Fertility and women’s labor force participation

in a low-income rural economy

Mattias Lundberg,1 Nistha Sinha, Joanne Kannan Yoong

2

First draft: 03/25/09

This Draft: 02/25/10

DRAFT ONLY DO NOT CITE

Abstract

This study examines changes in fertility and childbearing on the labor force

participation of women in rural Bangladesh. Since fertility is endogenous to other

decisions taken by the family, we separately identify the impact of changes in fertility on

changes in work by taking advantage of a family-planning program selectively

introduced in the district of Matlab. The family-planning program did have a significant

impact on certain aspects of fertility; but contrary to much previous research, fertility has

little or no impact on female labor force participation.

1 Corresponding author: mlundberg@worldbank.org

2 World Bank, World Bank, and Rand, respectively. We gratefully acknowledge the

financial support of the Hewlett Foundation, and the comments of David Canning, David

Lam, Michael Lipton, members of the PopPov Research Network and participants at the

Fourth Annual Research Conference on Population, Reproductive Health Conference in

Cape Town.

1

1. Introduction

This study examines changes in fertility and childbearing practices on the labor

force participation of women in rural Bangladesh. Since fertility is not exogenous to

other decisions taken by the family, we separately identify the impact of changes in

fertility on changes in work by taking advantage of a family-planning program selectively

introduced in the district of Matlab, Bangladesh that resulted in significant declines in

fertility, relative to the control areas. Our results show that, contrary to much previous

research, the number of children is only weakly associated with female labor force

participation. On the other hand, fertility may be correlated with the sector in which a

woman participates in the labor market.

2. Background

Birth rates are falling in many countries: families are smaller and dependency

ratios are lower than among previous generations. The World Development Report for

2007 argues that with the right combination of policies, these countries can reap a

―demographic dividend,‖ in which the generation of young people in whom families have

invested greater resources, and who are well-educated and healthy, provide the engine for

faster growth.1 Bloom and Canning (2005) argue that smaller families with fewer

dependents lead to greater savings and investment and faster, more widely shared growth.

As reproductive technology and better health care become more widespread, both

mortality and fertility fall, and the share of working-age population rises, relative to the

number of children. More children require more resources to care for them and a greater

share of mothers‘ time, leading to slower economic growth. Economies with fewer

children have more resources – both human and physical – for allocation to alternative

activities. Conversely, economies with higher dependency ratios consume relatively

more and save relatively less, thus lowering growth. Feyrer (2005) finds that changes in

1 It has been argued that a significant portion of the growth of the East Asian

tigers was due to the region‘s demographic transition and the large pool of well-trained

and healthy young people that came of age during the 1980s. But the demographic

dividend is not automatic: many countries in Latin America experienced the same

transition to lower fertility and lower dependency ratios, but were unable to achieve the

same rapid growth (World Bank 2006).

2

the age composition of the workforce are significantly correlated with changes in

aggregate productivity.

Casual examination of the data shows a clear correlation between fertility and

growth, and between fertility and poverty. Countries with high fertility are generally

poorer, on average, than countries with lower fertility, and they grow more slowly

(Figure 1). But cross-country correlations do not prove causality, either in the aggregate

or for any individual country. A number of studies have found causality going both ways

– from growth to demographic change and from demographic change to growth. And

Schultz (2004) finds that once the endogeneity is controlled, there is no clear relationship

between demographic composition and a country‘s savings rate; he argues that children

are not consumption items, rather they are a form of savings. Moreover, even if a

correlation could be found across countries, that would say nothing about the evolution of

fertility, growth, and poverty over time and within any particular country. That faster-

growing countries have lower fertility does not necessarily mean that any one country

will grow more quickly if its fertility rate declines.

Figure 1. Poverty and economic growth are correlated with total fertility

020

40

60

80

100

Perc

ent of popula

tion u

nder

$2/d

ay

2 4 6 8Total fertility rate per woman

Source: World Bank data

-.05

0

.05

.1

Annual gro

wth

of G

DP

per

capita

0 2 4 6 8Total fertility rate per woman

Source: World Bank data

Note: 71 countries; most recent TFR available (1995-2004), mean growth 1995-2004;

source: World Bank World Development Indicators data.

As fertility declines, childbearing patterns change in three ways: women may

postpone their first birth, space their births, or stop having children at an earlier age than

previous cohorts. Each of these changes may have a different impact on the opportunities

facing women and the decisions they make regarding work and family. The pattern of

3

fertility change differs considerably around the world. In some countries, such as India,

women are stopping childbearing at an earlier age than previous cohorts. In some

African countries, such as Chad, however, young women can expect to have six children

or more during their lifetime. In Ghana, and in the Philippines, women are increasing the

interval between births.

What motivates families to have fewer children? In poor societies, with high

mortality, having many children provides both income and insurance. Children provide

labor for current consumption and a means of support in old age. As mortality declines

and markets for both human and financial capital develop, fewer children are required to

obtain these benefits for the household. Also, as families – and especially women –

develop their human capital, and as demand for skilled labor increases with development,

wages rise and so does the opportunity cost of time, including the time required to raise

children. Fertility falls as women‘s education and independence increase. This suggests

that the primary determinant of fertility change is change in desired family size – that is,

the demand side.

Goldin (1994) and Szulga (2005) argue that changes in the structure of the

economy affect fertility: predominantly rural economies are characterised by economies

of scope between child care and work (such as farm labor). As countries become more

urban, the complementarity between work and childcare diminishes, increasing the costs

of childbearing.

Much of the evidence to date suggests that family planning programs have only a

small effect on fertility (Pritchett 1994; Joshi and Schultz 2007; Gertler and Molyneaux

1994; Pitt, Rosenzweig, and Gibbons 1994). But the effect is not zero: childbearing does

respond to changes in the availability of contraceptive technology and information. In

the Matlab (Bangladesh) experiment, the exogenous distribution of family planning

services led to significant declines in fertility. But it is unclear whether the provision of

information led to a drop in desired family size, or whether the provision of services

allowed families to achieve their desired size.

Just as changes in the opportunity cost of women‘s time and the opportunities

available to women affect desired fertility, so does fertility affect the opportunities

available to women. Clearly, a woman‘s capacity to perform physical labor is diminished

4

during the period surrounding childbirth. The need to care for a child restricts the time

that a mother can devote to wage labor. A study on urban households in India concluded

that the presence of children increases mothers‘ time in home production, with the effect

of younger children (aged 0-6 years) being six times the effect of older children (Malathy

1994). As a lower bound, the cost of this time is the wage that would be paid to a

substitute caregiver. The need to provide care diminishes as the child grows, permitting

the mother to return to work (alternately, the utility of working exceeds the utility of

remaining at home).

Childbearing may also have dynamic, path-dependent effects on working. Wages

partly reflect work experience: withdrawal from the labor force reduces the experience

that a worker can accrue, thus reducing wages, potentially below the reservation wage

and below the fixed costs of entering the labor market and searching for a job. Eckstein

and Wolpin (1988) estimate a dynamic model of labor force participation and fertility

that includes the impact of experience on wages. While the presence of children reduces

participation, the positive effect of experience on wages provides an incentive for

continued work. Conversely, absences from the labor market reinforce continued

absence, given lower wages and job search costs.

This raises the possibility that different methods for limiting fertility may have

different consequences for labor force participation and earnings. Women who choose to

delay fertility can obtain more education or work experience early; those who stop

bearing children earlier can then benefit from the higher wages accruing to uninterrupted

labor force participation later on.

The participation of women in wage labor and the sector in which they participate

is partly a function of the structure of the local labor market policies and institutions

(Fussell and Zenteno 1997, Del Boca et al. 2004). Participation is more sensitive to

fertility in some sectors than in others. In Morocco, for instance, the public sector is

more accommodating than the private sector to the needs of mothers with young children

(Assaad and Zouari 2002).

5

Most previous studies of the relationship between fertility and labor force

participation have relied on cross-sectional data. With cross-sectional data it is difficult

to correct for both the endogeneity of fertility and the impact of unobserved heterogeneity

among women. Previous papers have relied on natural experiments or on instrumental

variables estimation with questionable exclusion restrictions (see Rosenzweig and

Wolpin 2000). Engelhardt, Kögel and Prskawetz (2004) finds significant causality in

both directions using European data; and Kögel (2003) finds that the negative correlation

between fertility and employment has become significantly smaller (though not positive)

in Europe since the mid-1980s. On the other hand, McNown and Rajbhandary (2001),

using cointegration methods, find that female labor force participation responds

significantly to fertility shocks, but the effects from work to fertility are insignificant.

Rosenzweig and Wolpin (1980, 2000), use cross-sectional data and the occurrence

of twins in the first pregnancy to look at the impact of fertility on participation, and show

significant negative effects of additional children on mother‘s labor force participation.

Using a two-stage estimation strategy, Angrist and Evans (1998) first estimate the

probability of childbearing as a function of the sex of the first two children. Using cross-

sectional data from Korea, Chun and Oh (2002) instrument fertility decisions using the

sex of the first child. They find that having an additional child reduces labor force

participation by almost 40%, on average; but there is considerable heterogeneity across

families. Carrasco (2001) also uses family sex composition as an instrument for

subsequent births. Hotz et al. (2005) exploit the occurrence of spontaneous abortion

(miscarriage) as the instrument for childbearing. These instrumental variables papers

generally support the endogeneity of fertility and work decisions. The results obtained

under the assumption of exogeneity significantly underestimate the true impact of

childbearing on labor force participation.

But recent research has shown that IV methods do not necessarily provide

unbiased estimates. Heckman and Urzúa (2009) argues that where treatment – in this

case fertility – is estimated using IV, what is measured is the impact of changes in the

instrument on the outcome, and not changes in the treatment. The IV estimator estimates

the local average treatment effect, which is the average effect of the treatment for the

subsample of the population that is induced by a specific change in the value of the IV to

6

select themselves into treatment. In our data, as described below, we have the advantage

of random assignment of family planning interventions, which is an exogenous

instrument directly and intentionally related to fertility, but not to work.

2.1 Family Planning, Fertility and Labor Markets in Matlab, Bangladesh

The International Center for Diarrhoeal Disease Research, Bangladesh

(ICDDR,B) has maintained a demographic surveillance system in Matlab upazila (60

kilometers southeast of Dhaka) since 1966. The area is a low-lying plain located between

two tidal rivers. In the 1970s, this area was relatively isolated with no major towns or

cities, and high, persistent rates of poverty and mortality. The social structure of Matlab

has been relatively stable into the 1990s: the population has a large, relatively traditional

and conservative Muslim majority (over 85%). Matlab has seen a remarkable

demographic transformation over the last thirty years: infant mortality has fallen from

110 per thousand live births in 1983 to 65 in 1995, while total fertility has dropped from

more than 6 in 1976 to just over 3 in 1995 (Joshi and Schultz 2007).

In 1977, the ICDDR,B introduced Contraceptive Distribution and the Family

Planning and Health Services in 70 of the 149 villages covered by the demographic

surveillance system, although by 1987, seven villages in the control area had disappeared

due to river erosion, leaving 142 (ICDDR, B 2001). The Matlab family planning

program experiment was designed to test whether the provision of low-cost

contraceptives services could induce demographic change in a society in the absence of

economic development (Phillips et al. 1982). The intervention consisted primarily of

home visits by trained female outreach workers in the 70 villages (blocks A, B,C and D),

while the households in control villages (blocks E and F) continue to receive pre-existing

standard government family planning services (which generally required visits to a local

health clinic). Four of the six blocks of villages were assigned to the program, and all

currently married women residing in the treatment villages received the program

treatment (Phillips 1994). In the initial stages of the program, the outreach workers

provided home consultation and encouragement on contraceptive needs ever two weeks,

providing pills, condoms, foam tablets, injectables and intra-uterine devices as well as

7

clinic referrals for tubectomies or menstrual regulation. Supplies and consultation are

free in both the program itself and the government healthcare facilities. Additional

maternal and child health services including immunization and antenatal care were

phased in from 1982 to 1986.

In a comprehensive review of the impacts of the Matlab program, Joshi and

Schultz (2007) find that fertility declined around 15 percent in program villages

compared to control villages, and that the program appears to have also improved

women‘s health, incomes, and assets. They argue that the social returns to this program

go far beyond mere fertility reduction.

Many researchers have implicitly assumed that the program was randomly

assigned, although program assignment was actually made at the block-level rather than

by randomizing individual villages into treatment. However, Sinha (2005) shows that

there were no systematic differences between treatment and control villages prior to

program initiation. Joshi and Schultz (2007) show that treatment and control villages had

similar pre-program surviving fertility levels, although the total fertility rate is slightly

higher in control villages in 1974. There has been some diffusion of program

information into control villages because of migration among the villages due to

marriages (Phillips et al. 1988). However, Joshi and Schultz (2007) conclude that the

program has well-defined treatment and control areas, with roughly similar geographical,

demographic and socioeconomic conditions.

3. Motivating Theory and Empirical Approach

3.1. Motivating Theory

In this section, we frame the empirical analysis to follow by proposing a simple model

of a woman‘s fertility and her decision to work. Consider a household with one female

parent decision maker, endowed with a single unit of time. Utility for the parent is

defined over their consumption c, leisure time in the home d, and the number of children,

n. For any number of children, household utility is

U(c,d,n) = log(c ) + log (d) + log(n) + (1)

8

where > 0 and > 0 captures the household‘s preference for leisure and children

respectively, and is a stochastic error term. As children require time and money, this is

reflected in the time and budget constraints. We assume that working outside the home is

a binary decision l that entails a fixed number of hours outside the home h for

compensation w, and having n children imposes a time cost of childcare of b per child.

Firstly, the single unit of time is allocated to leisure d, childcare b and labor hours h:

d – hl – bn = 1 (2)

Secondly, the parent‘s earnings and other income A (asset income, or income transfers

from a male parent) are allocated between the adult and children, who consume g each,

such that

c + gn = wl + A (3)

If the parent decides to work, then their utility is described by U(1) = log(w +A) + log

(1- h - bn) + log(n) + 1; otherwise their utility is U(0) = log(A) + log (1- bn) +

log(n) + 0. The parent therefore decides to work if and only if there is a net gain in

utility from working, i.e.

l = I(U(1)-U(0) >0 ) (4)

where I is the indicator function, and the likelihood of working, Pr(l = 1) = Pr(D >1 - 0 )

where D = U(1)-U(0) = log(w +A – gn) - log(A – gn) + (log (1- h - bn) - log (1- bn)).

To understand how exogenous changes in fertility impact the likelihood of the

female parent working, we take the partial derivative of D with respect to n,

D

n

wg

(A gn)(A gn w)

bh

(1bn)(1 h bn) (5)

We note that the sign of this expression may be positive or negative, depending on the

values of the parameters. Specifically, the gain from working increases in the number of

children if and only if

wg

(A gn)(A gn w)

bh

(1bn)(1 h bn) (6)

9

Intuitively, as the number of children increases, the probability of working decreases if

parents place relatively more value on scarce leisure than the need for extra consumption.

On the other hand, if the need for extra consumption outweighs preferences for additional

leisure, parents will be more likely to work as family size increases.

For example, if we interpret d as time spent on housekeeping, we may consider

that certain types of employment such as home production are not mutually exclusive

with housekeeping or childcare, due to economies of scope. If home production is the

dominant form of labor, in the extreme case that h = 0, the parent is clearly more likely to

work as the number of children increases.

On the other hand, consider the possibility that the number of children affects the

resources of the household only through the time constraint, as in Bloom et al (2007). If

the analogous assumption, g = 0, is adopted, the partial derivative is unambiguously

negative. A drop in fertility thus yields the predicted increase in female labor supply that

contributes to what Bloom et al (2007) call the ―demographic dividend.‖

3.2. Empirical Approach

3.2.1. Basic Specification

We adopt a reduced-form empirical approach to the motivating theory sketched

above, by estimating probit regressions of the form

liv ni Xi ' Yv ' iv (7)

where l is a binary indicator for the work status of individual i living in village v, n is

the total number of children, X is a vector of individual control variables, Y is a vector of

village level controls, and is a stochastic error term. We are interested in , the which

describes the relationship between fertility and labor market participation.

However, it is important to note that in spite of controlling for individual or

household characteristics, estimating equation (7) above is unlikely to yield effects that

can be interpreted causally. While early studies treated fertility as an exogenous

determinants of female labor supply behavior (for example, Mincer 1962; Cain 1966;

Heckman 1974), a large body of more recent work has shown that fertility is most

10

plausibly regarded as endogenous if the household jointly chooses both the decision to

work and the number of children. Omitted variables, such as unobservable preferences,

may affect both labor supply behavior and fertility decisions, and n and in Equation (7),

are therefore likely to be correlated.

3.2.2. Identification

To deal with the endogenity of fertility and labor force decisions, we propose an

instrumental variables (IV) strategy based on the ―natural experiment‖ of the introduction

of family planning services in the Matlab area. In order for program exposure to be a

valid instrument, the program should be correlated with fertility outcomes but otherwise

should have no impact on female labor supply decisions. As noted above, previous work

has demonstrated that the intensive family planning program in treatment villages

significantly reduced fertility in program households through the take up of modern

contraceptive methods. However, whether or not program exposure satisfies the

exclusion restriction merits further discussion.

Firstly, the program should not result in changes in the monetary cost of obtaining

contraception large enough to plausibly impact women‘s need to generate labor income.

Contraception in both treatment and control villages remained free, with the program

primarily reducing the inconvenience and stigma cost of using modern methods.

The Matlab intervention has had a significant impact on fertility, over and above

the remarkable secular decline in fertility in the country as a whole during that period.

Phillips et al. (1982), Foster and Roy (1997) and Sinha (2005) show that women in

program villages have made use of modern contraceptive methods to reduce fertility, by

increasing birth spacing and postponing births over time. We re-examine the impact of

changes in childbearing over all, as well as the impact of different methods for limiting

fertility, and their consequences for labor market participation.

We might be concerned about program effects on other underlying factors apart

from fertility that affected women‘s decision to work. For example, repeated contact

with female health workers may have affected social norms related to working outside

the home. Alternatively, the maternal and child health services component of the

11

program may have increased the health (and hence, labor productivity) of mothers or

reduced the time needed to care for sick children. In their detailed review of the program,

however, Schultz and Joshi (2007) argue that the maternal and child health services

component of the program did not appear to explain a significant share of the variation in

child or maternal health in 1996.

Observed correlations between changes in fertility and employment, growth, or

other factors may also be merely coincidental, reflecting independent secular changes

over time or some cointegrated processes driven by other, unobserved, phenomena. In

the case of Bangladesh, Amin and Lloyd (1998), using data from the 1996/7 Bangladesh

Demographic and Health Survey, rule out any causal relationship from improvements in

the economic opportunities of reproductive-aged women or in women‘s autonomy to the

decline in fertility.2

Finally, the program should not exert demand side effects for labor. In this case,

one concern might be that expanded reproductive health services in program areas might

have led to greater demand for local female workers.

4. Descriptive Analysis and Sample Selection

Comprehensive socioeconomic surveys of Matlab were conducted in 1982 and

1996. For purposes of this study, data is available on 6068 women interviewed during

the 1996 Matlab Health and Socioeconomic Survey (MHSS), which surveyed 4,364

households residing in 141 villages in the overall surveillance area, covering both

treatment and control villages (Rahman et al. 1999). The primary sampling unit was the

bari, or the residential compound (containing a cluster of households). One or two

selected households from each selected bari were chosen for interview. One household

per bari was chosen at random, while the second household in multiple-household baris

was selected if it contained elderly relatives, so as to oversample elderly respondents.

2 The chain of causality is likely to be a bit more complex: Anderson and Eswaran (2005)

demonstrate that in rural Bangladesh, the income a woman earns away from her

husband‘s farm contributes significantly to her autonomy, and Eswaran (2002) shows that

a woman‘s autonomy influences both fertility and child mortality.

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The set of ever-married women for which data on family background and fertility

are complete comprises a sample of 1810 individuals. We restrict our sample to women

in program and nonprogram villages who were eligible to receive the program. The final

study sample is restricted to married women aged 20-55; we exclude women who did not

participate for biological reasons (i.e. report never using contraception due to infertility,

or report having been sterilized prior to 1972), giving a final sample of 1278 observations

(for most specifications), for which summary statistics are reported in Table 1.

Table 1. Sample descriptive statistics

Variable

Control

villages

Program

villages Total

T-test of

equality N

Age (years) 36.73 37.27 37.00 (1.04) 1278

Hindu 0.05 0.16 0.11 (6.60)** 1278

Husband's education 2.93 3.61 3.27 (3.17)** 1278

Education 1.92 2.19 2.05 (1.69)+ 1278

Ln(farmland) 0.41 0.42 0.42 (0.50) 1278

Currently working (percent) 74.26 69.54 71.91 (1.88)+ 1278

In informal sector 71.29 63.74 67.53 (2.89)** 1278

In formal sector 2.96 5.81 4.38 (2.49)* 1278

In home-based business 74.41 74.25 74.33 (0.07) 1278

Outside home 7.64 8.01 7.82 (0.24) 1278

Number of live births 4.98 4.40 4.69 (4.00)** 1278

Birth spacing (months) 46.26 50.23 48.18 (2.73)** 1039

Age at first pregnancy 18.85 18.83 18.84 (0.13) 1234

Number of births within past five years 0.90 0.70 0.80 (3.82)** 1255

**, *, +: significant at 1, 5, 10 percent, respectively.

Table 1 shows that married women in program areas and non-program areas have

the same average age, but otherwise differ on a number of relevant background

characteristics. In particular, program villages in 1996 have a smaller Muslim majority,

and have higher levels of education overall. They also appear to have higher levels of

education, but no difference in land ownership. Joshi and Schultz (2007) also report

differences in religious composition, asset ownership and education in the pre-existing

data. Women in program villages have a significantly lower level of realized fertility, as

measured by the number of live births; this appears to be due to increased birth spacing

rather than to a delay in the initiation of childbearing.3 Women in program villages are

3 Note that there are fewer observations on these alternative, more specific measures of

fertility.

13

less likely to be working. On the other hand, to the extent that they are working, they are

more likely to be employed in the formal sector than women in control villages.

Table 2 presents naïve estimates of program impact on a number of outcomes,

fertility-related, employment, and education. These regressions include program

exposure interacted with age dummies as well as the limited set of control variables for

the background characteristics described above. This table shows that the program did

have some effect on fertility, but no direct impact on education or labor force

participation.

The pattern of results in total live births is consistent with program effects: the

largest differences in fertility among women exposed to the program are among cohorts

aged 40-49. Those aged 55 and older, having been at least 40 at the time of program

inception, may be regarded as having completed childbearing, and thus their fertility

would not have been affected. An F-test on all cohort-program interactions jointly is

strongly significant. The significantly lower fertility among younger cohorts in general

reflects secular changes in fertility as well as the fact that these women are not likely to

have completed childbearing. The program also had significant impacts on other

measures of fertility: age at first birth, age at first pregnancy, birth spacing (the number of

months between births), and the number of births within the past five years.

14

Table 2. Naïve estimates of program effects, by age category

Number of

live births

Birth spacing

(months)

Age at first

pregnancy

Number of

births in past

five years

Currently

working Education

OLS OLS OLSOrdered

probitProbit OLS

20-24 -6.037** .. 2.012+ .. .. 1.770**

(0.58) .. (1.04) .. .. (0.57)

25-29 -4.732** 9.088** 2.249* -0.446* 0.298 1.555**

(0.57) (2.33) (1.03) (0.22) (0.22) (0.51)

30-34 -3.281** 11.296** 1.724 -0.911** 0.554* 0.881+

(0.58) (2.34) (1.05) (0.22) (0.22) (0.49)

35-39 -2.461** 13.376** 1.385 -1.845** 0.369+ 0.706

(0.59) (2.45) (1.06) (0.23) (0.21) (0.50)

40-44 -1.119+ 19.190** 1.139 -2.225** 0.559* 0.834+

(0.61) (3.78) (1.05) (0.25) (0.24) (0.50)

45-49 0.07 26.525** 1.06 -2.764** 0.390+ 0.256

(0.62) (4.01) (1.10) (0.27) (0.24) (0.49)

50-54 0.23 41.336** 0.236 -2.925** 0.27 0.11

(0.65) (5.47) -1.05 (0.29) (0.24) -0.47

55-59 .. 30.07013 .. -2.462** 0.096 ..

.. (20.58) .. (0.49) (0.43) ..

60+ .. .. .. .. ..

.. .. .. .. ..

Treatment village -0.313 10.897* 0.06 0.161 -0.208 -0.88

(0.74) (4.48) (1.23) (0.29) (0.22) (0.56)

Treatment*20-24 0.094 -0.484 -0.511 0.17 0.777

(0.76) (1.29) (0.39) (0.33) (0.75)

Treatment*25-29 -0.278 -3.049 0.822 -0.39 0.185 0.933

(0.76) (5.09) (1.29) (0.35) (0.29) (0.67)

Treatment*30-34 -0.343 0.105 0.204 -0.504 -0.014 0.615

(0.77) (5.01) (1.29) (0.34) (0.28) (0.63)

Treatment*35-39 0.153 -8.028 -0.003 -0.216 0.289 1.008

(0.77) (5.00) (1.31) (0.35) (0.29) (0.64)

Treatment*40-44 -0.627 -9.419 -1.115 -1.021* 0.095 0.84

(0.80) (5.94) (1.30) (0.40) (0.31) (0.64)

Treatment*45-49 -0.878 -7.490 -0.913 -0.388 0.039 1.463*

(0.83) (6.80) (1.38) (0.41) (0.31) (0.63)

Treatment*50-54 -0.456 -18.669* 0.163 .. .. 0.911

(0.84) (7.84) -1.31 .. .. -0.6

Treatment*55-59 .. -20.980 .. -0.371 -0.108 ..

.. (22.18) .. (0.71) (0.57) ..

Treatment*60+ .. .. .. .. ..

.. .. .. .. ..

Hindu -0.234 -4.581* 0.41 -0.03 -0.154 -0.372+

(0.15) (2.20) (0.27) (0.14) (0.13) (0.21)

Ln(farmland) 0.125+ -1.234 -0.108 0.037 0.102 0.403**

(0.08) (0.93) (0.16) (0.07) (0.08) (0.11)

Husband's education -0.049** -0.300+ 0.041 -0.025* -0.029** 0.468**

(0.01) (0.17) (0.03) (0.01) (0.01) (0.02)

Intercept 7.611** 30.703** 17.325** 1.986** 0.341+ -0.462

(0.56) (1.91) -1.01 (0.18) (0.18) -0.45

Joint significance, treatment

F-test (6.41)** (5.30)** (1.94)+ (2.28)* (0.96)

Chi-squared (3.85)

R2 0.568 0.1408 0.084 0.0217 0.455

N 1278 1039 1234 1255 1278 1278

**, *, +: significant at 1, 5, 10 percent, respectively.

15

Figures 1-4 present this information more intuitively. Each shows the predicted

values of selected outcomes, by age cohort, among women in program and non-program

villages. In each figure, the darker band represents control villages and the lighter band

represents program villages; the width of the bands is the 95 percent confidence interval

around the means. Figure 1 shows that the impact of the program on total fertility is

significant among women aged 25-34 and 40-49. In these cohorts, women in program

villages had significantly fewer children than their coevals in control villages.

Figure 1. Impact of treatment on total fertility, by cohort.

0

2

4

6

8

Tota

l n

um

ber

of liv

e b

irth

s

20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60+

Age category

Control villages Treatment villages

Figure 2 shows that the program also had a significant effect on birth spacing, but

only among women aged 25-34 (and possibly 20-34). Among older cohorts, the

estimates quickly become imprecise and insignificant. Figures 3 and 4 confirm that there

is no direct relationship between exposure to the family program and either working or

the level of education. There are clear trends over time (for education) and over age

cohort (for labor force participation), but there are no differences across treatment groups.

16

Figure 2. Impact of treatment on birth spacing, by cohort.

20

40

60

80

100

Bir

th s

pacin

g (

mo

nth

s)

20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60+

Age category

Control villages Treatment villages

Figure 3. Impact of treatment on labor force participation, by cohort.

-.5

0

.5

1

1.5

Curr

ently w

ork

ing

20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60+

Age category

Control villages Treatment villages

17

Figure 4. Impact of treatment on education, by cohort.

0

1

2

3

4

Ye

ars

of sch

oo

ling

20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60+

Age category

Control villages Treatment villages

Figure 5 below shows the relationship between current employment and fertility

in this sample. The bars represent the 95 percent confidence intervals around the means,

and the line is a density function reporting the share of women reporting births in each

category.4 The modal number of births is five. This figure shows clearly that as the

number of live births increases, the probability of currently working also increases. This

suggests a positive relationship, but given the endogeneity concerns discussed previously,

this can‘t by itself be given a causal interpretation. In the next section, we proceed to

causal inference.

4 This figure is top-coded at 10: the category ―10‖ includes the 47 women who report at

least ten births, including one reporting 13 and one reporting 17 births. The regressions

below include all reported measurements.

18

Figure 5. Correlation between fertility and employment.

0

.05

.1

.15

Sh

are

of w

om

en

re

po

rtin

g b

irth

s

.4

.6

.8

1

Pro

ba

bili

ty o

f cu

rren

tly w

ork

ing

0 2 4 6 8 10

Number of live births

5. Main Results

This section reports the main results of the examination of fertility on labor force

participation, controlling for age, religion, education of the woman and her husband, and

land ownership (in logs), and whether there is a health clinic in the village. The first

column in Table 3 looks at the simple impact of childbearing on labor force participation,

assuming exogeneity. This column confirms the positive correlation found earlier; the

other significant correlates of the decision to work are husband‘s education and the

presence of a health clinic. Columns 2-4 then examine the relationship between fertility

and labor force participation, instrumenting the fertility decision by exposure to the

Matlab family planning program (standard errors in all regressions are clustered by the

primary sampling unit, the bari).7

7 The results are also robust to the inclusion of additional control variables, such as

community characteristics (distance to market, etc.). Results available.

19

In column 2, fertility is instrumented by exposure to treatment, and the first stage

– the number of children ever born – is estimated as OLS. The results are presented as

marginal effects, so that exposure to treatment reduces the number of children by more

than one-half. However, even though exposure to the family planning program clearly

reduced fertility, the instrumentation also eliminates any correlation between fertility and

labor force participation. One possibility is that fertility decisions are partly the

consequence of the decision to work, rather than the primary driver of it.

Columns 3 and 4 repeat the IV exercise, but this time estimate fertility and labor

force participation in an arguably more appropriate fashion. Fertility, measured as the

number of children, is not strictly a continuous variable: rather it is truncated (between

zero and the maximum number in the sample, in this case 17), and discrete. Therefore, it

makes more sense to model the limits (Tobit) or the discreteness (ordered probit)

explicitly, as is presented in columns 3 and 4, respectively. To obtain these results, the

two equations were estimated recursively, using the cmp routine developed for Stata by

David Roodman (2009). These two provide a marginally better fit overall (as measured

by likelihood ratios), but in neither case is fertility significantly correlated with labor

force participation.

20

Table 3. Main results: impact of childbearing on working

1 2 3 4

Probit IV Probit IV Probit IV Probit

Currently working

Number of live births 0.021** 0.122 0.066 0.032

(0.01) (0.12) (0.06) (0.09)

Age -0.042+ -0.269 -0.135 -0.053

(0.02) (0.30) (0.16) (0.23)

Hindu1 -0.069 -0.168 -0.195 -0.211

(0.05) (0.14) (0.13) (0.13)

Highest education 0.008 0.03 0.025 0.022

(0.01) (0.02) (0.02) (0.02)

Husband education -0.011* -0.031* -0.032** -0.032*

0.00 (0.01) (0.01) (0.01)

ln(farm land) 0.025 0.064 0.073 0.078

(0.02) (0.07) (0.07) (0.07)

Clinic in village1 -0.090** -0.251** -0.258** -0.261**

(0.03) (0.09) (0.09) (0.09)

Number of live birthsOLS Tobit

Ordered

probit

Age 2.419** 2.532** 1.474**

(0.07) (0.07) (0.07)

Hindu1 -0.273+ -0.288+ -0177+

(0.15) (0.17) (0.09)

Highest education -0.091** -0.111** -0.059**

(0.02) (0.03) (0.01)

Husband education -0.001 0.004 0.004

(0.02) (0.02) (0.01)

ln(farm land) 0.147+ 0.144+ 0.073

(0.08) (0.08) (0.05)

Clinic in village1 0.015 0.022 0.002

(0.12) (0.13) (0.07)

Treatment village1 -0.628** -0.731** -0.355**

(0.09) (0.11) (0.06)

Intercept -9.434** -10.107**

(0.38) (0.44)

Wald Chi-squared 29.88** 23.29** 22.72** 20.62**

Log-pseudolikelihood (744.65) (3237.23) (3076.69) (3175.96)

N 1278 1278 1278 12781 Categorical variable

**, *, +: significant at 1, 5, 10 percent, respectively.

(marginal effects)

21

The Matlab surveys asked respondents to identify whether work is their primary

activity for the last year. For those who report ever working, the survey also elicited

occupation codes. Assuming that these occupation types correspond to the currently-

reported work, we were able to classify work into home-based production and outside

labor (primarily agricultural labor, operating a business or professional work), as well as

into formal-sector and informal-sector work. The vast majority of working women report

participating in home-based production: fewer than five percent can be classified as

working in the formal sector, and fewer than eight percent report participation in any type

of outside labor. The overwhelming majority report working at home, in activities such

as processing rice, raising poultry and livestock. The low rate of female participation in

wage labor and the predominance of home-based production suggests that the rural labor

market remains heavily influenced by pre-existing social norms, particularly the cultural

practice of female seclusion.

We re-estimate Equation (7) again using instrumental variables, with home-based

production, outside labor, formal- and informal-sector labor as separate dependent

variables.8 Columns 1 and 2 in Table 4 show the results for formal and informal-sector

work. These columns show that higher fertility, instrumented by exposure to the Matlab

program, increases the likelihood of informal-sector work and decreases the likelihood

that the woman will participate in the formal sector. This makes intuitive sense –

heightened family obligations may increase the value of the flexibility and ease of entry

provided by the informal sector. On the other hand, there appears to be no robust

correlation between fertility and decisions to work inside or outside the home.

8 Joint estimation, with sector choice as e.g. multinomial logit, failed to converge.

22

Table 4. Probability of working in formal or informal sectors

1 2 3 4

Currently working Informal

sector

Formal

sector In home

Outside the

home

Number of live births 0.194+ -0.299+ -0.138 0.000

(0.12) (0.16) (0.12) (0.17)

Age -0.467 0.804* 0.335 0.060

(0.28) (0.39) (0.29) (0.41)

Hindu1 -0.289* 0.370+ -0.410** 0.086

(0.14) (0.20) (0.12) (0.18)

Highest education 0.005 0.066+ -0.022 0.043+

(0.02) (0.03) (0.02) (0.03)

Husband education -0.023+ -0.023 -0.019 -0.007

(0.01) (0.02) (0.01) (0.02)

ln(farm land) 0.097 -0.192 0.226* -0.086

(0.08) (0.15) (0.10) (0.12)

Clinic in village1 -0.294** 0.189 -0.306** 0.001

(0.09) (0.14) (0.09) (0.13)

Wald Chi-squared 44.72** 46.17** 31.86** 4.79

Log-pseudolikelihood (3271.99) (2703.84) (3199.57) (2840.85)

N 1278 1278 1278 12781 Categorical variable

**, *, +: significant at 1, 5, 10 percent, respectively.

IV probit (marginal effects)

6. Alternative fertility outcomes: does it matter how fertility is affected?

In the introduction to the paper it was noted that the regulation of fertility can take

a number of forms: childbearing can begin later, or cease earlier, or the spacing between

births can increase. The descriptive statistics showed that the Matlab family planning

program seemed to have an impact on birth spacing, and that it probably has had a greater

impact more recently, as measured by the number of children born in the past five years.

On the other hand, it appears to have had no impact on the age at first pregnancy. In

other words, women in the Matlab program appear to have been regulating fertility by

23

increasing birth spacing, but not by postponing the initiation of fertility.9 One might

plausibly expect that birth spacing and the timing of initiation will have different

consequences for labor force participation. Later initiation will allow a young woman to

accumulate work experience and possibly more education. The health benefits of greater

birth spacing for both mother and child are well known, and greater intervals between

births may increase the returns to working for mothers who are healthier as well as

reduce the need to stay home to care for an infant.

Table 5 examines this question by repeating the analysis of Tables 3 and 4 for

three alternative fertility outcomes – birth spacing and the number of births in the past

five years. This table includes only the specific parameters of interest; control variables

and other parameters are available. Column 1 of this table shows that the Matlab

program had a significant impact on birth spacing. Women in program villages had a

four-month longer gap between births than women in control villages. However, this

increase in spacing did not appear to have any impact at all on the likelihood that a

woman entered the labor force. On the other hand, it did have an effect on the sector of

participation – women with longer intervals between births were more likely to work in

the formal sector, and less likely to work in the informal sector.

Column 2 of this table examines the impact of Matlab program exposure on the

number of births within the past five years. As with birth spacing, this has no discernible

impact on the probability of working, but does significantly affect the sector in which the

woman is more likely to work. Women with more recent births are more likely to work

in the informal sector, and much less likely to work in the formal sector.

9 We do not examine the cessation of fertility.

24

1 2

Birth spacing

(months)

Number of

births in past

five years

Working -0.021 0.522

(0.02) (0.43)

Informal sector -.027* 0.718**

(0.01) (0.33)

Formal sector .032* -0.881**

(0.01) (0.31)

Working in-home 0.006 -0.533

(0.02) (0.45)

Working outside the home 0.018 -0.064

(0.02) (0.72)

Impact of treatment on 4.310** -0.147**

fertility outcome1 (1.44) (0.05)

N 1039 12341

**, *, +: significant at 1, 5, 10 percent, respectively.

Table 5. Impact of childbearing on working (alternative

fertility outcomes)

These are the estimates from one of the five first-stage

regressions in each case. The parameters and standard

errors vary by less than one-tenth of one percent.

7. Alternative Instruments: Twin Births

Finally, we explored two additional variables as exogenous determinants of total

fertility. First, we used the experience of multiple births, as in Rosenzweig and Wolpin

(1980, 2000). We identified all members of the sample reporting a twin birth. Naturally,

the number of such occurrences is low – in our restricted sample, only 38 individuals in

our total sample (approximately 3%) reported a twin birth. To construct the indicator for

twin live births, we match the live birth history of the mother to the date of each live

birth, and classify as twins births that share the same date. We drop observations with

25

missing dates of birth or pregnancy histories, leaving an effective sample size of 1186

women.

Table 6 shows the results of this alternative identification strategy. Not

surprisingly, twin births lead the family to have to roughly one more child than they

would have had in the absence of twins. On the other hand, there is only a weak inverse

relationship between higher fertility and working out of the home.

Next, for those women who report having given birth, we identified those whose

first live birth was a son (about 54 percent). In this case we drop observations with

missing dates of birth or pregnancy histories, leaving a sample of 1231 women. The

table shows that having a male child first is not related to total fertility among women in

this sample.

1 2

Twin birth

First child

male

Working -0.015 0.525*

(0.16) (0.21)

Informal sector -0.024 0.539**

(0.15) (0.19)

Formal sector 0.034 -0.036

(0.25) (1.58)

Working in-home 0.115 0.064

(0.16) (0.93)

Working outside the home -.318+ 0.525*

(0.19) (0.17)

Impact of treatment on 1.422* -0.085

total fertility1 (0.29) (0.10)

N 1186 12311

**, *, +: significant at 1, 5, 10 percent, respectively.

Table 6. Impact of childbearing on working (alternative

instruments)

These are the estimates from one of the five first-stage

regressions in each case. The parameters and standard

errors vary by less than one-tenth of one percent.

26

7. Conclusions

This study examines changes in fertility and childbearing practices on the labor

force participation of women in rural Bangladesh. Since fertility is not exogenous to

other decisions taken by the family, we separately identify the impact of changes in

fertility on changes in work by taking advantage of a family-planning program selectively

introduced in the district of Matlab, Bangladesh that resulted in significant declines in

fertility, relative to the control areas. Our results show that, contrary to much previous

research, the number of children is only weakly associated with female labor force

participation.

Although it appears that fertility is not robustly or consistently related to labor

force participation overall, control over childbearing may affect the decisions made by

women concerning the sector of participation. Women with more children, those with

more young children, and those who bear children more quickly, at shorter intervals, may

be more likely to find employment in the informal sector. This is probably due to the

greater flexibility of employment in the informal sector.

27

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