rafmacalaba/datause-encoder-data huggingface dataset:
## **Figure 2: Ebola Orphan Count for Guinea, Liberia, and Sierra Leone**

<!-- Start of picture text --> 4,200<br>200<br>3,300<br>100<br>2,200<br>2,100 1,600<br>200<br>1,100<br>1,800<br>1,500<br>800<br><!-- End of picture text -->

# **Discussion**

The results presented here provide important information on the scale of impact of the current Ebola epidemic on the lives of children who lose their parents to the virus. To date, over 9,600 children under 15 years in the three most affected countries have been left orphaned as a direct result of the 2014 Ebola epidemic. While in absolute terms this represents a large number of children facing personal tragedy, it amounts to only 1.4% of the total 702,000 orphans in the same countries, calculated using parent mortality data from the DHS<sup>**16**</sup> and population data from the United Nations.<sup>**3**</sup> This represents a relatively small increase in the orphan burden. Across the three countries analyzed, 8% of 15 year olds had lost either one or both parents for some reason before the Ebola epidemic, but only 0.11% of children have been left orphaned as a result of Ebola.

5
the destination country. Therefore, part of the variation in unit export values can be explained by these country-specific covariates. In this regard, we use country-year dummies to control for all time-varying destination market aggregates, such as GDP and exchange rate. Further, Manova and Zhang (2009) demonstrated a positive relationship between the revenue exporters earn and the prices that they charge in the export market. For this consideration, we also include the total revenue that the firm earns in the export destination.<sup>14</sup> Another concern in estimating (2) is that export revenues and quantities may suffer from measurement error. To remedy this concern, we removed outliers, firm-product-destination combinations with export values below the 1<sup>st</sup> percentile or above the 99<sup>th</sup> percentile of the distribution, to reduce the bias associated with measurement error. The extensive fixed effects in our specifications also attenuate measurement error concerns.<sup>15</sup>

# **_<<A>>_ RESULTS**

After removing all observations lacking quantity or export value information, roughly 450,000 observations remain.<sup>16</sup> All of the regression results are derived from a panel-data fixed effect estimation in which the panel is a firm-product-destination triplet. All of the specifications include country-year dummies to control for aggregate variation in the export market. Table 5 contains the main results derived from estimating equation (2). In column (1), we report our estimates without controls. We include our controls in the remaining specifications. The variable log (exportrevenue)fdt denotes the total revenue that firm _f_ earns in market _d_ , log (exportrevenue)ft denotes the total export revenue of the firm, log (m)pdt is the average unit import value of the six-digit HS product _p_ in a particular destination, and log (employment)ft is the number of workers that firm _f_ employs. The availability of employment data determines the sample size in the last specification.

9
Figure 3: Comparison of Structural and Transient Poor, FOMD <!-- Start of picture text --> 1989<br>cL E 200 ___<br>_150<br>0 ~ --- ~' ~ ~ ~ .- .- structural poor<br>100 o<br>U. - transient poor s-<br>o 50<br>0.25 0.5 0.75 1 1.25 1.5<br>Alternative Poverty Line<br>i 80 __ ________ _____ 9909__<br>B 60<br>960 structural poor<br>o'-40 .<br>t°0 CD vtransient poor<br>- 20<br>0.215 0.5 0.75 1 1.25 1.5<br>Alternative Poverty Lines<br>E a. 200 _ ____ __-_ ________1991<br>4! 150. *<br>a 1i|| structural poor<br>o -100<br>20s 5Q transient poor<br>' O<br>0.25 0.5 0.75 1 1.25 1.5<br>Alternative Poverty Lines<br><!-- End of picture text -->

Note: Alternative poverty lines represent fractions of J$6198 (in 1991 dollars, which is the third decile of 1989 percapita expenditure).

32
of a SOE, physical commodities destined for export markets, or a future revenue stream. Certain types of collateralized borrowing can impair a government’s ability to meet or reschedule its liabilities and introduce significant macro risks. In countries with weak public investment management frameworks, the availability of funds in the immediate term may also induce borrowing countries to invest in large-scale infrastructure projects based on their ability to secure financing, rather than on the priority accorded to such projects in the government’s overall development strategy.

# III. Debt Vulnerabilities in BRI-Recipient Countries

Expected BRI investment is very large for a small number of countries. In our database, 66 percent of total BRI investment is expected to accrue to 7 countries, with Russia, Pakistan, Malaysia and Indonesia accounting for 50 percent of the total. Scaled by 2017 GDP, the median BRI investment amounts to under 6 percent of GDP, an amount which is not large compared to the investment needs of many countries, given that it would be disbursed over a multi-year period. Median annual BRI debt financing, could amount to slightly more than one percent of GDP if project financing would disburse over 5 years. However, in several countries the cumulative identified BRI investment surpasses 20% of 2017 GDP (Figure 5).

BRI investment takes place in countries with very different debt dynamics. Since 2012, median debt-toGDP ratios have increased for Low Income Developing Countries (LIDCs) and Emerging Markets (EMs)<sup>19</sup> , including BRI recipient countries (Figure 6 and Figure 7). One-third of BRI-recipient LIDCs, for which a recent debt sustainability analysis under the LIC DSF is available, are assessed to have high risk of debt distress according to the LIC DSF.<sup>20</sup> Nearly two-thirds of BRI-recipient EMs face elevated debt vulnerabilities, requiring high scrutiny (Figure 8 and Figure 9).<sup>21</sup> <!-- Start of picture text --> Figure 5. BRI Investment*<br>160% 140<br>In billions of U.S. dollars<br>140% 120<br>In percent of GDP<br>120%<br> 100<br>100%<br> 80<br>80%<br> 60<br>60%<br> 40<br>40%<br>20% 20<br>0% -<br>LAO TLS KHM GEO MNG ARM DJI ALB UZB KGZ PAK MYS TKM BIH NPL BGD MDV KEN MMR RUS TJK TZA JOR VNM BLR SRB LKA IDN MDA KAZ EGY UKR ARE IRN THA IRQ HRV AFG PHL ROU TUR IND AZE POL SYR<br><!-- End of picture text --> Source: WIND database and WDR.

- *Note: Total identified BRI investment between 2013 to 2018.

19 A list of countries is included in Annex I.

20 As of end September 2018.

21 Consistent with the definition adopted by the IMF for EM economies, high scrutiny countries are defined as those countries with (i) the public debt-to-GDP ratio equal or above 50 percent; or (ii) GFNs equal or above 10 percent of GDP; or (iii) have or are seeking exceptional access to Fund resources (IMF, 2013).

10
Policy Research Working Paper 10376

# **Abstract**

This paper uses an exogenous shock to wages from the world’s largest anti-poverty program to show that higher wages can lead to increased air pollution, likely by inducing farmers to shift into a labor-saving and mechanized production process. Using a difference-in-differences approach on the staggered roll-out of India’s Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA), combined with data on nearly 1 million fires, the paper shows that the frequency of agricultural fires increases by 21 percent after the shock. The increase in fires is concentrated in districts that appear more likely to mechanize the harvest. MNREGA did not lead to changes in area planted or tonnage produced in fire intensive crops. The estimates show that nationally, the shock increased the rate of particulate emissions from biomass burning by 30 to 50 percent. The results suggest that absent policies to correct for environmental externalities of mechanization at all stages of development, labor market shocks may lead to inefficient levels of mechanization.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at abehrer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
gasoline equals R$1.0451 and R$1.099 for each season (before any marketing margin to the retail gas pump is added).

Second, we calculate the _ad valorem_ tax called the ICMS which is 25 percent of the gasoline price given by ANP at R$1.54 and R$1.53, for 2010/11 and 2011/12, respectively. Therefore, the ICMS for gasoline A was R$0.512/liter and now it is R$0.5104/liter.

Third, we calculate the ICMS- ST tax, which is 56.35 percent of the ICMS tax calculated above. It is the total cost that is directly attributed to the ICMS. The value of this ICMS-ST is therefore R$0.289 for 2010/201 and R$0.288 for 2011/12. While there is no sales tax _per se_ in Brazil, every manufacturer, distributor, retailer, or provider of almost every type of merchandise or service pays the state _ICMS_ and passes the cost along to the consumer. It is largely a “hidden” tax, in that it is not reported on any receipt or directly on the price of the goods.

Finally, we add all the gasoline taxes to have the total gasoline taxes at R$ 1.2827 and R$1.2305 for these two periods, respectively.

The tax applied on anhydrous ethanol tA is only the PIS/COFINS of R$0.048/liter.

The tax on hydrous ethanol _tH_ has three components. First, we have _PIS/COFINS for the producers_ R$0.0048/l and PIS/COFINS for distributers at R$0.072. The producer price is given by CEPEA (Center for Advanced Studies on Applied Economics) at R$0.982 in 2010/11 and R$1.207 in 2011/12.

The margin for the distributer is R$0.05 for both periods. We add the PIS/COFINS taxes and the margin to the producer price (it give us R$1.04 for 2010/11 and R$1.26 for 2011/12) and from there we calculate the second tax that is the _ICMS_ .

To calculate the ICMS at 12 percent, we add the number we got above to the PIS/COFINS for the distributers (R$0.072) and multiply the sum by 12 percent. For 2010/11 the ICMS is R$0.151 and for 2011/12 it is R$ 0.182/liter.

Finally, we calculate the third tax, that is, the _ICMS ST_ . It is 25 percent of the values we got from the ICMS described above. The values calculated are R$0.038 and R$0.045 for 2010/22 and 2011/12, respectively.

The total hydrous tax is R$0.26 for 2010/11 and R$0.30 for 2011/12 (from March to February). In summary, the total hydrous tax is the sum of the PIS/COFINS for the producer and distributer and the ICMS taxes.

43
is consistent with entry, but it is imprecise (t-statistic is 2.4 and randomization inference p-value is 0.239). The visual reproduction of this regression, Figure A7(a), does provide some suggestive evidence in the form of the leftmost point just above the threshold. Binding vessels just above their nearest threshold are more likely to have been built after the policy was announced than nearby vessels (other points), but the difference is not statistically significant. We explore the potential for constructing vessels just above subsidy thresholds in greater detail in Section 5.2.2.

Column 6 of Table 5 uses a different measure of entry as the dependent variable: an indicator that equals 1 if the first year we observe a vessel in the GFW data is _not_ 2016. The indicator equals 0 if 2016 is the first year we observe a vessel in the GFW data. This measure of entry differs from the previous one in that it allows already-existing vessels that were not previously observed fishing to re-enter the fishery without rebuilding their vessel. We again estimate a positive but statistically insignificant effect of fuel subsidies on entry.

Finally, we measure exit with an indicator that equals 1 if the last year we observe a vessel in the GFW data is not 2020. The indicator equals 0 if 2020 is the last year we observe a vessel in the GFW data. We find no evidence that the subsidy policy increased the exit rate from the industry: while the coefficient is slightly positive it is not statistically different from zero (Column 7 of Table 5).

# **5.2.2 Comparing vessel characteristics before and after policy**

Of the vessels in our data, 23% were built during or after 2016. Though the regulator banned the entry of new distant water fishing vessels in 2013, firms may scrap their vessels, rebuild them, and remain eligible for fuel subsidies (Ministry of Agriculture, 2013b). We compare the distance to the nearest gross tonnage threshold of vessels built before 2016 and of vessels built on or after 2016. The regulator began two other policies in 2014 and 2015 that partially overlap with the fuel subsidy thresholds: a one-time renovation and reconstruction subsidy, and a vessel standardization regulation.<sup>25</sup> These regulations do not invalidate our

> 25Vessels built on or after 2015 are eligible for the renovation and reconstruction subsidy, which depends on gear, gross tonnage, and vessel length (Ministry of Agriculture, 2016). The vessel standardization regulations began in 2014 and they specify gear-specific standards for vessel characteristics like width (Ministry of Agriculture, 2014b, 2015). The regulator updated these standards in 2017, 2018, and 2021 (Bureau of Fisheries, 2018, 2021b; Ministry of Agriculture, 2017).

29
Nanchang, Shenzen, and Wenzhou are the fastest growing over the period 1999 to 2002. Note that each of these cities received an investment climate score of A or A+ (except for Nanchang which received a score of B+) in the Dollar et al. (2004) study compared to Benxi which received an investment climate score of B- in the study. When we look at growth rates over a smaller time horizon from 2001 to 2002, only firms in Shenzen seem to be growing faster than firms in Benxi. Our results in Table 4 are robust to controlling for 14 industry sector dummies.

To check whether our results are driven by outliers, we perform several robustness checks. We have re-estimated all our specifications by removing potential outliers (growth rates in excess of ± 1000%), by winsorizing the top 1% of the sales growth, reinvestment rate and productivity growth variables, and by using median regressions. Our results (not reported) remain unchanged in all cases. The coefficients of Bank Dummy are qualitatively similar to those in Table 4, indicating that our earlier results are not driven by outliers.

# **_5.2. Bank Finance and Firm Performance - Treatment Effects_**

In Table 5, we investigate the relation between bank financing and sales growth, reinvestment, and productivity growth, controlling for the selection of firms that obtain bank financing. The estimates of the selection equation (even numbered columns) indicate that firms that obtain formal financing are more likely to have collateral that they can pledge, are large companies organized as corporations, with some but relatively few (<6), competitors. Interestingly, although firms in highly competitive industries grow more slowly, there is little evidence that banks lend to them at a lower rate. The hazard 29
in the SILC is to compare the reliability of aggregate income in the SILC against the National Accounts figure of the Central Statistical Office. The study on the micro-macro gap between the EU-SILC data and national accounts shows that coverage of the household disposable income reported in Romania was the lowest in the European Union. The estimated total sum of EU-SILC disposable income was 33% (+/10%) of national accounts' gross household sector disposable income before any modifications. This share was higher than 50% in all remaining countries; it was higher than 75% in many countries. The coverage of wages and salaries in Romania in 2014 equaled 65% and was also the lowest in the European Union. It was nearly 90% or even above 100% in most countries. This situation might be explained by the high number of subsistence farmers and self-employed, but also due to incentives to set up a microenterprise and pay 3% on turnover (below the threshold of EUR 1 million per year and without the obligation to have a full-time employee until 2023) rather than have a labor contract to cover a tax wedge of 43% of gross income. The micro-macro gap at the EU level is caused mainly by measurement errors and conceptual differences (Törmälehto, 2019).

**The outcomes of this comparison are presented in Table 2.** The total compensation of employees in national accounts equals €87.0 billion. The estimate based on the EU-SILC data, which does not include social security contributions paid by the employer, is approximately €75.8 billion. The estimate based on the tax data, which does not include self-employed and social security contributions paid by the employer, is €67.0 billion. Although these estimates are not fully comparable, differences are probably small enough to be explained by varying data coverage.

**Following the literature, we assume that employees report their total income in survey data, including envelope payments and income earned in the informal economy** . The higher value of aggregate compensation of employees in the EU-SILC than in the tax data indicates that this is indeed a fact. Then, we compare the "true income" reported in the EU-SILC with the "reported income," which is determined by the tax income imputed in the EU-SILC dataset based on common characteristics of individuals available in both data sources. The comparison outcomes indicate the possible degree of underreporting of income in tax data.

13
# Cash and In-Kind Transfers in Humanitarian Settings: A Review of Evidence and Knowledge Gaps<sup>*</sup>

Dahyeon Jeong<sup>†</sup> Iva Trako<sup>‡</sup> Originally published in the Policy Research Working Paper Series on _April 2022._ This version is updated on _September 2022._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Systematic review, social protection, humanitarian settings, cash transfers, in-kind transfers, basic needs, financial outcomes, human development, gender, social cohesion

**JEL Classification:** D04, H84, I00

> * We are thankful for the support and guidance provided by Felipe Dunsch, Kristen McCollum, Jonas Heirman, and Andrea Cook from the World Food Programme’s Office of Evaluation (OEV). In addition, the team would like to thank Florence Kondylis, Marcus Holmlund, Benedetta Lerva, Patrick Premand, Vincenzo di Maro, Ugo Gentilini, Mattias Lundberg, and Margaret Grosh for their valuable comments and suggestions. We are also grateful to Eric Jospe and Nathalie Anne Kiersznowski for their research assistance. Finally, we would like to acknowledge the efforts of Emma Frankham, Meghan Quinn and OEV’s Comms Unit in providing copy-editing support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> † Economist. Development Impact Evaluation (DIME). The World Bank. Email: dahyeonjeong@worldbank.org

> ‡ Economist. Development Impact Evaluation (DIME). The World Bank. Email: itrako@worldbank.org
Table 3: Expected consequences from the reform (January 2011 survey).

||INFORMED|PEOPLE||
|---|---|---|---|
||Satisfaction rate|= 53.7 percent||
|Positive consequences||Negative consequences||
|Identify at least one|45.80|Identify at least one|44.50|
|Identify none|9.68|Identify none|9.35|
|NS/NR|44.52|NS/NR|46.13|
|**Total**|**100.00**|**Total**|**100.00**|
||UNINFORME|D PEOPLE||
||Satisfaction rate|= 24.0 percent||
|Positive consequences||Negative consequences||
|Identify at least one|13.10|Identify at least one|69.36|
|Identify none|40.40|Identify none|1.22|
|NS/NR|46.95|NS/NR|29.42|
|**Total**|**100.00**|**Total**|**100.00**|

Figure 3: Kernel density - perception about unsubsidized price (January 2011 survey).

<!-- Start of picture text --> 5$ 10$ 15$ 20$<br>Note: Kernel = Epanechnikov, Bandwidth = 0.3178.<br>.5<br>.4<br>.3<br>Density<br>.2<br>.1<br>0<br><!-- End of picture text --> 16
Policy ReseaRch WoRking PaPeR 4287

# **Abstract**

This paper analyzes results of a survey on debt management strategies conducted by the Banking and Debt Management Department of the World Bank. The analysis focuses on (1) whether a public debt management strategy exists in a given country, (2) whether it is made public, and (3) in which form it is imparted. The paper analyzes the distribution of the latter characteristics over different regions, income groups, and levels of indebtedness using graphical analysis. Using regression analysis, it investigates the extent to which basic economic factors can explain the characteristics of public debt management strategies across countries.

This paper—a product of the Banking and Debt Management Department—is part of a larger effort in the department to understand the development of public debt management strategies. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Martha Rosenquist, room MC7-126C, telephone 202-458-2602, fax 202-522-2101, email address Mrosenquist@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mmelecky@worldbank.org. July 2007. (37 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team