northwestern, West, Southwest, East, Central, and Northzone, respectively. We then estimate the ATT using the same methods employed in thissection.

6.3

Estimation Results Among Different Regions

We estimate the logit models of equation (3) for each subsample. In doing this, we employ
the same covariates used in Table 5, except the dummies for administrative zones. The
estimated propensity scores are then used as weights for weighted least square estimationof equation (4). The details results of these estimations are summarized in Appendix.
The distributions of estimated propensity scores for each sample are also presented inAppendix.
Table 8 reports the results of inverse probability weighting estimation of equation (4)for six subsamples. The dependent variables for a first child and children aged 6-14 aresame as those used in Table 7. The control variables are also same as those used in Table 7.
Column (1) through (3) report the estimated ATT for children of the households in bothTreatment 1 and 2. Let us first focus on the results for a first child. The estimation results
show that none of the coefficients are statistically significant for Northwest and West zone,
suggesting that marriages under the Pol Pot regime had no impacts on child educationof the households in these two zones. They also show that most of the estimated ATT
are statistically insignificant for East, Central, and North zone, suggesting that marriages
under the Pol Pot regime had less impact on education of a first child in these zones. On
the other hand, it is found that all the coefficients are statistically significant for Southwest
zone, which suggests that marriages under the Pol Pot regime had a negative impact onchild education for Southwest zone. Specifically, they lowered the proportion of graduatingfrom primary school by 3.6-8.8 percent and raised that of having never attended schoolby 5.2-6.7 percent.
Let us now focus on the results for children aged 6-14. The estimation results show thatnone of estimated ATT is statistically significant for Northwest, East, and North zone,
which suggests that marriages under the Pol Pot regime had no impact on child educationof the households in these zones. The results also show that most of the estimated ATT
are statistically insignificant for West and Central zone, suggesting that marriages under17

the Pol Pot regime had less impact on child education of the households in these zones.
On the other hand, statistical significance can be seen in all the coefficients for Southwest
zone. The results show that marriage under the Pol Pot regime lowered the proportion of
attending school by 8.3-8.6 percent and raised that of having never attending school by
8.8-9.4 percent.
We then evaluate the households in Treatment 1 and Treatment 2 by different administrative zones. The results are presented in column (4) through (6) for Treatment 1 andcolumn (7) through (9) for Treatment 2. Main findings for a first child are as follows.
First, marriages under the Pol Pot regime lowered child education for both Treatment 1
and 2 in Southwest zones. In particular, larger effects are found for Treatment 1. For example, marriages under the regime raised the proportion of having never attended schoolby 9.8-10.9 percent for Treatment 1, while they raised that by 4.5-5.4 percent for Treatment 2. Second, marriages under the regime lowered child education for Treatment 1, but
not for Treatment 2 in West, East, and Central zone. Third, most of the estimated ATT
coefficients are statistically insignificant, which suggests that marriages under the Pol Pot
regime had no impacts on child education for both Treatment 1 and 2 in Northwest andNorth zone.
Let us now move on the main results for children aged 6-14 by different administrative zones. It is found that marriages under the regime lowered child education for bothTreatment 1 and 2 in Southwest zone. In particular, larger effects are found for Treatment
1. Specifically, marriages under the regime lowered the proportion of attending school by
10.0-10.8 for Treatment 1 and by 7.8-7.9 for Treatment 2, while they raised the proportionof having never attended school by 11.2-11.6 for Treatment 1 and by 8.3-8.5 for Treatment 2. It is also found that marriages under the regime lowered child education of thehouseholds in West and Central zone for Treatment 1, but not that for Treatment 2. It isalso found that almost all the estimated ATT coefficients are statistically insignificant for
both Treatment 1 and 2 in Northwest, East, and North zone. This suggests that marriages
under the Pol Pot regime had no impacts on child education for the households in thesezones.
Overall, the results in Table 8 indicate that marriages under the Pol Pot regime lowered
the educational attainment of children in Southwest zone, while they had negative impacts
on child education for Treatment 1, but not for Treatment 2 in West and Central zone.
On the other hand, they had less negative impact on child education for both Treatment
1 and 2 in Northwest, East, and North zone.

18

7

Interpretation of the Findings

Our estimation results in Table 7 and 8 provide the following main findings. First, ourestimation results have shown the differences in educational investment for children between the households formed under the Pol Pot regime and those formed after the Pol
Pot regime. The investment in child education was lower for the households formed under
the Pol Pot regime than those formed after the Pol Pot regime. This suggests that forced
marriages employed in the Pol Pot regime had negative impacts on child education after
its collapse.
Second, our estimation results have also shown the differences between urban and rural
people. While the investment in child education was lower for the households formed under
the Pol Pot regime than for those formed after the Pol Pot regime for both urban andrural people, much larger difference is found for the urban people.
Third, our estimation results have also shown the differences among different regions.
The investment in child education was lower for the households in West, Central, and inparticular Southwest zone formed under the Pol Pot regime than for those formed after the
Pol Pot regime in each zone. On the other hand, less difference between those households
can be seen in Northwest, East, and North zone.
Fourth, our estimation results have also shown the differences between the householdswith a first child born in 1978 and those in 1979. Larger difference is found for thehouseholds with a first child born in 1978. This finding can be seen for both urban andrural people as well as for the base people in different administrative zones.
Our interpretation of these findings is that the differences in culture emerging in the Pol
Pot regime led to different impacts on child education. During the Pol Pot regime, urban
and rural people were treated differently. In particular, urban people were persecutedduring the Pol Pot regime. Historical documents report the degree of differences in control
among different administrative zones. For example, Southwest zone was a special zone
during the Pol Pot regime and people in Southwest zone were harshly controlled by the
Khmer Rouge (e.g. Kiernan 1996; Vannak 2003). The Khmer Rouge in East zone wasless brutal than those in other zones (e.g. Kiernan 1996). During the Pol Pot regime, thehouseholds with a first child born in 1978 appear to face harsher conditions than thoseborn in 1979, because they spent more time in raising the child under severer circumstancein the Pol Pot regime. The estimation results and these historical evidences then suggestthat a harsher social environment had more negative impacts on child education.

19

8Conclusion

This paper has analyzed the effects of the forced marriage system employed in the Pol Pot
regime on child education. Because no statistical data on forced marriages were available,
we have investigated the differences in educational investment for children between thehouseholds formed under the Pol Pot regime and those formed after the Pol Pot regime. In
particular, we have focused on the differences between urban and rural people and among
different regions and provided the empirical evidences suggesting that forced marriages
had negative impacts on child education. We have also argued that the culture emerging
in the Pol Pot regime had different impacts on child education. In particular, we havepointed out that harsher social environment had more negative impacts on child education.
Because we evaluate a part of the households formed under the Pol Pot regime, carefulattention needs to be paid to our interpretations. In addition, because the census data
employed in this study do not contain much information on household income, covariates
may not be fully adjusted between the household formed under the Pol Pot regime andthose formed after the Pol Pot regime.
Although these limitations are placed on, our results provide the implication thata ruling with strong political power such as dictatorship may trigger the emergence ofdifferences in culture. In this case, social environments may play an important role increating cultural differences, which may be persistent and have different effects on socioeconomic behavior.

20

References[1] Austin, P.C., and Mamdani, M.M. (2006): “A Comparison of Propensity Score Methods: A Case-Study Estimating the Effectiveness of Post-AMI Stain Use,” Statistics
in Medicine, 25, 2084-2106.
[2] Busso, M., J. DiNardo, and J. McCrary (2009): “Finite Sample Properties of Semiparametric Estimators of Average Treatment Effects,” Working Paper, UC - Berkeley.
[3] Dy, K. (2007): “A History of Democratic Kampuchea (1975 - 1979)”, Documentation
Center of Cambodia.
[4] Heckman, J. (1990): “Varieties of Selection Bias”, American Economic Review, 80,
331-318.
[5] Heckman, J., Ichimura, H., Todd, P. (1997): “Matching as an Econometric Evaluation
Estimator: Evidence from Evaluating a Job Training Program”, Review of Economic
Studies, 64, 487-535.
[6] Heckman, J., Ichimura, H., Todd, P. (1998): “Matching as an Econometric Evaluation
Estimator”, Review of Economic Studies, 65, 261-294.
[7] Hirano, K. and Imbens, G. W. (2001): “Estimation of Causal Effects using Propensity
Score Weighting: An Application to Data on Right Heart Catheterization”, HealthServices and Outcomes Research Methodology, 2, 259-278.
[8] Holland, P., (1986): “Statistics and Causal Inference”, Journal of the American Statistical Association, 81, 945-970.
[9] Kiernan, B. (1996): “The Pol Pot Regime: Race, Power, and Genocide in Cambodia
under the Khmer Rouge, 1975-79”, Yale University Press.
[10] Lunceford, J.K., and Davidian, M. (2004): “Stratification and Weighting via thePropensity Score in Estimation of Causal Treatment Effects: A Comparative Study”,
Statistics in Medicine, 23, 2937-2960.
[11] Marshall, G.N., Schell, T.L., Elliott, M.N., Berthold, S.M. and Chun, C.A. (2005):“Mental Health of Cambodian Refugees 2 Decades After Resettlement in the UnitedStates”, Journal of American Medical Association, 294, 571-579.
[12] Mollica, R.F., Donelan, K. Tor, S., Lavelle, J., Elias, C. Frankel, M. and Blendon, R.J.
(1993): “The Effect of Trauma and Confinement on Functional Health and Mental
21

Health Status of Cambodia Living in Thailand-Cambodia Border Camps”, Journalof American Medical Association, 270, 581-586.
[13] Mollica, Richard F., Mcinnes, K., Poole, C., and Tor, S. (1998): “Dose-Effect Relationships of Trauma to Symptoms of Depression and Post-Traumatic Stress Disorderamong Cambodian Survivors of Mass Violence”, British Journal of Psychiatry, 173,482-488.
[14] Nakagawa, K. (2008): “Gender-Based Violence During the Khmer Rouge Regime
Stories of Survivors from the Democratic Kampuchea (1975-1979)”, mimeo.
[15] Robins, J.M., and Rotnitzky, A. (1995): “Semiparametric Efficiency in Multivariate
Regression Models with Missing Data,” Journal of the American Statistical Association, 16, 285-319.
[16] Robins, J.M., and Rotnitzky, A., and Zhao, L.P. (1995): “Analysis of Semiparametric
Regression Models for Repeated Outcomes in the Presence of Missing Data,” Journalof the American Statistical Association, 90, 106-121.
[17] Rosenbaum, P. and Rubin, D. (1983): “The Central Role of the Propensity Score in
Observational Studies for Causal Effects,” Biometrika, 70, 41-55.
[18] Rubin, D. (1974): “Estimating Causal Effects on Treatments in Randomized andNon-randomized Studies,” Journal of Educational Psychology, 66, 688-701.
[19] Scharfstein D.O., Rotnitzky, A., Robins, J.M. (1999): “Adjusting for Nonignorable
Drop-Out Using Semiparametric Nonrespnse Models,” with comments and rejoinder,
Journal of the American Statistical Association, 94, 1096-1120.
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Missing Data Problems”, Journal of Econometrics, 141, 1281-1301.

22

.15
.1

.1

.15

(1a-1) Distribution of Educational Attainment (1b-1) Distribution of Educational Attainment
for First Child (Base People)
for First Child (New People)

1982

DensityDensity

1982
1981
1979

1979

.05

1978

0

0

.05

1978

1981

0

1

2

3
4
5
6
7
8
9 10
Educational Attainment (in Grade)
Treatment 1 (1978)
Control 1 (1981)

11

12

13

0

1

Treatment 2 (1979)
Control 2 (1982)

2

3
4
5
6
7
8
9 10
Educational Attainment (in Grade)
Treatment 1 (1978)
Control 1 (1981)

bandwidth=1.00

11

12

13

Treatment 2 (1979)
Control 2 (1982)

bandwidth=1.00

11(1a-2) Proportion of Children Aged 6-14 Who (1b-2) Proportion of Children Aged 6-14 Who
Are Currently in School (Base People)
Are Currently in School (New People)
1981

Estimated Probability
.4
.6
.8

Estimated Probability
.4
.6
.8

1981
1982
1979

1979

.2

1978

0

0

.2

1978

1982

6

7

8

9

10
Age

11

Treatment 1 (1978)
Control 1 (1981)

12

13

14

6

7

Treatment 2 (1979)
Control 2 (1982)

8

9

10
Age

11

Treatment 1 (1978)
Control 1 (1981)

bandwidth=1.00

12

13

14

Treatment 2 (1979)
Control 2 (1982)

bandwidth=1.00

1

1

Estimated Probability
.4
.6
.8

Estimated Probability
.4
.6
.8

(1a-3) Proportion of Children Aged 6-14 Who (1b-3) Proportion of Children Aged 6-14 Who
Have Never Attended School (Base People)
Have Never Attended School (New People)

1978

1979

1982

.2

.2

1979

1978

1981

0

1981

0

1982

6

7

8

9

Treatment 1 (1978)
Control 1 (1981)

10
Age

11

12

13

14

6

7

Treatment 2 (1979)
Control 2 (1982)

8

9Treatment 1 (1978)
Control 1 (1981)

bandwidth=1.00

bandwidth=1.00

Figure 1: Educational Attainments
23

10
Age

11

12

13

Treatment 2 (1979)
Control 2 (1982)

14

Treatment 1 (1978)

Control 1 (1981)
NortheastNorth

KratieNorthwest
CenterKratieNorthwest
CenterM ondulKiri

East

W est

NortheastNorth

M ondulKiri

East

W est
Percentage

Percentage

0.001 - 0.004

0.000 - 0.002

0.005 - 0.009

Southwest

0.003 - 0.005

Southwest

0.010 - 0.015

0.006 - 0.009

0.016 - 0.022

Kam pong Soam

0.010 - 0.015

Kam pong Soam

0.023 - 0.039

Treatment 2 (1979)

Control 2 (1982)
Northeast

North

CenterNortheastNorth

KratieNorthwest

KratieNorthwest
CenterM ondulKiri

East

W est

0.016 - 0.026

M ondulKiri

East

W est
Percentage

Percentage

0.000 - 0.003

0.000 - 0.003

0.004 - 0.007

Southwest

0.004 - 0.006

Southwest

0.008 - 0.013

0.007 - 0.009

0.014 - 0.023

Kam pong Soam

0.010 - 0.014

Kam pong Soam

0.024 - 0.044

0.015 - 0.022

Figure 2: Spatial Distribution of the Samples (Base People)

Treatment 1 (1978)

Control 1 (1981)
NortheastNorth

KratieNorthwest
CenterKratieNorthwest
CenterM ondulKiri

East

W est

NortheastNorth

M ondulKiri

East

W est
Percentage

Percentage

0.010

0.001 - 0.004

0.011 - 0.029

Southwest

0.005 - 0.015

Southwest

0.030 - 0.049

0.016 - 0.033

0.050 - 0.069

Kam pong Soam

0.034 - 0.060

Kam pong Soam

0.070 - 0.127

Treatment 2 (1979)

Control 2 (1982)
Northeast

North

CenterKratieNorthwest
CenterM ondulKiri

East

Percentage

0.003 - 0.010

0.001 - 0.005

0.011 - 0.024

Southwest

0.025 - 0.037

0.006 - 0.016
0.017 - 0.034

0.038 - 0.071

Kam pong Soam

M ondulKiri

East

W est
Percentage

Southwest

NortheastNorth

KratieNorthwest

W est

0.061 - 0.095

0.035 - 0.053

Kam pong Soam

0.072 - 0.098

0.054 - 0.079

Figure 3: Spatial Distribution of the Samples (New People)

24

.3

.2

.1

0

.2

.2

.2

Graphs by treat0Graphs by treat0Graphs by treat

0

.8

.4

1

0

.2

.4

.8

1

0

Propensity Score

.2

.6

.8

1

0

Propensity Score

.2

.6

.4

.4

.6

Treatment.6

Treatment(a-3-1) Sample 3-1.6Control.4.8Propensity Score

Treatment(a-2-1) Sample 2-1.6Control.4

Control(a-1-1) Sample 1-1

1

1

1

0

.2

.2

.2
Graphs by treat0Graphs by treat0Graphs by treat

.4

1

0

.2

.4

.8

1

0

Propensity Score

.2

.6

.8

1

0

Propensity Score

.2

.6

.4

.4

.6

Treatment.6

Treatment(a-3-2) Sample 3-2.6Control.4.8Propensity Score

Treatment(a-2-2) Sample 2-2.6Control.4

Control(a-1-2) Sample 1-2

.8

.8

.8

1

1

1

0

.2

.2

.2
Graphs by treat0Graphs by treat0Graphs by treat

Figure 4: Estimated Propensity Score (Base People)

.8

.8

.8

.2
.1
0
.4

.2
.1
0
.6

DensityDensityDensity

.6

.4

.2

0

.3

.2

.1

0

DensityDensityDensity

.3
.2
.1
0
.3
.2
.1
0

DensityDensityDensity

.4
.2
0
.2
.1
0

25

.4

0

.2

.4

.8

1

0

Propensity Score

.2

.6

.8

1

0

Propensity Score

.2

.6

.4

.4

.6

Treatment.6

Treatment(a-3-3) Sample 3-3.6Control.4

1

Propensity Score.8

Treatment(a-2-3) Sample 2-3.6Control.4

Control(a-1-3) Sample 1-3

.8

.8

.8

1

1

1

.3

.2

.1

0

.2

.2

.2

Graphs by treat0Graphs by treat0Graphs by treat

0.8

DensityDensityDensity

.6

.4

.2

0

.4

.2

0

.4

1

0

.2

.4

.8

1

0

Propensity Score

.2

.6

.8

1

0

Propensity Score

.2

.6

.4

.4

.6

Treatment.6

Treatment(b-3-1) Sample 3-1.6Control.4.8Propensity Score

Treatment(b-2-1) Sample 2-1.6Control.4

Control(b-1-1) Sample 1-1

1

1

1

Density
0

.2

.2

0

.2

Graphs by treat0Graphs by treat

.4

1

0

.2

.4

.8

1

0

Propensity Score

.2

.6

.8

1

0

.2

.6

.4

.4

.6

Treatment.6

Treatment(b-3-2) Sample 3-2.6Control.4.8Propensity Score

Treatment(b-2-2) Sample 2-2.6Control.4

Control(b-1-2) Sample 1-2

.8

.8

.8

1

1

1

Graphs by treat

Propensity Score

0

.2

.2

.2
Graphs by treat0Graphs by treat0Graphs by treat

Figure 5: Estimated Propensity Score (New People)

.8

.8

.8

.3
.2
.1
0
.5
0
.3
.2
.1
0

DensityDensity

.2
.1
0
.6
.4
.2
0
.3
.2

DensityDensityDensity
.1
0

26

.4

0

.2

.4

.8

1

0

Propensity Score

.2

.6

.8

1

0

Propensity Score

.2

.6

.4

.4

.6

Treatment.6

Treatment(b-3-3) Sample 3-3.6Control.4

1

Propensity Score.8

Treatment(b-2-3) Sample 2-3.6Control.4

Control(b-1-3) Sample 1-3

.8

.8

.8

1

1

1

Table 1: Age Table
Year
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983

(A)

0

1998

15

0
1

0
1
2

0
1
2
3

0
1
2
3
4

0
1
2
3
4
5

16

17

18

19

20

0
1
2
3
4
5
6

6
7
8
9
10
11
12
13
14
15
16
17
18

21

33

7
8
9
10
11
12
13
14
15
16
17
18
19

8
9
10
11
12
13
14
15
16
17
18
19
20

(B)
9
10
11
12
13
14
15
16
17
18
19
20
21

34

35

36

10
11
12
13
14
15
16
17
18
19
20
21
22

11
12
13
14
15
16
17
18
19
20
21
22
23

12
13
14
15
16
17
18
19
20
21
22
23
24

37

38

39

Note: The table shows ages of children aged 15-21 and women aged 33-39 in 1998 Census datafrom 1971 to 1983. Panel A shows those for children aged 15-21, while Panel B shows those forwomen aged 33-39. Note that the Pol Pot regime started in April 1975 and fell in January 1979.

27

Table 2: Detailed Procedures for Sampling
No.
(0)
(1)

Description of Condition
Total number of households in 1998 Census microdata
A first child in a household as of 1998 is 16 or 17 or 19 or 20 years old.

Keep the households that satisfy the following conditions.
(2)Household type is “normal” (not “homeless”, “boat”, “transient”).
(3)
All members of a household were born in Cambodia.
(4)
All members of a household have never lived outside Cambodia.
(5)
All members of a household speak Khmer as their fist language.
(6)
All members of a household believe in Buddhism.
(7)
There is mother in a household.
(8)
If a household satisfies with (7), mother’s marital status is “married” or“divorced” or “widowed” and that there is father in a household.
(9)
All children in a household have never married.
(10)
Educational attainments of father, mother, and children are neither missing
nor “other”.
(11)
There are no other/non relatives, grandfather/mother/child in a household.
(12)
If age of ith child is same as that of i + 1th child, birth place of ith childis same as that of i + 1th child.
(13)
Number of mother’s own children alive is equal to that of children living
together in 1998.
(14-1) Mother’s birth place is a provincial town or Phnom Penh. (New people)(14-2) Mother’s birth place is neither a provincial town nor Phnom Penh andthe birth place of a first child is same as that of mother. (Base people)
Treatment Groups(15-1) Households with mother aged 34-38 and a first child aged 20 (Treatment 1).
(15-1) Households with mother aged 34-38 and a first child aged 19 (Treatment 2).
Control Groups(15-2) Households with mother aged 34-38 and a first child aged 17 (Control 1).
(15-2) Households with mother aged 34-38 and a first child aged 16 (Control 2).
Samples
Sample 1-1: Treatment 1 and 2; Control 1 and 2 (base: 32,626; new: 3,019)
Sample 1-2: Treatment 1 and 2; Control 1(base: 18,194; new: 1,690)
Sample 1-3: Treatment 1 and 2; Control 2(base: 19,213; new: 1,771)
Sample 2-1: Treatment 1; Control 1 and 2(base: 28,957; new: 2,695)
Sample 2-2: Treatment 1; Control 1(base: 14,525; new: 1,366)
Sample 2-3: Treatment 1; Control 2(base: 15,544; new: 1,447)
Sample 3-1: Treatment 2; Control 1 and 2(base: 31,514; new: 2,901)
Sample 3-2: Treatment 2; Control 1(base: 17,082; new: 1,572)
Sample 3-3: Treatment 2; Control 2(base: 18,101; new: 1,653)

Obs.
2,188,177
351,071

349,814340,424
336,872
325,997
323,097
314,878
291,601
270,558
269,661
214,700
214,595
120,050
9,671
92,656102,327
1,230
3,993
5,223
14,661
15,76130,42235,64519,884
20,98431,652
15,891
16,991
34,41518,65419,754

Note: The table shows detailed results of sampling. While 2,188,662 households were contained in
an original data set, all the duplicated households, except one of them, were excluded. 2,188,177
households were used for sampling and fifteen conditions were imposed to develop six samples
for this study.

28

Table 3: Mother’s Marital Status
Treatment 1
MarriedDivorced
WidowedObservations

0.907
0.030
0.063
1,112

MarriedDivorced
WidowedObservations

0.864
0.034
0.102
118

Treatment 2 Control 1(A) Base People
0.914
0.918
0.029
0.034
0.058
0.049
3,669
13,413(B) New People
0.911
0.923
0.031
0.030
0.059
0.047
324
1,248

Control 2
0.917
0.033
0.050
14,432
0.939
0.027
0.034
1,329

Note: The table shows the proportion of married, divorced, and widowed households by different
groups and samples.

29

30

0.041
(0.197)36.917(1.193)41.009(4.399)2.195(2.581)4.044(3.106)0.013(0.115)0.043(0.203)36.475(1.269)39.451(4.021)
1.988(2.482)
3.722(3.077)0.013(0.112)

Base People
Treatment 2 Control 1
(2)
(3)
0.044
(0.206)36.144(1.331)38.718(4.043)1.920(2.458)3.452(2.992)0.014(0.116)

Control 2(4)
0.069(0.254)36.833(1.313)42.931(5.440)2.559(2.974)4.706(3.506)0.696(0.462)Treatment 1
(5)
0.058
(0.233)36.936(1.186)42.031(4.216)2.976(2.982)
5.302(3.659)0.590(0.493)0.053(0.224)36.594(1.269)40.505(4.391)
3.146(3.143)
5.206(3.548)0.582(0.493)

New People
Treatment 2 Control 1
(6)
(7)

0.041
(0.198)36.301(1.326)39.675(4.622)2.986(2.985)
5.063(3.576)0.588(0.492)Control 2(8)Observations
1,009
3,353
12,31013,229
102
295
1,152
1,248
Notes: The table shows the means and standard deviations of household characteristics by different groups and samples. The standard
deviations are reported in parentheses. The variable definitions are as follows. “Female head” is an indicator variable for a female
headed household. “Mother’s age” is mother’s age in years. “Father’s age” is father’s age in years. “Mother’s education” is the highest
grade completed for mother. “Father’s class is new” is an indicator variable for father who was classified as new people during the
Pol Pot regime. The definition of new people is same as that for mother.

Treatment 1(1)(A) Household Characteristics
Female head
0.049
(0.215)
Mother’s age
36.786(1.315)
Father’s age
42.169(5.381)
Mother’s education
2.077(2.559)Father’s education
3.836(3.167)
Father’s class is new
0.013(0.113)

Table 4: Descriptive Statistics

31
243

10.091(2.598)0.486(0.501)0.547(0.499)0.436(0.497)102714

10.300(2.556)0.478(0.500)0.681(0.467)0.304(0.460)295

19.000(0.000)0.363(0.482)0.529(0.500)0.149(0.357)2,951

10.204(2.537)0.509(0.500)0.725(0.447)0.259(0.438)1,152

17.000(0.000)0.475(0.500)0.546(0.498)0.128(0.335)

New People
Treatment 2 Control 1
(6)
(7)

3,363

10.380(2.614)0.494(0.500)0.721(0.449)0.260(0.439)1,248

16.000(0.000)0.502(0.500)0.509(0.500)0.138(0.345)Control 2(8)Notes: The table shows the means and standard deviations by different groups and samples. The standard deviations are reported
in parentheses. The variable definitions are as follows. “Age” is age in years. “Female” is an indicator variable for female. “Primaryschool” is an indicator variable for graduating from a primary school. “Never attended school” is an indicator variable for children
who has never attended school. “In school” is an indicator variable for children who are currently attending school.

37,191

32,821

Observations

8,475

10.246(2.603)0.490(0.500)0.611(0.487)0.369(0.483)10.149(2.540)0.492(0.500)0.619(0.486)0.362(0.481)

2,494

13,22920.000(0.000)0.382(0.488)0.392(0.491)0.324(0.470)16.000(0.000)0.498(0.500)0.334(0.472)0.204(0.403)

12,310Treatment 1(5)

Control 2(4)

Observations
1,009
3,353
(C) Characteristics of Children Aged 6-14
Age
10.19610.193(2.553)(2.560)Female0.481
0.486(0.500)(0.500)In school
0.565
0.600
(0.496)(0.490)
Never attended school
0.420
0.384
(0.494)(0.486)

Base People
Treatment 1 Treatment 2 Control 1
(1)
(2)
(3)
(B) Characteristics of a First ChildAge
20.00019.00017.000(0.000)(0.000)(0.000)Female
0.390
0.406
0.494
(0.488)(0.491)(0.500)Primary school
0.302
0.360
0.367
(0.459)(0.480)(0.482)
Never attended school
0.278
0.212
0.204
(0.448)(0.409)(0.403)

Table 4: Descriptive Statistics (Continued)

32

Sample1-3(3)0.388***(0.015)0.109***(0.004)0.012(0.008)0.031***(0.007)-0.060(0.163)-0.100(0.085)-0.243***(0.070)0.708***(0.065)0.654***(0.071)
0.788***(0.081)-0.061(0.449)0.353(0.243)-20.101***(0.574)
17,591-8699.5
0.117

Sample2-1(4)0.232***(0.027)0.105***(0.006)0.105(0.014)0.000(0.012)-0.034(0.290)-0.254*(0.143)-0.672***(0.126)
0.471***(0.107)0.242*(0.121)0.551***(0.131)-0.038(0.733)0.278(0.381)-16.066***(1.000)26,548-3974
0.074

Sample2-2(5)0.148***(0.028)0.108***(0.006)0.021(0.015)-0.017(0.012)0.024(0.299)-0.315**(0.146)-0.704***(0.129)0.418***(0.110)0.196(0.124)0.486***(0.136)-0.007(0.746)0.139(0.396)-12.316***(1.039)13,319-3325.1
0.069

Sample2-3(6)0.308***(0.028)0.118***(0.006)0.011(0.015)0.015(0.012)-0.075(0.298)-0.181(0.148)-0.635***(0.130)0.538***(0.111)0.304**(0.125)0.599***(0.137)-0.161(0.759)0.394(0.399)-18.745***(1.025)
14,238-3265.9
0.104

Table 5: Propensity Score Estimates (Base People)
Sample1-2(2)0.232***(0.015)0.088***(0.004)0.018**(0.008)0.004(0.007)0.071(0.162)-0.240***(0.084)-0.320***(0.069)0.588***(0.064)
0.521***(0.070)0.672***(0.080)0.012(0.437)0.167(0.236)-13.369***(0.578)
16,672-8944.2
0.067

Sample3-1(7)0.349***(0.016)
0.076***(0.004)0.015**(0.008)0.027***(0.007)0.006(0.165)-0.146(0.090)-0.172**(0.074)0.698***(0.068)0.680***(0.073)0.781***(0.083)0.080(0.479)0.299(0.251)-18.317***(0.602)
28,892-9608.3
0.074

Sample3-2(8)0.264***(0.017)0.073***(0.005)0.018**(0.009)0.012(0.007)0.066(0.177)-0.212**(0.094)-0.208***(0.077)0.642***(0.072)0.615***(0.078)0.730***(0.089)0.070(0.499)0.175(0.266)-14.326***(0.646)
15,663-7641.8
0.061

Sample3-3(9)0.423***(0.017)
0.093***(0.005)0.011(0.009)0.040***(0.007)-0.075(0.179)-0.075(0.096)-0.135*(0.078)0.761***(0.073)0.748***(0.079)0.842***(0.090)0.037(0.510)0.360(0.272)-21.071***(0.641)
16,582-7430.6
0.110

Notes: The table reports the results of propensity score estimations. The coefficients are estimated from logit models. Standard errors
are reported in parentheses. The unit of observation is a household. Northwest zone is the base case for the dummy variables ofadministrative zones. The dummy variables for Kompong Som and Mondok Kiri are excluded from estimations.
*** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

Observations
Log-likelihood value
Pseudo R-squaredIntercept

KratieNortheast zone

North zone

Central zone

East zone

Southwest zone

West zone

Father’s class is new

Father’s education

Mother’s education

Father’s age

Mother’s age

Sample1-1(1)0.317***(0.014)0.090***(0.004)0.015**(0.007)0.019***(0.006)0.003(0.149)-0.175***(0.079)-0.284***(0.065)0.643***(0.060)0.582***(0.065)0.725***(0.073)0.021(0.414)0.285(0.219)-17.350***(0.533)29,896-114280.080

33-10.515***(1.878)1,548-844.960.040

Sample1-3(3)0.336***(0.050)0.104***(0.014)-0.018(0.023)0.002(0.019)0.086(0.128)-0.200(0.213)-0.282(0.194)0.262(0.229)0.313(0.286)
0.386*(0.214)0.246(0.463)-0.916(1.104)0.560**(0.237)0.578(1.499)-17.726***(1.884)
1,645-829.98
0.087-15.660***(3.223)2,486-377.88
0.112

0.270
(0.354)

Sample2-1(4)0.230***(0.086)0.100***(0.018)-0.011(0.041)-0.012(0.034)0.332(0.233)-0.899**(0.412)-1.930***(0.507)-0.081(0.386)-0.028(0.480)0.740**(0.304)0.702(0.589)-11.335***(3.358)1,247-313.54
0.112

0.032
(0.366)

Sample
2-2
(5)
0.129
(0.089)0.105***(0.020)-0.017(0.041)-0.015(0.035)0.340(0.243)-0.810**(0.408)-1.957***(0.513)-0.166(0.400)-0.146(0.500)0.614*(0.319)0.627(0.634)-19.142***(3.387)1,341-306.55
0.150

0.519
(0.371)

Sample2-3(6)0.327***(0.090)0.114***(0.020)-0.001(0.044)-0.015(0.035)0.386(0.242)-1.003**(0.431)-1.975***(0.521)
0.042(0.397)0.009(0.497)0.809**(0.316)0.586(0.629)

Table 6: Propensity Score Estimates (New People)
Sample1-2(2)0.169***(0.050)0.080***(0.013)-0.025(0.022)0.000(0.019)0.130(0.129)-0.225(0.210)-0.375*(0.194)0.078(0.230)0.086(0.286)0.170(0.215)0.245(0.474)-0.582(1.096)0.030(0.227)

Sample3-1(7)0.279***(0.054)0.068***(0.012)-0.022(0.024)0.006(0.020)0.022(0.132)-0.024(0.220)-0.031(0.199)0.236(0.241)0.277(0.298)0.043(0.244)0.046(0.555)-0.383(1.056)0.299(0.246)1.746(1.460)-15.134***(1.996)2,695-892.640.041-11.073***(2.119)1,446-709.73
0.028

Sample3-2(8)0.191***(0.057)
0.066***(0.015)-0.027(0.025)0.004(0.021)
0.068(0.142)-0.037(0.232)-0.080(0.211)0.154(0.257)0.180(0.319)-0.088(0.259)0.031(0.595)-0.285(1.099)0.029(0.260)Sample3-3(9)0.356***(0.057)0.087***(0.015)-0.022(0.026)0.006(0.021)-0.002(0.140)-0.015(0.234)0.014(0.211)
0.345(0.256)0.430(0.319)0.135(0.259)0.066(0.583)-0.555(1.100)0.562**(0.270)1.000(1.488)-18.134***(2.122)
1,543-699.37
0.071

Notes: The table reports the results of propensity score estimations. The coefficients are estimated from logit models. Standard errors
are reported in parentheses. The unit of observation is a household. Northwest zone is the base case for the dummy variables ofadministrative zones. *** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

Observations
Log-likelihood value
Pseudo R-squaredIntercept

Mondol Kiri

KratieKompong Som

Northeast zone

North zone

Central zone

East zone

Southwest zone

West zone

Father’s class is new

Father’s education

Mother’s education

Father’s age

Mother’s age

Sample1-1(1)0.259***(0.047)0.083***(0.011)-0.020(0.021)0.001(0.018)0.101(0.118)-0.207(0.197)-0.329*(0.181)
0.158(0.212)
0.192(0.263)0.307(0.196)0.289(0.423)-0.711(1.055)0.294(0.211)
1.331(1.468)-14.685***(1.744)
2,797
-1081
0.054

34

-0.018*(0.009)0.020**(0.008)-0.014*(0.007)0.018**(0.007)-0.021(0.026)0.060***(0.021)-0.047**(0.019)0.048**(0.019)-0.029***(0.007)0.032***(0.007)-0.047*(0.027)0.067***(0.021)-0.052***(0.019)0.057***(0.019)Sample1-3(3)-0.097***(0.030)0.101***(0.029)-0.087**(0.038)0.155***(0.037)-0.040***(0.012)0.045***(0.012)-0.066***(0.014)0.064***(0.013)Sample2-1(4)-0.107***(0.032)0.111***(0.031)-0.119***(0.040)0.172***(0.039)-0.044***(0.013)0.049***(0.013)-0.072***(0.014)0.058***(0.013)Sample2-2(5)Table 7: Estimation Results-0.042***(0.008)0.023***(0.007)

Sample1-2(2)-0.099***(0.031)
0.098***(0.031)-0.076*(0.039)0.138***(0.040)-0.033**(0.013)0.039***(0.013)-0.055***(0.015)
0.061***(0.014)

Sample2-3(6)-0.038**(0.019)0.043**(0.019)-0.019(0.027)0.030(0.020)-0.018***(0.007)0.020***(0.007)-0.019**(0.008)0.009(0.007)

Sample3-1(7)-0.040*(0.021)0.045**(0.020)-0.024(0.029)0.030(0.022)-0.023***(0.007)0.026***(0.007)-0.030***(0.009)0.009(0.008)Sample3-2(8)-0.038*(0.021)0.040**(0.020)-0.011(0.029)0.034(0.021)-0.009(0.008)0.012(0.008)-0.006(0.010)0.007(0.008)Sample3-3(9)Notes: The table reports the coefficients from weighted least square regressions with propensity scores weighting. Propensity scores
estimated in Table 5 and 6 are used. 5 % trimming rule over common support (See Heckman et al. 1997) is imposed for all estimations
in both panel (A) and (B). The unit of observation is a child. For panel (A), the dependent variables are the indicator variablefor graduating from primary school (Primary School) and the indicator variable for having never attended school (Never Attended
School). All regressions include the following conditional variables: female, female head, mother’s and father’s age, mother’s andfather’s education and regional dummies developed from the interaction between province and urban or rural codes. Robust standard
errors are reported in parenthese. For the panel (B), the dependent variables are the indicator variable for currently attending school(In School) and the indicator variable for having never attended school (Never Attended School). All regressions include the following
conditional variables: child age, squre of child age, female, female head, mother’s and father’s age, mother’s and father’s education,and regional dummies. Standard errors clustered by household are reported in parenthese. *** significant at the 1 percent level, **significant at the 5 percent level, * significant at the 10 percent level.

-0.032***(0.008)Never attended school 0.023***(0.007)(A-2) Children Aged 6-14
In school-0.023***(0.006)
Never attended school 0.026***(0.006)(B) New People(B-1) First Child
Primary school-0.036(0.024)
Never attended school 0.065***(0.019)(B-2) Children Aged 6-14
In school-0.051***(0.018)Never attended school 0.055***(0.017)(A) Base People(A-1) First Child
Primary school

Sample
1-1
(1)

35

Sample1-2(2)Sample1-3(3)Sample2-1(4)Sample2-2(5)Sample2-3(6)Table 8: Heterogeneity Effects for Base People
Sample3-1(7)Sample3-2(8)Sample3-3(9)-0.031-0.030-0.032-0.063*-0.053-0.067*-0.021-0.024-0.019(0.022) (0.023) (0.024)(0.034)(0.036)(0.036)(0.025) (0.027) (0.028)
Never attended school
0.018
0.016
0.020
0.044
0.031
0.034
0.018
0.018
0.017
(0.019) (0.020) (0.021)(0.034)(0.035)(0.034)(0.022) (0.023) (0.024)(1-2) Children Aged 6-14
In school-0.025-0.018-0.033
0.016
0.014
0.014
-0.037* -0.030 -0.051**(0.019) (0.020) (0.021)(0.035)(0.037)(0.035)(0.022) (0.023) (0.023)
Never attended school
0.010
0.003
0.020
-0.022-0.020-0.020
0.022
0.012
0.034
(0.019) (0.020) (0.021)(0.035)(0.038)(0.036)(0.021) (0.022) (0.023)(2) West zone(2-1) First Child
Primary school-0.014-0.020-0.017 -0.089** -0.084** -0.091**
0.014
0.008
0.015
(0.026) (0.027) (0.031)(0.040)(0.041)(0.046)(0.029) (0.031) (0.033)
Never attended school
0.020
0.013
0.038
0.143*** 0.111** 0.152*** -0.020-0.023-0.010(0.022) (0.023) (0.025)(0.044)(0.043)(0.046)(0.023) (0.024) (0.025)(2-2) Children Aged 6-14
In school-0.024-0.025-0.022-0.056-0.054-0.056-0.014-0.016-0.015(0.020) (0.021) (0.022)(0.035)(0.035)(0.035)(0.022) (0.023) (0.024)
Never attended school 0.036* 0.041*
0.031
0.066*
0.062*
0.062*
0.027
0.032
0.024
(0.020) (0.021) (0.022)(0.035)(0.035)(0.035)(0.022) (0.023) (0.023)
Notes: The table reports the coefficients from the weighted least square regressions with propensity scores weighting. Propensity
scores estimated in Table 10 (Appendix) are used. 5 % trimming rule over common support (See Heckman et al. 1997) is imposed
for all estimations in both panel (1) and (2). The unit of observation is a child. For panel (1), the dependent variables are theindicator variable for graduating from primary school (Primary School) and the indicator variable for having never attended school(Never Attended School). All regressions include the following conditional variables: female, female head, mother’s and father’s age,mother’s and father’s education and regional dummies developed from the interaction between province and urban or rural codes.
Robust standard errors are reported in parenthese. For the panel (2), the dependent variables are the indicator variable for currently
attending school (In School) and the indicator variable for having never attended school (Never Attended School). All regressions
include the following conditional variables: child age, squre of child age, female, female head, mother’s and father’s age, mother’s andfather’s education, and regional dummies. Standard errors clustered by household are reported in parenthese. *** significant at the
1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

(1) Northwest zone(1-1) First Child
Primary school

Sample
1-1
(1)

36

Sample1-2(2)Sample1-3(3)Sample2-1(4)Sample2-2(5)Sample2-3(6)Sample3-1(7)Sample3-2(8)Sample3-3(9)-0.060*** -0.088***-0.036*-0.088*** -0.112*** -0.072** -0.057*** -0.083***-0.035(0.019)(0.019)(0.021)(0.034)(0.035)(0.036)(0.020)(0.021)(0.022)
Never attended school 0.058*** 0.067*** 0.052*** 0.105*** 0.109*** 0.098*** 0.048*** 0.054*** 0.045***(0.015)(0.015)(0.016)(0.033)(0.033)(0.033)(0.016)(0.017)(0.017)(3-2) Children Aged 6-14
In school-0.083*** -0.086*** -0.083*** -0.106*** -0.100*** -0.108*** -0.078*** -0.078*** -0.079**(0.015)(0.015)(0.016)(0.035)(0.035)(0.035)(0.015)(0.016)(0.016)
Never attended school 0.090*** 0.094*** 0.088*** 0.116*** 0.112*** 0.115*** 0.084*** 0.085*** 0.083***(0.014)(0.015)(0.015)(0.034)(0.035)(0.035)(0.015)(0.016)(0.016)(4) East zone(4-1) First Child
Primary school-0.019-0.033**-0.001-0.067*** -0.073***-0.052*-0.005-0.0170.013(0.014)(0.015)(0.017)(0.025)(0.026)(0.027)(0.016)(0.017)(0.018)Never attended school
0.013
0.023*-0.0090.050**
0.060***
0.024
0.002
0.009
-0.016(0.011)(0.012)(0.016)(0.022)(0.022)(0.025)(0.012)(0.013)(0.015)(4-2) Children Aged 6-14
In school
0.007
0.001
0.014
-0.008-0.012
0.001
0.012
0.005
0.019
(0.011)(0.012)(0.014)(0.020)(0.021)(0.022)(0.012)(0.013)(0.014)
Never attended school-0.0030.005-0.011
0.018
0.023
0.007
-0.009-0.001-0.018(0.011)(0.012)(0.014)(0.020)(0.020)(0.022)(0.012)(0.012)(0.014)
Notes: The table reports the coefficients from the weighted least square regressions with propensity scores weighting. Propensity
scores estimated in Table 10 (Appendix) are used. 5 % trimming rule over common support (See Heckman et al. 1997) is imposed
for all estimations in both panel (3) and (4). The unit of observation is a child. For panel (1), the dependent variables are theindicator variable for graduating from primary school (Primary School) and the indicator variable for having never attended school(Never Attended School). All regressions include the following conditional variables: female, female head, mother’s and father’s age,mother’s and father’s education and regional dummies developed from the interaction between province and urban or rural codes.
Robust standard errors are reported in parenthese. For the panel (2), the dependent variables are the indicator variable for currently
attending school (In School) and the indicator variable for having never attended school (Never Attended School). All regressions
include the following conditional variables: child age, squre of child age, female, female head, mother’s and father’s age, mother’s andfather’s education, and regional dummies. Standard errors clustered by household are reported in parenthese. *** significant at the
1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

(3) Southwest zone(3-1) First Child
Primary school

Sample1-1(1)Table 8: Heterogeneity Effects for Base People (Continued)

37-0.026(0.020)0.006(0.018)0.006(0.016)0.003(0.016)-0.034*(0.019)0.030(0.027)-0.029(0.021)
0.025(0.021)-0.037**(0.016)0.042***(0.016)-0.011(0.019)-0.005(0.027)-0.016(0.021)0.011(0.021)

Sample1-3(3)-0.049***(0.019)0.017(0.017)

Sample1-2(2)-0.027(0.036)0.025(0.035)0.010(0.030)0.008(0.038)-0.072**(0.028)0.078***(0.029)-0.080***(0.031)0.100***(0.032)

Sample2-1(4)-0.006(0.038)0.005(0.037)0.039(0.031)-0.019(0.041)-0.101***(0.029)0.105***(0.030)-0.102***(0.032)0.109(0.033)Sample2-2(5)-0.028(0.037)0.024(0.036)-0.007(0.032)0.043(0.041)-0.056*(0.030)0.065**(0.030)-0.073**(0.032)
0.098***(0.034)

Sample2-3(6)-0.023(0.021)0.019(0.021)-0.039**(0.018)0.009(0.027)0.002(0.016)0.004(0.016)-0.020(0.019)-0.011(0.017)

Sample3-1(7)-0.011(0.023)0.007(0.023)-0.024(0.020)-0.006(0.029)-0.022(0.017)0.026(0.017)-0.039*(0.021)-0.007(0.018)Sample3-2(8)-0.024(0.023)0.018(0.023)-0.044**(0.020)0.029(0.030)0.026(0.017)-0.018(0.018)-0.008(0.022)-0.016(0.019)Sample3-3(9)Notes: The table reports the coefficients from the weighted least square regressions with propensity scores weighting. Propensity scores
estimated in Table 10 (Appendix) are used. 5 % trimming rule over common support (See Heckman et al. 1997) is imposed for all theestimations in both panel (5) and (6). The unit of observation is the child education in the household. For panel (1), the dependent
variables are the indicator variable for graduating from primary school (Primary School) and the indicator variable for having neverattended school (Never Attended School). All regressions include the following conditional variables: female, female head, mother’sand father’s age, mother’s and father’s education and regional dummies developed from the interaction between province and urban orrural codes. Robust standard errors are reported in parenthese. For the panel (2), the dependent variables are the indicator variablefor currently attending school (In School) and the indicator variable for having never attended school (Never Attended School). All
regressions include the following conditional variables: child age, squre of child age, female, female head, mother’s and father’s age,mother’s and father’s education, and regional dummies. Standard errors clustered by household are reported in parenthese. ***significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

-0.040**(0.017)
Never attended school
0.011(0.016)(5-2) Children Aged 6-14
In school-0.017(0.014)
Never attended school
0.022(0.014)(6) North zone(6-1) First Child
Primary school-0.028*(0.017)
Never attended school
0.009(0.024)(6-2) Children Aged 6-14
In school-0.025(0.019)Never attended school
0.021(0.019)(5) Central zone(5-1) First Child
Primary school

Sample1-1(1)Table 8: Heterogeneity Effects for Base People(Continued)

Appendix

NortheastNorth

KratieNorthwest
CenterM ondulKiri

East

W est

Southwest
Kam pong Soam

Figure 6: Administrative Zone under the Khmer Rouge Regime
The figure shows the Democratic Kampuchea (DK) zone in 1977. This map was developed by the author with ArcGIS based on the degital layer provided by the website of
Cambodian Genocide Program at Yale university (http://www.yale.edu/cgp/). Census
microdata did not record the information based on this DK zone. To match the present
district codes with the DK zone, we first calculated the center of each district with ArcGIS.
We then matched these centers with DK zones.

38

Table 9: Definition of Variables
Variable
Definition
A. Dependent Variables
Primary School
1 if a first child in a household has graduated from a primary school,
0 otherwise
Never attended school 1 if a first child/ children aged 6-14 in a household has/have neverattended school, 0 otherwise
In school
1 if a child aged 6-14 in a household attends a school at the time of1998 census, 0 otherwise
B. Independent Variables
Female head
1 if female is head of a household, 0 otherwise
Mother’s age
The number of mother’s age at the time of 1998 Census
Father’s age
The number of father’s age at the time of 1998 Census
Mother’s education
The number of a highest grade completed for mother
Father’s education
The number of a highest grade completed for father
Father is new people
1 if father was classified as new people in the Pol Pot regime,
0 otherwise The definition of new people is as follows.
Father is defined as new people if his birth place was a provincial
town or Phnom Penh.
Age
The number of child age at the time of 1998 Census
Female1 if a child is female, 0 otherwise
Northwest zone
1 if a household has a first child born in Northwest zone, 0 otherwiseWest zone
1 if a household has a first child born in West zone, 0 otherwise
Southwest zone
1 if a household has a first child born in Southwest zone, 0 otherwise
East zone
1 if a household has a first child born in East zone, 0 otherwise
Central zone
1 if a household has a first child born in Central zone, 0 otherwise
North zone
1 if a household has a first child born in North zone, 0 otherwise
Northeast zone
1 if a household has a first child born in Northeast zone, 0 otherwise
Kompong Som
1 if a household has a first child born in Kompong Som, 0 otherwise
Kratie1 if a household has a first child born in Kratie, 0 otherwise
Mondol Kiri
1 if a household has a first child born in Mondol Kiri, 0 otherwise
Regional dummies
Indicator variables of interactions between province and urban/ rural
C. Other Variables
Married
1 if mother’s marital status is “married” at the time of 1998 Census,
0 otherwise
Divorced:
1 if mother’s marital status is “divorced” at the time of 1998 Census,
0 otherwise
Widowed:
1 if mother’s marital status is “widowed” at the time of 1998 Census,
0 otherwise

39

40

0.402***(0.046)0.105***(0.012)0.000(0.023)0.007(0.019)0.656*(0.345)-20.354***(1.721)
2,177-1015.5
0.093
0.299***(0.051)0.119***(0.013)0.020(0.025)-0.002(0.021)-0.657(0.561)-17.228***(1.915)1,748-768.96
0.090

0.076
(0.052)0.104***(0.013)0.034(0.024)-0.040*(0.022)-0.352(0.565)-8.408***(1.895)
1,704-796.96
0.047

Sample1-3(3)0.185***(0.048)0.082***(0.012)0.016(0.023)-0.018(0.019)0.552(0.337)-11.313***(1.785)
1,915-1016.6
0.035

Sample
1-2
(2)

0.011
(0.086)
0.113***(0.017)-0.005(0.044)-0.019(0.037)-0.541(1.023)-8.392***(3.127)2,880-368.05
0.048

0.222***(0.077)0.103***(0.015)0.009(0.038)0.002(0.031)0.499(0.533)-15.602***(2.858)3,306-519.2
0.053

Sample2-1(4)-0.105(0.089)0.123***(0.020)0.005(0.044)-0.040(0.039)-0.585(1.053)-3.830(3.233)
1,461-310.10
0.052

0.094
(0.081)0.106***(0.017)0.021(0.039)-0.014(0.032)0.470(0.553)-10.280***(3.014)1,587-430.43
0.043

Sample
2-2
(5)

0.134
(0.088)0.117***(0.019)-0.017(0.046)
0.002(0.037)-0.611(1.036)-12.392***(3.224)
1,505-307.650.067

0.327***(0.078)0.112***(0.016)-0.005(0.040)0.019(0.032)0.580(0.562)-19.174***(2.896)1,849-431.55
0.083

Sample2-3(6)0.249***(0.057)
0.096***(0.012)0.039(0.025)-0.023(0.023)-0.483(0.606)-15.438***(2.101)3,037-800.30
0.055

0.355***(0.052)0.073***(0.012)0.007(0.024)-0.005(0.020)0.558(0.354)-18.293***(1.942)3,504-1035.9
0.049

Sample3-1(7)0.138**(0.060)0.096***(0.014)0.046*(0.026)-0.042*(0.025)-0.323(0.635)-10.649***(2.202)
1,618-654.410.044

0.228***(0.056)
0.067***(0.014)0.016(0.026)-0.020(0.022)0.494(0.378)-12.615***(2.070)1,785-826.63
0.029

Sample3-2(8)0.364***(0.060)0.107***(0.014)0.033(0.027)-0.002(0.024)-0.702(0.634)-19.450***(2.226)
1,662-629.640.090

0.446***(0.054)0.087***(0.013)0.001(0.026)0.006(0.022)0.649*(0.387)-21.589***(1.997)2,047-824.24
0.085

Sample3-3(9)Note: The table reports the resluts of propensity score estimations. The coefficients are estimated from logit models. Standard errors
are reported in parenthese. The unit of observation is the treatment status of the household. *** significant at the 1 percent leve, **significant at the 5 percent level, * significant at the 10 percent level.

(1) Northwest zone
Mother’s age
0.311***(0.044)
Father’s age
0.088***(0.010)Mother’s education
0.008(0.021)
Father’s education-0.004(0.018)Father’s class is new
0.558*(0.310)Intercept-16.964***(1.647)
Observations
3,634
Log-likelihood value-1300.5
Pseudo R-squared
0.055(2) West zone
Mother’s age
0.184***(0.049)Father’s age
0.106***(0.011)
Mother’s education
0.028(0.023)
Father’s education-0.022(0.020)Father’s class is new-0.490(0.532)
Intercept-13.126***(1.786)
Observations
3,123
Log-likelihood value-991.72
Pseudo R-squared
0.057

Sample1-1(1)Table 10: Propensity Score Estimates for Base People in Different Administrative Zones

41

0.357***(0.036)0.116***(0.010)0.002(0.017)0.060***(0.015)-0.266(0.426)-19.592***(1.314)
4,562-1789.7
0.093
0.382***(0.027)0.134***(0.009)0.014(0.016)
0.033**(0.013)-0.196(0.328)-20.198***(1.015)4,733-2618.6
0.109

0.253***(0.027)
0.094***(0.008)0.021(0.015)-0.006(0.013)0.005(0.328)-13.764***(1.000)4,544-2722.2
0.051

Sample1-3(3)0.220***(0.036)
0.094***(0.010)0.010(0.017)0.040***(0.014)-0.191(0.419)-13.635***(1.349)4,262-1828.6
0.048

Sample1-2(2)0.324***(0.047)0.105***(0.011)
0.009(0.026)0.003(0.021)0.115(0.528)-18.975***(1.746)
6,666-1277.3
0.063

0.286***(0.076)
0.113***(0.014)-0.004(0.035)0.035(0.029)0.210(0.728)-19.157***(2.790)7,537-634.5
0.063

Sample2-1(4)0.247***(0.048)0.098***(0.011)
0.008(0.026)-0.023(0.021)
0.026(0.308)-15.077***(1.773)3,538-1101.2
0.050

0.219***(0.077)0.120***(0.015)0.004(0.036)0.021(0.029)0.293(0.738)-16.290***(2.895)3,686-545.09
0.059

Sample2-2(5)0.381***(0.049)0.134***(0.013)0.005(0.027)0.020(0.023)0.027(0.547)-21.515***(1.807)3,601-1027.5
0.1

0.341***(0.076)0.127***(0.016)-0.009(0.036)0.044(0.030)0.144(0.750)-21.058***(2.794)
3,986-537.1
0.089

Sample2-3(6)0.332***(0.028)0.091***(0.008)0.020(0.015)0.025**(0.013)-0.115(0.337)-17.577***(1.027)
7,451
-2978
0.062

0.308***(0.038)0.082***(0.009)0.008(0.017)0.059***(0.015)-0.394(0.467)-17.338***(1.391)
7,978-1958.6
0.053

Sample3-1(7)0.261***(0.030)0.082***(0.009)0.023(0.017)0.004(0.014)-0.042(0.364)-13.897***(1.104)4,197-2337.4
0.045

0.229***(0.040)0.078***(0.010)0.012(0.018)0.048***(0.015)-0.343(0.482)-13.571***(1.471)
4,127-1599.6
0.041

Sample3-2(8)0.395***(0.030)0.121***(0.010)0.015(0.017)0.044***(0.014)-0.233(0.365)-20.441***(1.120)4,386-2250.9
0.101

0.371***(0.039)0.098***(0.011)
0.003(0.018)0.068***(0.016)-0.426(0.489)-19.599***(1.431)
4,427
-1569
0.083

Sample3-3(9)Note: The table reports the resluts of propensity score estimations. The coefficients are estimated from logit models. Standard errors
are reported in parenthese. The unit of observation is the treatment status of the household. *** significant at the 1 percent leve, **significant at the 5 percent level, * significant at the 10 percent level.

(3) Southwest zone
Mother’s age
0.298***(0.034)
Father’s age
0.096***(0.008)Mother’s education
0.006(0.016)
Father’s education
0.052***(0.013)
Father’s class is new-0.246(0.402)
Intercept-17.319***(1.266)
Observations
8,113
Log-likelihood value-2263.7
Pseudo R-squared
0.061(4) East zone
Mother’s age
0.324***(0.025)
Father’s age
0.102***(0.007)
Mother’s education
0.018(0.014)
Father’s education
0.016(0.011)
Father’s class is new-0.068(0.298)Intercept-17.468***(0.919)Observations
7,798
Log-likelihood value-3526
Pseudo R-squared
0.069

Sample1-1(1)Table 10: Propensity Score Estimates for Base People in Different Administrative Zones (Continued)

42

0.278***(0.036)0.074***(0.011)
0.048**(0.019)0.007(0.015)0.189(0.412)-14.080***(1.336)
2,668-1584.2
0.047
0.253***(0.046)
0.065***(0.013)-0.040(0.030)0.020(0.024)0.107(0.535)-12.525***(1.763)1,444-904.67
0.035

0.376***(0.033)0.066***(0.009)0.044***(0.017)0.026*(0.014)0.136(0.363)-18.082***(1.220)4,571-2043.5
0.063

0.309***(0.043)0.072***(0.011)-0.032(0.027)0.011(0.021)0.028(0.470)-15.522***(1.599)2,422-1188.7
0.047

Sample1-2(2)0.368***(0.047)0.090***(0.013)-0.022(0.031)-0.006(0.024)-0.107(0.514)-17.690***(1.764)
1,488-888.32
0.07130.463***(0.036)0.072***(0.010)0.040**(0.019)0.045***(0.016)0.084(0.408)-20.844***(1.324)2,743-1524.5
0.098

Sample1-3(3)0.231***(0.077)0.110***(0.015)-0.030(0.051)-0.015(0.039)-0.217(1.034)-15.575***(2.877)2,040-447.710.064

0.159**(0.063)0.092***(0.014)0.081**(0.032)-0.012(0.028)-0.661(1.027)-12.661***(2.284)
3,904-677.67
0.043

Sample2-1(4)0.175**(0.079)0.110***(0.018)-0.037(0.052)-0.007(0.041)-0.218(1.075)-12.863***(2.994)
1,062-367.50.05960.066(0.065)0.102***(0.016)0.086**(0.034)-0.035(0.029)-0.708(1.052)-8.941***(2.382)2,001-564.23
0.042

Sample2-2(5)0.300***(0.080)0.120***(0.017)-0.021(0.055)-0.024(0.041)-0.256(1.049)-17.770***(3.014)1,106-360.99
0.089

0.248***(0.065)0.091***(0.014)0.075**(0.033)0.008(0.028)-0.569(1.045)-15.233***(2.324)2,076-558.37
0.062

Sample2-3(6)0.338***(0.049)0.047***(0.012)-0.033(0.030)0.023(0.023)0.115(0.505)-15.908***(1.807)2,294-993.94
0.03790.445***(0.038)0.052***(0.010)0.034*(0.018)0.039**(0.015)0.246(0.376)-20.309***(1.384)
4,398-1746.9
0.067

Sample3-1(7)0.279***(0.052)0.038***(0.015)-0.041(0.033)0.033(0.026)0.216(0.564)-12.705***(1.955)1,316-770.45
0.028

0.348***(0.041)0.059***(0.012)0.038*(0.020)0.020(0.017)0.282(0.423)-16.294***(1.497)2,495-1376.6
0.050

Sample3-2(8)0.394***(0.052)0.065***(0.014)-0.024(0.034)0.005(0.026)-0.032(0.547)-17.935***(1.952)
1,360-759.150.060

0.538***(0.041)0.057***(0.011)0.030(0.021)
0.061***(0.017)0.149(0.422)-23.229***(1.490)2,570-1316.7
0.105

Sample3-3(9)Note: The table reports the resluts of propensity score estimations. The coefficients are estimated from logit models. Standard errors
are reported in parenthese. The unit of observation is the treatment status of the household. *** significant at the 1 percent leve, **significant at the 5 percent level, * significant at the 10 percent level.

Observations
Log-likelihood value
Pseudo R-squaredIntercept

Father’s class is new

Father’s education

Mother’s education

Father’s age

Observations
Log-likelihood value
Pseudo R-squared(6) North zone
Mother’s age

Intercept

Father’s class is new

Father’s education

Mother’s education

Father’s age(5) Central zone
Mother’s age

Sample1-1(1)Table 10: Propensity Score Estimates for Base People in Different Administrative Zones (Continued)