rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3. Empirical Evaluation of Household WTP for Electricity Access**

To empirically test the model predictions, we use unique survey data from rural areas of three representative Sub‐Saharan African countries with deep pockets of extreme poverty: Burkina Faso, Rwanda, and Senegal. The data were collected between 2010 and 2013 for impact evaluations on electricity access interventions. All three countries are among the world’s poorest countries, classified as “low‐income” by the World Bank, and had very low rates of electricity access, particularly in ultra‐ poor rural areas at the time the survey.<sup>6</sup> The surveyed areas correspond to subcase b) shown in Figure 1 as very few (if any) households in the sample use diesel generation. The analysis thus focuses on the determinants of WTP by rural households for solar generation and grid electricity access.

# _3.1 Data and Quantification of WTP_

Identical WTP questions were asked in all three countries. Table 2 describes the basic information on the collected data sample.

In Burkina Faso, the data come from an impact evaluation of a Solar Home System electrification intervention in the rural province of Kénédougou supported by the Netherlands’ Ministry of Foreign Affairs (see Bensch et al. 2013). For this purpose, a random sample in villages, drawn in 2010, was offered a solar home system (SHS) on a fee‐for‐service basis. The WTP survey was only elicited among households that did not have any electricity source at the time of the survey. Most of the remaining households had already had a private solar panel bought on local markets. Grid electricity was not available among the surveyed population.

In Rwanda, the 2011 data are a representative random sample of rural beneficiaries of the nationwide electrification project EARP (Electricity Access Rollout Programme, see Lenz et at., 2017 and Peters et al., 2014). All these households were hence located in rural areas where grid electricity had not yet been available.
it is distributed, we first estimate the probability of default in 2020 for firms in each risk bin. To do so, we use the coefficients of the default risk model in Section 4 and plug in the 2019 information for the different regressors. This yields predicted _ex ante_ default values for 2020 for firms that use the credit program across different risk groups. As expected, the predicted default probability declines monotonically with risk, going from 18.2% for high-risk firms to 2.1% for low-risk firms (Table 7, Panel A, Column 4).<sup>61</sup> We then calculate a measure of total credit risk (i.e., expected loss) for each risk group by multiplying the dollar value of program loans (Column 2) by the default probability (Column 4).<sup>62</sup> As a proportion of GDP, the total credit risk related to formal firms corresponds to an expected credit loss of 0.27% (Panel A, Column 5). When using all guaranteed loans to formal firms and natural persons, the expected credit loss is 0.45% of GDP (Panel B, Column 5), which corresponds to a 9.8% default probability of the guaranteed credit (=0.45%/4.6%).

These estimates of the expected loss do not capture the increased uncertainty at the onset of the pandemic. They are calculated using the 2019 data available to banks and the government at the start of the pandemic. Thus, the estimated expected loss can represent a lower bound of the actual expected loss. To provide an alternative measure of the program’s cost and with the benefit of hindsight, we use data on the actual _ex post_ default rates after 2020 (Column 6).

The _ex post_ default rate is monotonically increasing in the risk categories, just like the _ex ante_ default rate, as shown in Section 4.1. Moreover, the _ex post_ default
|−3.7|−0.9|−3.1|
|21–30 YoE|−2.1|−0.3|−2.9|−4.9|−1.4|−3.4|
|31+ YoE|−2.1|−0.8|−3.6|−4.9|−1.5|−3.9|
|**Gender and area of residence**|||||||
|Male/Female|−1.4|−4.2|−1.1|−0.2|−2.3|−0.9|
|Urban/Rural|2.8|−3.8|−3.9|−0.5|−0.1|−3.2|

_Source:_ Calculations based on the SEDLAC database. See annexes A.1 and A.2 for details.

_Note:_ The underlying data represent the differences in the trends plotted in figures 9, 11, and 13. The bases for each category are primary or less in education, between 0 and 5 years of age for the potential experience, females for gender, and rural areas for area of residence. YoE = years of experience.

# 5. Conclusions

In this paper, we examine trends in labor income inequality in Latin America during the last 20 years. To estimate the inequality derived from the way the labor market values the different skills and demographic characteristics, we focus on the hourly earnings inequality of the main occupation of full-time workers between 15 and 65 years of age.

We identify a trend reversal in labor income inequality beginning in the early 2000s that followed a slight increase during the early 1990s. The increase of labor income inequality during the 1990s was associated with a reduction in real wages, most severe for the workers on less well paid jobs, particularly for those in the left half
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Policy Research Working Paper 10613

# **Abstract**

Reliable microeconomic data to understand the climate-migration nexus are virtually nonexistent. Nationally representative multitopic household surveys are rarely, if ever, explicitly designed for studying migration issues. Despite this limitation, most countries have no alternatives to the use of household surveys when it comes to analyzing complex multidimensional phenomena such as the interrelationship between climate change and migration. This paper offers a critical reflection on current challenges faced by multi-topic household surveys in responding to this need, but also, more importantly, on the many opportunities embedded in their use. Specifically, using the Living Standards Measurement Study as a case study, a conceptual framework, practical empirical guidance, and a methodological agenda are proposed to address data gaps and contribute to a more solid understanding of the climate-migration nexus.

This paper is a product of the Development Data Group, Development Economics. 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 apaolantonio@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
Policy Research Working Paper 9835

# **Abstract**

This paper revisits impact evaluation studies on the largest public workfare in the world, NREGA. In an environment where randomization is not feasible, I show why an impact evaluation exercise on NREGA should acknowledge the existence of an older program, SGRY. Using novel district-level expenditure data on SGRY, this article shows how ignoring the older program is likely to underestimate the general equilibrium impact of the employment policy on various relevant socio-economic outcomes. In most cases, ignoring SGRY underestimates NREGA’s impact by 30–40 percent.

This paper has been accepted for publication in _The World Bank Economic Review_ . 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 author may be contacted at girish.bahal@gmail.com.

_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
sup>14</sup>

> 11 Neoclassical growth model, since the seminal work of Lucas (1988), the concept of capital since is usually broadened from physical capital to include human capital.

> 12 For robustness checks we also tried institutionalized democracy measure from Integrated Network on Social Conflict Research which has a value between 0 and 10. This is a composite index which includes information on degree of participatory democracy, the existence of institutionalized constraints on the exercise of power by the executive, and other aspects of plural democracy, such as the rule of law, systems of checks and balances, and freedom of the press. Our results do not change.

> 13 By using shorter time spans we are left with a sample of approximately 100 countries by 2007. We have excluded few countries where the data on service exports was reported haphazardly for a limited time period; hence we only keep reliable data for panel estimations. 14 Initial, lagged levels are used to address potential endogeniety problems (see for example Barro and Lee (1994))

18
tend to hire from unemployment and earn large rents on their workers. At the same time, high-productivity firms can employ more workers as they not only hire from unemployment, but they can also poach workers from low-productivity firms. Poached workers can get a larger share of the surplus or rents as they can use their previous job as an outside option in wage negotiations.

The model highlights that a payroll tax cut increases overall employment and wages just like in the standard framework. Importantly, however, the impact of the policy is heterogeneous across firms. Low-productivity firms, which tend to hire from unemployment, benefit disproportionately from the policy as unemployed workers have limited bargaining power. At the same time, high-quality firms poach more of their workers from other firms, which means that workers are able to claim more of the surplus from the tax cut. Thus, the model suggests that employment effects are concentrated among low-productivity firms, while wage effects are concentrated among high-productivity firms.

We examine the empirical relevance of these predictions by studying the impact of an age-specific payroll tax cut in Hungary. In 2013 the monthly social security contribution decreased by HUF 14,500 ($66) for all over-55 private sector employees.<sup>3</sup> This led to a 5.3% decrease in the labor cost for an average over-55 private sector employee.

Using rich administrative data, we estimate the impact of the policy in a difference-indifferences framework, comparing men above the age cutoff to men just below it.<sup>4</sup> We find a large increase in employment in response to the policy. In response to the 5.3% decrease in labor costs, employment of the treated workers increased by 1.6%, implying a labor demand elasticity of -0.30 (s.e. 0.03). This increase in employment mainly came from nonemployment and inactivity. The change in self-employment and public sector employment was limited, reflecting that these workers were ineligible for the payroll tax cut
# **1 Introduction**

Maritime shipping is indispensable for international trade: ships transport 70% of the world’s trade value and 80% of the world’s trade weight (UNCTAD, 2015, 2017). Maritime shipping dates back to at least 5,000 years ago, and its history is well documented, including multiple waves in its development facilitated by technological advancements, wars, trade with colonies, etc. (Stopford, 2009). One of the more recent waves occurred in the 1950s when containerized shipping revolutionized maritime shipping. This technological breakthrough had a pronounced impact on trade volumes, patterns, and infrastructure (Hummels, 2007; Levinson, 2008; Blonigen and Wilson, 2018). The rate of change has further intensified in the last two decades with rapid fleet upgrades and expansions, improvements in infrastructure (port upgrading, expansions of Panama and Suez Canals), and changes in regulation and market structure.

In the last two decades (i.e., 2000-2020), the availability of qualitatively new and much more disaggregated data sets prompted a surge in empirical research of maritime shipping. This paper summarizes this research and complements it with novel data analysis and stylized facts. Understanding of the complexity underlying factors of the maritime shipping is even more important in light of the recent disruptions and high freight rates in maritime shipping caused by the COVID-19 pandemic. While we do not directly explore the most recent events, our review might be instrumental for this research as well.<sup>1</sup> Our focus is on container and dry-bulk maritime shipping.<sup>2</sup> Containerized maritime shipping is the dominant mode of international transportation: it accounts for two-thirds of total world trade. On the other hand, dry bulk shipping is used for commodities, and it accounts for about half of the total seaborne trade by weight.

We start by documenting several vital trends in maritime shipping. First, motivated by recent papers on the back-haul effects in maritime shipping (Wong
access only among the richest 60 and poorest 40 percent households; we do not have internet access information for every income level. As a result, our measure could be under-estimating connectivity for the relatively richer, and over-estimating connectivity for the relatively poorer households. To address this concern, we linearly interpolate internet access by income level based on those two estimates for each country to allocate internet. As seen in Figure 2c, this assumption does not change the results significantly either. The share of jobs that can be done in high income countries is identical due to widespread internet access. Similarly, the change in low-income countries is also minimal, but due to overall lack of internet access. The biggest change occurs in middle-income countries. In both upper- and lower-middle income countries, the share of jobs that require but lack internet access declines by around 0.5 percent, increasing the share of teleworkable jobs by the same amount.

Our final exercise uses data from the World Development Indicators (WDI) to allocate internet access. Since the data are not disaggregated by income level, we match the share of internet users by country by allocating connectivity to the highest earners. As an example, if 37% of the population has internet access, we assume that everyone in the top 37% of the income distribution has access and nobody in the bottom 63% has access. This procedure tends to increase the share of occupations amenable to teleworking across all country groups (Figure 2d). Higher wage occupations are more likely to be amenable to teleworking, and this allocation rule gives them preferential access to internet. The increase is about 2.3 percentage points in low-income countries, 5 percentage points in middle-income ones, and 3.1 percentage points in high-income countries. We also recalculated figure 1a under each scenario to see how the results change for each country and the results are available upon request. We see that the different assumptions explored in each of the scenarios (as presented in Figures 2a-d) do not lead to dramatic changes in
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.59% over 2011‐2015 and ‐ 0.56% over 2016‐2018 respectively. When we consider the additional decline of 1.8 million in the Syrian population discussed in the previous section, average years of schooling declines only marginally more, that is by 1.499 years. If we split this decline over time following the proportions in the first case, we obtain annual average growth rates in human capital of ‐2.64% over 2011‐2015 and ‐0.57% over 2016‐2018 respectively.

# _3.1.4 TFP_

The Conference Board estimates an average TFP growth of ‐9.5% over 2011‐2017 (from a low of ‐ 22.9% in 2012 rising to 2.8% in 2017). However, while the loss of TFP would be an important driver of loss in GDP potential, it could be that this high number reflects an underestimation of the destruction of physical and human capital.

Thus, as an alternative to The Conference Board’s calculations of TFP growth, we utilize Kim and Loayza’s (2019) model of determining TFP growth. The key element that feeds into this model is an overall index of TFP determinants, the determinants being education, infrastructure, innovation, institutions, and market efficiency. The composite index stood at 30.33 for Syria in 2010 on a scale between 1 and 100. We estimate the trajectory of this index over 2011‐2018 by calibrating its subcomponents. For the education index, we calculate a decline that is proportionate to the fall in average years of schooling of the working‐age population. Given that enrollment rates fall, and the exodus of Syrians increases noticeably only from 2013, we keep the average schooling years unchanged from 2010 to 2012, instead interpolating a decline over 2013‐2018, based on our calculations of average years of schooling for 2015 and 2018 respectively. For the infrastructure index,

> 36 See Milton (2019) for a discussion of how Syria
as discussed above. Since the probability of assignment to a flexible credit offer changed during the experiment (see Figure 1 and Appendix A), we adjust the standard estimation equation to avoid potential bias from correlation of client characteristics with the assignment probability. Following Gibbons, Suarez Serrato, and Urbancic, (2019), we estimate treatment effects separately for the two periods and calculate a weighted average based on the two periods’ sample frequencies. Formally, we estimate the following regression equation for client _i_ : where T _i_ is an indicator for assignment to a flexible contract and R1 is an indicator for receiving an offer in the initial period, Yi is the dependent variable. We include as additional controls the pre-intervention

> 9 We note that portfolio risk concerns raised by some credit officers in our study would be consistent with the lenders policy in Battaglia, et al. (2023), in which loan officers screened applicants for flexible loans based on their repayment histories and that of Barboni and Agarwal (2023)  in which the “repayment vacations” could not be taken in the first three months of the loan.

> 10 The rate of pass use among the sample of clients interviewed in the phone survey is only 18%. This is lower than the final rate from the administrative data since phone surveys were carried out, on average, six months into the loan. When controlling for time elapsed since loan disbursement, the reported rates of pass usage match closely with those of the administrative data.
Policy Research Working Paper 7600

# **Abstract**

This paper studies tax withholding on business sales, a widely used compliance mechanism which is ignored by public finance theory. A withholding scheme, whereby the payer in a transaction collects tax from the payee, is introduced in a standard evasion model with third-party reporting. If the taxpayer can fully reclaim the tax withheld, withholding is irrelevant to her evasion decision. However, in the presence of costly reclaim, low salience of enforcement or extensive margin compliance gaps, withholding can raise tax payment. To show this empirically, the study exploits a ten-year panel of income tax and sales tax records from 400,000 firms in Costa Rica, and over 20 million third-party information and withholding reports. First, an anatomy of compliance shows that firms are relatively compliant with third-party information on the extensive, intensive and payment margin. An event study then shows that coverage by third-party reporting leads to a 20–50 percent increase in reported taxable income. Finally, a difference-in-difference and regression discontinuity estimation of a withholding rate increase isolates the impact of withholding. A doubling of the withholding rate leads to a 33 percent increase in sales tax payment among treated firms and an 8 percent increase in aggregate sales tax revenue. The mechanisms are reduced misreporting and incomplete reclaim of the tax withheld. The large compliance impact of withholding rationalizes its widespread use in low compliance contexts.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at abrockmeyer@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