rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table S3. Disability adjusted life years lost to the diseases measured in the Service Delivery Indicators**

||**DALYs**<br>**(Disability-**<br>**Adjusted Life**<br>**Years, thousands)**|**YLDs**<br>**(Years Lived**<br>**with Disability,**<br>**thousands)**|**Prevalence**<br>**(thousands)**|**Incidence**<br>**(thousands)**|
|---|---|---|---|---|
|Diarrhoeal diseases|81,000|10,465|93,473|6,292,937|
|Diabetes|67,900|38,575|475,996|22,936|
|Neonatal encephalopathy<br>due to birth asphyxia and<br>trauma<br>_(closest in our study:_<br>_neonatal asphyxia)_|56,500|9,704|538,238|2,753|
|Tuberculosis|45,000|3,120|1,929,209|8,966|
|Malaria|45,000|1,468|136,085|208,768|
|Pneumococcal meningitis<br>_(closest in our study:_<br>_pneumonia)_|3,080|325|3,557|445|
|Maternal haemorrhage|2,230|61|1,660|6,988|

Source: Global Burden of Disease 2017.<sup>2,3</sup> 46
Figure 3: Youth Female Inactivity Rates <!-- Start of picture text --> 90% 83% 85%<br>78%<br>80%<br>67%<br>70%<br>60% 61%<br>57%<br>60%<br>49% 50% 51%<br>50% 45%<br>40%<br>26%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> Source: Authors’ computations based on data from ILO and KILM Figure 4 Difference between the Inactivity Rate of Adolescent Girls and Adult Women (Target 1), in percentage points <!-- Start of picture text --> 35% 32%<br>29%<br>30%<br>25%<br>20%<br>20% 18%<br>15% 13% 13%<br>11%<br>10% 7% 7%<br>6%<br>5%<br>1%<br>0%<br><!-- End of picture text --> Source: Authors’ computations based on data from ILO KILM and WDI 27
$11,000) although it had diminished to 55,691 pesos ($2,800) by 2006. In Colima, the municipality and the school are encouraged to raise another 20,000 and 30,000 pesos ($1,000 and $1,500). The state government matches each peso raised by the local and school communities to contribute to the development of the improvement plan. Hence, the maximum benefit a school can receive in a year amounts to 150,000 pesos ($7,600).

The monetary benefits are complemented with permanent support from the state government in the implementation of the school improvement plan and in the management of the funds. In Colima, this support is channeled through the four regional teacher centers, the CMR. These centers are also in charge of providing adequate professional development through workshops, courses and seminars for teachers and principals. Similarly, the CMR run workshops and distribute leaflets and information materials to encourage parental participation in school matters and activities. Formal rules mandating the participation of parents in the school improvement plan are a requisite for school participation in the program.

# **Data and Trends**

This analysis uses data collected for the RCT and five additional sources of data: the National Evaluation of Academic Achievement in School Centers (ENLACE), the administrative PEC data, local administrative achievement data and the administrative School Census data (SCD- 911). The data set utilized in this analysis includes Math and Spanish test scores from ENLACE, PEC program information, and school inputs from SCD-911 in the school years 2001-2002 through 2012-2013. The Ministry of Education (ME) produces these sources of data. The last data set is the index of disadvantage ( _indice de marginalidad_ ), which is a weighted average of literacy, access to basic public utilities, household infrastructure and average wages. All localities are ranked with ratings from very high, high, medium, low, and very low disadvantage. The National Population Council ( _Consejo Nacional de Población_ ,
enabling targeted recruitment of workers with the requisite skills, and include provisions for worker rights, such as wages, working conditions, and access to services (Biehler, Kipp and Koch, 2024, Chilton and Woda, 2022). While BLAs relax some regulatory barriers, they do not address all informational or financial barriers that limit broader migration.

Though conceived as a tool to facilitate migration, it is not obvious that BLAs necessarily increase migration for various reasons. First, significant numbers of migrants move between countries outside the framework of BLAs and BLAs could be designed to restrict or control the flow of migrant workers (Livnat and Shamir, 2022). Second, instead of increasing net migration flows, BLAs could be a policy tool to regularize the irregular migration already taking place (Bither and Ziebarth, 2018, Clemens, 2024). Third, even if BLAs may be intended to increase migration, the efficacy of such policies – like with many policies – is not obvious ex-ante. Such concerns may be particularly strong in settings with high costs of migration, high extent of misinformation, and low actual or perceived returns to migration as well as in settings where low capacity and monitoring limits the efficacy of BLA implementation.<sup>1</sup> Finally, even if BLAs were effective in facilitating migration, it is not clear _how_ effective they can be as a policy tool. Determining the magnitude of migration impacts and its persistence is important for migration policies.

To understand the impact of BLAs on migration, we combine two comprehensive data sources on migration and BLAs across all country pairs. The global bilateral migration data captures the migration (measured by people living in a country that is different from the country of birth) between all country pairs for each decade from 1960 to 2020, compiled by the World Bank and the United National Department of Economic and Social Affairs (UNDESA, 2020, World Bank, 2022). A comprehensive dataset on all BLAs for the same period comes from Chilton and Posner (2018),
# **Chapter 8 Challenges and potential research topics**

The previous chapters reviewed SAE methods, ranging from basic direct and indirect methods to modern model-based procedures designed to estimate general non-linear area indicators, defined in terms of a single continuous variable. For other important SAE topics such as model fitting methods and their properties, MSE estimation, prediction intervals or HB procedures, we refer the reader to Rao and Molina (2015).

This chapter outlines limitations in the current SAE literature encountered in certain practical situations. The issues or limitations listed below highlight research topics that might be of interest for practitioners and could potentially be addressed in the next 3-5 years.

1. _Estimation of general indicators with unit-level models in off-census years:_ A common limitation of model-based SAE procedures using unit-level data for estimating non-linear indicators, is the requirement of a census containing the values of the auxiliary variables for all population units. Census files are available every 10 years, and might be severely outdated in off-census years. Outdated census auxiliary information can result in significant biases in the result-

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One reason why this question might be asked is that the regulatory literature derived from Levy and Spiller (1994) emphasises country-specific constitutional, legal, economic, and political differences as being crucial for the success or failure of utility regulation. Hence, a highly reduced form model that abstracts from all those issues may well fail to reflect these local issues that seem to be so important in practice.

The answer to both these concerns lies in the importance of the country-specific _fixed effects_ . With 28 countries each having up to 21 years of data, we can obtain estimates of the fixed effects which should capture most if not all of the factors identified by Levy and Spiller and the subsequent literature. Hence, the estimated impact of eg enacting a regulatory law plus an autonomous regulator in Chile or Sudan (both countries in our sample) will be very different. That impact is the combination of (a) the predicted effect of the relevant regulatory variables **_plus_** (b) each country’s predicted fixed effect. The Chilean fixed effect is strongly positive relative to the sample average whereas that for Sudan is strongly negative.

In other words, the coefficients that we report are ‘highest common factor’ estimates of the impact of regulatory governance indicators where the fixed effects not just control for but effectively “wash out” all the Levy and Spiller and similar factors. But, this means that the regulatory governance effects that we report are not just _average_ cross-country sample effects but that they refer to a country with _average scores on country-specific fixed effects_ . Moreover, they are the impacts that one might expect, looking forward, for a country:

- With an average country specific fixed effect

- Implementing an average quality law

- Establishing an average quality autonomous regulator, etc.

It is for such a country that one might expect that implementing a best quality electricity regulator would increase per capita generation capacity in the long run by 15-25%.

The policy implication of this is, firstly, that the quality of overall country governance matters
a significant amount of variation in effect sizes.

# **3. Results**

## **3.1. Characteristics of Participants and the Study Sample**

Table 1 presents the demographic breakdown of the study sample, including the total sample that completed the surveys—both vaccinated participants who did not participate in the survey experiment and unvaccinated participants who were included in the survey experiments. Reflecting the typical social media user in LMICs, the sample skewed toward being male (52.4%), highly educated (43.6% tertiary education), and somewhat young (29.2% 18-29 years old).

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as well as from Madagascar (Dessy et al., 2020). Our findings support previous literature on children as ‘normal goods’ that increase as a result of greater wealth (Lindo, 2010; Amialchuk, 2013; Black et al., 2013; Lovenheim and Mumford, 2013)—but only partially, since we find that though fertility responses are higher among poorer households, these responses are concentrated in poorer households _in which women do not have a son_ , and we find no fertility response when husbands’ earnings increase (i.e., when we examine impacts on male intervention beneficiaries). Thus, we contribute to this literature by highlighting a key dimension that makes children a normal good: old age security.

In terms of methodology, our paper contributes to the growing but limited body of studies conducting secondary analyses of randomized controlled trials (RCTs) with the objective of drawing more generalizable conclusions. This literature includes pooling RCT databases from different countries to estimate the average impact of microcredit programs using Bayesian hierarchical models (Meager, 2019) and estimating the heterogeneous impacts of a similar program across contexts (Vivalt, 2015). Our study adopts a different approach by testing the impact of two interventions on a single outcome across multiple settings, strengthening the external validity of our results. Importantly, the experiments from which we draw our findings were not initially designed to generate or capture impacts on fertility. This approach to secondary analysis might be a first step towards further research exploiting the growing number of high-quality, RCT-generated panel datasets that are increasingly available to explore new research questions.

From a policy perspective, our findings suggest that strengthening women’s property rights is a promising avenue for reducing fertility in Sub-Saharan Africa. Interventions that secure household-level property rights without ensuring gender equality run the risk of inadvertently increasing fertility. Making sure that laws designed to advance gender equality in property ownership and inheritance are both on the books and put into practice, and increasing provisioning for widows and the elderly (e.g.,
who among all of these migrants are temporary migrants, however, is not always straightforward, especially if the survey is conducted in high-income OECD countries where permanent migration is more common. One way to identify temporary migrants is through questions on intentions to stay in the host country and for how long, which are collected by only a few surveys in destination countries (Dustmann and G ̈orlach 2016). Those include the German Socio-Economic Panel (GSOEP) in Germany or the “Trajectoires et origines” survey in France released by Ined-Insee in 2009. In some host countries, such as the Gulf States or Southeast Asia, the identification of temporary migrants is easier since, by definition, all migrants are only allowed to stay temporarily.

Survey data capturing migrants in destination countries are either longitudinal or crosssectional. Longitudinal surveys focusing on temporary migrants are very scarce. The most comprehensive destination country survey that follows migrants over time is the German Socio-Economic Panel (GSOEP). The GSOEP has a panel structure from 1984 and dedicates particular effort to tracking movements in and out of Germany, and to retain individuals when they return from temporary absences abroad (Constant and Massey 2002; Kırdar 2009). It also collects information on intended duration of stay in the country, which allows to identify temporary migrants, or at least migrants who _intend_ to stay temporarily. Given these features, this data set has been extensively used by studies on temporary migration including Dustmann (2003), Bellemare (2007), Van Baalen and M ̈uller (2008), Kırdar (2009), Dustmann and G ̈orlach (2016), and Adda, Dustmann, and G ̈orlach (forthcoming). One important limitation of the GSOEP for the study of temporary migration, however, is that it does not collect information on labor market outcomes outside Germany, in particular after return to the home country.

Other available surveys in destination countries capturing temporary migrants are crosssectional. As for the GSOEP, the survey “Trajectoires et origines” in France released by Ined-Insee
or perfectly elastic in supply (Z2) (Bell and Hazell 1980). In supply-constrained sectors (Z1), firms operate at full capacity, and output cannot increase without additional capital investment or introduction of new, more productive technology.

Total supply in each sector (Z) is modeled as the sum of inter-industry input demand (AZ) and final demand (F), where final demand includes consumption by households (  Y) and exogenous sources of demand such as exports (E). Income (Y) is related to production through a fixed value added share (v) in gross commodity output (Z), (Equation 1).

As indicated in equation (2), the SIO model permits output responses only in those sectors with excess capacity (Z2). Perfect substitutability between domestic and

> 12 Surveys of foreign tourists were implemented at two main entry points into the country—Tocumen Aiport and Canoas Pass—over the course of six weeks throughout different seasons in 2006 and 2007 and include data for 1,626 foreign tourists (surveyed at the Tucuman Airport and at Paso Canoas) that account for a total of 4,687 trips. Surveys of domestic tourism were implemented in July 2006 and April 2007 and collected data on trips completed throughout 2006; these surveys collected data for 3,498 Panamanian households (of which 34 percent report having gone on trips in the previous year). Data on expenditures is available for 1,604 foreign tourists in the sample; 252 tourists travel with a package and their expenditures are not included in the calculations in this paper. Thus, results shown here are based on averages for 1,352 foreign tourists with non-missing data on expenditures and who did not travel with a package.

> 13 This presentation of the SIO model is derived from Dorosh and Haggblade (2003), ―Growth Linkages, Price Effects and Income Distribution in Sub-Saharan Africa‖.  Journal of African Economies, 12(2).

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# **5 Robustness checks**

# 5.1 Sensitivity to Measurement Error

We assess the robustness of the above estimates to various amounts of measurement error, as inequality measures can be particularly sensitive to this source of bias. Following Chesher and Schluter (2002), we first produce overall inequality estimates correcting for some amount of measurement error. Assuming an error to signal variances ratio of 10% (resp. 20%) would decrease the estimated Gini from 47.1 to 45.3% (resp. 43.6%) and the Theil-T from 42.8 to 39.5 (resp. 36.1).<sup>25</sup> The assumed variance of measurement error needs to reach 40% of that of the signal for the Gini to go down to a level comparable to the uncorrected published statistics (40 instead of a published 40.3). Such level of measurement error is highly unlikely. Bound et al., 2001, give much lower orders of magnitude for measurement error in income, closer to 20%, and this is the range considered by Chesher and Schluter, 2002 in their application to Indonesian data. Hence, it is unlikely that measurement error explains the high observed inequality in the PSF survey. In addition, given ESPS data is as likely to be error-ridden as PSF, measurement errors certainly cannot account for the difference in the inequality estimates.

In the case of the PSF survey, because consumption data are collected at the cell level, measurement error will take place at that level. In such a case, working at the household level would help to average out some of this noise, while it would be maximal when working at the cell level. That would suffice to induce some intra-household inequality even if the true distribution is egalitarian. Although we already know, thanks to the analysis of section 3.2, that this within-household inequality is not pure noise, as it correlates with a number of observable characteristics of the households, it
(such as QALYs or DALYs) are even less solidly based for cross-national comparisons. Infant mortality is perhaps more reliably measured, but fails to capture mortality from many of the health conditions of concem which are responsive to health care, such as diarrhea and respiratory infections. Especially at moderate to low levels, infant mortality is dominated by perinatal mortality.

The first empirical result is the variation in mortality associated with income. Column I of Table 1 shows that a large part of the variation in (the natural log of) under-5 mortality can be "explained" by (the natural log of) GDP per capita and a set of region dummy variables only.<sup>7</sup> ' Even excluding the regional dummy variables, 84 percent of mortality differences can be "explained" by income alone.

The second, even more striking, result in column 2 of Table 1, is that when a few other variables are included over 94 percent of the variation in the under-5 mortality rate is explained. The level of female education, an estimate of the income distribution, a binary variable for whether the population of the country is predominantly Muslim, and the degree of "ethnolinguistic fractionalization" are each statistically significant and of plausible sign and magnitude.<sup>9</sup> ' Dummy variables are based on World Bank regions: East Asia and Pacific, Latin America and Caribbean, Middle East and North Africa, South Asia, Sub-Saharan Africa, and (excluded from the regression) "Rest of World."

**8** In order to ensure the robustness of these, and subsequent regressions (except the median regression), the two observations with the largest impact on the parameter vector are dropped from the sample.

9 When female education, income inequality, ethnolinguistic fractionalization, or access to safe water are missing, the variable is set to zero and a dummy variable equal to one is 6
The pattern in the Figure 3 differs from the findings by Deaton (2005), Ravallion (2003) and Karshenas (2003). Deaton and Karshenas both find a negative relationship between the gap and country income levels, with the gap being largest among the richest countries. In contrast, our sample shows that the gap is smaller among high-income countries than among middle income countries. An important part of the explanation for this is that compared to previous studies, our database contains more surveys overall from richer countries, and also more consumption surveys for richer countries and more income surveys for poorer countries. Moreover, we observe a very similar pattern for income surveys and consumption surveys, which Deaton and Ravallion did not. We believe this difference originates from income surveys being more heavily concentrated in richer countries in the Deaton and Ravallion samples, where they observed a larger gap, while consumption surveys were concentrated in poorer countries, where their gap was smaller. This pattern was particularly strong in the case of Deaton who focused the analysis of rich countries on the UK and the US, two high-income countries where the gap is particularly large. We also find relatively larger gaps in the UK and US, but these appear somewhat unique cases among rich countries.

A further possible reason we observe a smaller and narrowing gap among richer countries than previous studies may be linked to evolving practices and consolidation of data sources in these countries. Recently, it has been more common in rich countries to integrate administrative data, such as tax records, into survey data estimates. For example, the EU-SILC surveys, a major source for our income survey data for rich countries, explicitly allow countries to supplement income variables with administrative records, such as tax and payroll data (Jäntti et al., 2013). This practice helps align estimates, reduces underreporting in surveys, and contributes to a reduction in the gap between national accounts measures and survey measures. A caveat to this interpretation is that consumption surveys, which do not typically rely on any such administrative data
**POLICY** RESFARCH WORKING PAPER 2391

# Summary findings

Adjustment programs often emphasize protecting social spending-especially propoor spending-from cuts. Yet the incidence of fiscal contraction-and hence the case for action to protect public spending on the poor at a time of overall fiscal austerity-is an empirical question, which Ravallion addresses using data from Argentina.

Aggregate budget cuts in Argentina in the 1980s and 1990s typically brought proportionately greater cuts in social spending. "Nonsocial" spending was protected. But proportionate cuts for types of social spending that matter more to the poor were about the same as the cuts for those that tend to favor the nonpoor. Absolute cuts were in fact greater for "social insurance" that matters more to the nonpoor.

But spending on targeted social assistance and employment programs was more vulnerable to aggregate spending cuts than were more universal social services. Social spending was clearly exposed to fiscal contraction, but this was somewhat less true of propoor spending on things that also benefited the nonpoor.

So fine targeting may be a mixed blessing for the poor, bringing greater vulnerability to cuts, possibly when help is most needed. There is a strong case for action to protect propoor social spending at such times.

An externally financed workfare scheme in Argentina was far better targeted than other social spending but still had to ensure that a small but relatively well-protected share of the benefits went to the nonpoor.

The program was clearly subject to the same political economy constraints that influenced the incidence of past fiscal contractions in Argentina. The program expanded into poor areas when the budget increased but retreated from poor areas when the program was cut. It was the program's disbursements to nonpoor areas that were protected. Still, given the low wage rate offered, the direct benefits from the program were still likely to have favored the poor, even after the cuts.

This paper-a product of Poverty and Human Resources, Development Research Group-is part of a larger effort in the group to better understand the incidence of social spending. The study was funded by the Bank's Research