rafmacalaba/data-use-mentions-extended huggingface dataset:
the four different IPC classifications in the 2017 IPC announcement. The figure reports the averages of the variables in the months immediately before the 2017 IPC announcement.

There are three notable patterns apparent in Figure 2. First, the regions that were classified with the least severe food emergency in the 2017 IPC announcement (IPC 2) had significantly better food access than the rest of the country. For example, IPC 2 regions had an average FCS that was 31 percent higher and an average rCSI that was 75 percent lower than IPC 3 regions.

> Second, food access was very similar in regions with an IPC classification that was more severe than IPC 2. Both the average FCS and rCSI are nearly identical in all these regions and

> 20See Appendix 7. The relative ranking of governorates based on the share of respondents with either a poor or borderline FCS is similar, and the levels are nearly identical for a large number of governorates. However, in governorates that indicated the worst food access in both the mVAM and the EFSNA, the share of respondents with a poor or borderline FCS was higher in the EFSNA.

> 21See Appendix 8. Importantly, the WFP survey was not used as an input to the population-level estimates (IPC 2018). Also, the districts which are different between the two estimates tend to be districts that are large in area, but small in population (e.g., districts in Al Jawf, etc.).

> 22Table 1 only reports food assistance statistics from January 2017 and on, which was when the governoratelevel estimates begin in the publicly available data. However, the national share of assistance is reported since September 2015, and that data is reported in Figure 3a.

> 23The share of the mobile phone-using population receiving aid from the WFP is higher than the share reporting receiving food assistance because the averages are reported over different time periods. The average share of food assistance is reported between January 2017 and July 2018, whereas the breakdown of assistance by form and whether it was from
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **B. Engagement model: Bring decision makers on board**

As results from the use-case on COVID-19 became available, PURA and GBoS used these findings to inform their participation in the government task force for COVID-19. The early dissemination of results helped to inform decision makers and prompted requests for a scaled-up version providing realtime data during a quickly evolving health and economic crisis. Unfortunately, efforts to quickly turn this case-study into a fully functional data pipeline were delayed due to constraints in implementation capacity until late 2020. However, once the necessary hardware and training were provided, the CDR data pipeline became operational in early 2021.

Throughout the dialog with decision makers, presentation of findings in an easily accessible format and identification of specific policy recommendations strengthened the support and the interest in the project. Maps created an entry point for dialog with a technical and non-technical audience as they were visually appealing but still contained important lessons (see Figure 8). Moreover, interpretation of results and specific applications in the context of the pandemic supported communication. For instance, the team argued that findings from the use-case on COVID-19 could inform targeted testing initiatives, by concentrating efforts in areas of high mobility. When a full lockdown is not possible given the economic costs, this can also inform where social distancing policies should be enforced given higher mobility and associated with this increased risks of transmission. In addition, results demonstrated that the lockdown disproportionately affected urban areas by restricting economic activity, and relief and recovery efforts should therefore aim to address these adverse effects. These are but a few of the policyrelevant insights CDR data can deliver.

<mark>Figure 8.</mark> Weekly averages of distances traveled at the district level - ratio to the baseline

**17** | P a g e
# Annexes

## _Annex 1. Comparability with other existing SOE databases_

Most available SOE databases are not comprehensive as they either focus on collecting sectoral data or have limited coverage and scope. Databases that focus on specific sectors are primarily centered on economic sectors commonly associated with SOE presence, such as infrastructure and finance. Examples of infrastructure databases include the World Bank Database of Infrastructure State-Owned Enterprises (Herrera Dappe, et al., 2022), covering 19 countries and 135 SOEs between 2000 and 2018, and the 2017 State-Owned Enterprises Public Projects (SPI) database (PPIAF/The World Bank, 2017) compiled by the World Bank’s Public-Private Infrastructure Advisory Facility. Analyses of SOE presence in the financial sector include the work of La Porta, Lopez de Silanes and Shleifer (2002), who assembled data on government ownership of banks in 92 countries, the cross-country data set of state-owned banks compiled by Andrianova, Demetriades, & Shortland (2012) for 1997-2007, and the WB State Bank Privatization database that covers 70 countries between 1995-2017 (Can, Calice, Diaz, & Masseti, 2020).

Other existing databases compile national or regional SOE data, although with limited global country coverage and scope. Hence, a comprehensive, global cross-country database that identifies SOEs and their financial data has never been compiled. Existing efforts so far are limited to certain regions and in particular, coverage of developing countries has been lacking. For example, in 2012, 2015, and 2017, the OECD conducted an exercise to identify the presence of SOEs across 40 economies, which provided aggregated data for about 2,400 firms including information on the number of SOEs and their sectoral distribution based on a self-reporting survey tool of participating countries.<sup>54</sup> However, the OECD effort relied heavily on the local SOE definitions, which can vary substantially across jurisdiction. The IMF also collected firm-level data for about 10,000 SOEs leveraging the ORBIS database from _Bureau van Dijk_ for the period 2014-2016 although this one excludes SOEs operating
wage subsidy literature in several ways. First, we test the effect of a wage incentive for youths who are just graduating from secondary school.<sup>7</sup> At this career point, hourly wages of informal jobs are relatively attractive (Shehu and Nilsson, 2014) and youths may heavily discount and underestimate the rapid (relative) wage growth of formal jobs. Short-term wage incentives offered during the school-to-work transition are thus particularly important, not least because the first job has long-lasting impacts on career trajectories and lifetime earnings (Neumark, 2002; Kahn, 2010; Oreopoulos et al., 2012). Second, the incentive is paid monthly and is thus more attractive than bonuses paid in the more distant future, especially if beneficiaries heavily discount future payouts.<sup>8</sup> Finally, we link our study sample to monthly social security data, which addresses concerns of selective attrition and surveyor demand effects. It also allows us to precisely trace out employment effects over time, which is important given the high rates of worker churn, and thus estimate cumulative treatment<sup>9</sup> We also contribute to a nascent literature that explores the role of worker retention and on-the-job human capital accumulation in developing countries. In line with Donovan et al. (2020), our findings suggest that high turnover rates, especially in the informal sector, do not represent a reallocation of workers to more productive jobs. Donovan et al. (2020) and Bick et al. (2018) also conclude that the steeper wage-tenure profiles in developing countries are less likely to be due to human capital accumulation and more likely to result from firm selection in the presence of uncertain match quality. By contrast, our results suggest that at the very early career stage, formal employment offers substantial training and learning opportunities that are associated with wage increases. Importantly, when workers switch between formal jobs they do not experience a loss in wages, suggesting that the rapid wage increase in formal employment is at
with significantly lower gains from resource reallocation. We interpret this finding as indicative of the importance of counting with a database that properly accounts for the real distribution of value added shares in the economy.

## **7.1 Firm-Size Distribution: Survey vs Census**

To give some background to the analysis, the ES is briefly described as follows. The ES is an ongoing project of the World Bank to collect establishment-level data from several countries, particularly from low and middle income countries.<sup>17</sup> The dataset contains firm-level information including output and input measures in a harmonized fashion for 135 countries for at least one year since 2002. Two rounds of surveys were conducted in each of our sample countries.

To ensure the proper sample representation, the surveys rely on a stratified sampling technique. Three levels of stratification were used: sector of activity, firm size, and geographical location. In each country, regions are selected based on the extent of economic activity. The population of firms is stratified into three size strata: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). The degree of industry stratification depends on the size of the economy. In Ethiopia, sectors are classified into two strata: manufacturing and service, whereas in Ghana and Kenya the manufacturing is subdivided further into selected 2-digit industries according to their contribution

> 16Although there are some studies in Africa that examine productivity dynamics over the life cycle of firms using panel data Van Biesebroeck (2005), they rely on revenue-based measures of productivity. As a result, the dynamics of “productivity” over the life cycle may be misleading.

> 17The data are freely available from http://www.enterprisesurveys.org.

22
Walker, M. 2011. “PISA 2009 Plus Results: Performance of 15-Year-Olds in Reading,

- Mathematics and Science for 10 Additional Participants.” Australian Council for Educational Research (ACER) Press.

- World Bank. 2014. World Development Indicators. http://data.worldbank.org/country/moldova, [accessed 10.02.2015].

- Yang, D. 2008. “International Migration, Remittances and Household Investment: Evidence from Philippine Migrants’ Exchange Rate Shocks.” _The Economic Journal_ 118 (528): 591– 630.

Zhang, H., J.R. Behrman, C.S. Fan, X. Wei, and J. Zhang. 2014. “Does Parental Absence Reduce Cognitive Achievements? Evidence from Rural China.” _Journal of Development Economics_ 111 (0): 181–95.

**Figure 1.** Kernel density plots of the household asset index in 1999 and 2011

<!-- Start of picture text --> 1999 2011<br>−2 −1 0 1 2 −1 0 1 2<br>x x<br>non−migrant migrant non−migrant migrant<br>.8 1<br>.8<br>.6<br>.6<br>.4<br>density<br>.4<br>.2<br>.2<br>0 0<br><!-- End of picture text --> Source: Authors’ calculations based on CELB 2012 22
possible channels linking behavior to test scores – in particular, measures of student and teacher effort, as well as other factors (e.g., student attitudes toward school) that are not explicitly modeled above – to better understand the mechanisms underlying the reduced form estimates. We do not attempt to definitively disentangle the relative contributions of each channel in the overall program impact, as a number of possibly important channels (i.e., paying more attention in class) are difficult to measure in practice, and since this would likely require strong, untestable structural econometric assumptions.

# **<u>4. Data and Estimation</u>**

# **<u>4.1 The Dataset</u>**

The test score data were collected from the District Education Offices (DEO) in Busia district and Teso district. Test scores were normalized in each district such that scores in the comparison school sample (girls and boys together) are distributed with mean zero and standard deviation one. The complete dataset with both cohort 1 and cohort 2 students is called the _baseline sample_ (Table 1, Panel B). In the main analysis, we focus mainly on students with complete age, school attendance, and gender information, in schools that did not pull out of the program and for which we have baseline 2000 test scores and school ethnic composition, and call this the _restricted sample_ (Panel C). Note that average test scores are slightly higher in the restricted sample than in the baseline sample, since the students dropped are typically somewhat below average in terms of academic achievement, as discussed below.

Attendance data are based on four unannounced checks, one conducted during September to October 2001, and one in each of the three terms of the 2002 academic year. These were collected by NGO enumerators and they recorded baseline students actually in school on the day of the unannounced check as “present”. Attendance rates are below 80% for the baseline sample and a bit over
-31 -

# Avallabillty and Access to Electricity

Thirty years ago, the supply of electric power In most developing countrles was confined to owie or two large citles and the major source of power was small thermal plants. During the past thirty years, however, growth of the sector has been very high and now most major towns and many smaller towns have been supplied with electricity from an Integrated network using various kinds of power plants operated and developed as a system. Some countries now serve much of the urban population and have embarked on ambitious rural electrificatlon programs.

Installed capacity Increased by 9.7% p.a. between 1968 and 1982 compared to 10.6% p.a. In the 1950s and 19609. Power generation showed similar growth of 9.6% p.a. between 1968 and 1982, and 11.3% p.a. In the 1950s and 19609. Growth In the power sector has easily exceeded that of real GDP In most countries and also has been far higher than population growth rates, Indicating that overall access to electricity service has been Increaslnu.

Access to service can be measured by several Indicators, Including (I) generation per capita, which Indicates the amount of electricity per capita produced; (II) Insta:led capacity per capita; and (III) percent of the population actually connected to the public grid. Data for 51 countries In 1968 and 1982 showed that, on average, generation per capita Increased by 7% p.a. and Installed capacity by nearly 8% p.a. These growth rates compare very favorably with an average real growth rate of nearly 2% p.a. for per capita GDP over the same time period. In over 90% of countries, per capita generation and Installed capacity growth rates were more than double the real growth rate for GDP and In 57% of countrles more than three times as high.

Eariler data on the percentage of the populatlon having electricity connectlons were not readlly available, but data
Third, we demonstrate this using a matched difference-in-difference method introduced by Mueller and Rauh (2023) but at the local cell level. This method matches treated cells with other untreated cells on the predicted conflict risk before treatment. Treated and untreated units are then analyzed together using a new two-way fixed effects method introduced by Callaway and Sant’Anna (2021). If road completion is selected for de-escalating conflict dynamics, then this would be detected by the local conflict forecast and lead to significantly lower treatment effects. By contrast, and in line with the declared international stabilization strategy for DR Congo of the 2010s (see DFID 2011), we find that controlling for conflict risk during the construction period, if anything, increases the estimated treatment effect of road project completion.

To answer our second question, we use novel techniques to extract information on changes in road quality based on satellite images. This allows us to observe how long road rehabilitation has had effects on road quality, and whether decreasing road quality correlates with an uptake of violence. Our findings can be summarized as follows. First, we see a statistically significant reduction in the incidence of violence after road rehabilitation projects are completed. Depending on the estimation method, we find that the completion of a project reduces violence by around 5-10 percentage points. The association between roads and reduction in violence tends to be the strongest for violence against civilians. Within our sample, the improved quality of transport infrastructure thus has a beneficial effect on conflict dynamics and stabilization (a ’peace dividend’). Second, we observe statistically that the effect of road rehabilitation on the number of violent incidents diminishes significantly after some time. The dynamic treatment effects we estimate around the completion date show that violence is first reduced directly after completion of a road project, but rebounds to pre-rehabilitation levels within three years. These findings are consistent with the suggestion that rehabilitated roads deteriorate again after projects are completed. To further substantiate this, we combine machine learning with thousands of remote sensing data observations to estimate overall
extensive and intensive margins.<sup>2</sup> The median coefficient of variation for imports was 0.49 for quantities and 0.22 for unit values. The comparable values for exports were 0.59 and 0.26. Given that the shock to Mexico was primarily through demand, these numbers are consistent with a relatively elastic supply curve. In a small-country trade-in-tasks environment, it might not be surprising that U.S. firms would cut quantity first as a short-run response to the shock.

The picture that emerges from these simple figures and statistics is that the labor market in Northern Mexico was subjected to a significant external trade shock that induced the kind of volatility identified by Bergin et al. (2009), creating an excellent opportunity to study to link between offshoring and volatility. We describe the data used to study that link in the next section.

# **3. Data**

The empirics require trade data and labor-market data, and we discuss each below.

# **_3.1. Trade Data_**

The bilateral monthly trade data used for the econometric models originate from U.S.

customs records, specifically from the United States International Trade Commission’s data web interface. The monthly data were then summed over quarters. The industry classification system originating from the trade data (6-digit level of the Harmonized System (HS)) differs significantly from the industry classification system from the Mexican wage and employment data described below. We therefore constructed an industry classification concordance table to match the employment and wage data from the Mexican social security records with the U.S.-

> 2 Product-month observations with zero trade values or missing data were not used to compute the variation coefficients.

8
data on death rates by age. While these are readily available in countries with strong vital registries, in roughly the poorest quarter of countries such data are missing or incomplete. In these countries, the UN Population Division instead estimates death rates by age by linking together the limited available age‐specific mortality data with “model life tables” capturing the typical pattern of distribution of deaths by age. As noted above, data on stunting are increasingly available, but primarily

> 20 Commendably the GBD estimates of YLD are accompanied by confidence bands, which are very wide given the underlying data limitations.  For example, for most countries the confidence bands for adult YLD encompass nearly the entire range of point estimates of YLD, which transparently indicates the substantial uncertainty associated with these estimates.

> 21 These gaps are illustrated using metadata reporting the dates of the primary sources underlying the GBD estimates of the top 10 global causes of YLD.  Taking 2017 as a reference point, calculate the fraction of countries for which no data source was available for each cause in the previous 10 years (so that the data necessarily were imputed/extrapolated beyond 10 years).  This fraction is greater than 75 percent for developing countries, for 8 out of the top 10 global causes of YLD (anemia and diabetes are the only two exceptions).

40
# **5 Mechanisms**

In this section we aim at gaining further insights into the mechanisms that drive the baseline results, with an emphasis on disentangling banks’ soft budget constraints from their longtermism. The literature predicts that the occurrence of soft budget constraint problems in the banking sector is related to the degree of independence of banks from the influence of the government and of related parties and to the quality of monitoring and supervision of banks. Exploiting this prediction, in Section 5.1 we take a step towards disentangling a possible role of soft budget constraints by supplementing information from our main databases with country-level data on the independence, monitoring and supervisory quality of banks.

As for the more benign explanation of the baseline findings (banks buffer exporters from unforeseen shocks), banks’ ability to renegotiate contracts and insulate firms from unforeseen contingencies should especially kick in when ex ante it is hard to gather information about shocks that can hit export projects and when it is difficult to write detailed contracts that account for such unexpected contingencies (Allen and Gale, 2001). Based on these arguments, we conjecture that if the baseline results especially reflect domestic banks’ buffering and long-termism (rather than soft budget constraints) the effects should be stronger in the event of negative shocks, such as crises or recessions. We test this hypothesis in Section 5.2. Moreover, we would expect the effects to be relatively stronger for products that are informationally more complex and for which detailed, state-contingent contracts are harder to write. We test this hypothesis in Section 5.3.

## **5.1 Banks’ soft budget constraints**

As it is typically the case in the empirical literature, a direct test of soft budget constraints is not feasible. However, following the banking literature, we can capture the exposure of countries to a bank soft budget constraint problem by exploiting information on banks’ independence and on the regulation and monitoring to which banks are subject. Banks’ soft budget constraint attitudes can be more pronounced in settings