rafmacalaba/data-use-mentions-extended huggingface dataset:
provide them an incentive to turn informal as well. Generally, enforcement of the regulation is the crucial factor, not the extent of regulation itself.

The adverse effect of rigid regulation on incentive for operating formal has been largely documented in empirical literature, as has been summarized above. Johnson et al. (1997, 1998a, 1998b) present an empirical evidence of significant positive effect of overall regulation on shadow economy. Loayza et al. (2005) reach a similar conclusion as regards the effect of regulation on shadow economy. The study utilizes several measures of regulation and shows a positive effect of each of them including labor regulations.

We follow the OECD methodology (2004) for measuring the strictness of employment protection.<sup>21</sup> Data for old European countries and NMS-4 (Czech Republic, Poland, Slovakia and Hungary) are available from OECD in longer time series. Data for the rest of NMS group except Malta and Cyprus come from IZA database and were available for years 1999, 2003 and 2007 only. Therefore, NMS average in the following paragraph and Annex 2 refers to above mentioned four countries only for sake of comparability of development in time. Overall situation in European countries is shown in Annex 2.

The most liberal hiring and firing conditions were recorded in Denmark, Hungary, Ireland and Slovak Republic in period 2000-2007. France, Greece, Portugal and Spain found themselves on the opposite side of the spectrum. Southern European countries have the toughest regulation while the rules are more relaxed as one moves north. The most substantial changes leading to relaxation of employment protection in this period took place in Slovak Republic, Greece, Italy, Austria, and Portugal. On the contrary, Poland, Hungary and Ireland tightened their legislation moderately. Generally, EPL in NMS-4 is not as strict as in the other group―the average EPL index was significantly lower (1.9 in period 2000-2007). Old European countries recorded average EPL index at 2.4 with a decreasing trend in given period.
Section 1 presents a simple conceptual framework of the factors involved in dropout as well as the data for the empirical analysis, and discusses the patterns of dropout over the past several decades, comparisons with the rest of Latin America, and differences across subgroups within each country. Section 2 documents the economic consequences of dropping out, while Section 3 turns to the drivers of dropout, presenting empirical correlations and the available causal evidence. Section 4 considers the global evidence on what works to reduce dropout, and how this evidence informs the Central American experience. Section 5 concludes.

> 2 Highlighted for instance by Bruns and Luque (2015) and Cabrol and Székely (2013), among others.

> 3 A related study by Bassi, et.al. (2015) focuses on measuring secondary school dropout and graduation rates for the countries in the region, by using snapshots of household surveys up to the late 2000s. These authors conclude that dropout has declined, and point to the increase of school services and in the returns to secondary education as main causes for the reduction. Other related recent studies that analyze school dropout in the region, although not as the central focus of their investigation are Cárdenas, et.al. (2015) and Székely and Karver (2015), who look at the consequences of leaving school prematurely, and particularly the relation between dropout and the percentage of youth that are neither in school nor working. Another useful reference is the study by UNICEF (2013), which focuses on school coverage and attendance rates at the preschool and primary levels, for Central America specifically. The study clearly illustrates that for some of the countries in the sub-region, primary school participation continues to be a relevant challenge. Previous studies on the education sector of Latin America that include secondary dropout as part of their analysis (although not as the main focus) are World Bank (2005), and UNESCO (2008).

2
Conclusion

This paper analyzes income redistribution preferences and the effect of program design and enhanced transparency on willingness to give up a personal endowment. It uses data from a behavioral experiment conducted on a nationally representative sample of the Jordanian middle class. In contrast to opinion surveys, the experiment evaluated preferences using real trade-offs. It contributes to the literature on redistributive preferences by offering

> 28 The literature has shown that the demand for redistribution varies according to personal beliefs about the causes of poverty and success, with those who believe that people are poor because of bad luck or injustice or that success is the result of individual effort rather than luck being more prone to redistribution (Alesina, Glaeser, and Sacerdote 2001; Alesina and Glaeser 2004; Alesina and Angeletos 2005; Alesina and La Ferrara 2005; Alesina and Giuliano 2011; Charness and Rabin 2002; Konow 2010).

> 29 66 percent of the participants expressed a general belief that poverty is the result of bad luck or injustice rather than laziness, 37 percent believed that hard work usually brings success and 80 percent agreed that society should make people’s incomes more equal. These perceptions are in line with those in Latin America and Western Europe but in stark contrast with the United States, where government redistribution from the rich to the poor is less extensive. This might also be a factor behind the higher giving rates in our experiment compared to the classic dictator games or fundraising experiments in the United States.

16
the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

34
receive both internal remittances (from Guatemala) and international remittances (from USA). All values are weighted; standard deviations are in parentheses.

In 2000, 1 Guatemalan quetzal = US$0.128.

Source: Guatemala 2000 ENCOVI Survey, Instituto Nacional de Estadistica.

*Significant at the 0.05 level. **Significant at the 0.01 level.
methodology to a set of countries in Europe, namely France, Hungary, Italy, the United Kingdom (Brenner & Fazekas 2023).

The second unique feature of our proposed indicator set is that we track additional indicators of legislative and regulatory activities. Using these indicators as covariates allows us for a better understanding of these measures' contribution to enhanced legislative stability (and quality). We introduce each of the most relevant potential covariates below.

_Category of law_ : Laws and regulations might have different modification patterns and the category of legislation should account for that. As a categorical variable, it takes the value of 0 for regulations and 1 for laws.<sup>3</sup> Incorporating the category allows us to understand which type of legislation is prone to legal uncertainty and thus require closer attention in the future.

_Sector of law_ . Given that a standard classification of economic sectors is available across countries, such as NACE in the EU, it is possible to assign laws to economic sectors based on their texts and they matching to sector descriptions. This enables a closer look at those sectors that are particularly exposed to legal and regulatory instabilities and allows for targeted political counter-measures to protect businesses in these sectors. More details on this variable can be found in Section 3.3., below.

_Initiator of law_ . Knowing the initiator of legislation not only allows us to observe which actors are generally more active in drafting laws and regulations but also check for the initiators that are linked to higher levels of regulatory predictability or lack of thereof.

# **3.3 Sectoral Classification**

To analyze legislative predictability by economic sector, we have to assign laws and regulations to economic sectors. As our source legislative dataset does not contain such information, we classify laws into economic sectors in a separate analytic stage after the data collection process. The goal of matching laws and regulations to specific sectors is to identify if some are more exposed to unpredictable legislative environment compared to others.

In order to classify laws and regulations into
the risk index, and similar to estimates from the pooling model. Farmers cultivate more land (+0.11 SD), and use more seeds (+0.11 SD), more pesticides (+0.06 SD), and more fertilizers (+0.08 SD). However, the distributions of the posterior suggest that effects are more uncertain than in the pooling model. All 95% posterior intervals now include zero, and, for some outcomes, there is a sizable probability of negative effects. The 95%-interval is [ _−_ 0.02 SD, +0.25 SD] for cultivated area, [ _−_ 0.09 SD, +0.09 SD] for the crop riskiness index, [ _−_ 0.05 _− −_ SD, +0.21 SD] for fertilizers, [ 0.02 SD, +0.15 SD] for pesticides, and [ 0.02 SD, +0.25 SD] for seeds. Results are broadly robust to using the automatic priors (Figure A1) and to excluding any single study (Figure A2).

> 35For example, in India, Cole et al. (2017) show that the provision of index insurance induces farmers to shift production towards cash crops. In Burkina Faso, Stoeffler et al. (2022) show that the provision of an index insurance contract designed for cotton farmers led them to cultivate more sesame (an attractive cash crop in this context because it requires lower input and time investments).

17
**|0.349***|0.350***|0.592***|
||(0.086)|(0.049)|(0.032)|(0.082)|
|Precipitation|-0.156**|0.474***|0.267**|0.699***|
||(0.072)|(0.100)|(0.110)|(0.170)|
|Subnational FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|35.605|35.605|25.383|25.383|
|Observations|4,129|1,019|4,129|1,019|
|Equality test (Panel vs.<br>long differences)|p = 0|.383|p = 0.|029|
|Equality test (country<br>vs. subnational)|p = 0.000|p = 0.000|p = 0.000|p = 0.000|
|Number of countries|128|90|128|90|
|Number of regions|1,484|1,019|1,484|1,019|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level in Panel A and subnational level in Panel B. Regressions are weighted by region population. Inequality data are taken from the GSAP. Inequality and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. The data for the countrylevel analysis and the subnational-level analysis comes from our newly constructed database. The equality test p-values show the t-test between
exhibit higher values ( =0.677 for 1000-year flood scenario) than lower return periods ( =0.221 for 5-year flood scenario). Furthermore, we observe that network density and flood exposure influence network resilience RRin different ways. As is shown in Supplementary Table 4, the absolute values of network density coefficients<sup>2</sup> RR<sup>2</sup> RR<sup>2</sup> decrease with flood return period. That is, the change (negative) in average percentage of failed routes decreases given a one-unit increase in network density. This suggests that road density, as an indicator of network redundancy, contributes positively to overall network resilience yet the level of contribution decreases with higher flood intensity. As flood return period increases, the absolute values of the flood exposure coefficients first increase and then decrease with the 75-year flood as the inflection point. This agrees with the exposure pattern observed in Fig. 7a and suggests the diminishing impact of flood exposure on network resilience.

# **Discussion**

This study provides the first global evaluation of both direct (hazard exposure) and indirect (mobility and travel behavior) impact of flood hazards on road transportation networks. We define settlement cluster boundaries around the world based on global settlement distribution instead of administrative or selfdefined boundaries. We use these urban clusters as spatial masks to extract meaningful road networks from OSM. In addition, we leverage the latest global high-resolution flood maps which detail the extent and depth of pluvial and fluvial floods for 10 return periods. Combining these data sets with exposure mapping, travel simulation, and vulnerability analysis, we are able to better understand the far-reaching impacts of floods on road network resilience to flood hazards.

The impact of flood hazards on road networks in terms of both infrastructure exposure and mobility disruption (measured as the percentage of failed routes, travel delay, and travel distance increase) is heterogeneous across space. Specifically, the road networks in Africa, South Asia, East Asia, Southeast
|Yes|
|County-cohort<br>fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age fixed effects|No|No|No|No|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Barro and Lee 2013; Key Indicators of the Labor Market (KILM), International Labour Organization; Labour Force Statistics, Organisation for Economic Co-operation and Development (OECD); UN Population Prospects; World Development Indicators, World Bank; and World Bank staff estimations.

Note: Sample of countries is balanced across gender and age specific regressions. Business cycles defined as deviation of real GDP from Hodrick-Prescott-filtered trend. Sample includes balanced panel of 34 advanced economies and 104 EMDEs for 1987-2020. p- statistics are shown in parentheses.

54
and southernrelatively well. Only for the northeastern region the coverage of the sample is limited. This is partially due to the exclusion from the universe of Manipur, Mizoram, Tripura, Meghalaya, and Assam, five small states with relatively poor household survey data. It is also due to the shortcomings of village boundaries in the states of Arunachal Pradesh and Meghalaya. Not surprisingly, the places in the sample are quite different in size, with their surface varying from 0.1 to 464.7 km<sup>2</sup> .

# **3. Human judgment**

Places are usually classified as urban or rural based on a narrow set of indicators that most often includes population and administrative status. Sometimes additional metrics such as population density or the share of employment out of agriculture are considered as well. But even this may not be enough to ensure that the classification is consistent. Cities have diverse characteristics both across and within countries. This multiplicity of aspects to consider calls for an element of human judgment.

_Google images as virtual site visits_ A thorough subjective assessment would require visiting every city, town, and village in the sample and literally seeing how they look like. However, the time and cost implications of visiting 1,277 places scattered across a large country are dissuasive. To resolve a similar challenge, an emerging literature uses Google images as a proxy for information gathered through field surveys. Salesses et al. (2013) rely on over 4,000 Google street view images to crowdsource perceptions on safety and livability, for two major cities of the US. In a scaling up effort, Dubey et al. (2016) expand this approach to over 110,000 images from 56 cities. Naik et al. (2016) use the images associated with quantified human judgment as the training set and develop a model to further quantify neighborhood appearance for 19 cities in the US through machine learning. And Naik et al. (2015) apply the machine learning model to study
# **S2.2 Using Administrative Matched Employer-Employee Data**

Table S2.12 Variance Decompositions, AKM Model: Brazil, Costa Rica, and Ecuador

|_a. Brazil (data available 1995-20_|_14)_||||||||
|---|---|---|---|---|---|---|---|---|
||1995-98<br>(1)|1998-2001<br>(2)|2001-04<br>2004-07<br>(3)<br>(4)|2007-11<br>(5)|2011-14<br>(6)|Change<br>1995-2001<br>(2)-(1)|Change<br>2001-14<br>(6)-(3)|Change<br>1995-2014<br>(6)-(1)|
|Variance of worker efects|49 (0.299)|50 (0.280)|52 (0.252)<br>54 (0.230|)<br>56 (0.217|)<br>59 (0.216)|36 (-0.020)|31 (-0.035)|34 (-0.083)|
|Variance of frm efects|31 (0.188)|29 (0.163)|28 (0.133)<br>25 (0.107|)<br>28 (0.087|)<br>21 (0.075)|46 (-0.025)|50 (-0.057)|46 (-0.112)|
|2xCov. Worker and frm efects|13 (0.077)|14 (0.076)|13 (0.064)<br>12 (0.052|
expenditure very differently across goods (most notably, food), this can have implications for the overall gains from trade as well as for the inequality costs of trade. These differences and their implications are not captured by standard models based on homothetic preferences. There are, of course, papers that rely on non-homothetic preferences, and the conclusions from those models are consistent with the conclusions from our paper.

The other important dimension of heterogeneity is income heterogeneity. In our model, households earn income from various sources not limited to labor markets. In particular, income from sales of different agricultural products plays a crucial role in agrarian economies and these sales include returns to non-labor household factors such as land and assets such as tractors, ploughs, and more humble tools such as shovels, carts, and wheelbarrows. The “distribution” of these factors across households in the surveys is highly heterogeneous. Households thus have very different sources of incomes and this matters for welfare and inequality. This income heterogeneity is missing in many neoclassical models.

# **3 Estimating the Welfare Impacts of Trade Policy**

To estimate the welfare impacts of trade policy, we need to measure the different components of equations (5) and (7). The data needed to estimate impacts on consumption and production of traded goods, labor income and home enterprise income can be found in standard household surveys. Trade and trade policy data come from United Nations COMTRADE and UNCTAD TRAINS, which classify goods using the Harmonized System (HS) so that tariffs and imports are available at HS-6 level. Household surveys use different nomenclatures of goods produced and consumed. To match trade data and household survey data, we use and improve upon the templates and concordances developed by Nicita, Olarreaga and Porto (2014). In short, we first aggregate goods in the household surveys to 2-digit and 4-digit categories. We then aggregate tariff and trade data from COMTRADE to those categories. See Appendix A for details.

10
plausibly for the approach, no such requirements were made of subjects in this study.

> 2 For relevant background research on female empowerment, see Brody et al (2015), Doepke (2014), Ganle (2015), Prennushi and Gupta (2014) and Tromlmlerova (2015). Garikipati (2008) is interesting in the current context for the use of vulnerability and empowerment indicators, which are used in a study that concludes that women might be disempowered if loans are used for purposes by male household members. In our pilot work, this concern did not emerge as a significant issue – rather women tended if anything to have stories which showed how men came to support the program when they could see the potential benefits for the household that it brought.

3 Further related discussions of self-help groups can be found in Alemu et al (2018) Fafchamps and Ferrara (2012), Hasan (2017), Parida (2010), Seebohm (2013), Vinahagamoorthy (2017) and Weber (2014).

3
hazard level of the administrative units within a province.<sup>18</sup> Thus our analysis may be overestimating the country-province value-at-risk of natural hazards that do not affect all provinces equally (or affect them at differing magnitudes). Therefore, our results for physical risks should be considered as an upper-bound. Comparability caveats also arise since administrative units are not necessarily proportional to countries’ surface areas when compared across countries. On the one hand, we have countries like Dominican Republic, which has 31 provinces and one national district; on the other hand, Brazil, the largest country in LAC, is 175 times as large as Dominican Republic and has a lower number of provinces: 26 states and one federal district.

In a second step, we narrow our physical risk assessment to the portion of assets that banks allocate to households for home loan purposes, i.e. mortgages. We therefore combine the flood hazard mapping information with the banking system's outstanding mortgages within each country-province. Specifically, in line with recent empirical studies (e.g., Pagliari 2021; and Bank of England 2018), we measure physical risk exposure of mortgages to floods hazard. Mortgage repayments and property values could be significantly strained by floods via repair expenses and increased insurance costs. Publicly available information allows us to dive deeper into the physical risks associated with floods by analyzing the banks’ mortgage portfolios distribution at the province level for Chile, Colombia, and Mexico.

In a third and final step, we use geographical NPL data at the country-province level and natural disaster information to understand the dynamics of banks’ balance sheets in the aftermath of large-scale natural disasters.<sup>19</sup> Data availability limits the analysis to Argentina, Brazil, Chile, Mexico, and Peru. We define large-scale natural disasters as the most economically damaging climatological, hydrological, or meteorological natural disasters occurred in our country-province sample during the past ten years. Based on EM-DAT, this identification strategy provides us with the 2018 Argentine drought
# **Sovereign Defaults and Expropriations:  Empirical Regularities**

Maya Eden, Aart Kraay and Rong Qian Development Research Group, The World Bank ___________________________________ 1818 H Street NW, Washington, DC, 20433, meden@worldbank.org, <u>akraay@worldbank.org, rqian@worldbank.org. This paper was prepared as a background paper for the 2012 Multilateral Investment</u> Guarantee Agency (MIGA) Annual Report. We are grateful to Mark Wright and Mike Tomz for making their sovereign default and expropriation data available, and for helpful discussions about the data. We are also grateful to Daniel Villar and Persephone Economou for interesting discussions that motivated this work, to Manabu Nose for help with the Berne Union data, and to Christoph Trebesch and Juan Jose Cruces for kindly sharing summary statistics from their dataset of "haircuts" in sovereign default episodes. The views expressed here are the authors' and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.