rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2. Data**

To study regulatory arbitrage in the cross-border syndicated loan market we obtain data on syndicated loans from Loan Pricing Corporation’s Dealscan database for the period 19952016. This database contains loan level information including the identities and residencies of borrowers and lenders as well as lenders’ contributions to each loan. We use these data to construct aggregate loan volumes that are dependent variables in the regressions. In addition, we investigate credit ratios that reflect international banks’ credit provision through foreign subsidiaries relative to their total cross-border syndicated loan provision.

The Dealscan database provides only recent information on lenders and their ultimate parents. In practice, ownership links vary over time due to mergers and acquisitions. To address this issue, we rely on the Dealscan-Compustat link provided by Schwert (2018) that connects the most active lenders in Dealscan to their respective banking groups on a quarterly basis, reflecting mergers and acquisitions over time. We then define a lender as a foreign subsidiary if the banking group that it belongs to according Compustat is headquartered in another country than the lender itself as reported by Dealscan.<sup>5</sup> As we are interested in international bank regulatory arbitrage, we drop all loans provided by non-bank lenders and purely domestic loans where the ultimate parent bank and the borrower reside in the same country. Figure 1 plots the development of cross-border syndicated loan provision by banks over the 1995-2016 period, showing a generally upward trend that was temporarily reversed during the financial crisis of 2008-2009.

We match the loan data with borrower and lender country variables from various sources as detailed below. After these steps, we obtain a sample of 149,416 individual bank loans to

> 5 After merging with the Dealscan-Compustat link our database covers about 31% of the entire volume of crossborder syndicated loans in Dealscan, and about 18% of the number of cross-border syndicated loans.

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.0 million workers, of whom 1.9 million would be within formal firms. The Establishment Census generates a much larger number of workers, 9.2 million, of whom 4.5 million were within formal firms.

# **4.2.2. Distribution of firm characteristics across data sources**

We now examine firm characteristics across the four data sets to determine whether we are observing roughly the same firms. As shown in Figure 2, the two waves of the ELMPS have a somewhat higher proportion of one-person firms and are conversely somewhat under-represented in the three larger size categories. This may be due to branches of firms, which tend to be larger, being included as separate establishments in the EcC and EsC. Despite this tilt toward smaller firm sizes, the ELMPS data produces higher estimates of formality than the other two sources, as we have seen above. Besides the fact that EcC has a higher representation of two-person firms (35%) and the EsC a higher representation of threeor four-person firms (25%), these two data sources produce fairly comparable estimates in the three other size categories. Overall, firms in our universe skew toward the smaller size categories, with one or twoperson firms making up 66% to 86% of all firms in the universe and firms of 5-24 workers making up at most 10%.

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No targeting mechanism is ever perfect. And one might question whether we are using the right welfare indicator; possibly some of those we classify as “rich” according to current consumption (as measured in our survey data) would be deemed poorer by other (unobserved) criteria. So one might not want to overstate the significance of our finding that the self-targeting mechanism is far from perfect. However, the results in Table 4 do warn against the robustness of the generalizations made by some advocates of the EGS.

Table 6 gives our estimated aggregate supply of casual labor at each EGS wage rate. The overall wage elasticity (estimated as a regression of log labor supply on log EGS wage rate across the 7 observations) is 0.19 (with a standard error of 0.02). The elasticity is slightly lower for the poor; for the poorest quintile the elasticity is 0.16 (standard error of 0.02).

While past estimates of the labor supply function to casual labor may not be a good guide to the labor supply function in the presence of an EGS, it is still of interest to compare these elasticities to previous estimates. In a study using household level data for West Bengal in 197273, Bardhan (1984) obtained wage elasticities of labor supply to casual agricultural labor in the range 0.2 to 0.3 — similar to those implied by our estimates of labor supply at various EGS wage rates. However, our estimates are considerably lower than those reported in Kanwar (2004) who obtained labor supply elasticities in the range 0.8-1.5. (These estimates used the ICRISAT Village Level Surveys for semi-arid areas over 1975-1984.) What are the characteristics of those likely to benefit from the scheme? Table 7 helps answer this question by comparing a range of descriptive statistics between the adult sample as a whole, those who gain from the EGS and the sub-sample of gainers who are attracted into casual work at an EGS wage rate of Rs 50 per day. We find
https://www.worldbank.org/en/publication/gfdr/data/global-financial-development-database.</u>

> 4 For example, <mark>the World Bank Enterprise Survey is a firm-level survey covering a broad range of business environment topics. As of March 2023, data has been collected from more than 191,000 firms in 154 countries, including information on 44 Sub-Saharan African countries. For a detailed overview of the survey, please consult</mark> <u><mark>https://www.enterprisesurveys.org/en/enterprisesurveys.</mark></u>

> 5 See Demirgüç-Kunt, Klapper, Singer, and Ansar (2022) for description of the latest round of the Global Findex Database. Findex data were also collected in 2011, 2014, and 2017.

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<!-- Start of picture text --> Age Composition of Formal and Informal Employees<br>In European Countries, by Region. 2004-2009<br>SI RU SK PL LV HU RO EE UA BG CZ SE DK FI NO IT ES IL GR PT CY NL UK AT IE DE CH FR BE<br>East North South West<br>SI RU SK PL LV HU RO EE UA BG CZ SE DK FI NO IT ES IL GR PT CY NL UK AT IE DE CH FR BE<br>East North South West<br>15-24 25-54 55+<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Formal: Employee with a contract; Informal: Employee without a contract. Source: Calculation with ESS data

**Figure 7 Age composition of formal and informal employees, by country**

**_Education._** Figure 8 demonstrates that low-educated workers are over-represented in the shadow sector of most countries (exceptions to this rule include Slovenia, where the lowskilled are under-represented in the shadow sector, as well as Russia and Belgium, where they are represented proportionally). On the other hand, university graduates are underrepresented in the shadow sector everywhere except Czech R. and Romania. Similar results are presented in Boeri and Garibaldi (2005: Figure 2) for Italy in 1995-2002.

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**Growth, Poverty, and Inequality:** A **Regional Panel for Bangladesh**

Quentin T. WodonI Key words: growth, poverty, inequality, Bangladesh, panel data JEL classification: 132, D63, 040 Correspondence: qwodon~worldbank.org 'World Bank (LCSPR). This research was completed as a background paper for the poverty assessment of Bangladesh at The World bank. The research was made possible through a long term collaborative effort between the Bangladesh Bureau of Statistics and the World Bank. I am grateful to Martin Ravallion, Shekhar Shah, Bhaskar Kalimili, and Salman Zaidi for advise and/or comments.
appear to be competing options for households, with one almost always being chosen over the other. Only infrequently does a single household send family members both overseas and to another Indian state. In 1998, approximately 6.5 percent of migrant households had both domestic and overseas migrant members. This share had fallen in 2014, with less than 3.5 percent of migrant households having members both working abroad and within India.

> 9 Planning Commission, Government of India, data tables as of August 12, 2014.

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_|0.48 *|0.94|_0.45_|
|Number of Children|-0.12 *|2.35|-0.29|Number of Children|-0.02|2.00|-0.04|
|Mill's Ratio|_0.45_*|2.47|1.12|Mill's Ratio|-0.01|0.87|-0.01|
|Constant|-2.67 *||-2.67|Constant|1.87 *||1.87|
|Dependent Variable:||||Dependent Variable:||||
|Log Nat Expenditure||5.60|5.60|Log Nat Expenditure||7.32|7.36|
|Evaluated in pesos||270.64|270.23|Evaluated in pesos||1506.59|1568.72|
|R2|0.42|||R<sup>2</sup>|0.35|||

Source: Own calculations based on ENIGH 96 and DGPyP, SEP

* Significant at 5%, ** Significant at 10%

Italics: Reference category 15
proportion reticent, it is notable that four states have estimates of more than 50% and more than half of the states have estimates of over 25%.

# **4. Do Reticent Respondents Answer Sensitive Questions Differently?**

The main purpose of identifying reticent respondents is to determine in what way and by how much respondent reticence distorts responses to other questions in the survey. In Romania, AM found that reticent respondents typically report lower incidences of corruption and other sensitive acts compared to possibly candid respondents. Our analysis for Nigeria confirms this finding, but with one caveat: the extent that reticence matters appears to depend importantly on the wording of the sensitive questions. In particular, reticent respondents admit to sensitive acts at lower rates primarily when asked questions that are personal in nature or refer to the respondent’s own business, when the answers to questions are most likely to imply personal misbehavior on the part of respondents themselves.<sup>10</sup> We therefore structure the rest of this analysis according to how personally the sensitive questions in the Nigerian survey are worded. We organize a number of potentially sensitive questions from the survey into four groups, contained in Tables 4-7, and also examine some placebo questions in Table 8. In each table, we regress responses to the questions on the same set of control variables included in Table 2, and then a dummy taking the value one if the respondent is reticent, and zero otherwise, using OLS. For reasons of space we do not report the coefficients of the controls in the tables. Sensitive questions used in the analysis below have all been oriented the same way, so that a higher value of the dependent variable indicates admission to acts of a sensitive nature. To the extent that reticence effects are important, we should therefore expect

> 10 One possible reason for the difference between the Romanian and Nigerian results might be that the Romanian survey was specifically focused on corruption whereas the Nigerian survey had only a small proportion of questions on corruption.  Romanian respondents
with _Dowrycst_ .

# **8 Conclusion**

A large literature has shown that culture matters for economic outcomes. More narrowly, there is a small but emerging body of work that highlights the importance of marriage-related cultural norms and institutions for households’ decision-making. Despite the wide prevalence of bride-togroom marriage payments, i.e., dowries, in several developing countries, economists have not directly investigated their impacts on financial and childbearing decisions, and on human capital investments in children. This is due to both the lack of data on dowry and the lack of a credible identification strategy. In this paper, we make use of an under-utilized source of nationally representative dowry data from India and propose a novel estimation strategy to examine the impact of future dowry payments on current outcomes.

We find several important results. This is the first paper, that we are aware of, that proposes an alternative motive for savings behavior in dowry-paying societies: the prospect of lump-sum dowry expense induces firstborn-girl families to start saving more in advance relative to firstborn-boy families in rural India. The ability of parents to overcome barriers to savings, especially behavioral biases such as imperfect self-control, in the context of saving for dowry, suggests that it is crucial to take into account the cultural context while designing policies that seek to affect saving behavior. We find that the increased savings take the form of formal savings in financial institutions and not 21
Policy Research Working Paper 10914

# **Abstract**

This paper investigates trends in willingness to pay higher taxes to combat climate change in countries of Eastern and Central Europe and Central Asia between 2016 and 2023. Using data from the Life in Transition Survey, it shows that despite increasing attention from policy makers, scientists, and the media, the average shares of respondents willing to pay to combat climate change declined over this period. The paper tests several hypotheses that could explain the deterioration of public readiness to support climate change policies. The most likely explanation is the growing politicization of the climate change agenda in the region.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at mlokshin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
.

**Trade Proximity.** We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014. The data are classified according the 4-digit Standard International Trade Classification (SITC), Revision 2. Only products and commodities are considered. Then, we aggregate these bilateral trade flows at the country-level.

To classify trade flows into final and intermediate goods, we use a concordance table from SITC Rev. 2 to Broad Economic Categories (BEC).<sup>11</sup> We then classify goods into five categories: primary, semi-finished goods, parts and components, capital goods, consumption goods, and a residual category called goods non-specified. We group these categories into intermediate

> 8We used the data serie called “NY.GDP.MKTP.KD”

> 9With this setting, we mostly keep fluctuations that have a frequency between 8 and 32 quarters.

> 10We keep fluctuations between 32 and 200 quarters, following Comin and Gertler (2006a)

> 11The concordance table from SITC Rev2 to BEC can be found on the UN Trade Statistics webpage: `https: //unstats.un.org/unsd/trade/classifications/correspondence-tables.asp` .

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their accounts six or more times in a typical month. ATMs and electronic payment systems (debit cards, electronic bill payments, and the like) facilitate more frequent access to accounts. Indeed, adults with a formal account in high-income economies report most commonly using ATMs for withdrawals. Those in developing economies report most commonly making withdrawals over the counter in a branch of their bank or financial institution (figure 1.16).

In recent years the proliferation of “branchless banking” has received growing attention as a way to increase financial access in developing economies, particularly among underserved groups.<sup>21</sup> One mode of branchless banking centers on bank agents, who often operate out of retail stores, gas stations, or post offices. By taking advantage of existing infrastructure and client relationships, this way of operating makes it more cost-efficient to expand financial access. Bank agents can also be mobile, making daily or weekly rounds among clients. Few account holders report relying on bank agents (whether over the counter at a retail store or from some other person associated with their bank) as their main mode of withdrawal or deposit. But in several Asian economies—including Bangladesh, the Lao People’s Democratic Republic, Nepal, and the Philippines—more than 10 percent of account holders already report using bank agents, and this share is expected to grow globally. Over time the Global Findex data can serve as a benchmark for studies and policy interventions examining the effect of bank agents on financial access.

**22**

MEASURING FINANCIAL INCLUSION
During the second half of the 1990s, Panama, El Salvador, Guatemala, Nicaragua, Honduras, and Brazil also adopted reforms. The main missing players in the process of transforming the electricity sectors have been Costa Rica, Ecuador, Mexico, Paraguay, Uruguay, and Venezuela, although Costa Rica, Ecuador, Mexico, and Venezuela recently initiated actions toward restructuring.

Many authors have studied the impact of the reforms in the region, documenting an increase in labor productivity in all the countries where restructuring and privatization has taken place (see Rudnick, 1998; Fischer and Serra, 2000; and Rudnick and Zolezzi, 2001). The results obtained from these studies, however, rely on partial productivity ratios and focus on labor productivity. In this study we provide additional empirical evidence by using a more comprehensive labor requirement function approach to examine whether ownership type and type of regulatory regime have any systematic impact on labor productivity, labor productivity change, or both.

An important finding of our study is that there were substantial increases in labor productivity in the post-reform period (1994-2001). The current debate in the region is whether final consumers have benefited from this increase in labor productivity. As a first approach to address this issue, in this study we explore whether the increases in labor productivity have had an effect on lower prices for final consumers. To generate the evidence, we exploit a unique database described in an Appendix..

# **Model specification of electricity distribution**

The electricity distribution model used in this paper includes a variable input (the number of employees), an exogenous capital input (the kilometres of distribution network), and three exogenous outputs (the number of final customers, the total energy 3
# **4. The Effect of Migration on Fertility**

One possible channel through which migration may affect observed child health outcomes is through fertility. Migration may alter the fertility decision through a number of avenues, such as changes in household income and the opportunity cost of time and changes in knowledge about contraceptive practices. Since births that occur at very short intervals experience higher risks of infant death (see e.g. Bongaarts, 1987), the decision of whether and when to have a child may impact on child health outcomes, irrespective of any other effects of migration on health. We therefore first use the full sample of all women aged 15-54 in our sample to assess whether any differences in child health between migrant and non-migrant families are in part due to differences in fertility.

Our measures of child health are only for households that have had a child since 1 January 1994. The first four columns of Table 4 examine whether women aged 15-54 in migrant households are any more or less likely to have had a child in this period. Columns 1 and 2 use a probit specification, while Columns 3 and 4 use regression. The probit, OLS and instrumental variables results all show a small and insignificant effect of being in a migrant household on the likelihood that a woman gave birth after the start of January 1994. Columns 5 and 6 then investigate the impact of migration on the number of children ever born to women aged 15-54 as of the survey date. The OLS results show a significant positive effect of being in a migrant household. However, after instrumenting, we find this effect to be insignificant. We therefore conclude that household migration status does not affect either the likelihood a women had a child since 1 January 1994, or total fertility. For the remainder of our analysis we therefore consider health outcomes of children conditional on the children being born.

# **5. Does Migration Affect Child Health?**

# **5.1. The Impact of Migration on Infant Mortality**

Table
outcomes. The main explanatory variable we wish to correlate with women’s economic empowerment is the gap between the leave days allocated to mothers and to fathers. As noted in section 2, motivated by the work of Eckberg et al. 2013, we wish to investigate whether there is any correlation between the leave allocation to both parents and women’s labor market outcomes. Because there are some extreme values in this relationship, we express this variable in logarithmic form.<sup>9</sup> Additionally, because the relationship between the leave day gap and women’s outcomes may vary based on the number of days that a mother receives—for example, a gap of five days may have very different implications if mothers are allocated ten days, or if they are allocated 100—we also control for the number of days allocated to mothers. As leave policies may not be correlated with any changes in labor market statistics in the year in which the policies are implemented or changed, we lag the policy variables by three years.

There are several control variables that are included in our estimations. Firstly, because countries that have a more equal allocation of leave between parents may have a more gender-inclusive legal framework in general, we control for the overall level of legal equality within a country using the _Women, Business and the Law_ index score, recalculated without including the datapoints capturing family leave. Secondly, we account for the overall level of economic development in a country by including the log of gross domestic product (GDP) and its square term. This variable is measured with a three-year lag to lessen endogeneity concerns. Next, because we expect family leave policies to be most effective when there are more women in a country of child-bearing age, we include the percentage of women in a country between the ages of 15 and 44. Finally, we also control for the fertility rate within a country – while countries with relatively lower fertility rates have been at the forefront of the development of leave policies, more