rafmacalaba/data-use-mentions-extended huggingface dataset:
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Key institutional reforms are behind this progress. Liberalizing the market and opening to private capital have been instrumental. In the early 2000s the mobile market was monopolistic; in 2005 there were three operators in the market and a fourth expected by the end of 2010. The national operator, Onatel, was privatized in 2006, when Burkina Faso’s government sold a 51 percent stake to Maroc Telecom. As of 2009, the government sold a further 20 percent of Onatel’s capital in a public offering and

**Figure 22. Market concentration in Burkina Faso relatively low**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2005 2008<br>Herfindahl-Hirschmann Index<br>Mali Niger Cameroun Ghana Burkina Faso Benin Nigeria<br><!-- End of picture text --> _Source:_ World Bank 2009a.

_Note:_ Herfindhal-Hirschmann index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly; the lower the HHI, more diluted is the market power exerted by one company/agent.

an additional 6 percent was expected to be sold to employees. Burkina Faso has one of the lowest market power concentrations in West Africa (figure 22).

Flows of private resources **Figure 23. There is still room for increased private participation** to the sector in the period 1992– 100% 99 were minimal, at an annual 80% average of $0.001 million, but 60% they skyrocketed in subsequent 40% years: private participation 20% increased sixfold between the 0% periods 2000–03 and 2004
protection policies with “...a common argument is that tariffs are higher in recessions, because the political pressure from import-competing firms is then most pronounced. This explanation, however, is incomplete, since it ignores the political influence of other production sectors that might press for _less_ protection in recessions...In light of these competing political influences, the common argument for countercyclical tariffs fails to be convincing, as it does not explain why the political pressures from import-competing sectors dominate in recessions but not in booms.” Bagwell and Staiger (2003) also contains extensive reference to a number of empirical papers from earlier periods documenting the countercyclical relationship between business cycles and import protection.

> 3 Feinberg (1989) focused on the 1982-1987 period for the United States and found evidence that more antidumping cases were associated with dollar depreciations. Knetter and Prusa (2003) examine annual data for the US, Canada, Australia and the EU over 1980-1998 and find strong evidence of a relationship between antidumping cases and local currency appreciations over this longer time series of data. Crowley (2011) uses a cross-country, cross-industry panel from 1980-2001 and finds that the US imposed antidumping against foreign trading partners with weakness in manufacturing sectors.

4
effect of the dummy variable for skill _i_ in occupation _o_ . α is a constant, and ε is the error term. The dummy variable for a given skill _i_ of occupation _o_ is derived as follows:

> o 0, o � where _xoi_ represents the average count of skill _i_ in occupation _o_ and � is the mean of _xoi_ over all occupations, where _i_ is a vector of six skills subsets. Three of the skills subsets are cognitive skills: ıı xx thinking, communication, and organization. The other three are socioemotional skills: emotions, relationships, and personal growth. The dummy variable is used for easier interpretation of the LPM’s coefficients than the raw skills count variables would allow. This transformation, in the context of LPM, allows us to interpret the coefficients as percentage point increase (or decrease) towards the probability of being a given occupation digital level if the raw skills count for the variable is above (or below) the mean of the skills count over all occupations.<sup>27</sup>

We run the above analysis to be representative of the employed population of Malaysia (around 14 million of workers). Since we are interested in understanding the skills required by employers in a country-specific job market, we use the Malaysia subsample of the SEAD, with each occupation being weighted by the percentage of the employed population in it.<sup>28</sup> We carry out two additional analyses using different subsamples. First, we run the LPM with an unweighted sample to understand the correlation between skills within occupation, irrespective of the occupational composition of the workforce. Second, we carry out a similar analysis using the Cambodia, Thailand, and Vietnam subsamples. The results are similar to those from the Malaysia subsample so, in the interest of parsimoniousness, mostly present the results from the Malaysia subsample.<sup>29</sup> Results for the occupation skills profile (unweighted by the distribution of occupations in a particular country) and replications for other
Policy Research Working Paper 10383

# **Abstract**

While urbanization has great potential to facilitate poverty reduction, climate shocks represent a looming threat to such upward mobility. This paper empirically analyzes the effects of climatic risks on the function of urban agglomerations to support poor households to escape from poverty. Combining household surveys with climatic datasets, the panel regression analysis for Chile, Colombia, and Indonesia finds that households in large metropolitan areas are more likely to escape from poverty, indicating better access to economic opportunities in those areas. However, the climate shocks offset such benefits of urban agglomerations, as extreme rainfalls and high flood risks significantly reduce the chance of upward mobility. The findings underscore the need to enhance resilience among the urban poor to allow them to fully utilize the benefits of urban agglomerations.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Group, Development Economics. . 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:// www.worldbank.org/prwp. The authors may be contacted at snakamura2@worldbank.org and hdang@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 the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
To ensure that our study population was generalizable to the national population of Pakistan that has access to a mobile phone, post-stratified weighting was applied. The weighting corrected for differences in the achieved sample’s profile and what was expected as per the latest population census (2017). For creation of weights, two strata were used, namely: 1) Province (Punjab, Sindh, KP, and Balochistan) and 2) region (urban or rural). Thus, a total of 10 weights were created.

# Key Variables and Tools

The study focuses on the following key risk variables: parental distress, psychosocial stimulation, food insecurity, maternal education, ECE enrollment,<sup>3</sup> and living in a rural area. The two main outcome variables were child development for children under 36 months and school readiness for children 36-72 months. The age at which a parent believes their child should begin school was also analyzed to understand the association between parental belief and ECE enrollment. The following variables were constructed. A description of the independent variables follows.

## Outcome Variables

## **_Child Development for Children 0-3_**

The Caregiver-Reported Early Development Instrument (CREDI) is a globally validated, population-level, developmental assessment tool for children aged 0-3 years (McCoy et al., 2018). It has been used in over 17 countries and is designed to be culturally and linguistically neutral. The short form questionnaire was chosen given that data were collected using a phone survey and respondent fatigue was a concern. The short form is also recommended for population-level surveys. This version of CREDI has 20 questions to be completed by household parents or caregivers<sup>4</sup> about a selected child. The scale captures development across four domains: motor, language, cognition, and social-emotional development. There are 20 items per form, with items representing developmentally appropriate skills for each of six age groups ranging from 0-2 years (0-5, 6-11, 12-17, 18-23, 24-29, and 30-35 months
Often we need to answer questions such as: Do capital controls also serve as a conduit for favors to firms outside crises periods? What are the institutional characteristics that create this type of connection between firms and governments? Are there other policies that are used by other countries as substitute for capital controls to funnel resources to connected firms? Several approaches have been tried in the literature to address such questions. The simplest is to posit a relation between the variable of interest and a set of plausible determinants. One then uses multivariate techniques to describe the association between the variables of interest and the postulated instruments. Evidence of association is taken to support the existence of the postulated relation. This approach is problematic in that many of the plausible determinants may themselves not be truly exogenous, leading to well-known biases.

A second, widely adopted, approach follows Rajan and Zingales‟ (1998) study of the effect of financial development on the growth rate of industries. They posit that industries that depend on external financing grow faster in countries with well-developed financial systems. The identification problem here, though, is that in any country, the extent to which a firm depends on external financing is itself endogenous and influenced by the development of the financial system. The solution that Rajan and Zingales (1998) adopt is to use the extent industries in the U.S. depend on external finance to classify industries into financially and financially independent industries. This classification does not depend on conditions in developing countries or growth rates of industries in developing countries.

Once industries are classified using U.S. data, Rajan and Zingales use a difference-indifferences approach where they use variation across industries in their dependence on external finance and variation across countries in their level of financial development to assess the impact of finance on industry growth. The specific equation they estimate is as follows: Industry Growthi,k =  _i +_  _k +_  (External Dependencek x Financial Developmenti) _+_  Industry Sharei,k +  (Industryk x Countryi)
# **2.3.1 Spatial Feature Abstraction**

The minimum spatial resolution of weather data sets in this analysis is 0.0375 decimal degrees, or approximately four kilometers near the equator. At the same time, we find typically low dispersion of households and plots within enumeration areas (less than two kilometers from an EA centerpoint) across surveys used in this analysis. With this in mind, we expect the weather data sets to provide landscape-level contextual information, but not to capture field-level variation.

The most accurate spatial representation used in this analysis is household location. We assess five alternative representations, which will shed light on the effect of choices made in public dissemination of microdata and the usefulness of such data for research. In addition to household location, we use (1) the average of household locations within EA, (2) an anonymized (offset) EA location, (3) the full extent of anonymizing region (buffer of anonymized EA location), (4) the administrative unit associated with lowest-level locality variable in the public microdata, and (5) the administrative centerpoint.

# **2.3.2 Extraction Method**

The spatial features discussed above are a mix of point and polygon, or area, representations (see Figure 3). We evaluate two commonly employed techniques for merging values from raster data to household roster records using these feature types. For point features we extract weather time series using both simple and bilinear methods. The simple method extracts raster cell values by spatial intersection alone, not accounting for the point location within often arbitrary cell boundaries. The bilinear method computes the distance weighted average of values at four nearest cell centers. It is important to note that the bilinear method would be preferred for integration of continuous data like precipitation and temperature. However, as we are aiming to assess the added value of the more complex calculations in this context, both are evaluated. For polygon features we extract values using a zonal mean, or average of all cells
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We begin with a short literature review, describing the current state of the literature on defaults, expropriation and the relationship between them. In Sections 3 and 4 we describe the expropriation and default dataset. In Section 5 we explore in more detail a number of correlates of expropriations and defaults, both in the long and short run. In Section 6 we describe in more detail the evolution of FDI and debt around expropriation and default events to investigate in more detail the joint retaliation hypothesis. Section 7 concludes.

# **2.  Literature Review**

There is a large theoretical and empirical literature on sovereign default crises. On the theoretical front, questions that have been studied include: Why do sovereign debt markets exist when there is no straightforward legal mechanism to enforce repayment? (pioneered by Gersovitz (1981)); What are the punishments in case of default? (Bulow and Rogoff (1989), Fernandez and Rosenthal (1990) and Rose (2005)); What are the enforcement mechanisms for repayment? (Kletzer and Wright 2000, Cole and Kehoe 1998); and, What is the role of secondary markets in limiting sovereign default risk? (Broner et al. 2010), to mention a few. For the purpose of this paper, we focus more on the empirical literature that studies the determinants of sovereign defaults.

Default usually occurs when the country suffers a series of bad shocks, as is shown in LevyYeyati and Panizza (2006) and Tomz and Wright (2007). However, the empirical relationship between bad output realizations and defaults is not as tight as would be predicted by standard calibrated theoretical models of default. Other factors, such as behavior of creditors and international capital markets (as in Reinhart and Rogoff (2008)), might also influence countries’ default decision. This is because when taking the sovereign debt as given, conditioning on a given level of debt, tighter international financial conditions will make borrowing in bad times more expensive, and defaulting becomes a more attractive option
722-756.

Greene, W., 2003, _Econometric Analysis_ . Fifth Edition, Pearson Education.

GSO, 2011, _Migration and Urbanization in Vietnam: Patterns, Trends and Differentials_ .

GSO, 2010, _Statistical Yearbook of Vietnam 2010_ . Hanoi: Statistical Publishing House.

GSO, 2006, _Statistical Yearbook of Vietnam 2006_ . Hanoi: Statistical Publishing House.

GSO, 2005, _The 2004 Migration Survey: Major Findings_ . Hanoi: Statistical Publishing House.

GSO/UNDP, 2001, _Census Monograph on Internal Migration and Urbanization in Vietnam_ . Hanoi: Statistical Publishing House.

Ha, D. T. and G. Shively, 2008, Coffee Boom, Coffee Bust and Smallholder Response in Vietnam’s Central Highlands. _Review of Development Economics_ , **12** (2), pp. 312-326.

Hausman, J. and D. McFadden, 1984, Specification Tests for the Multinomial Logit Model. _Econometrica_ , **52** , pp. 1219–1240.

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the country fixed effects, these variables capture the impact of the variation in inflation and private credit from the long-run country average.

In Table 3 we present our robustness checks with regressions weighted by the inverse of the square root of the number of firms in the country-year survey. Because there is a large variation in the number of firms in each survey and our variables of interest are measured at the country-year level, the regressions reported so far give more weight to surveys with a larger number of firms, which will have a disproportional impact on the estimated coefficients. We find that weighting by the inverse of the number of firms has no impact on the significance of the Lerner index. We continue to find that low competition has a negative impact on firms’ access to finance.

10
where _T_ is the number of days in the heating season preceding the election year, and are the number of recorded daily instances of temperature switches and precipitation in day ττ,ii _t_ in municipality ττ,ii ss pp _i_ , and and are the indicator variables, which take the value of one if ] and<sup>mm</sup> ], and zero otherwise.<sup>12</sup> ττ,ii 11<sup>SS</sup> 11<sup>PPmm</sup> ss ∈SS(0, ss<sup>m</sup> ττ,ii We calculate the duration of the heating season each year based on the rule posted on the pp ∈PP(0, pp<sup>m</sup> We calculate the duration of the heating season each year based on the rule posted on the official website of the Moscow mayor<sup>13</sup> , which foresees that, _In Moscow, heating is turned on by order of the City Government if the daily average temperature is below 8°C for 5 days, and the weather forecast expects a further decrease. Heating is turned off according to the same principle - if the average daily temperature for 5 days keeps above 8 °C, and according to the weather forecast, it is expected to increase further._ To calculate the duration of the heating season, we use the archived daily temperature of Moscow VDNKh weather station, posted on the website of _Raspisaniye Pogodi Ltd_ , a weather forecast company based in Saint-Petersburg, Russia.<sup>14</sup> We use the same weather archive to obtain the recorded daily instances of temperature switches and precipitation for 13 weather stations located in the city of Moscow and Moscow oblast in close proximity to the city of Moscow. We apply the kriging procedure to interpolate these weather data and compute the switch and precipitation instances at the municipal level. The details are available in the Appendix.

Using the calculated
a significant gap in earnings between service and industry jobs, on one hand, and agriculture, on the other. However, once hours of work and other demographic characteristics are accounted for, this gap is no longer statistically significant. In agriculture, the average hours of work per week was 28 hours, which was significantly lower than other sectors like industry (37 hours) and services (46 hours). Many workers who engage in agriculture, agro-business and fishing in Kinshasa are indeed underemployed compared to other sectors. The underemployment in this sector is likely due to the lack of connectivity and other incentives for the development of agricultural production. However, these workers also have limited access to more productive jobs in urban centers due to higher costs of living and a lack of connective infrastructure—a topic that is discussed more extensively in the following section.

# **3.2. Spatial distribution of jobs**

The geographical distribution of jobs within urban Kinshasa reveals high concentration of jobs in a few pockets within the city. In Kinshasa, more than 70 percent of workers work at office or a particular, non-home workplace. For the spatial analysis of jobs within urban Kinshasa, we rely on the 2017 JICA Commuter Survey, which provides detailed information on the locations of jobs and means of transportation to reach those jobs.<sup>10</sup> Panel (A) in Figure 8 shows the estimated number of jobs per square kilometers at the quartier level. Panel (B) presents spatial variation in the index

> 8 A previous World Bank study in the DRC also highlights the importance of income disparities between men and women especially in urban areas where women appear to be disadvantaged (World Bank 2014). 9 World Bank (2018b, 39).

> 10 See Annex A for details on the 2017/18 JICA Commuter Survey.

16
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data specification of the model and present the empirical results. Section 5 concludes with a comparison of the MIMIC results with those from direct and indirect approaches.

# **2 Underreporting by firms around the world – the direct approach**

While most of the existing empirical research on the unofficial economy uses macro data, a number of recent papers have used firm-level survey data to analyze the determinants and consequences of underreporting of sales and/or output by firms (Johnson, et al. 2000, Gatti and Honorati 2008). These papers take advantage of the increasing availability of largescale and comparable firm-level surveys in which firms have been asked about their tax reporting behavior, such as in the BEEPS, (P)ICS and WBES surveys. Because the firms in these surveys are typically registered firms, the papers analyze informal activity by otherwise formal (registered) firms using the so-called direct approach.

Because of the sensitive nature of the subject, the question about reporting behavior is phrased in terms of actions of ‘typical firms in your area of activity’: _Recognizing the difficulties many enterprises face in fully complying with taxes and regulations, what percentage of total sales would you estimate the typical establishment in your area of activity reports for tax purposes?_ Researchers have explicitly or implicitly interpreted the firms’ responses to this question as indicators of underreporting at the firm-level.

These studies often report a sizeable degree of underreporting together with intuitive correlations with observable firm and investment climate characteristics. This suggests that these self-reported measures do capture underreporting, at least to some degree. Moreover, Johnson _et al_ . (2000) also note that the estimates of hidden activity from the 7