rafmacalaba/data-use-mentions-extended huggingface dataset:
jobs in some fields may fit certain gender stereotype beliefs held by employers. For example, many of the jobs advertised on the online portal for the finance field were for assistants, and a higher share of marketing jobs required knowledge of Microsoft Office and less advanced computer skills, as well as languages. This finding is in line with evidence found elsewhere. According to the World Economic Forum (2016), the IT industry has a significant gender gap in female hirees, whereas women are more highly represented in sectors related to services.

> 17 Unfortunately, no data are available to further verify this hypothesis. Implementing panel surveys in the future to carefully track the choices of graduates over time would be a good first step.

43
Policy Research Working Paper 6099

# **Abstract**

his paper uses a novel loan-level dataset covering lending by official creditors to developing country governments to construct an instrument for public spending that can be used to estimate government spending multipliers. Loans from official creditors (primarily multilateral development banks and bilateral aid agencies) are a major source of financing for government spending in developing countries. These loans typically finance public spending projects that take several years to implement, with multiple disbursements linked to the stages of project implementation. The long disbursement periods for these loans imply that the bulk of government spending financed by official creditors in a given year reflects loan approval decisions made in many previous years, before current-year macroeconomic shocks are known. Loan-level commitment and disbursement transactions from the World Bank's Debtor Reporting System database are used to isolate a predetermined component of government spending associated with past loan approvals. This can be used as an instrument to estimate spending multipliers for a large sample of 102 developing countries. The one-year government spending multiplier is reasonably-precisely estimated to be around 0.4, and there is some suggestive evidence that multipliers are larger in recessions, in countries less exposed to international trade, and in countries with flexible exchange rate regimes.

This paper is a product of the Macroeconomics and Growth 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 akraa@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
br>0,0 0<br>(c) Share of beneficiaries of matching grants who filed a (d) Share of beneficiaries of matching grants who<br>request to MEBF by month received approval for their request from MEBF by<br>month<br>Nov 2019 Dec 2019 Jan 2020 Feb 2020 March 2020 April 2020 May 2020 June 2020 July 2020 August 2020 Sep 2020 Oct 2020 Nov 2019 Dec 2019 Jan 2020 Feb 2020 March 2020 April 2020 May 2020 June 2020 July 2020 August 2020 Sep 2020 Oct 2020<br><!-- End of picture text --> <!-- Start of picture text --> 1,0 1,0<br>0,9 0,9<br>0,8 0,8<br>0,7 0,7<br>0,6 0,6<br>0,5 0,5<br>0,4 0,4<br>0,3 0,3<br>0,2 0,2<br>0,1 0,1<br>0,0 0,0<br><!-- End of picture text --> _Source:_ Monitoring data.

16
, or report the closest banking institution to be over ten blocks away. In the case of the banked population in Bogotá, 50% of respondents report a distance to the next bank of over 10 blocks or an unknown distance. In particular, 22% of the unbanked live more than 20 blocks from the next bank compared only 13.5% of the banked in Bogotá.

12. **In conclusion, the majority of the unbanked share the characteristics of the urban underclass** with lower income, lower education, closer links to the informal sector (and consequent economic and social insecurity), with a greater tendency to live outside higher income census districts where banks, commerce, religious and health centers are located. The unbanked show no particular difference of age or gender distribution from either the banked population or from the population at large nor do they differ from the overall population in terms of home-ownership. At the same time, the data reviewed above 49
|Gini Index|||0.00401*|0.00536**|0.00385*|
||||(0.00223)|(0.00241)|(0.00222)|
|Constant|-0.668***|-0.602***|-0.765***|-0.927***|-0.771***|
||(0.0191)|(0.0362)|(0.106)|(0.101)|(0.102)|
|Observations|184|168|145|122|96|
|R-squared|0.133|0.171|0.228|0.230|0.253|

_Note: The explained variable in these regressions are the fixed effects recovered from the pixel-level regression of cropland losses. Cropland per capita is from MODIS 2018; the Quality of Land Administration is from Doing Business 2020; GDP per capita is in current US$, the Gini Index uses the latest value reported in the World Development Indicators. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1;_ † _p<0.15_ .

We now turn to the assessment of the factors that drive movements into cropland, adopting the same two-stage procedure. For this, we focus on pixels within a 3-km distance of existing cropland in 2003 and estimate a regression similar to (1) but where the left-hand side variable is a dummy equal to 1 if the pixel transitioned to cropland in 2018 and 0 otherwise and where explanatory variables are the same as in Table 1. Results are presented in Table 4 below. All coefficients are highly significant, and the signs of these coefficients for distance to the city, the number of drought months, land suitability, and distance to the coast are opposite to what we reported in Table
and number of individuals caught by all sets. Unlike vessels in the electronic logbook data, the closures policy does not give third-party inspectors an incentive to misreport percentage juvenile because the regulator does not use landings data to determine closures during my study period.

Vessels report when each fishing trip begins and ends in the electronic logbook data. For each landing event by a vessel, I record the vessel’s most recent preceding electronic logbook fishing trip and the sets that occurred on the trip. I matched 93.1% of sets to landing

> level in the above procedure. 58 non-SNP sets (.04%) occur during a two week period without any SNP sets. I record the length distribution values, number of individuals caught, and number of juveniles caught as missing for these 58 sets.

> 45Whenever I create grid cells (of any resolution), I create a grid over Peru’s Exclusive Economic Zone. Peru’s Exclusive Economic Zone boundaries are from Flanders Marine Institute (2012).

> 46Recall from Section II that vessels do not underreport tons caught.

35
each transition type are presented in Figures 20 - 24, and discussed below.

## **Box 2. 1  Estimating Multivariate Models of Labor Force Transitions**

A multinomial logistic model is estimated using data from the Latvian Labor Force Survey 2002. This model is used to predict the probabilities of three outcomes: employment, unemployment or inactivity in 2002, conditional on being employed one year prior to the interview. A sample of 9,659 is included. The estimated model predicts the probability of each of the three outcomes based on the following explanatory variables: education level (6 categories), gender, ethnicity, age, employment status, occupation, ownership type, sector of economic activity (14 categories), and region of residence. Predicted probabilities are displayed in Figure 19 and Appendix Figures 13 - 17. Estimation results are presented in Appendix Table 19.

**Completed higher education clearly provides protection from unemployment** :  Figure 20 shows that the higher educated are relatively unlikely to experience job loss. Other things equal, the probability of making a transition from employment to unemployment within a year is just 0.02 for prime-age workers with higher education, two times smaller than the probability for their counterparts with basic education. The probability of exit to inactivity for persons with higher education is lower than amongst those who are less educated, especially for young and elderly workers.  On the other hand, those with general or technical secondary and postsecondary vocational education do not have significantly different job loss probabilities from those with basic education.  Those who were in their last year of university studies in 2001 have a high estimated probability of being unemployed in 2002. This post-university unemployment, however, is most likely related to labor force entry rather than job losing.

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# 4 Results

# 4.1   Farmers’ demand for extension services

We first test whether our intervention effects farmers’ decision to sign up for the service in the following year. This is the most objective measure of farmers’ demand we have access to, and it is sourced from administrative data from our partner OAF in Rwanda. In addition to tracking take up decisions among existing clients, we measure the impact of the intervention on locally-placed non-users. As described in section 3.1, we measure the incidence of drop outs and new member enrollment at the group level. Our empirical strategy is based on random assignment of farmers groups into the different treatments— Logbooks (L), Scorecards (S), Announcement (A), and their interactions (LS, AS). We estimate the following equation: ���� � �� ����� ������ � ������� ������� �������� ��� ��� (1) where, � indexes the farmer groups, � indexes the FO for group �, and � indexes the outcome. �� represents FO effects (for which FO dummies are included), and �� is an individual random error term. Results are shown in Table 4, where the top panel reports the total effect of being in each possible treatment arm, and the lower panel reports a number of additional statistics, including the p-values of differences in relevant treatment effects to guide inference.

First, we check whether our intervention did affect the structure of farmers’ groups. Groups can merge and divide up, leading to attrition from one season to another; our intervention may have affected this dynamic. We estimate (1) on a dummy indicating group survival (takes value 1 if the group remains active in the following season, 0 if it dies; col. 1, Table 4). The results indicate that our intervention did not
not validate this threshold score by means of in-depth psychiatric interviews. We therefore confirm the robustness of our main results using summed GHQ-12 scores. We also use another screening instrument called the Mental Health Inventory 5 (MHI-5) to further examine the robustness of our results. The MHI-5 is a more focused instrument that contains five Likert items aimed specifically at depression and anxiety (e.g. Cuijpers et. al. 2009). Appendix 1 lists each of these five items.

# 3. RESEARCH SETTING AND DESIGN<sup>8</sup>

# 3.1 LOCATION

Malawi, the setting for this research project, is a small and poor country in southern Africa. 81% of its population of 15.3 million lived in rural areas in 2009, with most people relying on subsistence farming. The country is poor even by African standards: Malawi’s 2008 GNI per capita figure of $760 (PPP, current international $) is less than 40% of the sub-Saharan African average of $1,973 (World Development Indicators Database, 2010). According to the same data source, net secondary school enrollment is very low at 24%.

# 3.2 SAMPLING

Zomba District in the Southern region was chosen as the site for this study. Zomba District is divided into 550 enumeration areas (EAs), which are defined by the National Statistical Office of Malawi and contain an average of 250 households spanning several villages. Fifty of these EAs lie in Zomba city, while the rest are in seven traditional authorities. Prior to the start of the experiment, 176 EAs were selected from three different strata: Zomba city (urban, 29 EAs), near rural (within a

> 8 This section draws heavily from Section 2 in Baird, McIntosh, and Özler (forthcoming), which provides more detail on the study design and the intervention.

7
We do not consider tax implications. Even without profit, tax credits can significantly contribute to mitigating the impact on capital.

- **NPL composition and provisioning.** A key assumption is that the NPL portfolio is equally divided among substandard, doubtful, and loss loans. We further assume that the corresponding provisioning rates are 20, 50, and 100 percent. As a result, we hypothesize an overall provision rate of 55 percent for all banks. This assumption could be relaxed using the information at the country level on provisions to nonperforming loans or using granular bank-level data on provisioning or on the ratio of the difference between gross loans and net loans over gross loans.

- **No collateral.** We assume that loans are not collateralized or that collateral cannot be liquidated sufficiently quickly.

- **Minimum capital requirements.** Data on country minimum regulatory capital requirements refer to year-end 2016 and are taken from the World Bank’s Bank Regulation and Supervision Survey. The minimum capital requirement is applied equally to all banks and does not take into account specific capital buffers (e.g., capital conservation buffer and capital buffers for systemically important institutions). Effective capital requirements may be also higher due to, for example, leverage ratio requirements (i.e., requirements on bank capital to unweighted assets ratio) or addons following supervisory stress test exercises.

Further, the empirical approach presents the following limitations:

- **Interdependence structure.** The CDBP does not capture non-linear distress dependencies among the banks in the banking system (Goodhart & Segoviano, 2009). For example, it does not incorporate knock-on effects and balance sheet interlinkages that may lead to contagion.

- **Type of shock.** The system-wide bank instability measure accounts for increases in credit risk. Other important sources of risk in EMDEs could be the sovereign-bank nexus (Feyen & Zuccardi, 2019), foreign exchange rate risk (Demirguck-Kunt & Detragiache, 1998), a sudden stop of capital inflows and sharp
0.62|0.69|
|Price of an inter-Africa ca|ll per minute|0.74|—|0.62|0.94|

_Source:_ Adapted from AICD, OTH, ART, SOCATEL, Orange, and World Bank ICT At-a-Glance. [1] = 2007.

[2] = 2010, WiMAX. — = Data not available.

The country has benefited from a huge expansion in the coverage and access of mobile communications. Mobile penetration rose from 0.3 subscriptions per 100 people in 2000 to 15.2 in 2009, but is still below the rates of fragile states. Nonetheless, with only around half the population covered by a mobile signal,<sup>12</sup> penetration remains behind the levels of comparable Sub-Saharan peers (table 11). The intensification of competition since 2004 and the actions of strategic investors from the Middle East and Europe in mobile operations<sup>13</sup> should help to boost access. But this may be difficult without universal service support, since it is estimated that only around half the population can afford to pay for service (figure 14) (Mayer and others 2009).

> 12 Figure refers to the largest mobile operator by subscriptions (Orascom Telecom 2010).

> 13 Bintel (a Dubai-registered company) purchased Nationlink, an existing mobile operator in 2007. Likewise, Orascom Telecom of Egypt purchased Telecel, another existing mobile operator, in 2008. France Telecom entered as the fourth mobile operator in 2007 through its Orange subsidiary.

30
it can be inferred that there is no substitution between working and schooling among extremely poor children. In other words, as already found by Ravallion and Wodon [21] in the case of Bangladesh, it seems that child labor does not displace schooling among extremely poor individuals in Bolivia. On the contrary, indigenous children became able to substitute between such goods.

There are three main caveats of the analysis. First, as mentioned, the absence of domestic work data could bias the obtained results with relation to the gender issue. Second, the analysis is carried out at the national level, disregarding any different dynamic that is occurring at the local level. Finally, the absence of a good instrument to control for the selection problem could be a potential source of bias.

The results have four main policy implications. Policies aiming to foster enrollment in rural areas should be promoted. At the same time, incentives and measures to stimulate school participation need to be revised in order to encourage child labor abandonment by the poorest. Moreover, a different setting of the installments of the BJP should be devised to avoid free riding and consequently promote human capital growth. Finally, educational policies should 17
||∆Electrici<br>(1)|ty consumpti<br>(2)|on in April 2020<br>(3)|
|---|---|---|---|
|∆light intensity, April 2020|0.579**<br>(0.230)|0.892***<br>(0.287)|1.251***<br>(0.519)|
|∆light intensity squared, April 2020||-2.789*<br>(1.607)||
|Constant|-0.188***|-0.138***|-0.135***|
||(0.0277)|(0.0393)|(0.04)|
|_N_|30|30|26|
|_light intensity_|all|all|<0.1|
|_R2_|0.184|0.266|0.195|

**Note:** Standard errors are in paranthesis. Arunachal Pradesh, Dadra & Nagar Haveli, and Daman and Diu are dropped from the analysis. * p <0.1, ** p <0.05, *** p <0.01

Table 4: Relationship between changes in electricity consumption and changes in nighttime light intensity at the state level related at the state level. To do so, we aggregate electricity consumption to monthly data and re-estimate the electricity consumption model presented above with monthly frequency. We can then compare the deviations of actual electricity consumption from the model prediction in April to the change in nighttime light intensity in April (compared to the year before).<sup>24</sup> For the whole sample, the linear coefficient is statistically significant at the 5 percent level (Table 4, column 1). However, as visible in Figure 5, since
"Infrastructure Coverage and the Poor: A Global Perspective" Komives, Whittington, and Wu opportunity to connect to the electricity network, the vast majority did so, regardless of their income level (this was not true for the other three infrastructure sectors). Moreover, when households had a choice among all four infrastructure services, it appears that they chose electricity first.

Fourth, few households in the sample relied on electricity as a cooking fuel. The vast majority of poor households in both rural and urban areas used wood, straw, dung, and/or thatch as their primary cooking fuel. In the poorest countries in the sample, even the majority of the richest rural households also relied on these basic fuels.

Fifth, although the majority of households in the pooled sample did not have an in-house water connection, relatively few households were using unimproved water sources (such as a river or stream) as their primary source. In urban areas very few households at any income level were using unimproved water sources. In rural areas between 20-30 percent of households in all except the richest income deciles relied on unimproved water sources. Water vendors were not a major water source for households in the four countries in the sample in which these data were collected. However, those households that purchased water from vendors were usually not paying much more per month than the likely full cost of private in-house water service (although the price per unit of water purchased from vendors is almost always higher than the price of water from piped distribution systems).

Sixth, in those countries in which the LSMS surveys collected information on toilets, latrines, and septic tanks, the majority of urban households had a toilet or latrine in their home. The greatest sanitation deficit existed among the rural poor; 80-90 percent of poor, rural households had no sanitation facilities of any kind. This will come as no surprise to those working in the water and sanitation sector.

21
respect to their impact on people with disabilities overall and by sex, age group, residence and other potential factors of intersectional disadvantage. More research is needed on program or policy evaluations. Not all such assessments need to be quantitative and large scale in nature. Qualitative, mixed methods and participatory studies are necessary on social protection programs and more broadly to complement the statistics produced in this paper by trying to understand the results in their complex contexts and by listening to voices and perceptions (e.g. Chaudhry 2019, Diaz et al (2015), Kuper et al 2016).

Consistent with earlier research (Washington Group on Disability Statistics (2016)), this paper shows that data sets with the WGSS and similar questions can be used to monitor the disability related targets of the 2030 Agenda. The Washington Group’s recommendation is to consider those who have at least a lot of difficulty in at least one functional domain as a threshold for disability. Our results suggest that analyses should try to incorporate the degree of functional difficulties through different degree categories or a functional score (Mitra 2018; Loeb 2020). Analyses that focus on persons experiencing a lot of difficulty leave out persons with some difficulty who, as shown in this paper, are also at risk of deprivations.

In addition, the range of development indicators on the radar screen of international and national organizations to monitor the SDGs for persons with disabilities should be expanded to more comprehensively monitor disability inequalities. Results in this paper suggest that considering indicators in addition to the SDG indicators such as the employment population ratio is necessary to monitor disability inequalities. Further, there is a need to include information on environmental barriers that could answer the question of why disparities exist.

Finally, for progress to continue in research and data analysis, internationally comparable disability questions need to become standard in censuses and general household surveys in LMICs, as well as in the monitoring systems of NGOs and governments. Results in this paper stress the need to produce disability prevalence and disability disaggregated statistics in