rafmacalaba/data-use-mentions-extended huggingface dataset:
larger focus on countries with high poverty rates.

Once we have selected the model that minimizes the mean absolute deviation following the details above, we will apply the selected model to predict poverty in the nearcasting and nowcasting years, which for the present paper are 2020 and 2021.

# **3 Data**

All poverty estimates used in this paper come from PovcalNet, which contains the World Bank’s official country-level, regional, and global estimates of poverty. Most of the data in PovcalNet comes from the Global Monitoring Database, which is the World Bank’s repository of harmonized multitopic income and expenditure household surveys used to monitor global poverty. PovcalNet contains more than 2,000 surveys from 168 countries covering 98% of the world’s population. The data available in PovcalNet are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption. By relying on the PovcalNet database, we ensure consistency with the official numbers used by the World Bank and United Nations for monitoring poverty, inequality, and related goals.

To predict poverty, we rely on variables from three databases. First, the World Bank’s World Development Indicators, which contain country-year information on nearly 1,300 variables covering a wide range of topics, such as health, agriculture, education, climate change, infrastructure and more (but as we will explain below, only a small selection of these can be used for the present exercise). Second, we rely on the IMF’s World Economic Outlook (WEO) database, which contains country-year information for about 50 variables related to macroeconomic outcomes, such as inflation, government debt, and unemployment. Third, we rely on remote sensing data from the Google Earth Engine, particularly, data on nighttime lights, rainfall, land surface temperature, impervious surface, cropland, and normalized difference vegetation, snow, and water indices. In contrast to WEO and WDI, the remote sensing data are both
<!-- Start of picture text --> Limited reconstruction<br>capacity<br>Reduced economic Long reconstruction<br>Amplifying<br>development period after disasters<br>feedback<br>loop<br>Reduced accumulation of Large economic cost of<br>natural disasters<br>capital and infrastructure<br><!-- End of picture text --> _Figure 3: Amplifying feedback loop that illustrates how natural disasters could become responsible for macro‐level poverty traps._ This type of feedback can be amplified by other long term mechanisms, like changes in risk perception that reduces investments in the affected regions or reduced services that make qualified workers leave the regions. Because of these mechanisms, the consequences of a disaster can last much longer than what is considered the recovery and reconstruction period.

# 4. Assessment methodologies

From this description of mechanisms and determinants of indirect losses, many scholars have attempted to assess these losses for real or potential events. To do so, they have mobilized various methodologies.

Higher‐order losses are sometimes measured using firm‐ or household‐level surveys (e.g., Kroll et al., 1991; Tierney, 1997; and Boarnet, 1998; Smith and McCarty, 2006). More frequently, they are estimated using economic models, including (i) microeconomic models at the household level (e.g., Dercon, 2004); (ii) econometrics models at the local level (e.g., Strobl, 2008) or the national level (Albala‐Bertrand, 1993; Skidmore and Toya; 2002; Noy and Nualsri 2007); (iii) input‐output (IO) models at the regional or national level (e.g., Gordon et al., 1998; Okuyama and Chang, 2004; Hallegatte, 2008); (iv) Computable General Equilibrium (CGE) models at the regional or national level (e.g., Rose et al.,
is that the impacts of the management training and consulting on sales and profits are economically large but are statistically weak.

This paper attempts to extend this line of research by using experimental data gathered before and after a management training program offered to MSEs in Africa. In our view, MSE entrepreneurs’ lack of management knowledge has a great deal to do with their location choices. Although the existing studies do not specify where their subjects are located, we suspect that they are located in industrial clusters because the vast majority of MSEs in developing countries are located in industrial clusters, that is, the geographic concentration of a number of firms producing similar and related products. Industrial clusters are spontaneously formed in a wide range of countries and sectors because of the benefits of localization economies (Nadvi and Schmitz, 1994). As documented in recent case studies, localization economies in clusters allow new entrants with little managerial and financial capital to start businesses (e.g., Schmitz and Nadvi, 1999; Ruan and Zhang, 2009; Sonobe and Otsuka, 2006, 2010). Moreover, like human capital, managerial capital may well be underinvested in due to failures in markets for knowledge and finance. Thus, it is not surprising that MSE entrepreneurs lack the basic knowledge and skills of management. Nonetheless, it is likely that many of them are now willing to learn about management because the profitability of their businesses have been declining due to intensified competition with an increasing number of new entrants producing similar products and the massive import of cheap products from newly industrialized economies.

This study provides an elementary management training program for MSE entrepreneurs in an industrial cluster. It examines whether they are willing to learn about management and to what extent the training improves the performance of their businesses. We find that the vast majority of the entrepreneurs invited to the training program attended 3
%|0.0113|+14%|+18%|
|**8**|+11%|0.0216|+6%|+15%|+13%|0.0323|+6%|+19%|+13%|0.0138|+10%|+16%|
|**9**|+9%|0.0319|+3%|+16%|+16%|0.0361|+9%|+23%|+12%|0.0164|+9%|+15%|
|**10**|||||||||||||
|**richest**|-30%|0.0764|-45%|-15%|+10%|0.0657|-2%|+23%|-3%|0.0274|-8%|+3%|

_Source: own estimates based on HIECPS 2005 and 2008_ Thus, the measurement error is not an explanation behind the observed pattern of pro-poor in the panel data. Indeed, replacing actual values with predicted on Figure 3, we would observe very similar shape to the observed.<sup>17</sup> We conclude from this, that panel data in Egypt convincingly show that the growth over 2005-2008 was pro-poor. But they also reveal a lot of mobility not only for the poor, but also for other parts of the distribution. The cross-section (i.e. if we look only at anonymous changes) might in fact hide tremendous amount of re-ranking that is taking place between the comparisons point and reveals only a tiny part of the actual turmoil associated with economic growth in the environment of high inflation.

> 17 Results available on request.

18
source of poverty reduction in the near future. This is especially the case because the headcount rate in Dar is now low enough, at 17.6%, that the potential for reducing it further is limited. Consequently, accelerating the rate of decline of poverty can only be accomplished by increasing poverty reduction in rural areas. While migration and urban growth can play secondary

> 4 Table 4 shows 5.35 of the population percent living in Dar. This figure is based on the weights in the 1991/92 HBS. Census data, which provides more definitive population information, show that the fraction living in Dar was 5.8 percent in 1988 and 7.4 percent in 2002.
Cost as % of<br>tax revenue|15%|1%|0%|0%|0%|4%|5%|0%|48%|7%|7%|4%|0%||

_Source_ : Own elaboration using data from the National Bank of Kazakhstan and audited financial statements of the banks supplemented with news reports. The total number of banks as of the start of the calendar year. _Note_ : The variable distress is computed considering all banks in the sample. We define an event as a bank-year observation where a bank is subject either to government intervention or private/market resolution of distress. The variable resolution is computed considering banks in the sample that have been subjected at least once to a resolution event over the period 2009-2021. The category operating refers to bank-year observations where a bank is not subject to an event in a specific year. Private resolution refers to bank-year observations where a bank is subject either to liquidation, merger, or reorganization in a specific year. The category bail-out captures bank-year observations where a bank is subject to government intervention in a specific year. The variable cost captures government expenditures related to bank bailouts.

# _4.3 Ownership type, systemic importance, and other control variables_

We are interested in analyzing how the ownership type and systemic importance affect the likelihood of distress, the use of specific resolution tools, and the associated fiscal expenditures. Because of the small sample size and the need to populate each category of ownership type over time, we select a parsimonious set of ownership types. Namely, if a bank is owned with a stake that is greater than 50 percent and ultimate operational control: (1) by private local shareholders or with the largest stake and operational control over the bank, the bank is classified as “ _private local_ ”; (2) by a restricted number of shareholders connected to PEP family, the bank is classified s Note that if a bank experienced multiple events of distress in one year,
impacts using a preanalysis plan. _The Quarterly Journal of Economics_ , 127(4), 1755-1812.

- Cho, Y., Denisova, A., Yi, Soonhwa, & Khadka, U. (2018). _Bilateral Arrangement of Temporary Labor Migration: Lessons From Korea’s Employment Permit System_ . Washington, DC: The World Bank.

- Clemens, M. (2010). The Roots of Global Wage Gaps: Evidence from Randomized Processing of U.S. Visas(No. 212). Retrieved from Center for Global Development website: https://ideas.repec.org/p/cgd/wpaper/212.html

- Clemens, M. A. (2011). Economics and Emigration: Trillion-Dollar Bills on the Sidewalk? _Journal of Economic Perspectives_ , 25(3), 83–106. https://doi.org/10.1257/jep.25.3.83

- Clemens, M. A. (2013). Why do programmers earn more in Houston than Hyderabad? Evidence from Randomized Processing of US Visas. _American Economic Review: Papers & Proceedings_ , 103(3), 198-202.

- Clemens, M. A., Montenegro, C. E., & Pritchett, L. (2018). The Place Premium: Bounding the Price Equivalent of Migration Barriers. _The Review of Economics and Statistics_ , 101(2), 201–213. https://doi.org/10.1162/rest_a_00776

- Clemens, M., & Tiongson, E. R. (2017). Split Decisions: Household Finance When a Policy Discontinuity Allocates Overseas Work. _The Review of Economics and Statistics_ , 99(3), 531–543.

- Das, N., de Janvry, A., Mahmood, S., & Sadoulet, E. (2018). Migration as a Risky Enterprise: A Diagnostic for Bangladesh. _International Migration Review_ , 0197918318775933. https://doi.
are presented in Table A4 of the Appendix. Both the direction and the magnitude of the estimated relation between employment in mining and per capita income growth are similar to those obtained using the (conditional) measure of employment in mining. For example, increasing the (unconditional) share of employment in mining by one standard deviation in a community located inside a homeland (0.037) is associated with a 4.8 percentage point higher growth rate, similar to that obtained using the conditional measure of employment in mining.

## **6.3 Complementary evidence from individual-level data**

To shed light on the underlying mechanisms driving the interaction between the dismantling of coercive institutions and rent-sharing we use individual-level data from the 10% sample of the 1996 South African population census. We restrict the sample to men between the ages of 15 and 65, working as an employee (i.e. not self-employed or working in a family business). In Table 5 we present results for regressing the log of individual wage on indicator variables for black, working in mining industry and its interaction.<sup>24</sup> In alternative specifications we account for individual attributes such as age, highest attained education indicators, occupation indicators, and province or district fixed-effects.

Consistent with the presence of resource rents in the mining industry and with the high degree of unionization relative to other industries, we find a large wage premium of around 40 percent associated to employment in this industry. At the same time, there is a large income gap against black workers of approximately 50 percent—in line with the lasting impacts of coercive institutions in place before the end of apartheid. We also observe that black mining workers initially benefited less from resource rents than non-blacks. This individual-level evidence is therefore fully consistent with the mechanisms emphasized in the main analysis of the paper.<sup>25</sup>

# **7 Alternative explanations**

Though our results point to the importance of the interaction between the dismantling of coercive
<u>Component 1: Survival. This component of the index reflects the unfortunate reality that not all</u> children born today will survive until the age when the process of human capital accumulation through formal education begins. It is measured using under‐5 mortality rates taken from the UN Child Mortality Estimates ( **Figure 1** ), with survival to age 5 as the complement of the under‐5 mortality rate. Data on under‐5 mortality are available for 198 countries, and much of the variation across countries in child mortality rates reflects differences in mortality in the first year of life.

‐ <u>Component 2: Expected Learning Adjusted Years of School. This component of the index combines</u> information on the quantity and quality of education. The quantity of education is measured as the number of years of school a child can expect to obtain by age 18 given the prevailing pattern of enrolment rates. It is calculated as the sum of age‐specific enrollment rates between ages 4 and 17. Age‐specific enrollment rates are approximated using school enrollment rates at different levels: pre‐ primary enrollment rates approximate the age‐specific enrollment rates for 4 and 5 year‐olds; the primary rate approximates for 6‐11 year‐olds; the lower‐secondary rate approximates for 12‐14 year‐ olds; and the upper‐secondary rate approximates for 15‐17 year‐olds. Data to construct this measure is available for 194 countries ( **Figure 2** ). The quality of education reflects new work at the World Bank to harmonize test scores from major international student achievement testing programs ( **Figure 2** ). The database covers over 160 countries. These are combined into a measure of expected learning‐adjusted years of school, using the conversion metric proposed in the 2018 World Development Report ( **Figure 3** ).

<u>Component 3: Health There is no single broadly‐accepted, directly
10 literature that the returns to experience decrease over time (Lagakos et al., 2018b; Jedwab et al., 2021c). If _<u>re</u>_<sup>_<r_ ̄</sup><sup>_e_, then the returns embody a college premium.</sup>

# **2.2. Returns Parameters**

We follow the methodology of Lagakos et al. (2018b) and Jedwab et al. (2021c) to estimate the returns to education and experience by college status: _r_ ̄ _s_ , _<u>rs</u>_<sup>, ̄</sup><sup>_re_and</sup><sup>_<u>r</u>_</sup> _e_<sup>.</sup> **Sources.** The data source is the _International Income Distribution Database_ (I2D2) of the World Bank. The database consists of a large number of household and labor force surveys and census samples. The data was initially compiled by the World Bank’s _World Development Report_ (WDR) unit between 2005 and 2011. The database has since been expanded.<sup>17</sup> Only select members of the WDR team or research team or individuals in charge of harmonizing the data can access the database. However, a significant number of the surveys can be accessed online or after entering an agreement with the countries’ respective statistical office.<sup>18</sup> Finally, the surveys are nationally representative and large enough for our purpose.<sup>19</sup> Jedwab et al. (2021c) also verify that the database generates global patterns that are consistent with patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank.<sup>20</sup>

**Sample Size.** I2D2 includes about 1,500 survey/census samples
)|-0.036<br>(0.093)|5,440|
|IHS(Ebola Cases)|||||||
|Total|1.015|0.057|0.065|0.05|0.014|5,440|
||(0.309)|(0.029)**|(0.038)*|(0.034)|(0.044)||
|Confrmed|0.887|0.02|0.024|0.016|0.007|5,440|
||(0.064)|(0.007)***|(0.01)**|(0.008)**|(0.012)||
|Negative|0.998|0.039|0.034|0.043|-0.008|5,440|
||(0.284)|(0.023)*|(0.03)|(0.029)|(0.035)||

_Notes_ : The sample includes 160 sections over 34 weeks. Standard errors clustered on section shown in parentheses. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Difference column reports the difference between the CM and NFA coefficients; the standard error is computed using the delta method. The bottom panel employs the inverse hyperbolic sine transformation (IHS( _y_ ) = log( _y_ + �(1 + _y_<sup>2</sup> )). Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

We take a number of steps to assess robustness. First, we re-estimate our pooled effect dropping one matched triplet at a time (Figure E.4), dropping each possible pair of matched triplets (Figure E.5)
thus analysts need to worry about selectivity bias when using these data. From an IG perspective, it might be useful to assess the potential magnitude of this selectivity bias by exploring the link between firm size and the probability of providing information about firm integration. Table 3 reports Probit estimates of the probability of a firm providing data for the three variables. Only the key variable on foreign input use appears to be systematically correlated with the size of firms, proxied by the (log of) the number of employees. Thus when using these data to do either descriptive or econometric analyses, analysts do need to worry about selectivity bias, because the probability of observing foreign input use is positively correlated with size. Hence we might spuriously conclude that size is an important determinant of integration, when in reality the problem for small firms might really be about reporting but this might not necessarily be an economic issue.

Figure 3 further explores the role of size. It plots the predicted values of foreign input use, export intensity and foreign ownership, from Fractional Logit estimation with the global dataset.<sup>7</sup> Size does appear to be systematically correlated with firm integration, but only the relationship with foreign input use appears to be linear; the other two appear closer to an exponential function. In any case, these results would imply that larger (and potentially more capable and productive firms) have either benefitted more from integration or would benefit more if future trade shocks reduce the costs of imported inputs or raise the relative price of exports. However, a quick inspection of results by industry

> 7 It is prudent to estimate this relationship with a Fractional Logit estimator, because the dependent variables are truncated and the relationship is not necessarily linear.

11
Page 7 for the next 15 years has been forecast at 2.9 percent<sup>3</sup> , indicating continued high urban population growth in the state for an extended period. As is commonly the case, Ahmedabad, the largest city in the state, has been growing more slowly than the total urban population of the state. It is also important to note that while population growth rates in both the AMC and the urban areas of Gujarat rose in the recent census, they are considerably lower than in the 1970s. These healthy growth rates and the relatively youthful age profile of the population clearly imply that household formation in the AMC—not currently measured at the corporation or urban agglomeration area—are likely to be buoyant and will require a substantial supply response to avoid major real estate price increases. However, rising prices and continuing large shares of the population in slums are not the result of out of control urbanization. Rates of growth are actually declining.

Table II. 1 Population and Urbanization Growth in Ahmedabad 1971-2011

||**1971**|**1981**|**1991**|**2001**|**2011**|
|---|---|---|---|---|---|
|**Population in '000**||||||
|Gujarat State|26,697|34,086|41,310|50,671|_60,384_|
|Gujarat-Urban|7,497|10,601|14,248|18,930|25,713|
|Ahmedabad MC(AMCext from 2001)|1,731|2,396|2,924|4,520|5,571|
|**Average Annual Pop'n Growth Rates**||||||
|Gujarat State||2.47%|1.94%|2.06%|1
# **C Details on outcome measures**

## Outcome Variable Descriptions

### **Psychological Well-being**

|PHQ9|The standardized total score of 9 questions from the Patient Health<br>Questionnaire-9 (PHQ9)|
|---|---|
|Life Satisfaction Index|A standardized average of survey responses to four questions from Di-<br>ener’s standardized scale, responses made along a seven-point Likert<br>scale.|
|Stress Index|The standardized total score from three elements of adapted from the<br>Cohen Stress scale. “How many of the last 7 days have you [been able<br>to fall asleep peacefully / felt nervous / felt frustrated]?”|
|Sociability (Total)|The total number of conversations in the past day with adults.|
|Sociability (Positive)|The total number of conversations in the past day with adults that the<br>respondent felt were positive.|
|Self-Worth Index|The standardized total score from the responses on a scale from 1 to 10<br>to two questions: “Think of a person you know who you most respect<br>and who brings greatest value to your [family / community]. If that<br>person is a 10, where would you put yourself?”|
|Locus of Control|The standardized total score from responses to four locus of control<br>questions. “In the last 7 days, how many days did you feel that to a<br>great extent your life is controlled by accidental/chance happenings...”|
|Allocation Decision Game|Indicator (yes / no) for response to an ofer to participate an allocation<br>committee to decide how money is spent. Participants are ofered the<br>opportunity to make a resource allocation decision for their community<br>or have another individual (an NGO worker, an “expert”, or
of domestic markets from world food price swings can be quantified using an Error Correction Model (Annex 6.1). The model regresses the log of the protection rate on the log of world prices and the deviation from long-term “equilibrium” food prices. The model estimates the degree of insulation to global price changes in both the short run (specifically, a negative coefficient on short-term changes in global food prices) and long run (specifically, a negative coefficient on the long-term relationship between domestic and global food prices). The sample used here includes annual data for 8 food commodity prices in 82 countries, of which 44 are EMDEs and 12 are LICs, during 1955-2011.

> 14The world price of U.S. Hard Red Wheat (HRW) increased from $196/mt to $440/mt.

> 15The world price of U.S. Hard Red Wheat (HRW) increased from $158/mt to $355/mt.

> 16Between January 2007 and June 2008, the world price of maize increased from $165/mt to $287/mt.

14