rafmacalaba/datause-encoder-data huggingface dataset:
investigate whether districts with higher preschool access induce a net migration of preschoolaged eligible mothers. Specifically, we regress the change in net migration of eligible mothers on either the change, or the lagged change, of private/public preschool density in the district. Appendix Table 9 reports our findings. We do not find any evidence of sorting for better preschool access.

# **7. Conclusion**

Prior studies have documented the mixed evidence of preschool access on maternal employment. We find that an additional public preschool per 1,000 young children in Indonesia increases eligible mothers’ work participation by 5.7-8.2 percentage points, or by 11-16 percent from the baseline mean. Mothers take up unpaid family work and jobs as agricultural workers. Improved private preschool density does not lend the same effect at the extensive margin, but it does increase the likelihood of working as an artisanal production worker. The tendency to work in informal sectors is in conjunction with the fact that preschools operate daily for less than half a day. We also document some evidence that improved preschools may particularly help mitigate the negative fertility effect of the first two children on maternal employment. The Indonesian experience with preschools may lend some guidance to other countries with low preschool enrollment and female work participation rates.

The benefits of expanding public preschool access are not limited to short-run gains in increased female employment. The World Bank (2016) argues that increasing FLFP is a more effective policy than attracting in-migrants or increasing elderly work participation to offset the shrinking workforce due to the rapidly aging economy. In this paper, we also elude to the welldocumented long-term benefits of early childhood education that tend to favor children born in poor households (Berlinski et al. 2009, Havnes and Mogstad 2015, Garcia et al. 2016; Brinkman et. al. 2017). A study in Indonesia suggests that the benefits from early childhood education intervention on children’s outcomes alone exceed the costs by an average of 6.01: the benefit-cost ratio is even higher for the poorest quintile (Chang et al. 2006). When conducting an economic analysis to assess the cost and benefits of preschool expansion, policy makers should keep all these various benefits in mind to ensure the right policy decisions are made.

zero change in (public/private) preschool density in district _j_ between 1993 and 1992. For the purpose of this test, we restrict our analysis to PODES years and collapse our individual-level panel of mothers to the district-level.

28
- Harbison, Ralph and Eric A. Hanushek, 1992, _Educational Performance of the Poor: Lessons from Northeast Brazil,_ Washington DC: Oxford for the World Bank.

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47
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45
_= r* * + *ERR*( f) *k_ so that the first term identifies the average rate of return to capital and the second parameter the impact of an increase in ERR on the overall return to capital. The results of estimating this equation using cross national data are presented in table 11. The estimated average return varies between 10.2 and 17.8 percent, a reasonable range of values. The impact of ERR on the overall rate of return is between 0.44 and 1.08 (with just two countries excluded). The estimates suggest that the return on Bank projects, the ERR is related one-for-one to the economy-wide rate of return which suggests it is a very good proxy for overall investment performance. This small piece of econometric evidence fortifies the arguments in earlier work for the use of the ERR as an indicator of the economy-wide rate of return.

However, the multicollinearity problems in identifying the interactive term are extremely severe. With just two variables, over 50 percent of the overall growth rate variance is explained and hence the F-tests of joint exclusion are overwhelming. However, the estimates of each term individually are very imprecise and cannot reject zero (or any other value for that matter) for the the individual terms. We are obviously not happy with this, but see no solution.

- 37 _-_
The predicted shadow ratings for the unrated countries are reported in Table 7. It can be seen that countries like Algeria that have little need for debt have shadow ratings well within investment grade. Mauritius has an investment grade shadow rating. A fairly large range of countries such as Dominica, Moldova, Armenia, Bhutan and Mali have below investment grade shadow ratings which are still in the middle of the B range or even better.

# 6. Relative risk ratings

With the vector of actual ratings for the rated countries and shadow ratings for the unrated countries, it is relatively easy to compute the relative risk rating of countries by sorting them in ascending or descending order. In this case, an improvement in relative rating of a country will usually be accompanied by a worsening of relative rating of another country. The complication arises when the average rating of the overall portfolio changes over time: in this case, it becomes important to compare rating changes of a country with the change in the average rating. Another source of complication is the choice of weight for a country for computing the world average. In our calculation, we use the GDP as weight, thus assuming that the larger a country, the greater is its impact on the world average rating. To avoid confusion that may arise from changes in the GDP over time, we use the data for 2008 to compute the weights.<sup>7</sup> The _relative risk rating_ , _RR_ , is then computed as: where rit stands for the rating of country _i_ in time _t_ and wi for the share of the country’s GDP in the world GDP in 2008. Since in our scoring system smaller numbers relate to better ratings, a negative _RR_ value would indicate a better than average rating performance. Similarly, when we compare 2008 and 2012, a higher positive value of relative risk rating, RR, would indicate a worse outcome.

The average ratings (not RR) for the world and separately for the high-income and the developing countries are shown for 2008 and 2012 in Figure 6. In 2008 the world average stood at 8.4 (approximately AA with negative outlook). By 2012 this had worsened to a score of 11.3 (AA- with a negative outlook), a downgrade of one rating notch. Most of this change arose due to a worsening of the weighted average of the high-income countries, from a score of 3.6 (AA+ stable) in 2008 to 7.5 (AA stable) in 2012. In contrast, developing countries had a slight improvement from 24.8 (BBB stable) to 24.2 (BBB positive).

> 7 This is an innocuous assumption when comparing ratings over two relatively close years as in the exercise below. As with the construction of index numbers a la Laspeyres and Paasche we can use the GDP weights of the start year or the end year. We prefer to use the start year for the pragmatic reason of more reliable data availability.

16
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26 which is close to the value of SN obtained above and confirms that the product-specific economies of scale more than offset the (assumed) diseconomies of scope.

These estimated ray economies ignore, however, the fact that the equation estimated is not a long-run equation but rather a short-run total cost equation. As in the case of Bitran-Dunlop study above, this means that one cannot obtain a true measure of ray economies of scale from the results reported in Wouters' paper.

# 7. Conclusions

It is evident from the above, and not surprising perhaps, that none of the studies of hospital costs in developing countries would appear to provide reliable evidence on all of the issues of interest to policy-makers.

Although the issue of over-capitalization is clearly important in its own right, it also has implications for the investigation of other issues. Thus even if one's interest lies with, say, economies of scale, the issue of over-capitalization cannot be ignored, since if hospitals are not in a long-run equilibrium, the estimated parameters will not provide reliable evidence on economies of scale. Estimating a short-run cost function, irrespective of whether or not one is interested in over-capitalization, which is the practice advocated by Cowing, Holtmann and Powers (1983), seems eminently sensible. It is therefore unfortunate that Dor, in his study of hospital costs in Peru, elected to estimate a long-run cost function. With regard to testing for over-capitalization, we have argued that since in practice it is difficult to separate fixed costs from variable costs, the test proposed by Cowing and Holtmann (1983) is unlikely in general to be appropriate, since it relies on the estimated equation being a variable cost function. Of the three short-run cost functions included in this survey, only in Anderson's was the sign of 6C/6K consistent with the equation being interpreted as a variable cost equation. But although the Cowing-Holtmann test seems valid in this case, the negative value of 6C/6K and the absence of information on the shadow price of capital make testing for over-capitalization impossible. In the case of the other two studies, where the positive value of 6C/6K suggests that the equation estimated is a short-run total cost equation, we argued that a more appropriate test would be to test whether 6C/6K=0. In both studies 6C/6K was positive, which interpreted literally implies over-capitalization. We argued, however, that this conclusion ought not to be taken at face value, since the estimated equations are not, as theory requires, additive in fixed and variable costs.

Of the studies included in this survey, only Wouters' analyses the issue of efficiency explicitly. Although her method for investigating technical inefficiency is somewhat ad hoc and falls short of the frontier production function approach, her approach to allocative inefficiency is fairly conventional. She finds evidence in her sample of under-employment of health workers relative to non-health workers,
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_Gender Equality and Economic Development: The Role for ICTs_ 28 <!-- Start of picture text --> Figure 1<br>Positive Correlation between Literacy Ratio and GDP per capita<br>25000 GDP per capita = -7335.84 + 159.10 Literacy Ratio<br> (-3.03) (5.72)<br>20000<br>15000<br>10000<br>5000<br>0<br>30 50 70 90 110 130<br>Ratio of Female to Male Literacy Rates in year 2000 (%)<br>Figure 2<br>Positive Correlation between Student Ratio and GDP per capita<br>60000<br>GDP per capita = -30744.4 + 419.73 Student Ratio<br> (-3.46) (4.59)<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br>55 65 75 85 95 105 115<br>Ratio of girls to boys in primary and secondary education in year 2000 (%)<br>GDP per capita in year 2000<br>GDP per capita in year 2000<br><!-- End of picture text -->
<mark>susceptible to the impacts of such disasters than those with higher income, emphasizing the intricate link between poverty and vulnerability to natural disasters.</mark>

# _3.3 Climate shock impacts_

Some evidence suggests that exposure is similar across poor and non-poor households or regions, and the differences in impacts are driven by resources available for recovery (Anttila-Hughes and Hsiang 2013; Hallegatte, Fay, and Barbier 2018). For example, in Honduras, the poor and nonpoor are equally exposed to hurricanes, but the effect on the poor is more severe ( Hallegatte et al. 2016). Similarly, there is no differential exposure to floods in Moldova, Rwanda, Slovak Republic, and Sudan, but the ability to recover and cope with flooding depends on relative socio-economic status and resources available (Hallegatte et al. 2016).

Consistent with earlier reviews, 80 percent of the studies in our sample find that the poor are more adversely affected by climate shocks than are other households—a finding that emerges from both CCDR and non-CCDR studies (Table 2). Greater impacts on the poor were identified in studies of droughts (92 percent), extreme heat (100 percent), and floods (88 percent; Table 5). Compared with studies of other shocks, studies of droughts were 19 percentage points more likely to show a greater impact on the poor than on other households. Studies of floods found a 28 percentage-point higher impact on the poor (Table 4, columns 3-4).

# _3.4 Potential channels_

The socio-economic effects of climate shocks can affect many domains (Carleton and Hsiang 2016). At the individual level, the impacts vary widely throughout the life cycle—and include mortality as well as temporal and persistent effects in education, health, and the labor market. The persistence of the effects is especially concerning in settings in which the duration of post-disaster assistance may be limited, and income losses push households further into poverty or extend the duration of poverty. For example, Hallegatte et al. (2016) show that the poor and non-poor are equally exposed to droughts in Niger, Peru, and Bolivia, but the poor are more adversely affected because they work predominantly in agriculture, where droughts reduce agricultural productivity. In the aggregate, these effects may reduce economic growth due to lower productivity and costly 9
relative to the available international evidence. Table 6 shows the undercoverage and leakage rates for the different approaches. The method we propose (panel B) outperforms the targeting method currently used in Chad (panel A) for all the poverty lines except the 25th percentile poverty line. The errors are not low overall, with the UNHCR/WFP/CNARR undercoverage rates ranging from 9% to 32% and the leakage rates from 12% to 36%, and our model-based undercoverage rates from 6% to 40% and the leakage rates from 9% to 41%. However, these methods perform relatively well when compared with international evidence. For example, Skoufias et al. (2001 <mark>) find that the undercoverage and leakage rates for the PROGRESA program in Mexico were 7% and 70%, respectively, for a poverty rate of 25%, a better performance on the undercoverage rate but a much worse performance on the leakage rate compared with the other two programs.</mark> The estimated targeting rates for Chad are also better than the median performance of similar scores for programs across the world (see Table 7). Coady et al. (2004) report an index of targeting performance obtained by dividing the proportion of beneficiaries falling within the target population by the proportion of beneficiaries that would result from a random allocation. For example, if the bottom 40% of the income distribution receives 60% of the funding, the performance indicator is 1.5 (60/40). The higher the indicator is, the greater is the performance of the targeting strategy. Table 6 reports this indicator for the 85 programs considered by Coady et al. (2004) (A), the UNHCR/WFP/CNARR targeting program (B), and our proposed methodology (C). Our methodology outperforms the UNHCR/WFP/CNARR targeting program and the median value of the programs covered by Coady et al. (2004), and the UNHCR/WFP/CNARR program does not perform poorly when compared with the international evidence.

# **VI. Limitations**

The objective of this work was to test how cross-survey imputation methods perform in estimating poverty for refugee populations, using Chad as a case study. Although the results of our crosssurvey imputation exercise show that key demographic variables from ProGres predict well the welfare measure captured in the PDM at the aggregate level, additional work is needed to assess how well this methodology performs in refugee contexts, particularly in poor countries and data scarce environments. To do this, data sets should ideally contain more detailed information on 14
Table 2: Sample of the study, São Paulo (2005-2009)

# **(A) Triple difference-in-differences. Locally and state managed schools**

||_M_|_unicipalities where_|_Other municipalities where_|
|---|---|---|---|
||_the local authoritie_|_s extended the winter break (G = 0)_|_the local authorities did not extend the winter break (G = 1)_|
||State|Locally|State<br>Locally|
||managed|managed|managed<br>managed|
||network|network|network<br>network|
|_Number of:_||||
|Municipalities|10|10|112<br>112|
|Schools|869|759|929<br>1,334|
|Students|144,655|110,606|99,172<br>139,017|

**(B) Difference-in-differences. Municipalities in São Paulo where there is at least one school managed by the local authority Locally-managed schools only**

||_Municipalities where_|_Other municipalities where_|
|---|---|---|
||_the local authorities extended the winter break (G = 0)_|_the local authorities did not extend the winter break (G = 1)_|
||Locally|Locally|
||managed|managed|
|_Number of:_|network|network|
|Municipalities|10|469|
|Schools|795|2,569|
|Students|118,804|239,778|

_Notes_ : To estimate the DiD, we consider all locally-managed schools with proficiency available since 2005 to be able to use more than one data point before the school closures. For the triple difference design, our sample includes the municipalities with at least one state and one locally-managed school available. The rows _students_ show the number of students enrolled in fifth grade. Source: Census of Education, 2009.