rafmacalaba/data-use-mentions-extended huggingface dataset:
9 discussion on conditional and absolute convergence, we recognize that significant differences exist between CEE and EU countries and that, therefore, a model based solely on the concept of absolute convergence would inadequately address the bridging of income gaps between CEE countries and the European Community. Hence, the model presented in this section will allow for differences in environments among countries, thus implying an analysis based on the concept of conditional convergence.

# **Box 2: Data** Used in the Econometric Estimation.

To estimate our growth equation we use cross-section data from the version Mark 5.6 of the Penn World Tables compiled by Summers and Heston (1991). This data is based on the United Nations International Comparison Project which aims at providing comparable national accounts information by using detailed price comparisons for over one-hundred products. The prices are then used to convert local currency country data into a common currency unit, therefore enabling real quantity comparisons across countries. This data is available from the NBER web site.

From this database we use the GDP per capita for the years 1960, 1965, and 1989, defined as persons in the labor force (i.e., the economically active population), and the share of GDP assigned to investment in physical capital, calculated as the annual average share for the period between 1960 and 1989. The rate of growth of the labor force is the average corresponding to the period between 1960 and 1989 and is calculated from the annual data on population and GDP per capita for that period, the latter both in terms of persons in the labor force as well as in terms of total population. We assume that the rate of technological progress and the depreciation rate are the same for each country, the sum of which is set equal to five percent a year. Setting the sum of technological progress and depreciation rate at 5 percent is in line with other studies, such as Mankiw, Romer, and Weil (1992) and King and Rebelo (1990). The data on human capital is
by the alignment of votes between recipient and donors in the United Nations General Assembly (UNGA) using data from Strezhnev and Voeten (2009). Annual measures of voting alignment range from 0 to 1 with higher values implying higher similarity and are calculated as in Kilby (2013) using all UN votes. We calculate the mean alignment of the largest three sovereign trust fund donors, the United Kingdom, the United States and the Netherlands, which we label as “the G3.”<sup>19</sup> We exclude the European Commission, the second largest donor, because its preference aggregation process is more complex (e.g., Reinsberg et al. 2014). Second, we add a dummy for a colonial relationship between a G3 donor and the recipient country. Third, commercial interests are measured by total G3 exports, using data from the IMF (2015). A thorough test of the importance of these donor‐interest variables in aid allocation decisions would require a different data set, with donor‐recipient‐year observations. In our analysis, we merely use these as control variables.

Table 2 displays the descriptive statistics for the dependent and control variables in our sample. The average recipient country receives 1.8 USD trust fund aid per capita, a relative minor inflow compared to average per capita aid of 6.7 USD from IDA<sup>20</sup> and 67 USD from other sources.<sup>21</sup> However, trust fund volumes have increased over the sample period and represent significant inflows for some recipient countries as indicated by the maximum value of more than 82 USD trust fund aid per capita. Average GNI per capita is 2,744 USD, which the Bank classified as a lower middle income country in the fiscal year 2012. The mean of the aggregate CPIA rating is just below the median value of 3.5, and the maximum value is almost 5. Recipient countries’ education, health and environmental policy ratings range
Policy Research Working Paper 10703

# **Abstract**

This paper describes the state of informal sector work in urban Sub-Saharan Africa, using household surveys from 26 countries representing 61 percent of the population of Sub-Saharan Africa and firm surveys from three countries. Five main conclusions emerge. First, the urban informal sector is large and persistent in Sub-Saharan Africa. Approximately 56 to 65 percent of urban workers are informal, half of whom are self-employed. Data from five countries suggest little systematic reduction in the prevalence of informality during the 2010s. Second, heterogeneity in the African informal sector cuts along demographic lines. Women are overrepresented in informal self-employment, men in informal wage work, and youth in unpaid employment. Third, while the urban informal workers are, on average, poorer and in less-skilled occupations than formal sector workers, the majority are not extremely poor and are in mid-skilled occupations. Fourth, informal enterprises are small and are challenged to survive and grow into job-creating firms. Few find much benefit from registration given the costs, both monetary (taxes) and transactional (information about the registration process). Fifth, access to urban public services (utilities) is weakly associated with the probability of working in an informal job, although access to mobile phones is high across all job types. If thriving urban jobs are to contribute to economic and social development in Africa, it will be crucial for policies and programs to take into consideration the heterogeneity in jobs, the profile of workers, and the urban context.

This paper is a product of the Social Protection and Jobs Global Practice and the Poverty and Equity Global Practice. 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 wcunningham@worldbank.org, dnewhouse@worldbank.org, fricaldi@worldbank.org, and
few lessons and provides useful insights into ways to go about it.

# **8. Bibliography**

<mark>Backiny-Yetna, P., Steele, D., and Djima, I. (2014)</mark> _<mark>. “</mark>_ <mark>The impact of household food consumption data collection methods on poverty and inequality measures in Niger”. World Bank Group Policy Research Working Paper 7090.</mark> <mark>Bee, Adam, Bruce D. Meyer, and James X. Sullivan (2012). “The Validity of Consumption Data: Are the Consumer Expenditure Interview and Diary Surveys Informative?” No. w18308. National Bureau of Economic Research.</mark> <mark>Beegle, Kathleen, et al. (2012). "Methods of household consumption measurement through surveys: Experimental results from Tanzania."</mark> _<mark>Journal of Development Economics</mark>_ <mark>98.1: 3-18.</mark>

- <mark>Borlizzi, Andrea, Del Grossi, Mauro Eduardo, Carlo, Cafiero (2017). “National food security assessment through the analysis of food consumption data from household budget and expenditure surveys: the case of Brazil’s Pesquisa de Orçamento Familiares 2008/09”.</mark> _<mark>Food Policy.</mark>_

34
We examine the determinants of FDI flows using the gravity-type empirical specification that has been widely used in previous studies such as the seminal work by Markusen et al. (2001) and Yeaple (2003). The specification is as follows: where subscripts i and j index host and parent countries, respectively, and the subscript t indexes year, BilateralFDI is the bilateral FDI flow from country i to country j, MktSize a vector of GDP of the host and source countries, HCdiff measures the host and source countries’ difference in human capital, Transport is a vector of transportation cost measures between country i and country j including air distance, sea distance, driving distance and driving time, Infrastructure is a vector of indices that measure the quality of trade-related logistic infrastructure including railway, airway, and port quality, PTAdepth measures the depth of the trade agreement between the host and source countries using the WTO RTA data, X is a vector of other country characteristics including a dummy variable for sharing border, a dummy for using same language, a dummy for sharing colonial ties, and a measure of business environment using the number of days required to start a business, D� and D� are vectors of country dummies used to control for all time-invariant country characteristics, and D� ia a vector of year dummies.

Figure A.3 shows positive and statistically significant correlations between FDI inflows and host countries’ GDP per capita, school enrollment, logistics performance, and port infrastructure quality. Countries with a higher income, higher levels of human capital, and better logistic and port infrastructure quality are shown to receive greater FDI inflows.

To examine empirically the effects of host-country economic characteristics, we estimate equation (1) first for a pooled sample of countries and then separating BRI and non-BRI countries to investigate how the determinants of investment flows might vary between BRI and the rest of the world.

Table 9 shows that most of the empirical regularities established in previous studies hold in our broad cross-country data. Most of the factors that have been
where is an error term, and ∆ denotes difference. The error correction representation of the selected ARDL model can be constructed by replacing the part of the lag of level μμ variables in equation (A3) with an error correction term ( _EC_ ).

# **A2. Data and estimation results**

Quarterly data on Cambodian exports to the EU and the US federal fund rate are obtained from the DOTS and the IFS databases published by the IMF, respectively. The EU’s GDP is used for its income, and its real value is calculated by using the GDP deflator. Both GDP and GDP deflator data for the EU are taken from the Eurostat database. The sample period of the estimation is from 1995Q1 to 2016Q4, based on data availability.

In the ARDL approach to cointegration, a statistical test to check the existence of the cointegration or long-run relationship among variables needs to be implemented in advance. Specifically, the F-test is used to test the null hypothesis of no cointegration, H0: _λ_ 1= _λ_ 2= _λ_ 3=0, against its null hypothesis, H0: _λ_ 1≠0, _λ_ 2≠0, _λ_ 3≠0. The computed F-statistic from this test is compared with its critical value available in Pesaran et al. (2001) for determining whether or not the null hypothesis should be rejected. Although the computed statistic does not support the existence of the cointegration relationship, its values varies significantly with different lag lengths. Following Bahmani-Oskooee and Nasir (2004) and Okada and Samreth (2013), the statistical significance of the error correction ( _EC_ ) term in the next estimation step may be examined to evaluate the existence of the cointegration relationship.

Equation (A3) in which the part of the lag of level variables is replaced by an error correction term ( _EC_ ) is estimated, using the ARDL approach to cointegration. The maximum lag order is set to four for this estimation.<sup>18</sup> Following Narayan (2004) and Okada
Change Mitigation? Evidence of Values, Preferences and Carbon Savings from a Choice Experiment in Ethiopia. World Bank Policy Research Working Paper 8499 (June).

- <mark>Gebreegziabher, Z. Beyene, A, Bluffstone, R, Martinsson, P, Mekonnen, A, Toman, M. (2018). Fuel Savings, Cooking Time and User Satisfaction with Improved Biomass Cookstoves: Evidence from Controlled Cooking Tests in Ethiopia.</mark> _<mark>Resource and Energy Economics</mark>_ <mark>52: 173-185.</mark>

- <mark>Marra, M., Pannell, D. J., & Ghadim, A. A. (2003). The Economics of Risk, Uncertainty and Learning in the Adoption of New Agricultural Technologies: Where are We on the Learning Curve?.</mark> _<mark>Agricultural Systems</mark>_ <mark>75(2-3), 215-234.</mark>

- <mark>Miller, G., & Mobarak, A. M. (2014). Learning about New Technologies through Social Networks: Experimental Evidence on Nontraditional Stoves in Bangladesh.</mark> _<mark>Marketing Science</mark>_ <mark>34(4), 480-499.</mark>

- Mørkbak, M.R., and Olsen, S.B. (2014) A Within-Sample Investigation of Test-Retest Reliability in Choice Experiment Surveys with Real Economic Incentives. _Agricultural and Resource Economics_ 59, 375-392.

- Price, J., Dupont, D., and Adamowicz, W. (2016) As Time Goes By: Examination of Temporal Stability Across Stated Preference Question Formats. _Environmental and Resource Economics_ 68(3), 643-662.

- <mark>Ruiz-Mercado, I., Masera, O., Zamora, H., & Smith, K. R. (2011). Adoption and Sustained Use of Improved Cookstoves.</mark>
## **_Impact of interest_**

**Third, the impact of these health and nutrition outcomes is the economic costs to individuals and society.** Economic costs are defined as the direct and indirect impacts of malnutrition on individual and national economic activity, including the costs of preventable mortality, costs of medical care and income lost to ill health, costs of impaired physical growth, and costs and losses linked to impaired cognitive growth (Global Panel 2016).

The measures of economic impact (i.e. cost) include disability adjusted life years (DALYs), the monetary value of losses of goods and services production in a country each year (gross domestic product, GDP), as well as various measures of human capital, such as years of educational attainment and value of future income/wage earnings. Studies on the cost of diet-related NCDs also frequently report the annual per capita health care costs associated with health services (hospitalization, consultations) and treatment of NCDs (medications), as well as indirect health care costs, such as work absenteeism and reduced work productivity.

# Literature search approach

We chose to conduct a scoping review as this type of review is useful for presenting a broad overview of the evidence for a specific topic, examining emerging aspects of research on that topic, clarifying key concepts and identifying gaps (Tricco et al. 2016). The scoping review aimed to assess the extent to which research on the contribution of unhealthy diets to nutrition and health outcomes and their associated economic costs in LMICs has been undertaken. This implied mapping the types of evidence available on which subpopulations (life stage groups) are represented, which diet metrics (or specific dietary factors) are used, and which outcome types and economic costs are considered.

As a first step, we conducted a comprehensive literature search to identify primary research articles that included all three components described in our conceptual framework, namely: a diet metric (or a specific dietary factor), a nutrition or health outcome, and
X. Liu, L. Song. 2011. “Internal Finance and Growth: Microeconometric Evidence on Chinese Firms.” _Journal of Development Economics_ 96(1): 79-94.

- Hanousek, J., S. Shamshur, J. Tresl. 2017. “Firm efficiency, foreign ownership and CEO gender in corrupt environments.” _Journal of Corporate Finance_ 59, 344-360.

- Harrison, G. W., J.A. List. 2004. “Field Experiments,” _Journal of Economic Literature_ 42(4), 1009-55.

- Huang, J., D.J. Kisgen. 2013. “Gender and corporate finance: Are male executives overconfident relative to female executives?” _Journal of Financial Economics_ 108, 822839.

- Hundley, G. 2001. “Why women earn less than men in self-employment.” _Journal of Labor Research_ 22(4), 817-29.

- Kalleberg, A., and K. Leicht. 1991. “Gender and organizational performance: Determinants of small business survival and success.” _Academy of Management Journal_ 34(1): 136-61.

- Kaufman, D., A. Kraay, M. Mastruzzi. 2004. “Governance matters III: Governance Indicators for 1996, 1998, 2000, and 2002.” _World Bank Economic Review_ 18(2),  253287.

- Keefer, P., S. Knack. 2005. “Social Capital, Social Norms and the New Institutional Economics.” In Cluade Menard and Mary Shirley, eds., _Handbook of New Institutional Economics,_ Sringer.

- Hallward-Driemeier, M., S. Wallsten, L.C. Xu. 2006. "The Investment Climate and the Firm: Firm-Level Evidence from China" _Economics of Transition_ , 13(1), 1-24.

27
18%|8%|
|Mean (months)|4.4|4.5|2.8|1.8|
|Median (months)|2|2|1|1|

Note: RoW is the rest of the world. Source: Authors’ compilation using PSA data

# **4 Methodology**

An event-study approach is employed to establish the effect of COVID-19 lockdown policies on international trade in the Philippines. This approach is preferred over impact-evaluation estimators such as the difference-in-differences (DiD) and Propensity Score Matching (PSM). DiD and PSM compare the outcome before and after a treatment has been imposed on a control and treatment group. Given that all countries in our sample imposed lockdowns, we do not have a non-treated control group (Majune and Addisu, 2021). Another advantage of the event-study approach is that it addresses concerns of lockdown anticipation. Since East Asia was the first epicenter of COVID-19, most countries started imposing restrictions earlier than the rest of the world (see Figure 1) which could have triggered the reaction of exporters and importers in the Philippines in anticipation of the imposition of lockdowns in partner countries. The event-study also enables us to observe the reaction of international trade to lockdown policies at high frequency (months) which often changed depending on the spread/wave of the virus in a country. The event-study model is specified as follows: 19
strategies employed by households in urban and rural areas, may translate into reduced income and reduced food availability at the household level.

Numerous studies have examined the impacts of natural disasters and extreme weatherrelated shocks on different dimensions of welfare (see Baez and Mason 2008, and World Bank 2010b for a thorough review of this literature). In general, they all show that agricultural incomes and, thus, food, basic non-food consumption and investments in human capital, health, nutrition and productive physical assets are likely to be negatively affected by extreme weather events. Many of these studies however, tend to rely on the perceptions of respondents about the incidence of different types of shocks or use data on rainfall and temperature as a tool (e.g. as instrumental variables) to analyze the effect of shocks to income on other outcomes, such as consumption or investments in human capital.<sup>24</sup> Hardly any studies use actual weather data to analyze the general relationship between weather and the level of welfare. A recent study by Skoufias, Vinha, and Conroy (2011) examines whether climatic variability, namely deviations of rainfall and temperature from their long run means, have significant impacts on the average wellbeing of rural households in Mexico. They report that the timing of the rainfall or temperature shock results in a substantial difference in its estimated impact on welfare. For example, per capita expenditures are 14 percent higher if the prior agricultural year (October to September of next calendar year) was at least one standard deviation drier than the average of the last 35 years (1951-1985). However, if the rainfall shock were to be defined within the time frame of the wet season (April to September) then neither positive nor negative rainfall shocks during that wet season appear to have any significant effect on household per capita expenditures.

In a study building on these insights, Skoufias, Essama-Nssah, and Katayama (2011) use data from rural Indonesia and consider two rainfall-related shocks: (i) a delay in the
)|(0.25)|(0.15)|(0.24)|(0.97)|
|Observations|2,000|2,000|2,000|2,000|2,000|
|Adjusted R-squared|0.66|0.27|0.63|0.34|0.08|

_Notes_ : (1) Higher the outcome variables, higher the conservativeness of beliefs and social expectations and higher the expected sanctions. (2) Reference group for province is Bagmati Province. (3) SEs clustered at the ward level. (4 Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. SEs clustered at the ward level.

Contrary to intuition, we find no evidence of respondents in rural areas being more likely to hold conservative beliefs either about norms around women’s household roles, or about women being in the public space as compared to those in urban areas. However, those in rural areas are more likely to expect negative sanctions for breaching a norm. These variables, together, explain between 27 and 66 percent of the variation in the social norms indices, and 8 percent of the variation in the sanctions index.

Geographically, respondents from Madhesh and Sudurpashchim Provinces are more likely to hold conservative beliefs and social expectations, as compared to those from Bagmati Province. On the other hand, respondents from Gandaki Province are more likely to be liberal. Relatedly, those who belong to the Madheshi ethnic group tend to be more conservative. These differences are supported by earlier literature showing that Nepal’s socio-cultural practices differ along the lines of castes and ethnicities. For example, Chakravarty et al. (2019) find that in Southern Nepal (Terai and Madhesh), women are more frequently confined to the 24
certain banks become too big to fail (see Koskela and Stenbacka, 2000; Beck, Demirguc-Kunt, and Levine 2006; Carletti, Hartmann, and Spagnolo, 2007; Schaeck and Cihak, 2008 among others).

Using bank-level data for the period 2002-2008, this paper examines the extent of bank competition in Russia. We contrast banking sector competition in Russia to that in similar sized emerging countries, namely Brazil, China, and India. Furthermore, because the Russian banking sector is quite fragmented, we compare measures of competition for different groups of banks. In particular, we conduct tests to examine differences between the top 20 banks vis-à-vis other banks, government-owned banks vis-à-vis privately-owned institutions, foreign banks vis- à-vis domestic banks, and business-oriented vis-à-vis consumer-oriented banks.

There are two main approaches to measuring bank competition: the structural approach and the non-structural approach. As the name suggests, the structural approach assesses bank competition by examining measures of market structure such as concentration ratios (the share of assets held by the top 3 or 5 institutions) or indices (e.g., the Herfindhal-Hirschman index). The theoretical justification for using concentration as a measure of competition comes from the socalled Structure-Conduct-Performance paradigm that postulates that fewer and larger firms (higher concentration) are more likely to engage in anticompetitive behavior.<sup>**3**</sup> However, studies have shown that at times concentration is not a reliable measure of competition (see Cetorelli, 1999) and the link between concentration and performance is not always positive as suggested by the Structure-Conduct-Performance paradigm (see Jackson, 1992). Furthermore, even concentrated banking sectors can be competitive if they remain contestable by promoting entry and exit.

> **3** For a discussion on the Structure-Conduct-Performance paradigm see Berger (1995).

3