rafmacalaba/data-use-mentions-extended huggingface dataset:
; and the (gendered) nature of parents’ expectations and aspirations for their child’s education, qualifications, and future career. In particular, considering the importance of literacy as a foundational skill (see also Gregory et al. 2021), the inclusion of an indicator of reading achievement would help control for gender differences relating to literacy levels and would allow more fine-grained examination of mathematical and scientific proficiency. Among international assessments, data from PISA at intermediate level or from a joint TIMSS and PIRLS assessment (such as TIMSS/PIRLS 2011) at primary level could be used for this purpose. At the national level, an administration of NALO that assessed reading as well as mathematics or science from the same students could also be used. Given that the majority of unexplained variance in the models presented in this paper was at the student level, extending future analyses in this way should provide further useful insights.

As noted above (section 5.3), a focused examination of teaching quality in Saudi Arabia — incorporating teacher characteristics, quality of teacher education, professional development, availability and use of resources, classroom management, professional collaboration, and pedagogy — would shed further light on some of the points raised in this paper. In particular, differences between the classroom environments of boys and girls, given the gender-segregated structure of the education system, merit closer inspection.

Finally, it would be useful to extend the work presented in this paper by drawing on data from other countries. In the first instance, subsequent research could focus on countries with similar cultural contexts such as other countries with comparable international data within the Gulf or MENA regions. Such research could examine (a) the extent of similarity between observed gender differences in Saudi Arabia compared to other countries, and (b) similarities and differences in the factors associated with student outcomes in each national context. Further work could also usefully examine factors associated with gender differences in single-gender compared to mixed-gender schools (see figure 2), particularly in, but not limited to
also faced much criticism, as fewer than 10 recruitment firms in Bangladesh handled all the recruitment interactions with Malaysia. The concerns of high cost and malpractice reemerged, and the program has been suspended since mid-2018. Bilateral talks are currently underway to resolve the issues and resume the flow of workers.

# 3. Data and empirical strategy

Our data come from 3,512 in-depth household interviews with G2G applicants between August and December of 2018. The first group is the Phase 1 lottery winners, referred to as T1, who won the lottery to migrate and were put in the first phase of intermediation. The second group comprises both Phase 2 and Phase 3 winners, referred to as T2, who won the lottery to migrate but were put into a deferred phase of intermediation. Eventually, this group only received partial (low) intermediation. The third group is the lottery losers, referred to as C, which will serve as the control group. **Figure 2** shows the various steps of the lottery program and the final study sample.

The survey included detailed modules on the migration, labor and earnings of all household members including the applicant and any other migrant members of the household. Interviews were conducted with the applicants themselves if they were present, or with a knowledgeable household member if the applicant was not present. The survey also had modules on household consumption, enterprises, housing and assets, debt position, and female decision-making of the household in Bangladesh. We also collected data on applicant and household characteristics that are unlikely to vary over time, as well as retrospective data on some pre-lottery outcomes.

## 3.1 Data collection

Constraints on data collection efforts limited the geographic coverage of the survey. Phase 1 winners (group T1) were spread thinly across Bangladesh. BMET limited the number of Phase 1 winners in each village to at most one applicant. Unions, which typically have about 6,000 households from a few villages, had an average of two Phase 1 (group T1) winners.
enrollment, we use the Average Treatment Effect on the Treated, which is the most popular parameter in the impact evaluation literature (Heckman et al., 1999). This parameter is expressed as follows:

> where _E_  _Y_ ( _D_  0) | _D_  1  is the expected value of educational variables of children whose parents have a disability had there not been a disability. This is not observed and has to be estimated. Once equation (1) is estimated, ATT can be computed as follows:

where _nD_ is the number of children whose parents have a disability, and  is the cumulative distribution function of the standard normal distribution (since we use a probit model).<sup>3</sup> Since the probit function is non-linear, and _AT_<sup>ˆ</sup> _T_ estimated by (3) can vary slightly across _X._ We can allow the effect of parental disability to vary more remarkably across _X_ by including interactions between _D_ and _X_ . The equation (1) becomes: The ATT is estimated by: ATT can be estimated for children who have the value of the _X_ variables equal to _x_ : Where _nD_ , _X_  _x_ is the number of children who have parents with disabilities and the value of the _X_ variables equal to _x._

> 3 We estimate the standard error of the ATT estimates by using a non-parametric bootstrap technique. More specifically, we repeatedly draw random samples from the original VHLSS 2006. Since the VHLSSs sample selection follows stratified random cluster sampling, communes instead of households are bootstrapped in each stratum (Deaton, 1997). The number of replications is 1000.

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136(3): 1447–1497.

- **Bryan, Gharad, Shyamal Chowdhury, and Ahmed Mushfiq Mobarak.** 2014. “Underinvestment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh.” _Econometrica_ , 82(5): 1671–1748.

- **Burchardi, Konrad B, Selim Gulesci, Benedetta Lerva, and Sulaiman Munshi.** 2019. “Moral Hazard: Experimental Evidence from Tenancy Contracts.” _Quarterly Journal of Economics_ , 134(1): 281–347.

- **Burgess, Robin, and Rohini Pande.** 2005. “Do Rural banks matter? Evidence from the Indian Social Banking Experiment.” _American Economic Review_ , 95(3): 780–795.

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Policy Research Working Paper 6317

# **Abstract**

The international migration of high-skilled workers may trigger productivity effects at the macro level such that the wage rate of skilled workers increases in host countries and decrease in source countries. The authors exploit data on international bilateral migration flows and provide evidence consistent with this theoretical hypothesis. They propose various instrumentation strategies to identify the causal effect of skilled migration on log differences of GDP per capita, total factor productivity, and the wages of skilled workers between pairs of source and destination countries. These strategies aim to address the endogeneity problem that arises when international wage differences affect migration decisions.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at volker.grossmann@unifr.ch and david.stadelmann@unifr.ch.

_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
. Prasad and Rao, 1990) or by bootstrap techniques (Gonzalez-Manteiga et al., 2007). In this paper the FH model is estimated using the Fayherriot Command in Stata (Halbmeier et al, 2019) with no transformation. The routine works similarly to the way the FH model is estimated in the EMDI and Povmap packages (Harmening et al., 2023) in R. Transformed versions of the FH models, using for example the arcsin transformation, are also available and can be considered when modeling proportions. Transformed FH models can be also estimated by using the EMDI and Povmap R packages.

# **3.1 Model selection and assessment**

The geospatial data listed in Table A1 were used to construct averages of zonal statistics both at the grid cell level and the target area level which are used as covariates in model (1). In addition, we include dummy variables at the region level. For model selection we use Lasso to select a set of predictor variables while avoiding overfitting. Estimation of the Lasso penalty parameter is implemented by minimizing the Bayesian Information Criterion (Zhang et al., 2010). The regional dummies are unpenalized and therefore are guaranteed to be selected in the model. Details are provided in Appendix B.

Broadly, the signs and patterns of the coefficients of the unit context model reflect a positive association between population and building density, and a negative association between welfare and remoteness, as proxied by agricultural production and a high prevalence of grassland and shrubland. Fitting model (1) in the five countries under consideration leads to R<sup>2</sup> values ranging from 0.19 in Chad to 0.32 in Niger. This range is consistent with similar applications in other contexts. In similar household-level models with aggregation of geospatial covariates at similar spatial scales, the R<sup>2</sup> was 0.30 in Tanzania and 0.27 in Sri Lanka when predicting per capita consumption, and 0.13 in Mexico when
# **IV Correlates of Intergenerational Mobility**

In this section, I explore a set of correlates of regional IGM with the aim of uncovering a set of stylized facts that help characterize its geography. A necessary caveat is that the set is relatively small given the difficulty of collecting data that is comparable for all the administrative units.<sup>18</sup> An additional and perhaps more important caveat is that the analysis does not provide any causal interpretation and is solely descriptive.

I run univariate regressions pooling all the countries linking IGM to geographical and initial conditions that have been discussed in previous studies on intergenerational mobility outside the continent (for example, see Alesina et al., 2021). This is done by estimating the following econometric specification: where _d_ = [ _up, down_ ], the dependent variable corresponds to the measure of upward or downward intergenerational mobility previously estimated for province/district _r_ in country c, _ηc_<sup>_d_denote country fixed effects,</sup><sup>_Zcr_and</sup><sup>_βd_are respectively the covariate and the coefficient</sup> of interest. The latter summarizing the linear association between intergenerational mobility and the covariate.

## **IV.1 Education of the old generation**

First, I analyze the share of the old generation that was able to complete primary education. Alesina et al. (2021) finds this measure to be strongly associated with mobility in Africa. This correlate in part reflects the initial outcomes at the province/district-level for parents. I compute this variable using an econometric specification similar to the one used to compute

> 18For example, Munoz (2021) and Van der Weide et al. (2020) use full-count census data for individual countries focusing on a much larger set of correlates.

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<!-- Start of picture text --> Public Private<br>Professional<br>Manager<br>Clerk<br>Sales<br>Service<br>Agricultural<br>Production<br>-.04 0 .04 .08<br><!-- End of picture text -->

# **Figure 7. Effect of preschools on mothers’ choice of occupation**

Note: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each dot represents the interaction coefficient of public/private preschool density in one’s district of residence and a dummy for having a preschool-eligible child on the probability of being employed in a certain occupation, standardized in z-scores to allow visual comparison across occupation categories. IFLS collects 2-digit occupation codes; we group occupations by their headings (see Table 8 for a list of top-3 occupations per heading). 95 percent confidence intervals are represented by horizontal lines. See Table 7 for non-standardized effect sizes and adjustments to multiple hypotheses testing.

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Policy Research Working Paper 10453

# **Abstract**

As climate change intensifies, dry rainfall shocks and - droughts are a growing concern. At the same time, scien tific evidence suggests that the world has surpassed the safe planetary boundary for green water, which is water stored in biomass and soil that is crucial for maintaining climate resilience. Yet, evidence at the global scale of these combined forces on economic growth is poorly understood. This paper attempts to fill this gap by using data on annual subnational gross domestic product for 82 countries from 1990–2014. Using rainfall shocks as plausibly exogenous variations in a spatially specific panel at the grid level, the analysis finds that the global effects of droughts on economic activity are substantial. Moderate to extreme droughts reduce gross domestic product per capita growth between 0.39 and 0.85 percentage point, on average, depending on the level of development and baseline climatic conditions, with low- and middle-income countries in arid areas sustaining the highest relative losses. In high-income countries, moderate droughts have no impact, and only extreme droughts have adverse effects, reducing growth by about 0.3 percentage point, a little less than half the impact felt in the low- and middle-income country sample for the same intensity of drought. Crucially, the impact of a dry shock of a given magnitude also depends on antecedent green water availability. The results show that increases in soil moisture in previous years can neutralize the harmful impacts from a dry shock, with suggestive evidence that local and upstream forest cover are key channels through which these impacts manifest. These findings have important implications for measuring the economic impact of droughts and can inform adaptation investments.

This paper is a product of the Water Global Practice. 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
**Table 6: Residual-Based ADF Test for Co-Integration on Key Variables in the ARDL Model**

Unit root tests for residuals *~~~~~~~~~~~~~ S r i: S* S* * * 4 * * S* * * * * * * * * * * * *I * * * * * * * * * * * * * *S Based on OLS regression of DLY on: INPT LY(-l) LLRT LI LT LFD 36 observations used for estimation from 1961 to 1996 Test Statistic LL AIC SBC HQC DF -4.3942 79.6475 78.6475 77.9305 78.4138 ADF(1) -4.5706 81.0263 79.0263 77.5923 78.5589 ADF(2) -3.7380 81.1517 78.1517 76.0007 77.4505 ADF(3) -4.2644 83.0405 79.0405 76.1726 78.1057 ADF(4) -3.3245 83.0510 78.0510 74.4660 76.8824 95% critical value for the Dickey-Fuller statistic = -5.2687 LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion Results obtained using Microfit 4.0 by Pesaran and Pesaran (1997).

# V. **Summary and Conclusions**

Our study, though a single country case study, has demonstrated that an economywide approach to analyzing economic effects of land registration offers considerable improvements over current and past approaches which have been sector-focused. We have deployed contemporary analytical approaches, including the ECM, to deal with nonstationarity that is inherent in time series data. We have at the same time used classical techniques such as the ARDL to model high order lags that characterize the response of output and economic growth to changes in land registration.

Our empirical analysis using the ECM has generated three unambiguous findings. First, land titling is found to have statistically significant and positive long-run
anticipation effects (one year ahead of EU fund spending) at the subnational level, partially driven by anticipated falls in investment (mostly in non-CEE countries). This could be because some investment is put off until it is eligible for EU funding, though more research is needed to test this hypothesis.

**Contribution and related literature.** Our paper contributes to a recent literature on the short-run growth impacts of EU fund spending in different countries and regions. We differ from the empirical literature mostly in terms of conclusions—we do not find evidence of large short-term multipliers—and we also offer some methodological improvements.

Coelho (2019) is the first paper to estimate short-run fiscal multipliers on EU spending, and, like us, she uses NUTS2 subnational data. Our paper seeks to improve on Coelho’s pioneering paper in three main ways: data quality, controlling for region-specific effects, and using an alternative instrumental variable (IV) approach. First, Coelho (2019) did not observe EU Fund expenditures at the NUTS2 level, and instead she interpolated them using country-level disbursement schedules combined with NUTS2 commitments. We have access to more recent data which should improve the accuracy of the estimates. Second, Coelho’s interpolation

> 9 Winsorizing is a less extreme approach than dropping outliers. Rather than dropping the extreme observations, winsorizing keeps them in the sample but rounds them to a bound.

> 10 The differences between our country-level results and those of Durand and Espinoza (2021) are mostly due to the selection of influential observations. Other approaches in the literature drop periods with austerity programs or drop observations that Cook’s distance or standardized residuals deem to be influential, often resulting in more dropped observations. Often these dropped observations are non-random; Coelho (2009, p582) finds these “are mostly observations with abnormally sharp output fluctuations and very low transfers changes”.

> 11 While there is some tentative evidence of an all-Europe subnational multiplier above one for the Cohesion Fund (mostly environmental and transport investment),
districts outside the ACCs using the same three-stage sampling procedure. Thus, the survey was designed with the secondary aim of generating results that are representative at the national level, at least for the four main regions that account for more than 90 percent of the agricultural production in Ethiopia (Bachewe, Koru, and Taffesse 2018). The analysis in this study uses data from 4,315 distinct households in 206 districts that produced maize or wheat at least twice in any of the three survey rounds. We also used administrative records from the ATA to define DSM adoption cohorts in the survey districts by crops.

Table A1 in the appendix presents the characteristics of sample households. Most households are male headed (about 90 percent), and the average household head is about 47 years old and completed less than four years of formal education. The average household consists of six members, owns 1.8 hectares of land, and keeps two oxen. On average, about 46 percent of sample households are members of agricultural cooperatives and use 82 kgs of fertilizer each year. The mean comparison tests between treatment (DSM) and control (non-DSM) households show statistically significant differences for several household characteristics, especially among DSM cohorts from 2014 to 2019. We account for those variables in the main analysis, which is discussed in the next section.

Next, we examine the descriptive statistics for our outcomes of interest—seed purchases and crop yields. Table 2 summarizes the descriptive results for the binary measure of fresh seed purchases (extensive margin) and the quantity of seed purchased per hectare (intensive margin) for maize and wheat, disaggregated by DSM status, cohorts, and survey rounds. Two key insights emerge here. First, post-treatment differences relative to the pre-treatment 2012 baseline are more pronounced and strongly significant in 2019 compared to 2016. For example, four out of the seven treated cohorts reported a higher quantity of maize seed purchases per hectare in 2019 compared to 2012, whereas only one treated cohort showed an increase in 2016. On the
Teacher absenteeism has been an issue in Indonesia. Chaudhury et al (2006) reported that 19% of Indonesian teachers were absent from work. Our survey relies on the principal’s estimate, and has a much better record of 6% absence rate. However, the variations remain large from school to school.

We use the average test scores for Indonesian language and math to measure the school outcome. The test is administered to the Grade 5 students in surveyed schools, and designed based on the core curriculum requirements. Students generally fair better in language than in math. Language test score also shows smaller standard deviation than math score.

**Figure 14: Distribution of Indonesian language and math test score**

<!-- Start of picture text --> a. Indonesian language b. Math<br>0 20 40 60 80 20 40 60 80 100<br>average bahasa score average math schore<br>.15<br>.1<br>.05<br>0<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> The results of first step estimates are presented in Table 2<sup>6</sup> . For schools’ direct spending on student activities, in addition to the significant effect of schools’ total budget and total enrollment, the result shows that the amount of spending is significantly affected by the influence of districts or subdistricts. Schools that reported districts/subdistrict as ―very influential‖ over schools ―planning and budget allocation‖ allocated more funds directly to support student activities. In addition, schools that send parents information on BOS spending also spend more on their students directly. Schools with a committee chair with at least senior secondary education also tend to spend more on

> 6 Full results in Annex 2.

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(0.7)|(0.8)|(1.0)|(0.9)|
|Number of working hours per month|180.0|187.2|188.7|188.0|
||(1.3)|(1.5)|(1.6)|(1.6)|
|Hourly wage (thousand VND)|18.1|19.5|20.4|24.2|
||(0.8)|(0.7)|(0.5)|(0.0)|
|Monthly wage (thousand VND)|3252.4|3554.0|3845.2|4404.3|
||(89.1)|(99.1)|(79.9)|(0.0)|
|Yearly wage (thousand VND)|39013.0|41878.6|46334.3|52749.0|
||(1434.5)|(1331.3)|(1131.8)|(0.0)|

Note: This table reports the employment variables of women with children age 1 to 5. Variables of wagepaying jobs, skilled jobs, and formal jobs are defined using the main occupation over the past 12 months. Employment consists of wage-paying employment, self-employed non-farm work, and self-employed farm work.

Wages are defined as the total wages, including main and secondary jobs. Standard errors of the mean are in parentheses. Wages are measured in 2016 prices. Source: Estimation from VHLSS 2012, 2014, and 2016.

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