rafmacalaba/data-use-mentions-extended huggingface dataset:
|Number of observations<br>12,408<br>29,645<br>2,221<br>5,926|
|---|
|Note: .01 - ***; .05 - **; .1 - *. Marginal effects from the estimation of a probit model are reported. Standard<br>errors are clustered at the level of the strata; rescaled weights are applied in the regressions. The models also|
|include controls for the region in which the firm is located, the sector in which it operates and the year in which it<br>began operations.|

While the global results provide an overview of the aggregate relationship between gendered laws and informality, in reality, there is significant heterogeneity between regions in our sample in terms of informality and legal equality between men and women. The informal economy accounts for 15-20 percent of GDP in advanced economies and 30-35 percent of GDP in emerging economies (Kelmanson et al., 2019); in Latin America and the Caribbean, it has been estimated to account for approximately 40 percent of GDP (OECD, 2018). As noted in Ohnsorge and Yu (2021), the variation in employment share in the informal sector between regions is even larger. Likewise, the prevalence of gendered laws varies significantly from one region to another. Data from the most recent _Women, Business and the Law_ report (World Bank, 2021), shows that the average global score on the WBL index was 76.1 points, but the regional average ranges from 95.1 points in OECD high-income economies to 51.5 in the Middle East and North Africa region. These regional differences highlight a need to understand the relationship between gendered laws and informality at a more disaggregated level.

Several interesting patterns emerge from the regionally disaggregated results presented in Table 3. The most prominent result is that, for firms with female participation in ownership, greater equality is only associated with a lower probability of beginning informally
output and technological development. The analysis also controls for time‐varying factors that are specific to states to capture state‐level policies. To ensure that later‐life health outcomes are measured in the same location of conception and birth where exposure occurred, the sample is restricted to individuals who have never migrated from their place of birth. In this way, our empirical strategy controls for a wide number of potential confounders in an effort to identify causal effects. To test for external validity, similar analyses are conducted in geographies outside India ‐ Vietnam and 33 countries in Africa.

Our results find that nitrogen exposure experienced by infants can have durable, long‐term impacts that stretch well into adulthood. In India, women exposed to nitrogen pollution in their earliest years of life are shorter on average in adulthood than women of similar circumstances who were not exposed to such pollution. Early‐life exposure to nitrogen pollution also lowers later‐life labor productivity and depresses adult wages, decreasing overall welfare. This finding is robust to several sensitivity and falsification tests. Analyses across different geographic settings in Vietnam and Africa that measure the impact of nitrogen pollution during early‐life provide further supporting evidence for the results found in India. Taken together, this paper provides new evidence that early‐life exposure to nitrogen pollution has enduring and irreversible costs on human capital, with decreases in height observed across different life‐stages: in adulthood (India), in childhood (Vietnam), and in infancy (Africa).

The rest of the paper is organized as follows. In Section 2 we describe the health and water quality data, the construction of the main variables used in the analysis as well as the procedure we use to match the health data to upstream pollution. Section 3 outlines our empirical strategy and Section 4 discusses the results. Robustness checks are provided in Section 5. Section 6 investigates the external validity of the results reported for India in other geographic settings such as Vietnam and Africa. Section 7 discusses the plausible mechanisms linking nitrogen
more resilient. More productive firms have longer survival times in the manufacturing of metal products, chemical products, food and beverages, and machinery and computing products.

Finally, we redo the analysis shown in Figures 2 and 3, this time assuming that exporters lose access to their external financing, as such financing is likely related to receipts in foreign currency or is in the form of letters of trade credit. Figure 7 shows that manufacturers of metals and metal products and manufacturers of machinery and computing products are most adversely affected by the collapse of export demand, with survival times reduced from 19 to 14 weeks. Conversely, retailers and the provision of other services are unaffected and remain the two sectors where firms are estimated to run out of working capital the fastest.

<!-- Start of picture text --> Figure 7: Median Survival Time (adjusted for exporters) based on Fixed<br>Costs by Sector<br>Other Services<br>Retail<br>Construction<br>Manufacturing of Chemical, Plastic, and Mineral Products<br>Manufacturing of Textiles, Apparel, and Leather Products<br>Other Manufacturing<br>Manufacturing of Food and Beverages<br>Manufacturing of Metals and Fabricated Metal Products<br>Manufacturing of Machinery, Computing, and Electrical ...<br><!-- End of picture text --> <!-- Start of picture text --> 12<br><!-- End of picture text --> <!-- Start of picture text --> 16<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,395<br><!-- End of picture text --
and stock markets for output growth (e.g., Demirg ̈u ̧c–Kunt and Maksimovic 1998; Levine and Zervos 1998; Beck and Levine 2004; Shen and Lee 2006), the economic and financial consequences of having a bank-based or a market-based financial system (e.g., Beck and Levine 2002; Demirg ̈u ̧c–Kunt and Maksimovic 2002; Levine 2002; Langfield and Pagano 2016; Bats and Houben 2020), as well as the evolving roles of banks and stock markets during the process of economic development (e.g., Tadesse 2002; Song and Thakor 2010; Demirg ̈u ̧c–Kunt, Feyen, and Levine 2013). We focus on specific transmission channels from banks and stock markets, utilizing disaggregated data on international trade that are available and directly comparable across different countries. One of the channels we examine also relates our work to the recent literature on banks and intangible capital (Dell’Ariccia et al. 2020; Beck et al. 2021) while our findings on large versus active stock markets complement the recent research on different characteristics of stock markets (Bae, Bailey, and Kang 2021).

Second, the paper connects to the strand of literature looking at the role of finance in international trade (e.g., Kletzer and Bardhan 1987; Beck 2002; Greenaway, Guariglia, and Kneller 2007; Manova 2008; Berman and H ́ericourt 2010; Amiti and Weinstein 2011; Ju and Wei 2011; Levchenko, Lewis, and Tesar 2011; Minetti and Zhu 2011; Bricongne _et al._ 2012; Chor and Manova 2012; Becker, Chen, and Greenberg 2013; Muˆuls 2015; Paravisini _et al._ 2015). Several papers in this area look at how the financial sector shapes export entry, export exit or export volume alongside some of the transmission channels that we also explore (e.g., Beck 2003; Svaleryd and Vlachos 2005; Hur, Raj, and Riyanto 2006; Manova
Cameroon became eligible for debt relief under the Enhanced HIPC<sup>5</sup> Initiative. In this context, the government adopted a Poverty Reduction Strategy (PRS) in 2003. The strategy is designed to cut the number of poor by half by 2015 through strong and sustainable economic growth. Cameroon reached the Completion Point in May 2006, after three full years of implementation of the 2003 PRS. This achievement signals the satisfaction of Cameroon’s development partners with the implementation of this strategy.

How much poverty reduction has this improved policy environment brought about? Preliminary analysis by the National Statistical Office based on the most recent household survey (2007) indicates that the overall incidence of poverty is still around 40 percent, about the same level as in 2001. The Gini index of inequality seems to have dropped a couple of percentage points from 41 percent in 2001 to 39 percent in 2007. These observations raise some interesting evaluative questions in terms of the social impact of economic growth in Cameroon. To what extent has the growth process been inclusive in Cameroon? What are the sources of observed variations (over time and across socioeconomic groups) in the distribution of economic welfare?

The purpose of this paper is to use available household level data, particularly the 2001 and 2007 surveys, to try to answer these questions using counterfactual decomposition of changes in the distribution of economic welfare. To put things into perspective, we present in section 2 a profile of growth, inequality and poverty for the period 1996-2007. In that section we use the Shapley decomposition to explain

> 5 HIPC stands for Heavily Indebted Poor Countries.  This initiative was launched in 1996 by the International Development Association (IDA, the World Bank’s fund designed to provide concessional credits and grants to the poorest countries) and the IMF.  The initiative was enhanced in 1999 to tighten its link with poverty reduction and to widen its scope and make it more efficient (in terms of speed of relief delivery).  Eligibility is based on three criteria: (1
Estimation Survey (PESS), which contains detailed estimates for each population type and administrative unit of interest.

The optimal allocation of interviews was subject to the following requirements:

- (i) 500 expected interviews in IDP settlements and 500 in nomadic populations;

- (ii) At least 600 interviews expected per administrative unit;

- (iii) Oversampled populations with

- Mogadishu (urban): 900 interviews, including IDPs;

- Kismaayo (urban) and Baidoa (urban): at least 500 interviews each;

- Coastal fisheries livelihood zones: at least 300 interviews;

- IDP host communities: 500 expected interviews.

Households are clustered into enumeration areas (EAs), with 12 interviews expected for each selected EA. A larger number of households per enumeration area would only marginally benefit the statistical estimation of indicators because of potential homogeneity among households in geographic proximity. A smaller number of households would result in fewer than 3 observations for each of the four optional modules capturing household consumption based on the Rapid Consumption Methodology, and thus affect the reliability of poverty estimates (see section 4).

The sampling design addressed the challenging security situation on the ground in two ways. First, a security assessment was conducted to exclude areas too dangerous for field teams to visit. Second, a micro‐listing approach was used in second‐stage sample selection to allow field teams to spend limited time on the ground. Replacement of sampling units during fieldwork followed a transparent and 5
>25<br>20.2<br>20 Serbia<br>15<br>10 8.7 7.7 7.8 10.1 EU 15<br>5<br>0<br>Total Men Women<br><!-- End of picture text --> Source: for Serbia LFS 2009; for EU-15: Eurostat Data in Focus 35/2009

**Table 4. Weekly hours usually worked by men and women in Serbia** **<u>(population age 15 and above)</u>**

|||**Percentage**<br>||
|---|---|---|---|
|**Weekly hours worked**|**Male**|**Female**|**Total**|
|10 or less hours|0.66|1.19|0.89|
|11-19 hours|0.99|1.37|1.16|
|20-29 hours|2.89|4.83|3.73|
|30-39 hours|5.93|7.81|6.74|
|40- 49 hours|66.12|71.91|68.62|
|50- 59 hours|10.47|7.13|9.03|
|60 or more|12.93|5.76|9.84|
|Total|100|100|100|

Source: Serbia LFS, 2009 The majority of labor force survey respondents in Serbia reports holding one job with only 6.3% of men and 2.9% of women engaging in additional paid activities outside their main job in 2009. As with part-time jobs, most of the additional work activities are performed in agriculture, which provides an extra-source of income to 64% of men and 39% of women who have more than one job
surveys. The first, conducted at regular intervals in most countries, is a socio-economic population sample survey that serves as training data for the PMT predictive model. The second is a targeting survey that collects current data to determine household eligibility. Due to the large population coverage of many social protection programmes, targeting surveys typically need to be administered to a significant share of the population<sup>1</sup> . Given the high cost of such extensive, country-wide data collection efforts, targeting surveys are typically conducted on a multiannual basis when carried out as a national survey sweep [Lindert et al., 2020]. Infrequent surveys delay assessment of new programme applicants and re-assessment of existing beneficiaries, resulting in programmes’ irresponsiveness to changing household circumstances.

If budgets were not limited, the most accurate PMT approach would be

> 1For example, the social registries that store targeting data covered 87%, 75% and 40% of Pakistan’s, Brazil’s and Indonesia’s populations respectively in 2015-17 [Leite et al., 2017], amounting to total records of ca. 360 million people.

2
by multiplying its predicted log real per capita consumption by 100.

The household head characteristics included in Model 1 are age, gender, marital status, and employment status (government, semi‐government, private, or self‐employment). Household demographics include categorical household size variables, the dependency ratio (the share of all household members that are older than 64 or younger than 15), and the highest education level of household members not currently enrolled in school. We include several household assets, including ownership of a computer, cooker, electric fan, refrigerator, land phone, washing machine, water pump, motorcycle, car or van, three‐ wheeler, and four‐wheel tractor. Housing quality and facilities variables included are bedrooms per person, an inside drinking water source, the use of electricity for lighting, the presence of floor tiles/terrazzo, walls made of brick/cabok/cement, and the presence of an inside toilet. Due to the household/family distinction, instead of considering the number of rooms within a household, we use rooms per capita.<sup>10</sup> Further, we consider the highest education level of all household members.

> 7 However, the precise definition of a family used by the Samurdhi program does not seem to exist, possibly to allow for context‐ specific interpretations of a family.

> 8See Appendix Table A1 for a comparison across models used in other papers developing a PMT in South Asia including Narayan and Yoshida (2005), Sharif (2009) and Hou (2008).

> 9 The spatial price index for a district is computed as the ratio of district poverty line to national poverty line.  Spatially price adjusted consumption is computed by deflating the nominal consumption by relevant spatial price indices of the districts. DCS computed the district poverty lines by using a spatial price index, considering the price variations among districts for the selected basket of food items consumed as per HIES 2016 survey.

> 10 We expect rooms per capita will be
# **Impact of Social Fund on the Welfare of Rural Households: Evidence from the Nepal Poverty Alleviation Fund**

Dilip Parajuli, Gayatri Acharya, Nazmul Chaudhury and Bishnu Bahadur Thapa<sup>1</sup> _World Bank_

**JEL** : H53, I38

**Keywords** : Poverty Alleviation Fund, Impact Evaluation, Instrumental Variables, Nepal

1 Dilip Parajuli is a Senior Economist in the South Asia Education Unit; Gayatri Acharya is a Country Sector Coordinator in the South Asia Agriculture & Rural Development Unit, Nazmul Chaudhury is a Country Sector Coordinator in the East Asia Social Protection Unit, and Bishnu Bahadur Thapa is a Junior Professional Associate in the South Asia Agriculture & Rural Development Unit. The authors would like to acknowledge collaboration from Poverty Alleviation Fund (PAF) Secretariat of the Government of Nepal in the implementation of this Impact Evaluation initiative, and from Tribhuvan University (TU) in the survey data collection. We would like to thank Jeffery Hammer, Atul Pokharel and Geeta Sethi for their significant support during the early stages of the Impact Evaluation design and survey activities. We would also like to thank participants at presentations made at the PAF, the National Planning Commission, Simeon Ehui, Amit Dar, Madhur Gautam, Elliot Mghenyi, Juan Carlos Osario, Venkatesh Sundararaman, and participants at a number of seminars and presentations for helpful comments on presentations and earlier versions of the paper. The findings, interpretations and conclusions expressed in the paper are entirely those of the authors and do not necessarily represent the views of the World Bank or its affiliates. Please send correspondence to dparajuli@worldbank.org.
**Figure 7: Forward Integration and (un)skilled labor content in exports at sectoral level**

**Figure 8: Backward Integration and (un) skilled labor content in exports at sectoral level**

Source: Authors’ using UNTAD-MERO data For the human capital index, this is positively correlated with FVA but negatively correlated with DVX. Moreover, there is a significant negative relation between oil exporting and (backward) GVC participation but a positive correlation between oil exporting and forward GVC integration.

22
unchanged across the two rounds. Mostly, they will be characteristics of the household head, such as the year of birth, educational attainment, ethnicity or religion and birth district. In addition, if the survey includes retrospective questions, these could be used, e.g., if the household owned a car or television at the time of the previous survey. As we have data for each year, we estimate the coefficient vector on these time-invariant explanatory variables. In practice we predict the round 1 income for households in the round 2 survey by ββ applying the coefficient vector resulting from the round 1 regression ( 1 ) to the characteristics of households in round 2 ( ). This prediction looks as follows for the observations of round 2: ββ ii2 xx ii ′ 1 . ii1 ii2 ii1 yy� = ββ�xx + εε̂ We turn first to the assumptions underlying this approach and will then discuss how estimates for the error term are obtained.

The method for estimating bounds on poverty transition quantities is predicated on two important assumptions. First, we need to assume that the underlying population from which the data are sampled is the same in both survey rounds. This implies that the method cannot be applied in cases where there are large shocks to the population, for example, if there were massive migration into or out of the country. More precisely, we want to ensure that the income model coefficients that we estimate in round 1 are good predictors for what the round 1 income of the households surveyed in round 2 would have been. Hence, to ensure stability of the reference population, we only include households whose heads are between 25 and 55 years in survey round 1.<sup>9</sup> Second, we need to make assumptions about the relationship between the error terms and . Specifically, we assume that these are, on average, positively correlated over time. In the presence of ii1 εε household fixed effects and persistence of shocks, this does not seem to be an unreasonable
29

- Quarmby, D.A., and J.J. Bates. 1970. "An Econometric Method of Car Ownership Forecasting in Discrete Areas," _MAUNotes 219,_ London: Department of Environment.

- Silberston, Aubrey. 1970. "Automobile Use and the Standard of Living in East and West." _Journal of Transport Economics and Policy 4_ (January).

- Summers, Robert, and Alan Heston. 1991. "The Penn World table (Mark 5): An Expanded Set of International Comparisons, 1950-1988" _Quarterly Journal of Economics, 56_ (May): pp. 327-368.

- Sweeney, J. L. 1978. _Energy Policy and Automobile Use of Gasoline._ Palo Alto, Calif: Stanford University Press.

- Tanner, J.C. 1962. "Forecasts of Future Numbers of Vehicles in Great Britain." _Roads and Road Construction._ 40: 236-274.

- Tanner, J.C. 1978. "Long Term Forecasting of Vehicle Ownership and Road Traffic." _Journal of the Royal Statistical Society,_ 141A(1): 14-63.

- Train, K.E. 1986, _Qualitative Choice Analysis: Theory, Econometric and an Application to Automobile Demand._ Cambridge, Mass.: MIT Press.

- Wheaton, William C. 1982. "The Long-Run Structure of Transportation and Gasoline LDemand." _The Bell Journal of Economics 13_ (Autumn): 439-454.

- Wildhorn, S. and others. 1974. _How to Save Gasoline._ Santa Monica, Calif.: Rand Corp.

World Bank. 1992. _World Development Report 1992._ New York: Oxford Univ. Press.

World Bank. 1997. _1997 World Development Indicators,_ Washington, D.C.

- World Resources Institute. 1996. _World Resources 1996-9 7._ New York: Oxford University Press.
|East and North Asia|17.2|
|---|---|
|Gulf and Red Sea|26.8|
|Mediterranean|20.3|
|Northern Europe|29.5|
|North America – East Coast|21.1|
|North America – West Coast|46.2|
|South Africa|64.6|

Finally, processing time when crossing an international border is also an important part of overall shipment time for international trade. To account for border delays in our computations, we include importing and exporting time from the “trading across borders” section in the World Bank’s Doing Business Database.<sup>10</sup> The data is available for each country considered in our analysis and Table 3 presents the average values aggregated by region. To get bilateral values for border delays for each directed country‐pair, we compute the total border delay as the sum of importing and exporting time for each origin and destination respectively. Whenever a border is crossed along the optimal route, the “border penalty” is incurred.

_Table 3: Export and Import processing time at the border (from the Doing Business Database)_

|**Region**|**Time to export: Border compliance**<br>**(hours)**|**Time to import: Border compliance**<br>**(hours)**|
|---|---|---|
|**East Asia & Pacific**|55.9|70.5|
|**Europe & Central Asia**|28|25.9|
|**Latin America & Caribbean**|62.5|64.4|
|**Middle East & North Africa**|62.6|112.3|
|**South Asia**|59.4|113.8|
|**Sub
# **1.2. Financial Sector Landscape of Sri Lanka**

Figure 1. Classification of financial sector players in Sri Lanka <!-- Start of picture text --> Financial Service Providers<br>Formal Semi - Formal Informal<br>Licensed Commercial Banks<br>Licensed Specialized Banks<br>Co-operative Societies<br>Licensed Finance Companies Rotating Savings & Credit<br>Micro Finance Institutions<br>Specialized Leasing Companies Association<br>Community Based Organizations<br>Licensed Microfinance Institutions Money Lenders<br>State-programs (e.g. Samurdhi,<br>Authorized Money Brokering Friends & Relatives<br>Divineguma)<br>Companies<br>Authorized Primary Dealers<br><!-- End of picture text --> Source: Updated by authors based on Kelegama et al. (2014) and Tilakaratna (2016).

As seen in Figure 1, Sri Lanka has a diverse financial sector which includes formal,<sup>9</sup> semi-formal, and informal financial service providers. There are currently 26 licensed commercial banks (including 13 foreign banks) and 7 licensed specialized banks (CBSL, 2018). Physical access points include over 6,000 bank branches/outlets and 5,000 ATMs island-wide. As of March 2019, bank branch density of Sri Lanka was at 16 branches per 100,000 adults,<sup>10</sup> nearly double the South Asian regional average of 9.4 per 100,000 adults.<sup>11</sup>

# **2. Comparative Analysis of the State of Financial Inclusion in Sri Lanka**

When analyzing financial inclusion, it is important to look beyond basic metrics such as ownership of a transaction account. It is the usage of such accounts and other financial services which is more critical. Sri Lanka has made noteworthy advancements in terms of basic financial inclusion metrics. For example, the number of adults with accounts at formal financial institutions stands