rafmacalaba/data-use-mentions-extended huggingface dataset:
implemented. The fact that the transfer represents a smaller percentage of expenditures for urban households might also help explain the results.

# **8. Conclusion**

In this paper we are able to exploit unique panel data to inform about the impact of Oportunidades on child nutrition. We contribute to the literature by measuring the effects using a representative survey that allows to assess the impacts in areas that were incorporated after the first implementation phase, which was focused on very poor rural communities. This is the only evaluation that relies on survey data outside the Oportunidades Evaluation samples. This analysis aims to inform an emerging literature about the potential pitfalls of scaling up programs which may be linked to changes in the characteristics of beneficiaries or differences in the context (AlUbaydli et al. 2019).

Our analysis focused on child nutrition. Nutritional status could be measured in a number of ways. In this project, we chose height as our marker of interest for a number of reasons. On the one hand, there is a well-establish positive association between adult height and earnings. Controlling for a number of individual characteristics, taller individuals earn, on average, higher earnings, and this relationship has been found to hold across a wide variety of contexts. On the other hand, we know from the nutritional literature that height at age two is a very strong predictor of adult height. Also, it has been shown that height is sensitive to nutritional inputs during the first years of life but not later in life. This feature is key for our identification strategy. Finally, height constitutes a longterm marker of nutritional status, as opposed to other indicators that measure short-term nutritional status. In summary, we have a marker that is responsive to nutritional interventions early in life (better nourished children are, on average, taller), a marker that measures long-term nutritional 34
increase in the skill profile of migrants as countries develop.

To observe these effects in the data, however, is challenging. Comprehensive data on the skill level of migrants, particularly time series data spanning several decades, do not yet exist. To provide some insight into the evolution of the skill profile of emigrants to high-income countries, this analysis turns to data from the United States, which is one of the biggest destinations for migrants from around the world, particularly for high-skilled workers. While not representative, data from this corridor can shed light on how emigration to high-income countries evolves in terms of skills as origin countries develop.

Data from the United States confirm that migrants become increasingly educated as origin countries develop. The increased educational attainment of the population also translates to increased educational profile of the migrants to the United States (figure 9, panel a). The relative share of the highly educated, with some tertiary education, among migrants increases steadily as origin countries develop. The share of the highly educated increase by 25 percentage points as origin countries’ incomes increase by 500 percent. The share of the medium-educated, with some secondary education, also increases by 10 percentage points.

The increased educational profile of migrants is driven not only by the higher educational attainment of the origin country population, but also by the increased rates of emigration as these countries develop. As panel b of figure 9 shows, the emigration rate of the highly educated increases much faster than those with lower levels of education as the origin countries develop. The probability that the highly educated would emigrate increases by 3 percentage points as origin countries increased their incomes by about 150 percent. The corresponding increase for those with lower levels of education is less than 1 percentage point. There is some indication of higher probability of emigration as countries increase their GDPs beyond 500 percent, but the estimates are noisier.

The evidence presented here is consistent with the increased skill profile of the migrants to high-return destinations as origin countries develop. This increase is driven by the changing educational
# C. Oral rehydration salts (ORS)

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Facilities without ORS in stock Facilities with ORS in stock<br>Cannot manage condition Can manage condition<br>Share of providers<br><!-- End of picture text -->

**Note** : Share of the most knowledgeable provider in the facility that can provide correct diagnosis and treatment for pneumonia, conditional on the facility having access or not to antibiotics. The estimates are (unweighted) mean outcomes across countries, with the country means calculated using country-specific sampling weights. The error bars represent the 95-percent confidence interval. Data are from clinical vignettes and visual inspections of storage facilities and consulting rooms from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Sierra Leone (2018) Tanzania (2016), Togo (2014), and Uganda (2013).

66
magnitude less (World Bank World Development Indicators). Indeed, FAO data classify 43 percent of the SSA land mass as semi-arid to hyper-arid. Even within the arid states, rain and water resources tend to be concentrated, leaving some subregions particularly arid and hence vulnerable to climate shocks. Climate change poses a serious threat. Western, central and southern Africa may experience a decline in their annual average rainfall by 4 to 5 percent as a direct consequence of climate change (Hoerling et al. 2006). Across SSA an estimated 60 percent of all land is at risk of desertification (Benson et al. 1997).

Improved water and land management can help stem or even reverse those patterns. The most recent household survey data from across the continent show that less than 5 percent of farming households have

> 9 Democratic Republic of Congo, Angola, Congo, Zambia, Cameroon, Mozambique, Central African Republic, Gabon, Sudan.

> 10 Foster and Rosenzweig (2011) suggest that, in India, once one values labor properly, small farms are typically less, not more productive than farms of five or more acres. Some recent cross-country evidence further suggests that policies meant to limit farm sizes may lead to productivity reducing misallocation of land (Adamopoulus and Restuccia 2014).
# Acronyms

|BPR|Business Process Re-engineering|
|---|---|
|DP|Development Partner|
|DPPA|Disaster Prevention and Preparedness Agency|
|DPPC|Disaster Prevention and Preparedness Commission|
|DRM|Disaster Risk Management|
|DRMFSS|Disaster Risk Management and Food Security Sector|
|DRM SPIF|Disaster Risk Management Strategic Programme and Investment Framework|
|DRMTWG|Disaster Risk Management Technical Working Group|
|EWRD|Early Warning Response Directorate|
|EWS|Early Warning System|
|FCB|Federal Contingency Budget|
|FDRE|Federal Democratic Republic of Ethiopia|
|FEG|Food Economy Group|
|FEWS NET|Famine Early Warning Systems Network|
|FSCB|Food Security Coordination Bureau|
|FSCD|Food Security Coordination Directorate|
|GoE|Government of Ethiopia|
|GTP|Growth and Transformation Plan|
|HEA|Household Economy Approach|
|HEP|Health Extension Program|
|HICES|Household Income and Expenditure Survey|
|HRD|Humanitarian Response Document|
|IFPRI|International Food Policy Research Institute|
|LEAP|Livelihoods, Early Assessment and Protection|
|LIAS|Livelihood Impact Assessment Sheet|
|LIU|Livelihood Integration Unit|
|MoANR|Ministry of Agriculture and Natural Resources (former Ministry of Agriculture<br>and Rural Development (MoARD))|
|Table 3 Average black mar|ket premi<br>**1980-89**|um _(perc_<br>**1990-93**|_ent)_<br>**1994-97**|
|---|---|---|---|
|Total<sup>a</sup>|82.0|78.2|20.3|
|East Asia|3.6|3.6|3.2|
|Middle East And North Africa|165.6|351.6|46.5|
|-Excluding outliers <sup>b</sup>|7.1|8.8|1.4|
|Latin America|48.7|13.1|4.4|
|South Asia|40.8|45.1|10.1|
|Africa|116.5|28.6|32.2|
|-ExcludingNigeria|112.1|25.8|9.6|
|_Notes:_<br>a<br>Sample of 41 develo<br>_Source:_<br>World Bank (2001)|ping count|ries. <sup>b</sup><br> Alge|ria and Iran|

There are good reasons to expect that, in this situation, a high protection regime will lock countries into continuing dependence on resource-based commodities which are typically less dependent on purchased intermediate inputs than is manufacturing, particularly in this era of production fragmentation. To allow further examination of this difference, Table 4 presents for a number of countries data on the cost structure of output and the effective rates of protection imposed on export-oriented activities. A striking feature is the top section of the tables is the much lower dependence of primary agriculture and resources commodities on intermediate inputs. This gives resource-based activities an opportunity to survive even in situations of very high protection.

It is, of course
crash is likely reported on Twitter). Therefore, these data can be used to target areas for design solutions where we are seeing high numbers of crashes consistently over time. In settings where there are limited or non-existent administrative records and, therefore, lack of any geolocated data, this tool can produce information in real-time for one of the most pressing challenges in developing countries.

Furthermore, by developing tools that generate time-stamped geolocated data and statistics from crowdsourcing on different “events” that are reported on social media, we can hope to expand data availability across other contexts and across issues beyond RTCs. For example, real-time traffic applications like RIDLR in India can be used to expand data on road safety. These improved tools can also help geolocate victims during a natural disaster or alert disaster management teams to the location of unsafe buildings or areas needing immediate attention. They can support law enforcement or communities to locate and respond to crimes, cases of violence against women, or police violence. Improved identification of the time and location of events can help to automate and accelerate policy response across a wide set of issues, potentially leading to better policy outcomes.

# **References**

1. Serajuddin U, Uematsu H, Wieser C, Yoshida N, Dabalen A. Data deprivation: Another deprivation to end. The World Bank. 2015;.

2. Notzon F, Nichols EK. Global Program for Civil Registration and Vital Statistics (CRVS) Improvement; 2015.

3. WHO. Global status report on road safety 2018. World Health Organization. 2018;.

4. IEAG. A World that Counts–Mobilising the Data Revolution for Sustainable Development. Independent Expert Advisory Group on a Data Revolution for Sustainable Development. 2014;.

5. GSMA Intelligence. The Mobile Economy 2020. London: GSM Association. 2020;.

6. Kemp S. Digital 2020: Global Digital Overview. Retrieved from Datareportal: https://datareportalcom/reports/digital-2020-global-digital-overview. 2020;.

7. Batty M. Big data,
be enough to offset the adverse effects of diminished employment on total output (Bauducco & Janiak, 2018; Seok & You, 2022). On the other hand, if increasing the minimum wage does not reduce employment, it may result in higher inflationary pressure (Majchrowska, 2022). Evidence from the US shows that the impact of the minimum wage on employment manifests over time through shifts in growth rather than an immediate decline in relative employment levels. They contend that commonly employed specifications in this field, particularly those incorporating state-specific time trends, need to be revised to capture these effects. Through analyzing three distinct state panels of administrative employment data, this research reveals that the minimum wage curtails job growth over multiple years. This conclusion is robust across various empirical specifications.

**In contrast, more recent investigations (Dube, Lester, and Reich, 2010; Allegretto, Dube, and Reich, 2011) found no adverse employment repercussions linked to minimum wage changes, but these results have been contested.** Nonetheless, Neumark, Salas, and Wascher (2014a) and Neumark, Salas, and Wascher (2014b) have cast doubt on these findings. Recently, studies have identified significant disemployment effects of the minimum wage (e.g., Baskaya and Rubinstein, 2016; Clemens and Wither, 2019; Powell, 2016). These

8
# **II. Methodological approach**

The study proposes a two-step methodology for updating the LAC region middle-class thresholds. The first step consists of constructing synthetic panels given the lack or scarcity of long panels in most countries. The second step defines the lower and upper middle-class thresholds.

## II.1 Constructing synthetic panels:

Ideally, a proper study of welfare dynamics entails following the same observation (household or individual) for at least two—or preferably, multiple—periods. However, in many developing countries panel data sets are not readily available, span few periods, or suffer from “non-random” attrition issues, hindering the capacity to study elements such as the factors that help households escape or remain in poverty (Dang and Lanjouw, 2013; Bourguignon and Moreno, 2015). To overcome the absence of panel data or longitudinal surveys, authors such as Deaton (1985), Deaton and Paxson (1994), and Pencavel (2006) have proposed methodologies to construct pseudo-panels by following similar age cohorts across multiple cross-section surveys. Nevertheless, as argued by Dang et al. (2014), these methodologies typically rely on having several rounds of cross-section surveys but do not allow for analyzing mobility at a more disaggregated level than the cohort. In addition, Fields and Viollaz (2013) argue that pseudo-panel methodologies might not perform well in predicting income mobility in some cases.

Dang et al. (2014) propose both a parametric and a non-parametric approach to construct synthetic panels and estimate an upper-bound (assuming zero correlation between error terms) and a lower-bound (assuming a perfect positive correlation between error terms) for the transitions using two rounds of cross sections. In addition, Dang and Lanjouw (2013, 2016) extend this method and calculate point estimates of poverty mobility based on the synthetic panels. However, this approach relies on the key assumption that the residual terms of the income equations in two periods are distributed according to a bivariate normal distribution.

To avoid the strong distribution assumption for residual or error
In this study, we use a variety of data sets. The WHO database<sup>4</sup> provided the COVID-19 data. The low and central values of the VSL for the US are from Robinson et al. (2017b) and Robinson and Hammitt (2016). The GNI and other data were extracted from the World Bank Development Indictors (WDI).

# **3. Results**

# **3.1 Estimates of the COVID-19 mortality costs over time**

**Table 1** shows the historical trend of COVID-19 mortality costs in Côte d'Ivoire. As the income elasticity of the VSL rises over time, regardless of the values of the central US VSL or low US VSL, the VSL for Côte d'Ivoire drops along with mortality costs. The Côte d'Ivoire VSL ( **Panel A of Table 1** ), for example, swings from US$ 734,103.81 to US$ 259,313.96 based on the central US VSL, indicating a drop of 64.68% of the VSL when the income elasticity of the VSL rises from 1 to 1.4. Similarly, for the year 2020, the mortality costs attributable to COVID-19 drop from US$ 98,369,910.86 to US$ 34,748,070.04, indicating a decrease in the mortality costs attributable to the COVID-19 pandemic of 64.68%. With an income elasticity of the VSL of 1, it is interesting to observe that starting in 2021, COVID-19-related mortality costs dropped precipitously, reaching US$ 90,294,768.92 in 2022. Using an income elasticity of the VSL of 1.4 ( **Panel A of Table 1** ), the mortality costs due to the COVID-19 pandemic decreased from US$ 148,586,896.52 (2021) to US$ 31,895,616.53 (2022). This decreasing pattern of mortality costs due
2 Maduro administration. The interaction of these two sources of variation corresponds to a plausibly exogenous _predicted migrant shock_ measure.<sup>3</sup> Our identification strategy exploits the fact that crisis-induced migrants tend to move disproportionately to municipalities where they have preexisting networks, formed before the beginning of the migration wave (Bartel, 1989; Greenwood and McDowell, 1986). Our identification assumption is that predicted migration shock is correlated with actual migration but do not affect educational outcomes independently or through any other channel. This is plausible after controlling for municipality-specific trends parametrized by key pre-determined municipality characteristics, which we choose using machine learning techniques.

We find that the migration shock increased the enrollment of both male and female foreign students. This effect is mainly driven by public schools, and it is stronger for younger kids, who enroll in primary school grades. We also find, however, negative externalities of the increased enrollment in terms of promotion rates and dropout rates. While falling promotion rates and increasing dropout occurs for both native and foreign students, the effects are larger for the former. Migrants, on the other hand, seem to exert more effort as suggested by their performance in standardized tests. Finally, we document that the mechanisms that explain these negative effects of the migration shock have to do with the deterioration of key school inputs in public schools. For instance, we show that the migration shock reduces the number of teachers and increases the ratio of pupils to teachers (and thus class size). Our results highlight important policy implications, which we discuss in the conclusion.

> 3Importantly, given the absence of longitudinal data on the places where Venezuelan migrants have settled over time, we use our predicted migration measure in a reduced-form specification instead of as an instrument for the endogenous actual settlement of Venezuelans. However, a visual comparison of Figures 2 **B.** and 2 **D.** suggests that the spatial distribution of the census-observed and the predicted number of cumulative Venezuelans in 2018 is very similar
**Women’s financial outcomes.** We use an OLS specification to estimate impacts on financial outcomes since we only collected this information in the endline survey. Almost 59% of women in the control group had saved any money during the past 12 months and 40% were able to pay FCFA 20,000 (approximately USD 34) in the case of an emergency (Table 12). We find a 13% percent increase in women’s likelihood of having saved in the last 12 months (significant at the 5% level) and a 25% increase in women’s reported capacity to mobilize financial resources in an emergency.

> 18We collected data on extensive margin labor market participation for all non-agricultural non-salaried activity. However, due to a mistake in the skip patterns in the programmed version of the survey questionnaire, we only collected information on hours worked in the last month and total monthly income for non-agricultural activity in a household enterprise, therefore excluding non-agricultural self-employment income from non-household enterprises. Thus, while our results in column 1 of Table 7 are unaffected, results in columns 2, 3, and 4 unfortunately exclude self-employment activities and could therefore understate any treatment effects.

18
includes worker-firm fixed effects, as well as worker and firm controls. The results indicate that returns to technical upgrading are positive and statistically significant in all these specifications. We also observe that the magnitude of the point estimate of interest is quite stable across these specifications, and points to positive wage effect of about 3%.

While the estimation sample includes only exporting firms, one might worry that the results are driven by only a subset of those firms. We therefore proceed by verifying the extent to which these results are specific to small, medium and large exporting firms. In Table 10 we split the sample according to a firm size classification of _Instituto Brasileiro de Geografia e Estat ́ıstica_ (IBGE), the national statistical institute. We observe that technical upgrading is associated with significantly higher labor earnings among firms in each of these categories.

The sample used in Tables 9 and 10 includes among the control group workers that received other types of training. Although the analysis in the previous section suggests that export participation does not impact the share of workers trained in those other categories, we cannot exclude the possibility that such training also affects labor earnings. We therefore verify if our estimates are sensitive to the exclusion of workers that received other types of training. In particular, we re-estimate (4) on a sample that excludes all observations that received any SENAI training course other than technical upgrading during 2009-2012. Appendix Tables A3 and A4 report the corresponding results. We observe that our findings remain qualitatively and quantitatively very similar when using this alternative sample.

# **5 Conclusion**

This paper has examined the effects of export participation on job training in a rich combination of worker-firm panel data for the Brazilian manufacturing sector, linked with detailed records on training activity from the main provider. Using industry-specific exchange rate movements to generate exogenous variation in export status at the firm-level, we have provided evidence that export participation tends to increase the share of workers who received training. In line with several intuitive theoretical mechanisms proposed in 15
that have not been documented. This has significantly limited potential demand, as the high price of insurance was a principal reason given by 64 percent of non-participating farmers for not purchasing the insurance – they faced liquidity constraints.

Households purchase the insurance at the beginning of the growing season, a time when many uses compete for available cash. Similar to the Malawi case, there is a lack of trust in the insurance process and limited education and financial literacy in the region. Formal legal protections are also viewed as relatively weak, further deteriorating the trust necessary for the success of the program. Limited salience of the insurance product can be improved by using different selling techniques, including door-to-door sales. The continued success of the program and its ability to scale, expand, and become self-sufficient is dependent on overcoming these barriers (Cole et al., 2010).

In Gujarat, India, the Self-Employed Women's Association (SEWA) has pioneered index weather insurance distribution, beginning in 2006 (Cole et al., 2011). Leveraging their longstanding, trusted presence as a trade union, SEWA's insurance sales operation has grown by an order of magnitude over this time, initially targeting 15 households in each of about 30 villages and in 2011 targeting about 70 households in each of 60 villages. Each year SEWA has used village meetings to introduce the insurance sales, followed by door-to-door marketing visits.

Between 20 percent and 40 percent of visited households have purchased the insurance, and the trend line is upward-sloping, but for research purposes the marketing outreach and the policy prices have been subsidized. A long-term randomized impact evaluation is ongoing, and will shed light on the dynamics of demand, the opportunities for greater production efficiencies, and the implications for consumption-smoothing. Consistent with the view that ambiguity aversion may influence adoption of index insurance (Bryan, 2010), in the Gujarati data the partial correlation of insurance demand with risk aversion is negative initially, but positive in the more recent data. This suggests that ambiguity about index insurance can