rafmacalaba/datause-encoder-data huggingface dataset:
following earlier literature that has documented associations between prevalence of young men and conflict. Finally, in this category of latent tensions, we include the country’s history of conflict, defined as the fraction of years since 1970 in which conflict, as defined in our dependent variable, is observed.

In the category of shocks, we gather several types of events that the existing literature has emphasized as increasing the risk of conflict. This category includes two external shocks: the income effect of changes in the terms of trade, which tends to be positively correlated with conflict in our sample, and number of neighboring countries in conflict, which also tends to be positively correlated with conflict in the country of interest. We also include a composite measure of the incidence of natural disasters, taken from Besley and Persson (2011). This is a binary indicator of whether any of the following types of natural disasters occur in a given country and year: extreme temperature events, floods, landslides and tidal waves. Finally, in this category we include per capita GDP growth, as a crude proxy for other shocks hitting the economy that might contribute to the risk of conflict.

The final category is intended to capture institutions that may reduce the likelihood of conflict, by offsetting some of the forces in the two previous categories. We use the World Bank’s Country Policy and Institutional Assessment as an omnibus proxy for the overall quality of policies and institutions. In the specific World Bank institutional context, this measure is of particular interest because the Bank’s past efforts to classify countries at risk of conflict have relied heavily on selecting countries with low scores on this particular indicator. Turning to measures of political accountability, we include the Freedom House composite indicator of civil liberties and political rights. We also use the Political Terror Scale, which is a long-standing effort to numerically code narrative descriptions of human rights violations in annual reports by Amnesty International, U.S. State Department and Human Rights Watch. Finally, we include in this category the log-level of real GDP per capita, following many papers that have documented a greater prevalence of conflict in low-income countries, and the logarithm of population, following previous papers that have documented an association between country size and conflict.

Table 2 provides descriptive statistics for these explanatory variables in the three-year period prior to the start of conflict and non-conflict episodes in our dataset. A glance at this table reveals many patterns that would be expected from the large empirical literature on conflict. Income inequality, ethnic fractionalization, income inequality across ethnic groups, natural resource rents as a share of GDP, and the history of past conflict are all on average higher prior to conflict events than non-conflict events. Interestingly, however, religious polarization is on average lower in conflict events. Turning to the variables in the institutions group, per capita income and the CPIA indicator are both lower prior to 21
56% increase). Although the point estimates are positive, we do not observe statistically significant impacts on the engagement with the child’s teacher in columns (3) and (4). We also explored whether parents in the treatment group and other household members were more likely to be part of the school committee. Here, column (5) shows a positive but statistically insignificant impact.

Third, DICIPE causes a small reduction in the labor supply of parents in a context with almost universal labor market participation. For example, 98.6% of mothers in control communities work and that fraction is only reduced by 1.3 percentage points for mothers of children in treated areas. While most in the literature show that eligible papers preschool or childcare programs can increase the labor market participation of (mostly female) caregivers (Berlinski and Galiani, 2007; Berlinski et al., 2011; Halim et al., 2022; Hojman and Boo, 2022; Donald and Vaillant, 2023; Halim et al., 2023; Mata, 2024),<sup>16</sup> these results on the reduction in the household labor supply of mothers are consistent with those of Krafft and Lassassi (2023), who find that pre-primary education may have decreased women’s employment.<sup>17</sup> Taken we together, find evidence that DICIPE increases parental time investments in children through stimulation, home play, and engagement with school principals and induces reductions in the likelihood of working.

Our qualitative analysis from the caregivers survey hints at the possibility that two mechanisms in treated communities are that potential why parental practices improved the program induced parents to be significantly involved in the preschool activities and that parents perceive positive changes in their children’s skills that may facilitate parentchild quality engagements. Despite the low compliance with the parenting education activities (instead of monthly sessions, they were held every three months), the families

> 16In a recent evidence review (Halim et al., 2023), the authors review 22 studies that plausibly identify the causal impact of childcare services on maternal labor market outcomes in lower and middle-income countries and find that all but one study report positive impacts on the extensive or intensive margin of maternal labor market outcomes.

> 17This small reduction in the labor supply of female caregivers may indicate that they are the ones investing more time in stimulation activities and with school engagement, being more likely to stay at home rather than participating in the labor market.

31
14 Companies with foreign ownership are more prone to pay bribes to government officials. Firms with some degree of foreign ownership are about 10 percent more likely to make informal payments to government officials than purely domestically-owned firms, _ceteris paribus_ . For Smarzynska and Wei (2000), corruption makes local bureaucracy less transparent and increases the value of using a local partner to cut through the bureaucratic maze. Furthermore, as pointed out by Kuncoro (2006), foreign ownership may make a firm more vulnerable to bureaucratic predation, and for this reason foreign firms typically have domestic partners – for their ability to ward off such predation. Moreover, the likelihood of a firm paying bribes in Mauritania decreases as the average monthly wage per worker increases. It is expected that firms that pay higher wages would hire more formal workers (and more skilled labor). As this can be interpreted as a measure of formality and legal compliance (labor rules), one should expect these firms to be less vulnerable to be harassed by officials.

The higher the percentage of senior management time spent dealing with government regulations each week (i.e., tax time), the lower the probability of a firm to pay bribes in Mauritania. A 10 percent increase in the tax time would be associated with a 3 percent decrease in this probability, everything else held constant. This result suggests that companies that comply with procedures are less vulnerable to bribe predation. Another possible interpretation is that more time spent dealing with government regulations may be reflected in having closer ties with officials and thus being less likely to be asked to pay bribes. Nevertheless, Svensson (2002) found that senior management in firms reporting that they had to pay bribes spend significantly more time dealing with government regulations than in enterprises that reported that they did not have to pay bribes.<sup>13</sup> As the number of dealings between firms and tax officials increases, so does the firm’s probability of paying bribes. Results show that one additional tax inspection (in a year) is associated with an increase of 16 to 17 percent in the probability of paying a bribe. In addition, firms that rate corruption as a major or severe obstacle to their growth and

> 13 A similar result was found by Gaviria (2000), who used perception-based data at the firm-level in 20 Latin American countries.
# **Box 5: Regional perspectives**

<u>Eastern Europe and Central Asia</u><sup>13</sup>

# **Key characteristics and trends in public debt and financial sector exposures**

Albania (80.6% of GDP), Ukraine (71.9% of GDP) and Kyrgyz Republic (60.5% of GDP) had the highest public debt levels in ECA no EU. Bulgaria (78.9% of GDP), Croatia (76.5% of GDP), Romania (49.4% of GDP), and the Czech Republic (45.4% of GDP) had significant shares of foreign currency debt. All four countries are not part of the euro area and the major share of their foreign currency issuances are denominated in euro. A large part of public debt in Armenia (2017), Azerbaijan (2016), Georgia (2018), and Ukraine (2017) is medium‐ to long‐ term maturity and denominated in foreign currency, while in Kazakhstan, public debt appears to be denominated in equal share of both local and foreign currency and in the Russian Federation the majority of the public debt is in local currency denominated.

According to 2016 Fitch data, Albania and Romania had over 20% of bank assets invested in government securities. In addition to the direct exposure, Romanian banks are indirectly exposed to the sovereign via their mortgage loan portfolio, as the majority of these loans are under the _Prima Casa_ program, where the states guarantees half of the mortgage. From 2012 to 2016 the share of sovereign debt in total bank assets increased by more than 10 percentage points in Ukraine and Serbia. This is partly explained by the financial crisis experienced in both countries, resulting in bank recapitalization with government bonds and greater investments in government assets as a risk mitigation technique given widespread non‐performing loan problems.

Some banking sectors also face substantial exposure to sovereign risks through state‐owned enterprises (SOEs). Turkmenistan and Uzbekistan are particularly exposed reflecting the economic structure with loans to SOEs accounting for over 50% and 40% respectively of total banking sector assets. However, banks are not the only institutions exposed to sovereign risks. The Unified Pension Fund in Kazakhstan invests 50% of assets in government papers and about 13% in SOEs. Following the 2017 exchange rate liberalization and currency devaluation in Uzbekistan, SOEs with foreign exchange denominated loans or with high import dependence experienced trouble servicing their debts and required government funds and debt restructuring. Banks received regulatory forbearance to avoid having to constitute provisions on those loans.

# **Concerns about financial sector exposure to sovereign risk and vice versa**

Large exposures to the sovereign and broader public sector in countries with weak fiscal positions may affect bank capitalization and credit growth, with a subsequent negative impact in the economy. In countries in which state‐owned banks (SOBs) dominate the banking sector, difficulties by the public sector to serve their debt would affect capitalization of SOBs, which 30
headed by an indigenous person, compared to only 7.4% in the rural coast, and 17.7% in urban areas.

Rural Peru shares many features with other traditional, rural societies. In-migration into communities is limited: more than 80% of adults in rural areas report that they were born in their current place of residence. Almost 90% of households are headed by males. Households tend to be slightly larger than those in urban areas (5.3 members, compared to 5.1 members), and to have more children under 16 (2.4, compared to 1.7). Labor market participation in rural areas is high: almost 80% of the population above age 14 r eports that they have a job. About three-quarters of the employed work in agriculture. On the coast, the main crops grown are rice, maize, beans and fruit; in the sierra, potatoes, other tubers, wheat, and quinua; and in the selva, rice, yuca, plantains and fruit. Much agriculture is small-scale, with peasant famnilies working small plots with traditional methods, limited use of fertilizer, and simple irrigation methods. One partial exception is the plantation agriculture for sugar, rice, cotton, and asparagus on the coast.

In general, the connection of rural households to many aspects of the "modern", formal economy is still quite limited: for example, the LSMS indicates that less than 2% of households in rural areas earn income from savings or stocks, and only about 5% have access to a pension or social security.

# **3. Understanding volunteering**

There are at least two ways to think of the determinants of volunteering. In one framework, volunteering is primarily decided by an individual's characteristics-for example, his wage and non-wage income. In an alternative framework, it is the characteristics of the community which matter-for example, the degree of "social capital", or the intensity of government activity which could crowd out volunteer efforts.

The simplest, labor-market based model of volunteering assumes that volunteering is a "consumption good".<sup>3</sup> People volunteer because they are altruistic (Arrow; Becker; RoseAckerman), or because they derive a "warm glow" from doing good (Tullock; Andreoni). Higher wages have an ambiguous effect on volunteering: they increase income but also raise the opportunity cost of time, making it more expensive to volunteer. By contrast, if volunteering is a normal good, the expected effect of non-wage income on volunteering should be positive.<sup>4</sup> In _3In_ a competing theoretical framework, volunteering is an investment in on-the-job experience and skills, which raises the expected future wage (Menchik; and Weisbrod; Freeman). This model does not have much applicability in rural Peru, where volunteering generally involves physical labor, and volunteers are unlikely to "build up their resumes" to command a higher wage.

_4_ Wealthier people may also choose to donate cash rather than volunteer time to achieve a certain level of production of "charity". The question of the relationship between donations of cash and labor has received 4
A river density index and the percentage area irrigated have also been derived for each district. These data are available for the country teams to validate, and for the derivation of appropriate independent variables for use in the Ricardian analysis.

These results are provided for all districts, not just those identified for analysis using the Ricardian approach (Section 1). Consequently, data are provided for 1421 political districts in total. The data are stored in individual Excel spreadsheets for each country. Within each spreadsheet the different data types (e.g. runoff, relative soil moisture storage, etc.) are stored in separate sheets, with the districts identified by a unique district number. The district numbers were obtained from the ArcView coverages provided by each country.

In Phase II, the climate change impact analysis was performed. Ten climate change scenarios, and five models for A2 and B2 were run through the hydrologic model to produce monthly results for 100 years: 2001–2100 producing 1200 monthly values. As requested by the economic modelers, the 1200 monthly values were processed into decadal average values (monthly) for precipitation, temperature and streamflow. This data was provided in two formats:

- i. Absolute value for the base case and 10 sets of decadal average _absolute_ values of precipitation , temperature and streamflow.

- ii. Absolute value for the base case and 10 sets of decadal average _changes_ from the _base_ for values of precipitation (% change from base), temperature (delta T in degrees from base) and streamflow (% change from base).

23
We start with the complete sample of banks in BANKSCOPE, resulting in a total number of bank-year observations of 54,038 (on average 6,755 banks per year). The sample we end up using is smaller, however, as we apply some selection criteria. First, we apply a number of outlier rules to the main variables corresponding to the 1" and 99gh percentiles of the distributions of the respective variables. This also deletes banks for which data on one of the main variables is not available, such as data on interest expense or personnel expense. We also delete countries with less than 50 bank-year observations (we need a reasonable number of bank-year observations for each country to estimate the H-statistics; we set the minimum number of observations to 50). This reduced sample consists of 37,107 bank-year observations. We also delete countries with data for less than 20 banks since we need at least 20 observations per country to get reasonable accurate H estimates for each country.<sup>4</sup> Furthermore, some countries in BANKSCOPE do not have adequate coverage of banks and only include the very large banks in the country. This reduces the sample by another 1,273 bank-year observations.

The final sample consists of 35,834 bank-year observations (4,479 banks on average per year). It is an unbalanced panel with the largest number of 5,002 bank observations for the year 1999.5 The final sample we use consists of 50 countries. A description and definition of the variables can be found in Table 1. In terms of number of banks, banks from France, Germany, Italy, Switzerland and United States dominate the sample. In each of these countries we have more than 1,000 bank-year observations (see also Table 2).6

> 4 We therefore drop observations from the following countries: Bahrain, Bolivia, Cyprus, El Salvador, Ireland, Israel, Jordan, Kazakhstan, Republic of Korea, Nepal, Saudi Arabia, Slovak Republic, Slovenia, Sweden, Tbailand, United Arab Emirates, and Vietnam.

> 5 The distribution of the sample across years is as follows: 3,934 banks in 1994; 4,327 banks in 1995; 4,633 banks in 1996; 4,731 banks in 1997; 4,852 banks in 1998; 5,002 banks in 1999; 4,741 banks in 2000; and 3,614 banks in 2001. The total number of bank-year observations is 35,834.

> 6 We need to exclude a large number of Japanese banks, because BANKSCOPE does not have data on personnel expense for most large Japanese banks. Our sample of Japanese banks is therefore much smaller than the actual

> number of Japanese banks.

13
economic activity fell sharply as sectoral GDP estimates have shown. Sectors such as tourism, hospitality and recreational activities were particularly hit hard as consumers reduced their exposure to the virus. We observe that jobs that have greater income risks are more likely to have experienced a reduction in demand too; so, there is a strong positive correlation between the supply and demand shocks. However, the demand shock may have continued for after government lockdown measures ended and other supply restrictions were lifted as the result of lower household spending capacity, and higher levels of uncertainty about the economy.

To capture the uneven impact of the pandemic experience on consumer spending across sectors, we follow Rio Chanona et al. (2020) who base their analysis on an earlier model developed by the US Congressional Budget Office (CBO, 2006) to generate scenarios to examine the possible impacts that could occur in the event of an influenza pandemic. The most-severe scenario, modeled after the SARS episode in east Asia, estimated that an 11.5 percent reduction in consumer spending would occur (CBO, 2006). For comparison, private consumption in 2020 in the Euro Area fell by 8.4% in real terms compared to 2019, and by 12.8% in the United Kingdom.<sup>6</sup> The CBO provides estimates of the size of the change in demand across two-digit categories of the North American Industry Classification System (NAICS); we convert these to two-digit categories used in the statistical classification system of the EU (NACE). Under a scenario that assumed an outbreak similar to the one that occurred during the Spanish flu pandemic of 1918-1919, the US Congressional Budget Office estimated that consumption in the hospitality and in arts, entertainment and recreation sectors would plummet by 80 percent (CBO, 2006). Under this scenario, it estimated that demand for healthcare services would rise by 15 percent, and that no major consumption changes would occur in other sectors, such as utilities, professional activities, financial and ICT services, public administration and education.

# 3. Data Sources

The primary data source used throughout the paper is the European Labor Force Survey (EU-LFS), which is constructed by harmonizing nationally representative labor force surveys in all of the 27 member countries of the European Union, in addition to Norway, Switzerland and the United Kingdom. The total sample size ranges between 4.5 million and 5.2 million observations, depending on the year. The dataset annually compiles detailed information on labor-market outcomes of the working-age population and their demographic characteristics, such as gender, age, and educational attainment. We focus on the survey results from 2018, prior to the COVID19 pandemic as well as from 2020, the first year of the pandemic.

The dataset provides information on whether the individuals were born in the country in which they currently reside. We do not observe the specific country of birth or citizenship status of the migrant workers, but we see nine world regions. These are (i) the high-income EU-15 countries 6 OECD (2021).

8