metadata: authors: 0: author: Abdurazzakova,Dilnovoz
1: author: Niu,Chiyu
2: author: Purevjav,Avralt-Od
count: Uzbekistan,Central Asia
docna: 0: docna: Working for Yourself or for Your Kids ?
            Childcare Expansion Policy in Uzbekistan
docty: Policy Research Working Paper
theme: Inclusive Growth,Economic Policy,Economic Growth and Planning,Structural Transformation and Economic Diversification
abstracts: cdata!: In developed countries, public childcare
            programs have increased maternal employment by easing time
            constraints. However, their impact in lower-middle-income
            settings with multigenerational households is less
            understood. In Uzbekistan, for instance, many households
            include multiple adult women, such as grandmothers and
            aunts, who traditionally do not work and can provide
            informal childcare, potentially lowering the demand for
            public services. This paper examines the effects of a recent
            preschool expansion policy (2018&#x2013;22) on
            women&#x2019;s labor market participation in this
            context. To assess the policy&#x2019;s causal impact,
            the paper utilizes variations in childcare coverage across
            districts over time. The results show that the childcare
            expansion policy led to a 12 percent average increase in
            female labor supply, with the strongest effects observed for
            families that value education but face financial
            constraints. In contrast, the availability of informal
            caregivers does not decrease the policy&#x2019;s effect.
            These findings challenge the idea that time constraints are
            the primary mechanism linking childcare expansion to
            women&#x2019;s employment. Instead, in this context,
            economic factors&#x2014;especially the need to afford
            childcare costs&#x2014;emerge as the main drivers.
display_title: Working for Yourself or for Your
            Kids ? Childcare Expansion Policy in Uzbekistan
listing_relative_url: /research/2025/01/34446022/working-yourself-or-your-kids-childcare-expansion-policy-uzbekistan
topicv3: Gender and Human Development m1326908 919,Child Development m1327025 2151,Jobs and Poverty m1327656 701,Labor Force Participation m1504050 2746,Female Labor Market m1504222 2950
publishtoextweb_dt: 2025-01-15T00:00:00Z
dois: 10.1596/1813-9450-11038
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 44
author_keys: 0: author_key: 2247938
1: author_key: 1388573
2: author_key: 1296634
model_extractions: Policy Research Working Paper 11038 Working for Yourself or for Your Kids? Childcare Expansion Policy in Uzbekistan _Dilnovoz Abdurazzakova Chiyu Niu Avralt-Od Purevjav_ Poverty and Equity Global Department January 2025
Policy Research Working Paper 11038

# **Abstract**

In developed countries, public childcare programs have increased maternal employment by easing time constraints. However, their impact in lower-middle-income settings with multigenerational households is less understood. In Uzbekistan, for instance, many households include multiple adult women, such as grandmothers and aunts, who traditionally do not work and can provide informal childcare, potentially lowering the demand for public services. This paper examines the effects of a recent preschool expansion policy (2018–22) on women’s labor market participation in this context. To assess the policy’s causal impact, the paper utilizes variations in childcare coverage across districts over time. The results show that the childcare expansion policy led to a 12 percent average increase in female labor supply, with the strongest effects observed for families that value education but face financial constraints. In contrast, the availability of informal caregivers does not decrease the policy’s effect. These findings challenge the idea that time constraints are the primary mechanism linking childcare expansion to women’s employment. Instead, in this context, economic factors—especially the need to afford childcare costs—emerge as the main drivers.

This paper is a product of the Poverty and Equity Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cniu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Working for Yourself or for Your Kids? Childcare Expansion Policy in Uzbekistan**

Dilnovoz Abdurazzakova, Chiyu Niu, Avralt-Od Purevjav JEL: J13, J22, I25, O15, D13 Dilnovoz Abdurazzakova: Central European University, Chiyu Niu: The World Bank, Avralt-Od Purevjav: The World Bank; Working paper: will be updated further.
# **1. Introduction**

Many developed and industrialized countries have expanded childcare programs to free up mothers’ time to encourage their participation in the workforce. And indeed, in these countries, the expansion of public childcare programs has often led to increases in maternal labor supply (Baker, Gruber, and Milligan 2012; Bauernschuster and Schlotter 2015; Berlinski and Galiani 2007; Clark et al. 2019; Müller and Wrohlich 2020). These effects have been strongest among mothers without access to informal caregivers, suggesting that time constraints have kept mothers from joining the workforce. Also lower-income countries have recently begun to expand preschool facilities. This expansion may be even more beneficial given the even larger gender gap in labor participation. At the same time, however, it does not seem to be time constraints that keep mothers from working in these countries. A typical household in this context includes multiple adult women, such as grandmothers, sisters, or aunts, who usually do not work. These women can serve as informal caregivers, and the need for public childcare services is not as pressing. This raises the question whether expanding public childcare in such contexts still has an impact on women’s employment and if so, why. In this paper, we attempt to answer to this question by studying the effect of a preschool childcare expansion policy, implemented in Uzbekistan from 2018 to 2022, on the labor market participation of all working-age women living with young children. The country’s typical household structure allows us to observe not only mothers, but also other potential caregivers within each household, like grandmothers, who may be affected by this policy reform. To get at the causal effect of the policy, we utilize variation in childcare coverage across different districts and over time. Our results show that the childcare expansion policy led to a 12 percent average increase in female labor supply, with the strongest effects observed for families that value education but face financial constraints. In contrast, controlling for educational preferences and income, the availability of informal caregivers does not influence the effect of the policy. These findings challenge the idea that time constraints are the primary mechanism linking childcare expansion to women’s employment. Instead, in this context economic factors—especially the need to afford childcare costs—emerge as the main drivers.

We start in Sections 2 and 3 by describing the context and the childcare policy reform as well as the data and sample used in our analysis. In Uzbek society, most mothers live with their in-laws, resulting in over 75% of households including at least two working-age women. Despite an abundance of informal caregivers, women employment 2
is low, with over 70% of households having no women in the workforce. Before 2017, only around 25% of children aged 3-6 were enrolled in preschool, well below averages in other Central Asian countries. Following the childcare expansion reform, the number of preschools has increased by over 3.5 times, and by 2022, preschool capacity reached 63% (Sankar, 2021). Despite this growth, preschool remains costly, with fees ranging from 25% to 200% of an average woman’s monthly earnings.<sup>1</sup> We use the Listening to Citizens of Uzbekistan (L2CU) survey conducted by the World Bank and Development Strategy Center, which includes a 2018 nationally representative baseline of 4,000 households and a monthly panel of 1,500 households through 2023. This data captures quarterly female labor supply and household outcomes. We measure preschool availability annually by district using data from the Uzbekistan Statistics Agency. We focus on households with children aged 1-6 who are most likely affected by the policy expansion.

In Section 4, we outline our empirical methodology. We adopt a quasi-experimental approach similar to Müller and Wrohlich (2020) and Nollenberger and Rodríguez-Planas (2015) to assess the impact of childcare availability on female labor supply. The gradual expansion of childcare facilities varied significantly across districts, providing both temporal and spatial variation that allows us to estimate the policy’s causal effect. Households in districts with above-median growth in childcare coverage since 2019 serve as our treatment group, while households in districts with later or minimal expansion form the control group. The key underlying assumption of our approach is that, in the absence of the policy, female labor supply would have followed a similar trend in both treated and control districts. To address potential confounding factors arising from unobserved regional differences, we include time and district-fixed effects in our analysis.

We present our findings in Section 5. Our primary empirical analysis shows that the childcare expansion policy increased women’s labor supply by 2.4 percentage points on average. This effect is particularly significant given the low baseline labor force participation rate for women, which averages just 20 percent. In relative terms, the policy led to a 12 percent increase in female labor force participation. Moreover, our heterogeneity analysis reveals that the impact was stronger for low- and middle-income households, as well as for those households prioritizing their children’s education. Notably, households with multiple adult women co-residing showed a stronger response to the policy compared to households where women live alone. Unlike previous studies where time availability was seen as the primary mechanism, our findings indicate that

> 1Source: https://cabar.asia/ru/uzbekistan-gosudarstvennyh-detskih-sadov-ne-hvataet-chastnye-dorogi

3
financial pressures and aspirations for children’s education can drive women’s decisions to enter the labor market in this context.

_Related literature and contribution._ The contribution of this paper is threefold. First, because of data availability issues, causal evidence on the effects of childcare expansion programs in developing countries is rare (Clark et al. 2019; Vuri 2016). The empirical literature mostly focuses on developed industrial countries with predetermined high female labor supply (Nollenberger and Rodríguez-Planas 2015; Havnes and Mogstad 2011; Kleven 2021). This is problematic because it is unclear whether findings from developed countries (with weaker gender norms on household responsibilities) generalize to this context where household structure is quite different.<sup>2</sup> Our findings indeed suggest that, in such contexts, childcare services can motivate mothers to work not primarily due to time availability, as emphasized in prior literature, but to afford the relatively costly preschool services and provide educational opportunities for their children.

Second, most previously studied childcare expansions have either been smaller in scale or spread over extended periods, leading to gradual increases in childcare access. In contrast, Uzbekistan’s reform was implemented over a relatively short three-year period, during which the number of kindergartens tripled and overall childcare coverage doubled. This rapid and substantial increase in childcare facilities presents a unique case of a strong exogenous supply shock that allows us to examine the effect on female labor supply more clearly (Morrissey 2017; Baker, Gruber, and Milligan 2012).

Third, the existing work primarily emphasizes the labor supply responses of mothers and often overlooks the actual responses of informal care providers to policy expansions. However, it is important to note that childcare can be easily substituted by other adult female members including grandmothers (Vuri 2016). And, thus, the existing studies could underestimate the aggregate response of the female population to childcare expansion policy reform. To illustrate, in Canada, the expansion of childcare facilities not only increased maternal employment, but also grandmothers’ labor supply (Karademir, P. Laliberté, and Staubli 2023). Utilizing household survey data from Uzbekistan allows us to observe not only mothers, but also informal care providers including grandmothers within the household, and observe their responses to this policy expansion reform.

> 2Average household size is 7 where usually 2 working age women co-reside. According to a 2018 survey in Uzbekistan, 43% of unemployed women are not seeking a job because they must take care of their household responsibilities, while only 7% of unemployed men reported the same reason (Strategy.uz, 2022).

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<!-- Start of picture text --> ae<br><!-- End of picture text -->
18,345, an increase of more than 3.5 times. By 2022, 63% of children aged 3-6 were enrolled in preschool programs, with about one-third attending PPP preschools. However, the expansion varied significantly across districts, both in timing and scale, creating temporal and spatial variation. This variation allows us to estimate the causal impact of the policy on female labor force participation (Sankar, 2021).

It is important to note that preschool education in Uzbekistan is not compulsory, and while enrollment has increased significantly, preschool services—especially private ones— remain costly.<sup>3</sup> On average, public preschool fees already represent around 25% of the average monthly earnings of women in regional areas.<sup>4</sup> Despite the high costs, many families are motivated to enroll their children because preschools across all regions follow a standard curriculum set by MOPSE, which focuses on teaching basic language, writing, and math skills as the main aim of the reform was to improve educational outcomes for children by better preparing them for primary school.

Meanwhile, primary education in Uzbekistan is free and compulsory, starting at the age of 6-7, with an enrollment rate of 94%.<sup>5</sup> The overall goal of expanding childcare facilities, particularly through PPP preschools, was to increase access to early childhood education and enhance children’s readiness for primary education, especially in regions where such services were previously limited.

# **3. Data**

_Survey data._ For our main estimation analyses, we use Listening to Citizens of Uzbekistan survey (L2CU) data conducted by the World Bank and Development Strategy Center (2022). The study comprises a 4,000-household nationally representative baseline survey conducted in June/July 2018, and since then a monthly “panel” survey of a subset of 1,500 households from the baseline survey until 2023. This survey is used to measure quar- terly female labor supply, individual and other household-level covariates since June 2018 in each district. Furthermore, Household Budget Survey (HBS) data – yearly pooled crosssectional data from 2015-2022 – is used to do robustness test estimations. The HBS serves as the fundamental data source for examining people’s standard of living and for devising additional measures to enhance the well-being of the population. Furthermore,

> 3Source: https://cabar.asia/ru/uzbekistan-gosudarstvennyh-detskih-sadov-ne-hvataet-chastnye-dorogi 4Source: https://stat.uz/uz/matbuot-markazi/qo-mita-yangiliklar/34143-yuridik-shaxs-maqomiga-egabo-lgan-korxona-va-tashkilotlarda-ishlovchi-xodimlarning-o-rtacha-oylik-nominal-hisoblangan-ish- haqi2022-yil-yanvar-dekabr

> 5Source: https://data.worldbank.org/indicator/SE.PRM.ENRR?locations=UZ

6
the survey results play a crucial role in computing indicators related to the low-income population, consumer price indices, and household sector accounts. Unfortunately, HBS lacks data on the specific districts where respondents reside or the urbanity status of their settlements. Instead, it only provides information on the regions where they live. As a result, our HBS analyses are limited to only 14 clusters due to this data constraint.

_Childcare provision._ There are 175 districts in Uzbekistan. We measure yearly available preschool provisions in each district using data collected from Uzbekistan Statistics Agency. Unfortunately, the Statistics Agency does not provide a direct preschool participation rate for each district, but it provides the statistics for the total number of children in different age group categories like the number of children aged 0-2, 3-5 and 6-7 living in each district. It also provides the number of children aged 3-6 who participate in preschool education for each district. We calculate the coverage rate by dividing the number of children at preschool by the total number of children aged 3-5 living in that district. Therefore, we may overestimate the participation rate as our denominator is missing the number of children aged 6 years old. Furthermore, we are also assuming that if preschool participation of children aged 3-6 increased in that district, the number of children aged 1-2 participating in preschool education also increased in a similar manner as we do not have official statistics on this age group of children. The calculated coverage rate for each district can be seen in the maps in appendix figures 1 and 2. In the HBS analyses, since the policy variable is measurable only at the region level, we obtained the direct preschool enrollment rate of the children aged 1-6 for each region over time from the Uzbekistan Statistical Agency. This data is used to assess the policy variable coverage rate for young children across different regions and time periods.

_Sample._ In our main estimation, we restrict our sample to the households in which the youngest children are aged 1-6 years old as these households are the ones potentially, affected by the policy expansion. In individual unit analyses, our sample is all working-age women aged above 15 and below 64 living in these households. In our sample, we have around 1,200 respondents in each period of time from 109 districts.

_Dependent variables._ We use several measures for our outcome variables. In the individuallevel analyses, the outcome variables are dummy variables, defined as follows:

- **Employment** : This is measured as a dummy variable, taking the value 1 if the respondent has been engaged in paid work, for profit or gain, within the past 7 days,

7
and 0 otherwise.<sup>6</sup>

- **Labor force participation** : This is also a binary variable, taking the value 1 if either the respondent is working (as described above) or actively searching for a job; otherwise, it takes the value 0.

In household-level analyses, the outcome variables are count variables, specifically:

- The total number of working women in the household.

- The total number of women in the household who are part of the labor force (either employed or actively searching for a job).

_Explanatory variables._ Our main variable of interest _Pol ic ydt_ is measured in two different ways in our main specification: 1) Coverage rate equals children’s preschool participation rate relative to 2018 in each district, and 2) Treated is a dummy variable that equals 1 when the district coverage rate increases above the median (17%) and 0 otherwise. Figure 2 shows how the coverage rates for control and treated districts change over time. The general set of individual control variables for the women includes her age (included in linear and quadratic form in all specifications), the level of education, and marital status. Further household variables include the number of working-age women, school-aged children, and young children in the household, number of working men in the household, along with household income status. We also add whether the household is extended, indicating that married women live with their in-laws.

Table 1 presents descriptive statistics of the sample women over time. In both samples (also presented in Appendix figures 3 and 4), around three-quarters of the households consist of at least two working-age female members. However, despite this, female employment is remarkably low, as in more than 70% of the households, none of the women are engaged in the labor market. The childcare coverage rate increased noticeably over time, from 40 percent in 2018 to 63 percent in 2022 on average. On average, the family size is quite large, with around 7 people per household. The majority of household types are extended, as approximately 80 percent of the respondents live in households where married women reside together with their in-laws. Thus, informal childcare arrangements can be quite often easily arrangeable within the household. Household and individual characteristics of women remain fairly consistent over time. The

> 6In the HBS survey, the question is slightly differently asked: In the last 7 days, have you _worked for someone_ who is not your household member as a worker? Hence we solely focus on employees and not selfemployed individuals in the HBS analyses.

8
TABLE 1. Descriptive statistics, overall provision of childcare, 2018–2022

||2018|2019|2020|2021|2022|
|---|---|---|---|---|---|
|N of LF women|0.58|0.57|0.43|0.36|0.32|
|N. of employed women|0.45|0.46|0.38|0.29|0.27|
|Labor Force Participation|0.25|0.23|0.18|0.16|0.15|
|Employed|0.19|0.19|0.16|0.13|0.13|
|Coverage rate|0.41|0.46|0.56|0.60|0.64|
|Age|36.47|36.96|37.24|37.20|37.27|
|Married|0.78|0.77|0.77|0.74|0.71|
|Higher education|0.09|0.09|0.09|0.08|0.07|
|Extended household|0.81|0.82|0.84|0.84|0.85|
|Mother|0.53|0.50|0.50|0.50|0.50|
|Grandmother|0.26|0.27|0.27|0.27|0.26|
|Other|0.21|0.22|0.23|0.24|0.23|
|Household size|7.40|7.46|7.42|7.47|7.45|
|N. of women|2.50|2.50|2.47|2.47|2.43|
|N. of children 7-15|0.82|0.88|0.85|0.88|0.91|
|N of children 1-6|1.62|1.63|1.61|1.61|1.61|
|N of employed men in the household|0.98|1.05|0.96|0.87|0.80|
|Households with children aged 1-2|0.26|0.27|0.23|0.21|0.20|
|Households with children aged 3-6|0.43|0.42|0.47|0.52|0.53|
|Households with children aged 1-2 and 3-6|0.31|0.31|0.30|0.26|0.27|
|HH income:||||||
|Lowest income|0.39|0.38|0.37|0.37|0.36|
|Middle income|0.34|0.35|0.34|0.35|0.35|
|Highest income|0.27|0.27|0.29|0.28|0.29|

_Note:_ The sample consists of working-age women aged 15 to 64 living in households with at least one child aged 1 to 6, excluding households with infants under the age of 1; _Source_ : World Bank: Listening to Citizens of Uzbekistan; Uzbekistan Statistical Agency: data on children preschool participation rate; own calculations.

summary statistics of respondents in the HBS datasets, which cover a longer period and are presented in Appendix table 1, demonstrate a considerable resemblance to the respondents in the L2CU dataset. It is evident that the household structure in Uzbekistan did not undergo significant changes over time.

# **4. Methodology**

_Empirical model._ Our analyses are among a sample of female respondents aged between 16-64, who are living with at least 1 youngest child aged between 1-6 years old in the household. We estimate the effect of this policy reform on these women’s labor supply since 2018. The applied generalized staggered difference-in-differences regression 9
estimation is formulated in a similar way to the approach used in Nolenberger and Rodriguez-Planas (2011) and Muller and Wrohlich (2020): where outcome _Yidt_ can be employment status of the respondent i, or the number of women who are working in household _i_ in district _d_ , at time _t_ . α<sup>_DD_</sup> is the treatment effect of the Policy variable on the outcome. _Pol ic ydt_ is the treatment variable indicating the presence or intensity of the childcare expansion policy for district _d_ , at time _t_ . _X_<sup>_′_</sup> is a set _idt_ of all personal and household-level covariates. β _k_ is a vector of coefficients associated with the control variables. _uidt_ is the error term, representing unobserved factors that affect the outcome variable. Standard errors are clustered at the district level to account for potential correlations or heterogeneity among observations within districts. In our HBS analyses, we adopted a similar methodology, but we utilized regions instead of districts. Since we had only 14 regions, we opted not to cluster standard errors and used robust standard errors.

_Identification assumption._ The primary assumption underlying staggered difference in differences is that female labor supply would have followed a similar trajectory in both treated and control districts had the policy not been implemented. The presence of parallel trends in the pre-period provides suggestive evidence supporting this assumption.

In Figure 2, the labor force participation rates of women in treated districts are compared to those in control districts. Before the policy implementation, female labor supply was slightly higher in the control districts. However, by 2021, women in treated districts had reached the employment levels of those in non-treated districts. It is impor- tant to highlight that the Covid-19 pandemic affected employment rates for both women and men across all regions of Uzbekistan, and these rates have not fully recovered (see Appendix, Figures 5 and 6). Additionally, we excluded 7 districts and the capital city from the sample (approximately 10% of the total) and reanalyzed the data, presented in Appendix Figure 5. These 7 districts showed irregular coverage rates, with notable fluctuations after the Covid-19 shock. The capital city was excluded due to its significantly higher coverage rates, which had increased well before the policy’s implementation and differed substantially from other districts. When these districts are removed, the trend in female labor force participation remains consistent. Appendix Figure 6 also shows labor force participation rates for men with young children and women without young 10
children, and their trends closely align in both treated and control districts, both before and after the policy implementation. Lastly, it is important to note that the short period between policy implementation and the start of data collection limits the analysis of the pretreatment period.

To address this limitation, we use HBS data to extend the pre-treatment period, even though we only have data for 14 regions in total. In Appendix Figure 7, the employment and coverage rates are shown separately for fast-implementing and slow-implementing regions. Before the policy implementation, the employment rate of women with young children and the coverage rate were both higher in the control regions compared to the treated regions. This discrepancy is largely due to the inclusion of Tashkent, the capital city, in the control group. Therefore, in Appendix Figure 8, we exclude Tashkent and re-analyze the data, showing that both the control and treated regions followed a relatively similar trend before the policy implementation. Over time, the employment rate of women in treated regions gradually caught up with that of women in control regions. It is also important to note that during the Covid-19 pandemic in 2020, when kindergartens were frequently closed, the employment rate of women with young children declined in control regions where coverage rates were higher.

Measuring the treatment at the regional level proved to be suboptimal, as district-level measurements revealed that all regions, except for Tashkent city, included both treated and untreated districts. As an alternative, we compared the employment rates of respondents living in households with young children to those living in households without young children (Appendix Figure 9). This comparison was particularly relevant as households with young children were the ones most likely to be impacted by the policy. From the graph, it can be observed that the employment rate of male respondents living with young children was slightly higher than those living without young children during all study periods. Although both groups of women had lower labor supply compared to men before the policy implementation, women living in households without young children had noticeably higher employment rates than women living with young children. After the policy implementation, this discrepancy decreased over time.

Further, we provide descriptive characteristics of our sample women for treated and not treated regions before and after the implementation of the policy in Appendix Table 2. As we can see from the table, on average women living in districts where preschool coverage increase is below the median are relatively similar to women who are living in districts with above median coverage increase in terms of individual and household characteristics. Before the implementation of the policy in nontreated 11
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# **5. Results**

## **5.1. Main estimation**

The estimates are based on our main sample which includes households with children aged 1–6 in Uzbekistan from 2018 to 2022. Our main specification includes individual and household covariates as well as time and district-fixed effects. The first two columns of Table 2 present an estimation of household-level analyses where the dependent variable is the number of women in the labor force and working women in the household. The rest of the columns show individual-level analyses among all working-age women living in those households. In the last four columns, we further separated mothers from the rest of the working-age women and estimated the effect of the policy reform separately. As we mentioned in Section 3, the policy is measured in 2 different ways: the annual coverage rate in each district relative to 2018 and further by comparing employment outcomes of women living in fast-implementing districts after the reform to the same group of women living in slow implementing districts.

We find a positive and statistically significant effect of the public childcare expansion on women’s labor force participation and employment rates. Specifically, our estimates suggest that a 10 percent increase in childcare coverage leads to a 1.38 percentage point increase in the probability of labor force participation and a 0.63 percentage point increase in the probability of employment for women. In districts where the coverage rate rose above the median (more than 17 percent), the policy increased female labor force participation by 2.4 percentage points and the employment rate by 1.7 percentage points. Additionally, after excluding 7 districts and the capital city (approximately 10% of the total sample) and reanalyzing the data (as explained in the section 4), we obtained similar results, which are presented in Appendix Table 3.

Although a 2.4 percentage point increase may seem modest, it is quite substantial when considered in the context of the low baseline labor force participation rate for women, which averages just 20 percent. This means the policy effect translates to a 12 percent increase in female labor force participation, a noticeable improvement given the entrenched social barriers that limit women’s access to the workforce. The magnitude of this increase indicates that public childcare expansion has a meaningful impact in encouraging more women to participate in the labor market.

Additionally, the findings show that this employment effect is shared almost equally between mothers and other women, as the estimated coefficients for both groups are relatively similar. However, while the policy successfully increased labor force 13
TABLE 2. Regression estimates, effects of childcare coverage on women employment

||Hous|ehold|Indivi|dual|Moth|ers|Other wo|men in HH|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Labor Force participa_|_tion_||||||||
|Coverage rate|0.230***||0.138***||0.144***||0.136***||
||(0.06)||(0.03)||(0.05)||(0.05)||
|Treated||0.049***||0.024***||0.026**||0.024**|
|||(0.02)||(0.01)||(0.01)||(0.01)|
|Mean|0.372|0.372|0.191|0.191|0.226|0.226|0.155|0.155|
|_Employment_<br>Coverage rate|0.103||0.063*||0.074*||0.053||
||(0.08)||(0.04)||(0.04)||(0.05)||
|Treated||0.036*||0.017*||0.017||0.017|
|||(0.02)||(0.01)||(0.01)||(0.01)|
|Mean|0.301|0.301|0.157|0.157|0.184|0.184|0.130|0.130|
|Individual controls|||Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|12454|12454|24517|24517|12417|12417|12100|12100|

_Note:_ The analysis is estimated among working age women living with young children aged 1 to 6, excluding households with infants under the age of 1. For household-level analyses, the outcome variable is the number of women in the labor force and the number of women employed. Standard errors in parentheses, clustered at district level; * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 _Source:_ World Bank: Listening to Citizens of Uzbekistan survey data 2018–2022; Uzbekistan Statistical Agency: data on children preschool participation rate; own calculations.

participation, the smaller effect on actual employment suggests that not all women entering the labor market are able to secure jobs. This discrepancy could be due to tight labor market conditions in Uzbekistan, where finding employment remains difficult despite an increase in job search activity among women.

# **5.2. Heterogeneity analyses**

To better understand how different types of households contributed to the overall employment effect and which groups benefited most from the policy reform, we conducted additional analyses focusing on different subgroups of women in the sample. These subgroups are based on income status, the availability of potential informal care 14
<!-- Start of picture text --> Coverage rate Coverage rate<br>Lower Lower<br>Middle Middle<br>—_e——_ —oe——_.<br>Upper Upper<br>-4 -3 -.2 1 0 A 2 3 4 A -8 -6 ~4 -2 0 2 A 6 8 1<br>Labor Force © Employment NofwomeninLF ® N of Working Women<br><!-- End of picture text -->
<!-- Start of picture text --> Coverage rate Coverage rate<br>1 Woman 1 Woman<br>2 or more Women 2 or more Women<br>——_e—_<br>-4 -3 2 1 0 A 2 3 4 -1 -8 -6 -4 -2 0 2 4A & 8 1<br>Labor Force © Employment NofwomeninLF ® N of Working Women<br><!-- End of picture text -->
<!-- Start of picture text --> Coverage rate Coverage rate<br>Not worried about good education Not worried about good education<br>Worried about good education Worried about good education<br>ee —e<br>-3 2 1 0 A 2 3 1-8 -6 -4 -2 0 2 4 6 8 1<br>Labor Force © Employment NofwomeninLF © Nof Working Women<br><!-- End of picture text -->
TABLE 3. Regression estimates, effects of childcare coverage on women’ employment.

||Hou|sehold|Indivi|dual|Moth|ers|Other|women|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Employed_|||||||||
|Coverage rate|0.419||0.174||0.264*||0.034||
||(0.246<br>)||(0.11)||(0.14)||(0.09)||
|Treated||0.0811**||0.033||0.038||0.025**|
|||(0.0336)||(0.02)||(0.03)||(0.01)|
|Mean|0.376|0.376||0.186||0.206||0.160|
|Individual controls|||Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3305<br>2|33052|61423|61423|35079|35079|26344|26344|

_Note:_ The analysis is estimated among working age women living with young children aged 1 to 6, excluding households with infants under the age of 1. For household-level analyses, the outcome variable is the number of women in the labor force and the number of women employed. Standard errors in parentheses, clustered at regional level;* _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 _Source:_ World Bank:Household Budget Survey data 2015–2022; Uzbekistan Statistical Agency: data on children preschool participation rate; own calculations.

_Different measurements of treatment._ In Appendix Table 3, we present regression estimations using various measurements of treatment variables. For the second treatment variable, we defined a dummy variable equal to 1 if the district coverage rate increased by more than 25% during the study period and 0 otherwise, following the approach used by (Kleven 2021). Our third policy variable takes the value of 1 if the district’s relative coverage rate is higher than the median coverage rate of 2021, considering data from 2018 onwards. Additionally, we included the baseline coverage rate as a control to account for regional differences before the policy implementation. Remarkably, all the measurements of policy variables yielded statistically significant estimates in our analysis.

_Placebo test._ Finally, in Appendix Table 4, we provide the estimation of the model for women without young children and for male respondents living with young children. These analyses are made because the policy is anticipated to have a more pronounced impact on women with young children, as they are commonly considered the primary caregivers. In line with our expectations, we did not find any consistent significant 18
effects of the policy implementation on the labor supply of these two groups of respondents.

# **6. Conclusion**

To conclude, in this paper, we study the effect of a childcare expansion policy reform implemented in Uzbekistan in 2018-2022 on the labor market participation of _all working-age women_ living with young children. Exploiting the unique household structure of the country allows us to observe not only mothers, but also other informal care providers within each household, including grandmothers and other working-age women who can potentially be affected by this policy reform. To get at the causal effect of the policy, we utilize variation in childcare coverage across different districts and over time. Treated households are the ones living in districts that had above-median growth in the childcare coverage rate since 2019. The control group faced such an expansion of growth only later in time or not at all (during the study period).

Our findings reveal that the childcare expansion policy increased female labor force participation by 12 percent on average. The impact was most strong among low- and middle-income households, as well as those prioritizing their children’s education, while highincome households and those with only one adult woman showed little change. These results suggest that many women entered the workforce to afford the relatively expensive preschool childcare and provide better educational opportunities for their children. This challenges the common notion that time constraints are the primary barrier to women’s employment in this context. Instead, economic factors—particularly the need to cover childcare costs—are the key drivers. Unlike previous studies, where time availability was seen as the main factor, our findings show that financial pressures and educational aspirations are critical motivators for women joining the labor market. With this project, we provide causal evidence of childcare expansion policy implementation for low-income developing countries where it can bring more crucial benefits to the economy. Secondly, the policy expansion we study is ongoing, which makes it more relevant to the current labor market situation.

Furthermore, although the policy successfully increased labor force participation, the smaller effect on actual employment is estimated. It suggests that not all women who entered the labor market were able to secure jobs. This discrepancy likely came from tight labor market conditions in Uzbekistan, where job availability remains limited despite a rise in job-seeking activity among women. This highlights a significant gap 19
between the desire to work and the availability of jobs, suggesting that childcare expansion alone may not be enough to improve women’s employment outcomes without simultaneous efforts to increase job opportunities.

_Limitations._ There are several limitations of this research project. First, in terms of outcome variables, labor force participation is measured as a dummy variable but not actually working hours, which could be an even more informative variable. Thus, the estimation mostly focusses on the external margin of employment. Furthermore, the wages of the respondents are not available in the survey. If such data were available, we could have calculated economic return from this implemented childcare program extension. Additionally, we do not actually observe whether the children in those households are actually going to preschool programs after the policy implementation, but only can estimate “intention to treat” as we measure if women living with young children change their labor market participation.

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