rafmacalaba/data-use-mentions-extended huggingface dataset:
Using a panel survey data set from the Pakistan Social and Living Standard Measurement Survey conducted in 2007/08 and 2010 and data from a household survey administered by IFC, this paper presents the first empirical evidence on the benefits of reliable electricity on households in Pakistan. The study uses a two‐stage propensity score–weighted fixed‐effects model to control for unobserved village‐ and individual‐specific effects that may simultaneously affect electrification status and the welfare outcomes of interest. The results show that electrification is associated with a broad range of social and economic benefits in Pakistan, including income and expenditure, better health outcomes for children, improved school enrollment and school completion for boys (but not girls), and increased women’s labor force participation and decision‐ making power.

All these benefits from expanding and improving electricity supply are important, although not all of them can be quantified in monetary terms. The potential gains in income growth alone are substantial. According to the 2014 Household Integrated Economic Survey, the average rural household in Pakistan earned PRs 26,452 ($253) a month in fiscal 2014. With estimated average income gains of 37 percent a year, the increase in monthly household income would be about PRs 9,787 ($93). Assuming the marginal cost associated with electricity generation and transmission is about PRs 12.2 ($0.12) per kWh, annual average per capita electricity consumption is 471 kWh, and the average household includes 6.7 people, the net per capita gain from gaining access to electricity is estimated at PRs 11,782 ($113) a year.

There is no consensus on the access rate of electricity in Pakistan. The official estimate based on household surveys suggests that about 5 million people remained off‐grid in 2016. Data from the 2017 census and utility connections lead to an estimate that is almost 10 times as high: almost 50 million people (36 percent of the population). Based on the more conservative figure of 5 million,
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# _3.1 Study region_

This analysis focuses on Latin American and Caribbean (LAC) cities with more than 50,000 people in 2010. According to (UN-Habitat, 2012), Latin America and the Caribbean is the most urbanized region on the planet with more than 80% of the population living in urban areas, and built-up area continues to expand despite deceleration in population growth. The LAC region is characterized by large diversity of climatic conditions, including from tropical to temperate climates; and the terrain also shows high variability in altitudes due to the presence of the Andean Mountain Range. That diversity in climate and topographic conditions translates into a high diversity in the shape that urban areas have taken. On the one hand, there are cities located in the mountains constrained by hilly terrain; on the other hand, there are also some cities located in the lower lands in flat areas, constrained only by the presence of large rivers or lakes, and yet other cities have grown located at the Caribbean and Pacific coasts, whose shapes reflect the coastline. Speaking of urbanized area, in the region it accounts for 0.4% of the land, a much lower value than Europe (2.3%) and the United States (1.2%), somehow closer to Asia (0.7%), but higher than the former Soviet Union region (0.2%) and Oceania (0.1%) (Y. Zhou et al., 2015). When compared to other regions of the world, LAC cities show denser populations than Europe and the United States, greater regularity and lower open space (Angel et al., 2010a; Huang et al., 2007). However, Inostroza et al. (2013) reported a general trend towards low density growing for some of the largest LAC cities.

# _3.2 Use of Night‐time Lights Data for urban extents delineation_

The nighttime lights data (NTL) are based on nighttime imagery recorded by
cropping allows the landlord and the tenant to share the inherent risk in agricultural production; in contrast, the tenant bears the entire risk under fixed rent (Cheung, 1969; Stiglitz, 1974; Ghatak and Pandey, 2000). Second, sharecropping can reduce the impacts of the tenant’s limited liability (Shetty, 1988; Basu, 1992; Laffont and Matoussi, 1995; Sengupta, 1997; Ray and Singh, 2001). Third, the control over the tenant in sharecropping might allow the landlord to ensure a more sustainable use of the land (Reddy, 1996). Fourth, sharecropping provides an incentive for the landlord to provide inputs, such as, pesticides, fertilizers and managerial support, to the tenant (Newbery and Stiglitz, 1979; Eswaran and Kotwal, 1985).

Motivated by anecdotal evidence and this last argument, we develop a theoretical framework which combines prior models (Eswaran and Kotwal, 1985) with the idea that when landlords migrate away from the village, the opportunity cost of providing managerial inputs increases. This results (for those who do not opt for land sales) in a preference for fixed over sharecropping arrangements.

To test this proposition we construct a district-level data set covering the first decade of the 21st century. The choice of time period is dictated by both data availability (we have detailed migration data only for the decades preceding 2001 and 2011) and contextual considerations: liberalization in the 1990s strengthened property rights, allowing for migration of the landed class (Giles and Mu, 2018). This period also saw an acceleration in the trend towards fixed rent arrangements: from 1992 to 2012, fixed rent agreements increased from 45% to 58% while sharecropping decreased from 45% to 31% (as a percentage of total rented in acreage). The largest drop happened in the last decade under consideration (Table 1).

Establishing a causal link from migration to rental contracts is challenging because of the presence of potential confounding factors. They include a major rural bank branch expansion
Policy Research Working Paper 6399

# **Abstract**

This paper uses primary data from rural north India to show that participation in a community-level female empowerment program significantly increases access to employment, physical mobility, and political participation. The program provides support groups, literacy camps, adult education classes, and vocational training for rural women in several states of India; the data are from Uttarakhand. The paper uses instrumental variables and truncation-corrected matching on primary data to disentangle the program’s mechanisms, separately considering its effect on women who work, and those who do not work but whose reservation wage is increased by participation. The analysis also finds significant spillover effects on non-participants relative to women in untreated districts. It finds consistent estimates for average treatment and intent to treat effects.

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 ekandpal@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
in recall NPS and recall ALT. Nor does it appear that respondents are making inferences based on their busiest week, the totals of which would be two or three times higher than those reported in the recall survey designs. Instead, although none of these reference periods provides a close approximation of the recall-reported person-season hour totals, the totals inferred from the most recent work experiences appear the closest to those obtained by recall, a finding consistent with those in Schwarz and Oyserman (2001).<sup>20</sup> Alternately, it might be that people look to the units or levels of aggregation that are intuitive or meaningful to them to form estimates of labor. If farmers tend to think about labor at the person level rather than the plot level, then they may erroneously substitute their person-level estimates for their person-plot level labor. Simulations using weekly data show that such a scenario may indeed be the case. Thus, we arrive at a total of 187 hours worked over the season in the weekly visits and 212 hours in the weekly phone data. These calculations are close to those reported at the person-plot level for recall NPS and recall ALT (121 hours and 146 hours, respectively) rather than those these groups report at the person level. Similarly, if, as weekly visit

> 20 In our context, the most recent period coincides with both the peak work period and a particularly culturally and economically salient one, namely, the harvest. For this reason, it is difficult to disentangle the effects of recency from those of work intensity or salience. For instance, if recall NPS and recall ALT household members reported labor based on the work they performed during the last weeks of the season, this could be because the most recent work performed is the easiest to remember, because this is a peak and time-bound work period, or because the work period coincides with the harvest, where work is most salient in terms of income gains.

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# B. Generation and transmission planning index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchamrk**|
|---|---|---|---|---|---|
|**Planning and Procurement**|95%|77%|59%|59%|70%|
|**Generation Planning**|86%|43%|71%|71%|56%|
|Countryhas ageneration masterplan|||||94%|
|Countryhas an overall energy plan|||||65%|
|Competent entity is responsible for producing the<br>plan|||||88%|
|Inter-governmental committee oversees the planning<br>unit|||||29%|
|Powergeneration systemplan is mandatory|||||19%|
|Plan leads to timelyinitiation ofprocurement|||||38%|
|Planning process is transparent andparticipatory|||||59%|
|**Transmission Planning**|100%|75%|50%|100%|72%|
|Competent entity is responsible for producing the<br>plan|||||100%|
|Explicitlylinked topowergenerationplans|||||88%|
|Plan is mandatory|||||29%|
|Planning process is transparent andparticipatory|||||71%|

# C. Generation and transmission procurement index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchamrk**|
|--
Table 6 also shows the average (Panel B) and the median (Panel C) Lerner indexes for banks in Russia and for banks in each of the comparator countries. This table confirms the finding that the Russian banking sector is less competitive than the Brazilian one, since the Lerner index which measures market power is higher for the former than for the latter. On the other hand, based on the Lerner index, we find that the Russian banking sector appears to be more competitive than that of China and also India, since the Lerner index is significantly lower for Russia than for these countries.

Given the large number and different types of banks in Russia, it is interesting to investigate whether the non-structural measures of competition for banks in Russia vary depending on the group of banks considered. Table 7 and 8, respectively, report the H-statistic and Lerner index for the following groups of banks: (a) the top 20 banks versus the remaining banks, (b) government-owned versus privately-owned banks, (c) foreign versus domestic banks, (d) business-oriented versus individual-oriented banks.

According to the H-statistics (Table 7), government-owned banks appear to be less competitive (have lower values of the H-statistic) than privately-owned banks, at the 10 percent significance level. Also, banks focused on lending to individuals are less competitive than those that concentrate on financing businesses. Based on the calculations and tests for the Lerner index (Table 8) we find that the top 20 banks appear to exert more market power relative to smaller banks. On the other hand, we find no significant differences in the competitive behavior of foreign and domestic banks.

17
literacy. In addition, school inspectors visited schools to assess the quality of teaching and lesson preparation. Steps were taken to assure independence in data collection and minimize risk of collusion between and teachers.

We find that at midline, after one year of program implementation, there is a sizable and statistically significant impact on test scores (about 0.24 standard deviations, sd) for the in-kind treatment arm, and a much smaller and statistically insignificant impact for the recognition treatment arm (about 0.13 sd). At endline, after two years of program implementation, the impact of the in-kind rewards had fallen by about 30 percent in magnitude (to 0.16 sd) and is no longer statistically significantly different from zero. The impact of recognition rewards remains smaller (0.09 sd) and statistically insignificant . We also find that at midline the impacts are larger for girls than boys. We find no evidence of strategic behavior on the number of students who were assessed in each year, in average enrollment rates, or in changes in enrollment. We provide suggestive evidence that the reduction in impacts at endline is linked to the growing Ebola epidemic that Guinea experienced at this time.

Effects differ by teacher gender. The impact of in-kind rewards are very similar across genders, 0.20 sd and 0.18 for female and male teachers respectively. In contrast, the effects of the recognition rewards is 0.23 sd for female teachers, while it is 0.01 sd (and insignificantly different from zero) for male teachers. The findings further suggest that the positive effects of either scheme for female teachers benefit both female and male students. However, the positive effects of the in-kind treatment for male teachers benefit only female students. This is consistent with targeted action since female students have lower test scores at baseline.

Finally, data from the school inspections suggest an improvement in teaching preparation and practice. Teachers in the in-kind treatment arm made better use of resources in the classroom, were better prepared for the
# **1. Introduction**

SARS-CoV-2 is a major global threat to human health, with 36 million cases and 1 million deaths worldwide as of October 9, 2020.<sup>_1_</sup> Governments have sought to limit the spread of the virus and mitigate the negative health outcomes of the disease through various policy measures that have limited travel, imposed quarantines and lockdowns, and closed businesses and schools.<sup>_11_</sup> In addition to the economic burden of COVID-19 related morbidity and mortality, government attempts to limit the spread of SARS-CoV-2 are having profound economic impacts, with the global economy projected to shrink by 8%.<sup>_2_</sup> To date, the greatest health and economic burden has been borne by the Americas and Europe.<sup>_1_</sup> However, recent evidence indicates that low-income countries, with their limited health system capacities, are likely to suffer infection and mortality rates similar to or greater than those currently suffered by upper-income countries.<sup>_5_</sup> While research on the health impacts of COVID-19 in low-income countries is rapidly emerging,<sup>_3_</sup> there is limited evidence on the socioeconomic impacts of the pandemic.<sup>_4_</sup> The evidence that exists relies primarily on pre-COVID-19 macroeconomic data and simulation models to forecast potential future scenarios based on assumptions about the disease spread.<sup>_6_</sup> In contrast, we rely on direct measurements of socioeconomic indicators to present evidence on the effects of the pandemic on households, individuals, and children living in low-income countries, as well as the actions that households are taking to mitigate these impacts.

An acute challenge emerging from the global pandemic is how individuals and communities are to strike the balance between the health benefits and the economic costs of managing the spread of the virus.<sup>_11_</sup> Even in high-income countries, which tend to be data rich in terms of
et al. (2012) find that along South Florida’s Gulf Coast, each kilometer of mangrove width reduces storm surge by 50 centimeters, while Del Valle et al. (2020) find a similar result in their study of how mangrove forest mitigates the impact of hurricanes on economic activity in Central America. Based on these initial findings, it is possible to interpret as hurricanes having an adverse effect on local economic activity, we explore whether mangrove forests can mitigate this effect in the Caribbean region.

To measure the level of protection that mangroves offer, we use two data sets with different calculations of mangrove width and test for potential nonlinear relationships. Our interest is in studying the effectiveness of this potential mitigation property as the mangrove width increases (see Section 2.3 for a description of how the two databases used for the econometric results calculate mangrove width).

Our results indicate that mangrove forests act as natural defense and mitigate the impact of hurricanes in local economic activity. We estimate three models, as in Del Valle et al. (2020), and discretize mangrove width data into various bins or compartments that correspond to its q-quantiles<sup>23</sup> in low-elevation coastal zones (LECZ). The first model uses _q=2_ , and we create a dummy variable for each bin and interact these variables with the damage index. We then take the resulting variables and include them in our two-way fixed effect econometric equation in place of the damage index. We repeat this approach for the second model using ff _q=3_ ; and the third model, _q=4_ . ff Excluding Cuba from the sample, figure 5 plots the results for 29 countries and territories, in three panels (a, b and c) for three models. Panel a shows point estimates for each of the models regarding the impact

> 23 A quantile is a set of values which divide a frequency distribution into equal groups, each one containing the same fraction of the total sample data.

17
_pi_ and _qi_ on investment, note that the likelihood of peace next year is given by Pr(peace next year) = (1 _− pi_ ) _∗ sit_ + _qi ∗_ (1 _− sit_ ) which is saying that the probability of peace next year switches from 1 _− pi_ to _qi_ when a country transitions enters recovery. That is, expectations change more in transition if _pi_ and _qi_ are large. We expect, for example, little changes in investments if investors can anticipate peace to be unstable (low _qi_ ).

In practice _qi_ could obviously vary by time as well. This will depend on what factors are taken into account by investors. If forecasts of instability are based on factors like political institutions, ethnic composition, climate or the availability of natural resources then we do not expect _qi_ to vary with time as these factors are fixed or only slowly moving. However, given our discussion of refugees, for example, we expect at least some factors to be time-varying.

> 44Besley and Mueller (2015a) show that an adaption of the same model can make sense of investment data more generally.

53
Thus, when analyzing determinants of informal finance, we find that gender, education and formal employment play a significant role. Given the widespread reach of self-help groups, particularly at the grassroot levels through agencies like cooperatives, where women are the main participants, our results confirm the popularity of informal finance in this demographic (of women, those informally employed, and those with lower levels of education).

# **5. Conclusions**

This paper analyzes the status and determinants of financial inclusion in Sri Lanka, with the objective of understanding its implications for inclusive growth.

We present data both from a comparative peer analysis using the Global Findex 2017, as well as country data from the 2018 National Financial Inclusion Survey. It is seen that gaps remain, particularly in the ‘use’ of formal financial services rather than just having access to the same. While Sri Lanka is ahead of its regional peers in terms of basic financial inclusion measured via access to a formal account and having formal savings, it lags its more aspirational upper middleincome peers in East Asia. Similarly, for credit and usage of digital finance, there is much room for the country to improve and thereby match its East Asian peers.

Given the implications of financial inclusion as an enabler for inclusive growth, understanding the determinants of financial inclusion for Sri Lanka is important. Our results indicate that being a male, being more educated and (possibly therefore) being formally employed increase a person’s access to, and usage of, formal finance. Thus, the gender gap is evident in financial inclusion. In a country where 52 percent of the population comprise women, combined with the demographic transition towards an increasing elderly population with higher longevity for women, the findings imply that urgent and targeted action is necessary for the financial inclusion of women and making them part of the economy.

Our analysis indicates the crucial role played by education in determining formal financial inclusion. While Sri Lanka has a high literacy rate with gender parity at all levels of education, the
The crime index reflects the number of crimes a person reports to have been subject to and ranges from 0 to 10. Similarly, the security index reflects the personal restrictions, such as not going out at night, a person is subject to due to perceived insecurities. This index ranges from 0 to 5. The environmental riks index ranges from 0 to 4. For a detailed overview of the indices see Annex 5. Source: World Bank estimates based on EHPM (2019)._

**Relying on the variables as they are reported in Table 1 could result in a biased estimator of vulnerability to poverty due to missing observations.** As soon as individuals report a missing observation in one of the variables included in the model, they are excluded from the estimation due to listwise deletion. This selective deletion of individuals might result in biased observations, which could be especially pronounced in the case of labor market variables, but also the share of children attending school. We therefore replace missing observations with zeros in these cases (Annex 6 presents the respective Summary Statistics). We verify the validity of our findings in a robustness check. There is one variable, which still reports a higher number of missing observations: the highest grade obtained. While we include this variable in our main empirical specification, we exclude it in one of our validity tests.

**We estimate results for the overall population but also distinguish between rural and urban areas; we also analyze the development of vulnerability to poverty over time and at the department level.** We estimate vulnerability to poverty at the yearly level for the overall population, but also the rural and

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