preschool availability and female labor force participation: … · 1 introduction in 2008, female...

Preschool Availability and Female Labor Force Participation: Evidence from Indonesia

EAST ASIA PACIFIC GENDER INNOVATION LAB WORLD BANK

DANIEL HALIM, HILLARY JOHNSON, ELIZAVETA PEROVA

JUNE 2017

PRELIMINARY DRAFT – DO NOT CITE

1 Introduction

In 2008, female labor force participation (FLFP) in Indonesia stood at 53.5 percent, lagging other

countries in the East Asia & Pacific region averaging at 67.7 percent (World Bank 2011).

Pooling National Labor Force Survey (Sakernas) data from 1994 to 20091, we also note that

female labor force participation stays relatively constant over the years, hovering around 49.2

percent. World Bank (2016) argue that increasing FLFP is a more effective policy to offset the

shrinking workforce due to rapidly aging economy compared to attracting in-migrants or

increasing elderly work participation. Halim, Johnson, and Perova (2017) suggest that the low

FLFP in Indonesia may be driven by childcare constraints instead of preference. Using event

study analyses, they found that the incidence of childcare reduces the probability of employment

and the effect is larger for two-generational households without access to informal childcare

provided by elder members of the household.

Blau and Currie (2006) propose that expanding access to public preschools serves dual purposes:

to encourage FLFP in their childbearing years and to improve early-childhood education and

development. In this paper, we will focus on the first purpose and estimate the elasticity of

maternal labor supply on access to public preschool in Indonesia. Presumably, mothers can

enroll their eligible children into preschools and free up some time from childcare and towards

employment. However, with an average of merely two-hours daily enrollment in Indonesian

preschools (World Bank 2006) it is not clear whether mothers can afford to be employed. Two-

hours might be too short for a formal employment and might yield only a small compensation,

which may not cover the implicit costs of enrollment, such as books, stationeries, uniforms, etc.,

even without the explicit cost of tuition fee in public preschools. Additionally, mothers may not

necessarily be induced to enroll their eligible children into preschools with low quality

preschools or due to cultural traditions, which favor keeping children at home (Myers 1995).

Studies from other regions generally suggest that access to public preschools positively affects

LFP of mothers. In Argentina, Berlinski and Galiani (2007) exploit a massive construction of

pre-primary schools in a difference-in-difference framework and find increases in pre-primary

school participation and maternal employment. Using a regression discontinuity design around

1 Excluded in the sample are young females below age 15.

the mandatory entry age into preschool in Argentina, Berlinski et. al. (2011) confirm the earlier

finding that access to preschool improves maternal employment. Studies in the US have also

exploited birth day-based eligibility to infer positive effects of preschool access on maternal

employment (Gelbach 2002, Fitzpatrick 2010, Barua 2014). Schlosser (2011) similarly shows

that free public preschool increases FLFP without necessarily changing their fertility, in the

short-run. Meanwhile, Cascio (2009) found that the positive effect of subsidized public preschool

on FLFP in the US is only statistically significant for single mothers whose youngest child is 5

and older and not on other mothers with eligible children.

Generalizing to other forms of childcare, other studies but one in Chile (Medrano 2009) point

towards a positive effect of access to childcare on FLFP. These findings pertain to different

contexts: Brazil (Paes de Barros, Olinto and Carvalho 2011), Canada (Lefebvre and Merrigan

2008, Baker, Gruber and Milligan 2008, Lefebvre, Merrigan and Verstraete 2009, Fortin,

Godbout and St-Cerny 2012), Colombia (Attanasio and Vera-Hernandez 2004), Ecuador (Rosero

and Oosterbeek 2011), Mexico (Angeles, et al. 2011, Calderon 2014).

In this paper, we exploit spatial and temporal variations in the access to public preschools and

age-eligibility to implement a generalized difference-in-difference framework and infer the

causal effect of public preschools on maternal employment. The official age of preschool

enrollment is between 4-62. Mothers may benefit from public preschools if they have access to

them when their children were age-eligible. Mothers from the same region with older children

who are not age-eligible, presumably, would not benefit and form our comparison group.

Similarly, mothers with age-eligible children from a low access to public preschool region may

find it more difficult to enroll their children into preschool and may not benefit as much. They

will also form a comparison group. Given the longitudinal nature of our data, we are also able to

absorb individual time-invariant fixed effects, such as ability, preference for work and children,

fertility, and fecundity. Our study will not be able to uncover the dynamic labor supply effect of

preschools, as Lefebvre, et. al. (2009) did, and the general equilibrium effect of preschools on

FLFP, such as through an increased demand for preschool teachers.

2 This age-restriction is not strictly enforced and there is a high frequency of enrollment for 3-years-old children as

well. Although, there is low frequency of preschool enrollment among 7-years-old children.

Our preliminary findings suggest that access to public preschool leads to higher maternal

employment of age-eligible children, but access to private preschool does not. Specifically, an

additional public preschool per 1,000 children raises maternal employment of age-eligible

children by 6.9 percentage points. This represents a 13.3% improvement from the average work

participation at 52%. This finding is robust to other specifications. Additionally, we find that

access to public preschool compensates work participation of mothers to 0-2 year old children

and improves work participation of mothers to 3-5 and 6-18 year old children.

We will proceed to discuss the context of preschools in Indonesia in Section 2, our data in

Section 3, our empirical strategy in Section 4, and our findings in Section 5. In Section 6, we will

explore caveats and implement robustness checks. Section 7 will then conclude our analysis.

2 Preschools in Indonesia

Early childhood education and development (ECED) services in Indonesia take place before

primary school starts, officially at age 7 although many enroll their children at age 6 and few at

age 5. ECED services in Indonesia can be largely categorized into two blocks: with or without

formal educational focus. There are three types of ECED with formal education focus: (1)

Taman Kanak-kanak (TK, kindergarten), (2) Raudhatul Afthal (RA, Islamic preschool), and (3)

Kelompok Bermain (KB, playgroup). We will specifically focus on TK (kindergarten) due to

availability of data. Aside from Islamic preschool and playgroups, we will also exclude non-

formal ECED such as childcare, Posyandu (village health posts), parent education group, and the

recently expanded PAUD program (early childhood education).

TK (kindergarten) program is a formal education aimed to prepare children between the age of 4-

63 for primary schools. Pre-primary school enrollment is not mandatory. It is center-based with

an average of 2 hours daily interactions (see World Bank (2006) for more details). It is

coordinated by The Directorate General for Management of Primary and Secondary Education

(DGMPSE), under the Ministry of National Education (MONE). World Bank (2006) reported

that TKs costed around 300 million rupiahs (or US$ 33,5294) to construct and only 305 out of

3 The age restriction is not strictly enforced. There is substantial enrollment of 3-year-old children in TKs. 4 At an annual exchange rate of US$1 = Rp 9,020 in 2006 (FRED)

48,000 total preschools in Indonesia were publicly supported. Pooling the village censuses

(PODES) across the years, we show that the average number of public and private preschools in

a district has steadily increased over time (Figure 1). Figure 2 shows the spatial distribution of

public preschools relative to the population of children age 3-6 across Indonesian districts in

2014. We will use the temporal and spatial variations of preschool availability to infer the causal

estimate of preschool access on maternal employment, which we will discuss in detail in Section

4.

Using household survey data, Indonesian Family Life Survey 3 (2000), we find that among

enrolled preschool students the median travel time to preschool is 10 minutes, in both public and

private preschools (Figure 3). We also find that the cost of attending preschool is significantly

higher in private preschool compared to public preschool. But there are more children between 3

and 6 who are enrolled in private preschool than in public preschools (Table 1). The last two

observations may not necessarily suggest that private preschools provide higher quality

childcare, and thus, command higher price and enrollment. Rather, it may simply underline our

earlier observation that the number of private preschools substantially dominate the few number

of public preschools.

3 Data

Preschool enrollment and maternal employment are endogenous choices driven by personal

preferences (for childbearing, childrearing, and work), ability, cultural traditions, biological

dispositions (such as fertility and fecundity), and other individual time-invariant factors. To

causally estimate the effect of preschool access on maternal employment, it is imperative to

control for these individual determinants. It can be accomplished through the inclusion of an

individual fixed effect if a long-time panel data is available. Hence, we will use the Indonesian

Family Life Survey (IFLS).

IFLS is a longitudinal household survey, first conducted in 1993 with subsequent tracking of the

same and split households in 1997, 2000, 2007, and 2014. It was first fielded in 13 (out of 275)

5 At the time, Timor Leste, now an independent country, was one of Indonesian provinces.

provinces back in 1993, which represent 83% of the national population (Frankenberg and

Karoly, The 1993 Indonesian Family Life Survey: Overview and Field Report 1993). It has

notably high re-contact rates with 87.8% of households surveyed in 1993 who were successfully

tracked or confirmed dead in 2014 (Strauss, Witoelar and Sikoki, The Fifth Wave of the

Indonesia Family Life Survey (IFLS5): Overview and Field Report 2016). In the first round,

more than 14,000 individual respondents were selected6 to provide detailed accounts of their

employment, current and historical—going back five years in 1988. This number grows to more

than 34,000 individuals in 2014. Combining the current and recall employment survey modules,

we could construct annual employment history from 1988 to 2014 for individuals who were

successfully tracked in all five waves.

It also surveyed ever married women between the age of 15-49 in detail about all their pregnancy

incidences—still in womb, resulting in livebirth, stillbirth, and miscarriage. In the first round,

close to 5,000 women were interviewed. Tracking the same women over time, allowed us to add

in subsequent pregnancies that occurred after the first wave. For each livebirth, respondents were

interviewed about the year of (or age at) childbirth. Thus, we can merge annual incidence of

pregnancies with annual employment for sampled women, regardless of co-residency status with

their children. To implement the fixed effect model, we further restricted our sample to females

who were found to be between the age of 19 and 45 in at least two waves. In the remaining text,

we will refer to this constructed panel data as the intergenerational panel.

Our measure of preschool access is obtained from pooling Village Census (Podes) cross-sections

from 1990 to 2014. Podes is fielded, roughly, once in three years. As briefly mentioned in

previous section, we have to focus on formal-education kindergarten (TK) because Podes, which

has the longest time-span and universal coverage of villages in Indonesia, only consistently ask

about TK. While Podes data is available at the village-level (two administrative levels below

districts), we decide to aggregate the number of preschools to district level, with consistent

6 In the first round, not all individuals within selected households were interviewed in detail. The procedure mainly

selected the household head and their spouse, two randomly selected children, and an individual age 50 and above

and their spouse (Frankenberg and Karoly, The 1993 Indonesian Family Life Survey: Overview and Field Report

1993). In the second round, the within-household sampling scheme grew to include all household members in the

original households (Frankenberg and Thomas 2000). Target respondents expanded further in subsequent rounds

(for more details, see: Strauss, et. al. (2004), Strauss, et. al. (2009), Strauss, et. al. (2016))

boundaries as of 19937, because the lowest geographic identifier publicly available in IFLS data

(and thus, our intergenerational panel) is the sub-district level. While it is possible to merge the

two datasets at the sub-district level, sub-districts are harder to harmonize over time and are not

the level at which the government allocates public goods provision. Since preschool access from

repeated Podes is not available annually, we will merge several years of Podes to one year of

intergenerational panel. The specific matching strategy is illustrated in Table 2.

Table 3 describes our constructed intergenerational panel. We have more than 220,000

individual-year observations for 10,000 individuals. Work participation is defined annually from

self-reported claims of employment in the past years and current year. Work participation

include individuals whose primary activity is working, who work at least 1 hour during the past

week, who have a job/business but were temporarily not working during the past week, and who

work at a family-owned (farm or non-farm) business during the past week. 52% females in our

sample were working, consistent with observations from Sakernas (National Labor Force

Survey) and Susenas (National Socio-economic Survey). Females in our sample completed, on

average, 7.7 years of schooling, halfway through their lower secondary education. In the pooled

sample of individual-year, 23% observations were found to be with a child between 0-2 and a

child between 3-5, and 50% observations were found to be with a child between 6-18.

Table 5 tabulates work participation of females in our sample by their motherhood status to

children in specific age groups and by access to public preschool in their district of residence. It

compares also work participation of eligible mothers (i.e. mothers of 3-5 year old children) to

non-eligible mothers with children in other age groups and/or without children and present the

difference-in-difference estimates. Our difference-in-difference estimates suggest that mothers of

age-eligible children outperform mothers of 0-2 children by 1 percentage point, perform as well

as mothers of 6-18 children, and outperform non-mothers and mothers of children 18 and older

by 4 percentage points. We concur that these estimates may be driven by non-random

placements of preschools (Pitt, Rosenzweig and Gibbons 1993) and individual

7 Districts frequently split to provide more fiscal-independence to economically growing regions. Especially, many

districts split following the fall of Soeharto’s regime in 1998 and since the decentralization process ensued in 2001.

In 1993, there were 290 districts in 26 provinces (excluding Timor Leste). By 2009, there were 497 districts in 33

provinces. To ensure that we are measuring preschool access within the same geographical boundaries over time, it

is imperative to maintain a consistent boundary.

preference/constraint. Therefore, we turn to our generalized difference-in-difference with

individual and district-year fixed effects in the next section.

4 Empirical Strategy

Conditional on individual time-invariant fixed effect, expansion or retraction of preschool access

in one’s district of residence should be orthogonal to individual idiosyncrasies. Provision of

public preschools in a district may not be synchronous with the aggregate demand for childcare

and driven instead by other factors unrelated to FLFP. Thus, we can exploit the exogenous

variations in the access to preschool in a district to estimate the labor supply elasticity of

maternal employment.

Pitt, et. al. (1993), however, argue that government programs are not randomly placed.

Specifically, they note that more family planning posts were allocated to areas with higher

utilizations of birth control (positive selection). In contrast, Duflo (2001) finds that massive

number of primary schools were constructed in educationally-backward areas (negative

selection). We confirm that allocation of public preschools is strongly and positively correlated

with provision of other public goods. While it is also strongly correlated with other economic

and demographic outcomes, it is not clear whether the selection is positive or negative (see more

details in the Appendix). Pitt, et. al. (1993) suggest including district fixed effect to solve the

endogeneity problem, if researchers have repeated cross-sections of district-level observations.

Since we have a longitudinal individual data, we can build upon their suggestion further with the

inclusion of district-year fixed effect. Aside from controlling district-specific, time-invariant,

characteristics and year-specific, district-invariant, characteristics, the district-year fixed effect

also absorbs district-specific and time-variant characteristics, such as changes in district regents

and their policies over public goods provision.

Specifically, we estimate:

𝑌𝑖𝑗𝑡 = 𝜑𝑖 + 𝜃𝑗𝑡 + 𝛼 𝐾𝑗𝑡𝐶35𝑖𝑗𝑡 + 𝛃𝐗𝐢𝐣𝐭 + 𝜀𝑖𝑗𝑡

where 𝑌𝑖𝑗𝑡 is the employment outcome of female i in year t in district j, 𝐾𝑗𝑡 is a measure of

accessibility of kindergartens in district j at time t, 𝐶35𝑖𝑗𝑡 is a dummy taking the value of 1 if

female i has preschool age-eligible children aged between 3 and 5 in year t, and 𝑿𝒊𝒋𝒕 is a vector

of time variant individual characteristics, which includes the lone term of 𝐶35𝑖𝑗𝑡. 𝜑𝑖 is the

individual fixed effect and 𝜃𝑗𝑡 is the district-year fixed effect. Standard errors are clustered at the

district-level j. The causal identification of 𝛼 rests on the assumption that conditional on

individual preference and variations in district-year characteristics, absent changes in preschool

access, mothers of age-eligible children supply maternal employment at similar rates.

As discussed in Section 2, the official preschool age is between 4 and 6. However, this age

criterion is not strictly enforced and, indeed, we find high frequency of 3-year-old children

starting preschools and continuing primary school at age 6. Since the age-cutoff is not strictly

enforced we will first proceed to define the age-eligibility to be between 3 and 5 to allow direct

comparison to other studies. We will return to this issue in Section 6.2.

5 Results

We first show naïve estimates of labor supply elasticity of maternal employment with respect to

preschool access using OLS and Probit without any fixed effects discussed above (Table 6). In

the first column, we pool public and private preschools. In the second and third column, we

focus on public and private preschools. Column 4 through 6 are defined similarly for Probit

estimations. First, we note that the pooled column (Column 1) is largely similar to private

preschool column (Column 3) because there are significantly more private to public preschools

(Table 4) in a district. Second, we note that conditional on having an age-eligible child additional

kindergarten is associated with lower probability of maternal employment in all columns. This

naïve estimate is confounded with individual preference, which may interact with preschool

access, and potentially positive selections in the placement of preschools, as indicated by

positive and statistically significant coefficient of number of kindergartens per 1,000 children.

For instance, if the government places preschools in areas with high average of FLFP and low

fertility females, then females who do have children in high preschool areas are “negatively”

selected and will supply lower than average LFP. The coefficient on urban residence is positive

and statistically significant.

Our preferred specification includes individual and district-year fixed effects to specifically

address those confounds. Conditional on individual and district-year characteristics, we find that

mothers of age-eligible children are 6.9 percentage points more likely to participate in the labor

force with an additional public preschool per 1,000 children in the district (Table 7, Column 2).

Access to private preschool has no effect on mothers of age-eligible children (Table 7, Column

3). We interpret these findings as a confirmation that expansion of public preschools is

exogenous to maternal labor supply decision, unlike private preschools, which are endogenous to

decision process. First, note that having an age-eligible child is associated with 1.4-1.9

percentage points higher likelihood of employment. This may be driven by age effect, as we will

discuss in the next section. Conditional on having an age-eligible child, having an additional

private preschool does not affect maternal employment. We expect to see a null result if the

supply of private preschools matches the demand of private preschools. Hence, in areas with

more private preschools, mothers are not more likely to work, and vice versa. Mothers that

would have worked and enrolled their child(ren) in private preschools have done so, and mothers

who have not would not enroll their child(ren) even if a new private preschool were to open. Our

results hold to various permutations of fixed effect treatments (not included in this paper).

6 Limitations and robustness checks

Since public goods are not randomly allocated (Pitt, Rosenzweig and Gibbons 1993), we are

required to include a district-year fixed effect in our specification. It constrains us from

estimating the “total” effect of public preschool on the entire population, i.e. mothers with and

without age-eligible children. For that purpose, we need to estimate:

𝑌𝑖𝑗𝑡 = 𝜑𝑖 + 𝜃𝑗𝑡 + 𝛼 𝐾𝑗𝑡𝐶35𝑖𝑗𝑡 + 𝛃𝐗𝐢𝐣𝐭 + 𝛾 𝐾𝑗𝑡 + 𝜀𝑖𝑗𝑡

The “total” effect of public preschool on the entire population is then 𝛼 + 𝛾, where 𝛾 is the

“spillover” effect of preschool on “untreated” females’ LFP. Notice, however, that the district-

year fixed effect, 𝜃𝑗𝑡 , completely absorbs 𝐾𝑗𝑡 . Thus, 𝛾 cannot be estimated in our specification.

The spillover effect can function in two ways, through: (1) dynamic labor supply effect of

preschools (Lefebvre, Merrigan and Verstraete 2009), and/or (2) general equilibrium effect of

preschools on labor supply. In the first channel, preschools allow mothers to work when their

children were age-eligible, accumulate longer work experience, and command higher wage rates

when their children get older and graduate from preschools. Longer work tenure and higher wage

rates presumably push FLFP upward later in the life cycle. Expansion of preschools may also

induce a general equilibrium effect on FLFP. For instance, expansion of preschools may open

new employment opportunities for mothers to work as preschool teachers or to set up shops near

the new preschools.

6.1 Robustness check: dynamic labor supply effect

We can partially address the first channel by looking at other age categories that might have been

affected by preschool access. Specifically, we estimate:

𝑌𝑖𝑗𝑡 = 𝜑𝑖 + 𝜃𝑗𝑡 + 𝛼1𝐾𝑗𝑡𝐶02𝑖𝑗𝑡 + 𝛼2𝐾𝑗𝑡𝐶35𝑖𝑗𝑡 + 𝛼3𝐾𝑗𝑡𝐶618𝑖𝑗𝑡 + 𝛃𝐗𝐢𝐣𝐭 + 𝜀𝑖𝑗𝑡

where 𝑌𝑖𝑗𝑡, 𝐾𝑗𝑡, 𝐶35𝑖𝑗𝑡, 𝑿𝒊𝒋𝒕, 𝜑𝑖, and 𝜃𝑗𝑡 are as described in Section 4. In addition, we include

𝐶02𝑖𝑗𝑡 and 𝐶618𝑖𝑗𝑡 in 𝐗𝐢𝐣𝐭, which are dummies indicating that female i in district j in year t has

children aged between 0 and 2 and children aged between 6 and 18 respectively. Non-mothers

and mothers of children aged 18 and older form our comparison group. 𝛼1 measures the impact

of having an additional preschool conditional on having only children aged between 0 and 2.

Similarly, 𝛼2 measures the impact of having an additional preschool conditional on having only

children aged between 3 and 5. Therefore, 𝛼1 + 𝛼2 quantifies the effect of preschool for mothers

who have one child between 0 and 2 and another child between 3 and 5.

Table 8 reports the findings. First, we find substantial age effect, in all three columns: pooled,

public, and private preschools. Having older children (i.e. 3-18) is associated with significantly

higher work participation and younger children (i.e. 0-2) is associated with significantly lower

work participation. Conditional on having children between 0-2, however, we find that additional

public preschool per 1,000 children improves work participation by 5.0 percentage points, which

roughly compensates the burden of rearing 0-2 children at 5.2 percentage points reduction to

work participation. In contrast, additional private preschool seems to exacerbate the childrearing

burden of 0-2 children by 0.4 percentage points. Conditional on having an eligible child (age 3-

5), additional public preschool per 1,000 children improves work participation further by 5.5

percentage points, a 10.6 percentage increase from the average female work participation. This

effect is not present for additional private preschool. We also find that conditional on having a

child between 6-18, additional public preschool improves work participation by 2.6 percentage

points.

6.2 Robustness check: eligible age between 3-6

Since the preschool eligibility age-cutoff is not strictly enforced and we find high frequency of 3-

year-old children starting preschools, we started by defining eligible age to be between 3 and 5

for direct comparisons to other studies. We could potentially expand the eligible age definition to

include children between age 3 and 6. We report our findings in Table 9. We find that mothers of

age-eligible children benefit in their work participation from additional public preschool by 7.5

percentage points, or 14.4 percent increase from the average female work participation. Having

an age-eligible child itself leads to 1.9 to 2.3 percentage points increase to work participation.

6.3 Robustness check: continuous measure of children

In our main specification, we interact the density of kindergartens in a district and an individual

dummy for having an age-eligible child. This binary indicator could potentially mask the non-

linear effects of childcare. In other words, it is unclear if mothers face an increasing, decreasing,

or constant returns to scale in providing care to preschool-aged children. We could investigate

this further by interacting instead the density of kindergartens in a district with the total number

of kids in a certain age category. In this case, 𝐶35𝑖𝑗𝑡 is a continuous variable and the marginal

effect of preschool is:

𝜕𝑌𝑖𝑗𝑡

𝜕𝐾𝑗𝑡= 𝛼2 𝐶35𝑖𝑗𝑡

Since 𝐶35𝑖𝑗𝑡 is no longer a dummy variable taking the value of 0 or 1, the coefficient of

interaction, 𝛼2, reported in Table 10 should be multiplied by the average number of aged 3-5

children owned to get the average marginal effect of preschool.

For instance, Table 10 Column 2 reports 𝛼2 being 0.05. From Table 3, we know that a mother at

any given year has on average 0.261 child between the age of 3 and 5. Multiplying these two

values yields the marginal effect of public preschool per 1,000 children to be 1.3 percentage

points increase in work participation per one age-eligible child.

7 Conclusion

Prior study suggests that the low female labor force participation in Indonesia is likely driven by

childcare constraint instead of preference alone (Halim, Johnson and Perova 2017). In this paper,

we confirmed that access to a formal childcare, such as formal-education preschool (TK), helps

improve work participation of mothers of age-eligible children. It is interesting that access to

public preschool, which provides minimal daily interactions with the children (roughly, 2 hours a

day), and thus, minimal relief to mothers’ childcare burden can lead to substantial improvement

in maternal labor supply: 13.3% increase from the average female work participation.

This finding shed light to a potential solution to improving female LFP in Indonesia. 67%

females in Indonesia live in two-generational households without access to informal childcare.

Providing childcare services to mothers can boost their labor supply, which can help expand the

national labor force and compensate for the aging workforce. In the future iterations of this

paper, we will further investigate observable characteristics of mothers who will likely take-up

and benefit from the expansion of preschools, which could help policymakers to better target the

program. Future research in this area should also consider the potential benefit of early childhood

education and development services in terms of child’s cognitive developments and future labor

market outcomes, which is beyond the scope of this paper.

Figures

Figure 1. Average number of public and private preschools by districts (with consistent boundaries as of 1993).

Figure 2. Spatial distribution of public preschools relative to the population of children age 3-6 across Indonesian districts in

2014

150

200

250

300

350

priva

te

0

5

10

15

public

1995 2000 2005 2010

year

public private

Note: data from Podes 1993-2011, aggregated to districts as they existed in 1993

Public Preschool per 1,000 children1.00 - 3.92 (57)0.50 - 1.00 (128)0.25 - 0.50 (107)0.00 - 0.25 (148)

Source: Podes 2014 and Pooled Susenas 2014

Public Preschool per 1,000 children age 3-6 in 2014

Figure 3. One-way travel time to preschool, in public and private preschool

0

.02

.04

.06

.08

Kern

el density

0 10 20 30 40 50

Travel time (mins)

Public Private

Note: median in public preschool = 10 mins, in private preschool = 10 mins

Tables

Table 1. Annual cost of attending kindergarten conditional on current attendance by type of kindergarten (in Rp10,000

increment)

Private Public Private-Public

mean sd mean sd b

school fees: registration fee 6.81 16.98 2.73 5.95 4.08***

school fees: other scheduled fees 5.69 14.87 2.11 3.90 3.57***

school fees: exams 0.10 0.73 0.00 0.02 0.10**

school supplies: books/writing supplies 2.45 4.91 1.15 2.30 1.30***

school supplies: uniform and sports 2.44 4.56 2.27 3.67 0.17

transp/pocket money: transp. costs 1.71 8.14 0.11 0.69 1.60***

transp/pocket money: food/housing costs 6.15 12.95 4.14 7.03 2.01

transp/pocket money: special courses 0.41 6.03 0.00 0.00 0.41

school expenses: other 0.54 3.14 0.00 0.00 0.54***

Observations 430 76 506

Table 2. Matching strategy of the intergenerational panel (IFLS) to Podes

IFLS

PODES

1988-1990 1990

1991-1993 1993

1994-1996 1996

1997-2000 2000

2001-2003 2003

2004-2005 2005

2006-2008 2008

2009-2011 2011

2012-2015 2014

Table 3. Summary statistics of females aged between 19-45 in at least two IFLS rounds

(1) (2) (3) (4) (5)

VARIABLES N mean sd min max

Work participation 227,559 0.520 0.500 0 1

Years of schooling 224,064 7.791 4.492 0 19

Total kids between:

0-2 227,559 0.256 0.486 0 6

3-5 227,559 0.261 0.495 0 6

6-18 227,559 0.958 1.219 0 14

Fraction with kids between:

0-2 227,559 0.235 0.424 0 1

3-5 227,559 0.237 0.425 0 1

6-18 227,559 0.508 0.500 0 1

Number of private kindergartens per 1,000 children

aged 3-6

227,039 4.584 3.250 0 33.08

Number of public kindergartens per 1,000 children

aged 3-6

227,039 0.156 0.221 0 3.703

Number of id 10,340

Table 4. Summary statistics of kindergartens

Variable Obs Mean Std. Dev. Min Max

Public 2,022 9.182987 11.57477 0 162 Private 2,022 218.6024 229.4607 0 1739

Table 5. Female LFP by motherhood status to certain age groups and availability of preschool in district of residence

Work in past week

Availability of preschool in district of residence

High Low Difference

(1) (2) (3)

Mother of 3-5 child

Mean 0.543 0.502 0.0406

Std. error (0.00305) (0.00304) (0.00431)

Observation 26533 26859

Mother of 0-2 child

Mean 0.46 0.423 0.0368

Std. error (0.00306) (0.00301) (0.0043)


Diff (3-5, 0-2) 0.0915 0.0815 0.01

Std. deviation (0.00473) (0.00469) (0.00666)

Mother of 6-18 child

Mean 0.631 0.585 0.0455

Std. error (0.00201) (0.00205) (0.00287)


Diff (3-5, 6-

18) -0.108 -0.104 -0.00415

Std. deviation (0.00381) (0.00384) (0.00541)

Others (non-mothers and mothers of children > 18)

Mean 0.417 0.43 -0.0138

Std. error (0.00266) (0.00265) (0.00376)


Diff (3-5,

others) 0.102 0.0608 0.0409

Std. deviation (0.00646) (0.00636) (0.00907)

Table 6. Naive specification without fixed effects: OLS and Probit

(1) (2) (3) (4) (5) (6)

OLS Probit

VARIABLES All Public Private All Public Private

Num. kindergartens * has kid 3-5 -0.008*** -0.019 -0.008*** -0.009*** -0.019 -0.009***

(0.002) (0.022) (0.002) (0.003) (0.023) (0.003)

Num. kindergartens per 1000 children 0.024*** 0.112*** 0.024*** 0.025*** 0.114*** 0.025***

(0.002) (0.037) (0.002) (0.002) (0.038) (0.002)

Has kid age 3-5 0.049*** 0.005 0.048*** 0.050*** 0.005 0.049***

(0.015) (0.009) (0.014) (0.015) (0.009) (0.015)

urban 0.077*** 0.069*** 0.078*** 0.078*** 0.069*** 0.079***

(0.016) (0.017) (0.016) (0.016) (0.017) (0.016)

Observations 227,039 227,039 227,039 227,039 227,039 227,039

R-squared 0.027 0.008 0.027

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 7. Maternal labor supply elasticity of preschool access, with individual and district-year fixed effects: preferred

specification

(1) (2) (3)

VARIABLES All Public Private

Num. kindergartens * has kid 3-5 0.002 0.069*** 0.001

(0.001) (0.016) (0.001)

Has kid age 3-5 0.017** 0.014*** 0.019**

(0.008) (0.005) (0.008)

urban 0.007 0.006 0.007

(0.011) (0.011) (0.011)

Observations 227,039 227,039 227,039

R-squared 0.126 0.126 0.126

Number of id 10,340 10,340 10,340

Individual FE yes yes yes

District FE no no no

Year FE no no no

District-Year FE yes yes yes


*** p<0.01, ** p<0.05, * p<0.1

Table 8. Maternal labor supply elasticity of preschool access: robustness check on the dynamic labor supply effect

(1) (2) (3)


Num. kindergartens * has kid 0-2 -0.004*** 0.050*** -0.004***

(0.001) (0.017) (0.001)


(0.001) (0.015) (0.001)

Num. kindergartens * has kid 6-18 -0.001 0.026* -0.001

(0.001) (0.014) (0.001)

Has kid age 0-2 -0.029*** -0.052*** -0.028***

(0.009) (0.006) (0.009)

Has kid age 3-5 0.017** 0.013** 0.019**

(0.008) (0.006) (0.008)

Has kid age 6-18 0.083*** 0.074*** 0.084***

(0.009) (0.006) (0.009)

urban 0.008 0.007 0.008

(0.011) (0.011) (0.011)

Observations 227,039 227,039 227,039

R-squared 0.135 0.135 0.135

Number of id 10,340 10,340 10,340



Year FE no no no



*** p<0.01, ** p<0.05, * p<0.1

Table 9. Maternal labor supply elasticity of preschool access: robustness check on eligibility age cutoff

(1) (2) (3)



(0.001) (0.015) (0.001)

Has kid age 3-6 0.022** 0.019*** 0.023***

(0.009) (0.006) (0.009)

urban 0.006 0.006 0.006

(0.011) (0.011) (0.011)

Observations 227,039 227,039 227,039

R-squared 0.126 0.127 0.126

Number of id 10,340 10,340 10,340



Year FE no no no



*** p<0.01, ** p<0.05, * p<0.1

Table 10. Maternal labor supply elasticity of preschool access: robustness check using continuous measure of total kids and

flexible age groups

(1) (2) (3)


Num. kindergartens * num. kid 0-2 -0.004*** 0.036** -0.005***

(0.001) (0.016) (0.001)

Num. kindergartens * num. kid 3-5 -0.000 0.050*** -0.000

(0.001) (0.014) (0.001)

Num. kindergartens * num. kid 6-18 0.000 0.001 0.000

(0.001) (0.007) (0.001)

Num. kid age 0-2 -0.013* -0.037*** -0.013

(0.008) (0.006) (0.008)

Num. kid age 3-5 0.025*** 0.017*** 0.026***

(0.007) (0.005) (0.007)

Num. kid age 6-18 0.037*** 0.037*** 0.037***

(0.003) (0.002) (0.003)

urban 0.008 0.007 0.008

(0.011) (0.011) (0.011)

Observations 227,039 227,039 227,039

R-squared 0.136 0.136 0.136

Number of id 10,340 10,340 10,340



Year FE no no no



*** p<0.01, ** p<0.05, * p<0.1

References

Angeles, Gustavo, Paola Gadsden, Sebastian Galiani, Paul Gertler, Andrea Herrera, Patricia

Kariger, and Enrique Seira. 2011. "Evaluación de impacto del programa estancias

infantiles para apoyar a madres trabajadoras."

Attanasio, Orazio, and Marcos Vera-Hernandez. 2004. Medium and Long Run Effects of

Nutrition and Child Care: Evaluation of a Community Nursery Programme in Rural

Colombia. Working Paper, Institute for Fiscal Studies.

Baker, Michael, Jonathan Gruber, and Kevin Milligan. 2008. "Universal Child Care, Maternal

Labor Supply, and Family Well‐Being." Journal of Political Economy 709-745.

Barua, Rashmi. 2014. "Intertemporal substitution inmaternal labor supply: Evidence using state

school entrance age laws." Labour Economics 129-140.

Berlinski, Samuel, and Sebastian Galiani. 2007. "The effect of a large expansion of pre-primary

school facilities on preschool attendance and maternal employment." Labour Economics

665-680.

Berlinski, Samuel, Sebastian Galiani, and Patrick J. Mc Ewan. 2011. "Preschool and Maternal

Labor Market Outcomes: Evidence from a Regression Discontinuity Design." Economic

Development and Cultural Change.

Blau, David, and Janet Currie. 2006. "Pre-School, Day Care, and After-School Care: Who’s

Minding the Kids?" In Handbook of the Economics of Education, Volume 2, 1163-1278.

Calderon, Gabriella. 2014. The effects of child care provision in Mexico. Working Paper, Bank

of Mexico.

Cascio, Elizabeth U. 2009. "Maternal Labor Supply and the Introduction of Kindergartens into

American Public Schools." Journal of Human Resources.

Duflo, Esther. 2001. "Schooling and Labor Market Consequences of School Construction in

Indonesia: Evidence from an Unusual Policy Experiment." American Economic Review

795-813.

Fitzpatrick, Maria D. 2010. "Preschoolers Enrolled and Mothers at Work? The Effects of

Universal Prekindergarten." Journal of Labor Economics 51-85.

Fortin, Pierre, Luc Godbout, and Suzie St-Cerny. 2012. Impact of Quebec's Universal Low Fee

Childcare Program on Female Labour Force Participation, Domestic Income, and

Government Budgets. Working Paper, Research Chair in Taxation and Public Finance.

Frankenberg, Elizabeth, and Duncan Thomas. 2000. The Indonesia Family Life Survey (IFLS):

Study Design and Results from Waves 1 and 2. Santa Monica, CA: RAND.

Frankenberg, Elizabeth, and Lynn Karoly. 1993. The 1993 Indonesian Family Life Survey:

Overview and Field Report. Santa Monica, CA: RAND.

FRED. 2017. FRED, Federal Reserve Bank of St. Louis. June 15. Accessed June 15, 2017.

https://fred.stlouisfed.org/series/CCUSSP02IDA650N.

Gelbach, Jonah B. 2002. "Public Schooling for Young Children and Maternal Labor Supply."

American Economic Review 307-322.

Halim, Daniel, Hillary Johnson, and Elizaveta Perova. 2017. Could Childcare Services Improve

Women's Labor Market Outcomes in Indonesia? Policy Brief, Washington D.C.: World

Bank.

Lefebvre, Pierre, and Philip Merrigan. 2008. "Child-Care Policy and the Labor Supply of

Mothers with Young Children: A Natural Experiment from Canada." Journal of Labor

Economics.

Lefebvre, Pierre, Philip Merrigan, and Matthieu Verstraete. 2009. "Dynamic labour supply

effects of childcare subsidies: Evidence from a Canadian natural experiment on low-fee

universal child care." Labour Economics 490-502.

Medrano, Patricia. 2009. "Public day care and Female Labor Force Participation: Evidence from

Chile."

Myers, Robert G. 1995. "Preschool Education in Latin America: A "State of the Practice"."

Paes de Barros, Ricardo, Pedro Olinto, and Mirela Carvalho. 2011. "The Impact of Access to

Free Childcare on Women's Labor Market Outcomes: Evidence from a Randomized Trial

in Low-income Neighborhoods of Rio de Janeiro."

Pitt, Mark, Mark Rosenzweig, and Donna Gibbons. 1993. "The Determinants and Consequences

of the Placement of Government Programs in Indonesia." World Bank Economic Review

319-348.

Rosero, Jose, and Hessel Oosterbeek. 2011. "Trade-offs between Different Early Childhood

Interventions: Evidence from Ecuador."

Schlosser, Analia. 2011. "Public Preschool and the Labor Supply of Arab Mothers: Evidence

from a Natural Experiment."

Strauss, John, Firman Witoelar, and Bondan Sikoki. 2016. "The Fifth Wave of the Indonesia

Family Life Survey (IFLS5): Overview and Field Report."

Strauss, John, Firman Witoelar, Bondan Sikoki, and Anna Marie Wattie. 2009. "The Fourth

Wave of the Indonesian Family Life Survey (IFLS4): Overview and Field Report."

Strauss, John, Kathleen Beegle, Bondan Sikoki, Agus Dwiyanto, Yulia Herawati, and Firman

Witoelar. 2004. "The Third Wave of the Indonesia Family Life Survey (IFLS): Overview

and Field Report."

World Bank. 2006. Early Childhood Education and Development in Indonesia: An Investment

for a Better Life. Washington D.C.: World Bank.

World Bank. 2016. Live Long and Prosper - Aging in East Asia and Pacific. Washington D.C.:

World Bank.

World Bank. 2011. World Development Report 2012 - Gender Equality and Development.

Washington D.C.: World Bank.

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