rafmacalaba/data-use-mentions-extended huggingface dataset:
br>National<br>20% 0.005<br>Urban<br>10%<br>US$1.90 PPP<br>0% 0<br>0 50 100 150 200 0 50 100 150 200<br>Total cons. per capita per day (Oct. 2016 SSP) Total cons. per capita per day (Oct 2016 SSP)<br>Poverty headcount<br>South AfricaNamibiaBotswanaZambiaCentral African RepublicLesothoSwazilandGuinea‐BissauRwandaCongo, Rep.BeninSeychellesSouth SudanCameroonMalawiMozambiqueComorosDjiboutiChadZimbabweNigeriaTogoAngolaMadagascarGhanaCongo, Dem. Rep.Cote d'IvoireUgandaSouth SudanSenegalBurundiTanzaniaMauritiusSudanBurkina FasoNigerSierra LeoneGuineaEthiopiaLiberiaMaliMauritaniaSao Tome and Principe<br>Kernel Density<br><!-- End of picture text -->

**Note: Figure 3 includes low income and lower middle‐income countries with poverty data post‐2008. All data for South Sudan refers to the seven states covered by the HFS.**

# **Source: Authors’ own calculations based on NBHS 2009, HFS 2015‐2017, World Bank WDI, and IMF World Economic Outlook.**

Such high levels of deprivation translate into widespread hunger and food insecurity. Disruptions to agricultural production and the near hyperinflationary increases in prices of most staple foodstuffs have left most households struggling to find enough food to sustain themselves (World Bank, 2018). Widespread fighting and large‐scale displacement over several consecutive planting seasons have disrupted many households’ normal agricultural activities, resulting in increasingly large production deficits each year and widespread food insecurity (FAO and WFP, 2017). This has had a devastating effect on livelihoods, given that except for a few oil enclaves the productive structure of South Sudan is one of a rural pastoralist society where more than 4 in 5 people practice subsistence agriculture (World Bank, 2018, 2016). Food security has continuously deteriorated since late 2012, sometimes even reaching famine conditions in certain vulnerable counties. During the most recent harvest season in 2017, a time

> 33 Data for real GDP per capita in 2011 PPP for South Sudan were obtained from the
employment during the second and third years of the foreign investment, this positive effect disappears during the fourth year, which suggests some sort of staff consolidation exercise contributing to productivity increases.

We also look for spillovers on local firms, stemming from the presence of foreign owned companies. Following Fons-Rosen et al. (2014), we define horizontal “competition spillovers” in narrowly defined sectors (four-digit classification), as the entry of highly competitive foreign owned companies is likely to impact on local competitors. At the same time, we define a “knowledge spillover” variable that captures foreign presence in the same two-digit sector, excluding output produced by foreign-owned companies in the same four-digit sector (which would be the direct competitors). We find that, even if there is a small and negative competition spillover impacting domestic companies in narrowly defined sectors, there is also strong evidence of large and positive knowledge spillovers to domestic companies in the broader two-digit sector. This is a very important finding, since Turkey is unique among its neighboring countries in terms of getting full knowledge spillovers from FDI for domestic firms that are not in direct competition with the multinationals. Thus, we argue that there is a strong case for the design of policies aimed at increasing foreign direct investment attraction in Turkey, with the aim of helping unleash economic development.

To reply to our second research question, how Turkey can attract larger FDI inflows, we build a panel of data for 16 Eastern European economies (and Turkey) over the period 1999-2012. Drawing from an eclectic theoretical framework based on existing literature, and following Sánchez-Martín et al. (2014), we include in our empirical experiment a series of macroeconomic, endowment and infrastructure-related, institutional, and risk variables, and conduct a series of cross-country estimations using fixed effects. In line with previous literature focusing on Eastern and Central Europe (Cartensen, 2004, Bevan, 2004), exchange rate stability, mobile phone penetration, government stability, law and order, privatization, and EU candidacy are all significant determinants
job types.<sup>43</sup> The CMMHH data on job and employment status transitions from before COVID to the winter of 2020 reveal that net levels of non- “PFPS” jobs were much better sustained than those of PFPS jobs. Job losses persisted into the winter, accelerating for non-PFPS workers slightly more than for PFPS ones to a higher level during the 60 days prior to the CMMHH survey. As shown in Table 5-4, 20.6 percent of temporary workers in the formal sector and 19.3 percent of informal workers were permanently laid off or suspended without pay relative to 15.1 percent of PFPS workers. Still, on a net basis, informal and temporary jobs in the _Table 5-4 Jordan: Labor Market Impacts of COVID-19 in 2020-2021 by Employment Type in March 2020 (Percentage of those Employed Pre-COVID Reporting Impacts in the previous 60 days)_

|By pre-COVID employment status in<br>2020|Temporary<br>layoff/<br>suspension<br>(withoutpay)|Permanent<br>layoff/<br>suspension|Reduced<br>hours|Reduced<br>pay|Delay in<br>wage|
|---|---|---|---|---|---|
|PFPS|5.7|15.1|14.1|10.9|15.8|
|Private sector formal non-permanent<br>workers|14.1|20.6|20.4|18.5|28.6|
|Private sector informal workers|18.9|19.3|21.0|23.6|23.3|
|Public sector workers|3.2|2.3|18.6|4.7|4.4|
|Total|8.8|10.9|18.3|12.2|13.4|

Source: CMMHH Jordan, 2021
# Figure 8. Policies to strengthen drivers of potential growth

## A. Investment growth

<!-- Start of picture text --> Percent 2011-21<br>10 Baseline (2022-30)<br>Investment surge (2022-30)<br>8<br>6<br>4<br>2<br>0<br>AEs EMDEs EMDEs Exp. Non-exp.<br>excl. CHN<br><!-- End of picture text -->

## C. Life expectancy

<!-- Start of picture text --> Number of years 2011-21<br>85 Baseline (2022-30)<br>Health improvements (2022-30)<br>80<br>75<br>70<br>65<br>60<br>AEs EMDEs EMDEs Exp. Non-exp.<br>excl. CHN<br><!-- End of picture text -->

## B. Secondary schooling completion

<!-- Start of picture text --> Percent 2011-21<br>100 Baseline (2022-30)<br>90 Education improvements (2022-30)<br>80<br>70<br>60<br>50<br>40<br>30<br>AEs EMDEs EMDEs Exp. Non-exp.<br>excl. CHN<br>D. Female labor force participation<br>Percent 2011-21 Percent<br>70 Baseline (2022-30) 40<br>Improvements in labor markets (2022-30)<br>30<br>60<br>20<br>50<br>10<br>40 0<br>AEs EAP ECA LAC SSA<br>EMDEs MNA (RHS) SAR (RHS)<br>EMDEs<br>excl. CHN<br><!-- End of picture text -->

## D. Female labor force participation

Sources: Penn World Tables, World Bank.

Note: AEs =
# **I. Introduction**

Economists have long explored how debt levels affect stock returns. The literature, dating back at least to Modigliani and Miller (1958), showed that in a frictionless world, the value of a firm is unaffected by how a firm is financed. In the presence of frictions, firms face a trade-off linked to the benefits and costs of debt financing (see Harris and Raviv, 1991).<sup>2</sup> The equity of a levered firm is riskier than the equity of an otherwise identical firm with less leverage. The value of equity falls more when the firm leverage is higher for a given fall in the value of the firm’s assets.<sup>3</sup> The shock caused by the Great Recession allowed for the analysis of the role of debt levels in mediating the impact of the Recession on firm performance.<sup>4</sup> In the present paper, we exploit the 2020 oil crash to explore the role of debt in mediating the effect of a fall in the value of the firm’s main assets, oil reserves, on stock returns of oil and gas firms in the United States.

Oil firms in the U.S. have accumulated a significant amount of debt during the period of relatively high oil prices. Data from Compustat reveal that the simple average total debt for all available U.S. oil firms jumps from about 25% of total assets during 2009-2014 to about 40% during 2015-2019. The COVID-19 pandemic and an oil price war sent oil prices to an all-time low in the first half of 2020. Amid the collapse in oil prices, commentators have raised concerns about the sustainability of debt accumulated by oil firms.<sup>5</sup> They argued that lower prices combined with high debt levels could lead to a cascade of bankruptcies.<sup>6</sup> Moreover, the size of the oil and gas sector has grown rapidly, raising
total assets growth (sales growth) for equity and bond users is on average 18 (20) percent per year, but only 9 (16) percent for nonusers. In India, total assets growth (sales growth) is 16 (17) percent per year for users of equity markets, 18 (18) percent for users of bond markets, and less than 6 (10) percent among nonusers. Notwithstanding the large differences in the median firm size, the growth rates of equity

> 23 Throughout the paper, we report the data in constant 2011 US dollars.

> 24 Although the size of total assets for the median firm without an issue seems small, this figure is consistent with that calculated by Allen et al. (2012) using the Prowess CMIE database.

20
within the same commodity categories, with the former using more capital-intensive technologies than the latter, leading to the patterns of comparative advantage observed in the data. This is consistent with the findings in Schott (2003).

In Table 8 we evaluate the goodness of fit of our preferred estimations for the sample of low human-capital economies and for the sample of high human-capital countries. The first set of columns reports summary statistics for the group of countries with a low skilled-unskilled labor ratio, followed by the high-skill sample. For each group of countries, the table shows the observed average net exports for each industry, the mean regression residuals reported in Table 6 for each group of countries, and the ratio of the mean residual over the observed net exports.

Table 8 suggests that the model does a relatively good job at explaining cross country variation in net exports. Most of the ratios of the average industry-specific residuals over the – observed average net exports are relatively small on average about 0.09% and 0.61% for the sample of low-skill and high-skill economies, respectively. Only for handful industries, the average residuals are larger than 4% relative to net exports; for example, the model overpredicts the size of net exports in Tobacco industry for low-skill economies and in Petroleum refineries and Glass manufacturing in high-skill economies, whereas high-skill economies are observed to have less net exports in Non-ferrous metals than the model predicts. Hence, we conclude that the model with heterogeneous Rybczynski coefficients seems to provide an acceptable set of predictions for both sub-samples.

# **4 Conclusion**

This paper makes two contributions to the literature. First, it extends the empirical literature on the estimation of factor proportions as determinants of international trade patterns to a large set of diverse economies, including developing countries. By relying on trade data, rather than on production data, and by following Leamer (1995), the estimation sample 14
# **2. Description of the survey and the dataset**

The World Bank Group (WBG) has developed a brief firm survey instrument to collect data measuring the impact of the COVID-19 pandemic on the private sector. The questionnaire _checks the pulse_ of businesses measuring the impact on some critical dimensions: operations of the business, sales, liquidity and insolvency, labor adjustments, firms responses, expectations and uncertainty about the future, and preferred mechanisms of public support. The appendix shows the standard version of the questionnaire.

In this paper we describe the results using harmonized data based on the implementation of the first wave of the business pulse survey in 51 countries over 6 regions.<sup>6</sup> In most countries interviews were conducted over the phone, but in a few countries such as Colombia or Turkey, the questionnaire was administered online. In 31 of these countries, a fresh sample of businesses was collected and the survey was implemented in collaboration with private sector associations, statistical agencies, and other government agencies (mainly Ministries of Finance and Economy). Data for the remaining 20 countries were collected as a follow-up of the World Bank Enterprise Survey, using a questionnaire that excluded some questions from the standard version.<sup>7</sup> The data include micro, small, medium, and large businesses, across all main sectors (i.e. agriculture, manufacturing, retail, and other services, including construction). Micro-businesses account for 51% of observations in the cross-country data; only around 8% of observations report more than 100 employees before the COVID-19 shock (January 2020; Figure A2). Most businesses in the data are in wholesale and retail (28%), manufacturing (17%), and food preparation services (15%; Figure A3). The sampling frame in some countries excluded micro-firms and/or businesses in agriculture (see Table A2 and Table A3) and in some countries, when micro-businesses were included, the survey instrument offered simplified
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)|(0.004)|(0.004)|
|tc<sup>(12+)</sup>||||0.000296<br>(0.002)|
|E[Enroll_|_control,year_>_97]|0.683|0.693|0.693|0.693|
|E[Enroll_|_treat,year_>_97]|0.767|0.778|0.778|0.778|
|E[tc_|_treat,year_>_97]|6.897|6.863|6.863|6.863|
|E[tu_|_treat,year_>_97]|2.867|2.896|2.896|2.896|
|E[famsize]|6.609|6.609|6.609|6.609|
|Observations|21321|17432|17432|17432|
|R-squared|0.153|0.116|0.121|0.121|

_Note:_ Standard errors are clustered at the locality level and are presented in parentheses. * _p <_ 0 _._ 10, ** *** _p <_ 0 _._ 05, _p <_ 0 _._ 01. Dependent variable: _Enrollit_ . Sample: unbalanced panel of children aged 12-15 years in 1997-1999 from eligible (poor) households with two parents and one parent reports to be the head of the household. tc denotes the weekly, per-capita transfer offered to child _i_ in year _t_ . tu denotes the weekly, per-capita transfer offered to child _i_<sup>_′_</sup> _s_ siblings under the age of 12. tc<sup>(12+)</sup> denotes weekly, per-capita transfer offered to child _i_<sup>_′_</sup> _s_ siblings aged 12-15 years. _y_<sup>_f_</sup>
# 5.2 Beyond Labor Income

The above results find no evidence of poverty traps in Mexican labor income. One potential concern is that some of the households with very low or zero labor income could be non-poor due to other sources of income, such as autoconsumption, transfers, rent and interest. As these households are not poor, we would not expect to find them in a poverty trap. However, their inclusion among other households with similar labor income levels who rely much more on labor income in the estimation of income dynamics is likely to reduce the chance of detecting poverty traps for this latter group.

We therefore examine the robustness of our results to the use of more comprehensive measures of household welfare. We use data from Mexico’s National Income and Expenditure Survey, the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH). This survey has been taken biannually since 1992, and we use the six rounds from 1992-2002.<sup>11</sup> To allow direct comparison with the urban employment survey, we use only data from cities of population 100,000 and above. As with the ENEU data, we restrict our analysis to households with heads aged 25 to 49, and define cohorts based on five year birth intervals of the household head within three education groups. This results in a sample size of between 3,000 and 4,000 households in each round. Further restricting the number of observations per cohort in a round to be at least 100 results in a pseudo-panel of 19 cohorts observed over 6 time periods.<sup>12</sup> We consider four measures of household welfare. The first measure is total household labor income from the principle occupation of each member, which corresponds to the measure we have used in the ENEU. The second measure is total monetary income of the household, which includes household income from

> 11 Earlier rounds of this survey were also conducted in 1989 and 1984. The inclusion of these surveys would require estimating income dynamics over
<!-- Start of picture text --> Production and capacity utilization rates Manufact eopany pezation ourabl<br>110 . anufacturing: Durable and Non Durable<br>United States, 1972-2011 85 105<br>Latte oO= 100 * At Fi<br>= 95 1. ot<br>90 “ ee" . 753rn 90 a ao ry "| XY<br>: f° He here<br>y Oo 85<br>80 we 70<br>ans vu<br>Fa 80<br>a<br>70 69 75<br>2006-06 2007-06 2008-06 2009-06 2010-06 2006-03 2007-03 2008-03 2009-03 2010-03<br>— Production, Total index, base 100 in 2007 — Manufacturing<br>Production, Durable manufacturing, base 100in 2007 Durable manufacturing<br>beastaesanaee i ilizati i seeeeeees Nondurable manufacturin;<br>Capacity Utilization Rates, Total Index (right) Source: Board of Gavemrorsofthe red i<br>Employment Levels Since Peak in January 2008 Employment Levels Since Peak in January 2008<br>Total Employment in Private Sector: Goods vs Services Manufacturing and Construction<br>;<br>2 LO —PePe _<br>a SX a<br>" NO —<br>2008m1 2009m1 time;; 2010m4 2011m1 2008m1 2009m1 time;; 2010m1 2011m1<br>— Total Private ————— Goods Producing —— Manufacturing ——— Construction<br>——— Services ——— Durable goods ———— Non Durable goods<br>Source: author's calculations based on Bureau of Labor Statistics Source: suthor's calculations based on Buresu of Labor Statistics<br><!-- End of picture text -->

<!-- Start of picture text --> Employment Levels Since Peak in January 2008<br>Manufacturing and Construction<br>2 LO —PePe _<br>SX a<br>NO —<br>2008m1
# _Rainfall_

Monthly precipitation data for a network of 171 rainfall stations and their respective geo-coordinates were obtained from the Department of Hydrology and Meteorology in Nepal. Since each of the DHS sample clusters is geo-coded as well, we are able to match each child to a precise time series of monthly precipitation. The location of rainfall stations and DHS clusters are shown in Figure 1. Geographic coverage of the rainfall stations is fairly expansive and well-balanced throughout Nepal. Though there are fewer stations in the northern, mountainous, parts of the country, these are also areas with sparser population.

A key task is to match precipitation records from the weather stations to each of the DHS clusters. The usual practice in the literature is to match each locality – a cluster or the geographic center of an administrative unit – to the geographically closest weather station (Yang and Choi, 2007; Maccini and Yang, 2009; and Menon, 2009). However, this approach may give rise to inaccuracies if there is high prevalence of missing records in the data, stations have been affected by site relocation, or if cluster and station differ considerably in terms of topographic characteristics (e.g., elevation). Instead, therefore, we predict climate or weather based on a station’s observable characteristics (latitude, longitude, elevation, and interactions among these variables) and impute values for each of the DHS clusters based on their corresponding characteristics (Nordhaus et al., 1994, use a similar procedure).

We calculate normal rainfall for every month using the average precipitation in that month over the 26 year period 1972-97. Month-to-month precipitation patterns are broadly similar across the four major regions and topographic zones of Nepal as seen in Figure 2.

8
stylized model:

> component analysis of the asset list. Dividing the divisible assets by the squared root of household size as an equivalent scaling rule does not change figure 1 in any qualitative way.

> 17. This figure is remarkably similar to the one reported in the 2013 Global Corruption Barometer: 37% of households in Moldova that came into contact with education authorities paid bribes in the 12 months before the survey (Transparency International <u>2013). We focus on the likelihood of paying informal fees rather than the values</u> paid since we assume the decision to participate in the informal fee scheme to be the most affected by a change in preferences. Note that we added one LCU to each private expenditure to ensure that the log exists.

> 18. See figure S.2 for an illustration.

9
|x|

NOTES: Sample of second- and higher-order births. Each column is a separate regression, with the same specification to column (3) in Table 3. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE. The vector _Xijt_ comprises mother’s age at birth, household wealth quintiles, caste, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

66
gender gap is not statistically different from zero in either the unconditional or the conditional specification. However, this average effect is hiding important heterogeneity across sectors. Women-led businesses in hospitality are comparatively more likely to report falling in arrears (a statistically significant gap of 6.4 pp). We also find that the gap is large and significant and to the disadvantage of women among businesses in the third quartile of the mobility drop (3.7 pp), but not among countries in the top quartile.

16The World Bank Gender Data Portal.

15
48 percent are both poor and below the Sisben threshold (which should be close to the actual eligibility requirement), more than 15 percent of the beneficiaries are neither.

**Table 4: Share among Beneficiaries in Groups according to the ENCV 2013,** **<u>percent</u>**

|Sisben eligible|71.65|
|---|---|
|Poor|60.59|
|Indigent|23.64|
|Sisben eligible or poor|84.25|
|Sisben eligible andpoor|48.00|

To obtain a better grasp of the role played by the eligibility criteria, we have run simple kernel regressions. Figures 1 and 2 show the relationship between the actual receipt of the program benefit by an age-eligible person and the household-level Sisben III score. Figure 1 shows the regression for each year, and figure 2 the pooled results across all years, but by area. The threshold values are 39.32 for residents of the 14 major cities, 43.63 for residents of other urban areas, and 35.26 for rural areas.

**Figure 1: Kernel Regression of Program Receipt on the Sisben Score, by ENCV Round**

11