rafmacalaba/datause-encoder-data huggingface dataset:
three month period. The second two columns compare the impact of having an additional community radio station to that of an additional radio station of any other type, confirming again that community radio is a particularly special media in this context, in terms of communicating information about health issues.

**Table 2a:  Community Radio and Village Exposure to Health Programs**

|Dependent variable: Number of health<br>programs broadcast in the three months<br>prior to the survey|Community<br>radios|Including total<br>number of other<br>radios|Disaggregating by<br>radio type|
|---|---|---|---|
|Community radio received by village (#<br>stations)|129.14<sup>***</sup><br>(43.00)|126.27<sup>***</sup><br>(47.21)|124.66<sup>***</sup><br>(39.67)|
|All radio received by village (# stations)||2.77<br>(17.94)|19.26<br>(0.00)|
|Public radio received by village (#<br>stations)|||-51.28<br>(52.52)|
|Commercial radio received by village (#<br>stations)|||68.83<sup>*</sup><br>(34.61)|
|Religious radio received by village (#<br>stations)|||-63.98<br>(63.10)|
|Observations<br>|210|210|210|
|_R_<sup>_2_</sup>|0.80|0.80|0.81|

Note: OLS with commune fixed effects. Standard errors in parentheses, clustered at commune level. *** Significant at 1% level; ** Significant at 5% level; * Significant at 10% level. Dependent variables is the number of health programs received by a village, over the three months prior to the survey team’s visit, as determined by information supplied by the managers of the radio stations whose signals reach the village.

Villages that receive an additional community radio station also have access to 40 additional education programs over a three month period (the first column in Table 2b). The second column of Table 2b shows that each additional radio station of any kind is associated with approximately 19 additional education programs. Additional _community_ radios, however, are associated with 20 _more_ education programs than the average additional station. The third column disaggregates radio station types, distinguishing the contribution of community, public, commercial and religious radio stations to education programming. Community radios make, by far, the largest contribution to such programming compared to public and commercial radio. Although religious stations are also more likely to broadcast education programming, this is likely to include more sectarian shows compared to the more secular programming provided by community radio. Public and commercial radios, with less need to raise funds by selling air-time to donors or government ministries, contribute relatively little education programming.

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Using data on school enrollment from other children in the household, we document large, positive, and statistically significant impacts on preschool attendance for younger siblings, of about 85% of the estimated treatment impact for target children. This result speaks to the cost-effectiveness of the DICIPE program, as newly built preschools will be there to benefit future generations.

Our paper makes important contributions to the literature in at least three ways. First, our study contributes to recent mixed evidence on the impacts of preschool construction programs in developing countries. Although some studies document positive impacts of preschool construction programs (Berlinski et al. 2008, 2009 in Uruguay and Argentina, and Donald and Vaillant 2023 in the Congo, for example), others find small or null effects (see Blimpo et al. 2022 in the Gambia and Castro and da Cruz 2023 in Brazil). Notably, Bouguen et al. (2018) suggest caution based on the results from a randomized evaluation of a preschool construction program in Cambodia, where the impacts on cognition are negative for the cohort with highest program exposure due to substitution from primary to preschool. Our contribution here is twofold. In addition to showing that the preschool construction program we study in Mozambique led to increased enrollment in primary school and positive impacts on skill formation, we also highlight the unique features of DICIPE that may explain these particularly high impacts, namely the interaction between . the program and the local primary school system _by design_ Second, our research also speaks to the evidence on early childhood development (ECD) programs implemented _at scale_ that aim to improve child development (Bos et al., 2024; Attanasio et al., 2022; Bailey et al., 2021; Sylvia et al., 2021; Attanasio et al., 2014). Existing studies find significant improvements of at-scale ECD programs on child’s cognitive skills. Our paper contributes to these findings by investigating the effects of a program that was implemented through a public-private partnership, which can be a key policy consideration for resource-constrained governments in developing countries, with the aim of developing or expanding capacity in the preschool sector.

Third, our study adds to the debate about why ECD programs have long-term effects if the effects seemingly dissipate in the medium term (Elango et al., 2015; Yoshikawa 5
establishments. These surveys invariably have non-response and so the inability to confirm a firm’s operating status due to a refusal may just as likely be an indication of implementation as it is of firm exit. For this reason, we choose not to consider refusals to the screening stage as indication of a firm exiting the market. While this method is necessarily conservative, it does avoid the inclusion of firms that are not in fact operating while maintaining a listing in the business registry or a physical, but dormant, establishment. Incidentally, this is a problem not completely avoided by using census data, as other researchers have defined firm exit to include those firms whose employment levels drop below a certain threshold (such as 10 employees); for simplicity’s sake we forego making similar decisions here, though this would be possible with the available data. In this way, we intend to most accurately indicate those firms that have in fact ceased operations or exited the market, though we concede that this approach is conservative and may underestimate actual magnitudes of firm exit.

A second issue arises from the semi-frequent nature of the Enterprise Surveys. As noted above, it is generally regarded that firms entering the market tend to be smaller and face a higher likelihood of exiting the market as they have not yet scaled up production, invested in sunk-cost capital (for instance, see Rosenbaum and Lamort (1992) and Austin and Rosenbaum (1990)), or have had the chance to accumulate knowledge or managerial expertise and know-how (see Bloom and Van Reenen (2010); indeed our own empirical findings in the following section confirm the effect of firm incumbency on the likelihood of survival. A consequence of this relationship, though, is that with elapsed time periods between survey rounds, a certain number of new-entrant firms are likely to begin operations and close their doors before the follow-up round of the ES. We have no way to measure these exiting firms and so regard our measure of firm exit as a lower bound. Further work incorporating external data sources on entrants<sup>8</sup> and exiting firms, with consideration for the compatibility of the specified universe of inference.

While we note that the previous two points indicate that our measures of firm exit are necessarily conservative, the role of the source sample frame should also be noted. The quality of various censuses and registries certainly varies substantially, and thus studies relying on these sources – and those that define market exit as disappearance from those records – also depend on the completeness and quality of the original data. As we note, the ES do not benefit from such breadth, justifying an approach such as the one we have taken. The ES implementation takes pains to record

> 8 One such data source is the World Bank's Entrepreneurship Survey and database (http://econ.worldbank.org/research/entrepreneurship).

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<!-- Start of picture text --> TABLE 1: Taxes by E-filing Usage<br>NO YES NO YES<br>Franc | no” ves | no ves no ves| no’ “ves | no ves | no” ves |<br>TOTAL| 88 910] 781 217|93 905] 129 869] 96 902] 353 645)<br>*Source: USAID, 2008<br><!-- End of picture text -->
4 shows a stagnant trend for IPV experience across all countries. Bangladesh has the highest share of women who experience physical or sexual violence by their husband/partner.<sup>24</sup>

# **Stylized fact 2: Urbanization, education, and higher incomes do not seem to be correlated with better outcomes for women.**

The results discussed up to this point refer to the average woman in each birth cohort. However, it may be the case that there is dispersion around the average results and that the trends have been different for some groups of women. A recurring theme in debates about gender disparities is that these disparities should decrease, if not disappear, as countries develop. Higher levels of income, education and urbanization usually support the closing of the gaps between women and men. The fact that these gaps remain may indicate that social norms are acting as a barrier to gender equality over and above the usual barriers faced by poorer and less educated, rural women. In fact, things can be more complex as the direction of influence is not unequivocally positive. Education and place of residence can ease the pressures of social expectations or norms to marry young. Education may operate by changing a woman’s goals and opportunities away from early marriage. Place of residence may change the nature of social norms that women are exposed to, but it is not clear that in urban areas they encounter norms that are less conservative than those in rural areas. When families migrate from rural to urban areas, as is common in South Asia, the influence of their relevant reference group may diminish if urban areas are more heterogeneous or dispersed. However, this might also work in the opposite direction as cities are considered more “dangerous” for women and hence their “purity” more needing of protection and control. Communities also tend to concentrate in specific neighborhoods and socially segregate themselves even in urban areas, which can further strengthen the influence of social norms. Using the regression approach described in the methods section and by focusing on more affluent, better educated, and urban women within each cohort, this section addresses the questions of whether the trends are different and the gender gap is closing for these groups of women in South Asia.

This heterogeneity analysis for labor market participation shows that women in the top 20 percent of the wealth distribution have enjoyed stronger progress when compared with their poorer counterparts. However, more educated women, or women living in urban areas do not seem to have followed a different path than less educated or rural women. So, this confirms that the stagnant trends and decadal

> 24 Although IPV prevalence is not static and likely to change over the life cycle, we do not show the age by cohort graphs because the variable has too few data points in the DHS data set to claim any meaningful result.

20
availability of key public services, such as medical service points, clean water sources, crop irrigation, and kindergartens. We describe these demographic and socio-economic characteristics at regional (North/South) and gender dimensions (Table 2).

**Table 1: Sample size,** **<u>gender and ethnic composition</u>**

||Sa|mple size|of which,|_share of :_|
|---|---|---|---|---|
||Households|Individuals aged 18+|Females|Kyrgyz|
|**Total rural**|**1,671**|**5,145**|**0.52**|**0.72**|
|North|723|2,075|0.51|0.67|
|Issyk-Kul|169|458|0.52|0.92|
|Naryn|99|290|0.48|0.89|
|Talas|99|233|0.49|0.98|
|Chui|356|1,094|0.52|0.45|
|South|948|3,070|0.53|0.75|
|Djalal-Abad|338|946|0.51|0.72|
|Batken|147|419|0.52|0.96|
|Osh|463|1,705|0.53|0.72|

Source: LiK 2012<sup>8</sup> An overview of individual characteristics reveals some interesting (and statistically significant) differences on regional and gender lines. For example, the population in northern oblasts seems to have slightly more schooling compared to southern oblasts. Further, risk taking attitudes (e.g. establishing own business, etc.) are higher for individuals who are both male and who reside in northern oblasts. Labor market participation is expectedly higher for males than for females with almost 80 percent of males active in labor markets compared to 49 percent of females. This suggests the inactivity of rural women in labor markets could be an important factor of participation in communal life.

Social capital, proxied here by reliance on social networks and duration of residency, is a consideration in the analysis as an indicator of better opportunities and a proactive attitude of individuals including interest and participation in community issues. As Table 2 demonstrates, the extent people rely on each other and duration of residency, which were used to proxy social capital in this paper, seems to be generally stronger among males and among residents in the south.

Trust in public institutions is a crucial factor for strong citizen participation in local budget processes therefore suggesting that lower level of trust leads to less citizen interest and participation. The survey data reveals an interesting fact: residents in southern oblasts express greater trust in people in their

> 8 For details, see

> http://www.diw.de/de/diw_01.c.100313.en/forschung_beratung/projekte/projekte.html?id=diw_01.c.345525.en

7
l

# Table 24: BENEFIT AND COST STREAMS FOR VTE GRADUATES (CFAPF)

||Secondary<br>Level|Post-<br>Secondary<br>Level|Teacher<br>Training|Apprenticeships|
|---|---|---|---|---|
|Benefits|||||
|Starting Salary Increment<br>Final Salary Tncrement<br>Duration of Benefit Stream (Years)|195,396<br>625,011<br>35|684,264<br>2,110,327<br>37|327,014<br>1,080,977<br>32|24,215<br>77,455<br>35|
|Costs I/|||||
|Private Costs|602,894 (100)|<br>1,226,753 (100)|<br>919,968 (100)|426,240 (100)|
|Social Costs|2,831,395 (470)|<br>8,450,783 (689)|<br>3,920,142 (426)|426,240 (00)|
|Duration of Cost Stream (Years)|2.79|3.09|3.20|3.05|

1/ Index in parenthesis.
Our findings bring new experimental results on pay-for-performance to the literature on the recruitment of civil servants in low- and middle-income countries. Existing papers have examined the impact of advertising higher _unconditional_ salaries and career-track motivations, with mixed results. In Mexico, Dal B ́o et al. (2013) find that higher base salaries attracted both skilled and motivated applicants for civil service jobs. In Uganda, Deserranno (2019) finds that the expectation of higher earnings discouraged pro-social applicants for village promoter roles, resulting in lower effort and retention. And in Zambia, Ashraf et al. (forthcoming) find that emphasis on career-track motivations for community health work, while attracting some applicants who were less pro-social, resulted in hires of equal pro-sociality and greater talent overall, leading to improvements in a range of health outcomes. By studying pay-for-performance and by separately manipulating advertised and experienced contracts, we add evidence on the compositional and effort margins of a different, and widely debated, compensation policy for civil servants.

How the teaching workforce changes in response to pay-for-performance is of interest in high-income contexts as well. In the United States, there is a large (but chiefly observational) literature on the impact of compensation on who enters and leaves the teaching workforce. Well-known studies have simulated the consequences of dismissal policies (Chetty et al., 2014b; Neal, 2011; Rothstein, 2015) or examined the role of teachers’ outside options in labor supply (Chingos and West, 2012). Recent work has examined the District of Columbia’s teacher evaluation system, where financial incentives are linked to measures of teacher performance (including student test scores): Dee and Wyckoff (2015) use a regression discontinuity design to show that low-performing teachers were more likely to quit voluntarily, while Adnot et al. (2017) confirm that these ‘quitters’ were replaced by higher-performers. In Wisconsin, a reform permitted approximately half of the state’s school districts to introduce flexible salary schemes that allow pay to vary with performance. In that setting, Biasi (2019) finds that high-value-added teachers were more likely to move to districts with flexible pay, and were less likely to quit, than their low-value-added counterparts. Our prospective, experimental study of pay-for-performance contributes to this literature methodologically but also substantively since the Rwandan labor market shares important features with high-income contexts.<sup>1</sup>

> 1Notably, there is no public sector pay premium in Rwanda, which is unusual for a low-income country and more typical of high-income countries (Finan et al., 2017). The 2017 Rwanda Labour Force Survey includes a small sample of recent Teacher Training College graduates (aged below age 30). Of these, 37 percent were in teaching jobs earning an average monthly salary of 43,431 RWF ,

4
# The Bank for Agriculture and Agricultural Cooperatives

The VRF is not the only, or even necessarily the most important, source of credit for Thai households. The Bank for Agriculture and Agricultural Cooperatives has an extensive network of rural lending. Of the households covered by the 2004 socioeconomic survey, 23% borrowed from the VRF only, 15% borrowed from both the VRF and BAAC, and 6% borrowed from the BAAC only. These figures differ slightly from those presented earlier because they only refer to the two most important loans incurred by any given household. But the fact that many households borrow both from the VRF and the BAAC raises the possibility that our earlier results may be picking up the effect of BAAC borrowing and attributing it to VRF borrowing.

We therefore applied our propensity score matching approach to borrowing from the BAAC, and report the results in Table 10. For each comparison (i.e. row in Table 10) we estimated separate propensityscore equations. From Table 10 it is clear that those who borrowed from the BAAC in 2004 were comparably poor to, and somewhat more dependent on farm income than, VRF borrowers.

The first point to note is that, based on the results of the propensity-score matching analysis set out in Table 10, borrowing from the BAAC, with or without other loans, is associated with substantially higher expenditure per capita (+6.5%) and income per capita (+6.1%). This effect is larger than that of borrowing from the VRF (expenditure per capita rises 3.3%, income per capita by 1.9%, as shown in Table 4).

The most striking finding is that the _combination_ of borrowing from the BAAC and VRF has particularly powerful effects, and is associated with 9.1% higher expenditure and 8.5% higher income. Loans from these two sources appear to be complementary. A plausible interpretation is that many households, particularly farm households, are credit constrained, even if they borrow from the BAAC; the VRF, by relaxing these constraints, enables them to boost their incomes. It is noteworthy that borrowing from the BAAC but not VRF, or from the VRF but not BAAC, has a small and only marginally significant effect on expenditure levels and an even weaker effect on incomes. This hints at a real, but moderate, degree of “lumpiness” in investment, where the full return on using borrowed money is only obtained when the sum is large enough.

The propensity score matching results appear, on balance, to show that VRF borrowing raised household income and expenditures on average, and that much of the productive effect operated in agriculture. In the next section we use a different approach, instrumental variables, further to check the robustness of these results.

_Village Fund, draft of June 29, 2009_ _Page 15 of 34_
productivity is ambiguous.

There is wide consensus in the published estimates that MNREGA increased unskilled wages by between 5% and 8%, increased labor participation in public works, and may have led to declines in the supply of labor to the private sector (Azam, 2011; Berg et al., 2012; Imbert and Papp, 2015). Imbert and Papp (2015) show that MNREGA increased wages of low-skill workers by 5% on average and this increase is concentrated in the dry season, when they show the bulk of MNREGA work occurs, and is accompanied by a decline in private sector labor supply of 1.3%. When they focus on the states in which the fraction of time spent on public works projects by rural, prime age adults was above 1 percent (“star” states), they find wages increased by 9% and private sector employment fell by 3%. Deininger and Liu (2013) show that MNREGA led to an increase in the accumulation of non-financial assets in the medium run. Raghunathan and Hari (2014) show that farmers plant riskier crops after the implementation of MNREGA which increases their incomes above the direct wage support of the program. Berg et al. (2012) suggests that the wage increase takes between 6 and 11 months after program implementation to materialize and is biased towards low-skill labor.

In the only large-scale RCT on MNREGA to-date (Muralidharan et al., 2016) (MNS) show that improving the implementation of MNREGA increases wages by 7%.<sup>14</sup> They focus only on Andhra Pradesh but note that the similarity in the size of their estimates on the impact of improving MNREGA to the impacts of initial implementation highlight the importance of implementation heterogeneity. They also do not find substantial evidence of impacts on migration but note that their migration data differs from the data used in previous studies.<sup>15</sup> The impact of MNREGA on education also suggests MNREGA tightened local labor markets. Shah and Steinberg (2015) show that MNREGA decreased the educational attainment of 13-16 yearolds by decreasing school enrollment. The effects are similar for boys and girls. They suggest that

> 14I describe a replication exercise of my main results using treatment in this RCT in Appendix 6.

> 15Other attempts to estimate the impact of MNREGA on migration have been limited but suggest it may have had important impacts. A government review of the impacts of MNREGA claims that the program led to a 27% decline in cross-district migration caused by economic distress. One of the few academic studies of the impact of MNREGA on migration finds a reduction in rural to urban migration of around 8% (Imbert and Papp, 2014). While not direct evidence of reductions in migration, several studies have estimated a positive spillover of MNREGA on wages in neighboring districts that is hypothesized to operate via reductions in inter-district migration (Prasann, 2016; Muralidharan et al., 2017). These estimates suggest that wages in neighboring districts may have increased by as much as 9% and that the estimated impact of MNREGA on wages in districts in which it was implemented may be substantially underestimated due to these spillovers.

9