rafmacalaba/data-use-mentions-extended huggingface dataset:
br>Rural 2021 Rural2022 Rural 2023<br><!-- End of picture text --> Note: Results are from entire sample of wave 2, wave 3 and wave 4 households.

Most natural hazards threaten in greater proportion those in the bottom poorest quintiles compared to households in richer quintiles. To examine whether exposure to hazards differs by socio-economic status, the data was disaggregated by household consumption quintile.<sup>8</sup> By the end of 2022 and by early 2023 a larger share of individuals in the poorest two quintiles reported facing threats to most natural hazards compared to those in the middle- and high-consumption quintiles (Table 1). For example, while 75 percent of individuals in the bottom quintiles reported living in a household exposed to earthquakes, this was 8 The SWIFT methodology was used to simulate consumption expenditure. Specifically, wave 1 of the HFS (collected in JulyAugust 2021) included questions on poverty correlates to distinguish relatively poorer households from relatively richer households. These questions were drafted following Haiti’s ECVMAS 2012 household survey and were then used to predict the position of respondent’s household in the consumption distribution and to subsequently create wealth quintiles. To assign wealth quintiles to households in the wave 2 data, we restrict the sample to the panel of households that were interviewed in both rounds 1 and 2 and we assume that households maintained their quintile position in wave 2. Using this approach, of the 2,361 observations in wave 2 only 1,487 observations were panel and could be assigned the consumption quintile. For this and subsequent sections where data from wave 2 is disaggregated by quintile, the sample is restricted to only these panel households. Given that all observations in wave 3 and wave 4 were new, we applied the same methodology used for wave 1 data, to assign wealth quintiles to the households. Out of a total of 1,530 observations in the wave 3 data we could assign wealth quintiles to 1,529 households. Data disaggregated in this note
and sociodemographic factors may determine the probability of migration. Women may face specific barriers in their place of origin that might prevent them from migrating and accessing better economic opportunities.

**This study analyzes the determinants of interstate migration in Mexico during the period 2015-2020 and the earnings gains from the migration decision.** The study focuses on women and identifying macroeconomic push and pull factors influencing internal mobility flows and individual factors affecting the propensity to migrate. Push and pull factors at the aggregate level are estimated using a gravity-type model, while the propensity to migrate is estimated using a discrete choice model that accounts for different migration reasons. Also, this study aims to measure whether women in Mexico successfully increase their income and wellbeing by migrating internally. Income gains are estimated using a double selection model that accounts simultaneously for the migration and work decisions.

**The primary source of information is the latest Census of Population and Housing (Census of 2020).** The Census allows for the identification of internal migrants during the period 2015-2020, and it also provides information on the reasons for migration. This study limits the internal migration analysis to migration across states, including the country's capital Mexico City. There are 32 federal entities in Mexico: 31 states and the capital Mexico City, which constitutes a separate entity without being formally a state.

**Around 3.4 percent of the population residing in Mexico in 2015 moved to another state during the period 2015-2020, representing nearly four million people.** The internal migration rate was higher among men than women (3.6 against 3.3 percent). However, women accounted for almost half (49.8 percent) of the total internal migrants in 2020. The migration patterns during the period 2015-2020 provide evidence

> 2 See Gibson and McKenzie, 2012; Clemens, 2010; McKenzie et al., 2010; Stillman et al., 2009; Clemens et al., 2008; among others.

1
across advanced economies and EMDEs, particularly since the variance of productivity is three times larger in EMDEs than in advanced economies, given assumptions required on common parameters for panel estimations. For each economy, the VAR consists of the log-level of labor productivity, logemployment per capita (including self-employment), the log share of investment (gross-fixed capital formation), and separately consumption in GDP, and consumer price inflation.

Macroeconomic aggregates such as GDP and employment are from the World Bank’s World Development Indicators (WDI) database and The Conference Board’s Total Economy Database (TED) for employment. Employment and GDP data are extended where available with WDI and ILO employment estimates and Penn World Table 9.1 GDP data where possible. The majority of economies have full data coverage over the same period over which the growth accounting components are available, and the average sample length is 40 years for EMDEs and 45 years for advanced economies. Hence, here, annual data is used to estimate the VARs. This choice reduces the degrees of freedom in the VAR estimation, while at the same time significantly lengthening the period over which the VAR is estimated for many EMDEs. The span of the data is critical for identifying 8
# **SUMMARY**

Fiscal rules have become an important subject of policy debate in the Western Balkans. The rise in public debt during the global financial crisis focused attention on strategies for restoring sound public finances, while plans to join the European Union (EU) mean that the countries will need to be in a position to adopt and comply with the EU’s fiscal rules. This paper aims to help the countries make decisions about fiscal rules in the period before they join the EU.

Fiscal rules are long-term quantitative constraints on the government’s spending, deficit, debt, or other fiscal aggregate. A few decades ago, only a handful of countries had fiscal rules; now more than 90 do, including, of course, the member states of the EU. All the countries of the Western Balkans apart from North Macedonia have established fiscal rules.

Fiscal rules are designed to limit the government’s discretion over fiscal policy and thereby to limit the fiscal deficit and the growth of public debt. There is evidence that strong fiscal rules (e.g., those entrenched in the constitution and those for which compliance is monitored by an independent agency) foster fiscal discipline, at least in countries with a tradition of large deficits. But fiscal rules, especially self-imposed rules, cannot always be rigorously enforced: a government that can establish a fiscal rule may be able to abolish, circumvent, or ignore it. Thus, the mere enactment of fiscal rules may not achieve much. Yet if fiscal rules enjoy widespread support, and compliance with them is politically important, they may guide fiscal policy even if they are difficult to enforce.

This paper’s assessment of the fiscal rules in the Western Balkans has five main findings:

- A rough estimate suggests that the debt and overall-deficit rules have been complied with a little more than half the time. This is not out of line with the experience of other regions and is consistent with the difficulty of enforcing national fiscal rules.

- An online survey conducted
# **7.4   Treatment Effects on Environmental Contamination**

Medical evidence suggests that the transmission of diarrhea and respiratory infections is mainly through the hands, which constitute disease vectors carrying respiratory microorganisms and fecal material to the domestic environment of the susceptible child. The use of soap reduces germs and bacteria in hands, thus reducing the risk of becoming sick. It is expected that those practicing handwashing with soap at key junctures will have a lower presence of bacteria such as _Escherichia Coli_ or total coliforms on their hands, thereby reducing the risk of contamination when handling, preparing, or serving drinking water at home. Thus, the study collected samples of drinking water<sup>49</sup> from a subsample of 600 households among the school component sample and its control group. These samples were collected during the administration of the questionnaire, and analyzed to detect the presence of _Escherichia Coli_ and total coliforms. Consistent with previous results, the microbiological analysis of drinking water samples summarized in Table 4 shows that the prevalence of _Escherichia Coli_ and of total coliforms was lower in the treated households than in those of the control group, but none of the effects are statistically significant at conventional levels.

# **7.5   Treatment Effects on Child Health**

The last step in the causal chain is to assess whether the positive results found on exposure to the campaign—knowledge of appropriate handwashing practices, access to soap and water, and handwashing with soap behavior—resulted in health improvements. Table 5 summarizes the effects of the treatments in a wide range of indicators of child health and well-being, including symptoms of illness, nutrition, parasite infestations, and anemia. The results show that overall none of the treatments had a significant effect on those variables.

Because the mass media plus DCC provincial-level treatment alone had no effects on previous outcomes, we did not expect to find any impact on child health. If there were significant estimates, they would probably not be causally attributed to the treatment given that it did not reach the
Table 6: Squared Semi-Partial Correlation Coefficients of Job Quality Ratio from Table 5

|Variable|Correlation Coef.|
|---|---|
|Age|0.0125|
|Male|0.0130|
|Educ - High School|0.0063|
|Educ - College|0.0001|
|Migrant|0.0007|
|Public Firm|0.0035|
|Large Firm|0.0036|
|Formal|0.0059|
|Self-Employed|0.0000|
|Employer|0.0016|
|Union|0.0053|
|Urban|0.0016|
|ln(GDP/capita)|0.0000|

_Notes_ : Authors’ calculations using SEDLAC data. Each value represents the proportion in variance of _JQ_<sup>_R_</sup> explained by that control variable.

between job quality and each control variable, after removing the effects of the other control variables. Age and gender have the largest impact on job quality. This is to be expected since older workers are more likely to have tenure than younger workers and have accumulated more human capital. Also, men are likely to be paid more, and thus more likely to have a higher quality job.

These characteristics may have different effects across the job quality dimensions. Therefore, we run the same regression as above, except we replace job quality with an indicator for each particular dimension of job quality. Since the dependent variables take on values of 0 or 1, we run logit regressions. These results are shown in Table 7.

In the regression with health insurance as the dependent variable, an indicator 28
economic gains. We also test for differential impacts across regions for similar reasons.

# _Comparison between Study Samples and Universe of Applicants_

A key concern for external validity is the extent to which the studied samples are representative of the population from which they were drawn. In the environmental analysis, we analyzed the universe of polygons within 25 points of the cutoff, which includes 97% of the applicants. However, to examine deforestation, we begin with applicant polygons that include more than 50% forest cover in 2000, which eliminates 43% of the area submitted for consideration during our study period. Therefore, our results cannot shed light on program impacts in areas without land that is easily classified as forest, which includes many types of vegetation important for the biodiversity program, such as the low shrubs and cacti characteristic of arid regions. This is a fundamental limitation of having to fall back on the Hansen et al. data set.

With regards to the survey data, survey participant characteristics indicate they are very similar to the universe of eligible applicants to the program for the years studied. **Table 2** compares environmental and social characteristics of the communities that were surveyed with the rest of the population of properties that applied in 2011-2014. Our survey sample of 862 properties represents 30% of the available universe of 2,839 _ejidos_ and _communidades_ that met the minimum program requirements in these years (after dropping repeat applications and 52 properties without a matching code for the _Sistema Padrón e Historial de Núcleos Agrarios-PHINA_ ). To assess differences between our survey 12
<!-- Start of picture text --> Figure 3 - How many countries have laws that restrict unfair practices?<br>No restrictions<br>Abusive collection practices<br>Unfair selling practices<br>Deceptive advertising<br>Breach of client confidentiality<br>0 50 100 150<br>Number of Countries<br>Source: Financial Access Database<br>Figure 4 - Which FIs have to disclose upon opening & periodically?<br>Banks<br>Regulated FIs<br>Unregulated FIs<br>0 20 40 60 80<br>% of Countries<br>Disclosure upon opening<br>Periodic statements required<br>Source: Financial Access Database<br><!-- End of picture text --> 22
|0.508|
|||(0.361)||(0.225)||(0.337)||(0.353)|
|Damage on crop (%)||-1.072**||-1.914***||-0.581*||-0.401|
|||(0.478)||(0.332)||(0.339)||(0.423)|
|Constant|144.1***|177.3|126.0***|196.3|81.48***|-183.6**|129.8***|374.6*|
||(28.29)|(119.2)|(19.27)|(124.0)|(18.71)|(86.99)|(48.08)|(208.9)|
|Observations|2,928|2,841|2,397|2,336|1,685|1,629|1,302|1,240|
|Adjusted R-squared|0.181|0.359|0.109|0.379|0.303|0.544|0.053|0.528|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|

Note: Regression of realized production on expected produdction, planted area and plant productivity loss, and a number of plot-, manager-, and household controls as well as inputs and zone fixed effects. Controls: Inputs - Plot area, total labor, any hired labor dummy, seed quantity, improved variety dummy, inorganic fertilizer quantity, any organic fertilizer dummy; Household - dependency ratio, any HH member has primary education dummy, agricultural asset index; Manager - female
refugees actually do so but we check if these observations might have an influence on our results. We therefore run all our regressions on a sample from which we drop the observations where the canton of residence differs from the initially assigned canton (7.5% of observations). There are hardly any differences between these results and those reported in Tables 4, 5 and 6.<sup>42</sup>

# **6 Concluding Remarks and Policy Implications**

In this paper, we analyze the integration of refugees in the Swiss labor market by looking at trajectories of employment rates and by analyzing the impact of initial conditions on these trajectories. We combine data from a longitudinal dataset, which covers the universe of refugees and migrants in Switzerland over 1998-2018, with data on attitudes toward refugees and migrants, which are derived from popular voting outcomes at the canton level. We find that, on average, the employment rates of refugees increase slowly over time to reach, ten years after their arrival, a level which is about 15 percentage points lower than the employment rates of comparable natives. However, this integration profile depends on the refugees’ age upon arrival: those who were younger at arrival integrate more rapidly into the labor market and attain higher employment rates in the long run. We also consider migrants from non EU-15 countries as a comparison group. According to our estimates, which take differences in origin countries into account, labor market integration takes more time for refugees than for migrants, although the convergence seems slightly more rapid than in other European countries (Fasani et al. 2020a). Ten years after arrival, employment rates of refugees are 4 percentage points lower than those of migrants and this difference persists in the long run. There are some gender differences. Employment rates of male refugees increase initially more rapidly than employment rates for female refugees but native-refugee employment gaps are similar for men and women in the long run.

When we investigate the impact of initial conditions on the employment of refugees over the lifecycle
our sample, Kyrgyz Republic comes out at the top with an estimated sibling correlation of 0.38 which is also the lowest among the 53 countries in Table 1. Turkiye (previously known as Turkey) and Armenia share the unfortunate distinction of the lowest intergenerational mobility in this region with an estimated sibling correlation of 0.55.

# (5.2) Evolution Over Time: Estimates from Decade Wise Birth Cohorts

As noted earlier, in many countries, the sample size for the 1960s birth cohort is too small for credible estimation of sibling correlation. We thus focus on the three decade wise birth cohorts, from the 1970s to the 1990s. The children born in the 1970s are likely to face signicantly dierent economic and educational policies when compared to the children born in the later decades. There were two major policy developments in the 1980s and the following decades that might have aected the educational opportunities of children. First, there were economic liberalization and reform across many developing countries including trade liberalization, privatization and deregulation. The reform yielded impressive economic growth and substantial reductions in poverty in many countries, but at the same time increased income inequality (World Bank (2006)). Second, there were dramatic expansion of schools in many developing countries.<sup>34</sup> Many countries also implemented compulsory primary and secondary schooling in the decades of 1980s-2000s. As a result, signicant gains in school enrollment and schooling attainment were achieved over these decades (World Bank (2018)). Did the poverty reduction and the expansion of schooling and other educational policies outweigh the countervailing eects of inequitable growth and might have actually expanded the educational opportunities for the children in the later decades? Are there important regional dierences in the evolution of inequality of educational opportunities over these decades? We make some progress on these questions in this section.

Figure 2 presents the estimates of sibling correlation for the six regions dis-aggregated by the decade of birth (1970s-1990s birth cohorts).<
program delivered small, regular, foreseeable, monthly transfers, which are distinct from the large transfers delivered as emergency response following seasonal shocks. Third, and most importantly, the transfers took place for 18 months between January 2011 and June 2012, and this limited duration was clearly communicated to beneficiaries at the beginning of the program. A follow-up survey collected 18 months after the end of the transfers (36 months after the baseline survey) provides a rare opportunity to assess long-term transfer impacts. Specifically, we can analyze the sustainability of program impacts after households have made investments,

> 10 In polygamous households, the recipient was typically the first wife.

> 11 _Tontines_ are often referred to as a rotating savings and credit association (ROSCA). Project monitoring data suggest that _tontine_ funds were mostly used by beneficiaries to purchase livestock.

> 12 Niger had the lowest Human Development Index (HDI) in the world in 2012 (see <u>http://hdr.undp.org/en/statistics/).</u>

10
# f. Predictions across time are much less accurate than predictions across space

In contrast to the many studies that have used satellite imagery to predict welfare levels across space, only two published studies to our knowledge have evaluated intertemporal predictions using geospatial data. Yeh et al. (2020) attempt to use daytime imagery to predict changes in wealth measured in the Demographic and Health Surveys. The CNN was only able to explain 15 to 17 percent of the estimated changes across African villages, however. When using self-reported changes in assets from the most recent survey, the figure rose to 35 percent. When aggregating up to districts, and using self-reported changes from the endline survey, imagery can explain about half of the variation in self-reported changes. However, self-reported changes are subject to recall error and may only capture major changes in assets.

Meanwhile, Khachiyan et al. (2022) look at variation across census blocks in the US, and find that a CNN trained on daytime imagery predicted half of the change in population density between 2000 and 2020, and 42 percent of the variation in income change between 2000 and 2017. Both Khachiyan et al (2022) and Yeh et al (2022) use convolutional neural networks for prediction. Further studies would be very useful to test the ability of different methods and data sources to predict changes over time.

# g. Summary of key lessons on using geospatial data for small area prediction

Overall, the main conclusion from this nascent literature is that geospatial data are strongly predictive of geographic variation in wealth and poverty. Exactly how predictive accuracy varies depending on a myriad number of factors. In general, wealth is easier to predict than consumption. Nonetheless, when national geospatial estimates of poverty or wealth are compared against census-based estimates for sampled areas, the R<sup>2</sup> appear to consistently range from about 0.74 to 0.95, as shown in Table 1.

Because geospatial data are particularly predictive of population density (Leasure et al, 2020, Engstrom
health: beneficiaries of the combined treatment arm (IPT-G+) were 10 pp less likely to have minimal depression compared with those who received IPT-G alone at the 12-month followup. These negative effects were still present and statistically significant at the 24-month follow-up. There were no impacts on human capital outcomes. We hypothesize that the negative impact of cash reflects the fact that it was distributed right before the onset of the COVID-19 pandemic – an unexpected negative shock. Exploratory analysis of data from the follow-up surveys suggests that the adolescent beneficiaries were frustrated by their inability to put the cash towards their own goals (such as starting a small enterprise), since they had to divert part or all of the transfer towards the essential needs of their families. We discuss this finding in more detail in Section 3.4.

Our study contributes to a growing literature on the impact of CBP/IPT interventions on mental health in LMICs. Lay-provider led therapies have received considerable interest, and were shown to be effective (in the short run) by Singla et al. (2017), who conducted a meta-analysis of 27 studies with a highly significant average effect size of 0.4 standard deviations. However, a number of more recent studies have produced longer-term results and the findings are mixed. Baranov et al. (2020) showed sustained effects of the Thinking Healthy Program (THP), which is a CBT-like intensive intervention adopted by the WHO, on depression among women with perinatal depression in Pakistan after 7 years - with lasting effects on financial empowerment and investments in children, particularly among mothers who gave birth to daughters. In contrast, Bhat et al. (2022) and Maselko et al. (2020) evaluate peer-delivered programs based on THP to women suffering from maternal depression in South Asia and find no impacts on depression or other outcomes 3 to 4 years post-intervention.<sup>4</sup> In another notable contribution, Blattman et al. (2023) evaluate a groupbased CBT intervention targeted to young men actively involved in