rafmacalaba/data-use-mentions-extended huggingface dataset:
should become more immediately apparent to government staff that we have relatively little idea as to what identifies these firms, and so there is a role for expanding the program to serve a broad range of firms and then scientifically measuring impact to see who the program works best for.

3. _Focus more on eligibility criteria and on making it easier for firms to apply:_ the most innovative companies may not be those which are already formal, large, and long-established. Market failures may apply more to younger and smaller companies, so ensuring that eligibility criteria do not rule these firms out seems a good idea. But just as important as the initial criteria is making it easy for firms that meet these criteria to find out about the funds, apply without undue burden, and receive the funding promptly. Incentives for both government and World Bank operational staff are often focused more on getting projects launched and ensuring money is spent, rather than on the efficient delivery of services once projects have been launched.

4. _Use evaluation techniques that can obtain more power out of relatively small samples:_ despite all these efforts, in many African countries there are just not that many firms with more than one or two workers, and the projects will likely end up funding fewer than 100 firms a year. McKenzie (2011b, 2012b) discusses how one can obtain more power out of such sample sizes by restricting attention to a more homogeneous set of firms, and taking multiple measures on them. An extreme case is illustrated in Bloom et al. (2012), which conducted an experiment with only 20 textile firms in India, but as these firms were all in the same sector and weekly production data were collected, impacts of management consulting were still possible to measure. In practice, matching grant programs are unlikely to have the same level of homogeneity of firm production technologies and the same richness of data, but steps in this direction can still be made.

5. _Practice “little IE” as well as “big IE”:
**Figure 2: Power supply gap in the Dominican Republic**

<!-- Start of picture text --> 2,500.0<br>2,000.0<br>1,500.0<br>1,000.0<br>500.0<br>-<br>2003 2004 2005 2006 2007 2008<br>Maximum Electricity Demand Maximum supplied power<br>Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct<br><!-- End of picture text --> Source: National Interconnected Electric System Coordinating Organism, published at the www.sie.gov.do In the same way some factories have chosen to privately generate electricity in order to avoid sudden production stops, households cannot fully rely on this faulty system either. Since electricity blackouts are frequent in the Dominican Republic, buildings increasingly count with alternative electricity supply systems. The so-called “inversores” (electrical inverter) are used to supply energy stored in batteries (direct current) to alternating current. They are the cheapest and less sophisticated private solution to electricity shortfalls in the country. On the other hand, “generadores” (generators, normally using fuel-oil) are normally found in newer and more expensive blocks of flats and houses.

Given this context, it would be interesting to calculate auto generation capacity installed by private hands (both companies and families) and also expenditure on auto generation, which can illustrate the substitution of public goods.

In 2001, a survey conducted under the supervision of the National Commission for Energy concluded that auto generation capacity (2,715 MW at the time) was already similar to installed power in the national interconnected system (see BID, 2010). In order to update these figures, and given that electricity generators are not manufactured in the Dominican Republic, we have used customs statistics to estimate added imported capacity in the last decade. Private

[
Policy Research Working Paper 7577

# **Abstract**

This study investigates the existence of political rents in bank lending, using a comprehensive loan-level data set of the universe of commercial loans in Mexico from 2003 to 2012. Identification relies on changes in the state of origin of a senate committee chairman as a source of exogenous variation in firms’ political relationship. The study finds that banks offer favorable loan terms to politically connected firms with larger loan quantities, lower loan spreads, longer maturities, and lower collateral requirements. Furthermore, political loans exhibit higher default rates. To isolate the bank supply channel, a rich set of fixed-effects is included with various specifications. The favorable lending increases with the strength of a firm’s political connection, varies gradually along the political cycle, and is mainly offered by large and domestic banks. Consistent with the quid pro quo hypothesis, the study finds that banks that extend political loans receive significantly more government borrowings with better credit quality. The study also shows that the greater credit supply due to political connection leads to a large and significant increase in firm-level employment and assets. The study provides estimates of the total social cost of political lending and net revenue for banks that are engaged in rent - provision activity. Finally, a series of robustness tests are per formed to rule out alternative mechanisms and explanations.

This paper is a product of the Finance and Private Sector Development 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 authors may be contacted at cruizortega@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
# **Impacts of COVID-19 on the Income and Mental Well-Being of Cismen, Ciswomen, Transgender, and Non-Binary Individuals: Evidence from the 2020 COVID-19 Disparities Survey**

Tanima Ahmed, Toni Joe Lebbos, Sean Howell, Erik Lamontagne, and Joshua Seth Wimpey<sup>†</sup>

## **JEL Classifications** : I10, I31, J15

**Keywords** : COVID-19, transgender, non-binary, income, consumption, mental distress

> † The authors are affiliated with the World Bank (Ahmed, Lebbos, and Wimpey), Carleton University (Lebbos), LGBT Foundation (Howell), and with Joint United Nations Programme on HIV and AIDS (Lamontage). The corresponding author is Tanima Ahmed (tahmed9@worldbank.org). The authors thank Lee Badgett, Jose Antonio Cuesta Leiva, Maria Beatriz Orlando, Jorge Luis Rodriguez Meza, and Clifton John Cortez for their invaluable feedback. All findings, interpretations, and any remaining errors in this paper belong to the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
the analysis controls for male homicides, male hospitalizations for assault, and reports by health care professionals of violent incidents against men per 100,000 men ages 15–49.<sup>10</sup> It also controls for three factors that are correlated with the bargaining power of women within the household. First, data of the Brazilian electoral court are used to construct a gender ratio of educational achievement—specifically, high school completion—for each municipality by year. Given that elections are held only every other year, the analysis imputes, by municipality, the average education ratio of surrounding years for each year between election years. For example, the high school completion ratio of women relative to men in 2013 is computed as the average of the ratios in 2012 and 2014. Second, the analysis takes advantage of the fact that conditional cash transfers have been shown to influence women’s bargaining power in Brazil (Litwin, Perova, and Reynolds 2019). So, data of the Ministry of Social Development (Ministério do Desenvolvimento Social) on the number of households receiving the social program Bolsa Família family allowance and the value disbursed by the program in each municipality by year are combined with population data to control for the share of women receiving the Bolsa Família benefit as well as the per capita benefit in each municipality-year. Third, the analysis controls for the share of the population represented by women ages 15–49 because the “scarcity” of women may influence how women are treated (Grosjean and Khattar 2019).

The analysis also controls for the log population of women ages 15–49, the per capita number of health clinics and hospitals, and the per capita number of hospital beds. **Sample Selection** Initially, 409 municipalities that showed all zeros in all outcomes in all years were excluded. RAIS data were missing for one additional municipality. Brasília, the capital city, was also excluded because wages for government positions are occasionally coded to Brasília, and this anomaly might generate a small
with liquidity constraints.<sup>1</sup> In contrast to short-term capital flows, long-term foreign investment is much more likely to be valuable to host economies, in particular if the investment takes the form of new or expanding production plants.

Moreover, foreign investment inflows are generally accepted as a means to incorporate new knowledge from abroad. The theory of the multinational firm proposes that multinational corporations have a technological advantage over local firms that outweighs the cost of doing business in external markets (Caves 1996, Markusen 2002). The inflow of new knowledge may benefit domestic firms through imitation and learning (Findlay 1978, Mansfield and Romeo 1980, Blomström 1986), increasing competition in local markets, facilitating human capital mobility among firms (Fosfuri et al. 2001, Glass and Saggi 2002) and vertical linkages (Rodríguez-Clare 1996, Markusen and Venables 1999), thereby increasing the productivity level and sustaining a higher growth rate.

Despite the fact that the economic benefits of increasing FDI inflows are well established in the theoretical literature, the evidence on technology spillovers is far from conclusive in both firm-level and country-level data.<sup>2</sup> For example, Aitken and Harrison (1999) could not establish any evidence of a positive technology spillover from multinationals to domestic firms in Venezuela in the 1980s. On the other hand, Görg and Hijzen (2004) find that imitation and learning can take place when local firms are geographically close to multinationals and have enough absorptive capacity. Using firm-level data for Lithuania, Javorcik (2004) discovers that spillovers may occur through backward linkages between multinationals and their local suppliers. Another finding is that labor turnover might act as an important channel. As multinationals train the workforce in their operations abroad to use new technologies, technology diffusion might also occur due to a labor turnover from multinationals to domestic firms or if locals establish their own business (Djankov and Hoekman 2000).

Unlike most of the microeconomic evidence, studies using aggregated FDI data found questionable support that FDI in itself significantly boosts
Figure 1: Expectation of Losing Job and Income <!-- Start of picture text --> 54% 54% 52%<br>43%<br>40% 40%<br>31% 29% 32%<br>24%<br>Cismen Ciswomen Transgender Non-Binary All<br>Expect to lose job or be unemployed (conditional on had a job on the day of<br>the survey interview)<br>Expect income to reduce at least 30 percent<br><!-- End of picture text --> N=4454 for expect to lose job; and N=7427 for expect income to reduce at least 30 percent.

The disproportionate impact faced by transgender and non-binary people in relation to jobs is also reflected in their expectation of reduced income. More than half, 54 percent of transgender people and 52 percent of non-binary people expect their income to decline by at least 30 percent. However, compared to transgender and non-binary people, a much lower 40 percent of cismen and 29 percent of ciswomen expect the same (Figure 1). The marginal effects of the probit model estimation in Table 3 shows that non-binary people are 7 percentage points more likely to expect an income reduction of at least 30 percent due to the pandemic than cismen.

Nonetheless, one may reason that the expectation of economic loss does not necessarily reflect the actual loss of job and income. Using the COVID-19 Disparity Survey, it is not possible to infer the realized loss of jobs and income for transgender and non-binary people compared to cisgender people. In the absence of global evidence on the impact of COVID-19 on job loss of LGBTI people, we are unable to compare our results widely. The findings in this paper, however, are consistent with the existing empirical evidence on the US economy, suggesting that LGBTI people and their families experienced a higher rate of job and wage loss during the pandemic than non-LGBTI people (Movement Advancement Project 2020). In the US, LGBTI
0.002|-0.0001|-0.002|
||(0.003)|(0.002)|(0.003)|(0.004)|
|Foreign Exchange reserves/GDP|0.009***|0.015***|0.011***|0.595|
||(0.002)|(0.002)|(0.002)|(0.495)|
|Country specific trends|x|x|x|x|
|Year effects|x|x|x|x|
|Country effects|x|x|x|x|
|Seasonal Dummies|x|x|x|x|
|Within R-squared|0.89|0.88|0.85|0.97|
|Nbr. obser.|288|380|420|112|

Note: Robust clustered standard errors in parentheses. Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.
Kumar, A.K. Shiva, 1996. "UNDP's Gender Related Development Index: A Computation for Indian States," Economic and Political Weedky, April 6, 1996.

Miller, Barbara D., 1982. The Endangered Sex, Neglect of Female Children in Rural North lIndia. Ithaca and London: Cornell University Press.

Miller, Barbara D., 1989. "Changing Patterns of Juvenile Sex Ratios in Rural India, 1961 to 1971," Economic and Political Wekly, June 3, 1989.

Miller, Barbara D., 1993. "On Poverty, Child Survival and Gender: Models and Misperceptions," Third World Planning Review 15(3):6-13.

Murthi, Mamta, Anne-Catherine Guio, and Jean Dreze, 1995. "Mortality, Fertility, and Gender Bias in India; A District Level Analysis," Popuation and Developen Review 21(4):745-782 Pritchett, Lant, and Laurence H. Summers, 1996. "Wealthier is Healthier," Joumnal of Human Resources, 31(4):841-868.

Rosenzweig, Mark R., and T. Paul Schultz, 1982. "Market Opportunities, Genetic Endowments, and Intrafamily Resource Distribution: Child Survival in Rural India," American Economic Review, 72(4):803-815.

Rutstein, Shea Oscar, 1984. "Infant and Child Mortality: Levels, Trends, and Demographic Differentials," World Fertility Surveys Comparative Studies No. 43. Voorburg, Netherlands: International Statistical Institute.

Sen, Amartya, 1989. "Women's Survival as a Development Problem," Bulletin of he American Academy of Arts and Sciences, 43.

Srivastava, Dr. B.P. 1997. Personal communication on work undertaken at the Institute of Population, Environment and Development, Lucknow, India.

Svedberg, Peter, 1990. "Undernutrition in Sub-Saharan Africa: Is There a Gender Bias?" Journal of Development Studies, 26:469-486.

Svedberg, Peter, 1996. "Gender Biases in Sub-Saharan Africa:
12 Like all other countries, except for Moldova, Bulgaria was not an outlier. Its governance was in the positive range and its institutional development lagged less than 60 percent behind the level of highly industrialized economies. The net cumulative FDI inflows predictably exceeded US$ 500 per capita putting it well ahead of countries that had negative scores of governance and low EBRD scores. On the other, Moldova attracted less FDI than then the structural index would suggest. The reason is straightforward: the absence of political stability as captured in the value of governance index.

Similarities between the results derived from plotting cumulative FDI inflows and levels of governance and structural reforms come as no surprise. Both indexes are highly correlated, with the value of correlation coefficient of 97 percent (Table 6). Both indices are also similarly strongly and positively correlated with cumulative FDI inflows per capita, with correlation coefficients exceeding 80 percent. It is interesting to note that the variation in values of CPI and FDI inflows is also positively correlated, albeit somehow weaker than the two other indices.

**Table 6:** _Values of correlation coefficients among corruption, governance, reform progress and FDI_

||CPI|Governance|EBRD|
|---|---|---|---|
|Corruption Perception Index (CPI)|1|0.83|0.77|
|Governance aggregate index|0.83|1|0.97|
|EBRD-basedindex|0.77|0.97|1|
|Cumulative net FDI|0.75|0.84|0.82|

Source: See Table 5.

Data for Bulgaria as well as for Romania provide strong support to the link between progress in governance and FDI inflows. The 1990-2003 time profile of FDI flows to Bulgaria per capita also appears to coincide with changes in the values of structural reform index (see Table 4 and Figure 2 above). So does Romania’s time profile. Until 1997 annual FDI inflows to Bulgaria and
increased during last 5 years (a finding which was not evident when registered unemployment rates and officially reported wages were considered in Section B). Unfavorable developments have been especially significant in terms of activity of both genders in Kurzeme and men’s activity in Latgale. Disparities in unemployment and earnings have increased because in Riga and Riga district unemployment rates were falling and wages growing much faster than elsewhere. As far as the main cities are concerned, earnings rankings of Ventspils and Daugavpils have gone down substantially.

43
financing obstacles. Loans per capita enters negatively and significantly at the 10% level in regression (6), but loses significance once we control for financial development. The loanincome ratio enters positively and significantly when we control for Private Credit/GDP. Deposits per capita does not enter significantly in either regression, while the deposit-income ratio only enters significantly when we control for financial development.

The economic effect of outreach on firms’ financing obstacles varies across the different indicators. A one standard deviation change in outreach indicators is associated with 0.07, 0.11, 0.05 and 0.16 lower financing obstacles in the case of geographic branch penetration, demographic branch penetration, geographic ATM penetration, and demographic ATM penetration, respectively.<sup>23</sup> This compares to a standard deviation of 0.44 in general financing obstacles across countries. Thus, cross-country results suggest that demographic penetration of bank outlets is somewhat more important than geographic penetration.

The results in Table X also suggest that financial intermediary development is not robustly associated with firms’ financing obstacles, once we control for our outreach indicators. While Private Credit/GDP enters significantly and negatively by itself (column 1) and when controlling for indicators of deposit and loan use, it loses significance once we control for branch and ATM penetration indicators. The R<sup>2</sup> statistics suggest that while financial intermediary development and controls for firm characteristics explain 40% of cross-country variation in firms’ financing obstacles, and banking system outreach alone explains 28-50% of variation, together the independent variables explain 49-79% of cross-country variation.<sup>24</sup>

> 23 We multiply one standard deviation of the respective outreach indicator (Table III) by the Table X coefficient in the regression including Private Credit/GDP.  The effect size is larger if we instead use the coefficients from the regressions excluding Private Credit/GDP.

> 24 We also experimented with regressions where we include GDP per capita instead of Private Credit/
psychosocial support services. Finally, a limitation of our phone surveys is that we measure time spent on learning activities, but not how much learning has taken place. Continuing to monitor these students over time to measure learning effects is therefore needed.

# **References**

Bureau of Labor Statistics (2019) _American Time Use Survey Questionnaire 2011-18_ <u>https://www.bls.gov/tus/tuquestionnaire.pdf</u> [accessed 30 April, 2020] Cabrera, José María León and Anatoly Kurmanaev (2020) “Ecuador’s Death Toll During Outbreak Is Among the Worst in the World”, _New York Times_ , April 23, <u>https://www.nytimes.com/2020/04/23/world/americas/ecuador-deaths-coronavirus.html</u> [accessed 28 April, 2020].

El Comercio (2020) “Ministerio de Salud confirma primer caso de coronavirus en Ecuador”, <u>https://www.elcomercio.com/actualidad/salud-confirma-primer-caso-coronavirus.html</u> [accessed 29 April, 2020] Giannini, Stefania (2020) “Prioritize health and well-being now and when schools reopen”, UNESCO, April 30, <u>https://en.unesco.org/news/prioritize-health-and-well-being-now-andwhen-schools-reopen</u> [accessed 30 April, 2020].

Goldstein, Dana, Adam Popescu and Nikole Hannah-Jones (2020) “As School Moves Online, Many Student Stay Logged Out”, _New York Times_ April 6, <u>https://www.nytimes.com/2020/04/06/us/coronavirus-schools-attendance-absent.html</u> [accessed 28 April, 2020] Le Nestour, Alexis, Samba Mbaye, Justin Sandefur, and Laura Moscoviz (2020), “Phone survey on the Covid crisis in Senegal", April 23, "report_ENG_v1.pdf”, https://doi.org/10.7910/DVN/9XE95F
that there was no change, and Milanovic finds an increase of 3 Gini points between 1988 and 1993 followed by a decline of 1 Gini point in the next five years.<sup>26</sup> His most recent (and unpublished) calculations for 2002, show another small increase of about 1 Gini point. Thus, according to Milanovic, there are zig-zags. They are explained, in the early 1990s, by the slow growth of rural incomes in India and China and economic collapse of Eastern Europe both of which contributed to global inequality. When both developments reversed in the next five-year period global inequality decreased. But these are zig-zags caused by specific economic events in large countries, not a trend.

This lack of unanimity on changes, and disagreement on whether there is any trend at all, stem not only from the differences in methodologies but paradoxically also from the very similar results that all authors obtain regarding the overall _level_ of inequality. The reason is as follows. Different methodologies yield similar inequality levels but they do so with quite a lot of noise caused by measurement problems. Mean incomes, whether obtained from surveys or national accounts, are not consistently calculated and key data sources disagree among themselves. The computation of Concept 2 inequality using GDI per capita—a metric on which there is apparently least dispute—from World Bank or Maddison’s data series will differ by several Gini points. This is because, as pointed out by Sutcliffe (2003), Maddison’s data include estimates for a number of war-torn or otherwise “excluded” countries like Sudan, Afghanistan, Somalia, the Congo, Cuba, North Korea etc. that are almost invariably poor and not included in the World Bank database. In addition, Maddison’s growth rates for China are less than the official rates and those used by the World Bank. It is then not surprising that when one superimposes estimates of national distributions on one or another set of GDIs per capita to generate global inequality, the choice of the GDI
5|
|Global banking CDS|990|128.9|64.9|33.7|229.7|
|Asset volatility|990|1.4|1.1|0.3|4.0|
|Loan demand|990|-0.3|0.2|-0.6|-0.1|
|Infation<sup>*</sup>|986|9.9|13.3|-29.4|122.9|
|Growth|990|3.1|6.3|-40.5|67.5|
|GDP|990|58,897|137,326|157.2|1,197,708|

> _†_ Summary statistics are provided for the sample between 2001Q4 and 2011Q1. The crisis period was defined as the period between 2007Q3 and 2009Q2.

> * The inflation statistics reported here suppress the outlier cases where QoQ inflation exceeds 1,000 percent.

24