rafmacalaba/data-use-mentions-extended huggingface dataset:
proximity) between location _g_ of individual/firm _i_ and the nearby main road or the interaction between this variable and the submarine Internet treatment indicator. _HistRoad_ is the shortest distance to the historical network built from _i,g,t_ Murdock’s 1959 map (see below for the rationale and details on this instrumental variable), and _Submarine ∗ Internet ∗ HistRoadi,g,t_ is its interaction with the treatment variable. As additional instruments, we use geographic characteristics ( _geographyi,g,t_ ) including terrain elevation and lightening intensity, which are clearly exogenous. _Xi,g,t_ is a vector of control variables including age, gender, education, and place of residence; we also control for year fixed effect, country fixed effect, and grid cell by connectivity fixed effect as in the second stage equation below.

In a second stage, we examine the simultaneous causal impacts of the accessibility to major roads and the availability of Internet on employment status and other labor-market outcomes by estimating equation (2), which combines the two aforementioned strategies: with _Yi,g,t_ being individual _i_ ’s employment outcome (or firm _i_ ’s outcome). The variable _RoadProximityi,g,t_ is the proximity to the nearest major road. It is a continuous variable representing the negative distance to the nearest main road. The proximity to the nearest main road is measured by taking the length of the shortest straight line between the place of residence and the closest major road. However, given the fact that roads’ networks are probably not randomly built across cities in such direct routes, OLS estimates might be biased. Therefore, we use two instrumental variables, namely the land roughness and the distance obtained from exogenous historical theoretically built networks.

_Submarinei,g,t_ is a dichotomous variable taking the value one when a fiber node in a given area is connected to fast internet (submarine cables). The variable _Interneti,g,t_ takes the value one when the grid in which the individual _i_ is
9449|9070|18519|
|||**Mobil**|**ity Estimates f**|**romQuadratic**|**CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0_**|6.81|5.80||**_IGME0_**|0.55|0.65|
|**_ES5_**|9.39|8.82||**_IGME5_**|0.485|0.556|
|**_ES10_**|11.61|11.37||**_IGME10_**|0.414|0.464|
|**_ES16_**|13.82|13.82||**_IGME16_**|0.330|0.353|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at state-level in braces; *** p<0.01, ** p<0.05, * p<0.1.
This new extended data set includes information on the lending entity, the borrowing country, the amount of the loan, the year of loan agreement signing, the type of resources involved, the nature of collateralization, when available, projects to be financed by the loan and repayment terms. The information collected on each loan is compiled from a variety of sources, including African government sources, lending entities, Extractive Industries Transparency Initiative (EITI) reports, AidData’s Global Chinese Development Finance Dataset (Dreher et al. 2021), investment reports from multilaterals, as well as fieldwork and interviews with experts. Primary source information is then complemented with a review of the financial press.

The data has been checked against the World Bank’s Debtor Reporting System (DRS). The DRS collects detailed loan-byloan information on new public and publicly guaranteed external debt. The DRS does not include information on any collateral features of loans, however. We therefore also contacted relevant WB country economists or national debt offices to verify the information where possible.

While significant effort has been spent on ensuring the accuracy of the data, the reliance on mixed sources and methods may justify a level of caution and verification in the use of facts and figures from this research in further analysis, including referring directly to the underlying data sets and sources for specific details. RBL contracts are rarely publicly available. Nevertheless, whenever publicly available, we analyzed contract text. These were identified through NRGI’s oil, gas and mining contracts5 and Gelpern (2021).

Our research identified 30 RBLs made in the 2004 to 2018 period in Sub-Saharan Africa that match the focal area of our review and where sufficient minimum information was available to incorporate into our data set. These minimum criteria consisted of having details on both the lending and borrowing entity, the size of the loan, the year the loan was agreed and confirmation that the loan had a repayment period beyond a single year.

Our data set is by no means comprehensive, and there are certainly more RBLs which
|---|---|---|---|---|---|---|---|
|Country<br>A|Country<br>B|in B<br>relative to<br>A|Overall<br>govt.<br>budget<br>in GNP|Share of<br>education<br>in govt.<br>budget|Share of<br>primary in<br>education<br>budget|Primary<br>education<br>budget in<br>GNP|demo-<br>graphic<br>burden|relative<br>teacher<br>salaries|All<br>sources|
|200|400|1.43|0.0|33.0|-13.2|19.8|17.3|62.9|100%|
|400|800|1.39|6.6|20.5|_-15.3_|11.8|19.6|68.6|100 %|
|800|1,500|1.32|18.9|8.8|-13.8|13.8|19.3|66.9|100 %|
|1,500|3,000|1.32|31.3|0.0|-16.9|14.4|23.4|62.2|100%|
|3,000|10,000|1.46|51.4+-|-16.1|-22.9|12.3|31.9|55.8|100%|

Source: authors' calculation-b-ased on data in table 5.

Increased government spending on primary education contributes between 12 and 20 percent of the increase (see column 7), reflecting the expansion of overall government
**Attrition and Take-up Tables**

Table A1: Attrition

|||Attriter<br>midline survey|
|---|---|---|
|T1|(safe spaces)|0.021|
|||(0.041)|
|T1|+ Leaders|-0.063*|
|||(0.035)|
|T2|(safe spaces + IGA)|-0.063<br>(0.041)|
|T2|+ Leaders|-0.091***<br>(0.030)|
|T3|(safe spaces + husbands’ clubs)|-0.098**<br>(0.042)|
|T3|+ Leaders|-0.016|
|||(0.043)|
|T4|(control spillover)|0.002<br>(0.031)|
|T1|= T1 + L|.0621|
|T2|= T2 + L|.496|
|T3|= T3 + L|.128|
|T1|= T2|.0927|
|T2|= T3|.496|
|T1|= T3|.0166|
|T1|+ L = T2 + L|.427|
|T2|+ L = T3 + L|.0964|
|T1|+ L = T3 + L|.328|
|N||3,785|
|Co|ntrol mean|.238|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, OLS regression with standard errors in parentheses clustered at the locality level. IGA = income-generating activity; OLS = ordinary least squares. The dependent variable for column (1) is a dummy for attrited individuals at the follow-up survey. The p-values of
Additional information on irrigation requirement for dry-season rice cultivation under various soil types and their infiltration capacity<sup>17</sup> has been used to estimate groundwater irrigation.

The social vulnerability is defined here by the lack of nutrition in children under the age of five. Four indicators measuring the level of under-nutrition in children: the number of underweight and severely underweight, and stunted and severely stunted children at the upazila level of Bangladesh (Figure S3) are used here to characterize social vulnerabilities. According to the 2012 Undernutrition Maps of Bangladesh, underweight and severely underweight children are those, whose standardized weight-for-age is more than two and three standard deviations, respectively, below the median for the international reference 28
# **1. Introduction**

The impact of bank competition on financial markets and firms is an important topic for policymakers and researchers alike.<sup>1</sup> Interest on this subject intensified during the recent global financial crisis, as many questioned whether high competition was partly to blame.<sup>2</sup> At the same time, the downfall of some institutions as a result of the crisis and the emergency measures taken by some governments to deal with this episode - such as mergers, bailouts, recapitalizations, and extension of guarantees- have led to concerns about the future of bank competition and its potential implication for access to bank finance.<sup>3</sup> How bank competition affects firms’ access to finance is in itself a much debated question in the economic literature and in policy circles and, as we discuss below, the theoretical predictions and the empirical evidence on this subject are mixed. Combining multi-year firmlevel surveys with panel country-level data on bank competition for 53 countries, this paper offers new evidence on the link between competition and firms’ access to finance. In particular, our paper evaluates whether competition improves access to finance and analyzes the extent to which different features of the environment in which banks operate affect the link between competition and access.

Theory provides ambiguous predictions regarding the impact of competition on access to finance. The conventional _market power hypothesis_ argues that competition in the banking system reduces the cost of finance and increases the availability of credit. On the other hand, the _information hypothesis_ argues that, in the presence of information asymmetries and agency costs, competition can reduce access by making it more difficult for banks to internalize the benefits of investing in building lending relationships, in particular, with opaque clients (Petersen and Rajan, 1995; Marquez, 2002; Hausewald and Marquez, 2006).

> 1 _The Economist_ magazine hosted a virtual debate on this topic on June 1st, 2011. See http://www.economist.com/debate
attitudes towards gender equality and perceived community norms (particularly on the role of women in- and outside of the household), opinions on marriage, hope and aspirations for the future, as well as their answers to a strengths and difficulties questionnaire (SDQ).<sup>31</sup> Adolescents were also asked to provide information on the quality of the relationship with their parents, time use, and their involvement in household chores. Parents were asked about their own marriage history, decision-making power and attitude towards intimate partner violence (IPV), gender attitudes and perceived norms, behavioral characteristics (conformity, risk preferences), aspirations for their children, and opinions on FGM and early marriage.<sup>32</sup> The midline and endline questionnaires were very similar to the baseline but collected additional information 29The COVID-19 pandemic reached Somalia in March 2020 and the country went into lockdown for several months. In July 2020 most of the government restrictions in Somalia had been lifted and schools reopened in August 2020. The lockdown also explains the relatively long survey period as survey teams tried to match the timing of surveys to the timing of the rollout of the intervention in each of the 6 districts.

> 30The respondent for the adult module was typically one or both parents or guardians. Enumerators were instructed to survey the mother and whenever possible, the father too. The baseline sample consists of 57% of households with one female respondent, 9% with one male respondent and 34% with both a female and a male respondent. Data is representative of mothers for all rounds of the survey. However, data from fathers should be considered a more selective sample as it was difficult to track and survey fathers for all rounds.

> 31Scales to measure attitudes and mental health outcomes were changed from a 3-point Likert scale at baseline to agree/disagree options in the follow-up surveys to make it easier for adolescents to respond accurately. In the Appendix we detail the individual statements in the attitudes index and present the Cronbach’
data set is representative in exactly the same way as the original LSMS data set, and can be used to make reasonable simulations about changes in expenditures and poverty at the national level. .

# **4. Results**

What is the effect of geographic targeting with alternative indicators in Peru? As a first step towards answering this question, I present estimates of poverty and allocations of funds by region. About two-thirds of the population of Peru lives in urban areas, and well over a third of these urban residents live in the capital city, Lima. Table 1 decomposes poverty measures and allocations by alternative geographic targeting indicators into three categories: Lima, other urban, and rural. The values in each cell correspond to the proportion of total poverty or the proportion of total allocations by region, so that every row sums to 100%. Here, and for all of the results presented in the paper, individuals in the household surveys are weighted with the appropriate expansion factors.

Since the no targeting scenario makes an equal transfer to every Peruvian, regional allocations when there is no targeting correspond exactly to the fraction of the population living in each region. A comparison of these allocations with the various poverty measures shows that poverty in urban areas is below average: about 29% of the population lives in Lima, but only 14% to 22% of total poverty was found there in 1994, and 13% to 20% in 1997. Other urban areas account for about 36% of the population, and about one-third of poverty. Poverty in rural areas, by contrast, is well above average: just over one-third of the population lives in rural areas, but between 44% and 55% of poverty was found there in 1994, and as much as 47% to 57% in 1997.

Table 1 suggests that geographic targeting using any of the indicators under consideration appears to approximate the distribution of poverty reasonably well. When any of these indicators is used for geographic targeting, about one-half of all resources are
for MoLSA is correctly referred by the PPMT. Referral failure – the probability that ineligible households are refereed. Failure to refer – the probability that a household in the humanitarian database who is eligible for MOLSA is not referred by PPMT.

**<u>Step 6. Disaggregated analysis:</u>** The disaggregated analysis is conducted using summary statistics. In the first analysis, we calculate the share of cross eligible households across a population of interest, such as displaced and returnee households, female-headed households, households with high disability and chronic illness, households with no working members, and households in different governorates. In the second disaggregated analysis, we calculate the share of (cross) eligible households that are willing to be referred to the government support.

# 4. Results

# 4.1 The PMT score distribution

We apply the MoLSA PMT, Legacy VA, SEVAT, and the two PPMT models to the data collected from the field test. The correlation and kernel density of the PMT scores are shown in Table A1 and Figure A1 in the Annex. The MoLSA-PMT score ranged from 3.9 to 6.4, with a mean of 5, indicating that a larger share of the population lies at the right-hand (richer) side of the distribution. The MOLSA-PMT scores are expressed in a natural log and the IQD equivalent of the PMT would be calculated by an exponential function of the score, as shown in Table 2. The SEVAT score and the PPMTs show similarity in distribution with the MoLSA-PMT score. However, the Legacy-PMT has a unique distribution, ranging from 0 and 52, with 19.6 as the mean. The two PPMT distributions are wider than those of the MoLSA-PMT and SEVAT but lean towards the left-hand (poorer) side. While the SEVAT and the two PPMTs positively correlate with the MOLSA-PMT, the legacy PMT is negatively correlated with the MOLSA-PMT. This is expected as they use widely different targeting variables.

# 4.2 PMT cutoffs and eligibility proportion

Table 2 shows the
, coverage level and average yield. Studies have shown that producers with lower APH yields also have significantly higher variability of yields. For this reason, different Rate Classes are established for producers with different APH yields. Finally, other expenses and capital costs are added.

APH premium rates are calculated by multiplying the premium rate at 65 percent coverage level by a rate relativity factor that is the same for all crops and all regions. For example, to find a 75 percent APH rate, one multiplies the 65 percent premium rate by 1.53. Babcock, Hart and Hayes (2002) show that these constant rate relativities may be inconsistent with the underlying probability distribution of yields and therefore premium rates may not be actuarially sound. They show that farmers who plant crops in region where the 65 percent pure premium rate is above 3 percent face unsubsidized premiums that are too high at the 80 percent coverage level. This discrepancy may explain why 65 percent coverage was the most popular coverage level in higher-risk areas.

# **_5.2.  Rating a New Insurance Scheme_**

In the absence of historical indemnity data, indemnity payments of a new crop insurance scheme can be simulated from the yield data. As discussed in Section 3, the data generating process of yields can be estimated through parametric or non-parametric methods, depending on the data available and a priori beliefs. The distribution function of indemnity payments is thus derived from the distribution function of yields. However, this procedure does not capture the impact of insurance on farming practices, contrary to the ratemaking procedure described earlier. In other words, the yields observed are those 24
Policy Research Working Paper 9078

# **Abstract**

The ability to manage financial risk is especially important for people earning their living through agriculture. Many farmers only get paid once or twice a year, and households need to stretch their earnings across the year by saving or borrowing money. Moreover, agricultural production faces a variety of risks related to both production and markets because of their exposure to weather and disease shocks. Households engaged in agriculture may thus especially benefit from financial inclusion—access to and use of formal financial services. This paper explores the topic of financial risk management in agriculture—how adults who rely on growing crops or raising livestock as their household’s main source of income manage financial risk and use financial services. The paper summarizes new data based on a nationally representative survey of about 15,000 adults in 15 lower-middle- and low-income Sub-Saharan African economies collected as part of the World Bank’s Global Findex database. The majority of these adults reported suffering a bad harvest or significant livestock loss in the past five years, and most bear the entire financial risk of such a loss. Most adults in agricultural households lack the financial tools—such as insurance, accounts, savings, and credit—that could help them manage financial risks.

This paper is a product of the Development Research Group, Development Economics. 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 lklapper@worldbank.org and dsinger@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
in accessing and adopting more sophisticated technologies, including efforts to address the systemic barriers that hinder women’s access to financial resources. Second, our results highlight the importance of technology in shaping the relationship between female management and firm performance, indicating that promoting technology adoption could effectively reduce the gender gap in firm performance.

The remainder of the paper is structured as follows. Section 2 describes the data. Section 3 describes the methodology used to identify the association between firms with female managers, performance, and technology. Section 4 provides descriptive statistics and initial correlations between female-managed firms and productivity as well as technology. Section 5 shows the main results and examines the role which these technological differences may have in explaining productivity differences. section 6 explains women’s higher returns to the use of technology. section 7 explores the differences in the determinants of technology adoption between businesses managed by women and those managed by men. section 8 concludes with policy implications.

# **2 Data**

Our study is based on the new data from the Firm-level Adoption of Technology (FAT) survey developed by Cirera et al. (2020). The survey was implemented in 11 countries, including both developing and developed economies. It includes a nationally representative random sample of firms with five or more employees in agriculture, manufacturing, and services. Our main dataset includes information on firms in advanced economies (the Republic of Korea, Poland, Georgia, and Chile), middle-income countries (Brazil,<sup>1</sup> Ghana, Kenya, Senegal, and Viet Nam), and low-income countries (Ethiopia and Burkina Faso).<sup>2</sup> Unlike most studies exploring the gender gap in general purpose technologies, the FAT survey offers detailed information on the adoption of several technologies across different business functions associated with general business or sector-specific business tasks.

## **2.1 Technology measure**

The FAT survey covers over 300 technologies across almost 50 business functions, divided into general business functions (
deficiencies in infrastructure (lack of water, sanitation, and/or electricity), HHs that are built with inadequate materials (walls, roofs, and floors), and HHs that are overcrowded (e.g., Cristini and Moya (2008) cited in Lora et al. (2010); Szalachman 2000). Some authors expand this definition adding aspects such as insecurity of tenure (e.g., Habitat 2016), telephone coverage (e.g., Lora et al. 2010), or location and accessibility to markets (e.g., Acolin & Green 2017; Isalou et al. 2014). Others proxy the qualitative housing deficit by focusing on one qualitative aspect only such as access to drinking water (e.g., Gilbert 2001; ECLAC 1996). Yet another alternative to estimating the qualitative housing deficit is to survey HHs’ satisfaction with their homes. Gallup recently conducted such a perception-based survey asking respondents about their satisfaction with the availability of good, affordable housing. The results highlight a wide gap in satisfaction levels across countries which reflect the diversity of housing markets across the world (Gallup 2021).

These varying definitions and approaches towards the qualitative and quantitative housing deficit are particularly problematic when comparing deficit numbers. Diverging definitions of adequacy lead to different estimation methodologies of housing deficits and therefore render the comparison of housing deficits across studies meaningless. This becomes particularly apparent with the varying scope and number of qualitative dimensions considered to contribute to the qualitative housing deficit across studies. In particular, the aspect of overcrowding is ambiguous. It is sometimes considered to be contributing to the qualitative housing deficit, the quantitative housing deficit, or to both. A recent World Bank study regarding the housing deficit in Peru, for instance, highlights the impact of varying adequacy definitions. The study shows that the qualitative housing deficit equivalent to 23 percent of the country’s housing stock could rise to 68 percent when a stricter definition of housing adequacy is applied (World Bank et al.