rafmacalaba/data-use-mentions-extended huggingface dataset:
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# I **Introduction**

The relationships between growth, inequality, and poverty in developing countries have been discussed at some length. The standard view is that broad-based economic growth is poverty reducing<sup>2</sup> . Yet, growth may also be associated with rising inequality, which then tends to offset part of the gains from growth for the poor. This point was first made by Kuznets (1955) who suggested that rural to urban migration would result in an inverted-U relationship between growth and inequality since urban areas have not only higher standards of living, but also higher inequality. Kuznets' views or extensions thereof remain present in the literature today (Anand and Kanbur, 1993; Watkins, 1995; Ram, 1995), even though they have been challenged or at least qualified recently (Papanek and Kyn, 1986; Bourguignon and Morisson, 1990; World Bank, 1990; Fields, 1989; Chen and Ravallion, 1997; Bruno et al., 1996)3.

While the theoretical arguments for explaining the links between growth, inequality, and poverty have been refined over time, most of the empirical work is still based on international panel data with growth, poverty, and inequality measures for a large number of countries at a few points in time. Empirical studies focusing on a single country have relied on somewhat less satisfying methodologies. First, researchers using single surveys have estimated the point elasticity of poverty to growth and inequality using formulae provided by Kakwani (1993) and Kanbur (1987). Although useful for short term comparative statics, these elasticities say nothing about the longer term relationships between growth, poverty, and inequality. Second, researchers have decomposed changes in poverty measures 2 The World Bank's (1990) World Development Report on poverty recommends growth as a privileged path for poverty alleviation, provided it is accompanied by policies to promote access to education, health and social services, and also by the provision of safety nets, especially during adjustment periods.

3 For example
in a real stakes environment. Measuring the effects of debt forbearance on borrower behavior is challenging for several reasons. Payment deferrals are typically endogenous to borrower and lender characteristics, and episodes of debt forbearance also tend to coincide with large economic crises that further confound the impact of repayment flexibility on borrower behavior and expectations. We overcome these challenges by working with a leading consumer finance lender at a time when a national consumer debt moratorium policy was in place in India. Importantly, the policy guidance at the time of the experiment encouraged lenders to provide further relief but did not strictly _require_ them to offer repayment deferrals to all customers. This feature of the policy enables us to issue moratorium offers that are fully compliant with the terms of the national policy and to randomize whether a customer is offered debt relief as well as the manner in which moratorium offers are presented. The offers in our experiment are fully truthful, because the experiment takes place at a point in time when both the statement that the moratorium is offered as a result of regulatory guidance and the statement that it is offered by the lender are true.

In the first part of the paper, we examine how randomized moratorium offers affect borrower expectations and loan repayment. In addition to the main effects on repayment of the loans covered by the moratorium offers, we examine the impact of forbearance offers on _overall credit discipline_ . To do so, we combine repayment data from our partner firm with credit bureau information that captures the _universe_ of loan payments for customers in our sample. We establish that borrowers who receive a moratorium offer from their lender prioritize payments for loans covered by the moratorium and are significantly less likely to default, which holds both for those with and without outstanding loans 2
traditional cropping is subject to the stochastic occurrence of flooding. If so, then annual flooding may account for significant differences in local supplies and prices of both fish and milk, two important sources of animal protein.<sup>10</sup> Religious culture may be a second source of nutritionally-significant differences in diet. The three Indian states are predominantly Hindu, while Bangladesh is predominantly Muslim. Many Hindus are vegetarians, with potentially-important implications for the role of diet in the incidence of child wasting and maternal anemia. Although it is perfectly possible to compensate for the lack of animal protein in vegetarian diets, this may require nutrition education, incentives and provision of diet additives which are sometimes difficult to achieve in practice.

A third, related factor involves nutrition education. At this point, we have no information on regional differences in nutrition education at different levels of schooling. However, we should note an important difference in the India and Bangladesh surveys that may have related implications. Both surveys ask detailed questions about mothers’ child-feeding practices. However, Bangladesh DHS 2011 asks specifically about meat (beef, pork, lamb, chicken, etc.), while India National Family Health Survey 2015-16 excludes this question. This is pure speculation on our part, but the exclusion may reflect a sensitivity about dietary issues in India that affects school instruction in nutrition. And, of course, there may well be differences in nutrition education across regions that reflect other factors.

> 10 As noted by Dasgupta et al. (2018), fish are a particularly important source of micronutrients that are critical for child health.  Fish are also an important source of dietary iron, which is critical for the prevention of anemia.

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work, however, focuses on metrics that use female-owned, incumbent businesses to determine labor market resources for new entrants.

In all of our estimations, we control in some format for the size of the incumbent districtindustry employment. This is important given that entrepreneurs often leave incumbent firms to start their companies. Klepper (2010) shows in detail the importance of this spawning process in the history of Detroit and Silicon Valley, and many econometric studies find the existing business landscape the most important factor for the spatial location of new entrants (e.g., Glaeser and Kerr 2009). We model both the totalemployment in incumbent firms for the districtindustry and the count of female-owned incumbent businesses specifically.

The first agglomeration rationale is that proximity to customers and suppliers reduces transportation costs and thereby increases productivity (e.g., Fujita et al. 1999). To test the importance of this mechanism within the manufacturing sector, we measure the extent to which districts contain potential customers and suppliers for a new entrepreneur. We begin with an input-output table for India developed by India’s Central Statistical Organization. We define Inputi←k as the share of industry i's inputs that come from industry k, and Outputi→k as the share of industry i's outputs that go to industry k. These measures run from zero (no input or output purchasing relationship exists) to one (full dependency on the paired industry). These shares are calculated relative to all input-output flows and are not symmetrical by design (Inputi←k≠Inputk→i, Inputi←k≠Outputk→i).

We summarize the quality of a district d in terms of its input flows for an industry i as Inputdi = - ∑k=1,...,Iabs(Inputi←k – Edk/Ed), where I indexes industries. This measure simply aggregates absolute deviations between the proportions of industrial inputs required by industry i and district d's actual industrial composition, with E representing employment among incumbent firms
costs associated with the search for suitable foreign input suppliers creates new forms of cross‐border policy effects compared to a situation where goods are produced in a single location. Therefore, in order for cross‐border production to operate efficiently, national policies including disciplines such as competition policy, investment, infrastructure and institutions should be harmonized across countries.<sup>13</sup> Linking deep agreements and GVC‐related trade<sup>14</sup> The relationship between deep agreements and GVC‐related trade is formally estimated using a structural gravity equation. An augmented gravity equation is estimated for 189 countries,<sup>15</sup> using data from 1990 to 2014, to investigate the relationship between the depth of an agreement

> 13 Antras and Staiger (2012), Lawrence (1996).

> 14 This section is based on Osnago, Rocha and Ruta (2017), forthcoming

> 15 See appendix table A2 for the list of countries that are included in the regression.

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For both poverty and inequality, we find evidence that points to larger climate change effects at the subnational level than those estimated using more aggregated, country-level data, particularly in regions where temperature change has the largest effects. Our heterogeneity analysis further shows that countries in South Asia and Sub-Saharan Africa are more vulnerable to warmer temperature, but the effects of colder weather are also observed among countries in Europe and Central Asia.

Our study makes several new contributions to the literature. First and most importantly, we offer the first global assessment of warmer temperature on _both_ poverty and inequality, exploiting a novel global subnational panel database that we constructed based on the Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021). Several attempts were made to understand the direct effects of global warming on poverty and inequality, but none examines these outcomes together.<sup>2</sup> For example, analyzing cross-sectional household survey data from 24 Sub-Saharan African countries, Azzarri and Signorelli (2020) show that a one degree increase in long-term temperature is associated with a 2.8 percentage point increase in poverty. Paglialunga _et al._ (2022) use data from 150 countries and find that a one percent temperature increase is associated with a 0.5 percentage point increase in the Gini index.<sup>3</sup> This lack of evidence poses an important, and perhaps quite urgent, challenge given the recent public debate of whether richer countries should take more responsibilities for the costs of climate change that correspond to their shares of the pollutants (Birnbaum _et al._ , 2022; Popovich and Plumer, 2021). As an example, 80 percent of global greenhouse gas emissions are currently produced by G20 economies—the world’s largest economies—but these economies can only price 49 percent of CO2 emissions from energy use (OECD, 2021).

> 2 There are also a number of studies investigating the effects of natural disasters on temporary (transient) poverty (e.g., Sawada
## **Use of financial services is not completely explained by financial depth** Adults with an account at a formal financial institution (%)

<!-- Start of picture text --> 100 •••••• • • • • • • • • • • •• •<br>• • • • •<br>80 • • •<br>• •<br>•• • • • • • •<br>•<br>• •<br>60 • • •• •<br>• •<br>• •<br>4020 • • • • • ••• • ••••• • •• • ••• • • • • • •••••• • ••••••••• • •• • • • • • • • • • •<br>•••• • ••••• •<br>•• • • •<br>0 50 100 150 200 250<br>Domestic credit to private sector<br>(% of GDP)<br>Note: Domestic credit data are for 2010.<br>Source: Demirguc-Kunt and Klapper 2012; World Bank, World Development Indicators database.<br><!-- End of picture text -->

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## **Self-reported barriers to use of formal accounts**

This suggests that financial depth and financial inclusion are distinct dimensions of financial development—and that financial systems can become deep without delivering access for all.<sup>12</sup> The large variation in account penetration among economies with similar levels of national income and financial depth also suggests that there is likely to be room for policy interventions to increase financial inclusion.

# SELF-REPORTED BARRIERS

The Global Findex survey, by asking more than 70,000 adults without a formal account why they do not have one, provides insights
questions relevant to the drivers of job loss or jobs at risk were asked of permanently closed firms (in either round).<sup>33</sup> Therefore, we had to adopt a few simplifying assumptions to account for such closures: (1) that sales for permanently closed firms had fallen to zero; (2) that the coefficient on sales in the job loss equation was equally valid for permanently closed firms (even though job losses cannot exceed 100 percent); (3) that newly entering firms had a negligible impact on net PFPS jobs; and (4) that the effects of the other significant variables in the job loss equation (which are not observed for these firms) were swamped by the drop in sales to zero for permanently closed firms. Unfortunately, there is no avoiding such admittedly strong assumptions in the absence of more complete data.

Once we have estimated job loss levels by sector, we must next adjust the proportion of job losses by occupational category. We consider it unlikely that firms adjust their staff levels for each occupation in equal proportion as they adjust their overall workforce levels. Some types of labor input are “fixed” and others “variable.” Unfortunately, there is a substantial disparity in the level of details on occupational categories in the WBES and LFS surveys. That is, the WBES survey asks only about “production” and “non-production” workers, whereas the LFS asks about occupation and job content.<sup>34</sup> We attempt to estimate the share of job 32 For example, prediction models may be selected on the basis of cross validation methods, and where dimensionality reduction is required, through machine learning methods.

33 Firms that had closed permanently only answered section H questions in CFUBWES, which include closed year/month, whether they implemented certain measures before closure, and whether the firm is expected to reopen in the future. 34 These questions are intended only for manufacturing firms, but some firms with main products being services also
Policy Research Working Paper 6102

# **Abstract**

Clean, safe energy for rural areas is an important component of green growth and sustainable development. Biogas could be an important contributor, if its record in reality lives up to its expected potential. This paper provides a preliminary assessment of biogas use by smallholder farmers in rural China, using data collected from 2,700 households in five provinces. The authors find that user satisfaction is high, and environmental and economic benefits appear tangible. There are strong indications of reduced use of wood and crop residues for fuel. Less time is spent on collecting fuel wood and cooking, which is especially beneficial to women. Adopters also save on fertilizers, because of the use of biogas residues. Moreover, problems with suspension of biogas use, whether due to technical or human factors, remained limited. However, few tangible benefits to respiratory health were detected. Overall, these findings are grounds for optimism about the potential for of smallholder biogas to contribute to more sustainable development, in China and beyond.

This paper is a product of the Poverty and Inequality Team, Development Research 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 author may be contacted at lchristiaensen@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
# **Figure 6. Probabilities of Female-Headed Households Falling in Poverty in Second Year Conditional on Being Non-poor in First Year (percentage)**

<!-- Start of picture text --> Egypt 2017-2020 Iraq 2007-2012<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Jordan 2010-2013 Mauritania 2014-2019<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Palestine 2011-2017 Tunisia 2015-2021<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>FHH wo/children FHH w/children Non-FHH<br>14<br>16<br>14 12<br>12 10<br>10 8<br>Percentage (%) 8 6 Percentage (%)<br>14 16<br>12 14<br>10 12<br>8 10<br>Percentage (%) 6 8 Percentage (%)<br>55 10<br>50 9<br>45 8<br>40 7<br>35 6<br>Percentage (%) 30 5 Percentage (%)<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that enters poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps.

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Tilton, 2004).<sup>42</sup> It then scales its tax upward according to the size and presumed profitability of the larger projects, capturing resource rent flows as they occur. The tax has nevertheless been measured to be still somewhat regressive and distortionary and it likely suppresses some exploration activity (Davis and Smith, 2020). Industry leaders see a recent proposal to add a royalty to the tax rate as a tax on more than resource rent; they claim the royalty would cause them to reconsider investment plans already announced under the current tax regime.<sup>43</sup>

# Section 5: Empirical Findings from Copper Projects in Selected Countries

The model of mining companies’ project advancement presented above suggests that decisions on project investment first happen at the exploration stage and then at each subsequent stage when an investment commitment to advance the project further is needed. Firms will not advance a project any further if their evaluation shows a project stage has a negative after-tax NPV. The analysis also demonstrated how the results of successive evaluations as the project advances are adjusted to account for perceived political risk in the country context in which the project takes place. This adjustment is typically made by applying a higher discount rate as a proxy for rigorously adjusting individual components of t projected cash flow. The implication of this is that a mining project located in a country with low perceived political risk is more likely to advance than an identical mining project in a country perceived to have high risk. An empirical analysis of the impact of political risk on project advancement is presented below using data on copper projects in selected countries. This shows that political risk alone has had a profound impact on the investment in mining projects from the exploration stage through final commitment to build the mine.

## A. Project Grade and Country Characteristics

The compensating factor at a project with higher costs due to higher political risk is faster and higher revenue. The principal way a project can produce faster revenue is through reduced bureaucratic delays. Higher revenue
in the market, contributing to the sluggish growth of aggregate productivity. Cavalcanti et al. (2021) present further evidence on rm dynamics using data from Relação Annual de Informações Sociais (RAIS), a matched employer-employee administrative dataset covering all formal rms in Brazil that follow rms and workers over time. They nd that rm size in Brazil is increasing but concave in age over the rst 15 years of rm life, on average rising 50 percent relative to its entry size. This growth is signicantly less than the lifecycle growth for manufacturing plants reported by Hsieh and Klenow (2014) for the U.S., which show 8-fold average growth over 30 years, but greater than the roughly 1.25-fold increase reported for India. Ulyssea (2020a) analyzes both formal and informal rm dynamics in Brazil, combining rm-level data with a structural model. The paper documents that in, both sectors, rms display an increasing and concave age-size prole. However, the growth in size is signicantly higher for formal rms. The results show that after 10 years there is a 50 percent growth in the sample that includes only formal rms and their formal workers in contrast with the 20 percent average growth that one obtains when using both formal and informal rms. The author shows that the age-size prole using only formal rms and workers in Brazil is very similar to the one documented for Mexico by Hsieh and Klenow (2014). However, once he incorporates informal rms, the age-size prole of Brazilian rms becomes much closer to that of Indian rms, which is remarkably atter. These facts show that dynamic selection takes place in both sectors but is substantially weaker in the informal sector. They also suggest that the lack of dynamism found in the Indian data might also be present in other highinformality countries. Failing to incorporate informal rm dynamics may therefore lead to a substantial underestimation of the lack of dynamism among developing countries' rms.<sup>14</sup>

# 4 Competitive distortions and productivity

There are signi
yields for virtually all crops are lower for poor (small) farmers and higher for rich (large) farmers, thus turning the farm-size productivity relationship upside down. Furthermore, the lanid rental market leads to land transfers from poor and labor-abundant small holders, to rich and relatively labor scarce households (Kevane 1996). The reason is that capital market imperfections combined with reasonably functioning land and labor markets and a technology that is not supervision intensive make it more attractive for small credit-constrained households to rent out land and work for a wage than to engage in owner-cultivation without capital inputs. By contrast, in panel data from Burkina Faso an inverse farm size-productivity relationship was observed even though a positive presence of correlation between yields and cash inflows from non-agricultural employment suggests the presence of capital market imperfections (Udry 1996). The conclusion is that imperfections in land, labor, credit, and

> **29** Due to the covariance of production risks, crop insurance is very difficult and forward markets to insure against price risk are often unavailable to small producers due to high transaction costs.

> The difficulty of land acquisition through borrowing by would-be smallholders, in spite of their productivity advantage, has been highlighted by Binswanger and Elgin (1998) and Carter and Mesbah (1995). Furthermore, they point out that by exhausting access to credit for land acquisition, the ability to borrow for working capital is eliminated.

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