rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5531

# **Abstract**

According to World Bank policy, countries remain eligible to borrow from the International Bank for Reconstruction and Development until they are able to sustain long-term development without further recourse to Bank financing. Graduation from the Bank is not an automatic consequence of reaching a particular income level, but rather is supposed to be based on a determination of whether the country has reached a level of institutional development and capital-market access that enables it to sustain its own development process without recourse to Bank funding. This paper assesses how International Bank for Reconstruction and Development graduation policy operates in practice, investigating what income and non-income factors appear to have influenced graduation decisions in recent decades, based on panel data for 1982 through 2008. Explanatory variables include the per-capita income of the country, as well as measures of institutional development and market access that are cited as criteria by the graduation policy, and other plausible explanatory variables that capture the levels of economic development and vulnerability of the country. The authors find that the observed correlates of Bank graduation are generally consistent with the stated policy. Countries that are wealthier, more creditworthy, more institutionally developed, and less vulnerable to shocks are more likely to have graduated. Predicted probabilities generated by the model correspond closely to the actual graduation and de-graduation experiences of most countries (such as Korea and Trinidad and Tobago), and suggest that Hungary and Latvia may have graduated prematurely—a prediction consistent with their subsequent return to borrowing from the Bank in the wake of the global financial crisis.

This paper is a product of the Human Development and Public Services 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 sknack@worldbank.org or hrogers@worldbank.org.

_The Policy Research Working Paper Series
attacks. The consumption basket of exposed households was 3% more expensive for these items, providing some evidence that higher food prices could also be another relevant mechanism through which a terrorist incident affects consumption levels.<sup>26</sup> Figure 4: Distributional effect across consumption percentiles in Mogadishu <!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>p=0.0 p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 p=0.9 p=1.0<br>Consumption percentile<br>Estimated impact on log of core consumption<br><!-- End of picture text --> Source: Authors’ calculations based on data from the SHFS and ACLED. Note: The vertical lines depict 95% confidence intervals.

We extend the analysis to investigate how the impact on consumption differs across consumption percentiles and the spatial variation of this negative effect.<sup>27</sup> For these extensions, we use our most complete specification from the DiD approach, the sample of households from Mogadishu and the sampling weights of surveys. Figure 4 plots the point estimates for the effect of incidents on core consumption from a quantile regression. For the negative and immediate impact of terrorist attacks on consumption, we find a heterogeneous effect across different parts of the consumption distribution. The point estimates increase with the consumption decile. Incidents affect exposed households from most of the consumption distribution, except those in the top 20%. The estimates for
the general perception among investors that these the states have the “best” business environment in India. Responses to the World Bank Investment Climate surveys seem to confirm that this reputation holds among domestic small and medium sized firms as well. What we have grouped as moderate FDI states are also ranked higher by respondents of the World Bank survey than the remaining six states. If self-selectivity of businesses to or away from better business environment cities takes place on unobservable factors, we should observe it at the level of mobility between high FDI states and low FDI states as well.

The first panel of the table reports OLS estimates of equation (7) with location dummies aggregated into the three regions of FDI flows with moderate FDI states as the base group. The second panel presents random effects (GLS) estimates of the same specifications. We report t-values based on robust standard errors in both cases. Within each panel, specifications differ only to the extent of exclusion restrictions intended to highlight correlation among exogenous variables. The specifications that we should focus on in each panel are the first two. The difference between the two within each panel is that we adjust for differences in product market power only in one of them. The most obvious significance of the omission is that a halving of the goodness of fit of both the OLS and the GLS regressions.

Comparison of the GLS estimates with their OLS counterparts suggests the presence of heterogeneity bias: our estimate of rho is statistically different from zero in all alternative specifications, and the GLS estimates of the effects of economic geography are as a rule significantly different from their OLS counterparts. This is setting aside the problem of selectivity bias to which we now turn, and of which there are signs already in Table 5. One sign is that, although labor regulation comes out as a statistically significant influence on productivity in the GLS panel, this is not the case with power shortages. The GLS estimates suggest that a doubling of the percentage of managers complaining about labor regulation would be associated with a four-percentage point
- 62 -

sources of employment data. Data should therefore be taken with a certain amount of caution. Military employment is taken form the International Institute for Strategic Studies, The Military Balance. 1995/96 and relates to 1996. GDP estimate and Wages and salaries as percent of GDP figures are taken from the IMF Report SM/951243 of September 20, 1994 and relate to 1993. They are confirmed by Stephen Lister's (EC4C2) draft discussion paper on Public Administration dated 24 September 95. Average wage estimate is also from IMF Report (monthly for 1993). Lister's draft paper indicates that monthly wage must be multiplied by 15 to obtain yearly wage.

# **Azerbaijan**

Data on paid employment in non-agricultural activities are taken from Statistical Handbook 1995: States of the Former USSR and are for 1994.

Government employment, education and Health employment is taken from Statistical Handbook. 1995 States of the Former USSR and relate to 1994. Health employment is also from the same source and corresponds to category " Health care, social security, physical culture and sports" and relates to 1994. Education employment is also from the same source and relates to 1994.

GDP and wages and salaries estimates are from WB's Statistical Handbook 1995: States of the Former USSR, and relate to 1993. Average wage is taken from IMF Report No. SM/94/128 of May 26, 1994 and relates to 1993.

# **Belarus**

Unemployment data are drawn from The Belarussan Labor Market drafted on June 11, 1993 (p. 14). Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Central and Local Govemment employment are staff estimates based on information present in the OECD's Local Governments in the CEE and CIS: an anthology of descriptive Dapers as well as that present in the IMF Report No. SMW95/194 of August 11, 1995, and relates to 1992. Data on employment
, 2002). Key data drawn from the report is shown in Table 2.

adult equivalent basis used for the poverty figures. It is also possible that a slightly different consumption aggregate than that employed here was used to calculate mean consumption.

> 3 It would be possible to examine the sampling frame issue in more detail by comparing the distributions of household characteristics in the 2002 census and the 2000/01 survey for Dar es Salaam. If the distributions match closely, drift in the sampling frame is unlikely to have had a large effect on the poverty figures. If the distributions do not match, the problem could be addressed by reweighting the survey data to match the distribution of characteristics in the census data. The issue of reweighting for Dar es Salaam will be addressed as part of the ongoing poverty mapping project.
example, the gross enrollment ratio (GER) in primary education increased from 58 percent in 1979 to 101 percent in 2019. During the same period, the GER in secondary education increased from 27 percent to 112 percent. Although increased access to education is a positive development, these large gains in access have not been accompanied by similar improvements in students’ learning outcomes.

Overall, learning outcomes remain below expectations in Saudi Arabia. Data from TIMSS 2019 show that, in mathematics, Saudi Arabia ranks 53rd of 58 countries (i.e., below 52 countries) in grade 4 and 37th of 39 (outperformed by 36 countries) in grade 8. In PISA 2018, less than half (48 percent) of 15year-old students in Saudi Arabia achieved minimum proficiency in reading and almost no student was a top performer (i.e., achieving levels 5 or 6; OECD 2019). Additionally, only 27 percent of Saudi students in the same age group achieved at least minimum proficiency in mathematics, compared to an Organisation for Economic Co-operation and Development (OECD) average of 75 percent. In particular, learning outcomes in Saudi Arabia are low relative to the country’s level of wealth, and studies (Patrinos and Angrist 2019) have identified Saudi Arabia as an outlier when examining Harmonized Learning Outcomes<sup>4</sup> relative to gross domestic product (GDP) worldwide. Although, as noted above, significant gender differences in achievement can be found in other countries — sometimes in favor of boys, and sometimes in favor of girls (Mullis et al. 2017; 2020) — the gender differences observed in Saudi Arabia are consistently among the largest in the world, with girls showing a consistent advantage, across grade levels and subject areas.

> 4 A composite indicator of learning outcomes at the country level, based on data from large-scale assessments such as TIMSS, PISA, Progress in International Reading Literacy Study (PIRLS), and early-grade reading or mathematics assessments (Angrist et al. 2021
0.1|
|Lightingfixtures and fittings andparts|8124|2653|0.3|0.0|0.1|0.0|1.1|4.6|
|Miscellaneous articles of materials of div.58|8939|2549|0.3|0.0|0.3|0.2|0|0.4|
|Hygienic andpharmaceutical articles of rubber|6281|1885|0.2|0.0|5.6|0.2|0|4.1|

_Source_ : Calculated using UN COMTRADE Database _Notes_ : A value of 0.0 represents less than US$100 in exports; blanks represent zero exports.

35
Liu, Jing, Nico van Leeuwen, Tri Thanh Vo, Rod Tyers, and Thomas Hertel. 1998a. “Disaggregating Labor Payments by Skill Level in GTAP,” GTAP Technical Paper No. 11, Purdue University, West Lafayette, Indiana.

Liu, Jing, Nico van Leeuwen, Tri Thanh Vo, Rod Tyers, and Thomas Hertel. 1998b. Aziz Elbehri, and Truong P. Truong. _Global Trade, Assistance and Protection: The GTAP 4 Data Base_ , Center for Global Trade Analysis, Purdue University, West Lafayette, Indiana.

McCallum, J. (1995). National borders matter: Canada-US regional trade patterns. The American Economic Review, 85(3), 615-623.

OECD (2013), “Interconnected Economies: Benefiting from Global Value Chains”, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/9789264189560-en OECD (2015) “Participation of developing countries in global value chains – implications for trade and trade-related policies”.

OECD (2015a), “Contribution of Trade Facilitation Measures to the Operation of Supply Chains”, forthcoming OECD Trade Policy Paper, <u>http://www.oecd-ilibrary.org/trade/oecd-trade-policyworkingpapers_18166873</u> OECD (2015b), “Developing countries participation in global value chains and its implications for trade and trade related policies”, OECD Trade Policy Papers, No. 179, OECD Publishing, Paris. DOI:http://dx.doi.org/10.1787/5js33lfw0xxn-en Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., &Pietronero, L. (2012). A new metrics for countries' fitness and products' complexity. Scientific reports, 2, 723.

Timmer et al. (2012), The World Input-Output Database (WIOD): Contents, Sources and Methods, WIOD Background document available at <u>www.wiod.org.</u> Timmer, Marcel
starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating. Institutions were then assigned a country. An _h_ - index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2). The final rank column denotes the global rank among all institutions.
sup>14</sup> The sample focused on settlements where the Roma population’s share equals or is higher than the national share of Roma population, also known as “marginalized Roma.”<sup>15</sup> “Non-Roma” refers to the non-Roma population living in close vicinity to the marginalized Roma and is not meant to be representative of the total population of the country.<sup>16</sup> The Roma estimates presented from now on in this paper do not include non-Roma.

**The analysis in this paper relies on labor market and time usage data from the 2017 RRS.** The data were collected in 4 modules: management section, household members profile, the status of the household, and attitudes of the randomly selected respondent. For the labor market and labor income variables, modules 2 and 3 were used.<sup>17</sup> We estimate wives’ and husband’s labor market status and

> 12 The 2017 RRS was conducted in Albania, Bosnia and Herzegovina, Kosovo, North Macedonia, Serbia, and Montenegro. 13 Those who self-identified as “Gypsies” in the survey are categorized as Roma by interviewers.

14 In a first phase, enumerators approached the externally identified Roma household and in a second stage the implicit self-identification is used. Questions on self-identification, interviewer identification, and language were included in both survey rounds so that the surveys allow for different definitions of ethnicity and a more nuanced discussion of the different groups.

15 In 2011, the sample was restricted to areas within settlements in which Roma represented 40 percent or more of the population. In 2017, areas in which Roma represented between 10 and 40 percent of the population were also included. These areas represent between 36 and 55 percent of the unweighted country samples.

16 The non-Roma neighboring population refers to population living within 300 meters of the Roma settlements. 17 Definitions for employment and unemployment are consistent with the International Labour Organization (ILO) definitions. The employed
.1 issues**

Intergenerational mobility studies have been fraught with econometric challenges that have arisen because of unobservable heterogeneity, including the inheritance of genetic endowments such as ability and preference across generations. The partial correlation observed in the data might be mainly driven by the transmission of preference and ability between parents and children. Previous studies attribute the partial, but high correlations between parents’ and children’s educational outcomes to nature and nurture, among other factors (Becker and Tomes, 1986; Haveman and Wolfe, 1995; Black and Devereux, 2011; Checchi et al., 2013). Nature refers to a genetic transmission of the ability of a parent to a child. Able parents have a higher chance of producing to have more able children who can attain higher levels of education without special parental investment. For instance, a child might learn skills through observation without any additional effort from parents (Haveman and Wolfe, 1995; Basu and Getachew, 2015). Nurture pertains to the amount of time and economic investments of parents on a child’s human capital accumulation.

The standard approach in tackling unobserved heterogeneity is to use instrumental variables. The challenge is to identify exogenous variables that affect parental educational attainment, but do not have any effect on children’s educational attainment. However, the instrumental variables used widely in the literature such as family background variables tend to affect children’s outcomes, including our main interest here, education. Some studies use data on adoption (Plug, 2004; Plug and Vijverberg, 2003) and twins (Behrman and Rosenzweig, 2005) to isolate the effect of nature from the effect of nurture. However, these studies are limited to developed countries, where reliable data are available. Other studies compare the effect of nature and nurture on social mobility and find that nurture is relatively more important in explaining parent-child education transmission (Checchi et al., 2013; Haveman and Wolfe, 1995). In the absence of quasi-experimental data and credible instruments, we limit our analysis to the
A final note of caution around the estimation results is that there is most likely measurement error in our variables that could potentially lead to downward bias in the estimates. This is not dealt with in the current draft.

We now present the data and regression results, followed by the statistical analysis and robustness testing.

**3. Presentation of results of the derivation of vulnerability relationships for the impact of drought on poverty in Ethiopia**

This section discusses the results of the vulnerability relationships estimated. We first present the descriptive statistics, and then the tables of results. The next section discusses the statistical learning methods attempting to validate the regression results.

- **a. Data**

The data used by Hill and Porter (2014) to establish vulnerability to poverty relationships is used as the main dataset for the initial regression results. We use the data collected in the 2005 and 2011 rounds of the nationally representative Household Income and Consumption Expenditure and Welfare Monitoring Surveys (HICES/WMS). These contain information on just under 25,000 households in each year. The key information recorded in the HICE used to calculate vulnerability is expenditure on food and other items. The WMS records household assets and characteristics as well as a fairly detailed module on self‐reported adverse events (referred to as shocks throughout). In both years they were administered by Ethiopia's Central Statistics Agency (CSA).

The advantage of using the HICES‐WMS for vulnerability analysis is that they are relatively large, nationally representative, comparable across years and allow measures of vulnerability to be estimated at the household level. This allows us to look at the relative importance of geographic and household factors in determining vulnerability, and it also allows us to examine how vulnerability varies across certain groups of households.

As noted above, we exclude pastoral areas of Ethiopia from the preliminary analysis as we believe the relationship between rainfall and consumption to be structurally different to that of other regions. The descriptive statistics for the two datasets are shown in table 3.
checks, etc.). And for digital marketing practices, this verification occurred by collecting and auditing digital footprints (e.g. Twitter handles, Facebook page addresses, Instagram pages, Website URLs, etc.).

These 41 business practice measures are then formed into six indices (see Appendix 4 for components): Finance and Accounting (10 practices); Marketing and Sales (9 practices); Operations and HR (11 practices); Verified Traditional Practices (10 of 30 practices); Verified Digital Marketing Practices (11 practices); Overall Index (41 practices). Each index takes the form of the proportion of business practices used by firms and therefore ranges from 0 to 1. The mean firm in the control group implemented 47.3% of the 41 practices at the time of the first follow-up, and 45.1% at the time of the second follow-up. This shows that firms had room to improve their business practices.

<u>Firm Growth. We used multiple measures of sales, profits and employees to proxy for firm growth.</u> Two measures of firm sales were included (total sales last month; total sales last year), with each winsorized at the 99<sup>th</sup> percentile then transformed using the inverse hyperbolic sine (IHS) function. Similarly, two measures of firm profits were used (total profits last month; total profits last year). Both were winsorized at the 99<sup>th</sup> percentile then IHS transformed. These 4 measures were also combined to construct a fifth measure: an overall Sales/Profit Index (computed by summing up the standardized z-score of each measure). As an additional proxy of growth, we also included two measures of firm employees. The first was total employment (sum of all wage/salary workers, casual/daily workers, apprentices/interns and unpaid workers) winsorized at the 99<sup>th</sup> percentile. The second