rafmacalaba/data-use-mentions-extended huggingface dataset:
70<br>60<br>50<br>Food Price Index, 2016 = 100<br>2000M1 2000M12 2001M1 2001M12 2002M1 2002M12 2003M1 2003M12 2004M1 2004M12 2005M1 2005M12 2006M1 2006M12 2007M1 2007M12 2008M1 2008M12 2009M1 2009M12 2010M1 2010M12 2012M1 2012M12 2013M1 2013M12 2014M1 2014M12 2015M1 2015M12 2016M1 2016M12 2017M1 2017M12 2018M1 2018M12 2019M1 2019M12 2020M1 2020M2<br><!-- End of picture text --> _Source:_ International Monetary Fund Primary Commodity Prices (January 2000-February 2020). _Note:_ Food Price Index, 2016 = 100, includes Cereal, Vegetable Oils, Meat, Seafood, Sugar, and Other Food (Apple (non-citrus fruit), Bananas, Chana (legumes), Fishmeal, Groundnuts, Milk (dairy), Tomato (vegetables)).

Despite favorable initial conditions, the virulent and rapid global diffusion of Covid-19 could lead to the emergence of problems in global food markets in the coming months. Declines in food supplies could result from a number of factors, including labor shortages as people become sick or are prevented to go to work due to containment policies and to disruptions in supply chains due to logistics bottlenecks within 2
Figure **I:** Access to Bond Markets <!-- Start of picture text --> Debt-Equity Ratios *<br>5-<br>4-<br>3-<br>2<br>85 8687 88 8990 9192 939495 96 9798<br><!-- End of picture text --> <!-- Start of picture text --> Short-Term Debt/Equity *<br>3.0<br>2.5<br>10<br>85 8687 8889 90 919293 9495 9697 98<br><!-- End of picture text --> <!-- Start of picture text --> Short-Term Debt/Equity *<br>0.&<br>0.7.<br>0.6<br>0.<br>0.4<br>0.111<br>97 98 85 86 87 88 89 90 91 92 93 94 95 96 97 98<br>Firms that Issued Bonds in Foreign Markets<br>Firms that Did Not Issue Bonds in Foreign Markets<br><!-- End of picture text --> <!-- Start of picture text --> Long-Term Debt/Equity *<br>2.5<br>2.0-<br>1.5<br>1.0.<br>0.5<br>0.0 L .wl<br>85 86 87 88 89 90 91 92 93 94 95 96 97 98<br><!-- End of picture text --> Source: **IFC** Corporate Finance Database andWordScope

***** Ratios are averages across firms
**Table 3 Performance Indices for Selected Occupations**

|**Occupation**|**Prestige Index**|**Education Index 1**<br>**(Census 2000)**|**Education Index 2**<br>**(Census 2000)**|
|---|---|---|---|
|Accountants and auditors|65.4|92.8|16.1|
|Bank tellers|43.3|54.8|13.7|
|Cashiers|29.5|31.8|12.3|
|Civil engineers|68.8|95.8|16.4|
|Clergyand religious workers|63.3|89.8|15.8|
|Computer software developers|60.5|95.0|16.2|
|Farm workers|24.5|19.6|10.0|
|Judges|71.5|93.3|16.2|
|Laundryworkers|31.8|20.1|11.2|
|Lawyers|74.8|99.9|17.0|
|Mathematicians and mathematical scientists|63.5|99.4|16.6|
|Physicians|86.1|99.7|17.0|
|Police,detectives,andprivate investigators|59.7|84.1|15.1|
|Registered nurses|66.5|98.1|15.9|
|Secondaryschool teachers|66.4|99.5|16.8|
|Secretaries|46.1|64.0|14.1|
|Taxi cab drivers and chauffeurs|28.1|42.5|12.7|

**Table 4. Estimates Based on Equation (1)**

|**EDUCATION INDEX 1**||**
this affects demand and/or supply of credit, our estimates may be biased. In order to assuage such concern, we augment our baseline specification with an extensive set of additional regressors that virtually cover any dimension available in the WBES survey. In our robustness checks, we account for past sales and productivity growth, capacity utilization, as well as granular information on main destination markets (local, national, or international), managerial characteristics (experience of the managers, female managers) and ownership (government ownership and female owners). All of which capture different dimensions of firms’ fundamentals. Moreover, we control for relevant characteristics of the local environment that may be spuriously associated with informality: the size of the city, measures of corruption (bribery depth or gifts to officials) and crime (loss from thefts and vandalism), as well as other shocks linked with the localization of the company (number and length of electric outages), together with geographic area fixed effects interacted with more granular sector controls (2-digit ISIC Rev. 3.1 level). Furthermore, in some specifications we enrich the vector _X_ with controls for the issuance of the loan, whether the company was originally informal, and the number of years since formal registration occurred (see below). Finally,

> 7Geographical areas are defined as the localization group used as a stratum in the WBES sampling scheme. From the sampling methodology note of the WBES: “Geographical distribution is defined to reflect the distribution of the non-agricultural economic activity of the country; for most countries this implies including the main urban centers or regions of the country.”

9
11

# Cross‐Regional Perspective

Broadly, access expansion mirrors economic development. Does access in Africa as a function of income or depth of poverty fall in line with the rest of the world? To answer this question, access in different countries was compared as a function of the poverty gap at $3.10 and $1.90 and per capita GDP. Correlation coefficients between access and the two measures of the poverty gap and per capita GDP are statistically significant at 1 percent, with correlation coefficients ranging from ‐0.72 for the logarithm of GDP per capita at the market exchange rate to ‐0.89 for the poverty gap at $3.10.

Figure 1 plots the relationship between access and the poverty headcount at $3.10 for 2012. There is a downward sloping line on which most countries in regions other than Africa lie, whereas several countries in Africa lie markedly below, as do Cambodia and Vanuatu. When access is regressed on any one of the measures of the poverty gap or per capita GDP together with 1/0 regional dummies, Africa is the only region for which the coefficient for the regional dummy is consistently negative and statistically significant at 1 percent. The predictive power of the poverty gap at $3.10 was the highest, followed by the poverty gap at $1.90, the logarithm of per capita GDP at purchasing power parity, and finally the logarithm of per capita GDP at the market exchange rate.

Figure 1: Relationship between poverty gap and electricity access in 2012 _Source_ : World Bank staff analysis using data from World Bank 2016 and World Bank and IEA 2015. _Note_ : Bubbles are in proportion to population.

Figure 2 compares access and the poverty gap in Africa in 2012 with corresponding values in three other regions in 1990. China stands out as a (positive) outliner, with the access rate far above that of other countries at similar levels of the poverty gap. Several countries in Africa again lie below the global trend line. When
), 2020. doi: 10.1016/j.jbankfin.2020.1. URL `https://ideas.repec.org/a/eee/jbfina/v119y2020ics0378426620302004.html` .

- David Bounie, Abel Franc ̧ois, and Patrick Waelbroeck. Debit Card and Demand for Cash. _Journal of Banking & Finance_ , 73:55–66, 2016. doi: 10.1016/j.jbankfin.2016.0. URL `https://ideas. repec.org/a/eee/jbfina/v73y2016icp55-66.html` .

- David Bounie, Youssouf Camara, Etienne Fize, John Galbraith, Camille Landais, Chloe Lavest, Tatiana Pazem, and Baptiste Savatier. Consumption Dynamics in the COVID Crisis: Real Time Insights from French Transaction & Bank Data. CEPR Discussion Papers 15474, C.E.P.R. Discussion Papers, November 2020a. URL `https://ideas.repec.org/p/cpr/ceprdp/15474. html` .

- David Bounie, Youssouf Camara, and John Galbraith. Consumers’ Mobility, Expenditure and Online- Offline Substitution Response to COVID-19: Evidence from French Transaction Data.

36
# III. Trends in income growth and poverty reduction

Information on real income per capita for households in Uganda across time is presented in Figure . The data represent weighted averages of income from crop farming, livestock production, wage employment (in agriculture and non-agriculture sectors) and non-farm self-employment. All values are in 2011 prices. Figure 4 provides the same information, but for households that were in the bottom 40 percent of the consumption distribution during at least one of the survey rounds. Figure 3 and Figure 4 show that agricultural income is the main source of income for households, particularly for those in the bottom 40 percent. Together, crop and livestock income comprised 57.5 percent of the income of Ugandan households in 2012 and 70 .2 percent of the income of the bottom 40 percent. Within agricultural income, crop income dominates livestock income.

Income from non-farm self-employment is the second most important source of income when considering all households and it is followed by non-agricultural wage income. Finding a measure of non-agricultural self-employment income that compares well to the measures of gross agricultural income used in this analysis is not straightforward. Much self-employment income comes from trade and taking only gross sales does not give an idea of how much was earned. We use net self-employment income in the analysis, which is gross self-employment income net of raw materials, operating expenses and wages paid to others. Raw materials account for 81 percent of these expenditures. Operating expenses and wages paid to others account for 12-13 percent of gross income, suggesting that self-employment income would be a marginally more important source of income were these expenses not netted out. Non-farm self-employment income is also the second most important income source for poor households, but only just. For poor households non-agricultural wage income is also a very important source of income.

Figure 3 and Figure 4 indicate substantial growth in real per capita agricultural incomes from 2006 to 2012 based on household survey data. On average, real per capita crop income grew by 9 percent per year, and by 8 percent for the
.51|2.38|6.08|-1.57|-5.72|1.07|6.79|
|Georgia|1.49|0.27|2.16|-0.68|-0.27|1.25|1.53|
|Ghana|-0.50|0.52|1.19|-1.69|-0.84|-1.02|-0.19|
|Guatemala|2.82|0.33|3.78|-0.96|-0.19|2.57|2.76|
|Guinea|2.56|0.85|4.93|-2.38|-1.63|1.82|3.45|
|Guinea-Bissau|1.47|2.06|5.89|-4.42|-4.62|-1.69|2.93|
|Indonesia|2.88|0.38|3.13|-0.25|0.63|3.05|2.42|
|Iraq|1.33|0.25|1.79|-0.46|0.57|1.48|0.91|
|Jordan|6.55|1.06|6.87|-0.32|2.64|7.58|4.94|
|Kenya|2.83|1.25|7.64|-4.82|-2.24|1.61|3.85|
|Kyrgyz Republic|1.10|0.30|1.82|-0.72|-0.21|1.01|1.22|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of country’s
This is one of the first studies to explore the effect of armed conflict on the labor market outcomes of men and women who were of school age or just completed school when the conflict started. In particular, this study examines the effect of the 1992-1998 armed conflict in Tajikistan on the educational attainment and labor market experiences of the birth cohort that was of school age during the conflict and who lived in the more conflict affected areas. The study combines the 2003 and 2007 Tajik Living Standards Surveys data to address this question.

2
, e.g., Bekaert and Harvey (2000), Henry (2000b), Kaminsky and Schmukler (2008).

> 2 Related research examining the impact of domestic financial development on allocation of physical capital includes, e.g., Wurgler (2000), Beck and Levine (2002), Love (2003), and Fisman and Love (2004, 2007).

> 3 Bekaert et al. (2007) use country-time panel data covering 50 countries between 1980 and 2002 and show that financial openness is an important factor in aligning growth opportunities with actual growth. Larrain and Stumpner (2017) rely on firm-level data from 10 post-communist countries in Central and Eastern Europe and provide evidence that capital account liberalization helps financially constrained firms to demand more capital and produce at more efficient levels. Gopinath et al. (2017) explore capital misallocation using firm-level data from Spain and extend part of their analysis to five other countries in Western Europe. Both papers rely on databases provided by ORBIS-AMADEUS that cover most of the manufacturing firms in the respective countries. Varela (2018) looks at impact of lifting the restrictions on international borrowing by using firm-level census data covering all manufacturing firms in Hungary.

3
Simplistic models of accessibility impose costs, as the failure to measure accessibility in units of time leads to incorrect or vague assessments of service facility catchment areas. Traditional linear measurements do not account for the impact of terrain types, slope, presence of roads, etc. on travel conditions. These factors are especially relevant in rural Nepal where a hypothetical child could live within 2 linear kilometers of a school, but be on the wrong side of a river, valley or mountain, or simply 1000m below the school.

# Measurement approaches

Most country‐scale research into remoteness in Nepal uses basic weighted indices of subnational units, suitable for high‐level analysis but not for measuring localized travel times or facility level accessibility. A recent example comes from Dempsey (2016), who assessed remoteness at the Village Development Committee (VDC) level using a simple weighted linear combination (WLC) model. She graded different input factors by their level of remoteness and weighted them based on expert judgments from staff at the United States Agency for International Development (USAID). Elsewhere, the World Bank has calculated a Rural Access Index for subnational administrative units in Nepal and other countries based on road network coverage and quality (Roberts et al. 2006 / Iimi et al. 2016). Huber (2015) went one step further and created a rasterized cost time travel model for Nepal, even including a separate monsoon model to reflect the sharp seasonal changes in accessibility where roads are poor. But to create this raster Huber interpolated missing values from the results of a network analysis (Rodrigue et al. 2009) of road vector lines, ignoring off‐road travel and travel impedances from terrain, landcover, bridges, etc. He also calculated remoteness in terms of travel times to Kathmandu, not in terms of services. Reaching further back, Donner (1972) published a map showing path lengths in units of porter days. All the above studies use incomplete and partial transportation data sets, particularly of pathways, implying a degree of inherent error.
# **1. Introduction**

This paper seeks to identify interviewer effects using household data collected in Sudan. The data is from a pilot agricultural survey conducted in 2018 by the Central Bureau of Statistics (CBS) in collaboration with the World Bank in Kassala State. Kassala lies in the eastern part of Sudan bordering Eritrea and is viewed as having high smallholder agricultural potential. It was chosen for the pilot agricultural survey in part for this reason but also because of its diverse agriculture including rainfed, irrigated and mechanized crop production, as well as livestock husbandry. The drier north of the state is pastoral whereas the wetter central and southern parts of the state are primarily sorghum-based agro-pastoral livelihood systems.

The pilot survey is a good opportunity to identify interviewer effects because of the authors’ close involvement throughout the survey. This involvement included questionnaire and sample design; enumerator and supervisor training and piloting; post survey debriefing with enumerators and supervisors; and data cleaning and analysis.

# _Interviewer effects_

Interviewer effects are the effects that interviewers can have on survey or census data quality. In the case of surveys, which are the focus here, interviewer effects are the difference between the true population parameter and the survey estimate that is attributable to interviewer errors. Interviewer effects can broadly be divided into three categories: coverage errors, non-response errors and measurement errors. Coverage errors are errors in the sample frame as a result of mistakes in listing or under coverage of difficult to access households. Non-response errors result from interviewer’s inability to solicit a response from a sampled respondent. Finally, measurement errors, which are the focus of this paper, occur when an interviewer does not solicit or record the correct response from the respondent (West and Blom, 2017).

There is a long history of the study of interviewer effects on survey data collection (Kish, 1962). In their recent comprehensive review of the literature on interviewer effects, West and Blom (2017) identify six types of interviewer characteristics that have been related to measurement error in
# **IV. Methodology and Data**

## **a. Sample Description and Sampling Technique**

Our primary source of data comes from a survey covering three consecutive cohorts of EF trainees (from 2010 to 2012), with two rounds of data collection for each cohort.<sup>24</sup> Figure 1 depicts the impact evaluation timeline.

We sample at the training event and the applicant level. The main sampling frame for data used in this study consisted of all EF training courses sponsored in a given year. The number of training events comprising the sample frame ranges from 598 (in 2010) to 711 (in 2012). Table 1 reports the number of events and participants by year.

Sampling into this study included a combination of stratified, random and convenience sampling. First, we selected a subset of training events occurring between the months of January through April.<sup>25,26,27</sup> Second, from the universe of training events offered during these four months, we randomly selected up to 15 districts. Third, from that list of training events occurring in these districts, we randomly selected 20 percent of the training events. Finally, a survey team visited each sampled training event on the day when applicant selection took place. Each event’s ranking sheet listed the shortlisted applicants from the top-scorer to the bottom and indicated the threshold, or minimum score needed to gain admission to the course. From this ranking sheet, the survey team selected applicants whose scores were within 20 percent of the threshold for admission to training events. The sampled applicants above the threshold comprise this study’s treatment group, while those below the threshold make up the control group. Immediately following the sampling of applicants, a baseline survey was conducted on the treatment and control groups, before the results of the selection process were announced.

Table 2 shows the resultant sample of events for the three cohorts. The 2010 event sample comprised 64 events across 30 districts.
500|
||0|0|
|**Mexico**|**Ecuador**|**Argentina**|
|12,000<br>**140%**|60<br>**123%**|500<br>**97%**|
|10,000|50|400|
|4,000<br>6,000<br>8,000|20<br>30<br>40|200<br>300|
|2,000|10|100|
|0|||
||0|0|

**Source:** Own elaboration, credit to SMEs obtained from the Financial Access Survey (IMF) and from the MSME Finance Gap Report (IFC) if missing.

**Note:** Heat map bar depicts the credit to SMEs covered by PPCG as percentage of the financial system credit to SMEs as in Figure 4. Two of the three PCGs in Argentina did not implement COVID-19 programs. We couldn’t compile complete information for _Banco de Desarrollo Productivo_ (BDP, Bolivia), and _Sociedad Administradora de Fondos de Garantía Reciproca_ (SAFGR, Honduras). In the case of BDP its guarantees granted by the end of 2020 totaled less than USD 1 million, and around USD 33 million in SFGR in Honduras. Besides, the guarantees granted by FSG in El Salvador stood at USD 38 as of 2019.

20
Second, we find empirically using a database of 26 SWF investments in Africa over the period 1985-2013 that: i) about half of SWF investments in Africa are commodity-fueled funds, which raises some concerns given the end of the commodity super-cycle; ii) Real Estate and Hotels are the most popular sector for SWFs in Africa, totaling 42 percent of investment, while only 10 percent of their investments are in infrastructure<sup>1</sup> ; iii) SWFs that invest in Africa mainly originate from Asia, and Africa is the region that received the smallest share of SWF investments.

Third, in terms of econometric specification, we regress the logarithm of the investment share on return rate, risk and networking parameters using cross-country data over the period 19852013. Our empirical results show that rate of return has a positive and significant effect on SWF investments, while volatility exerts a negative but not significant effect. Most importanly, we also find that SWFs with a political bias are more likely to invest in Africa. Our results remain robust to the introduction of the following control variables: the Linaburg-Maduell transparency index, school, inflation, macroeconomic variables (government consumption and budget balance), trade, religion, corruption, property rights, and the use of legal origin as controls.

The rest of the paper is organized as follows. Section 1 investigates some facts of SWF investments in Africa. Section 2 presents the econometric investigation of the determinants of SWF investments. Finally, section 3 provides policy implications and concludes.

# **1 Stylized facts**

## **1.1 What do we know about SWFs in Africa?**

Using data from the Sovereign Wealth Institute (SWI) transaction database, we document information on SWFs in Africa according to several variables. Table 1 shows African SWFs, their capitalization, type, and origin.

In terms of numbers, the world market share of sovereign wealth funds reached a record amount of $6,831 trillion in September 2014 (see Sovereign Wealth Fund Institute)
ambitious goal. As these insurance markets mature, they are likely to significantly improve risk management opportunities among Indian households and entrepreneurial firms.

# Acknowledgments

We thank BASIX for their generous provision of data and technical advice. We also express particular gratitude to K. P. C. Rao for his efforts in coordinating field experiments and collecting survey data in Andhra Pradesh; to an anonymous referee, Mangesh Patankar; to the editor, Chetan Ghate; and to our coauthors, especially Shawn Cole, for their insights. The views expressed in this chapter are those of the authors; they do not reflect the opinions of the Federal Reserve Bank of New York or the World Bank.

# References

## _A. Our academic research on rainfall insurance_

Cole, Shawn, Xavier Giné, Jeremy Tobacman, Petia Topalova, Robert Townsend, and James Vickery. 2009. Barriers to Household Risk Management: Evidence from India. Harvard Business School Working Paper 09–116. http://ssrn.com/abstract=1374076.

Giné, Xavier, Robert Townsend and James Vickery. 2007. Statistical Analysis of Rainfall Insurance Payouts in Southern India. _American Journal of Agricultural Economics_ 89: 1248–1254. http://ssrn.com/abstract=1100218.

- ———. 2008. Patterns of Rainfall Insurance Participation in Rural India. _World Bank Economic Review_ 22: 539–566. http://ssrn.com/abstract=1318107.

- ———. 2010. Forecasting When It Matters: Evidence from Semi-Arid India. Mimeo, World Bank.

## _B. Other cited papers_