OECD DEVELOPMENT CENTRE

Working Paper No. 217(Formerly Webdoc No. 5)

THE IMPACT OF EDUCATIONON FERTILITY AND CHILD MORTALITY:

DO FATHERS REALLY MATTERLESS THAN MOTHERS?

by

Lucia Breierova and Esther Duflo

Research programme on:Social Institutions and Dialogue

November 2003DEV/DOC(2003)15

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TABLE OF CONTENTS

RÉSUMÉ ...........................................................................................................................4

SUMMARY ........................................................................................................................4

I. INTRODUCTION............................................................................................................5

II. PROGRAM, DATA, AND IDENTIFICATION STRATEGY .............................................8

III. EFFECTS ON EDUCATION AND DIFFERENCES IN EDUCATION .........................13

IV. RESULTS: FERTILITY AND CHILD MORTALITY .....................................................16

V. CONCLUSION ............................................................................................................19

BIBLIOGRAPHY..............................................................................................................31

OTHER TITLES IN THE SERIES/ AUTRES TITRES DANS LA SÉRIE..........................32

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RÉSUMÉ

Ce document profite d’un programme massif de construction d’écoles mis en œuvre en Indonésie entre 1973 et 1978 pour déterminer les effets de l’éducation sur la fertilité et la mortalité infantile. L’impact changeant, dans le temps et dans les régions, de ce programme de construction d’écoles produit des variables instrumentales pour le niveau d’éducation moyen du ménage et pour les différences de niveau entre le mari et la femme. Les auteurs montrent que l’éducation des filles a davantage d’influence sur l’âge du mariage et du premier enfant que l’éducation des garçons. Cela étant, l’éducation des filles et des garçons semble avoir le même impact sur la réduction de la mortalité infantile. Les auteurs indiquent que l’estimation par les moindres carrés ordinaires de l’impact différentiel de l’éducation des femmes et des hommes pourrait bien être biaisée par l’incapacité à tenir compte de la constitution des couples par affinité sociale.

SUMMARY

This paper takes advantage of a massive school construction program that took place in Indonesia between 1973 and 1978 to estimate the effect of education on fertility and child mortality. Time and region varying exposure to the school construction program generates instrumental variables for the average education in the household, and the difference in education between husband and wife. We show that female education is a stronger determinant of age at marriage and early fertility than male education. However, female and male education seem equally important factors in reducing child mortality. We suggest that the OLS estimate of the differential effect of women’s and men’s education may be biased by failure to take in to account assortative matching.

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I. INTRODUCTION

The role of male and female education on fertility and human capital formation is a central question for development economists and policy makers. Numerous studies report strong associations between parental education and child mortality or other measure of children’s human capital (see Strauss and Thomas (1995) for a survey of the literature). Significant effects of maternal schooling have also been reported for a variety of inputs into child health (e.g. number and timeliness of prenatal visits, likelihood of obtaining immunisations, etc.). Several of these studies also report that female education is more strongly associated with these outcomes than male education. This evidence has been used as an argument in favour of targeting educational expenditures towards girls. However, most of these studies are based on correlation between years of education and the outcomes of interest, often after controlling for community or family background variables1

. Part of the correlation between parental education and human capital may thus reflect the influence of unobserved background variables correlated with education. Further, the difference between the effects of maternal and paternal education is particularly likely to be biased upwards in absolute value, for two reasons. First, because girls are less likely to be educated, the omitted variable bias might be larger for girls than for boys (because girls’ education may be determined more strongly by family background than boys’ education). Second, the comparison between the coefficients of husband’s and wife’s education might be obscured by a correlation between the wife’s education and unobserved characteristics of her husband, through the functioning of the marriage market: more educated women may be able to marry men who care more about their children.

A few studies have tried to address the omitted variable bias due to the woman’s unobserved abilities by using data on the mother’s siblings to control for family fixed effects (see Wolfe and Behrman, 1987; and Strauss, 1990). Wolfe and Behrman use data on mother’s siblings in Nicaragua to control for the characteristics of her family. They find that once the mother’s family fixed effect is removed, the association between mother’s schooling and child health disappears. Strauss uses data on extended families living together to control for household fixed effects. He finds that the correlation is attenuated once household fixed effects are controlled for2. Behrman and Rosenzweig (1999) use data on monozygotic twins to address both problems together and to

1. When the parents’ family background variables are added as controls, the estimated magnitudes

tend to decline, but the association remains strong and significant (see Thomas et al., 1990).

2. However, fixed effect methods remove a large part of the variation in the data, and exacerbate the measurement error problem, which tends to bias the coefficient downwards.

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investigate the impact of mother’s schooling on her child’s schooling in the United States. They set up a model where a child’s schooling is determined by her parents’ unobserved abilities and observed education, and where more educated women marry more able men. They show that in this model, under certain structural assumptions, data on monozygotic twins (with different education levels) and their children can be used to identify the effect of mother’s education on child education, controlling for genetic ability and the assortative mating effect. Their results suggest that the effect of mother’s education on child education is actually marginally negative. These provocative results, while they may not carry over to the effect of mother’s education on child health in developing countries, suggest that it is worthwhile taking seriously the hypothesis that the difference between the effect of maternal and paternal education may be overstated.

In this paper, we take advantage of a large-scale school construction program, which took place in Indonesia in the 1970s, to construct instrumental variables estimates of the effect of average parental education and the difference between father’s and mother’s education on fertility and child mortality. In 1973, the Indonesian Government launched a major school construction program, the Sekolah Dasar INPRES program. Between 1973-74 and 1978-79, 61 807 primary schools were constructed — an average of two schools per 1 000 children. Duflo (2001) linked the 1995 intercensal survey of Indonesia (SUPAS) with district level data on the number of INPRES schools built between 1973-74 and 1978-79. The exposure of an individual’s to the program was determined both by her district (kabupaten) of birth and by her year of birth. After controlling for district and year of birth fixed effects, interaction dummy variables indicating the age of the individual in 1974 and the intensity of the program in his region of birth were used as exogenous variables, and as instruments for education in the wage function. This paper uses the same data sets, and replicates the analysis for women and their husbands. The estimates suggest that each school built for 1 000 children increased years of education by 0.15 for the first cohort of women fully exposed to the program, and 0.26 for their husbands. It increased the probability that a woman graduated from primary school by 3.5 per cent and that of her husband by 2.7 per cent. To instrument for average education in a family, we combine the interactions of year of birth dummies and the level of program in the region of birth of each partner3. To instrument for the difference in years of education between the husband and the wife, we add a single instrument, based on the observation that, when husbands are not exposed to the program, their wives are increasingly likely to be exposed to the program as they get younger. The interaction of a dummy for whether the husband was born too early to be exposed to the program, the age difference between the husband and the wife, and the intensity of the program can thus be used as an instrument for the level of program in the region of birth (after controlling for the interaction between the husband’s exposure dummy and the age difference).

The estimates suggest a strong and significant effect of education on child mortality, but no significant difference between the effects of male and female education. For fertility, the estimates suggest a very different picture, where the difference in

3. This supposes that we observe complete families. We explicitly deal with selection issues below.

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education has a strong effect, suggesting that the wife’s education is a stronger determinant of fertility decisions than husband’s education.

The remainder of the paper is organized as follows. In Section II, we describe the data, the INPRES program, an the identification strategy. In Section III, we present the results on education. In Section IV, we present the mortality and fertility results. Section V concludes.

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II. PROGRAM, DATA, AND IDENTIFICATION STRATEGY

II.1. Program and Data

The Sekolah Dasar INPRES program was one of the programs implemented by the Indonesian Government to redistribute oil revenues across Indonesian regions. It is described in more details in Duflo (2001). Starting in 1973, the Indonesian government emphasized the need for equity across provinces. Oil revenues were used to finance centrally administered development programs, the presidential instructions (INPRES). As a result of the oil boom, real expenditures on regional development more than doubled between 1973 and 1980, and the Sekolah Dasar INPRES program became very big. Between 1973-74 and 1978-79, 61 807 primary schools were built across the country. This represented more than one school per 500 children. Each school was built for three teachers, and 120 pupils. Once an INPRES school was established, the government recruited the teachers and paid their salary. An effort to train more teachers paralleled the INPRES program.

The program was designed explicitly to target children who had not previously been enrolled in school. The general allocation rule was that the number of schools constructed in each district was proportional to the number of children of primary school age not enrolled in school in 1972. There is thus a negative correlation between the number of schools per capita constructed in each region and enrolment rate in before the program.

The data used in this paper come from the 1995 intercensal survey of Indonesia (SUPAS), matched with administrative data on the number of schools sanctioned for each district (kabupaten). It is administered to 150 000 households. The survey contains a fertility history model administered to all women over 15 present in the household. The module has questions on the date of birth of all children ever born, whether they are still alive, and their date of death if they are dead. The survey also records the date and region of birth of each member of the household, their marital status, and their relationship to the head of the household (which, in most cases, allows us to match husband wife). Table 1 present descriptive statistics on the sample. There are 148 845 women in the sample, aged 23 to 50 in 1995; 122 818 of them have children. The average education of women is lower than that of men (6.16 versus 7.15). The fertility is not very high by the standard of a developing country (1.37 children are born before the woman reached age 25). Out of these children, 0.22 have died, including 0.075 before one month, and 0.16 before age one.

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II.2. Identification Strategy: Effect of the Program on Education

The date of birth and the region of birth of an individual jointly determine her exposure to the program. Indonesian children normally attend primary school between the age of 7 and 12. A child born in 1962 or before was 12 or older in 1974, when the first schools were constructed, and therefore would get only a minimal exposure to the program (less than 3 per cent of children born between 1950 and 1962 were still in primary school in 1974). As we explained above, the district of birth is a second dimension of variation in the intensity of the program: children born in a region where the enrolment rates in 1972 were low are very likely to be educated in this region, and thus to be exposed to a high program intensity. Based on this observation, Duflo (2001) proposed to use the interaction between an individual’s cohort and the number of school built in his region of birth to evaluate the impact of the program. For example, the difference between the education of men who were aged 2 to 6 in 1974 (exposed) and that of men who were aged 12 to 17 in 1974 (unexposed) is 0.47 in regions that got more schools, and 0.36 in regions that got less schools. The difference in these differences (0.12) can be attributed to the program, under the assumption that, in the absence of the program, the increase in years of educational attainment would not have been systematically different in low and high program regions. This assumption can be checked by running the same differences in differences between cohorts who were not exposed to the program.

We use the same strategy in this paper to estimate the effect of the program on the education of women aged 22 to 45, their husbands aged 22 to 50, and the average education in the household. We are also interested in the difference between the husband’s and the wife’s years of education. To identify it, we can use the interplay between the husband’s age, the age difference between the husband and the wife, and the level of the program in the husband’s region of birth4. If the husband was not exposed to the program (because he was more than 12 in 1974), the younger his wife, the more likely she is to have been exposed to the program. The difference between the years of education of the husband and that of the wife will thus be more strongly correlated with their age difference in regions where many schools were built. To illustrate, we present simple comparisons in Table 2. Each cell presents the coefficient and the standard error of the difference between husband’s and wife’s education on the age difference between the husband and the wife. In the first row, we restrict the sample to husbands who were not exposed to the program. In the first column, we run the regression in “low program” regions (defined as the regions where the residual of the number of schools constructed on the number of children is positive). In the second column, we run the regression in “high program” regions. The coefficient is 0.021 and significant in low program regions (older husbands are more educated than their wives, perhaps reflecting the selection in the marriage market). However, in the high program region, the coefficient is very close to 0. The difference between these two coefficient is negative (-0.018) and significant, which is what we expected. Of course, this could be due to the fact that the marriage market functions differently in low and high program regions. When the husband is 4. We do not explore variation in the husband and the wife’s region of birth, because in a large fraction

of the households (74 per cent) husband and wife were born in the same region.

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exposed to the program, however, since most wives are younger than their husband (95 per cent), it is likely that they were both exposed. Therefore, we expect a smaller difference (or none at all) between the correlation between the difference in age and the difference in education of husband and wife. This is shown in the second row in Table 2. Indeed, the coefficients of the difference in age on the difference in education are now not significantly different (the difference in the coefficients is -0.004, with a standard error of 0.0114). This suggests that the difference among exposed husbands was indeed due to the program, rather than to some region specific effect. In Section III, we implement a more general, regression based, version of this identification strategy.

II.3. Identification: Effects on Fertility and Child Mortality

We will extend this strategy to construct instruments for education in the equations that determine age at first marriage, fertility and child mortality. Under the assumption that, in the absence of the program, the pattern of fertility and child mortality across cohorts would not have been different in regions that got more schools than in regions that got fewer schools, we can compare the change in fertility or mortality across regions and over time (as we did for education). Under the assumption that the program itself did not affect anything else than the quantity of education, the interactions of time and the level of program can then be used as instruments for education for the outcomes of interest.

There are several potential problems with these assumptions. First, there may be differential time trends across regions, not due to the program. Since older women had their children at earlier dates than younger women, even though fertility and child mortality are measured in the same year, the cohort pattern reflects evolution over time. For example, the reduction in fertility or child mortality may have been faster in program regions in the absence of the program if these regions started with a higher level of fertility or child mortality. This is however likely to affect cohorts smoothly over time, rather than only the cohorts affected by the program. We will thus check whether there are differential trends among the cohorts that were not exposed to the program. In addition, for each regression, we present a specification where we add controls for enrolment rates in 1971, interacted with year of birth dummies. This should capture time-varying factors correlated with pre-program enrolment rates5. Second, the fertility and child mortality histories are not measured over a period of the same length for older and younger women. Specifically, the fertility history of the younger women is censored. This may lead to a spurious difference in difference if regions where more schools were built tend to have higher fertility (or mortality). To address this, we will use as dependent variable the number of children born (or dead) before the woman reached 25 (the youngest women in the sample are 22). A final problem is potential sample selection. A woman’s own education and the family average education can be calculated irrespective of the marital status of the woman, or our ability to match her with her husband.

5. We will also compile a data set on the availability of family planning and health care centres across

regions, to verify directly whether it is correlated with the program. It should be noted, however, that Pitt et al. (1993) and Gertler and Molyneaux (1994) do not find any effect of family planning clinic on fertility, using fixed effect specifications.

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However, the difference in the education of the husband and the wife can be calculated only when we are able to match a husband and a wife. In the sample, there are 148 845 women aged 22 to 45 : 17 675 of them were never married; 8 785 are not married any more (widowed or divorced); and 11 459 are married, but we were not able to match them with their husbands (we were only able to match a woman with her husband if he or she is the head of the household). Restricting the sample to women who can be matched with a husband can introduce a selection bias in our estimates, if the probability of having a husband is related to our instruments. We will show below that in fact, it does not seem to be the case. However we address this problem by running all regressions in two samples: the sample restricted to those where we were able to match a woman and a man, and a “completed” sample, where, when we do not observe a woman’s husband, we impute the data on the husband. Husband’s age is imputed as the mean age of actual husbands for each year of birth of the woman; program variables are imputed as the mean of these variables for the husbands in the province of birth of the woman; and husband’s education is imputed as the mean education of actual husbands for each year and province of birth of the woman. In all of these regressions, we control for interactions of wife’s year of birth dummies and region of birth dummies with a dummy indicating whether the husband’s data was imputed.

The first two problems may affect the interpretation of the interactions between the level of the program and husband’s and wife’s year of birth, but probably not that of our instrument for the difference in education, the interaction between husband’s exposure, difference in age between husband and wife, and the level of program. Before proceeding, it is nevertheless useful to think about the interpretation of this coefficient. Since the instruments are based on variables that the husband can observe (level of program and age difference), the interpretation given to the 2SLS coefficients of women’s and men’s education depends on the underlying model of the marriage market, and how it was affected by the program.

To interpret the coefficient, we can think of a very simplified model (used in Behrman and Rosenzweig, 1999) in which the child outcomes depend on the mother’s education, the father’s education, the mother’s unobserved ability, and the father’s unobserved ability. Husbands and wives are not randomly matched, but choose each other on the marriage market. The instrumental variable method identifies the effect of giving one more year of education to a random woman before her marriage on child health. Because the marriage intervenes after the woman has completed her education, the future husband can base his choice of wife on the education of the woman. This coefficient will therefore incorporate the effect of assortative mating. Specifically, it will incorporate the average unobserved quality of the men who choose to marry a woman with the education predicted by the instrument, over and above the direct impact of the husband’s education, which is included in the regression (and instrumented). This is the parameter of interest for a woman who considers whether to get an education or not, since by getting an education, she will increase the survival probability of her children, not only through her own capabilities, but also through the effects of the marriage market. However, as pointed out by Behrman and Rosenzweig (1999), this is not the parameter of interest if the government is considering raising the average education of all women. In this case, since all the women are more educated, the entire distribution is shifted and the husband each of them chooses is the same as if none of them had

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received an education. The relevant parameter for policy decisions in this case is the causal effect of the women’s education on child health, keeping her husband’s characteristics fixed. In the presence of assortative mating, our instrumental variables estimates are upper bounds (in absolute value) for the effect of the difference in education between husband and wife.

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III. EFFECTS ON EDUCATION AND DIFFERENCES IN EDUCATION

III.1. Reduced Form Evidence: Effect of the Program on Education

The identification strategy discussed above can be implemented in a simple regression framework. As in Duflo (2001), we run the following specification, separately for women and their husbands.

where dil is a dummy that indicates whether individual i is age l in 1974 (a year-of-birth dummy). In these unrestricted estimates, we measure the time dimension of exposure to the program with 22 (27 for the husbands) year of birth dummies. Individuals aged 24 (29 for the husbands) in 1974 form the control group, and this dummy is omitted from the regression. Each coefficient l1γ can be interpreted as an estimate of the impact of the program on a given cohort.

In the estimation of this equation, as well as in the rest of the paper, we do two adjustments to the standard errors. First, we aggregate the data to cells grouping households by husband’s year and region of birth, and wife’s year and region of birth. The regressions are then weighted by the sum of the weights in each cell. This takes care of the correlation between households with the same characteristics. Second, we correct the standard errors in these aggregate regressions for auto-correlation of an arbitrary form between observations in the wife’s region of birth (as suggested in Bertrand et al. (2001)6.

There is a testable restriction on the pattern of the coefficients l1γ . Because

children aged 13 and older in 1974 did not benefit from the program, the coefficients l1γ

should be 0 for l > 12 and start increasing for l smaller than some threshold (the oldest age at which an individual could have been exposed to the program and still benefit from it).

In Figure 1, we show the coefficients l1γ for women (in solid lines) and men (in dotted lines). Each dot on a line is the coefficient of the interaction between a dummy for 6. In practice, we use the stata “cluster” command, at the level of the wife’s region of birth”. The

standard errors are thus larger than those reported in Duflo (2001), which was not implementing this correction.

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being a given age in 1974 and the number of schools constructed per 1 000 children in the region of birth. For women and men, these coefficients fluctuate around 0 until age 12 and start increasing after age 12. As expected, the program had no effect on the education of cohorts not exposed to it, and it had a positive effect on the education of younger cohorts. The coefficients are jointly significant for age 2 to 12 in both equations (The F-statistics for the interaction between age 2 to 12 in 1974 and the program are respectively 2.89 and 2.26, for males and females), and insignificant for age 13 and older.

Next, we run a similar specification that combines the husband’s exposure to the program to the wife’s exposure to the program to explain the average level of education in the household. Namely, we run the following specification, in the complete sample (with imputed husbands)7.

(2)

where Sirqkl is the average education of household i, in which the husband and the wife were born in regions r and q, respectively, and in years k and l, respectively. Pr is the level of program in the husband’s region of birth, Pq is the level of program in the wife’s region of birth, h

ird (resp. wird ) is a dummy equal to 1 if the husband (resp. the wife) is

τ years old in 1974. Xirqkl is a vector of control variables, the enrolment rate in each partner’s region of birth, interacted with their year of birth dummies, and interaction of a dummy indicating whether the husband’s data is imputed and year of birth of the wife dummies on the one hand and region of birth of the wife dummies on the other hand. The interpretation of these coefficients is the same as above: They should be more or less constant till age 12, and then start increasing. The results are presented in Figure 2. The pattern is very striking: the coefficients of the husband and the wife are almost on top of each other, and have the same pattern as they have as in the individual regressions. Both husband’s and wife’s education have contributed to the increase in average education.

This regression supports the identification assumption for the education equation: the interactions are jointly significant after age 12 for both genders (the F-statistics are 1.82 for men, 1.76 for women, and 2.25 jointly for both genders), and jointly insignificant before age 12 (the F-statistics is 1.46 jointly for both genders).

7. The results in the sample where both woman and husband are observed are almost identical.

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III.2. First Stage Results: Effect of the Program on Education and Differences in Education

We now impose that the program had no effect on the generations that were not exposed to it. To obtain the impact of the program on average education, we run the following specification:

where the notation is the same as above.

The results are presented in Table 3, columns 1 and 2. The coefficients for both men and women have the expected pattern: they are positive and increasing, and jointly significant (with a F-statistics of 2.25). The significance of the men’s instrument is not very high (the F-statistics is 1.38 without controls), lower than that of women’s instrument. One of our concerns in this paper is to estimate the impact of the difference in education between the husband and the wife. As we described above, the interaction between a dummy indicating whether the husband was exposed to the program, the age difference of the husband, and the level of program in the region of birth, is likely to predict the difference in education (but not the average level of education). This leads to the following first stage specification:

In addition to the control variable previously mentioned, Xirqkl now includes the

variables Tk * (l – k) and (1 – Tk) * (l – k). The coefficients of interest in this regression is λ2, which should be negative and significant. Testing whether λ1 is zero provides a useful specification check. The same specification, with the average education as the dependent variable, will be the first stage for average education in the specification where we instrument both for average education and the difference. In this case, we expect the coefficients of both interactions to be insignificant.

Columns 3 to 6 present the first stage results for average education and difference in education. As expected, the coefficients of the interactions are both insignificant in the first stage regression for average education. The precious conclusions are otherwise unchanged, and the instrument set is jointly significant. In the regression for the difference in education, however, λ2 is negative and significant (with a t. statistic of -3.00), while λ1 is insignificant. Therefore, this instrument seems indeed to be capturing the effect of interest. The F-statistics of the joint set of instrument is 1.86 (p=0.0096).

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IV. RESULTS: FERTILITY AND CHILD MORTALITY

IV.1. Reduced Form Results

In Table 4, we present F-statistics of regressions analogous to equation 2, for four outcomes yirqkl: total number of children, number of children by age 25, total number of children who ever died, number of children who died before the woman reached age 25.

We present the F-statistics for testing whether the set of i

r2γ are jointly significant for τ > 12 and for τ less or equal to 12, for i = h and w.

In the regressions on the total number of children, none of the F-statistics is significant: there is no indication that the program was associated with a reduction in the number of children or the number of children born before the woman turned 25. The picture is different for the mortality regressions. Here, the F-statistics are all jointly insignificant for the pre-program variables, but the woman’s interactions are significant after the program, and the set of interaction is jointly significant at the 10 per cent level.

Thus, these results suggest that overall the program may have been effective in reducing mortality, but not fertility. The fact that none of the pre-program interactions are jointly significant is reassuring: it suggests that the effect on mortality is not due to omitted region-specific trends correlated with the program (unless they changed for this specific cohort of woman).

IV.2. Restricted Reduced Forms

In Table 5, we present restricted reduced form using specifications analogous to the first stage for education, with the instrument for the difference (Table A) and without it (Table 5B). In Table 5A, in the regressions using the total number of children born (or the number of children born before the woman turned 25), neither the woman’s instrument, nor the man’s instrument, are jointly significant. The pattern for the mortality variables (total number of children that died or total number of children that died before the woman turned 25) is more similar to the pattern we had for average education: the wife’s instruments are jointly significant, while the husband’s instrument are not. Jointly, the instruments are significant at the 5 per cent level in all the regressions with control for enrolment rates.

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In Table 5B, we introduce the variable indicating the difference in exposure to the program by men and women. Interestingly, a different pattern appears in the fertility and the mortality regressions. In the fertility regression, the interaction between the husband’s age, the age difference between husband and wife and the level of program in the region of birth is significant: in particular, when the husband is not affected by the program, the interaction between the age difference and the level of the program is negative, and significant at the 10 per cent level. This parallels the negative coefficient we found in the equation for the difference in education. This suggests that the program, by reducing the difference in education between husband and wife, may also have decreased fertility. In the child mortality equations, however, there is no similar effect of the difference in exposure to the program.

IV.3. Instrumental Variable Estimates

Tables 6 and 7 present the OLS and Instrumental variables estimates of the effect of average education and difference in education on a larger number of outcomes related to fertility or child mortality.

and

The excluded instruments for equation 5 are the set of interaction between age in

1974 and intensity of the program in the region of birth (for both husband and wife). In equation 6, we add to this set the interaction between husband’s exposure to the program, age difference between husband and wife, and level of the program in the region of birth (we control for the age difference interacted with the husband’s exposure to the program).

In Tables 6 and 7, the first two columns present the OLS estimates (of the average education in the household in the first column, the average and the difference in the second column). Columns 3 to 6 present the IV results, with or without controls for initial enrolment.

OLS and 2SLS deliver similar results for the age at marriage and the probability that the woman is currently married. Age at marriage is significantly associated with education (the 2SLS estimate is 0.38 for the average education in the household, suggesting that each year of education is associated with an increase of 0.38 in the age at marriage), and women’s education matters more than men’s education (conditioning on the average education, a greater difference in education between husband and wife reduces marriage age). Education does not seem to be correlated with current marriage status8. 8. This shows that our instruments are not correlated with the probability to be selected in the sample

of “complete” couples, and thus that results are not very likely to be biased by sample selection.

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The 2SLS results on the number of children ever born are somewhat noisy: the point estimate of the effect of average education on the number of children ever born is similar to the OLS (-0.09), but is not significantly different from 0. The 2SLS estimate effect of the difference in education is almost as large (in the opposite sense), but not significant either. The results of the number of children born before the woman turned 15 and 25 are more interesting. In the case of the woman born before age 15, both average education and the difference in education matter, suggesting once again that women’s education has a larger impact on early pregnancy than men’s education. In the case of the number of children born before the woman turned 25, the average education does not seem to matter, but the difference in education does matter. In other words, when the education of the man increases relative to that of his wife, the number of children in the household is predicted to increase.

In Table 7 we present the child mortality results. We obtain very similar results for total number of child who died, mortality before one month, mortality before one year and mortality before five years. Average education in the household has the effect of reducing child mortality, and there is no significant effect of the difference between husband’s and wife’s education. When we restrict the sample to death occurring before the woman was age 25, we find negative estimates as well, although they are less significant.

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V. CONCLUSION

The INPRES program led a to large increase in the education of women as well as men. This increase resulted, not only in higher incomes, but also in lower age at marriage, lower number of very early births, and lower child mortality. Thus, the estimates reported in this paper confirm the findings of the earlier literature, that parental education has a strong causal effect on the reduction of child mortality.

The results on the difference between the effect of male and female education are more nuanced. Female education seems indeed to matter more than male education in determining age at marriage and number of children born before the woman reaches 15 or 25. On the other hands, these estimates do not confirm the intuition derived from OLS specifications (including the OLS specification in this paper) that female education has a stronger causal impact on child mortality than male education. The 2SLS estimates of the differences in education between male and female are never significant. Note that in the presence of assortative matching, the 2SLS remain lower bounds of the effect of the difference between male and female education (to the extent that “good husbands” may prefer wife with higher education, as predicted by the instruments).

In extension to this work, we will present direct evidence of assortative matching. First, each partner’s education has a causal effect on his or her partner education. Second, the OLS estimate shows a positive effect of the education of the wife on her husband’s wage, which disappear after instrumenting.

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BIBLIOGRAPHY

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STRAUSS, J. and T. DUNCAN (1995). “Human Resources: Empirical Modelling of Household and Family Decisions” in J. BEHRMAN and T.N. SRINIVASAN (eds.), Handbook of Development Economics, Vol. 3A(9), Ch. 34, pp. 1885-2023, North Holland, Amsterdam.

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OTHER TITLES IN THE SERIES/ AUTRES TITRES DANS LA SÉRIE

The former series known as “Technical Papers” and “Webdocs” merged in November 2003

into “Development Centre Working Papers”. In the new series, former Webdocs 1-17 follow

former Technical Papers 1-212 as Working Papers 213-229.

All these documents may be downloaded from:

http://www.oecd.org/dev/papers or obtained via e-mail (cendev.contact@oecd.org)

Working Paper No.1, Macroeconomic Adjustment and Income Distribution: A Macro-Micro Simulation Model, by François Bourguignon, William H. Branson and Jaime de Melo, March 1989. Working Paper No. 2, International Interactions in Food and Agricultural Policies: The Effect of Alternative Policies, by Joachim Zietz and Alberto Valdés, April, 1989. Working Paper No. 3, The Impact of Budget Retrenchment on Income Distribution in Indonesia: A Social Accounting Matrix Application, by Steven Keuning and Erik Thorbecke, June 1989. Working Paper No. 3a, Statistical Annex: The Impact of Budget Retrenchment, June 1989. Document de travail No. 4, Le Rééquilibrage entre le secteur public et le secteur privé : le cas du Mexique, par C.-A. Michalet, juin 1989. Working Paper No. 5, Rebalancing the Public and Private Sectors: The Case of Malaysia, by R. Leeds, July 1989. Working Paper No. 6, Efficiency, Welfare Effects, and Political Feasibility of Alternative Antipoverty and Adjustment Programs, by Alain de Janvry and Elisabeth Sadoulet, January 1990. Document de travail No. 7, Ajustement et distribution des revenus : application d’un modèle macro-micro au Maroc, par Christian Morrisson, avec la collaboration de Sylvie Lambert et Akiko Suwa, décembre 1989. Working Paper No. 8, Emerging Maize Biotechnologies and their Potential Impact, by W. Burt Sundquist, October 1989. Document de travail No. 9, Analyse des variables socio-culturelles et de l’ajustement en Côte d’Ivoire, par W. Weekes-Vagliani, janvier 1990. Working Paper No. 10, A Financial Computable General Equilibrium Model for the Analysis of Ecuador’s Stabilization Programs, by André Fargeix and Elisabeth Sadoulet, February 1990. Working Paper No. 11, Macroeconomic Aspects, Foreign Flows and Domestic Savings Performance in Developing Countries: A ”State of The Art” Report, by Anand Chandavarkar, February 1990. Working Paper No. 12, Tax Revenue Implications of the Real Exchange Rate: Econometric Evidence from Korea and Mexico, by Viriginia Fierro and Helmut Reisen, February 1990. Working Paper No. 13, Agricultural Growth and Economic Development: The Case of Pakistan, by Naved Hamid and Wouter Tims, April 1990. Working Paper No. 14, Rebalancing the Public and Private Sectors in Developing Countries: The Case of Ghana, by H. Akuoko-Frimpong, June 1990. Working Paper No. 15, Agriculture and the Economic Cycle: An Economic and Econometric Analysis with Special Reference to Brazil, by Florence Contré and Ian Goldin, June 1990. Working Paper No. 16, Comparative Advantage: Theory and Application to Developing Country Agriculture, by Ian Goldin, June 1990. Working Paper No. 17, Biotechnology and Developing Country Agriculture: Maize in Brazil, by Bernardo Sorj and John Wilkinson, June 1990. Working Paper No. 18, Economic Policies and Sectoral Growth: Argentina 1913-1984, by Yair Mundlak, Domingo Cavallo, Roberto Domenech, June 1990. Working Paper No. 19, Biotechnology and Developing Country Agriculture: Maize In Mexico, by Jaime A. Matus Gardea, Arturo Puente Gonzalez and Cristina Lopez Peralta, June 1990. Working Paper No. 20, Biotechnology and Developing Country Agriculture: Maize in Thailand, by Suthad Setboonsarng, July 1990. Working Paper No. 21, International Comparisons of Efficiency in Agricultural Production, by Guillermo Flichmann, July 1990. Working Paper No. 22, Unemployment in Developing Countries: New Light on an Old Problem, by David Turnham and Denizhan Eröcal, July 1990.

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Working Paper No. 23, Optimal Currency Composition of Foreign Debt: the Case of Five Developing Countries, by Pier Giorgio Gawronski, August 1990. Working Paper No. 24, From Globalization to Regionalization: the Mexican Case, by Wilson Peres Núñez, August 1990. Working Paper No. 25, Electronics and Development in Venezuela: A User-Oriented Strategy and its Policy Implications, by Carlota Perez, October 1990. Working Paper No. 26, The Legal Protection of Software: Implications for Latecomer Strategies in Newly Industrialising Economies (NIEs) and Middle-Income Economies (MIEs), by Carlos Maria Correa, October 1990. Working Paper No. 27, Specialization, Technical Change and Competitiveness in the Brazilian Electronics Industry, by Claudio R. Frischtak, October 1990. Working Paper No. 28, Internationalization Strategies of Japanese Electronics Companies: Implications for Asian Newly Industrializing Economies (NIEs), by Bundo Yamada, October 1990. Working Paper No. 29, The Status and an Evaluation of the Electronics Industry in Taiwan, by Gee San, October 1990. Working Paper No. 30, The Indian Electronics Industry: Current Status, Perspectives and Policy Options, by Ghayur Alam, October 1990. Working Paper No. 31, Comparative Advantage in Agriculture in Ghana, by James Pickett and E. Shaeeldin, October 1990. Working Paper No. 32, Debt Overhang, Liquidity Constraints and Adjustment Incentives, by Bert Hofman and Helmut Reisen, October 1990. Working Paper No. 34, Biotechnology and Developing Country Agriculture: Maize in Indonesia, by Hidjat Nataatmadja et al., January 1991. Working Paper No. 35, Changing Comparative Advantage in Thai Agriculture, by Ammar Siamwalla, Suthad Setboonsarng and Prasong Werakarnjanapongs, March 1991. Working Paper No. 36, Capital Flows and the External Financing of Turkey’s Imports, by Ziya Önis and Süleyman Özmucur, July 1991. Working Paper No. 37, The External Financing of Indonesia’s Imports, by Glenn P. Jenkins and Henry B.F. Lim, July 1991. Working Paper No. 38, Long-term Capital Reflow under Macroeconomic Stabilization in Latin America, by Beatriz Armendariz de Aghion, April 1991. Working Paper No. 39, Buybacks of LDC Debt and the Scope for Forgiveness, by Beatriz Armendariz de Aghion, April 1991. Working Paper No. 40, Measuring and Modelling Non-Tariff Distortions with Special Reference to Trade in Agricultural Commodities, by Peter J. Lloyd, July 1991. Working Paper No. 41, The Changing Nature of IMF Conditionality, by Jacques J. Polak, August 1991. Working Paper No. 42, Time-Varying Estimates on the Openness of the Capital Account in Korea and Taiwan, by Helmut Reisen and Hélène Yèches, August 1991. Working Paper No. 43, Toward a Concept of Development Agreements, by F. Gerard Adams, August 1991. Document de travail No. 44, Le Partage du fardeau entre les créanciers de pays débiteurs défaillants, par Jean-Claude Berthélemy et Ann Vourc’h, septembre 1991. Working Paper No. 45, The External Financing of Thailand’s Imports, by Supote Chunanunthathum, October 1991. Working Paper No. 46, The External Financing of Brazilian Imports, by Enrico Colombatto, with Elisa Luciano, Luca Gargiulo, Pietro Garibaldi and Giuseppe Russo, October 1991. Working Paper No. 47, Scenarios for the World Trading System and their Implications for Developing Countries, by Robert Z. Lawrence, November 1991. Working Paper No. 48, Trade Policies in a Global Context: Technical Specifications of the Rural/Urban-North/South (RUNS) Applied General Equilibrium Model, by Jean-Marc Burniaux and Dominique van der Mensbrugghe, November 1991. Working Paper No. 49, Macro-Micro Linkages: Structural Adjustment and Fertilizer Policy in Sub-Saharan Africa, by Jean-Marc Fontaine with the collaboration of Alice Sindzingre, December 1991. Working Paper No. 50, Aggregation by Industry in General Equilibrium Models with International Trade, by Peter J. Lloyd, December 1991. Working Paper No. 51, Policy and Entrepreneurial Responses to the Montreal Protocol: Some Evidence from the Dynamic Asian Economies, by David C. O’Connor, December 1991. Working Paper No. 52, On the Pricing of LDC Debt: an Analysis Based on Historical Evidence from Latin America, by Beatriz Armendariz de Aghion, February 1992. Working Paper No. 53, Economic Regionalisation and Intra-Industry Trade: Pacific-Asian Perspectives, by Kiichiro Fukasaku, February 1992. Working Paper No. 54, Debt Conversions in Yugoslavia, by Mojmir Mrak, February 1992. Working Paper No. 55, Evaluation of Nigeria’s Debt-Relief Experience (1985-1990), by N.E. Ogbe, March 1992. Document de travail No. 56, L’Expérience de l’allégement de la dette du Mali, par Jean-Claude Berthélemy, février 1992. Working Paper No. 57, Conflict or Indifference: US Multinationals in a World of Regional Trading Blocs, by Louis T. Wells, Jr., March 1992. Working Paper No. 58, Japan’s Rapidly Emerging Strategy Toward Asia, by Edward J. Lincoln, April 1992. Working Paper No. 59, The Political Economy of Stabilization Programmes in Developing Countries, by Bruno S. Frey and Reiner Eichenberger, April 1992. Working Paper No. 60, Some Implications of Europe 1992 for Developing Countries, by Sheila Page, April 1992. Working Paper No. 61, Taiwanese Corporations in Globalisation and Regionalisation, by Gee San, April 1992. Working Paper No. 62, Lessons from the Family Planning Experience for Community-Based Environmental Education, by Winifred Weekes-Vagliani, April 1992. Working Paper No. 63, Mexican Agriculture in the Free Trade Agreement: Transition Problems in Economic Reform, by Santiago Levy and Sweder van Wijnbergen, May 1992. Working Paper No. 64, Offensive and Defensive Responses by European Multinationals to a World of Trade Blocs, by John M. Stopford, May 1992. Working Paper No. 65, Economic Integration in the Pacific Region, by Richard Drobnick, May 1992. Working Paper No. 66, Latin America in a Changing Global Environment, by Winston Fritsch, May 1992. Working Paper No. 67, An Assessment of the Brady Plan Agreements, by Jean-Claude Berthélemy and Robert Lensink, May 1992.

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Working Paper No. 68, The Impact of Economic Reform on the Performance of the Seed Sector in Eastern and Southern Africa, by Elizabeth Cromwell, June 1992. Working Paper No. 69, Impact of Structural Adjustment and Adoption of Technology on Competitiveness of Major Cocoa Producing Countries, by Emily M. Bloomfield and R. Antony Lass, June 1992. Working Paper No. 70, Structural Adjustment and Moroccan Agriculture: an Assessment of the Reforms in the Sugar and Cereal Sectors, by Jonathan Kydd and Sophie Thoyer, June 1992. Document de travail No. 71, L’Allégement de la dette au Club de Paris : les évolutions récentes en perspective, par Ann Vourc’h, juin 1992. Working Paper No. 72, Biotechnology and the Changing Public/Private Sector Balance: Developments in Rice and Cocoa, by Carliene Brenner, July 1992. Working Paper No. 73, Namibian Agriculture: Policies and Prospects, by Walter Elkan, Peter Amutenya, Jochbeth Andima, Robin Sherbourne and Eline van der Linden, July 1992. Working Paper No. 74, Agriculture and the Policy Environment: Zambia and Zimbabwe, by Doris J. Jansen and Andrew Rukovo, July 1992. Working Paper No. 75, Agricultural Productivity and Economic Policies: Concepts and Measurements, by Yair Mundlak, August 1992. Working Paper No. 76, Structural Adjustment and the Institutional Dimensions of Agricultural Research and Development in Brazil: Soybeans, Wheat and Sugar Cane, by John Wilkinson and Bernardo Sorj, August 1992. Working Paper No. 77, The Impact of Laws and Regulations on Micro and Small Enterprises in Niger and Swaziland, by Isabelle Joumard, Carl Liedholm and Donald Mead, September 1992. Working Paper No. 78, Co-Financing Transactions between Multilateral Institutions and International Banks, by Michel Bouchet and Amit Ghose, October 1992. Document de travail No. 79, Allégement de la dette et croissance : le cas mexicain, par Jean-Claude Berthélemy et Ann Vourc’h, octobre 1992. Document de travail No. 80, Le Secteur informel en Tunisie : cadre réglementaire et pratique courante, par Abderrahman Ben Zakour et Farouk Kria, novembre 1992. Working Paper No. 81, Small-Scale Industries and Institutional Framework in Thailand, by Naruemol Bunjongjit and Xavier Oudin, November 1992. Working Paper No. 81a, Statistical Annex: Small-Scale Industries and Institutional Framework in Thailand, by Naruemol Bunjongjit and Xavier Oudin, November 1992. Document de travail No. 82, L’Expérience de l’allégement de la dette du Niger, par Ann Vourc’h et Maina Boukar Moussa, novembre 1992. Working Paper No. 83, Stabilization and Structural Adjustment in Indonesia: an Intertemporal General Equilibrium Analysis, by David Roland-Holst, November 1992. Working Paper No. 84, Striving for International Competitiveness: Lessons from Electronics for Developing Countries, by Jan Maarten de Vet, March 1993. Document de travail No. 85, Micro-entreprises et cadre institutionnel en Algérie, par Hocine Benissad, mars 1993. Working Paper No. 86, Informal Sector and Regulations in Ecuador and Jamaica, by Emilio Klein and Victor E. Tokman, August 1993. Working Paper No. 87, Alternative Explanations of the Trade-Output Correlation in the East Asian Economies, by Colin I. Bradford Jr. and Naomi Chakwin, August 1993. Document de travail No. 88, La Faisabilité politique de l’ajustement dans les pays africains, par Christian Morrisson, Jean-Dominique Lafay et Sébastien Dessus, novembre 1993. Working Paper No. 89, China as a Leading Pacific Economy, by Kiichiro Fukasaku and Mingyuan Wu, November 1993. Working Paper No. 90, A Detailed Input-Output Table for Morocco, 1990, by Maurizio Bussolo and David Roland-Holst November 1993. Working Paper No. 91, International Trade and the Transfer of Environmental Costs and Benefits, by Hiro Lee and David Roland-Holst, December 1993. Working Paper No. 92, Economic Instruments in Environmental Policy: Lessons from the OECD Experience and their Relevance to Developing Economies, by Jean-Philippe Barde, January 1994. Working Paper No. 93, What Can Developing Countries Learn from OECD Labour Market Programmes and Policies?, by Åsa Sohlman with David Turnham, January 1994. Working Paper No. 94, Trade Liberalization and Employment Linkages in the Pacific Basin, by Hiro Lee and David Roland-Holst, February 1994. Working Paper No. 95, Participatory Development and Gender: Articulating Concepts and Cases, by Winifred Weekes-Vagliani, February 1994. Document de travail No. 96, Promouvoir la maîtrise locale et régionale du développement : une démarche participative à Madagascar, par Philippe de Rham et Bernard Lecomte, juin 1994. Working Paper No. 97, The OECD Green Model: an Updated Overview, by Hiro Lee, Joaquim Oliveira-Martins and Dominique van der Mensbrugghe, August 1994. Working Paper No. 98, Pension Funds, Capital Controls and Macroeconomic Stability, by Helmut Reisen and John Williamson, August 1994. Working Paper No. 99, Trade and Pollution Linkages: Piecemeal Reform and Optimal Intervention, by John Beghin, David Roland-Holst and Dominique van der Mensbrugghe, October 1994. Working Paper No. 100, International Initiatives in Biotechnology for Developing Country Agriculture: Promises and Problems, by Carliene Brenner and John Komen, October 1994. Working Paper No. 101, Input-based Pollution Estimates for Environmental Assessment in Developing Countries, by Sébastien Dessus, David Roland-Holst and Dominique van der Mensbrugghe, October 1994. Working Paper No. 102, Transitional Problems from Reform to Growth: Safety Nets and Financial Efficiency in the Adjusting Egyptian Economy, by Mahmoud Abdel-Fadil, December 1994. Working Paper No. 103, Biotechnology and Sustainable Agriculture: Lessons from India, by Ghayur Alam, December 1994. Working Paper No. 104, Crop Biotechnology and Sustainability: a Case Study of Colombia, by Luis R. Sanint, January 1995. Working Paper No. 105, Biotechnology and Sustainable Agriculture: the Case of Mexico, by José Luis Solleiro Rebolledo, January 1995.

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Working Paper No. 106, Empirical Specifications for a General Equilibrium Analysis of Labor Market Policies and Adjustments, by Andréa Maechler and David Roland-Holst, May 1995. Document de travail No. 107, Les Migrants, partenaires de la coopération internationale : le cas des Maliens de France, par Christophe Daum, juillet 1995. Document de travail No. 108, Ouverture et croissance industrielle en Chine : étude empirique sur un échantillon de villes, par Sylvie Démurger, septembre 1995. Working Paper No. 109, Biotechnology and Sustainable Crop Production in Zimbabwe, by John J. Woodend, December 1995. Document de travail No. 110, Politiques de l’environnement et libéralisation des échanges au Costa Rica : une vue d’ensemble, par Sébastien Dessus et Maurizio Bussolo, février 1996. Working Paper No. 111, Grow Now/Clean Later, or the Pursuit of Sustainable Development?, by David O’Connor, March 1996. Working Paper No. 112, Economic Transition and Trade-Policy Reform: Lessons from China, by Kiichiro Fukasaku and Henri-Bernard Solignac Lecomte, July 1996. Working Paper No. 113, Chinese Outward Investment in Hong Kong: Trends, Prospects and Policy Implications, by Yun-Wing Sung, July 1996. Working Paper No. 114, Vertical Intra-industry Trade between China and OECD Countries, by Lisbeth Hellvin, July 1996. Document de travail No. 115, Le Rôle du capital public dans la croissance des pays en développement au cours des années 80, par Sébastien Dessus et Rémy Herrera, juillet 1996. Working Paper No. 116, General Equilibrium Modelling of Trade and the Environment, by John Beghin, Sébastien Dessus, David Roland-Holst and Dominique van der Mensbrugghe, September 1996. Working Paper No. 117, Labour Market Aspects of State Enterprise Reform in Viet Nam, by David O’Connor, September 1996. Document de travail No. 118, Croissance et compétitivité de l’industrie manufacturière au Sénégal, par Thierry Latreille et Aristomène Varoudakis, octobre 1996. Working Paper No. 119, Evidence on Trade and Wages in the Developing World, by Donald J. Robbins, December 1996. Working Paper No. 120, Liberalising Foreign Investments by Pension Funds: Positive and Normative Aspects, by Helmut Reisen, January 1997. Document de travail No. 121, Capital Humain, ouverture extérieure et croissance : estimation sur données de panel d’un modèle à coefficients variables, par Jean-Claude Berthélemy, Sébastien Dessus et Aristomène Varoudakis, janvier 1997. Working Paper No. 122, Corruption: The Issues, by Andrew W. Goudie and David Stasavage, January 1997. Working Paper No. 123, Outflows of Capital from China, by David Wall, March 1997. Working Paper No. 124, Emerging Market Risk and Sovereign Credit Ratings, by Guillermo Larraín, Helmut Reisen and Julia von Maltzan, April 1997. Working Paper No. 125, Urban Credit Co-operatives in China, by Eric Girardin and Xie Ping, August 1997. Working Paper No. 126, Fiscal Alternatives of Moving from Unfunded to Funded Pensions, by Robert Holzmann, August 1997. Working Paper No. 127, Trade Strategies for the Southern Mediterranean, by Peter A. Petri, December 1997. Working Paper No. 128, The Case of Missing Foreign Investment in the Southern Mediterranean, by Peter A. Petri, December 1997. Working Paper No. 129, Economic Reform in Egypt in a Changing Global Economy, by Joseph Licari, December 1997. Working Paper No. 130, Do Funded Pensions Contribute to Higher Aggregate Savings? A Cross-Country Analysis, by Jeanine Bailliu and Helmut Reisen, December 1997. Working Paper No. 131, Long-run Growth Trends and Convergence Across Indian States, by Rayaprolu Nagaraj, Aristomène Varoudakis and Marie-Ange Véganzonès, January 1998. Working Paper No. 132, Sustainable and Excessive Current Account Deficits, by Helmut Reisen, February 1998. Working Paper No. 133, Intellectual Property Rights and Technology Transfer in Developing Country Agriculture: Rhetoric and Reality, by Carliene Brenner, March 1998. Working Paper No. 134, Exchange-rate Management and Manufactured Exports in Sub-Saharan Africa, by Khalid Sekkat and Aristomène Varoudakis, March 1998. Working Paper No. 135, Trade Integration with Europe, Export Diversification and Economic Growth in Egypt, by Sébastien Dessus and Akiko Suwa-Eisenmann, June 1998. Working Paper No. 136, Domestic Causes of Currency Crises: Policy Lessons for Crisis Avoidance, by Helmut Reisen, June 1998. Working Paper No. 137, A Simulation Model of Global Pension Investment, by Landis MacKellar and Helmut Reisen, August 1998. Working Paper No. 138, Determinants of Customs Fraud and Corruption: Evidence from Two African Countries, by David Stasavage and Cécile Daubrée, August 1998. Working Paper No. 139, State Infrastructure and Productive Performance in Indian Manufacturing, by Arup Mitra, Aristomène Varoudakis and Marie-Ange Véganzonès, August 1998. Working Paper No. 140, Rural Industrial Development in Viet Nam and China: A Study in Contrasts, by David O’Connor, September 1998. Working Paper No. 141,Labour Market Aspects of State Enterprise Reform in China, by Fan Gang,Maria Rosa Lunati and David O’Connor, October 1998. Working Paper No. 142, Fighting Extreme Poverty in Brazil: The Influence of Citizens’ Action on Government Policies, by Fernanda Lopes de Carvalho, November 1998. Working Paper No. 143, How Bad Governance Impedes Poverty Alleviation in Bangladesh, by Rehman Sobhan, November 1998. Document de travail No. 144, La libéralisation de l’agriculture tunisienne et l’Union européenne : une vue prospective, par Mohamed Abdelbasset Chemingui et Sébastien Dessus, février 1999. Working Paper No. 145, Economic Policy Reform and Growth Prospects in Emerging African Economies, by Patrick Guillaumont, Sylviane Guillaumont Jeanneney and Aristomène Varoudakis, March 1999. Working Paper No. 146, Structural Policies for International Competitiveness in Manufacturing: The Case of Cameroon, by Ludvig Söderling, March 1999. Working Paper No. 147, China’s Unfinished Open-Economy Reforms: Liberalisation of Services, by Kiichiro Fukasaku, Yu Ma and Qiumei Yang, April 1999.

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Working Paper No. 148, Boom and Bust and Sovereign Ratings, by Helmut Reisen and Julia von Maltzan, June 1999. Working Paper No. 149, Economic Opening and the Demand for Skills in Developing Countries: A Review of Theory and Evidence, by David O’Connor and Maria Rosa Lunati, June 1999. Working Paper No. 150, The Role of Capital Accumulation, Adjustment and Structural Change for Economic Take-off: Empirical Evidence from African Growth Episodes, by Jean-Claude Berthélemy and Ludvig Söderling, July 1999. Working Paper No. 151, Gender, Human Capital and Growth: Evidence from Six Latin American Countries, by Donald J. Robbins, September 1999. Working Paper No. 152, The Politics and Economics of Transition to an Open Market Economy in Viet Nam, by James Riedel and William S. Turley, September 1999. Working Paper No. 153, The Economics and Politics of Transition to an Open Market Economy: China, by Wing Thye Woo, October 1999. Working Paper No. 154, Infrastructure Development and Regulatory Reform in Sub-Saharan Africa: The Case of Air Transport, by Andrea E. Goldstein, October 1999. Working Paper No. 155, The Economics and Politics of Transition to an Open Market Economy: India, by Ashok V. Desai, October 1999. Working Paper No. 156, Climate Policy Without Tears: CGE-Based Ancillary Benefits Estimates for Chile, by Sébastien Dessus and David O’Connor, November 1999. Document de travail No. 157, Dépenses d’éducation, qualité de l’éducation et pauvreté : l’exemple de cinq pays d’Afrique francophone, par Katharina Michaelowa, avril 2000. Document de travail No. 158, Une estimation de la pauvreté en Afrique subsaharienne d’après les données anthropométriques, par Christian Morrisson, Hélène Guilmeau et Charles Linskens, mai 2000. Working Paper No. 159, Converging European Transitions, by Jorge Braga de Macedo, July 2000. Working Paper No. 160, Capital Flows and Growth in Developing Countries: Recent Empirical Evidence, by Marcelo Soto, July 2000. Working Paper No. 161, Global Capital Flows and the Environment in the 21st Century, by David O’Connor, July 2000. Working Paper No. 162, Financial Crises and International Architecture: A “Eurocentric” Perspective, by Jorge Braga de Macedo, August 2000. Document de travail No. 163, Résoudre le problème de la dette : de l’initiative PPTE à Cologne, par Anne Joseph, août 2000. Working Paper No. 164, E-Commerce for Development: Prospects and Policy Issues, by Andrea Goldstein and David O’Connor, September 2000. Working Paper No. 165, Negative Alchemy? Corruption and Composition of Capital Flows, by Shang-Jin Wei, October 2000. Working Paper No. 166, The HIPC Initiative: True and False Promises, by Daniel Cohen, October 2000. Document de travail No. 167, Les facteurs explicatifs de la malnutrition en Afrique subsaharienne, par Christian Morrisson et Charles Linskens, octobre 2000. Working Paper No. 168, Human Capital and Growth: A Synthesis Report, by Christopher A. Pissarides, November 2000. Working Paper No. 169, Obstacles to Expanding Intra-African Trade, by Roberto Longo and Khalid Sekkat, March 2001. Working Paper No. 170, Regional Integration In West Africa, by Ernest Aryeetey, March 2001. Working Paper No. 171, Regional Integration Experience in the Eastern African Region, by Andrea Goldstein and Njuguna S. Ndung’u, March 2001. Working Paper No. 172, Integration and Co-operation in Southern Africa, by Carolyn Jenkins, March 2001. Working Paper No. 173, FDI in Sub-Saharan Africa, by Ludger Odenthal, March 2001 Document de travail No. 174, La réforme des télécommunications en Afrique subsaharienne, par Patrick Plane, mars 2001. Working Paper No. 175, Fighting Corruption in Customs Administration: What Can We Learn from Recent Experiences?, by Irène Hors; April 2001. Working Paper No. 176, Globalisation and Transformation: Illusions and Reality, by Grzegorz W. Kolodko, May 2001. Working Paper No. 177, External Solvency, Dollarisation and Investment Grade: Towards a Virtuous Circle?, by Martin Grandes, June 2001. Document de travail No. 178, Congo 1965-1999: Les espoirs déçus du « Brésil africain », par Joseph Maton avec Henri-Bernard Solignac Lecomte, septembre 2001. Working Paper No. 179, Growth and Human Capital: Good Data, Good Results, by Daniel Cohen and Marcelo Soto, September 2001. Working Paper No. 180, Corporate Governance and National Development, by Charles P. Oman, October 2001. Working Paper No. 181, How Globalisation Improves Governance, by Federico Bonaglia, Jorge Braga de Macedo and Maurizio Bussolo, November 2001. Working Paper No. 182, Clearing the Air in India: The Economics of Climate Policy with Ancillary Benefits, by Maurizio Bussolo and David O’Connor, November 2001. Working Paper No. 183, Globalisation, Poverty and Inequality in sub-Saharan Africa: A Political Economy Appraisal, by Yvonne M. Tsikata, December 2001. Working Paper No. 184, Distribution and Growth in Latin America in an Era of Structural Reform: The Impact of Globalisation, by Samuel A. Morley, December 2001. Working Paper No. 185, Globalisation, Liberalisation, Poverty and Income Inequality in Southeast Asia, by K.S. Jomo, December 2001. Working Paper No. 186, Globalisation, Growth and Income Inequality: The African Experience, by Steve Kayizzi-Mugerwa, December 2001. Working Paper No. 187, The Social Impact of Globalisation in Southeast Asia, by Mari Pangestu, December 2001. Working Paper No. 188, Where Does Inequality Come From? Ideas and Implications for Latin America, by James A. Robinson, December 2001. Working Paper No. 189, Policies and Institutions for E-Commerce Readiness: What Can Developing Countries Learn from OECD Experience?, by Paulo Bastos Tigre and David O’Connor, April 2002. Document de travail No. 190, La réforme du secteur financier en Afrique, par Anne Joseph, juillet 2002. Working Paper No. 191, Virtuous Circles? Human Capital Formation, Economic Development and the Multinational Enterprise, by Ethan B. Kapstein, August 2002.

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Working Paper No. 192, Skill Upgrading in Developing Countries: Has Inward Foreign Direct Investment Played a Role?, by Matthew J. Slaughter, August 2002. Working Paper No. 193, Government Policies for Inward Foreign Direct Investment in Developing Countries: Implications for Human Capital Formation and Income Inequality, by Dirk Willem te Velde, August 2002. Working Paper No. 194, Foreign Direct Investment and Intellectual Capital Formation in Southeast Asia, by Bryan K. Ritchie, August 2002. Working Paper No. 195, FDI and Human Capital: A Research Agenda, by Magnus Blomström and Ari Kokko, August 2002. Working Paper No. 196, Knowledge Diffusion from Multinational Enterprises: The Role of Domestic and Foreign Knowledge-Enhancing Activities, by Yasuyuki Todo and Koji Miyamoto, August 2002. Working Paper No. 197, Why Are Some Countries So Poor? Another Look at the Evidence and a Message of Hope, by Daniel Cohen and Marcelo Soto, October 2002. Working Paper No. 198, Choice of an Exchange-Rate Arrangement, Institutional Setting and Inflation: Empirical Evidence from Latin America, by Andreas Freytag, October 2002. Working Paper No. 199, Will Basel II Affect International Capital Flows to Emerging Markets?, by Beatrice Weder and Michael Wedow, October 2002. Working Paper No. 200, Convergence and Divergence of Sovereign Bond Spreads: Lessons from Latin America, by Martin Grandes, October 2002. Working Paper No. 201, Prospects for Emerging-Market Flows amid Investor Concerns about Corporate Governance, by Helmut Reisen, November 2002. Working Paper No. 202, Rediscovering Education in Growth Regressions, by Marcelo Soto, November 2002. Working Paper No. 203, Incentive Bidding for Mobile Investment: Economic Consequences and Potential Responses, by Andrew Charlton, January 2003. Working Paper No. 204, Health Insurance for the Poor? Determinants of participation Community-Based Health Insurance Schemes in Rural Senegal, by Johannes Jütting, January 2003. Working Paper No. 205, China’s Software Industry and its Implications for India, by Ted Tschang, February 2003. Working Paper No. 206, Agricultural and Human Health Impacts of Climate Policy in China: A General Equilibrium Analysis with Special Reference to Guangdong, by David O’Connor, Fan Zhai, Kristin Aunan, Terje Berntsen and Haakon Vennemo, March 2003. Working Paper No. 207, India’s Information Technology Sector: What Contribution to Broader Economic Development?, by Nirvikar Singh, March 2003. Working Paper No. 208, Public Procurement: Lessons from Kenya, Tanzania and Uganda, by Walter Odhiambo and Paul Kamau, March 2003. Working Paper No. 209, Export Diversification in Low-Income Countries: An International Challenge after Doha, by Federico Bonaglia and Kiichiro Fukasaku, June 2003. Working Paper No. 210, Institutions and Development: A Critical Review, by Johannes Jütting, July 2003. Working Paper No. 211, Human Capital Formation and Foreign Direct Investment in Developing Countries, by Koji Miyamoto, July 2003. Working Paper No. 212, Central Asia since 1991: The Experience of the New Independent States, by Richard Pomfret, July 2003. Working Paper No. 213, A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI), by Maurizio Bussolo, Mohamed Chemingui and David O’Connor, November 2003. Working Paper No. 214, Ratings Since the Asian Crisis, by Helmut Reisen, November 2003. Working Paper No. 215, Development Redux: Reflactions for a New Paradigm, by Jorge Braga de Macedo, November 2003. Working Paper No. 216, The Political Economy of Regulatory Reform: Telecoms in the Southern Mediterranean, by Andrea Goldstein, November 2003.