rafmacalaba/data-use-mentions-extended huggingface dataset:
receive large per capita transfers, offsetting reductions in private intra-household transfers or employment, and the impact of taxes needed to finance poverty alleviation programs.

# _The data set_

For all of the estimations below, I combine information from two sources: district-level averages of the infant mortality rate, the FONCODES index, and the measure of imputed poverty, and household-level data on expenditures. District-level averages are available from INEI, and household-level data can be estimated from the 1994 and 1997 LSMS, both of which were executed by the Peruvian think-tank Cuanto.

District-level data can be used to estimate the proportion of total funds that would be allocated to every district under alternative targeting regimes. Further dividing this fraction by the total population of the district in question allows us to calculate the proportion of funds that would be allocated to every individual. Finally, multiplying this proportion by the total budget available for poverty alleviation programs, we can estimate per capita transfers.

The LSMS can be used to estimate the expenditures of the households in the sample (3,558 for 1994, and 3,840 for 1997) and, dividing total household expenditures by household size, for household members (18,362 for 1994 and 19,562 for 1997). These estimates can be combined with information on poverty lines to calculate the headcount index, poverty gap, and P2 measure at a national level before any transfers take place.8 The 1994 and 1997 Peru LSMS drew households from 364 and 397 clusters, respectively, and the accompanying literature lists the districts from which each one of these clusters was drawn. Observations in the LSMS can be coded manually with district identifiers which match those used by INEI, and district-level and household-level data can then be merged. Having done this, we keep only those observations for which there are matching codes for place of residence in both data sets--in effect, discarding the district-level information for all but the 199 and 238 districts which were sampled in the 1994 and 1997 LSMS, respectively.<sup>9</
Table 16: Heterogeneous effects of payment delays on total spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|A<br>Assets|bove median:<br>Patience|
|1-day delay|-438.000<br>(1627.285)|3278.722<br>(2362.448)|5.191<br>(2038.724)|1150.498<br>(2047.030)|-1387.772<br>(2369.923)|
|8-day delay|-1192.477<br>(1700.539)|2282.270<br>(2161.679)|-2120.740<br>(1701.914)|569.199<br>(2041.364)|-2118.200<br>(2420.014)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Immediate group|16755.85|12634.49|17367.79|15774.29|16009.12|
|R-squared|0.296|0.443|0.341|0.301|0.286|
|P-value: one day delay = 8 day delay|0.666|0.685|0.208|0.779|0.782|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|B<br>Assets|elow median:<br>Patience|
|1-day delay|3048.881<br>(2339.617)
the most recent round of data collected for each country. While the Enterprise Surveys focus primarily on firms in developing economies, data have also been collected for several high-income countries and, therefore, our sample spans all regions and income categories.

# <u>Women, Business and the Law index</u>

The Women, Business and the Law (WBL) index measures the legal inequalities that a woman faces as she navigates her working life, from the time she enters the workforce through to her retirement. There are 35 individual data points underlying the index, aggregated into the following eight indicators: The _Mobility_

> 6 Additional information on the Enterprise Survey methodology can be found at https://www.enterprisesurveys.org/en/methodology

> 7 The data we use in our analysis were published on February 16, 2021.

6
development of schooling and vocational aspirations and self-esteem. Note that vocational training was not included in any of these six countries.

The program has changed slightly since the time of the study and varies somewhat between countries. In the past, Compassion worked in tandem with local schools (which is true of our data from Guatemala and the Philippines) but more recently has operated through local church-affiliated tutoring centers. We study the impacts from children involved in the program in India and Bolivia when small cash transfers were given to parents of participating children, but not in other countries. Other than these differences the program across countries is highly standardized.

Through the use of local enumerators, we were able to locate 93.5% of the families of these formerly sponsored children, who by the time of the survey were aged seventeen to fortythree. Our field personnel were unaffiliated with Compassion, in order to reduce bias in the responses of our subjects. We administered our survey first-hand to households of formerly sponsored children, a random sample of nonparticipating households in nineteen program villages, and a random sample of households in thirteen neighboring, nonprogram villages. The survey questionnaire was administered to family members (typically parents or adult siblings) present at the time of the survey, and data were obtained on all adult siblings in the household cohort, including the nonsponsored siblings of sponsored children. We also administered the survey to 50–75 randomly selected households with children in a similar cohort age that did not participate in the program in program villages, as well as 50–75 randomly selected households with children in a similar cohort age in nearby nonprogram villages.

Overall, our data contain information on educational and vocational outcomes, monthly labor income, consumer good ownership, and dwelling quality on 1,860 formerly sponsored children, 3,704 of their unsponsored siblings, 2,136 individuals of a similar age from nonparticipating families in villages where the Compassion program operated, and 2,444 individuals from similar, nearby villages without the Compassion program.

There are several
that countries that liberalized in the last 5 years have higher probability of a crisis. There is no evidence that liberalized financial system increases the probability of a crisis.

Finally, in the fourth column we present the results using our complete data set. 8 The results are consistent with the previous findings. Using this specification, the probability of a banking crisis in countries that have had financial liberalization in the past _5_ years is higher by almost 9%.

To conclude, the results provide support to the model's prediction that the probability of a crisis is higher following a financial liberalization. There is little evidence that banking crisis is more likely to occur in liberalized financial system after controlling for the period following the liberalization.

# 4.3. **Transparency**

In this section we turn to test whether the increase in the probability of a crisis depends on transparency as the model predicts. That is, do countries that liberalize their financial sector are more likely to have a banking crisis if transparency is poor? One difficulty in carrying out this test is that it is hard to find a good data about transparency. Thus, we use three different measures to construct proxies for transparency. First, we use an index of corruption as a proxy for transparency. The source of the corruption index is Political Risk Services, Syracuse, New York (ICRG indices). The index ranges from 0 to 6, where highly corrupted countries take a value 6 while non-corrupted countries take a value of 0. Using this index we create a transparency dummy variable that takes the value of 1 if the corruption

> **8** Our sample has six more countries: Argentina, Brazil, United Kingdom, Nepal, Singapore, and South Africa.

18
, bringing it to the same level as the CDIS data. For 2009–12, when the CDIS and UNCTAD data are available, we use the CDIS data when the value for a given bilateral link for a given year is positive or when the CDIS value is zero and the

> 27 As a robustness check, we analyzed alternative methodologies: using only the data on assets, using primarily the data on assets and filling in the missing values with the data on liabilities if available, and using primarily data on assets and filling in the missing values by assuming that those assets in missing years grew at the same rate as liabilities (when this growth rate was available). The regression estimates using these alternative criteria were quantitatively similar to the ones we report using the maximum between assets and liabilities.

> 28 To calculate the ratio, we need the IMF and UNCTAD data for at least one year during 2009–12 for each country pair in the databases. If this is unavailable, the UNCTAD data are not leveled up and the FDI values not covered by the CDIS data are coded as missing observations.

35
Consistent with a limited trade openness, Brazil’s participation in global value chains (GVCs) is low and is relatively stronger on the seller (forward) side than on the buyer (backward) side. GVCs represent a new path for trade, whereby a country does not need the capability to produce an entire export good but instead contributes a segment of its production process. The latest OECDWTO TiVA data for 2011 suggest that Brazil’s participation in GVC is reduced when compared to international peers and is relatively stronger on the seller (forward) side than on the buyer (backward) side.<sup>8</sup>

**Figure 3. Participation in GVC Brazil vs comparator countries, 2011 (value added as share of domestic and foreign gross exports)**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Forward Backward<br><!-- End of picture text --> Source: World Bank staff elaboration using OECD TiVA data The limited openness to trade reflects the use of protectionist trade policies. This includes high tariff barriers to import. Brazil’s average (trade-weighted) tariff rate was 8.3 percent in 2015, the highest rate in comparison to other emerging and advanced economies (Figure 4).<sup>9</sup> Overall, apart from the trade liberalization episode of the early 1990s, when tariffs fell from extremely high levels of 90 percent to 20 percent in wearing apparel, and except for small occasional amendments – such as the inclusion of tariff hikes in 1995 to account for the Mercosur list of exceptions, the generalized tariff increase in 1997 and the temporary rise (for 100 products) in 2012 - the import tariff structure in Brazil has not seen major changes since 2004 (Figure 5).
*|**65%**|**61%**|**80%**|63%|
|**Corporate Governance**|100%|74%|57%|85%|62%|
|_Accountability_|_100%_|_58%_|_58%_|_92%_|60%|
|_Autonomy (SOEs)_|_100%_|_89%_|_56%_|_78%_|63%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform KPLC operates under an annual performance contract with the MoEP, with targets set for key performance indictors based on the concept of the “balanced scorecard.” The company reports its performance to the government at least quarterly. Though KPLC performs well on most items and is known to have a long history of strong corporate culture, the discretionary influence of government should be kept in mind when interpreting the score. The cause for doubt is confirmed when one considers that (i) the board’s decisions have largely been in line with government positions, (ii) the government has removed board members before their term is up, and (iii) the board has failed to perform on all ten areas defined in the legal and regulatory framework as “explicit responsibilities.” When it comes to financial management, KPLC stands head-and-shoulders above other countries in the region and compares favorably with the broader global benchmark group (table 9). As KPLC’s shares are traded on the Nairobi Stock Exchange, its financial reporting is done in compliance with the International Financial Reporting Standards. The company is ISO-certified and has set procedures for budgeting operating and capital expenditures, as well as for other processes. KPLC has set internal auditing procedures with adequate reporting and supervision mechanisms. Because it is a

> 17 Data for utility governance indicators were collected in 2015.
derivatives which further spurred the real estate boom.<sup>4</sup> Rising wages and the real estate boom initially helped increase fiscal revenue and allowed deficits to remain in line with the Maastricht criteria (except for Greece), in spite of government expenditure that rapidly rose due to increasing government wage bills and social transfers. Yet, with growth increasingly driven by unsustainably high domestic consumption, periphery countries lost export competitiveness and the manufacturing sector declined. At the same time, core countries‘ competitiveness and their external surpluses improved, as a result of wage restraint and the relative undervaluation of the euro compared to the earlier national currencies.

4. The global financial crisis since September 2008 led to a recession in Europe and triggered the burst of the real estate bubble; both resulted in a ballooning of fiscal deficits and a massive deterioration of debt indicators that set the stage for the sovereign debt crisis in the Euro Zone that began with the Greek crisis in early-2010.

<!-- Start of picture text --> Percent change, Figure 1: Euro Zone growth: Projections and reality<br>year over year<br>4 Actual<br>Projected<br>3<br>2<br>1<br>0<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br>-1<br>-2<br>-3<br>-4<br>-5<br>Note: Projection data from previous year of actual GDP growth.<br>Source: World Economic Outlook, various years.<br><!-- End of picture text --> 5. The dynamics between core countries, especially Germany, and non-core countries in the Euro Zone appear analogous to those between East Asian surplus economies and the United States in the run-up to the global financial crisis.<sup>5</sup> Excess consumption in the United States brought about by a real estate boom and low interest rates allowed East
In order to address this shortcoming, we propose an alternative scenario where pollution intensities change across countries and time. The scenario is constructed taking as the base the technique effects estimated by Grether, Mathys, and de Melo (2007) for 62 developed and developing countries during 1990–2000.To our knowledge, this is the only study that estimates and identifies the technique effect for a large sample of countries<sup>10</sup> . It does so by combining different databases providing pollution estimates at the country level. For most countries the data are available only until 2000, which prevents us from developing a full decomposition exercise. We circumvent the data problem by regressing the estimate of Grether, Mathys, and de Melo against the possible determinants of technical changes (please see regression results in ~~the Appendix~~ <u>Annex 1). While initially we consider several determinants (such as</u> initial per capita GDP, trade-to-GDP, manufacturing output to GDP, growth in per capita income, and different measures of human capital), model selection analysis helps us to focus on two dependent variables—initial GDP per capita and ratio of trade to GDP—which allows us to project the rate of adjustment in pollution intensity for each of the CA-4 countries in the 1999–2008 period. The rates are then applied to IPSS pollution intensities in order to construct new emissions data series. We assume that technical changes are homogeneous across sectors.

The remaining data used in the analysis include the following indicators: value added, exports, imports, number of workers, wages of skilled workers, and wages of unskilled workers. All indicators are disaggregated at the two-digit industry level. Table A1 in the <u>Annex</u> ~~appendix~~ presents the summary statistics and source of each indicator. While value added and trade data are used directly in the analysis, labor indicators are used to calculate industry-specific factor shares. Following Grossman and Krueger (1992) we assume that
<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B2. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Resilience dimension (40% for Resilience, and 20% for the rest of dimensions)._

# Peru

<!-- Start of picture text --> B2.1. FOSD: Counting Vector B2.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.4<br>1<br>.8 .3<br>.6 .2<br>CCDF<br>.4 Difference .1<br>.2 0<br>0<br>-.1<br><!-- End of picture text --> B2.3. SOSD: Social Sustainability Index B2.4. Rank correlations 42
- Bertrand, M. (2011). New Perspectives on Gender. In Ashenfelter and Card, ed. Handbook of Labor Economics, 4:1543- 1590. North Holland: Elsevier.

- Broeck, V., & Kilic, T. (2018) Dynamics of off-farm employment in Sub-Saharan Africa: A gender perspective. World Bank Group Policy Research Working Paper. 8540

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- Carneiro, P., Crawford, C. and A. Goodman (2007). The Impact of Early Cognitive and NonCognitive Skills on Later Outcomes, CEE Discussion Paper 0092.

- Contreras, D., de Mello, L. & Puentes, E (2011) The determinants of labor force participation and employment in Chile. _Applied Economics_ , 43:21, 2765-2776, DOI: 10.1080/00036840903373303

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- DTDA (2020). Ghana Labor Market Profile, 2020. Danish Trade Union Development Agency

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- Fay, D. and
Pakistan (Pakistan’s Central Bank, SBP hereafter)—The Export Finance Scheme (EFS) and the Long-Term Finance Facility for Plant & Machinery (LTFF)—that provide loans to exporters at concessionary interest rates to finance short-term working capital and longterm investment in machinery and equipment, respectively. These schemes are large and well established in Pakistan. EFS, which started to operate in 1973, provided loans worth US$3.8 billion per annum between 2015 and 2017, or 17.4 percent of Pakistan’s total exports. While LTFF is more recent and smaller in size than EFS, its loans outstanding are equivalent to 1.3 percent of the country’s exports over the same period.

To carry out our empirical analysis we combine customs transaction data that contain the universe of international trade transactions for firms in Pakistan with data from SBP that tell us which firms used EFS and LTFF and the value of the loans they requested from commercial banks in each fiscal year over the period 2015-2017. Our data shows that the firms benefiting from these loans are substantially larger than the average exporter and are concentrated in the clothing, textiles and apparel sectors.

In order to address the non-random selection of exporters into the export finance support programs, we use a ‘doubly robust’ matching estimator combined with covariate adjustment developed by Wooldridge (2007), following the policy evaluation literature. Doing so allows us to construct an appropriate comparison group to estimate what would have been the export performance of firms that made use of EFS and LTFF had they not participated in these schemes. Our estimates of the probability that firms participate in each scheme confirm that size (in terms of export sales) is a very strong predictor of participation, as well as being an importer and exporting a large share of products for which the schemes are available.<sup>1</sup> Crucially, for the purposes of identification, even after controlling for the aforementioned observable characteristics determining firms’ participation in the schemes, there remains substantial unexplained
7.0|8.1|6.0|

_Source:_ World Bank 2010; Bullock 2009. Derived from AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). — = Not available.

_Note:_ * With 2.5 passenger-km equivalent to 1 TU, 1 tonne-km equivalent to 1 TU. TU = traffic unit.

**Figure 10. Evolution of SITARAIL’s freight traffic over time**

<!-- Start of picture text --> 1,000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Total traffic in million traffic units (TUs)<br><!-- End of picture text --> _Source:_ AICD 2006; WB/AfDB 2009 _. Note:_ **x** = Projection for 2010.

# **Challenges**

In spite of its impressive operational performance, SITARAIL is in financial distress due to the accumulation of losses made during the crisis period and the related financial liabilities it owes to stateowned asset-holding companies. SITARAIL’s total financial war-related losses are equivalent to $38.6 million. They include: (i) physical damage ($5.7 million), (ii) loss of income to employees ($9.7 million), (iii) nonpayment of state-owned asset-holding companies’ debt ($8.6 million), (iv) concession fee 18
The forced nature of the decisions is also evident from descriptive survey data about the time available to prepare for migration and the ability to capitalize assets. Figure 3 presents the share of refugees in Kurdistan and Jordan<sup>9</sup> who took one day or less, between one and seven days, and more than seven days to prepare for moving. The vast majority (more than 75 percent) of refugees in Kurdistan and Jordan had a week or less to prepare, with half the Syrian refugees in KRI and 36 percent of Syrian refugees in the Jordan sample having at most a day to prepare. In addition, a natural consequence of forced displacement is the decapitalization of the fleeing population through the loss of assets. The SRHCS allows us to describe this process by comparing immovable asset ownership prior to displacement with the status of these assets upon displacement (Figure 4). Focusing on the two types of assets with higher ownership rates in 2010, dwellings and vehicles, more than 60 percent of the Syrian refugee households owned a dwelling in 2010, irrespective of their country of asylum in 2015, and more than one-third report that it was destroyed at the time of displacement. Similarly, for vehicles (cars, trucks, and motorcycles), which were owned by roughly one-fifth of refugees (results not shown). Among owners of dwellings, only a tiny fraction—ranging from less than 1 percent in Lebanon to almost 4 percent in Kurdistan—was able to sell their dwelling in preparation to leave. For vehicles, the corresponding number ranges from roughly 15 percent in Jordan to almost 50 percent for Syrian refugees in Kurdistan.

# **_B. First and Subsequent Destinations_**

To estimate the degree of decision-making power of Syrians under forced migration, we examine the distance to their first destination of refuge. Theoretically, events and circumstances outside of people’s control largely determine where they end up under forced migration. We model how far refugees traveled by exploring the relationship between total driving time from refugees