OSIPP Discussion Paper: DP-2013-E-001

Is a gender gap in net school enrollment a reflection of thegender wage gap in the labor market? Evidence using household
data from Vietnam
January 16, 2013

Tien Manh Vu†
Ph.D Candidate, Osaka School of International Public Policy,
Osaka University[Key words] School dropouts・Returns to schooling・Wage・Gender gap・Vietnam[JEL Codes] I24, I25, J31[Abstract]
The paper estimates both the gender gap in wage and net schooling enrollment from
Vietnam household data. The results imply a reflection of gender wage gap in thelabor market in hazard of school withdrawals. Generally, males have higher incentive
to terminate their schooling to join the labor force. Males would have 43.8 percent
higher in participating the labor market and gain 18.4 percent of wage per hour higher
than females. Also, we observe 16–44.4 percent lower in probability for males toenroll in school, especially, the school withdrawal rate accelerates at higher speed
after the age of primary school. Meanwhile, females would have an incentive tocomplete junior, senior high school and 3–year college thanks to higher speed gain in
wage. Besides, family having a combination of a household head working for a state–owned firm and his spouse working as self–employed would best facilitate their co–
residing children and grandchildren for more years of schooling. Finally, the current
education subsidy and tuition fee reduction policy do minimal to reduce the hazard ofschool dropouts among beneficiaries.

†Contact: Osaka School of International Public Policy, Osaka University, 1-31
Machikaneyama, Toyonaka, Osaka 560-0043 Japan. Tel/Fax: +81-(0)6-6850-5656. E-mail:t-vu@osipp.osaka-u.ac.jp.
[Acknowledgments] The author acknowledges the useful suggestions of Hisakazu Matsushige,
Miki Kohara, Masaru Sasaki, Hiroyuki Yamada, participants in the graduate seminars chaired
by Tsunehiro Ootsuki, and by Charles Yuji Horioka of Osaka University, and Rafael Laliveof University of Lausanne. All remaining errors are ours.

1 Introduction
During the economy transition, Vietnam have enjoyed high gross domestic productgrowth rate for almost over 5 percent per annum from 1990–2008 (GSO 2010) withincreasing number of “active” firms1 from 42,288 in 2000 up to 155,771 in 2007
(GSO 2012). Such shift needs more skilled workers and skill–based wage would haveestablished. For example, higher income for workers in productions and services
would be a measure for newly established firms to absorb labors. Thus the choice ofschooling more than primary education would become more important to eachindividual’s future income. These assumptions encourage us to verify and compare
the corresponding changes in school enrollment by gender.
The main purpose of the study is to investigate the gender gap in both wage
and net school enrollment using data from the 2008 Vietnam Household Living
Standard Survey (VHLSS). Also, we are interested in the efficiency of policies on
education and linkage between parents’ (grandparents’) wage to the schooling choicesof the children. Unlike previous studies, we examine the wage in the labor market byusing in the Heckman sample selection models with greatest combination of 33
occupational types and 88 industries where 101,306 individuals are working.
Meanwhile, the school enrollment gender gap is analyzed in Cox hazard models andregarding the effect the family background as well as education policies. In addition,
we link education premium with schooling choice decision by using some control
variables (which are highly valued in the labor market) in the hazard models andcomparing the marginal effects between returns to education and hazard of dropouts.
We obtain four major findings. First, males are more likely to involve insalary work, at about 43.8 percent higher in probability and benefit 18.4 percent
higher wage than females. However, females have a relatively increment incentive of
4.51–9.49 percent for an additional degree from junior high school up to 3–year
college. Second, males are facing higher school dropout hazard and the higher
acceleration to dropout than females from age 12–22. Third, we find a combination ofa household head working for a state–owned firm and his spouse working as self–
employed would reduce the hazard of school dropout to the lowest. Finally, although
the tuition fee reduction policy is widespread to almost 40 percent of individuals aged
7–22, the policy has minimal connection to an additional year in schooling. Similarly,
the education subsidy does not create any statistically significant gain for net school
enrollment.
The remainder of the paper is organized as follows. Section 2 presents
previous advances in estimating the gender gap in wage and school dropouts as wellas theory bases for our hypothesis. Section 3 introduces the data used and Section 4
describes our empirical methods. Section 5 details our findings. Section 6 provides
our conclusions.

1“Active” means the firms actually have employee to do business and create some business cost
during the year.

12 Background to gender gap in wage and schooling choice
2.1 Advances in estimating gender wage gap and school choices
The returns of schooling have been examined for several decades since Mincer (1958).
The returns of schooling would vary among population with observable factors suchas school quality and education of the parents (Card 1999). Heckman, Lochner andTodd (2008) estimate the marginal internal rates of return for different schoolinglevels and find relatively larger returns to graduating from high school than tograduating from college given that both have been increasing overtime.
Schooling contains endogeneity problems. The common is the unobservableof “ability”. Second, schooling would be the optimization behavior of individuals andfamilies (Griliches 1977). Third, the aggregate log of earnings can be right skewed incontrast with original Roy model that assumes log skills are normally distributed(Heckman and Honoré 1990). Furthermore, we learn from Card (1999) that it would
be difficult to decide whether the higher education cause higher earnings observed
from better educated individuals or whether individuals with greater earning capacity
chose to have more years of schooling. Thus we realize intercepting the schoolingdecision in the early life would be important because wage earning capacity is still
potential.
The estimation technique is improved significantly by the development oftheory and econometric methods to get an insight to unobservable characteristics,
ability. Measurement errors can be justified by using potentially instrumental
variables such as parental education (Willis and Rosen 1979), tuition fees, minimum
school–leaving age, college proximity (Card 2001) and test score (Card 1999), andusing identical twins data (Ashenfelter and Krueger 1994) where genetic difference inendowments can be controlled.
There are some explanations for gender wage gap. The first reason could be
the difference in gender specific factors. For example, women are likely to be
involved in shorter and discontinuous work due to childbirth, child rearing andhousework (Mincer and Polachek 1974) and time constraint between market work andhouse work (Becker 1985). Thus women are more likely to have less experience
and/or less incentive to invest or be invested in on–job training and education.
Similarly, women with different marital status and number of children devote
different proportion of her lifetime to the world of work (Mincer and Polachek 1974).
Second, the wage structure (distribution) could explain for the gap. Fortin andLemieux (1998) indicate the decline in the gender wage gap during 1979–1991 washighest at the middle centile of the wage distribution. This is due to the distribution of
wage function of females became less skewed to the right and had similar shape tothat of males. Also, we acknowledge that the gender wage gap would be lower at theentry of the labor market, larger in the middle and the other end of exit (Tansel andBodur 2012). Topel (1994) shows wage inequality differs by regions. On the otheraspect, changing the nature and condition of work, i.e increasing computer use, would
open more job opportunity to females (Weinberg 2000). There is also an unexplained
gap. For example, Fortin (2005) finds the impact of soft factors such as greed,
ambition, altruism, locus of control, gender role attitude, family values and income
expectations would have impact on behaviors, wages and gender wage gap. Finally,
the gender wage gap can be a purely gender discrimination. Goldin and Rouse (2000)
2

find the shift to “blind” auditions can explain 25 percent of the increase in thepercentage of female members in the orchestras from 1970–1996. Because music
directors in charge of hiring new musicians, publicly disclosed their belief thatfemales had lower musical talent.
Literature reports several ways to decompose the gap. Detected genderdifferences in wage consist of differences in the endowments of the sampledindividuals, in coefficients and unexplained part, probably due to discrimination.
Works by Oaxaca (1973) and Juhn, Murphy and Pierce (1991) are of the extent andlater widely applied in other studies. However, the decomposition considers only themean wage difference in the two latter parts. Thus quantile regressions further
examine whether the gender wage gap is larger (smaller) in either ends of wage
distribution (Buchinsky 1994, Gardeazabal and Ugidos 2005, Tansel and Bodur2012).
Some studies examine the school choice focusing in college degrees indeveloped countries and primary schools in developing countries. For example, Willis
and Rosen (1979) observe the expected lifetime earnings would influence the decision
to attend college. Brown and Corcoran (1997) find the gender difference in choosing
schooling content relates to gender wage gap among college graduates. Meanwhile,increasing returns to education would raise (primary) school enrollment (Foster andRosenzweig 1996). Similarly, increasing wealth would induce parents invest more forchildren’s education in Vietnam (Glewwe and Jacoby 2004). However, literaturereports some time lags or even mismatches at the time that individual left school.
Card (2001) indicates different individuals would finish their schooling at the time
when the marginal return to last year of schooling would either above or under the
average marginal return of the population. However, using the National Longitudinal
Survey of Youth (NLSY) 1979, Cunha and Heckman (2007) find 50 percent of the
ex–post variance in returns to college is predictable at the time the individual making
the choice of schooling. More specifically, the study predicts that 13.86 percent ofhigh school graduates would rather be college graduates and 17.15 percent of college
graduates would have stopped their schooling with a high school diploma. Besides,
Glewwe and Kremer (2006) suggest that the elasticity of demand for schooling ismore likely higher for females than males in developing countries. Thus policies andprograms on education may increase enrollment rates of females without having theintended target.
2.2 Theory base for our hypothesis
In decision theory, individuals make decision on schooling choice by
maximizing their utilities. We assume that individuals are able to evaluate theirlearning ability. We also assume that their learning ability is unlimited given that they
are provided with enough resources to learn. Hence, individuals consider the expected
present value of an additional year in schooling, , versus the expected forgone
income inyears if they work and the cost of learning, and otherunobservable utilities such as preference on learning, psychic cost, job searching cost.

(1)3More specifically, individuals make decision of schooling choice at time––(2)
in which,
is sum of expected future income after obtained the degreeofyears of schooling.
are cost to obtain the degree andexpected forgone income at current education level within
additional year of
schooling.
is other unobservable (unmeasurable) utilities such as: abilities,
preference, psychic cost (job searching cost), etc.
We suppose that all individuals can observe the wage function and/or
distribution, e.g from the wages of their relatives and acquaintances. The present
value of current expected log wage (if the individual is working) is,(3)

where is the schooling premium. Therefore, expected forgone wage at current
education level can explain for the education choice. The wage gender gap, in turn, asa part of wage function, can also explain for the education choice.

3 DataThe data in use is cross section data, common known as Vietnam Household Living
Standard Survey 2008. The sample size is 45,945 households with 289,948
individuals. The sampling method is two–stage stratified by using 3,045 sample units
taken from master sample of population census. Two parts of the survey arecontaining necessary information about income and education.
For income analysis, our definition of labor force and definition of working
are different from the concept from the survey. The labor force in our concept consistsof two types of people, working and non–working individuals. Working individuals
are defined as people who have wage (salary) in the year 2007 for their main job. By
the definition of the survey, main job is an activity consuming most time among otherjobs (economic activities). In Vietnam, an individual can have more than a single job
and economic activities to generate income. For example, an individual can work ascivil servant during the day and as self–employed for family business at night, such as
producing ceramic crafts. VHLSS 2008 collects detailed information of first main
job2, the second job and so on. Non–working individuals are those who do not havewage (salaries) for their first main job. They are seeking for such kind of job or doingsome other activities as substitutions for the main job. Besides, we further restrict thelabor force by age and possibility to (re)enter to the labor force. Individuals in thelabor force are at the age permitted by Vietnamese Laws of Labor. They have neither
retired nor reached to the age of retirement by the laws. In addition, they are neither
enrolling in any kind of school nor expecting for school entrance exams. Labor force
does not contain individuals who are disabilities, too weak to work, facing serious
illness and taking early retirements. Descriptions of selected sample for income
analysis are as in Table 1.
2

Main job is defined in VHLSS as earnings required the highest number of time devoted to. If an
individual has two equal time spending on jobs, highest earnings job is recorded as main job.

4

[INSERT TABLE 1 HERE]
From the sample, we can observe a “labor force” relatively young with anaverage of 36.33 years of age and females occupy 49.25 percent. In addition, paid
work as main job (most time consuming and highest earning job) attracts only 33
percent of the total labor force. 43.97 percent of individuals are self–employed, 72.59
percent of which is in agriculture, forestry and fishery sectors. Therefore, we argue
that sample selection would be the most serious problem in estimating the returns toschooling. Also, we notice that the distribution of wage distribution of both males andfemales are right–skewed although that of males is less than that of females as seen in
Graph 1. The gender gap would be minimal in the far right tail whereas the gender
gap would be significantly different in the left and the right middle of the distribution.
[INSERT GRAPH 1 HERE]
For education analysis, we define net school enrollment as individuals who areenrolling in any kind of school at the year 2008. However, the individuals are 7–22
years of age and do not repeat any grade3 since they have entered to primary school.
This is important as suggested by Glewwe and Kremer (2006)4. Besides, we defineschool leavers as individuals aged 7–22 and have a record of school attendance5. The
sum of school enrollments and school leavers confound the total individuals who havebeen made decision on schooling. In order to further drill on the time of making
decision, we construct two panels. Panel 1 is as what we defined. Panel 2 further
restricts to individuals who were enrolling in a school in the year 2007. Therefore,
Panel 1 deals better with accumulation of schooling choice made by individuals ofinterest in all past years while Panel 2 facilitates to analyze the school decision madein 2007. Panel 2 also enables us to integrate some educational policy variables.
Descriptive statistics for the two panels are shown in Table 2 and 3.
[INSERT TABLE 2 AND 3 HERE]

4 Empirical methods and specifications
4.1 Heckman sample selection with two steps for wage regression
We apply Heckman sample selection model with two steps (Heckman 1979) toestimate the gender wage gap as we are unable to observe wage of approximately 68
percent of total individuals who are engaged (ready to be engaged) with somebusiness activities6.
We acknowledge that the model is not able to decompose the gender gap into
3

Appendix 1 shows descriptions of Vietnam education system.
The study indicates principals and teachers have incentives to exaggerate the number of students
enrolled in developing countries. Thus the definition of net enrollment rate is more appropriate. Net
enrollment rate is the number of children enrolled in a particular level of schooling who are of the age
associated with that level of schooling divided by all children of the age associated with that level ofschooling (Glewwe and Kremer 2006).
5
We acknowledge that 1,327 individuals (2.5 percent) in Panel 1 aged 7 or above have never entered
to any school.
6
Glewwe and Kremer (2006) record this problem is serious in developing countries.
45difference of endowments, of coefficients and of unobservable factors. Also, themodel does not consider the interaction between the coefficients of individual
characteristics with the gender. In addition, the model does not divide data into
different quantiles of wage and consider the gender wage gap in each quantile.
However, we would argue that sample selection would be the most serious problem in
detecting the gender wage gap in Vietnam. Besides, the general mean of gender wage
gap is of our interest because it is easiest to be observed by any individual. Thus thegeneral mean of gender wage gap is more likely to be the signal for each individual toalign their years of schooling at their early life. The quantile gender wage gap would
be more precise and more important for individuals at their mid–career. Thus we try
to reduce the effect of occupational types and industries’ differences, which are more
likely to lead to different gender wage gap in different part of the wage distribution,by introducing corresponding control variables.
We assume that the selection can be predicted from the individual
characteristics
such as sex, marital status (Das et al. 2003), age, work experience,from the place of residence such as rural or urban, employment opportunities(economic regions can be a proxy), and highest educational attainment. In theselection stage,
𝑧′

𝑠 𝑙 𝑐

1 𝑖𝑓𝑧
𝛽 𝜀≥𝑧
.
{
0 𝑖𝑓 𝑧 < 𝑧(4)

In the outcome stage,
𝑦

𝑤𝑎𝑔

′ 𝛽′

𝜖.

(5)

The wage estimate is Mincer regression and can be explained by ′ characteristics
such as sex, age, working experience, rural or urban area of residence, economic
regions, highest diploma obtained, highest vocational degree obtained, characteristicsof the enterprise such as types of ownership and industries.
4.2 Hazard model for the probability of leaving school
Meanwhile, we deploy a hazard model to estimate the probability of withdrawing(dropout) from school for individuals aged 7–22. We omit all individuals who areattending in schools but their ages are more than 2 years difference to the age of anindividual starting primary school at 7 years of age and continuously adding one
grade annually. The period of study is the time since an individual enters primaryschool at 7 years of age until either the individual leaves the school or the survey ends.
At the age 7, all individuals are considered to be at school as primary school iscompulsory in Vietnam. The individuals are also at risk of dropout every year after
the age 7. If an individual does not have a status of “at school” in 2008, the individual
is considered as already left school (termination). If the individual is “at school”, theindividual is regarded as right censored data. It is reasonable to assume that all
censored data will fail or finish their schooling sometime after 2008.
Suppose that the year of schooling, started from 7 years of age when an
individual enters primary school and finishes when the individual terminates6

7
schooling, follows. The survival function, indicating the probability that anindividual continue for higher school grade at time , is then1.
1 at
0. The hazard function,, is the instantaneous rate of
failure or the rate of terminating school in such a short period of time:(6).

The cumulative hazard function is
∫

∫

.

(7)

In general, the likelihood function of an individual experiencing school termination is(8)
In case of right censored data
(9)
Thus the likelihood function of both censored and uncensored individuals can be
rewritten as∏(10)
4.3 Specifications
4.3.1 Dependent variables
In wage regression, the dependent variable is logarithm of wage per hour in 12 monthprior the survey. The wage per hour is the ratio of total wage by total working hour.
The total wage is a sum of the total wage, bonus and any other income related to wage.
Meanwhile, the total working hour is a product of sum of total hour working per day,total days of working in a month, and total months of working in 12 months prior to
the survey.
In school dropout analysis, the dependent variable is the years of education. If
the individual has left school, years of education is the highest school grade ever becompleted. If the individual is enrolling in a school by the time of survey, it is thecurrent grade minus 1 by the definition of VHLSS.
4.3.2 Independent variables
Independent variables in wage analysis include individuals characteristics
such as sex, marital status, age, working experience, highest obtained academic
degree, highest obtained professional degree, residing area (rural/urban and economy
regions), ownership of the enterprises (classified by GSO), 88 industries where theemployers are mainly involving, and 33 occupation types (classified by GSO).
In school dropout estimates, independent variables consist of individuals andfamily characteristics including sex, household income (household income adjusted
by root squared of number people in the household), whether the individual is the
only son and/or the grandchild of the household head and residing area (rural/urban
and economy regions). Also, we add information about the household head and his

7of

We simplify all functional forms without displaying covariates. For example,, wheredenotes independent variables.

is used instead7

spouse8, including education, their employers’ type if they are working. This would
be a reasonable assumption as the difference in wage among household head and his
spouse, if any, would be the easiest to be observed. Besides, the information could
help to control a possible interference of household heads (spouses) to the schoolingchoice of children within the household, given the heads (spouses) acknowledges of
their wage premium. Also, working in different employer types may result in different
time schedule which might influence the time for rearing or support their children(grand children). However, since there are 5,498 (1,327) individuals missing
information about their household head (head’s spouse), we analyze the information
in a separate estimation. Also, in a separate data, the Panel 2, we add three dummies
for the status of education policy recipients such as tuition fee reduction, scholarship,
education subsidies. Ratio of tuition fee over the total expenditure on individuals’
education is included. The information only appears among individuals who
experienced school enrollment in 2007. Detailed explanation for both dependent andindependent variables can be seen in Table 2, 3 and 4.
[INSERT TABLE 4 HERE]
In addition, we introduce an interaction term between sex and the variable ofinterests. In wage function, it is the highest degree obtained by the individuals. If theinteraction term is significant, the coefficient of the interaction term can explain forthe difference in the incentive to complete the highest degree among the sex ofinterest. In a hazard model for school dropouts, the corresponding coefficient would
show the difference in effort to add an additional year in schooling among the sex ofinterest. As the magnitude of the interaction effect in non–linear models is not
equivalent to the marginal effect of the interaction term and the its statisticalsignificance is not calculated by standard software (Ai and Norton 2003). Thus, weapply the tips by Buis (2007) to calculate and interpret the interaction term.
4.4 Marginal effect over sex and the variable of interest
There are three main reasons for us to conduct marginal effect over sex and thevariable of interest. The first is that Heckman et al. (2006) present evidence that themarginal return does not equal to the average return which is often estimated from
Mincer regression. The second is lying to the fact of the coefficient of the interaction
term in non–linear models is not equivalent to the marginal effect. Besides, we
acknowledge that the marginal effect to complete a degree would be higher in the
final year of degree completion (sheepskin effects). Heckman, Lochner and Todd
(2008) indicate that the levels and trends in rates of return estimated from Mincermodel are misleading for many schooling levels.

5 Results
5.1 Differences in labor participation rate and gender gap in wage
Estimates of the probability of participation would capture the general image of thelabor market of Vietnam, as shown in Table 5. People living in different economic
8

In fact, we convert all female household head as household head spouse and male household head
spouse as household head for the selected data.

8

regions and/or rural (urban) area also experience different labor participation rate.
Highest of these rates are Southeast and Red River Delta (as the base). Probably, thetwo regions embrace two largest cities in Vietnam, Ho Chi Minh City and Hanoi
where most firms are located. In general, estimates show individuals with higher levelof education tend to have higher participation rate, which agrees with Becker (1962)or they are self–selected to the labor market. Meanwhile, FDI firms, state–owned
firms and private–owned firms would be attractive to employees, at least novice, forpaying 29.6, 18.9 and 15.3 percent higher wage per hour than collectives and family
owned business, respectively. However, the percentage of individuals having morethan 12 years of schooling among state–owned firms in our selected sample is 64.35
whereas the numbers are 27.05, 28.47 and 3.38 in FDI firms, private firms and familybusiness, respectively. Thus state–owned firms’ employees would have a different
pay scale which may put heavy weights on education degrees. In addition, theemployees would have added up their degrees while working as a response to thatscale. Similarly, a 3–year vocational course graduates can earn 11.5 percent higher
than the one without the degree. Besides, while coefficients of region dummies in thefirst step illustrate the probability to participate in the labor market, the corresponding
coefficients in the wage regression show the regional wage difference. Though Red
River Delta would be where proportion of wage earners is the country second highest
rate, the region is also the country second lowest wage paid per hour, holding otherelse constant. Besides, the return to education would be increasing if individuals holdhigher degree up to as equal as master degree and then slightly decreases. Especially,
upper secondary graduates would receive 15.4 percent higher wage than the basewhile the return would jump to 33.7 if individuals hold some college degrees.
[INSERT TABLE 5 HERE]
Females are less likely to participate in labor market, with 43.8 percent lower
in probability and suffer a wage of 18.4 percent lower (as seen in Table 5). Especially,
the interaction term between gender and the highest degree show an interesting
feature. Although experiencing from the wage gap of 12.4 percent lower than males,
comparing among females, females would have additional wage gains by higher
education. As shown in Table 5, the interaction terms between female and degree ofjunior, senior high school, 3–year college, and 4 or 5 year university can explain for acorresponding 4.51, 6.19, 9.49 and 6.0 percent wage gains. The increment in wage
gains would be an incentive for females to accomplish the four year degrees.
Nevertheless, the incentive would be minimal in primary school as the corresponding
coefficient is statistically insignificant. It is very likely that compulsory education
during primary school with no tuition fee fill the gap if any.
5.2 Differences in probability to leave school and the gender gap
In general, individuals at age groups 12–15, 16–18, 19–22 would be 100, 243.8 and419.6 percent more likely to leave school than the age group 7–11 as seen in the Panel
1, Table 6. There are several possible reasons. First, the age group 7–11 is undercompulsory education. Second, individuals have different ability, and people withhigher ability are scarce than the average. Third, there would be lower accessibility tohigher level of education, such as under–developed higher education services. Fourth,individuals may leave school for economic reason, such as for working. However, in9Panel 2 consisting of relatively higher ability people, the corresponding coefficientsshow a decrease from 85.4 to 98.31 percent in dropout rate in older age groups.
Sheepskin effect, the phenomenon of highest return to the last grade of one educationlevel, can also be an explanation. As individuals try to get the degree, they would not
dropout in the middle of one school level. This agrees with our interpretation in Panel
1.
[INSERT TABLE 6 HERE]
Besides, differences in school dropouts among economic regions are
significant. In Panel 1 shown in Table 6, individuals in North Central Coast would be
5.3 percent less likely to dropout. Interestingly, the region is often considered as
poorest. Mekong River Delta region would have the highest school dropout rates in
the country, with 148.2 percent probability higher than Red River Delta. In Panel 2,
differences among economic regions are less serious but the evidence of country’s
highest dropout rate in Mekong River Delta is confirmed with 15.8 percent difference.
Individuals in North East are 27.2 percent higher in probability to dropout school. In
contrast, South East would have a lower dropout probability.
Family background would help to control different aspect to the probability todropout as decision of school choice would be an interaction among individuals, theirfamily (Griliches 1977) and the society. We argue for the role of society such as
educational policies and labor market settings. We prefer to add such information offamily background to trade off with bias selection (4,475 individuals are left behind).
Apart from the core variables which have consistent effect in the previous analysis,
information of family background would explain some interesting facts. In Table 6,analysis 2, higher educated household head and his spouse would lead to 7.9–8.7
lower probability to leave school earlier. Interestingly, there is a different attitude ofhousehold head and spouse to their children and/or grandchildren. We once observed
the highest returns to schooling among individuals working for FDI, state–owned, andprivate firms, respectively. However, the school dropout hazard is highest amongfamilies having the head working for FDI firms (62.5 percent higher) and lowest
among a combination of the head working for state–owned firms (with 13.3 percent
lower) and the head spouse is self–employed (with 10 percent lower). Where the headis in FDI firms (in private firms), their children and grandchildren are facing an
excessive dropout hazard of 62.5 (30.5) percent as shown in Table 6 (7), analysis 2. It
could be a reflection of wage inequality to the next generation’s schooling choiceeither in an active or inactive manner. A possible explanation is that the difference
can be a result of difference in time flexibility of the head (head spouse) available forchild rearing. If education can be a proxy for skill–based pay which is more likely toapplicable in non–state sector, the differences between effect of FDI (private) firms
and that of family business are more likely the time rearing the children. For instance,
if the head spouse is self–employed, she would gain more flexible working time andthus having more time to educate the next generation.
[INSERT TABLE 7 HERE]Household income level would lower down the probability of dropout to 7.2–
30.4 percent as shown in Table 6 and 7. In addition, if the individuals are living with10

grandparents, they are more likely to be kept in schools with 18.1–51.8 percent
increment of probability compared with the others living without grandparents.
Notably, the difference between the individual who is the only co–residing son andthe base is 18.1 percent higher as shown in Table 6, analysis 1. We once try toestablish one–daughter family to compare single gender effect. Unfortunately, thesample does not contain any of such family. Probably, the son preference leads to ahigher investment on the education of the only son in the family. However, in otheranalysis, the corresponding coefficients are statistically insignificant.
Panel 2 allows us to have an insight to the effect of educational policies on the
dropout rates (net enrollment rates). A tuition fee reduction would contribute minimal
to the dropout rates of the individuals despite that 41.9 percent of the individuals are
the beneficiary of this policy (see Table 4). Similarly, education subsidy receivers
would have the same probability as others to dropout. In contrast, scholarship
receivers would benefit 29.2–30 percent higher in probability to continue theirschooling. Perhaps, there would be two kinds of scholarship receivers: those whohave excellent academic records and those who have some economic disadvantages.
Thus if individuals are only the former, the coefficients show their ability to continue
higher education level. If they are the later or both, it is difficult to have policy
implication from the result. In addition, the ratio of tuition fee payment over the total
expenditure on education would have an interesting implication for policies on
education. If the ratio increases one unit, it explains for 71.6 percent increase inprobability to be in schools in the following year. In Vietnam, public education
dominates and the tuition fee is set by the authorities. Thus policies to decrease or
increase tuition fee cannot determine themselves the probability to dropout rates.
Instead, the sensitive to education expense and the other component of education
expense than tuition fee should be considered. On the other hand, if tuition fee isalmost all education expense, individuals are more likely to be in school, otherwise
the inverse applies.
The gender could explain for 16–44.4 percent higher in probability for females
to stay in school. In other words, females are less likely to terminate their schoolingthan males. However, among females, those aged 16–18 are 27.2–31.5 percent more
likely to dropout than other females aged 19–22 in Panel 2. We will further present
the difference in the following sub–section.
5.3 Gender gap in wage and school withdrawals
In wage regression analysis, we have estimated the interaction term between genderand the degree obtained, which could be an incentive for females to stay longer inschool. The result suggests that the incentive would explain for the lower school
dropout rate of females. More specifically, females would have incentive to complete
junior, senior high school, 3–year college and 4 or 5 year university than males. In
hazard model for school dropouts, we observe an inverse corresponding gender
dropout gap of 27.2–31.5 percent.
[INSERT TABLE 8 HERE]
We further analyze the marginal effect over gender and one variable of11interest in both wage and school dropout analysis. In general, males often have higher
marginal effect over wage per schooling level as shown in Table 8. The difference
would explain for higher (lower) relative school withdrawal (dropout) rates among
lower (higher) ability individuals. In addition, the corresponding wage gap (the speedof gains) would still be the incentive for females staying longer in junior, senior highschool and 3–year college. In Table 8, the marginal effect is higher for females whenmoving from primary school to junior high school (1.801 compared with 1.052), fromjunior high school to senior high school (2.945 and 3.015), and form senior highschool to 3–year college (53.466 and 164.315). Meanwhile, as seen in Table 9, themarginal effect of hazard rate (also, the speed lost) over sex and age group is higher
for males in all age groups. These results confirm and validate our previous analysis.
Females have incentive of future return to education to move (complete) up to junior,senior high school and 3–year college. In contrast, males are more likely to join thelabor force than females.
[INSERT TABLE 9 HERE]
Unfortunately, we are not able to divide the age group 19–22 to different
learning routes from the data, for instance, first year in 3–year college is treated as
first year in 4 or 5 year university. Thus the marginal effect of hazard rate over sex
and age group when an individual moves from age 16–18 to age 19–22 is the total
effect of different routes. Therefore, we are unable to provide explanation for thedifferences in hazard marginal effect from the two age groups. In addition, as we limit
the age of individuals in the data of school dropout analysis to 22, we are also unable
to compare the later progression to master and doctor degree.

6 Conclusions
The study has examined the gender gap in wage among wage earners aged 15–60(15–55) for males (females) and school withdrawal (dropouts) hazard amongindividuals aged 7–22. There would be a corresponding higher school withdrawal(dropouts) rate among males while the schooling premium is generally higher formales than females. We also observe the wage incentives among women to complete
junior, senior high school and 3–year college. The speed of withdrawal (dropouts)
among males would be higher at the corresponding age groups. Besides, the younger
generation is given a relatively higher education while the only co–residing son of thehousehold head would have a similar benefit. Meanwhile, the employer types, whichshow a significant difference in wage, of the household heads and his spouses, in turn,would have a significant influence to the hazard of school withdrawal (dropouts) inthe (grand) children co–residing in the household. More specifically, a combination
between a head working for a state–owned firm and a head spouse working as theself–employed would decrease the hazard of dropout from school of the (grand)children to the lowest.
We acknowledge some limitations in our study. First is the limitation of thecross section data in use. The schooling premium may differ or change overtime
according to the difference in returns to skills in the labor market (Heckman et al.
1998). Similarly, the dropout in Panel 2 is just the figure of period 2007–2008 which12can be affected by the economic settings. However, to our best knowledge, there
would be no panel data and/or longitude data from Vietnam to eradicate the problem.
Second, the hazard model replies heavily from data selection and distributional
assumptions. Third, there can be a problem of sample selection in hazard model as
1,327 individuals (2.5 percent) in Panel 1 have never entered to any school. Fourth,omitted variables are always problems to both estimates of returns to schooling andestimates of hazard dropouts. For example, our estimates do not consider thedifference in accessibility to school among individuals, quality of school and teachers
as suggested by Glewwe and Kremer (2006). However, we prefer this to be able toestimate some key factors from the labor market as a guide to choose number of years
in schooling. In addition, we are unable to distinguish the difference in career choices
in post high school and link with the difference in the industries and occupational
types. Nevertheless, we believe future studies would overcome our limitations whendata are adequate.

13

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Appendix 1 General descriptions of education system in Vietnam
Since 1995, the Laws of Education in Vietnam have been changed two times, in 2005
and 2009. However, the main characteristics of grade remain. Individuals enter
primary school at age 6 as a compulsory education. After 5 years in primary school,
individuals can enter to lower secondary school which lasts for 4 years. Right after
lower secondary school, individuals have two choices but both require an entrance
examination. The first is to enter upper secondary school for 3 years. The second is tojoin professional high school for 4 years whose graduates are recognized as
equivalent to upper secondary school graduates plus a recognized degree inprofessional education and training (the degree holders have different wage ranking
regulated by laws for employees in public sectors). After successfully passing theexams to be recognized as an upper secondary graduate, an individual can join
entrance exams to the university which last for 4 or 5 years or for 3 year college
which has lower requirement and less competitive entrance exams. The entrance
exams to universities and colleges are regulated by Ministry of Education andTraining in cooperation with each academic agent. Individuals are encouraged to takeentrance exams to universities, if fails, their exams result is still usable for theselection to 3 year college.

15

0

.2

.4

.6

.8

Graph 1 Kernel density estimations of log wage per hour by gender

-4

-2

0
2
Log wage per hour
Females
Total

4

6

Males

16

Table 1 Descriptive statistics on the labor force in VHLSS 2008
Category

Total

Males

Males
(%)

Labor force
1. Working
2. Not working

101,306
33,42567,88132,336
12,20851,413
20,51330,90014,817
5,211

50.75
61.37
45.52
45.82
42.69

9,913
3,457
770
986

4,649
276
487
533

46.89
7.98
63.25
46.9

2.1 Self–employed in agriculture, forestry and fishery sectors
2.2 Self–employed in other sectors than agriculture, forestry andfishery sectors
2.3 Do not have the first main job but have the second or third job
2.4 Do housework only
2.5 Cannot find a job
2.6 For some other reasons, they cannot work17

Table 2 Descriptive statistics for main variables in wage analysis

log wagemarried (1 if married)age
work experience (in years)
rural (1 if in rural area)
Red River Delta
North East
North West
North Central CoastSouth Central Coast
Central Highland
Southeast
Mekong River Delta
No general education
primary (school grad.)junior (high school grad.)senior (high school grad.)college (3–year)
university (4 or 5 year)
master (degree)phdother degree
no vocational degree
vocation 6 (months)
vocation 1 (year)
vocation 2 (year)
vocation 3 (year)
illiteratefamily firms
family businesshousehold businessstate–owned firms
collectives
private firms
FDI firms

AllObs
33,425
101,306
101,306
96,093
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
101,306
96,09396,09396,09396,09396,09396,09396,093

Mean
2.084
0.739
36.33011.397
0.749
0.186
0.148
0.056
0.096
0.087
0.068
0.138
0.220
0.187
0.278
0.307
0.172
0.015
0.039
0.001
0.000
0.001
0.901
0.038
0.020
0.037
0.003
0.064
0.005
0.647
0.157
0.108
0.006
0.055
0.021

Std. Err.
0.644
0.439
11.612
9.605
0.433
0.389
0.356
0.230
0.295
0.281
0.252
0.345
0.414
0.390
0.448
0.461
0.378
0.120
0.194
0.033
0.015
0.024
0.298
0.191
0.142
0.188
0.058
0.245
0.070
0.478
0.364
0.310
0.080
0.229
0.143

Males
Obs
20,51351,41351,41350,11751,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41351,41350,117
50,117
50,117
50,117
50,117
50,117
50,117

Mean
2.139
0.724
36.52410.938
0.755
0.184
0.149
0.055
0.096
0.087
0.070
0.137
0.223
0.162
0.275
0.319
0.186
0.012
0.044
0.001
0.000
0.001
0.882
0.050
0.029
0.035
0.004
0.049
0.005
0.586
0.209
0.115
0.007
0.063
0.015

Females
Obs
12,91249,89349,89345,97649,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89349,89345,97645,97645,97645,97645,97645,97645,976

Mean
1.998
0.754
36.13011.898
0.744
0.189
0.148
0.057
0.096
0.087
0.067
0.139
0.217
0.212
0.282
0.295
0.158
0.018
0.034
0.001
0.000
0.000
0.922
0.025
0.012
0.039
0.003
0.080
0.005
0.714
0.102
0.099
0.005
0.047
0.027

18

Table 3 Descriptive statistics for Panel 1
All

Males

Variable

Obs

Mean

enrollment
education (in years)
age
log household income
Head's self–employed
Head's in state–owned firms
Head's in collectives
Head's in private firms
Head's in FDI firms
Head spouse's self–employed
Head spouse's in state–owned firms
Head spouse's in collectives
Head spouse's in private firms
Head spouse's in FDI firms
Head's education
Head spouse educationrural
only son of the Head
grand child of the Headage 7–11
age 12–15
age 16–18
age 19–22
Red River Delta
North East
North West
North Central CoastSouth Central Coast
Central Highland
Southeast
Mekong River Delta

53,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16047,662
51,83353,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,16053,1600.666
7.273
14.557
5.711
0.725
0.056
0.006
0.028
0.004
0.800
0.041
0.002
0.015
0.006
7.518
6.491
0.789
0.202
0.081
0.261
0.300
0.246
0.194
0.179
0.146
0.058
0.118
0.095
0.091
0.129
0.184

Std.
Err.
0.472
3.473
4.160
0.761
0.446
0.230
0.076
0.165
0.064
0.400
0.198
0.045
0.120
0.078
3.899
3.996
0.408
0.386
0.272
0.439
0.458
0.430
0.395
0.384
0.353
0.234
0.322
0.293
0.287
0.335
0.388

Females

Obs

Mean

Obs

Mean

27,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74424,882
27,061
27,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,74427,7440.643
7.235
14.673
5.742
0.728
0.055
0.006
0.028
0.004
0.801
0.040
0.002
0.015
0.006
7.490
6.461
0.790
0.387
0.078
0.258
0.291
0.242
0.209
0.177
0.145
0.058
0.118
0.095
0.092
0.129
0.187

25,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41622,78024,772
25,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,41625,4160.691
7.314
14.430
5.676
0.722
0.057
0.006
0.028
0.005
0.799
0.042
0.002
0.014
0.006
7.549
6.523
0.787
0.000
0.084
0.264
0.310
0.249
0.177
0.181
0.148
0.059
0.118
0.095
0.090
0.128
0.181

19

Table 4 Descriptive Statistics for Panel 2
All

Males

Variable

Obs

Mean

enrollment
education (in years)
age
log household income
Head's self–employed
Head's in state–owned firms
Head's in collectives
Head's in private firms
Head's in FDI firms
Head spouse's self–employed
Head spouse's in state–owned firms
Head spouse's in collectives
Head spouse's in private firms
Head spouse's in FDI firms
Head's education
Head spouse educationrural
only son of the Head
grand child of the Headage 7–11
age 12–15
age 16–18
age 19–22
Red River Delta
North East
North West
North Central CoastSouth Central Coast
Central Highland
Southeast
Mekong River Delta
ratio of tuition fee
education subsidies
scholarship
tuition fee reduction

37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
34,07736,71737,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,646
37,64637,52137,646
37,646
37,6460.940
7.106
12.985
5.758
0.715
0.069
0.007
0.033
0.005
0.789
0.053
0.002
0.017
0.007
8.165
7.161
0.770
0.195
0.099
0.358
0.372
0.218
0.052
0.201
0.146
0.050
0.132
0.103
0.088
0.125
0.154
0.115
0.098
0.140
0.419

Std.
Err.
0.237
3.395
3.536
0.759
0.452
0.253
0.081
0.178
0.069
0.408
0.224
0.050
0.128
0.085
3.803
3.937
0.421
0.378
0.298
0.479
0.483
0.413
0.223
0.401
0.353
0.218
0.339
0.304
0.283
0.331
0.361
0.160
0.298
0.347
0.493

Females

Obs

Mean

Obs

Mean

18,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97817,25018,511
18,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,97818,91018,97818,97818,9780.940
7.004
12.920
5.786
0.717
0.070
0.007
0.033
0.004
0.791
0.053
0.002
0.018
0.007
8.177
7.159
0.772
0.387
0.096
0.367
0.370
0.212
0.051
0.202
0.144
0.053
0.133
0.103
0.087
0.124
0.153
0.117
0.097
0.116
0.420

18,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,668
16,827
18,206
18,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,66818,611
18,66818,66818,6680.941
7.210
13.050
5.729
0.712
0.068
0.007
0.033
0.005
0.787
0.053
0.003
0.016
0.007
8.152
7.163
0.768
0.000
0.101
0.349
0.373
0.224
0.053
0.200
0.147
0.046
0.132
0.103
0.089
0.127
0.155
0.113
0.100
0.165
0.418

20

Table 5 Wage function estimates(1)
VARIABLES

log wage(2)
RobustStd. Err.
0.01910.00200.00000.00130.00000.00940.01660.02960.01460.00990.01980.00910.00930.01300.01310.01470.04000.03690.07090.12200.08850.01910.01880.01900.03260.02330.09700.26600.17400.01940.02320.02780.03800.01610.01140.02040.00900.0141

RobustselectStd. Err.
–0.438***0.0091–0.0180*** 0.0005(3)
Inversemills ratio

RobustStd. Err.

sex–0.184***age
0.0345***age^2–0.000428***
work experience
0.0235***work experience^2–0.000246***rural–0.0784***–0.313***0.0108
North East
0.0191–0.607***
North West0.113***–1.013***0.0265
North Central Coast–0.0540***–0.428***0.0181
South Central Coast
0.0369***–0.0255
0.0180Central Highland
0.161***–0.625***0.0213
Southeast
0.227***
0.103***
0.0159
Mekong River Delta
0.0710***–0.133***0.0146
primary (school grad.)0.0743***–0.0763*** 0.0157
junior (high school grad.) 0.0922***–0.01850.0161
senior (high school grad.) 0.140***0.125***
0.0185college (3–year)
0.310***
1.631***
0.0417
university (4 or 5 year)
0.494***
1.749***
0.0309master (degree)
0.779***
1.996***
0.1950phd
0.573***
7.304–
other degree
0.330***
1.174***
0.1910sex*primary–0.0255
sex*junior
0.0451**
sex*senior
0.0619***sex*college
0.0949***sex*university
0.0600**sex*master0.0695sex*phd
0.416
sex*other degree
0.252
vocation 6 (months)
0.01940.682***
0.0220vocation 1 (year)
0.0588**0.801***
0.0305vocation 2 (year)
0.0956***
1.173***
0.0249vocation 3 (year)
0.115***
1.126***
0.0780
illiterate–0.0459***
0.0389*0.0234state–owned firms
0.189***
collectives–0.0479**
private firms
0.153***
FDI firms
0.296***
married
–0.164***0.0117
lambda–0.0803** 0.0361
Constant
1.528***
0.04870.785***
0.0272
Observations
101,306
101,306
101,306
Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Coefficients included but notdisplayed are 87 dummies for 88 industries where the employers have main business, and 33 dummies
for occupational types. GSO classifies industries and occupational types in the survey.

21

Table 6 Gender gap in probability to leave school in Panel 1(1)
Haz. Ratio
0.766***
0.608***(2)
VARIABLES
Robust Std. Err.
Haz. Ratio Robust Std. Err.
sex
0.01920.840***
0.0192log household income
0.01920.786***
0.0118Head's self–employed
0.999
0.0361
Head's in state–owned firms
0.877**
0.0458Head's in collectives
0.966
0.124
Head's in private firms
0.999
0.0691
Head's in FDI firms
1.625***
0.219
Head spouse's self–employed
0.900***
0.0335
Head spouse's in state–owned firms
0.904
0.0567
Head spouse's in collectives
0.973
0.185
Head spouse's in private firms
0.964
0.0845
Head spouse's in FDI firms
1.094
0.147
Head's education
0.913***
0.00321
Head spouses' education
0.921***
0.00331rural
1.245***
0.02771.052**
0.0272
only son of the Head
0.936***
0.02090.989
0.0241
grand child of the Head
0.819***
0.04
0.482***
0.0457age 12–15
2.009***
0.233
1.902***
0.236
age 16–18
3.438***
0.39
3.176***
0.385
age 19–22
5.196***
0.589
4.292***
0.519
sex*age 7–11
1.119
0.181
0.893
0.16
sex*age 12–15
1.009
0.05430.945
0.0552sex*age 16–18
0.984
0.03420.941
0.0366
North East
1.284***
0.03121.133***
0.032
North West1.559***
0.0566
1.191***
0.0479North Central Coast
0.947*
0.02830.959
0.0331
South Central Coast
1.172***
0.03650.846***
0.0316
Central Highland
1.758***
0.05871.228***
0.0467
Southeast
1.894***
0.057
1.158***
0.0407
Mekong River Delta
2.482***
0.0621
1.367***
0.0422
Observations
51,83345,191
Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Model in use is Cox proportional
hazard.

22

Table 7 Gender gap in probability to leave school in Panel 2(1)
VARIABLES

Haz. Ratio(2)
Robust Std.
Err.
0.054
0.0275

Haz. Ratio

Robust Std.
Err.
0.05980.0342
0.0925
0.106
0.282
0.191
0.594
0.129
0.14
0.449
0.272
0.558
0.00926
0.00905
0.0562
0.05660.175
0.03530.00882
0.005
0.27
0.193
0.125
0.07850.119
0.06840.0711
0.09430.07080.0762sex
0.656***
0.688***
log household income
0.843***
0.928**
Head's self–employed
1.041
Head's in state–owned firms
0.970
Head's in collectives
1.198
Head's in private firms
1.305*
Head's in FDI firms
1.704
Head spouse's self–employed
1.255**
Head spouse's in state–owned firms
1.037
Head spouse's in collectives
1.055
Head spouse's in private firms
1.304
Head spouse's in FDI firms
1.442
Head's education
0.975***
Head spouses' education
0.977**rural
1.122**
0.0566
1.022
only son of the Head
0.916
0.05340.910
grand child of the Head
0.918
0.107
0.899
age 12–15
0.146***
0.03450.133***
age 16–18
0.0294*** 0.00763
0.0311***age 19–22
0.0169*** 0.00451
0.0172***sex*age 7–11
1.327
0.333
0.940
sex*age 12–15
1.172
0.185
1.131
sex*age 16–18
1.373***
0.128
1.269**
North East
1.272***
0.07021.315***
North West
1.117
0.108
1.182*
North Central Coast
0.989
0.0641
0.973
South Central Coast
0.936
0.0691
0.877
Central Highland
1.034
0.0917
1.004
Southeast
0.775***
0.06820.738***
Mekong River Delta
1.158**
0.076
1.007
ratio of tuition fee over total expenditure on
0.311***
0.03930.299***
0.0408education
education subsidies
1.123
0.097
1.028
0.103
scholarship0.700***
0.05060.708***
0.0573
tuition fee reduction
0.934
0.05610.929
0.0602
Observations
37,41432,939Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Model in use is Cox proportional
hazard.

23

Table 8 Marginal effect of log wage over sex and degree obtained
Marginal effects
sex#diploma
sex#no degree
sex#primary
sex#juniorsex#senior
sex#college
sex#university
sex#mastersex#phd
sex#upper degree – sex#lower degree
no degree–primaryprimary–junior
junior–seniorsenior–college
senior–univesity
university–mastermaster–phd

Malesexp(b)
7.832
8.288
8.339
9.419
13.39818.02825.24223.027exp(b)
1.579
1.052
2.945
53.466
5,478.685
1,358.479
0.109

P>|z|
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
P>|z|
0.000
0.520
0.000
0.000
0.000
0.000
0.463

Femalesexp(b)
6.413
6.534
7.122
8.225
13.32716.31222.42127.533exp(b)
1.128
1.801
3.015
164.3153,251.152
449.724166.035

P>|z|
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
P>|z|
0.226
0.000
0.000
0.000
0.000
0.000
0.452

24

Table 9 Marginal effect of hazard rate to withdraw from school over sex and group ofage in Panel 1, Analysis 1.
Marginal effects

Males

sex#age groupsex#age 7–11
sex#age 12–15
sex#age 16–18
sex#age 19–22
sex#upper age – sex#lower age
age 7–11 to 12–15
age 12–15 to 16–18
age 16–18 to 19–22

exp(b)
0.106
0.207
0.340
0.546
exp(b)
1.106
1.142
1.229

Females
Pr>|z|
0.000
0.000
0.000
0.000
Pr>|z|
0.000
0.000
0.000

exp(b)
0.095
0.168
0.271
0.430
exp(b)
1.076
1.108
1.173

Pr>|z|
0.000
0.000
0.000
0.000
Pr>|z|
0.000
0.000
0.000

25