rafmacalaba/data-use-mentions-extended huggingface dataset:
We assembled a new database with information on budget allocations for every social protection program implemented in response to the pandemic in 155 countries. The database derives the universe of social protection programs from the Global Database on Social Protection Responses to COVID-19 (Gentilini et al. 2022), with a cut-off date for policies announced before June 1, 2021. Using these data, we estimate the relative contributions of income protection and job protection policies to the overall social protection response.

Our empirical model estimates the relationship between four economic outcomes (GDP growth, unemployment, rates of labor inactivity, and poverty) and variables representing expenditures on the two types of social protection policies and country-level controls. Our analysis shows that, in the short run, higher expenditure on job protection is associated with a more robust economic recovery, increased employment, and decreased inactivity and poverty rates; expansion of income protection measures appears to have no significant economic effect. We conduct a series of robustness checks with empirical specifications that use alternative definitions of our dependent variables. These estimations qualitatively confirm our main results on the greater effectiveness of job protection policies in promoting economic recovery during the second half of 2020 and the first half of 2021.

We then take our analysis further to account for the effects of pre-pandemic characteristics of social protection systems. We find that social protection response had a significant economic impact in countries with weaker pre-pandemic social insurance systems. In countries with broader social insurance coverage, income and job protection programs appear to have a limited impact on post-pandemic recovery. We speculate that some programs implemented during the pandemic - particularly those protecting jobs - may have been redundant in these countries as automatic stabilizers were already in place.

This paper contributes to the emerging literature on the effectiveness of various types of social protection programs in facilitating post-pandemic recovery. To our knowledge, it offers one of the first empirical cross-country analyses of the short-term impact of job protection and income protection programs on economic growth, unemployment, labor inactivity, and poverty. The findings of this paper could be used
# **3 Types of Debt Relief and their Effects**

This section reviews the different types of debt relief available to households, and the existing evidence on their effects on borrower behavior and access to finance. There are many different forms of debt relief that can be grouped into _de facto_ and _de jure_ debt relief, as well as temporary and permanent debt relief measures. These policies are not mutually exclusive and are often available concurrently, especially in a crisis. We focus on describing the broad features of each type of debt relief policy as well as evidence from studies measuring the effect of specific policies. International comparisons of debt relief programs and their effects are notoriously difficult because of idiosyncracies in program design and the lack of internationally comparable outcome measures.<sup>6</sup>

## **3.1 Loan Default**

The simplest form of debt relief is loan default or delinquency, which occurs when a borrower makes zero or less than the minimum required payment on a loan.

While default can provide _de facto_ debt relief, it can have significant costs in terms of future access to credit. In recent years, many jurisdictions have expanded their credit reporting systems to include smaller personal loans and microloans in individual credit records. In most cases, non-repayment of any formal loan will therefore trigger a negative credit bureau entry and reduce a borrower’s future access to formal credit. By making it easier for creditors to learn about past defaults, the presence of credit registries can increase the costs of default, thus reducing the moral hazard costs of debt relief.

It is unclear, however, to what extent borrowers internalize this cost. In a study of Indonesian credit card borrowers, for example, Bursztyn et al. (2019) find that nearly half of all respondents are not aware of the existence of a credit bureau or the consequences of non-repayment. When consumers do not fully internalize these costs, this can increase the moral hazard costs of debt relief policies.

In microfinance, one factor that
examine how spatial distribution of population and sectoral employment shares respond to a shock to transport costs. We consider a country which consists of two cities: _K_ and _J_ . City _K_ uses

> 7For Mali, we do not have access to more recent transit data.

> 8There was a very brief period of political violence in 2011 in CIV that started in March 2011 and ended on April 11, 2011 with the arrest of Laurent Gbagbo by French forces.

> 9The share of manufacturing employment was less than 5 percent during the pre-CIV crisis period in both countries.

8
# 2 Post-conflict poverty projections: Review of existing estimates

## 2.1 Post-conflict estimates based on nowcasting methodologies

Nowcasting methodologies are typically used to fill data gaps in countries that lack up-to-date surveybased poverty estimates. Nowcasting approaches vary in terms of both methodological assumptions as well as in terms of data sources used in the analysis.

The simplest nowcasting approach – which is used by the World Bank in the production of global poverty estimates – relies on information from national accounts (per capita GDP or household final consumption expenditure) to predict poverty. More specifically, this nowcasting methodology assumes that growth (or shrinkage) in GDP per capita registered since the country’s last poverty estimate is fully passed through to the consumption vector that is measured in household surveys (Prydz et al. 2019). Poverty is then estimated using this predicted consumption vector under a “distribution neutral” assumption i.e., assuming that inequality does not change at that each individual is equally affected by the growth/contraction in welfare. While the impact of growth on poverty reduction is well-known (Kraay, 2006; Ferreira and Ravallion, 2009), concerns have emerged related to possible un-reliability of GDP measures in developing countries (Angrist, 2022), as well as the extent to which growth is passed through to welfare in fragile and conflict affected countries (Box 2).

_Box 2: Growth rate pass-through in FCS Economies_ The assumption of full pass-through works fairly well in economies that are not in Fragile and Conflict-Affected Situations (FCS). However, available evidence suggests that a full pass through assumption is likely inappropriate in case of conflict.

In order to estimate any difference in pass-through in FCS settings, Corral et al. (2020) fitted a regression model on a sample including all countries for which survey consumption estimates are available in PIP over the period 2000 to 2019. In particular, the annualized growth rate in the survey per capita consumption is regressed on annualized growth in GDP per capita, together with an interaction of annualized growth in GDP per
# **1. Introduction**

Corruption hinders firm productivity by diverting resources away from their best uses. It does so, among other channels, by increasing the premium on rent seeking, creating secrecy and uncertainty and undermining public resources. Corruption also promotes excessive regulation, further empowering bureaucrats to extort rents and amplifying corruption’s negative effects on productivity (e.g. Shleifer and Vishny 1993; Banerjee 1997; Aidt 2016). Adverse effects of corruption on talent, investment, innovation, entrepreneurship, growth and public resources are well documented in the literature (e.g. De Soto 1989; Murphy et al. 1991, 1993; Mauro 1995; 1998; Tanzi and Davoodi 1997; Wei 2000; Djankov et al. 2002; Gamberoni et al. 2016; d'Agostino et al. 2016; Cieslik and Goczek 2018).

Empirical studies such as Meon and Sekkat (2005) and De Rosa et al. (2010) show that corruption is more damaging for economic performance at higher levels of regulation or lower levels of governance quality. Some studies also find that corruption eases the burden of excessive regulation and thereby improves economic performance (e.g. Leff 1964; Lui 1985; Dreher and Gassebner 2013). However, as pointed out in several studies, such as Campos et al. (2010), there is no widespread evidence for the latter. One reason is that corruption may speed the bureaucratic process in the short run, but in the long run, it will lead to more regulation and cause more damage in the economy.

Building on the above literature, in this paper, we use firm-level survey data on 39,732 firms in 111 countries collected by the World Bank’s Enterprise Surveys between 2009 and 2017 to test the hypothesis that corruption impedes firm productivity more at higher levels of regulation. Corruption is measured with three indicators: bribe rate (measured as the percentage of bribes in firms’ sales), bribery incidence (percentage of firms from which bribe is
# **3.  Data**

Our data come from two recently-administered surveys that incorporate the methodology of the global Demographic and Health Surveys (DHS): the India National Family Health Survey (NFHS-4), 2015-16 (IIPS 2017) and the Bangladesh Demographic and Health Survey 2011 (NIPORT 2013). We have used Bangladesh DHS 2011 instead of DHS 2014 because the latter does not include maternal anemia measures. Table 1 displays summary statistics by province/state for DHS clusters in the regression data set. Overall, the sample contains data on 124,327 individuals in 4,241 DHS clusters. Figure 1 displays the cluster locations.

Our child and maternal health variables are measured identically in the two surveys. Wasting is based on child weight-for-height measures converted to Z-scores, based on WHO’s Child Growth Standards (WHO, 2006).<sup>7</sup> Using a standard cutoff criterion, we define our child wasting variable as 1 for Z-scores less than -2.0 and 0 otherwise. Anemia is based on the measured hemoglobin (h) level (in grams/deciliter) in a droplet of blood. After adjustment for altitude and rounding to one decimal place, women are assigned to anemia categories as follows: severe (h ≤ 7.0 g/dl); moderate (7.1 ≤ h ≤ 9.9); mild (10.0 ≤ h ≤ 10.9 [pregnant women], 10.0≤ h ≤ 11.9 [other adult women]; non-anemic (h ≥ 11.0 [pregnant women], h ≥ 12.0 [other adult women]. Using these categories, we define our maternal anemia variable as 1 for severe and moderate anemia and 0 otherwise.

> 7 Z-scores are calculated from tables standardized for age and gender, so we do not include these variables in our regression equations.

7
capacities. In our sample, 67 percent of plants have a capacity lower than 30 MW.<sup>2</sup> Next, we augment this dataset with market-level measures of the degree of hydrological drought faced by hydropower plants at monthly frequency during the 2000 to 2020 period. The process of computing these measures of drought involves several steps. In the first step, we follow existing literature and use runoff anomalies to measure hydrological drought (Herrera-Estrada et al., 2018; IEA, 2021; Qiu et al., 2023). Runoff is the depth of water accumulated over time in the soil and is a valuable indicator of drought or flood conditions. Runoff anomalies indicate a period where water availability is above or below normal. Specifically, we define runoff anomalies as the difference between the monthly runoff and its corresponding average value over the reference period (2000 to 2019). To measure runoff anomalies, we use data from the International Energy Agency Weather for Energy Tracker database<sup>3</sup> (IEA and CMCC, 2022). This database provides information derived from the ERA 5 reanalysis on monthly total runoff anomalies (surface and subsurface) measured in millimeters per hour (mm/h) at a spatial resolution of 0.25<sup>_◦_</sup> x0.25<sup>_◦_</sup> .

In the second step, we determine the watershed of each hydropower plant in the sample. The watershed is the area over which water would accumulate for use by the hydropower plant. To compute these areas, we use the hydro basin polygons (areas where water collects and may flow) from the HydroSHEDS database<sup>4</sup> (Lehner and Grill, 2013). This dataset is produced from digital elevation maps and hydrological models. Next, following standard engineering practices for each hydropower plant, we delineate the watershed by tracing all of the upstream basins (Pfastetter level 12) that flow in the direction of
- E1: Gas (IER index: 2 all the time)

- E2: Water and water waste (water)

- G: Retail trade and repair

- I: Transportation 1/2(rail + roads)

- I1: Telecom (telecom)

- J: Finance 1/2(banking + financial )

H+K: Other business-related services (hotels and restaurants, real estate, rent, informatization, R&D, agencies) 1/5( ssp + price_lib + trade_lib+ competition+ financial)

# **Index without utilities and retail trade**

– For four services sub-sectors Transport, Telecom, Finance, and Other business-related services (hotels and restaurants, real estate, rent, informatization, R&D, agencies) – we map the sub-sector with EBRD indices of reforms as follows:

- I: Transportation 1/2(rail + roads)

- I1: Telecom (telecom)

- J: Finance 1/2(banking + financial )

H+K: Other business-related services (hotels and restaurants, real estate, rent, informatization, R&D, agencies) 1/5( ssp + price_lib + trade_lib+ competition+ financial) 40
-10-

**TABLE 1.4**

**Thi** Size of Our Sample in **Terms** of Value Added (US Census Concept) by Manufacturing Branch. (1975), (national currencies and percentages)

||Braz|il||Nex|ico.||US|A||
|---|---|---|---|---|---|---|---|---|---|
|<br>|Universe<br>(mill.<br>cru|Sample<br>zeiros)|%|Universe<br>(mill.|Sample<br>pesos)|**_%_**|Universe<br>(mill|Sample<br>. USS)|%|
|Food Products|34,681|6,564|18.9|<br>28,964|5.808|20.1|<br>39,985|2,522|<br>6.3|
|Beverages|5,494|2,022|36.8|<br>1 2 <sup>,</sup>9 9 4<sup>a</sup>|6,875a|52.9|8.110|2,130|26.3|
|Tobacco|3,212|3,212|100.0|<br>1,817a|<br>1,8178|100.0|<br>3,722|3,722|100.0|
|Textiles & Wearing<br>Apparel|<br>**b**<br>27,247|10,247|37.6|<br>19,678|8,365|42.5|<br>26,793|
||LMI market share|0.00529**|0.00345|-0.00685***|-0.00152|
|---|---|---|---|---|---|
|||[0.00249]|[0.00306]|[0.00253]|[0.00278]|
|LIC|Export share|0.00835|0.01259+|-0.01389**|-0.01417*|
|||[0.00695]|[0.00772]|[0.00650]|[0.00727]|
||LI market share|0.00529**|0.00345|-0.00685***|-0.00152|
|||[0.00249]|[0.00306]|[0.00253]|[0.00278]|
|Number of firm-year observations||8,064|7,471|7,936|7,442|
|Number of firms||2,720|2,462|2,648|2,450|

Source: GEOSTAT Statistics Survey of Enterprises for 2006-2017.

Notes: Robust standard errors clustered at firm level in brackets. Includes turnover, firm fixed effects, and year and industry effects. *** p<0.01, ** p<0.05, * p<0.10, + p<0.20.

16
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data and/or localities.<sup>11</sup> On the other hand, only a small part of the variance appears to be correlated with firm-level characteristics once we control for industry and location-specific effects. ‘Variance2’ in figure 1 is the additional variance that can be explained by firm size and ownership dummies. Ownership is captured by 5 ownership categories and firm size by 3 size categories in all countries. Using more size categories or a polynomial function of a continuous size (i.e. employment) variable does not increase the variance explained much. The figure shows that the firm-level characteristics firm size and ownership explains a small percent of the observed variance once we control for the variation across industries and locations. ‘Variance 1’ is uniformly much larger than ‘Variance 2’. Although one might also include other firm-level characteristics besides firm size and ownership, there are no obvious other firm-level variables that will be able to explain much more of the total variance.

Therefore the largest part of the explained variance in reported underreporting is indeed across industries and localities, rather than across firms within industries and localities. This suggests that the responses are indeed biased towards the average behavior of other firms in the environment making them less indicative of underreporting by individual firms.<sup>12</sup>

> 11 The relatively low percentages explained for Colombia, Germany, Greece, and Portugal are probably due to the fact that in these surveys individual cities are aggregated into size classes.

> 12 Unless one assumes that underreporting behavior is mostly random and uncorrelated with observable firm characteristics.

10
where θ1 is expected to be positive and θ0 is a constant as suggested by equation (25).

# 3.2 Data

The estimation of equations (27) and (28) requires information on U.S. importers and Latin American exporters’ political organization, and data on elasticity of export supply and tariff preferences by industry and by Latin American country. Data on MFN and preferential tariffs as well as exporters’ political organization cover the years from 1997 to 2000. The paper employs data on importers’ political organization from the period 1991-2000. By calculating the average of the data over these years, we perform cross-section regressions to estimate the coefficients of equations (27) and (28). All the variables are organized at the HS 8-digit and ISIC 3-digit levels. Several issues related to the data set are discussed at length next.

MFN and Preferential Tariffs MFN and Preferential tariffs are calculated using customs data provided by the United States International Trade Commission (USITC). The original data is available at the tariff line level (HS 8-digit level) and covers the period between 1997-2000. Before proceeding with the calculation of the MFN and preferential tariffs, we average out the observations for these four years by HS 8-digit code and by country. U.S. MFN tariffs are determined by the ratio between duties paid on U.S. imports and the value of U.S. imports from all over the world that entered under the MFN regime for each HS 8-digit line. Similarly, preferential tariffs are determined by the ratio between duties paid on U.S. imports and the value of U.S. imports by Latin American country and by HS 8-digit line.

Using actual duties paid and the value of imports to calculate tariffs allows the determination of actual tariff preferences (i.e., those actually granted at customs and not ‘on paper’).<sup>35</sup> The data on MFN and preferential tariffs has been aggregated at the ISIC 3-digit

> 35The data on
public policy interventions would be doomed to ineffectiveness. If pioneering is already common in fragile states, it could be increased to the level appropriate for the state’s level of income.

**Figure 1. Distribution of pioneering firms by income group**

<!-- Start of picture text --> FCS Non-FCS<br>UMIC 44.44 55.56 UMIC 46.47 53.53<br>LMIC 42.63 57.37 LMIC 54.43 45.57<br>LIC 63.23 36.77<br>LIC 55.05 44.95<br>0 50 100<br>0 50 100<br>Pioneers Non-pioneers Pioneers Non-pioneers<br><!-- End of picture text --> _Source:_ Calculations based on the World Bank Enterprise Surveys (2018) _Note:_ LIC = Low Income; LMIC = Lower-Middle Income; UMIC = Upper-Middle Income Pioneers overall tend to be larger, more experienced, domestic rather than foreign-owned, maleowned and run, and a result of a partnership rather than a sole proprietorship. They also tend to be more productive, rely more on training and international certification, and are more frequently encountered in manufacturing. Simple averages across groups of all firms and pioneers in fragile and non-fragile developing countries highlight these differences, which are largely consistent between fragile and non-fragile countries (see Appendix Tables A2 and A3).<sup>13</sup> Firms’ particularities, however, might have little to do with pioneering, and more to do with the sector characteristics where they are active, i.e., the structure of production in fragile markets. For example, there may be a lot of domestic pioneers because foreign ownership is not prevalent in the sectors and markets where they are active. Controlled averages in a simple linear framework allow to net out such influences by looking at deviations from sector averages across countries. Such a calculation highlights how pioneers deviate from their peers across different contexts,
2002|0.365|0.156|0.090|0.245|0.102|0.060|
|2003|0.373|0.163|0.097|0.249|0.106|0.064|
|2004|0.345|0.145|0.083|0.222|0.093|0.054|

Notes: 1 - Rocha (1993) regional poverty lines. See Appendix 2 for details. 2 - The "administrative poverty line" is set as R$100 per person per month, in September 2004 values. Source: Authors' calculations from the PNADs.

Behind the overall decline, poverty dynamics in Brazil over the last two decades have been marked by considerable volatility, which largely reflected macroeconomic instability. Unsurprisingly, therefore, the volatility was more pronounced in the unstable decade of the 1980s, with a sharp increase during the 1981-83 recession, and a substantial decline during the recovery that took place between 1984 and 1986. All three measures are at their minimum in

> 16  See Foster, Greer and Thorbecke (1984).

> 17 The Thai (Indonesian) poverty incidence is calculated with respect to a poverty line of US$ 2-a-day  (US$ 1-aday), both in 1985 prices and using PPP exchange rates. See Ahuja et. al. (1997, pp. 7 and 33).
and Sjöholm, 2003; Egger and Kreickmeier, 2013; Fabri et al., 2003; Fosfuri et al., 2001; Heyman et al., 2007; Javorcik, 2014; Tomohara and Takii, 2011).

The World Bank Enterprise Surveys confirm the existence of substantial aggregate wage premia in the majority of developing countries (see Fig. 9). The levels of premia seem to be associated with intensity of skill use and existing wage gaps, ranging from high levels in countries where unskilled labor is more intensively employed in foreign multinationals to lower levels where skilled labor is abundant. A concentration of foreign MNEs in activities with higher returns or skill intensity could well explain aggregate differences between multinational and domestic firms; i.e., differences within sectors might be much less pronounced. The fact that average wages hide variations across skill remuneration in developing countries does not invalidate the findings of the literature; it rather shows that foreign multinationals’ wage advantages do not accrue automatically.

> 4 Heymanet et al. (2007) draw from the following studies in estimating this range: Aitken et al. (1996), Bandick (2004), Conyon et al. (2002), Doms and Jensen (1998), Driffield and Girma (2001), Girma et al. (2001), Griffith (1999), Görg et al. (2002), Haddad and Harrison (1993), Lipsey (1994), Lipsey and Sjöholm (2004), and Sjöholm and Lipsey (2006).