rafmacalaba/data-use-mentions-extended huggingface dataset:
The fast and broad-based debt re-accumulation from 2013, and the increasing exposure to market risks raises concerns about debt sustainability. The number of Sub-Saharan African countries at high risk of debt distress, based on the LIC Debt Sustainability Framework (DSF), increased significantly, from eight countries in 2013 to eighteen by the end of 2018.

This section discusses key trends of public debt in Sub-Saharan Africa drawing from annual data of 45 countries in the region.<sup>2</sup> For each of these countries, public debt refers to general government gross debt. Public debt is evaluated at three points in time: 2007, after the delivery of the first wave of debt relief under HIPC and MDRI to 17 of 30 countries; in 2013, before the plunge in commodity prices in 2014 and the year that marks a turning point in debt dynamics changing noticeably; and in 2018, for which the most recent data for most of the countries are available.

# **2.1. Public Debt Dynamics**

From 2013 to 2018, the level of public debt has increased on average from 38 percent of GDP to 59 percent of GDP while the median public debt increased from 34 to 55 percent over the same time period. Note that the (GDP-) weighted average public debt in the region excluding Nigeria (the largest economy in the region and with low debt levels after the GDP rebasing) increases from 42 percent in 2013 to 62 percent in 2018 ( **Figure 2** ).<sup>3</sup>

**Figure 2. General Government Gross Debt in Sub-Saharan Africa** _(% GDP)_

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>SSA Average SSA Median SSA Weighted Avg.<br>excluding Nigeria<br>2007 2013 2018<br><!-
of the public health system (Alegana et al. 2013). In the data only 12 out of 1,416 health post-month observations are missing in 2013. In addition, 99% of suspected cases were tested parasitologically in the ve districts analyzed. Since both malaria cases and imported cases are calculated based on case data, as long as utilization is relatively uniform across the country, it should not bias results. Based on the DHS data for all the regions, a health facility was visited for fever in children under age 5 in 46% of cases (ANSD and ICF International 2015). The standard deviation of this utilization across regions is 6.5 percentage points. While in the main analysis, I assume uniform utilization, I include a robustness check where cases are scaled by regional utilization in the DHS.

# 2.5 Population Movement Data

The data used to measure short term movement come from phone records made available by Sonatel and Orange in the context of the Data for Development Challenge (Montjoye et al. 2014). The data come from the second phase of the Challenge and consist of 15 billion call and text records for Senegal between January 1, 2013 and December 31, 2013 for all of Sonatel's user base.<sup>14</sup> The data contain information on all calls and texts made or received by a SIM card, their time, date and location of the closest cell phone tower, which enables tracking of SIMs in space as they make calls from dierent tower locations. The data are anonymized, with a random ID provided that makes it possible to track the same SIM over time, but no identifying information on the individuals. On average there are 1,657 calls or texts per ID during the year, and on average an ID has a call or text on 155 days.

Each tower is assigned a health district based on its GPS coordinates. I follow previous literature to assign individuals a daily health district location based on the cell tower
the results of the Sudan national PMT with the inclusion of employment information in the form of the proportion of household members that have ever worked for income to total household members. Although this information is strongly and statistically significantly associated with per capita household consumption. Including this information does not impact the targeting performance of the PMT in a meaningful way. Employment information is generally difficult and time consuming to collect in household surveys, consisting of a series of questions to capture the employment status of each household member of working age and the type of employment, for example, salaried or non-paid, family workers.

20
a better understanding of patterns of household energy use for cooking with a view to refining government support to household use of LPG. In contrast to the Multiple Indicator Cluster Surveys (Kojima 2021c) or Demographic and Health Surveys,<sup>6</sup> which are valuable sources of information about primary cooking, lighting, and heating energy sources but contain no information about energy stacking, data from national household expenditure surveys enable not only calculation of how much households spent on commercial forms of energy but also assessment of whether households had abandoned use of polluting fuels. The study was confined to Sub-Saharan Africa, the region with the largest access gap and therefore the rate of abandonment of polluting fuels and technologies is presumably correspondingly the lowest. Examining the extent of abandonment and incremental costs of doing so could inform policy makers as they consider different forms of support for the purpose of achieving universal access to clean household energy.

# **Study Description**

This study analyzed national household expenditure surveys conducted between 2014 and 2019 in 24 Sub-Saharan African countries. The names of the surveys, dates of data collection, and annual expenditures per capita expressed in 2022 U.S. dollars can be found in annex 1. The countries included those with very large price subsidies for LPG (Angola and Sudan in particular) and those with no price subsidies. LPG users were defined as those with non-zero expenditures on LPG. The questionnaire in Cameroon asked about the possession of LPG stoves and cylinders, and households with such possession were also considered to have been LPG users. Consistent with the definition provided by the World Health Organization, this paper considers electricity, LPG, biogas, solar cookers, and ethanol clean forms of energy—although questions about ethanol were asked only in Nigeria where only a handful of households in urban areas cited ethanol as their primary cooking fuel—and all other fuels are considered polluting, including charcoal and kerosene.

6 https://dhsprogram.com/.
work activities that generate income for the owners of production means in each economy. Its other unique advantage is the possibility of tying the individual income of the entire working population to the characteristics of the workplaces where such income is generated.

These strengths come with limitations. First, the unit of observation is not the business but the individual, and employers are not identified. Therefore, we can only characterize the employmentweighted business size distribution, not its unweighted version. Second, we are constrained to a reduced number of size bins, already limited in the original data sources and further reduced in our analysis for comparability across countries.<sup>3</sup> Third, because the data are at the level of individual workers, we have no information on capital stock or other business characteristics. Our only measurable business characteristics are size, sector, and labor productivity, measured from the earnings of employees and owners, which yield proxy measures of productivity distorted by the presence of labor market power and labor regulations.

We summarize here the basic characteristics of the data sources used for the different countries in our sample. Details are provided in the Appendix. For Latin America, we use the 2019 national household surveys, with the exception of Chile (2017) and Mexico (2018). The microdata for most countries are publicly available, and we use harmonized versions to ensure comparability across countries. We have data for 11 LATAM countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Mexico, Paraguay, Peru, and Uruguay.<sup>4</sup> We also have data for three LDCs in Asia (India, Pakistan, and Nepal) for which we use information from their 2019 labor force surveys (2018 in the case of Nepal).<sup>5</sup> Our advanced economies include all countries in the European Union, the U.S., the Republic of Korea, Japan, and Australia. We use the 2019 microdata from
**The focus of this work is to identify individuals who, through the remittance data, appear to be foreign workers or carrying out transactions on behalf of other foreign workers.** Unlike our earlier work (World Bank, 2020), this exercise does not attempt to identify individuals who appear to be irregular workers (see Annex 1 for a comparison of methodologies used). Rather, the main objective is to estimate the total number of foreign workers between March 2017 and February 2018, looking at the monthly numbers as well as the annual average size of the foreign worker population. Additionally, the current method uses the difference between the remittance analysis and lawfully-employed foreign population data from MOHA to estimate potential irregular workers. The specific steps are described below.

# **Step 1. Create a sample of potential foreign workers using objective variables of individual observations (remittance transactions).**

- (i) We restrict the sample to individual remitters between the ages of 18 and 55 years. According to Malaysia’s foreign worker system, foreign workers must be between the ages of 18 and 45 years at the time of their work permit application, and they are allowed to stay in Malaysia for up to 10 years from their entry.

- (ii) Remittances from Malaysian citizens are excluded except in cases where the remitter sends money to multiple recipients in different countries indicating employment as the source of funds, which may indicate that the transaction was done on behalf of multiple foreign workers.

- (iii) As in World Bank (2020), we exclude those individuals whose monthly remittance amount is more than US$650 (two standard deviations above the mean), as the average monthly wage of foreign workers is not more than US$650. As a point of reference, the World Bank KNOMAD surveys (2015) show that Vietnamese workers in the manufacturing sector send, on average, US$200/month to their family members back home.

- (iv) We exclude observations with transaction amounts
<mark>Kahn, Matthew E. "Demographic change and the demand for environmental regulation."</mark> _<mark>Journal of Policy Analysis and Management: The Journal of the Association for Public Policy Analysis and Management</mark>_ <mark>21, no. 1 (2002): 45-62.</mark> <mark>Kahn, Matthew E. "Environmental disasters as risk regulation catalysts? The role of Bhopal, Chernobyl, Exxon Valdez, Love Canal, and Three Mile Island in shaping US environmental law."</mark> _<mark>Journal of Risk and Uncertainty</mark>_ <mark>35, no. 1 (2007): 17-43.</mark> <mark>Kahn, Matthew E. "The death toll from natural disasters: the role of income, geography, and institutions."</mark> _<mark>Review of economics and statistics</mark>_ <mark>87, no. 2 (2005): 271-284.</mark> <mark>Kahn, Matthew E., and Matthew J. Kotchen. "Business cycle effects on concern about climate change: the chilling effect of recession."</mark> _<mark>Climate Change Economics</mark>_ <mark>2, no. 03 (2011): 257-273.</mark> <mark>Kleinnijenhuis, Alissa, Tobias Adrian, and Patrick Bolton.</mark> _<mark>The Great Carbon Arbitrage</mark>_ <mark>. No. 2022-07. Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, 2022.</mark> <mark>Knittel, Christopher R., and Shinsuke Tanaka.</mark> _<mark>Driving behavior and the price of gasoline: evidence from fueling-level micro data</mark>_ <mark>. No. w26488. National Bureau of Economic
# **Notes**

i Monthly income was presented as percentage increase in income compared to the respondent’s current monthly income from his or her main economic activity.

ii Some early studies attempted to overcome these difficulties using panel data, with mixed results (Brown 1980, Duncan and Holmlund 1983). Another approach is to use information on job duration – namely, to estimate preferences for specific job benefits by examining whether workers stay longer in jobs with those benefits (Gronberg and Reed 1994, Reed and Dahlquist 1994). However, it is not clear that duration models solve the key problem of unobserved heterogeneity, since an important identifying assumption they make is that the _involuntary_ separation rate is the same across jobs with different benefits. More recently, a number of studies have estimated search models to identify the total value of non-wage attributes (Sullivan and To 2014, Hall and Mueller 2018) and the contribution of non-wage attributes to variation in observed wages (Taber and Vejlin 2016, Sorkin 2018). However, only a few of these papers distinguish between different types of attributes: Dey and Flinn (2005, 2008) focus on health insurance, while Bonhomme and Jolivet (2009) use self-reported satisfaction with five job characteristics.

iii These conditions include extremes of heat, cold, wetness, vibrations or hazards. The authors also found positive but not statistically significant WTP for jobs that do not require frequent stooping, kneeling, crouching or crawling, and for jobs that are not repetitive.

iv The job attributes covered in their study are type of work, working conditions, working times, distance to job/commuting time, and job security.

v The survey was conducted by the BRAC Institute of Governance and Development (BIGD), BRAC University.

vi The sample included 50 _mouzas_ from Dhaka, 7 from Gazipur, 4 from Narayanganj and 19 from Chittagong. vii The replacement rules for the household selection were as follows: If the enumerator was unable to talk to someone at the selected household, or if the potential
Policy Research Working Paper 6218

# **Abstract**

This paper uses a large cross-country dataset to empirically examine factors associated with sovereign defaults on external private creditors and expropriation of foreign direct investments in developing countries since the 1970s. In the long run, sovereign defaults and expropriations are likely to occur in the same countries. In the short run, however, these events are uncorrelated. Defaults are more likely to occur following periods of rapid debt accumulation, when growth is low, and in countries with weak policy performance, and defaults are not strongly persistent over time. In contrast, expropriations are not systematically related to the level of foreign direct investment, to growth, or to policy performance. Expropriations are however less likely under right-wing governments, and are strongly persistent over time. There is also little evidence that a history of recent defaults is associated with expropriations, and vice versa. The paper discusses the implications of these findings for models that emphasize retaliation as means for sustaining sovereign borrowing and foreign investment in equilibrium, as well as the implications for political risk insurance against the two types of events.

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 authors may be contacted at meden@worldbank.org, akraay@worldbank.org, or rqian@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 of the authors. They do not necessarily represent
. Village heads, in turn, report taking action on these priorities. These effects extend to preferences expressed by citizens more socially distant from village officials, suggesting that improved information flows make policy more representative of the entire community. We illustrate these accountability dynamics by tracing the full causal chain from policy diagnosis to action for one public good: in villages with worse transport costs at baseline, a proxy for road quality, turnover increases the likelihood that bureaucrats recognize road deficiencies and report citizen complaints, and that village governments respond with road investments.

We next examine whether these shifts in bureaucratic processes and engagement improve the performance of village governments. Consistent with this, turnovers improve the quality of service provision, as measured in both administrative and survey data. Restricting attention to villages that held elections before 2021, the most recent year with administrative service-provision data, we find an increase of about 0.5 standard deviations in a standardized service index. This effect is driven by locally managed services such as garbage collection and street lighting. These gains appear only in villages where the head has no relatives employed in the village government, illustrating the role of declining bureaucratic nepotism as an important channel. They are also larger in villages whose last election occurred several years earlier (2015–2017) rather than more recently (2018–2020). Thus, the benefits of leader turnover may take time to materialize, perhaps because they must first offset the short-run disruptions associated with bureaucratic turnover, as in Akhtari et al. (2022).

2
# **4.3 Are the effects driven by outliers?**

One way of assessing the robustness of the estimates effects is to examine whether they are driven by outliers, i.e. specific industries only. Figure 5 plots the change in the number of employees within an industry, calculated as the log of employees in November 2013, the last month in the data, minus the log of the average number of employees in the pre‐2009 period. The figure shows the distribution of these calculated changes, separately for eligible and ineligible industries. One of the ineligible industries (manufacture of asbestos‐based products) experienced a particularly large drop in employment of 130%. However, overall the distribution of employment changes in eligible industries lies to the right of the distribution of employment changes in ineligible industries, suggesting that the positive effect of the program on employment was not concentrated in a few industries only. A Kolmogorov‐Smirnov test of equality of distributions rejects equality with a p‐value of 0.017 (0.025 if manufacture of asbestos‐based products is excluded from the group of ineligible industries). Also, there are no outliers in terms of increases in the number of employees for eligible industries.

I test to see how the estimated program effects would change when weakening the influence of the negative outlier (manufacture of asbestos‐based products) by winsorizing this outlier. For all months from January 2009 on, I replace the log number of employees in this industry with the log average pre‐ 2009 value plus the drop in employees for the industry that experienced the second largest drop in employment in that month, calculated as log employment in that month minus average log employment over the pre‐2009 period. Columns 5 in table 3 and table 4 shows the results in the winsorized data. The estimated effects are somewhat smaller in magnitude, but they are still statistically significant.

# **4.4 Falsification exercise**

Although the analysis uses eligible and ineligible industries that displayed the
sales (EGP)|579,873|599,057|4,843,432|
|Exporter (yes=1)|0.05|0.07|0.08|
|Value Machinery (EGP)|1,066,584|11,700,000|2,619,007|
|Cost per worker (EGP/worker)|20,329|20,543|65,725|
|Age of firm (years)|11.71|15.48|18.97|
|Foreign ownership (yes =1)|0.10|0.10|0.08|
|Female ownership (yes=1)|0.05|0.03|0.09|
|Registered (yes=1)|0.98|0.97|0.84|
|Training (yes=1)|0.03|0.13|0.08|
|Foreign technology (yes=1)|0.01|0.02|0.06|
|Imported inputs (yes=1)|0.26|0.42|0.42|
|Indirect exporter(yes=1)|0.01|0.02|0.05|

Notes: Number of observations equals 198. Sample is restricted to the 2013-2016-2020 panel and excludes firms missing information on number of employees. Weighted using panel weights. Source: Author elaboration using Enterprise Survey for Egypt.

28
(Lee, 2002). Second, to account for the small number of clusters in each of our treatment groups, we estimate alternative p-values using randomization inference (Athey and Imbens, 2017).

We estimate separate effects for our full sample and for the subsample of households with a child aged 0 to 2 as well as estimating separate child-level effects for children within this age-range to examine whether childcare centers have differential impacts for women with young children.<sup>11</sup>

# **4 Results**

## **4.1 Baseline Characteristics**

Table 1 presents balance on baseline women’s and household characteristics for the full sample. Women in our sample are aged 31 on average and two thirds are in a monogamous marriage. Approximately 46% of respondents have no formal education. In comparison, 64.5% of women in Burkina Faso and 48.7% of women living in urban areas have no formal education according to the latest Harmonized Survey on Household Living Conditions, conducted in 2018/2019. Thus, education levels in our urban sample are similar to those for women in the urban population in general. Almost 98% of women report having worked for at least one hour in the last 30 days, which is consistent with respondents’ participation in the public work activities. On average, women in our sample worked 33 hours per week in the last month. Time use data reveal that in the last 24 hours, women in our sample spent on average of 6.7 hours caring for their children, 2.5 hours on domestic chores and 3.4 hours doing paid work. Women in the control group spent more time working per week (37 hours for women in the control group, compared to 28 hours on average for women in the treatment group, _p<_ 0.05), and more time on domestic chores (2.6 hours in the control group, compared to 2.4 hours in the treatment group, _p
that, ...rst, the e¤ect of vertical production linkages on MNC subsidiaries’ relative performance increases in the incidence of the crisis in host countries. Vertical MNC subsidiaries exhibited a greater advantage over local counterfactuals in countries with larger negative demand shocks. In contrast, the e¤ect of vertical production linkages is not signi...cantly associated with the incidence of the crisis in MNCs’home countries.

> 18 Recall that country-industry speci...c factors that uniformly a¤ect the performance of foreign owned and domestic establishments have been controlled for in the matching analysis by the pairing of foreign owned subsidiaries and local establishments from the same country and industry. Estimated parameters of country dummies and intercepts are suppressed in the table because of space consideration.

> 19 We also used countries’GDP growth rates and import growth rates as alternative proxies and found similar results.

17