rafmacalaba/data-use-mentions-extended huggingface dataset:
|Highest usual hours of work in household||||0.054|0.489|6.032|
|X Other worker ill||||[0.068]|[0.512]|[5.760]|
|Highest usual hours of work in household||||‐0.024|‐0.103|‐6.212|
|X Other worker ill X Female||||[0.113]|[0.657]|[7.469]|
|Net Effect on a Male w/ Highest Earn/Hours|0.135|0.838|9.106|0.122|0.713|8.565|
|P‐value|0.007|0.007|0.006|0.009|0.069|0.068|
|Net Effect on a Female w/ Highest Earn/Hours|‐0.124|‐0.511|‐0.780|‐0.024|0.134|‐1.079|
|P‐value|0.081|0.259|0.875|0.706|0.607|0.950|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,951|3,948|3,948|3,951|3,948|3,948|
|R‐squared|0.507|||0.507|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Usual earnings and usual hours of work taken from reports in baseline survey. All specifications include individual fixed effects. Column
shocks in the first and second seasons of life have an impact on child health is not enough to say that they have a long-lasting effect, because they might affect the younger children but not the oldest.

## Table 7

To analyze this issue we group children into cohorts according to the number of completed rainy season that they have experienced since birth, and then interact each cohort with rainfall deviations in different time periods as explained in Equation 3. Examining exogenous shocks in several periods at early life for each cohort, allow us to identify the most critical periods, and ultimately to understand the persistency of their impacts. Results presented in columns 1 and 2 of Table 7 suggest that weather shocks (both rainfall and temperature) are transitory, since it is only the last season shock that matters for HAZ scores ( _ΔR1_ and _ΔT1_ for the first cohort and _ΔR2 and ΔT2_ for the second cohort). In the last two columns we split the sample between boys and girls to see if there is any difference across gender groups. Again, results are indicative of similar (in magnitude) transitory impacts of weather shocks for both groups.

The short-lived nature of past weather shocks seem to indicate that children who have experienced a shock can rapidly catch-up with their cohort. Our findings are in line with some results reported in the epidemiologic literature, but seem to contradict Maccini and Young (2009) who report that shocks during the first 12 months since birth have an effect on socioeconomic outcomes of women as adults.

# **6. Conclusions**

In this study, we investigate the extent to which rainfall shocks affect the welfare of children in rural Nigeria. Understanding the socio-economic consequences of rainfall variability is a prerequisite for the integration of adaptation activities into poverty reduction efforts in Nigeria. This is especially relevant in the context of global climate change and the projected increase of weather variability.

We used data from the 2003 and 2008 rounds of the Demographic and Health Survey that contain measures on two key dimensions of child welfare such as child
WPS3907

# **Trade and Harmonization: If your institutions are good, does it matter if they are different?**<sup>**1**</sup>

# **Roumeen Islam World Bank**

# **Ariell Reshef New York University**

# Abstract

Good institutional quality (function) and similar institutional design (form) can promote international trade by reducing transactions costs. We evaluate the relative importance of function versus form in a gravity model, using an indicator of different legal systems as a proxy for differences in form together with indicators of overall institutional quality. We find that good institutions promote trade much more than similar legal systems and have much more explanatory power. This effect is economically large –up to 10 times the effect of different legal systems. Moreover, better infrastructure matters as much as good institutions.

# World Bank Policy Research Working Paper 3907, May 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 We wish to thank Decio Coviello, William Easterly and William Martin for helpful suggestions. Authors’ contact details: rislam@worldbank.org, ariellr@nyu.edu
0.2|2.6|
|PPP $5.5|3.1|12.7|
|Nationalpovertyline|4.2|14.7|

Note: The table shows loss and gains based on households’ consumable income, which is defined as market income – direct and indirect tax payments + direct transfers. It excludes in-kind transfers (for education or health care). PPP = purchasing power parity, in 2011 U.S. dollars. a. “Fiscal impoverishment” is the extent to which fiscal system (of taxes and transfers) either (a) make a substantial portion of the poor even poorer, of (b) make the nonpoor poor. In other words, if the poor pay more in taxes than they receive in transfers, they are fiscally impoverished (Higgins and Lustig 2016). b. “Fiscal gains to the poor” is the opposite of fiscal impoverishment, representing more in gains from transfers than they pay in taxes (Higgins and Lustig 2016).
# **1. Introduction**

In 2018, the Financial Stability Board (FSB) launched an evaluation of the effects of G20 financial reforms on the financing of small and medium-size enterprises (SMEs). According to FSB (2019), reforms such as the Basel III bank regulatory framework shall boost financial stability and systemic resilience in the long term. But they may be associated with some short-run costs. The global community is interested in learning if any such short-run costs may be incurred by SMEs— the main job providers and conduits of shared prosperity. Because banks provide SMEs with most of their external finance, the main transmission mechanism from the reforms to SME growth could work through bank credit provision to SMEs (FSB 2019).

Although the FSB evaluation focuses on the group of G20 jurisdictions, this paper conducts a similar evaluation for SMEs in countries covered by the World Bank Enterprise Surveys. More specifically, the paper uses firm-level panel and repeated cross-sectional data for 32 countries from the Enterprise Surveys together with banking system–level data from the World Bank’s FinStats, individual bank-level data from Fitch (to create matched firm-bank data for a subset of firms), and with the Bank of International Settlements Financial Stability Institute Survey of Basel III implementation in emerging markets and developing economies (EMDEs). The Enterprise Surveys offer some benefits in comparison with other available databases, for example, allowing us to examine a broad measure of access to finance in the EMDEs—like the European Central Bank’s Survey on the Access to Finance of Enterprises,<sup>1</sup> which was used as the leading data set in the FSB evaluation.

We examine whether Basel III capital requirements,<sup>2</sup> the first and most prominent regulation of the Basel III package, had any short- to medium-term effects on SME access to finance in EMDEs. Access to finance as a constraint reflects the firm’s overall perception of difficulties in accessing financing and is tracked before and after the Basel
- Guenther, R. and J. Lee (1988). _Partial Differential Equations of Mathematical Physics and Integral Equations_ . Dover Publications.

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- Kobus, M. (2015). Polarisation measurement for ordinal data. _Journal of Economic Inequality 13_ (2), 275–97.

- Kobus, M. and P. Milos (2012). Inequality decomposition by population subgroups for ordinal data. _Journal of Health Economics 31_ , 15–21.

- Lazar, A. and J. Silber (2013). On the cardinal measurement of health inequality when only ordinal information is available on individual health status. _Health Economics 22_ (1), 106–13.

- Levy, H. (2006). _Stochastic Dominance: Investment decision making under uncertainty_ (2nd ed.). Springer.

- Mendelson, H. (1987). Quantile- preserving spread. _Journal of Economic Theory 42_ , 334–51.

- NIPORT, Mitra and Associates, and ICF International (2009). Bangladesh demographic and health survey 2007. Report, Dhaka, Bangladesh, and Calverton, Maryland, USA.

- NIPORT, Mitra and Associates, and ICF International (2013). Bangladesh demographic and health survey 2011. Report, Dhaka, Bangladesh, and Rockville, Maryland, USA.

- NIPORT, Mitra and Associates, and ICF International (2016). Bangladesh demographic and health survey 2014. Report, Dhaka, Bangladesh, and Rockville, Maryland, USA.

38
capitalization to GDP, all from the World Bank Global Financial Development Database. During episodes of banking crisis in emerging markets, the issuance of corporate bonds and the stock market infrastructure may play the role of a “spare tire” by providing an alternative source of long‐term debt finance for large firms (Oh and Rhee, 2002; Chan et al., 2012). By contrast, Levine, Lin and Xie (2015) argue that a good financial infrastructure, in the form of adequate investor protection, rather than a large bond or stock market, helps support firm performance during a financial crisis.

Banking system policies – such as those related to bank competition and contestability– are expected to impact the terms and availability of bank loans, including the maturity of these loans (see Love and Martinez Peria, 2015). This would be in line with the notion that the entry of new financiers could reduce the rents that incumbent banks extract by rolling‐over short‐term loans (Rajan, 1992). Contestability of the banking system could matter even more during a crisis as it may affect firms’ ability to be less dependent on a pre‐determined set of banking relationships and access loans from diverse lenders. To account for this effect in our estimations, we include a variable that captures the regulatory requirements for bank entry. This variable comes from Barth, Caprio and Levine (2013) and higher numbers reflect tighter regulations for bank entry.

Appendix table A2 lists the definitions and data sources for the variables used in our empirical analysis.

20
fuel source).<sup>8</sup> Each data set observes the household’s “primary sampling unit” or PSU, which corresponds to the household’s village, town, or neighborhood as designated in each survey’s sample design. The analysis also merges in data at the district level from the 2011 Indian Census, reported separately for urban and rural areas of each district. These data provide detailed information about a household’s location. For each data set, each household is weighted using the provided household-level sample weights.

# **II Descriptive Statistics and Preliminary Evidence**

To explore the relationship between religion and sanitation, the analysis begins with descriptive statistics that are separated by religion and urban/rural status. Specifically, the data are split by whether the head of household is Hindu or Muslim, as well as by whether the household is in a rural or urban location.<sup>9</sup> Using data from the IHDS, Hindu households are on average 18 percentage points less likely to have a latrine than Muslim households (Table 1). Yet this overall difference masks significant heterogeneity across urban and rural areas. In urban areas, the gap between Hindu and Muslim latrine ownership is 4 percentage points, whereas in rural areas the gap is 18 percentage points.<sup>10</sup> The large overall difference in latrine ownership between Hindu and Muslim households is similar to the rural gap between Hindu and Muslim households, which reflects several features of the data. Latrine ownership is substantially lower in rural areas, and Hindus live disproportionately in rural areas as compared to Muslims.<sup>11</sup> Further, a majority of both

> 8The availability of these variables vary slightly across data sets: the IHDS data do not include information on homeownership, and only the IHDS data include the occupation of the household head and the distance to the closest fuel source.

> 9“Urban” is defined in the 2011 India Census, which
history; (iii) experience with the PES and private employment service providers; and (iv) a battery of questions to characterize each of the jobseeker’s “Big 5” personality traits.<sup>5</sup> The baseline survey had a response rate of 59.3 percent (914 out of 1,541 beneficiaries participated in the survey). There was no difference in response rates across the two treatment groups, likely because the survey was administered before any services were provided by the private employment service provider. Table 2 below shows the timelines of the program and impact evaluation related activities.

> 5Given the relatively high rates of refusal to participate in the end-line survey, we have not used these data for analysis of the impact evaluation. As there were no systematic differences across treatment and control groups, analysis of the endline survey could be included as an additional robustness check.

10
the range 2013-present social protection data is counted as missing. This results in data being available for 92 countries, such that the inclusion of a social protection indicator limits global coverage. Similarly for 2010, data for the closest year to 2010 is used as long as the survey falls within 2005-2015.

The indicator used captures households currently receiving social protection, when conceptually what we would like to capture is whether or not a household is eligible to receive social protection should the need arise. The indicator we are using is very imperfect as someone in a formal job at the top end of the income distribution, not currently receiving social protection would be counted as vulnerable even though they could rely on social insurance instruments should the need arise. In future work, we will explore the possibility of using eligibility criteria to refine this measure. Additionally, we note that ASPIRE’s coverage is estimated using the national representative household surveys, which may capture primarily the largest programs in the country. As a result, the extent to which information on specific transfers and programs is captured in these household surveys can vary across countries.

While poverty and education data are available from the same survey source, this is often not the case for social protection data, and assumptions are needed to estimate social protection at the household level. In future work a more detailed approach could be used to impute whether a household is potentially covered by social protection systems, however for the purposes of this analysis we use information on social protection coverage rates by income quintile to assign each household a probability that they receive social protection. The probability for each household is equal to the coverage rate for the income

> 19 The ASPIRE database is available at https://www.worldbank.org/en/data/datatopics/aspire. As the majority of surveys used in the ASPIRE database are the same surveys in the GMD, we can explore all three dimensions together (poverty, education, and social protection) at the household level in future analysis.

> 20
evaluation to highlight its impacts on key seed system indicators means that DSM’s proponents have had very little to show, apart from achievements in coverage extracted from standard monitoring data.

This study addressed this gap by examining the impact of DSM on seed purchases and yield for two crops that were the program's initial focus. It used a quasi-experimental difference-indifferences approach that leverages the staggered scale-up of DSM to demonstrate how the approach led to increased maize yield, primarily by improving farmers’ purchase of hybrid maize seed. Specifically, the study finds that DSM has resulted in a 15-percentage-point increase in the proportion of farmers purchasing maize seed, a 45 percent increase in the quantity of maize seed purchased per hectare, and an 18 percent increase in maize yield. However, DSM’s effects on seed purchases and yields are not statistically significant for wheat.

These crop-specific differences may be partly attributable to differences in the reproductive biology of maize compared to wheat—a difference that incentivizes private participation in maize seed markets more readily than in wheat seed markets. These implications suggest a need for more nuanced policies, institutional arrangements, and market development strategies to accelerate the adoption of improved varieties across a wider range of crops. Clearly, the hybrid maize market will remain more attractive to private investment as DSM becomes the standard channel for distribution. This market may even expand and improve more rapidly—with greater investment, newer traits, better seed traceability, and lower prices for farmers—if state-owned enterprises withdraw or reallocate their resources to other functions, such as early-generation seed production and seed sector capacity development. The same may be possible for other crops where hybrid vigor plays a role, for example, in certain vegetable crops. For OPV maize, wheat, teff, and other crops, there will still be a need for public investment in seed sector development, given the limited impact of DSM. The same may be the case for many other grain and legume crops, where weak returns to private investment are common.

However, this should not
Gallup World Poll.” _Journal of Economic Perspectives_ 22 (2): 53–72.

- Demirguc-Kunt, A., and L. Klapper. 2012. “Measuring Financial Inclusion: The Global Findex Database.” Policy Research Working Paper 6025, World Bank, Washington, DC.

- Demirguc-Kunt, A., L. Klapper, and L. Zingales. 2012. “Trust in Banks.” Working paper, Development Research Group, World Bank, Washington, DC.

- Demirguc-Kunt, A., and R. Levine. 2009. “Finance and Inequality: Theory and Evidence.” _Annual Review of Financial Economics_ 1: 287–318.

- de Soto, Hernando. 2000. _The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else._ New York: Random House.

- Djankov, S., C. McLiesh, and A. Shleifer. 2007. “Private Credit in 129 Countries.” _Journal of Financial Economics_ 2 (84): 299–329.

**42**

MEASURING FINANCIAL INCLUSION
Policy Research Working Paper 10143

# **Abstract**

This paper uses high-frequency phone survey data from Ethiopia, Malawi, Nigeria, and Uganda to analyze the impacts of the COVID-19 crisis on work (including wage employment, self-employment, and farm work) and income, as well as heterogeneity by gender, family composition, education, age, pre-COVID19 industry of work, and between the rural and urban sectors. The paper links phone survey data collected throughout the pandemic to pre-COVID-19 face-to-face survey data to track the employment of respondents who were working before the pandemic and analyze individual-level indicators of job loss and re-employment. Finally, it analyzes both immediate impacts, during the first few months of the pandemic, as well as longer run impacts through February/March 2021. The findings show that in the early phase of the pandemic, women, young, and urban workers were significantly more likely to lose their jobs. A year after the onset of the pandemic, these inequalities disappeared and education became the main predictor of joblessness. The analysis finds significant rural/urban, age, and education gradients in household-level income loss. Households with income from nonfarm enterprises were the most likely to report income loss, in the short run as well as the longer run.

This paper is a product of the Development Data Group, Development Economics and the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at apalacioslopez@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.
system. Those same individuals could be measured as poor at market income, when pensions are not included.

These individuals would be, in a sense, “false poor” as their most likely outcome (in retirement) without a public contributory pension system would be an income level (in retirement) that depends on the accumulated earnings on assets held in privately arranged retirement accounts. The creation of this “false poor” population would also automatically cause an overestimate of the redistributive impact of fiscal policy.<sup>15</sup> Therefore, the _baseline results_ (discussed below) are based on the PDI treatment of the Ugandan public contributory pension system. Nonetheless, throughout the report, we also present the results produced under the PGT treatment and overall, the results are very similar. Market Income plus Pensions is thus our baseline “pre-fiscal” income (income prior to any government intervention), and pensions are not included in the list of fiscal instruments.

# 3.4. Allocating Assumptions

## 3.4.1. Direct Taxes

Households who are PAYE-liable are identified directly in the UNHS consumption expenditures module as households that reported paying income tax. PAYE amounts paid (as reported in the consumption module) were used to generate shares of total PAYE revenues contributed by UNHS households. The amount of PAYE allocated in the survey was generated by first scaling total PAYE collections in administrative and budget reporting such that the ratio of total PAYE revenues in administrative records to National Accounts (NA) Household Final Consumption Expenditure was equivalent to the ratio of PAYE collected from UNHS households to total UNHS Consumption Expenditures. The PAYE burden for each household was then determined by applying each household’s share to the scaled PAYE amount.

## 3.4.2. Direct Transfers

The SCG and NUSAF programs provide direct cash transfers to eligible individuals; whereas UWEP and YLP provide soft loans to qualifying _groups._ The UNHS does not allow direct identification of beneficiaries for any of these programs. Instead, we use the program