rafmacalaba/data-use-mentions-extended huggingface dataset:
remain in traditionally female sectors.<sup>3</sup> These findings are consistent with micro-level data results found by Campos et al. (2015) in Uganda and Alibhai et al. (2015) in Ethiopia who find that firms owned by women in male-dominated sectors, on average, are far more profitable than firms in traditionally female sectors. Next, we compare the female crossovers and non-crossovers earnings with two categories of male enterprise owners: men in male-dominated sectors and men who operate in traditionally female sectors. We document a hierarchy of earnings consistent with the presence of both horizontal and vertical segregation with men in male-dominated sectors on top; women in male-dominated sectors and men in female-concentrated sectors in the middle tier; and women in female-concentrated sectors at the bottom.

These findings are also consistent with cross-country evidence from the developing world. Research relying on World Bank enterprise surveys finds a significant gender gap in sales across countries in Sub-Saharan Africa, Eastern Europe and Central Asia, and Latin America (Bardasi et al. 2011; Islam et al. 2018). In SubSaharan Africa, sales of female-owned businesses are 31 percent lower than businesses owned by their male counterparts (Bardasi et al. 2011). The results for Eastern Europe and Central Asia are corroborated by a separate study that found majority female-owned enterprises to have lower sales and lower total factor productivity than those that were majority male-owned (Sabarwal and Terrell, 2008). Results of a gender gap are less conclusive when entrepreneurship is measured in terms of profits, number of employees, or business survival rates as opposed to revenues.

While existing literature examines industry-based differences in profitability for developing countries, this phenomenon has been less well studied for developed countries. Hence, we next examine the profit differentials across male and female firms by country grouping.<sup>4</sup> For developing countries, our previous ranking holds. However, in developed countries we find that while female crossovers do earn more than female non-crossovers, male firms in female- and male-dominated sectors
The results suggest that membership in government-supported programs contributes little to reducing starvation during monga, but does help to reduce hunger during the non-monga period. Perhaps this explains why safety-net support during monga was insufficient to substantially reduce seasonal hardship, but did help to alleviate it, albeit inadequately, during the non-monga period. Nonetheless, both sets of results clearly show that safety-net programs affect both transitory and chronic poverty, which is consistent with the findings of Devereux (2002) for poor African countries and unlike those of Ravallion, van de Walle, and Gautam (1995).<sup>7</sup>

# **6. Summary and Conclusion**

The basic objective of this paper has been to assess the role of social safety-net programs in mitigating seasonal deprivation (monga) using recent survey data of hard-core poor households in the greater Rangpur region of Bangladesh. We have also explored whether safety-net programs, which are overwhelmingly seasonal in nature, can avert the consequences of seasonal shocks or seasonality of income and employment. Although the extent of program coverage is limited by the availability of funds, we find that programs, where in place, are well-targeted. However, program inputs are not distributed in accordance with the perceived notion of area-specific poverty incidence. We also find that some programs, such as Old Age Allowance, are year-round, reaching the most vulnerable groups (e.g., the elderly). Interestingly, the distribution of program benefits is not random but is determined by a host of factors, including a household’s physical and human assets, plus the availability of alternative programs (e.g., microfinance in the vicinity of potential program beneficiaries).

Interestingly, the results of our impact assessments show that safety-net programs have a positive effect on mitigating monga. More importantly, the programs mitigate both seasonal and

> 7 We also tried to differentiate the potential effects of short-term versus long-term safety-net measures; however, the sample size for long-term measures was too small to run separate regressions and thus separate estimates
). Throughout the paper, we consider the distance between an individual or a household and the nearest road as the distance between the centroid of the cluster in which that individual or household is located and the nearest road. Similarly, in the WBES, the distance between a firm and the

> 4More details on this sample are displayed in Table (A3) in the Online Appendix.

> 5The LSMS component of our final data set is summarized in the Online Appendix Table (A4).

> 6Figure (A1) in the Online Appendix illustrates the dynamics of the road network of three countries in Sub-Saharan Africa over the period from 1965 to 2014.

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a 10-year sovereign bond during the month. “Domestic exposure” is the ratio of the bank’s total holdings of domestic sovereign bonds to the bank’s total assets (divided by 1000). A constant term is included (coefficient not shown). Standard errors clustered at the bank level appear in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. Sources: ECB Individual MFI Balance Sheet Statistics, European Banking Authority, Bankscope, Bloomberg, SNL Financial, and Datastream.

# 9 Discussion and conclusions

This paper reexamines the theoretical relationship between the expectation of a government bailout of bank debtholders (depositors and bondholders) and bank risk taking. After the Global Financial Crises, the regulatory policy debate has centered on how to make ...nancial institutions safer and more resilient to shocks. Within that debate, the classical argument that explicit (typically deposit insurance) or implicit (typically the expectation of a public bailout of bondholders) government guarantees on bank liabilities entail a moral hazard e¤ect that increases bank risk taking has played a prominent role. Yet, this argument critically relies on debtholders and depositors’ability to price bank risk at the margin. Put di¤erently, it requires the cost of bank liabilities to re‡ect changes in bank behavior. In contrast, absent this ability, the presence of government guarantees improves bank incentives by reducing the cost of their liabilities and hence increasing their charter value.

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the two-step method (Kim et al. 2020) described below to decompose consumptions into quantities imported from all COOs. The imported quantities are proportional to COOs’ shares of total domestic supply, which can be obtained from FBS and TM data sets.<sup>5,6</sup> STEP 1. Using FBS trade data to allocate the share of each FBS item _f_ consumed in country _c_ (Bangladesh) between imports and domestic production. i i i <u>f</u> %i i i i = i i i i + i + i i i i f,cc ii ff,cc STEP 2. Using the share of TM items iidd i ppddi _t_ exported from iippff,cc iiddss ddhaappaai _e_ into _c_ to allocate the imported share of ff,cc iiff,cc − iieei iiff,cc _t e_ into _c_ FBS item _f_ among exporting countries.

> 4 All the GLEAM-i data in this work refer to the data acquired from FAO’s GLEAM-I tool version 2.0 accessed in December 2017 and made available in Kim et al.’ s supplementary data.

> 5 FAO. FAOSTAT Trade Matrix database. Available at: https://www.fao.org/faostat/en/#data/TM, accessed 24/12/2021.

> 6 FAO. FAOSTAT Food Balance Sheet. Available at: https://www.fao.org/faostat/en/#data/FBS, accessed 24/12/2021.

6
explaining output per worker, they do not directly address the question of what constitutes intangible capital.

In Section 4, we directly investigate the composition of intangible capital, by analyzing the relative contributions of human capital and institutional/social capital using regression analysis. As in World Bank (2006), potential correlation between the regressors and the error term (arising, for example, from measurement error or omitted variable bias) is an issue in the empirical estimation. The intangible capital residual includes, by construction, (i) any assets not accounted for in the tangible capital estimates, for example, some minerals (e.g. diamonds, platinum), fisheries and groundwater, not accounted for in the calculation of natural capital, and (ii) any errors in the estimation of tangible capital and/or of total wealth.

Unlike World Bank (2006), we have a panel dataset with observations for 115 countries for the years 1995, 2000 and 2005. The use of country and time fixed effects helps us to mitigate omitted variables bias as long as the unobserved variables are constant over time and/or across countries. In addition, we take a number of steps to reduce measurement error in the variables used in the analysis. Our indicator of intangible wealth accounts for net foreign financial assets; we use several indicators of human capital that are a function of health status in addition to years of schooling; we analyze the robustness of the results to alternative measures of institutional capital and to the exclusion of outliers. Finally, we estimate an instrumental variables regression in which differences in European settler mortality rates (from Acemoglu, Johnson and Robinson, 2001), distance from the equator, and the percentage of the population speaking a Western European language as their first language today (from Hall and Jones, 1999), are used as instruments for current institutions and human capital. Overall, our results show that human capital is an important factor, in both statistical and economic terms, in explaining intangible capital.

# *
business environment constraints.

The World Bank Enterprise Surveys provide a large set of objective measures of business environment constraints. In addition to subjective information on access to finance as an obstacle, the questionnaire also collects objective information on aspects of financial access, allowing us to create several variables: _Loan_ is a dummy variable indicating whether a firm has a loan or line of credit from a financial institution or an overdraft facility _. Credit constraint_ is a dummy variable indicating whether an establishment did not apply for loans or lines of credit for one or more of the following reasons: application procedures for loans or lines of credit are complex, interest rates are not favorable, collateral requirements are too high, the size and maturity of loans are insufficient, getting bank loans requires making informal payments, or the establishment did not think its application would be approved.<sup>4</sup> _Sales credit_ is a dummy variable indicating whether the firm has positive purchase of its material inputs or services paid for after delivery (about 70 percent of firms

> 3 ln(1+ _X_ ) is considered approximately equal to ln( _X_ ). We therefore use ln(1+ _X_ ) to compute the log of the number of employees, since some firms have zero employees in a specific year but not in both years.

> 4 No dummy variable is included for firms applying for new loans or lines of credit whose applications were rejected because this information is available for only 14 percent of firms in the sample.

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# **5 Annex: Detailed RPSR Indices**

- A. Generation and transmission planning index for Uganda and comparators, 2015

||**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Generation Planning**|86%|43%|43%|43%|56%|
|Countryhas ageneration masterplan|||||94%|
|Countryhas an overall energy plan|||||65%|
|Competent entityis responsible forproducingtheplan|||||88%|
|Inter-governmental committee oversees theplanningunit|||||29%|
|Powergeneration systemplan is mandatory|||||19%|
|Plan leads to timelyinitiation ofprocurement|||||38%|
|Planning process is transparent andparticipatory|||||59%|
|**Transmission Planning**|75%|100%|75%|75%|72%|
|Competent entityis responsible forproducingtheplan|||||100%|
|Explicitlylinked topowergenerationplans|||||88%|
|Plan is mandatory|||||29%|
|Planning process is transparent andparticipatory|||||71%|

- B. Generation and transmission procurement index for Uganda and comparators, 2015

||**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Procurement of Generation**|100%|50%|100%|95%|85%|
|There
# **3 Data and Empirical Specification**

## **3.1 Data on Child Health**

I use micro data on infant and neonatal mortality from country-specific DHS (DHS Program, 2015). All other information on children and mothers is also obtained from the DHS. The survey questionnaires are standardized which allows for cross-country analysis and each survey consists of a nationally representable sample of women of childbearing age (15 to 49 years). The women answer detailed questions regarding their personal characteristics, family situation, the survival of their children, etc. In these surveys, mothers give detailed information on the birth dates and death dates, if applicable, of all their children ever born (up to 20 children) so that a panel of children’s infant and neonatal mortality can be obtained. One country survey is thus enough to secure a panel of children. For the purpose of analyzing the effects of debt relief under the HIPC Initiative I select all 31 HIPCs for which there is at least one survey available between the country’s Decision Point date and mid-2014 and all countries that participate in the IMF’s PRSP approach to economic and social development or are Sub-Saharan African or Latin American countries where at least one survey has been conducted between 2004 and mid-2014. If there are more than one survey that fulfill these criteria, I choose the latest available survey to maximize the number of post-Completion Point observations. Table 2 presents a complete list of the 56 countries in the sample and information on the DHS used in the empirical analysis.<sup>5</sup> The main child health outcomes in this paper, infant mortality and neonatal mortality, are defined as a baby dying before reaching his or her first birthday or month of life, respectively. Children born less than twelve months prior to their mother’s interview are dropped since it is not possible to know whether they will survive their first birthday. Also, only children born after their country’s year of independence are included in
300|
|Footwear and Leather Products|129,231|<br>48,101<br>|<br>239,700<br>|
|Wood and Paper Products|524,402|<br>1<sup>64 </sup>595b|2,642,700|
|Chemical Products|177,920|<br>157,170|<br>9b3,100|
|Rubber and Plastic Products|120,866|<br>53,363|585,000|
|Stone, Clay and Glass Products|311,361|<br>100,714|<br>588,800|
|Metal products|429,539|206,509|2,505,800|
|Electrical Machinery|170,425|<br>114,382|1,523,600|
|Machinery and Transport Equipment|<br>595,580|<br>178,678|3,571,200|
|Other|146,260|<br>34,113|<br>893,200|
|Total Manufacturing|3,671,656c|1,674,340d|17,173,800e|
|Total Population|104,851,000|60,153,000 2|15,973,000|

Ratio of Total Engaged in Manufacturing to Population (percent) 3.50 2.78 7.95

- (a) employment in the footwear industry (95,358 employees) was reallocated from wearing apparel to footwear and leather.

- (b) includes 25,989 employees in petroleum refining which are not covered by the industrial census Resumen General, but taken from SPP, 1981.

- (c) excludes 152,682 employees in head offices and auxiliary activities.

- (d) excludes 69,448 employees in head offices and auxiliary units.

- (e) excludes 1,128,400 employees in administrative offices and
Following completion of the technical training, treatment A and C individuals received a certificate and then graduated from the program after conclusion of the on‐the‐job training sessions. Participants in groups B and C were invited to form business clubs composed of members of their own choosing from the pool of program beneficiaries. The aim of the business clubs was to raise repayment rates, although group liability was not enforced because individuals who may have participated in a club could establish their own businesses and obtain secure microloans individually. After the business club participants in treatment groups B and C had completed their training, HELP‐SL was to assist them through microfinance facilitation. However, the Ebola crisis disrupted the implementation of this initiative, thereby limiting the intervention’s relief of capital constraints to the cash stipend provided to participants in all three treatment groups.

Competing hypotheses posited by local and international policy makers about what interventions might be effective in relieving constraints to youth employment informed the design of the three packages, although the lack of a labor force survey since before the civil war limited knowledge about the current labor market dynamics. The first hypothesis was that youth lacked the job‐specific technical skills that were necessary if they were to be absorbed in the labor market, preferably in wage work. The second was that youth lacked the combination of entrepreneurial skills and capital that would enable them to engage in a business on their own, for example, by creating and expanding a household enterprise. The third was that capital was the binding constraint, and the impact of a capital infusion should therefore be tested alone against the impact of training; however, testing this hypothesis was infeasible from a political economy and operational perspective.

The impact evaluation baseline data collection was carried out between August 26 and November 14, 2013, during program enrollment and covered the 3,319 shortlisted individuals who participated in the lottery process. The randomization yielded balance between the treatment and control groups (appendix B). Of the 23 variables tested, only three main differences were
**Figure 6:** Share of the growth of LE in the growth of _PALE_ 1 and _PALE_ 0 _._ 5, 19902019

<!-- Start of picture text --> Year<br>LE growth contribution, θ = 1 LE growth contribution, θ = 0.5<br>80<br>70<br>60<br>50<br>%<br>40<br>30<br>20<br>10<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Reading: in 1991, the growth of life expectancy contributed to 10% of the growth of _PALE_ 1 and to 25% to that of _PALE_ 0 _._ 5.

Turning now to deprivation as captured by the _EDθa_ ˆ index, Figure 7 reports the evolution of _ED_ 1 _,_ 70 in the world during the same period.<sup>24</sup> The evolution of _ED_ 1 _,_ 70 illustrates the vast progress made against deprivation at the world level. While a newborn expected to have 58% of its normative lifespan either lost to mortality or spent in poverty in 1990, this expected loss falls to 18% in 2019. Unlike PALE, ED can be directly decomposed into each of its components, as indicated in the graph. In particular, the share of the lifespan deprivation component increased over time: from 30% in 1990 to 51% in 2019. As a result, the neglect of mortality in most poverty measures amounts to missing an increasing and significant share of total deprivation.

# **4.3 Improving on comparisons based on the headcount only**

Our indices also allow for comparing countries in which life expectancy and poverty evolve in opposite direction, as in the Pakistan-Bangladesh comparison presented in Table 2 or in the comparison between the years 1990 and 2007 in South
the synthetic household data relies on detailed Census data, which is not available everywhere, as well as being very computationally intensive for large countries. As such, the quality of the synthetic household output, including which variables can be considered, is dependent on the level of detail of input data, in particular the socioeconomic survey data. We present a method, using a Small Area Estimation technique, to add an additional variable that is not originally in the Census data to the synthetic household data. However, this might not be feasible for other variables or using different survey data. As such, moving forward, less complex versions of the methodology presented could be developed and tested, such that it can be more universally applied.

In future work, the methodology could be enhanced by 1) incorporating socioeconomic projections and associated changes in exposure and vulnerability to enable a more comprehensive future scenario analysis, 2) employing a stress-testing approach to better capture uncertain hazard inputs and cross-correlations and spatial dependence between hazards and infrastructure networks, and (3) test portfolios of interventions to evaluate their synergies and trade-offs, (4) more directly incorporating the health status of households, and (5) incorporating employment and household income as additional household characteristics that could be affected after disaster impact.

Overall, our work demonstrates the added value in terms of improving quantitative estimates of hazard-induced welfare impacts. First, the synthetic household survey data enables an 35
Figure 10. Average annual GDP growth by country group <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>RR Non-RR Rest of the World RR<br>Pre boom Boom Post boom<br>%<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012). Source: author’s calculations based on World Bank data.

Following the boom, the group averages fell to 2.7% and 3.8%, respectively. Here the drop was biggest in the resource-rich group, and fell to below the pre-boom average growth rates. This indicates the average effects saw a bigger boom for non-resource-rich Africa, as compared to the resource exporting countries in the region. While the average effects mark a bigger bust for the resource exporters. However, if we account for population growth in these countries, GDP growth rates look less favorable. After the boom, resource-rich Africa’s GDP growth did not keep up with population growth and resulted in negative GDP growth per capita (Figure 11).

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