rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 3.  Interest Rate Simulations for Uncovered Debt Ratios**

<!-- Start of picture text --> Uncovered Debt Ratios Simulations<br>80.00%<br>70.00%<br>60.00% Baseline<br>50.00%<br>40.00% Interest rate shock 4%<br>30.00%<br>20.00% Interest rate shock 8%<br>10.00%<br>0.00%<br>5.<br>1. Mining 2. Industry Hotels Energy<br>8. Financial<br>3. General<br>Trade/Services 4. Construction Transportation 6. Tourism and 7. Utilities and<br><!-- End of picture text -->

# **4.2 Shocks to Earnings**

In addition to stressing the interest payments, corporate data allows for simulations and stress tests of shocks to corporate earnings. The EBITDA is the key ingredient in the calculation of interest coverage and uncovered debt ratios discussed above. Shocks to corporate earnings, i.e. to EBITDA, will be directly reflected in corporate ability to repay their debts.

While the interest rate shocks were the same as in bank stress testing scenarios, the earnings shocks are not directly comparable to macro economic bank stress testing scenarios. Therefore, we develop separate scenarios for earnings shocks based on historical volatility of earnings. Table 9 reports historical median growth rates in EBITDA by sector. The firm-level yearly EBITDA growth is calculated, then the sector-level median is reported for each year.<sup>11</sup> One observation from Table 9 is high volatility of EBITDA growth – with wide swings between periods of high growth, or high contraction.<sup>12</sup>

> 11 Table 4 reports nominal growth rates. Inflation in Jordan was ranging between 1.5-6% in the years in the sample. Thus
2017), is that there is a systematic relationship between TFP growth measured using only factor data on capital and labor, and TFP growth that also measures natural resources as a factor of production. If we denote TFP growth excluding natural resources as g���� and with natural resources as g����, it will be shown below that Where s� is the GDP share of natural resource rents, while g� and g� are the growth rates of fixed capital and natural resources measured in volume terms. Given the high resource dependence of developing countries, this formula suggests there are potentially significant biases when measuring TFP growth in developing countries while ignoring natural resources as production factors.

# _Analytical framework_

The framework we use for estimating TFP growth with natural resources as a factor of production is inspired by the recent work of the OECD. In what follows we adapt the approach 4
World Bank Gender Data Portal|
|**Labor force participation rate for females with intermediate education (% of**<br>**female working-age population with intermediate education)**|World Bank Gender Data Portal|
|**Labor force participation rate for females with advanced education (% of**<br>**female working-age population with advanced education)**|World Bank Gender Data Portal|
|**Gender wage gap (unadjusted)**|ILO & OECD|
|**Wage and salaried workers(or employees), female(% of female employment) **|World Bank Gender Data Portal|
|**Female share of employment in senior and middle management(%)**|World Bank Gender Data Portal|
|**Attitudes with respect to whether men should have more right to a job than**<br>**women whenjobs are scarce**|World and European Values Surveys|

# _4.2 Estimation results_

Considering the correlation between leave policies and women’s labor force participation, table 2a shows that a higher gap between the leave allocated to mothers versus fathers is negatively correlated with women’s participation in the labor market. The absolute number of leave days allocated to mothers is also negatively associated with women’s participation in the labor market. If we consider the association between leave policies and labor supply disaggregated by education, the results are not intuitive. A larger leave gap is associated with a greater labor force participation rate for women with the lowest levels of
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For vocational high schools, amortization was calculated from cast figures of the Third Education Project, assuming an economic life of twenty years for buildings and five years for equipment. For other Institutions, we used amortization figures calculated by ONFP.

No cost calculations were made for adult training centers, because the available data base was much weaker than for the other institutions. Moreover, courses offered by these centers range from a few days to several months. Thus, a calculation of costs per student is really meaningless, and instead costs should be calculated per student-day (or even per student-hour, since many courses are only held for a few hours per day). Data on the total number of student-days or student-hours are, however, not available (see also Appendix 4).

The post-secondary level cost data are those for the National Institute of Higher Technical Educaion (INSET), which offers curricula in many different fields. INSET is an independent institute with its own budget, from which were derived the figures in Table A3.2. The calculation of housing allowances and technical assistant salaries used the same assumptions and sources as for the secondary level. Scholarship figures are only known for INSET and IPNETP combined, and they were allocated on the basis of number of students. Amortization figures are those of ONFP.

For teacher training, three types of institutions were distinguished: training centers for primary school teachers (CAFOP/ENI),
reaches companies in the form of debt, it tends to increase their indebtedness and default risk (Myers (1997); Hennessy et al. (2007)).<sup>13</sup> Thus, matching support instrument with the dominant channel of transmission of the shock for the firm is critical in making firm support effective.

Figure 10: Relationship between access to public support and key firm-level outcomes <!-- Start of picture text --> Public access and expected change in sales Public access and financial fragility<br>SAR countries SAR countries<br>20 1.2<br>1<br>0<br>.8<br>.6<br>-20<br>.4<br>-40 .2<br>0 .2 .4 .6 0 .2 .4 .6<br>Average predicted probability of accesing public support Average predicted probability of accesing public support<br>Expected change in sales<br>Probability of falling in arrears<br><!-- End of picture text -->

# **5. Conclusion and policy recommendations**

Firms in South Asia had a disproportionately larger impact of the pandemic. Evidence from Business Pulse Surveys conducted by the World Bank in collaboration with the IFC in South Asian countries suggests that only two-thirds of the businesses are currently open in the region compared to 80% in other non-high income countries. The average revenues among these businesses have declined by 64% relative to a 46% decline in other non-high income countries. Results also point to the elevated financial fragility among South Asian firms and the dwindling confidence about their future during the crisis. BPS results show that nearly two-thirds of the South Asian firms are likely to fall into arrears compared to 43% in other non-high-income countries. South Asian firms also have a more pessimistic outlook such that they expect their sales to decline by 19% relative to the expectation of an 8% decline in other non-high income
**|-1.323***|-1.354***|
||(0.039)|(0.040)|(0.182)|(0.185)|
|Observations|4072|4072|2142|2142|
|Wald_F||0.159||1.883|
|Wald_p||0.691||0.173|

Coefficients and standard errors from linear models of the primary outcomes on intervention group, fixed effects for randomization strata, fixed effects for child sex, and intervention-by-sex interaction terms. Standard errors are clustered by cluster (group of villages). There were 121 clusters in total. The WASH institutions index was calculated by rescaling each variable in the index (e.g., presence of WASH committee) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units. The Wald F is a test statistic for the null hypothesis that all of the interaction term coefficients are zero. * p<.1, ** p<.05, *** p<.01"
particularly large, with over 160 employees at inception, appear to roughly double their size early on, then stabilize, meaning that in a few short years they have added nearly 180 jobs, more than tenfold the raw increase by firms that are born as micro.

Lastly, table 7 displays the same base models, with the (log) wage bill — that is the total remuneration cost, including wages but also social security payments — per worker. In line with several other instances in the literature on the wage-size-effect, the model controls for the proportion of workers with secondary-level education, which is positively and significantly associated with a higher wage bill per worker, though this coefficient loses significance when micro firms are excluded, pointing to a segmentation of the very smallest firms. Similarly, the correlation between the average wage bill and labor productivity is positive. However, the size story is less consistent. Though large firms do enjoy a “wage premium” relative to other firms, the story is not consistent (that is relative to the omitted micro category) across all increasing size groups.

# **V. Conclusion**

Taken broadly, Ghana has been experiencing sustained economic growth, which has been bolstered by recent oil production. What is more, evidence from macro-level data shows that recent growth has not been jobless, showing expansions of employment at pace with GDP growth. Despite this growth, the formal private sector measured through the WB ES 2013 data is small and in aggregate, the number of jobs in the formal sector is still negligible compared to the size of the labor force measured through the national household survey (GLSS6) in the same year.

Indeed, using micro-level evidence affords the opportunity to disaggregate these effects. One recent account argues that employment growth lagging GDP expansion had been the result of labor being absorbed into slow-growing industries (Aryeetey and Baah-Boateng 2015, World Bank 2016); the evidence presented here supports that finding, indicating that these sectors may be lagging behind in their absorption of labor relative to their
the degree of substitutability across varieties of a given origin country is stronger than that between origin countries—see Crowley et al. (2022). In these settings, DTAs may have very different consequences on the export performance of large and small firms at destination, with large firms unambiguously benefiting the most from the trade agreement.

The impact of DTAs on small and less productive exporters also depends on whether the new agreement has an impact on the market structure of the origin country. If the number of exporters is unaffected by the entry into force of a deep trade agreement, small incumbent exporters may benefit through an increase in exports even if these gains are milder relative to large exporters. Conversely, if the DTA has an impact on the market structure in the origin country by increasing the number of exporters, small exporters may also experience a reduction in their exports (and market share) at destination, and eventually may be induced to exit from the export market due to enhanced competition.

All in all, while DTAs are expected to have an unambiguous positive effect on the export value of large and high-productivity firms, the effect on small and less productive firms is an empirical question. The within-origin competition effect of DTAs can be so strong to offset the reduction of fixed and variable costs and induce the exit of small firms from the destination market. This effect has relevant welfare implications. If small and less productive firms exit from a specific destination market and large and high-productivity firms survive and thrive, resources are reallocated from low- to high-productivity firms. This selection effect implies adjustment costs, but also an increase in the average productivity of firms. The heterogeneous trade effects of DTAs on low- _versus_ high-productivity firms, and the consequent selection effect, is at the core of the empirical investigation in the rest of this paper.

# **3 Data and descriptive evidence**

Our empirical analysis is based on two main World Bank data sources: (i) the Exporter Dynamics Database (Fernandes et al., 2016) providing
2. _Tech/Sales_ (the Census category of “Technical, Sales and Administrative Support Occupations,” occupation codes 203-389). Dental hygienists (204) and computer programmers (229) are examples.

3. _Service_ (combination of the two smallest Census categories: “Service occupations,” occupation codes 403-469, and “Farming, Forestry and Fishing Occupations,” occupation codes 473-499). Childcare workers (406), firefighters (417), and waiters (435) are examples.

4. _Craft_ (the Census category of “Precision Production, Craft and Repair,” occupation codes 503-699). Automobile mechanics (505) and Drywall installers (573) are examples.

5. _Laborer_ (Census category “Operators, Fabricators, and Laborers,” occupation codes 703-889.). Printing-press operators (734) and truck drivers (804) are examples.

The only departure we make from the Census categories is to combine the last two categories into the ‘laborer’ occupation, since these two are quite small. In addition to sector and occupation, we group workers into two education groups, workers with no college education, workers with some college education.

# **2.2 The Switching-Cost Function**

We can now show how we implement the switching-cost function _Ct_ ( _i, k, j, l, s_ ) empirically. In principle, we could assume a different value for the moving cost for each value of ( _i, j, k, l, s_ ) and estimate each one, but this would create a vast number of parameters and make identification of those parameters impossible. Therefore, we need to parameterize the moving cost function somehow. We have attempted to build a specification that is rich but at the same time parsimonious, and have allowed for four types of effect on switching cost that might or might not turn out to be important in the data. First, we allow for the possibility that some industries or occupations are harder to get into than others. This leads us
specified in equation 1, where we model heterogeneity by GDP and an index of security. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the security index and columns labeled “high” evaluate the estimated model at the 90th percentile of the security index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

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variation in outreach. Section 4 shows the predictive power of our indicators relating them to household- and firm-survey based indicators of use of financial services. Section 5 examines the correlation of the outreach indicators with other country characteristics, as well as regulatory and policy variables. Section 6 relates the outreach indicators to cross-country survey indicators of firms’ financing obstacles. Section 7 concludes.

# **2.  Data: Indicator Sources and Definitions**

This paper presents a new data set that seeks to measure the access to and use of banking services across 99 countries in 2003-2004. Specifically, the objective of this dataset is to construct indicators of access to physical bank outlets and use of banking services (in particular credit and deposit services). For this purpose, we developed a questionnaire that we circulated among bank regulatory agencies across countries. The main questions from this survey focus on obtaining information on the number of bank branches, number of ATMs, and the aggregate number and value of bank loans and deposits.<sup>6</sup> For countries that did not provide responses to our questionnaire, we gathered data from alternative sources, including government publications and official websites. A detailed list of all the sources used for each country can be found in appendix Table A.1.

Our survey refers exclusively to deposit money banks – all financial institutions that have “liabilities in the form of deposits transferable by check or otherwise usable in making payments” (IMF 1984, p. 29) - for two main reasons. First, in a majority of countries, the banking sector intermediates most of the funds in the economy. Second, the banking sector is regulated and statistical information for this sector is easier to obtain and higher in quality than

> 6 We also included questions on payment transactions (value and number) and on the distribution by size of bank loans and deposits.  However, most countries were unable to provide answers to these questions; hence it is not possible to conduct a systematic analysis of these data.
robustness check, we assume that someone working in the 80th percentile at home earns only the median wage income abroad. The corresponding relative wage measure is denoted by _RelWage80to50ij,t_ .

For relative GDP and relative TFP between destination and source countries, denoted by _RelGDPij,t_ and _RelTFPij,t_ , respectively, we use Penn World Tables and the UNIDO World productivity database. In particular, GDP data have better availability than wage data such that the number of observations increases. Details on variable definitions, data sources, and the summary statistics of the employed variables are presented in the appendix (table A1).

As indicated, although recent empirical literature has focused on the impact of income differences on migration patterns, we aim to examine the opposite channel. In a first attempt to address endogeneity, we replace the high-skilled emigration rate in 2000 by the lagged one in 1990, denoted by _SMigij,t-1_ , in OLS regressions. Doing so allows for the possibility that the TFP effects of the migration flows of skilled workers (for instance, through innovation activity) take time to come into effect.

Second, we explore potential instruments for the high-skilled emigration rate for 2000. We use the lagged rate of expatriates in 1990 emigrating from country _i_ to _j_ , denoted by _TotalMigij,t-1_ , as an instrument for _SMigij,t_ , thereby predicting the rate of highskilled emigrants by the lagged rate of all emigrants. This approach is motivated by the notion that a larger percentage of emigrants from a certain source country already living abroad act as a signal to potential high-skilled migrants regarding the destination country’s openness and its administrative bodies’ treatment of foreigners. The presence of more emigrants to a certain destination creates mobility cost-reducing network effects 17
sector market structure, the institutional environment, and specific government policies to relax barriers to account use.<sup>25</sup> We include a number of variables to proxy for the cost of opening, maintaining, and using an account. _Cost of Opening a Bank Account_ , _Cost of Maintaining a Bank Account_ , _Cost of Direct Credit_ , and _Cost of Debit Cards_ are all central banks’ assessments of the costs of payment and associated services of these respective categories. All these variables are dummies that take the value 1 if the country’s central bank perceives the costs as medium to high, and 0 if it perceives them as negligible to low.<sup>26</sup> The data come from the World Bank Global Payment Systems Survey (World Bank, 2010). Ex ante, we expect all these variables to have a negative impact on the likelihood of using bank accounts.<sup>27</sup> We also include the dummy variable _Offer Basic or Low Fee Account_ , which takes the value 1 for countries where the government requires banks to offer a basic or low-fee account to low-income clients (CGAP, 2009). We expect this variable to be positively correlated with the use of formal accounts.

Documentation requirements are measured by information collected from regulators on “know your customer” (KYC) requirements to open accounts: (i) proof of identity through government-issued ID, (ii) proof of identity through any ID, (iii) proof of nationality or legal status in country, (iv) proof of address, (v) proof of income, and (vi) proof of employment. We

> 25 Our results excluding country fixed effects are also robust to the inclusion of lagged five-year average inflation. The coefficient is insignificant in predicting account ownership or high frequency of use. The coefficient for savings is significantly negative, but does not affect the significance of our country-level variables. Results available upon request.

> 26 To address our concerns on the subjectivity of
with cases. In addition, sewer and water burst density was based on spatiotemporally referenced complaints data, which is obviously a proxy measure with a number of reported and recorded issues. Data from direct inspections of sewer and water networks would have been a more reliable measure but such data was not available to the researchers.

The focus of this analysis has been to study what factors might be associated with a cholera outbreak and could explain the spatial heterogeneity in the outbreak. An area of future research would be to explore what may lead to some of these factors, especially sewer bursts, in order to be able to predict areas at risk for future outbreaks. Above all, this analysis shows the importance of emphasizing maintenance above and beyond new construction, especially when thinking of extending an already poorly maintained system.

# Acknowledgments

The authors acknowledge with thanks the financial support received from the Global Water Security and Sanitation Partnership (GWSP). The authors also acknowledge the cooperation of officials from City of Harare (CoH) Water and GIS Departments, in particular Toine Ramaker from VEI, Dr. Manangazira from the Ministry of Health, Zimbabwe; in country counterparts at the World Health Organization (WHO); and the Country Management Unit (CMU) of the World Bank in Zimbabwe, in particular then Country Manager Mukami Kariuki, Senior Operations Officer Tonderai Fadzai Naome Mukonoweshuro and Health Specialist Consultant Chenjerai Sisimayi for facilitating contact with counterparts and local dissemination of findings.

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