rafmacalaba/data-use-mentions-extended huggingface dataset:
Digital wage payments are also cheaper and safer for recipients. In Liberia, teachers who were paid digitally saw the cost of receiving their payments fall 92 percent (including bus fare) from $25 per paycheck to $2. This cost reduction was accompanied by an increase in time spent teaching—because they were paid digitally, teachers no longer had to travel to town to receive their wages and instead spent more time in the classroom (Dusza, 2016). Eliminating travel also improves recipient safety, as noted in the section on digital payments and crime (Wright et al., 2017).

Additional benefits of digital wage payments come from their speed and efficiency compared with manual cash payments. Outcome data collected from firms that digitized wage payments (i.e., not in comparison to a control group) suggests that these benefits apply also to the firms making them. In Jordan, three garment factories that digitized their wage payments saw the time needed to make payments decline by between 66 percent and 70 percent, depending on the type of account the wage was paid into (BTCA, 2021a). Similarly, in India, after transitioning to digital wage payments, the clothing company GAP saw savings equal to the wages for 16 fulltime workers per month (BTCA, 2021b). In Senegal, 82 percent of businesses reported that they felt safer with digital payments because they did not need to travel with all employee paychecks (BTCA, 2021a).

# **4.3 The benefits of digitalizing agriculture payments**

In developing economies, 160 million banked, and 145 unbanked adults received agricultural payments in cash (Global Findex 2021). In contrast to wage and government payments, agricultural payments have a very low rate of digitalization: of the 11 percent of adults in developing economies who received them, three quarters received them in cash. The one exception to this trend is Sub-Saharan Africa, where the share of adults receiving agricultural payments is higher at 26 percent—more than twice the developing economy average—
data sources and empirical strategies used to test the real options hypotheses, focusing on temperature uncertainty at the grid level to explore the potential negative relationship between temperature uncertainty and firms’ investment decisions.

# 3 Data

## 3.1 Data source

Firm-level data. We employ Orbis as our principal firm-level data source, which includes comprehensive financial statements for organizations on a global scale. Administered by Bureau van Dijk, the Orbis database integrates information from a variety of origins, including official company registries and regulatory filings. Its extensive coverage, particularly concerning balance sheet items and profit 6
, Cambridge, MA.

D'Arcy, M. and M. Nistotskaya. 2018. "The Early Modern Origins of Contemporary European Tax Outcomes." _European Journal of Political Research_ 57 (1): 47-67. D’Arcy, M., M. Nistotskaya and O. Olsson. 2024. "Cadasters and Economic Growth: A Long-Run Cross-Country Panel." _Journal of Political Economy_ 132 (11): 3785-3826.

Dachis, B., G. Duranton and M. A. Turner. 2012. "The Effects of Land Transfer Taxes on Real Estate Markets: Evidence from a Natural Experiment in Toronto." _Journal of Economic Geography_ 12 (2): 327-354.

Deininger, K. W., D. A. Ali and R. Neyter. 2023. "Impacts of a Mandatory Shift to Decentralized Online Auctions on Revenue from Public Land Leases in Ukraine." _Journal of Economic Behavior & Organization_ 213: 432-450. delCarpio, L. 2015. "Are the Neighbors Cheating? Evidence from a Social Norm Experiment on Property Taxes in Peru." Princeton, NY.

34
>All Women Men B20% B40% T20% Rural Urban Rural Urban Rural Urban<br>women women men men<br>By gender By quintile By locality By locality and gender<br>Services Industries Agriculture<br><!-- End of picture text --> _Source:_ World Bank 2019b.

# **_Education and economic opportunities_**

Low educational attainment has far-reaching impacts across domains, including on people’s earnings and standards of living (Wodon et al. 2018). A recent study confirms that educational gaps between boys and girls in Sudan are still striking, and they perpetuate gender inequality, with child labor also contributing to keeping future generations uneducated (Berenger and Verdier-Chouchane 2015). Enrollment rates in Sudan have increased across the board in the last few decades, but especially noticeable is the 20 percentage point increase in girls’ enrollment in primary school and 16 percentage point increase in secondary school between 2001 and 2017 (compared to 15 and 8 percentage point increases for boys in primary and secondary school, respectively) (Figure 11). The primary enrollment increase was mainly driven by an increase in girls’ attendance in the poorest quintiles in urban areas. The difference in enrollment between the poorest and richest quintiles was 9 percentage points at the primary level and 11 percentage points at the secondary level. Although enrollment rates have increased, Sudan is still below the average of Sub-Saharan Africa countries at the primary and secondary levels. Sudanese boys have 7.9 expected years of schooling and girls 7.3 (WDI 2020, data for 2015). Access to school is affected 16
policy institutions like the China Development Bank, and private sector lenders). The importance of bilateral non-Paris Club lenders has increased significantly, and China is now the largest official creditor to developing countries (Horn, Reinhart, and Trebesch 2020; G30 2021). The growing number of private creditors and range of financial instruments further complicates debt resolution, although credit enhancements by MDBs could encourage private creditor participation in restructuring (Friedrich-Ebert-Stiftung and Consensus Building Institute 2021).

Lack of debt and investment transparency. The growing diversity of creditors and complexity of debt instruments has been associated with greater uncertainty about the level and composition of debt, as not all creditors are bound by a single set of reporting standards and loan terms are often confidential. In 2019, of the 17 LICs with available data, minimum requirements in debt recording were met by only eight, and monitoring guarantee requirements were met by only four. Due to shortcomings in accuracy, timeliness, coverage, and completeness of debt records, only four of these 17 countries met the minimum requirements for debt reporting and evaluation (Essl et al. 2019; World Bank 2019). Of 59 countries eligible for IDA borrowing, only one-third reported private sector external debt statistics (World Bank and IMF 2018). This raises the risk that public sector debt is higher in some EMDEs than reported. This risk is compounded by indirect and hidden debt, especially debt incurred by state-owned enterprises and public-private partnerships (Melecky 2021).

In addition, a lack of clarity about commitments encumbers debt restructuring negotiations, scrutiny of borrowing decisions, and efforts to ensure that borrowed funds are well spent (World Bank, 2021i; Friedrich-Ebert-Stiftung and Consensus Building Institute 2021). Debt sustainability can be undermined by policies that impose strict nondisclosure clauses on government borrowers, require major liens and collateralization, and place guaranteed debt repayments in state-owned enterprises (G30 2021). For example, since 2015, Chinese lenders have made heavy use of confidentiality clauses in their debt contracts (Gelpern et al. 2021). From 2015 onwards
Anderson (2008).<sup>13</sup> Following the existing literature, six health measures are used to construct the health index from the data available in IFLS 5: the general health status (GHS) as perceived by the visiting health worker, self-reported health status, dummy for not being underweight (using the information on body mass index (BMI)), natural logarithm of lung capacity score, dummy for no hypertension, and the adult height (in centimeters).<sup>14</sup> The math test index uses scores from three separate tests which measure different aspects of mathematical skills. A separate cognition index, uses scores from the Raven test and two memory tests, available in IFLS 5.<sup>15</sup> The final index is the economic index which combines income and years of education completed for males. Because of low labor force participation among females, a dummy indicating if a woman is in the labor force (or in school) is used, instead of the income measure. All the indices and the constituent variables are constructed in such a way that a greater value implies a better outcome. Data Appendix section S5 of the Appendix discusses the details (source, construction, motivation, and so on) of all constituent variables. Table 1 reports the summary statistics of outcomes of interest for male and female samples separately.

District-level information on health facilities is added to the IFLS data for the year 1983 from PODES to deal with possible confounding factors. PODES is a village-level survey data. To arrive at the figures for district level, the available village level data is aggregated at the district level.<sup>16</sup> 13The Appendix, section S4 titled “Construction of the Summary Index”, explains the construction of the index . 14The midwife program can also affect other dimensions of health like the likelihood of diabetes and inflammation level in the body. The IFLS collects the information on HbA1C (a measure of blood sugar level)
Which side of this debate is correct is an empirical question. However, while a large and increasing number of studies explore the topic, virtually all of them aggregate effects at the household level rather than allowing for heterogeneity within the household and focus on contemporaneous effects during the immediate reservation period. For a variety of reasons, these may be very different from longer-term changes in behavior, the change of which is the ultimate goal of reservations. To assess the extent to which such policies achieve their objective, it will be important to complement them with evidence on the extent to which reservation-induced effects persist over time. Exploring longer-term effects could also help in the understanding of the channels through which such effects materialize an issue of great policy interest.

India is of particular interest in this respect because, in an effort to overcome long-standing discrimination by gender and caste, far-reaching political decentralization in the early 1990s was combined with regulations mandating that a share of elected leadership positions be „reserved‟ for women and other disadvantaged groups. As villages subject to reservation are chosen randomly in any given period, differences between reserved and non-reserved locations can be interpreted causally. We draw on a large national sample to assess impacts of female reservations on the perceived quality with which public goods are delivered, individuals‟ level of political participation and willingness to make a matching contribution to provide local public goods, and their self-reported ability to hold leaders to account. As our data were collected some 15 years after this policy had taken effect, yet still with about one-third of villages never having been reserved, we can examine whether – and, if yes, how – reservations have affected long-term outcomes. Beyond academic interest in such results, recent debates in India about extending female reservations to the state and national levels imply that such evidence can also inform the country‟s ongoing policy debate.

From a methodological perspective, reservation-induced effects may be gender-differentiated and persist beyond the „reserved‟ period. The latter may be due to learning about political processes by previously excluded voters or reversal
and is frequently updated.<sup>47</sup> The classification rule adopted by the fDi Markets is different from that by the MRIOT. We first aggregate the FDI data at the project level to the countrysector level, and then we do the concordance manually.

To explore the mechanism through which the GVC participation affects the strength of FDI spillovers, we use the _World Bank Enterprise Survey_ data. This data set provides rich firm- level information with firms classified into the same sector levels as the MRIOT. We leverage the firmlevel data to test if FDI is positively associated with firm R&D propensity and whether a firm with lower GVC participation engages less in the R&D faced with FDI in its sector.

# 4.2 Metrics and variable definitions

The main GVC measures of a country-sectors are constructed using the derivations in Wang, Wei and Zhou (2017). The variables are defined as follows, with the label in parenthesis. We first calculate the forward and backward GVC participation separately.

**_Forward GVC participation (GVC_forward_** ) is defined as the share of the value-added created by a country-sector used for intermediate exports. It reflects the strong links that a sector has with buyers abroad, and generally tends to be in the upstream segment of the production process.

**_Backward GVC participation (GVC_backward)_** is defined as the ratio of the domestic and foreign value-added in intermediate imports over the total output of a country-sector. It reflects strong links as a purchaser of foreign goods and thus is as a sector more likely to be located in the downstream segment of the production process **_._**

**_GVC participation (GVC)_** is defined as the simple average of the **_GVC_forward_** and **_GVC_backward._**

Figure 6 shows the variation of GVC participation across all 61 countries. The mean value has not changed much in the eight years to 2017, it remains around 2.5 though the skewness has led
and quoted in New York or London. Similarly, we note that only a subset of the 1,400 bonds for which we have pricing data were in default at some point. Many bonds, therefore, appear in the bond price dataset but not in our default and haircut dataset and vice versa.

Table 2 and Figure 3 provide an overview the data coverage. This is complemented by Figure B3 in the Appendix, which shows the data coverage by country. The apparent gaps in the sovereign bond price and return data series can largely be explained by the fact that countries tapped New York and London markets irregularly. In the first half of the 19th century (after Waterloo), less than 30 countries had sovereign bonds traded in London. The sample grows markedly after 1850, especially between 1870 and 1913, a period that has been termed the “first era of financial globalization” and is characterized by large-scale capital flows from London to periphery countries. After WW1, New York joined London as the second dominant financial center of the world, and our sample continues to grow, reaching the first peak in the late 1920s. By 1929, after an extended issuance boom (see, for instance, Winkler (1933) or Wynne [1951]), our sample covers more than 80% of all independent sovereigns. Specifically, more than 300 sovereign bonds by more than 50 sovereigns were actively traded in London and New York (as of 1929 there were 63 countries worldwide).

Table 2: The bond pricing database: Countries and bonds included

|<br>|**Total**<br>**sample**<br>|1815-1869|1870-1913|**By era**<br>1914-1945|1946-1980|1989-2016|
|---|---|---|---|---|---|---|
|Number of countries covered|91|30|45|52|43|67|
|Share
# **1 Introduction**

Environmental, social and governance (ESG) considerations play a key role in companies’ business strategy worldwide. They guide investors, financial institutions, customers and suppliers to understand the capability of companies to produce value for a broad range of stakeholders (not only for companies’ shareholders, but also communities, workers, consumers, the environment, etc.) in a long-term perspective.

Investors are generating an increased and more sophisticated demand for ESG information, but producing such information is generally complex. Disclosure of ESG information by firm is very limited everywhere; it is mainly done by listed companies. However, SMEs not only represent 98% of firms, and 67% of total employment in Europe (Saez-Martinez, 2016) and even more in emerging countries (ILO, 2019), they are also responsible for 60%-70% of pollution globally (Hillary, 2017, and Miller, 2011). Smaller companies, despite a large number of initiatives,<sup>2</sup> have neither legal obligations, resources, nor relevant incentives to put in place systems to give full disclosure of ESG-relevant information. The picture is even more complex in emerging countries, where only a small number of firms is listed, the average company size is smaller, and ESG information is even more limited if not totally absent. For SMEs (typically not covered by ESG rating agencies) and especially for emerging countries, the urgent need for ESG firm-level information and disclosure is in sharp contrast with its scarce availability.

Between 2018 and 2020, the World Bank Group (WBG), the European Bank for Reconstruction and Development (EBRD), and the European Investment Bank (EIB) fielded the Enterprise Survey (ES) in 41 countries in the Middle East and North Africa (MENA), Southern Europe, Central, Eastern and Southeastern Europe, and Central Asia. More than 28,000 interviews were completed, answered by business owners and top managers. The survey addresses formal (registered
have limited access to public infrastructure and services, but the constraints on physical access to job and product markets are bigger concerns for the rural poor (see the case studies by Fan and his various co-authors or van de Walle and Cratty 2004 for recent detailed studies). At the same time, access to network utilities is less important because many of the rural poor prefer more costeffective local solutions for their needs—such as solar energy, water pumps, and satellite-based telecommunications. In the context of urbanization, the growth of megacities is fast becoming a major source of demands for additional infrastructure, creating a sense of urgency in some policy circles.<sup>13</sup> But this is criticized by some academics as favoring excessive urban concentration (see Vernon 2002). New infrastructure also creates additional incentives for rural-urban migration .

These differences in concerns have a long history in the academic literature, and there is not much news to report in this area. But new debates in policy circles are also starting to make

> 11 The minimum population threshold for defining urban areas varies dramatically among countries, but “urban” is typically characterized by density of settlement in a contiguously built-up area, by the structure of economic activity, and sometimes by administrative attributes.

> 12 Diversification of income sources is a key component of rural poverty reduction strategies, and one that depends on infrastructure to be effective (see Ellis 1998 for a survey). In a survey of the literature identifying a poverty trap similar to the one observed in regions with large rural populations, as in Africa, Booth (2004) lists eight factors used by all authors to explain poverty that is mostly rural in those regions. One is poor land and sea transport infrastructure, which makes market development unusually difficult. Fan and others. (2004a, b, c) provide impressive evidence on the various channels through which infrastructure contributes to poverty reduction and show how different these channels can be across as well as within countries.

> 13 There are more than 400
Policy Research Working Paper 9106

# **Abstract**

Many developing countries use employment guarantee programs to combat poverty. This paper examines the consequences of such employment guarantee programs for the human capital accumulation of children. It exploits the phased roll-out of India’s flagship Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGA) to study the effects on enrollment in schools and child labor. Introduction of MGNREGA results in lower relative school enrollment in treated districts. The authors find that the drop in enrollment is driven by primary school children. Children in higher grades are just as likely to attend school under MGNREGA, but their school performance deteriorates. Using nationally representative employment data, they find evidence indicating an increase in child labor highlighting the unintentional perverse effects of the employment guarantee schemes for Human capital.

This paper is a product of the Knowledge and Strategy Team, 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 ssekhri@virginia.edu.

_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
), the results suggest that the poverty rate decreases after direct taxes and social benefits are taken into account but increases upon the imposition of indirect taxes. In other words, social transfers cannot fully offset the burden of taxes on poverty. Table 3 shows that the share of the population whose consumable income is below a poverty line is higher than that for market income, except for the extreme poverty line of $1.9/day. The same trend applies if poverty is anchored on the national relative poverty line at 60 percent of median equivalized income.

Similarly, poverty gap and poverty severity are reduced when direct taxes and social benefits are introduced, but the effects are reversed after considering indirect taxes. This finding is robust across different absolute poverty lines and the relative poverty line. Since households do not observe the monetary value of health and education transfers, these items are not included in the computation of the poverty headcount, following standard practice.

> 2018); MALI (Hounsa, Coulibaly and Sanoh, 2019); MEXICO (Scott, 2014); MEXICO (Scott et al., 2018); MEXICO (Scott et al., 2018); MONGOLIA (Freije and Yang, 2018); NAMIBIA (World Bank, 2017); NICARAGUA (Icefi, 2017c); NIGER (Hounsa, Coulibaly and Sanoh, 2019);  PANAMA (Martinez-Aguilar, 2018); PARAGUAY (Gimenez et al., 2017); PERU (Jaramillo, 2014); PERU (Jaramillo, 2019); POLAND (Goraus and Inchauste, 2016); ROMANIA (Inchauste, et al, 2016); RUSSIA (Popova, 2019); SOUTH AFRICA (Inchauste et al., 2017); SRI LANKA (Arunatilake, 2019); TANZANIA (Younger, Myamba, and Mdadila, 2019); TUNISIA (Jouini et al., 2018); UGANDA (Jellema et al., 2018); UKRAINE (Bornukova et al., 2019); URUGUAY (Bucheli, 2019) and VENEZUELA (