rafmacalaba/data-use-mentions-extended huggingface dataset:
- Ma, Yuan, Qiang Zhang, and Qiyue Yin (2021). “Top management team faultlines, green technology innovation and firm financial performance”. _Journal of Environmental Management_ 285, 112095.

- Marin, Giovanni and Francesco Vona (2021). “The impact of energy prices on socioeconomic and environmental performance: Evidence from French manufacturing establishments, 1997-2015”. _European Economic Review_ 135, 103739.

- Martin, Ralf, Mirabelle Muûls, et al. (2012). “Anatomy of a paradox: Management practices, organizational structure and energy efficiency”. _Journal of Environmental Economics and Management_ 63, 208–223.

- Martin, Ralf, Laure B. de Preux, and Ulrich J. Wagner (2014). “The impact of a carbon tax on manufacturing: Evidence from microdata”. _Journal of Public Economics_ 117, 1–14.

- Masson-Delmotte, V. et al. (2018). “IPCC, 2018: Annex I: Glossary”. _Global Warming of 1.5C. An IPCC Special Report on the impacts of global warming of 1.5Â°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty_ . Cambridge, UK and New York, NY, USA: Cambridge University Press, 541–562.

- Masuda, Yuta J., Brianna Castro, et al. (2019). “How are healthy, working populations affected by increasing temperatures in the tropics? Implications for climate change adaptation policies”. _Global Environmental Change_ 56, 29–40.

- Masuda, Yuta J., Teevrat Garg, Ike Anggraeni, Kristie Ebi, et al. (2021). “Warming from tropical deforestation reduces worker productivity in rural communities”. _Nature Communications_ 12. Number: 1 Publisher: Nature Publishing Group, 1601.

- Masuda, Yuta J., Teevrat Garg, Ike Anggraeni, Nicholas H. Wolff, et
Policy Research Working Paper 9925

# **Abstract**

China’s lending boom to developing countries is morphing into defaults and debt distress. Given the secrecy surrounding China’s loans, also the associated defaults remain “hidden”, as missed payments and restructuring details are not disclosed. This paper constructs an encompassing dataset of sovereign debt restructurings with Chinese lenders and finds that these credit events are surprisingly frequent, exceeding the number of sovereign bond or Paris Club restructurings. Chinese lenders follow a resolution approach reminiscent of 1980s Western lenders; they seldom provide deep debt relief with face value reduction. If history is any guide, multi-year debt workouts with serial restructurings lie in store.

This paper is a product of the Office of the Senior Vice President and Chief Economist, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at shorn1@worldbank.org; creinhart@worldbank.org; and Christoph.trebesch@ifw-kiel.de.

_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
3.52|
|Min||0.4|6|.0||2.6|
|Max|2|6.9|1|4.3||18.8|

_Notes:_ Countries covered: (A) with a national minimum wage: FR, BE, UK, IE NL; (B) without a national minimum wage: AT, CH, DE, DK, FI, NO, SE.<sup>a</sup> Countries without a minimum wage assigned sample mean. *, **, *** - coefficient is significantly different from zero at 10%, 5%, 1% level, respectively. _Sources:_ Dependent variables: Calculations with ESS data (see Table 1 for details). Explanatory variables - see Annex 1 (Table A1) _._ Model estimates - own calculations ( _Stata_  simultaneous estimates with clustered robust standard errors have been applied after fitting linear models by OLS).

44
*

<mark>Estimates of trade costs for Modes 1, 2 and 4 featured in the World Trade Organization’s</mark> _<mark>World Trade Report 2019</mark>_ <mark>are derived from multiregional input-output data found in the World Input-Output Database (WIOD 2016) and experimental data from the Asian Development Bank (ADB-MRIO). The results are based on data for 44 countries across 34 sectors.</mark> <mark>Trade costs are estimated using a sector-level gravity model specification proposed in Egger et al. (2018). First, the coefficients on country-pair dummies ( d</mark> ı <mark>) are obtained from a fully saturated gravity model with appropriate parameter constraints. Second, these estimates are transformed using a sectoral</mark><sup>�</sup> <mark>elasticity of substitution ( θ ): T</mark> i <mark>= (d</mark> ı <mark>)</mark><sup>−1/θθ�</sup> <mark>To obtain the estimates of T</mark> i <mark>, international and domestic trade data from the World Input-Output Database (WIOD) is used and an experimental data set from the Asian Development Bank (ADB-MRIO). The parameter θ is estimated for each sector in Egger</mark> _<mark>et al</mark>_ <mark>. (2018). A higher θ means a more elastic reaction of demand to prices, and hence a higher responsiveness of import demand to trade frictions. Generally, θ takes on lower values for services than for manufactures, implying that trade in services reacts less to changes in trade costs.</mark> In the subsequent analysis, the factors which explain i , are identified by running a regression analysis and using the results to decompose the explained variation in i into different types of trade costs. TT TT 17
‘climate change mitigation technologies’ and classify job postings as green technology-related if they mention these keywords (or close synonyms). We then match the job postings data to firm-level data including balance sheet information and energy consumption from Factset and the Carbon Disclosure Project (CDP).

We first show that growth in openings related to green technologies was modest from 2014 to 2018, before increasing slightly in 2019 and then accelerating rapidly in 2022. The share of all job postings mentioning green technologies more than doubled between 2019 and 2022. Rapid growth in green technology-related hiring was strongest in Europe but occurred in three quarters

> 1See H ́emous and Olsen (2021) for a recent review.

> 2For example, while Newell, Jaffe and Stavins (1999), and Delarue, Ellerman and D’haeseleer (2010) find positive impacts of carbon prices on the adoption of different green technologies, Klemetsen, Rosendahl and Jakobsen (2020) and Calel (2020) do not find any such effects.

> 3Lightcast was formerly Burning Glass Technologies.
**<u>Figure 1: CDFs of (a) Profits and (b) Sales</u>**

<!-- Start of picture text --> 0 1000 2000 3000 4000 5000<br>Estimated Profits Last Month (win 5%)<br>0 5000 10000 15000 20000<br>Sales Last Month (win 5%)<br>Book Only Book & Movie<br>Book & Assistance All Three<br>Control<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> This figure plots the cumulative density functions for profits and sales from the final follow-up survey, 18 months after the intervention. Both estimates are winsorized at the 5% level.
**Figure 2: Poverty rates in Kazakhstan using international poverty lines**

<!-- Start of picture text --> 70.0%<br>60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>$1.9 PPP $3.2 PPP $5.5 PPP<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source: the Kazakhstan HBS, Author’s calculations_ Among the Central Asian countries, **Tajikistan** traditionally has the highest rates of poverty, though in recent years the gap with Kyrgyzstan has been falling. As is the case with Kazakhstan, the country monitors poverty using a national official definition that is distinct from the World Bank’s poverty line. According to the national series, the poverty rate fell from over 72 percent in 2003 to 47 percent in 2009. The rate of extreme poverty measured at the international poverty line of $1.9-a-day is below 2 percent. According to a different method of measuring national poverty which began in 2012, poverty continued to decline from 37.4 percent in 2012 to 29 percent in 2017. The poverty rate in Tajikistan fluctuates strongly between seasons and about half of the population falls into poverty (by the national definition) at least once during the year (figure 3).

11
Figure 4 presents data for 2020 across the same regions as in Figure 1. All regions are increasing from January to February, and most at similar rates. However, in March, all regions show a marked change, with four of the six decreasing coincident with the start of the crisis. South Asia shows a dip in May and June, before rebounding in July and August. Four regions continue decreasing over the few months following the onset of the crisis, while Sub-Saharan Africa seems to recover and even increase rather substantially in the last several months of the year. Finally, consistent with the NO2 results shown in Figure 2, North America shows a steep increase in the second quarter of the year followed by a steady decrease in the next three months. A rebound is observed only in the last quarter of the year.

.

When we disaggregate by country income in Figure 5, we see a similar dip in March in three of the four categories, but these dips are relatively small. The only category that appears to have a sustained decrease is lower middle income; nightlights decrease in March, April, and May, before showing a brief increase in the following months. None of the categories shows consistent decreases from the previous year. In fact, high-income countries show the lowest increases but are right at zero for the entire year. The other three categories are above zero in almost all months and sometimes substantially above zero.

Overall, NO2 appears to correlate with the onset of the crisis in upper-middle-income and highincome countries, but nightlights do not seem to exhibit a consistent pattern.

# 3.2 Google data

a. Mobility Figure 6 presents the Google mobility data for all the countries in our sample. The baseline for most areas is February, which is why we see all of the trends clustered around zero (i.e. no change from the baseline) in that month. Visits to all locations outside residences decreased markedly in March, bottoming out in April, with the largest relative decreases in transit stations
# References

Alesina, A., and Perotti, R. (1999). Budget Deficits and Budget Institutions. In Fiscal Institutions and Fiscal Performance, 13-36. Cambridge, MA: _National Bureau of Economic Research_ .

- Alesina, A., and Devleeschauwer, A., and Easterly, W., and Kurlat, S., and Wacziarg, R. (2003). "Fractionalization ." _Journal of Economic Growth, 8(2),_ 155-194.

- Alesina, A., F.R. Campante, and G. Tabellini. (2008). Why is Fiscal Policy Often Procyclical?. _Journal of the European Economic Association, 6_ , 1006-1036.

- Aizenman, J., Jinjarak, Y., Nguyen, H. T. K., and Park, D. (2019). Fiscal space and governmentspending and tax-rate cyclicality patterns: A cross-country comparison, 1960–2016. _Journal of Macroeconomics_ , 60, 229-252.

- Ardanaz, M. and A. Izquierdo. (2017).  Current Expenditure Upswings in Good Times and Capital Expenditure Downswings in Bad Times? New Evidence from Developing Countries. _InterAmerican Development Bank, IDB-WP-838_ .

- Arezki, R., and M. Bruckner. 2012. Commodity Windfalls, Polarization, and Net Foreign Assets: Panel Data Evidence on the Voracity Efect. _Journal of International Economics, 86(2),_ 318-326.

- Blume, L., and Voigt, S. (2008). Federalism and Decentralization- A Critical Survey of Frequently Used Indicators _. MAGKS Joint Discussion Paper series in Economics_ , No. 21-2008, Marburg: Philipps-University Marburg.

- Blume, Lorenz, J. Muller, and S. Voigt. (2009). The Economic Effects of Direct Democracy—A First Global Assessment. _Public Choice, 140(3-4),_ 431-461.

- Chinn, M., and H. Ito. (2006). What Matters for Financial Development? Capital Controls, Institutions, and Interactions.
**Table 7: Sales price regressions for agricultural land**

||**By type**|**of land**||**Commercia**|**l agric. land**||
|---|---|---|---|---|---|---|
||**Commercial**|**Personal**|**With reg**|**istry data**|**With bu**|**yer data **|
|Area (ha)|0.1081***|-0.2159***|0.2584***|0.2377***|0.1897***|0.3439***|
||(0.0022)|(0.0046)|(0.0052)|(0.0054)|(0.0086)|(0.0158)|
|Area^2|-0.0241***|0.0563***|-0.0946***|-0.0911***|-0.0554***|-0.1083***|
||(0.0015)|(0.0019)|(0.0043)|(0.0042)|(0.0063)|(0.0129)|
|Buyer is legal entity|0.2607***|0.4668***|0.2095***|0.2685***|||
||(0.0051)|(0.0136)|(0.0131)|(0.0198)|||
|# transactions|<br>-0.0121***|<br>-0.0427***|
25 Data available from INTA (1998) for seven extension-providing companies indicates a range of cost of extension provision for 1997/98 that varies between $70.98 and $88.43 per producer, based on the company. The average cost for 1997/98 of $79.67 is significantly lower than that of 1996/97 ($88.83), and that of 1995/96 ($101.18). The cost effectiveness trend over the last three years of ATP2 operations is a reflection of both the experience gained by the private companies, and by the competition regulated by INTA.

Table I 1: Extensionists' allocation time for extension in the 7 private companies of ATP2 as of 10/1996

||Numberof|Working Days||Distribut|ion of Tec|hnicians|' Time am|ong Act|ivities||
|---|---|---|---|---|---|---|---|---|---|---|
|Company|Technicians|per Month|Visits of|Producers|Technican|'s Training|Office|Work|Mi|sc.|
||||Days|%|Days|%|Days|%|Days|%|
|I|10|24|20|83.3|2|8.2|1|4.2|1|4.2|
|2|7|24|20|83.3|2|8.3|2|8.3|-|-|
|7|6|23|18|78.2|2|8.7|2|8.7|1|4.4|
|6|8|24|20|83.3|3|12.5|1|4.1|-|-|
|3|11|
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

## **Figure 12. Evolution of seats and city pairs in Sudan**

<!-- Start of picture text --> a. Seats b. City pairs<br>4,000,000 60<br>3,500,000<br>50<br>3,000,000<br>40<br>2,500,000<br>2,000,000 30<br>1,500,000<br>20<br>1,000,000<br>10<br>500,000<br>- 0<br>2,001 2,004 2,007 2009 (Est)<br>2,001 2,004 2,007 2009 (Est)<br>Total Total International<br>International<br>Intercontinental excluding flights between NA and SSA<br>Between North and South Intercontinental Between North and South<br>Domestic<br>City pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems. NA = North Africa; SSA = Sub-Saharan Africa.

# Water supply and sanitation

## **Challenges**

Around 85 percent of Sudan’s populace lacks access to safe sources of water (figure 13). Over 25 percent of the population relies on surface water and almost 60 percent relies on wells and boreholes. Sudan’s reliance on wells and boreholes for water supply is higher than for any other country in its peer groups and is becoming a challenge for the country. Fewer than half the wells and boreholes in Africa provide safe water (table 10).

Sanitation access in Sudan is bimodal. On the one hand, 40 percent of the population has
be assigned to each of these two sectors. Following Herrendorf et al. (2014), we allocate investment value added to each sector using constant shares. As these authors argue, the quantitative relevance of this assumption should be relatively small because total investment is a relatively small share of GDP. To estimate the share that we should attribute to services, we use input-output data for Brazil from the World Input-

> 18Recall that this is not the case in our model of unbalanced growth. Nevertheless, we believe that this strategy is reasonable for the purpose of calibrating the interest rate.

33
provided collateral worth 4.4 times the value of their loan, much more than the required 1.9 collateral value to loan ratio required for other bank loans of a similar size (Figure 10). This high ratio can in part be explained by the fact that firms were required to submit land titles for loans as small as IDR 15 million (US$ 1000). Nevertheless, KUR’s large collateral requirements are not just a function of a few extreme cases: for the median borrower, collateral requirements for KUR are still larger than for other loans of a similar size (1.7 compared to 1.4).

Figure 9: Almost all KUR borrowers were required to submit collateral to receive KUR **% firms who were required to submit collateral, by type of loan** <!-- Start of picture text --> 100%<br>100% 95.7% 92.5%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Micro KUR Small KUR Non-KUR<br>Commercial Loan<br><!-- End of picture text --> Source: Author calculations using survey of KUR borrowers Figure 10: KUR borrowers present collateral that is valued at multiple times the amount of the loan **Ratio of collateral value to loan amount** <!-- Start of picture text --> 5<br>4.4<br>4.5<br>4<br>3.5<br>3<br>2.5<br>1.9<br>2 1.7<br>1.4<br>1.5<br>1<br>0.5<br>0<br>KUR Other loans KUR Other loans<br>Median Average<br><!-- End of picture text --> Source:
|Total expenditures<sup>12</sup>|37,971|31,334|_7,249_|.276|
|---|---|---|---|---|
|Share of energy expenditure on total expenditures|0.04|0.07|_‐0.03_|.001|

_Note:_ The ITT depicts the difference in means at the follow‐up stage between the whole treatment and control group, including also non‐complying households. We control for all stratification and re‐randomization characteristics. Detailed estimation results can be found in the supplemental appendix. Exchange rate as of November 2011: 1 USD = 607 FRW).

_Source:_ Household data set 2011/2012.

As a consequence, we observe a significant reduction in expenditures for small batteries, but not for the larger batteries since households use their Pico‐PV kit predominantly for lighting but only very seldom to run their radio. The consumption of candles is also significantly reduced. In addition, we find a moderate reduction in expenditures on cell phone charging, although the difference is not significant. Estimating an ATT only among mobile phone users by employing the random treatment assignment as an instrument shows a statistically significant reduction of costs for phone charging of 1,662 FRW (2.74 USD). The average household that pays _._ for charging the mobile phone pays 1,400 FRW per month (2.31 USD) In total, energy expenditures without cooking energy are 557 FRW (0.92 USD PPP) lower in the treatment group. This difference is statistically significant. If we compare this to the total household expenditures it shows the importance of energy expenditures for the household budget: The share of energy expenditures without cooking decreases by three percentage points from seven percent to four percent.

> 12 This difference seems not to be driven by the treatment. The (nonsignificant) difference in total expenditures had already existed at baseline
|0.054***|
|||(0.017)|(0.010)|
||Observations|10695|10359|
||AdjustedR<sup>2</sup>|0.133|0.119|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the country-industry level in columns.

To make the adjusted R<sup>2</sup> comparable in each panel, for each dependent variable, the observations are the same as the sample is limited to that all channel variables have observations.

29
phone data using a data mining approach that is agnostic about the outcome variable. Our approach is instead tailored to one outcome, repayment. We extract a set of objects that may have an intuitive relationship to repayment, and then compute features that summarize these objects. We focus on features with an intuitive relationship because implementation partners can be wary of ‘black box’ methods, and indicators that have a theoretical link are more likely to have a stable relationship to the outcome of interest. While our approach is likely to extract some features similar to Blumenstock et al. (2015), it will also measure more nuanced features that would not have been generated by a generic method.

Phone usage captures many behaviors that have some intuitive link to repayment. A phone account _is_ a financial account, and captures a slice of a person’s expenditure. Most of our indicators measure patterns in how expenses are managed, such as variation (is usage erratic?), slope (is usage growing or shrinking over time?), and periodicity (what are the temporal patterns of usage?). In particular, individuals with different income streams are likely to have different periodicities in expenditure (formal workers may be paid monthly; vendors may be paid on market days). We also capture nuances of behavior that may be indirectly linked to repayment, including usage on workdays and holidays, and patterns of geographic mobility, which can reflect information on employment. Although social network measures may be predictive (who one is connected to may reflect one’s level of responsibility or ability to access resources), we include only basic social network measures that do not rely on the other party’s identity (degree, and the distribution of transactions across contacts), as we are hesitant to suggest that a person’s lending prospects should be affected by their contacts. While many traditional credit scoring models aim to uncover a person’s fixed type (whether the person is generally a responsible borrower), the high frequency behavior we
the number of years the current proprietor has been operating it), and £ is an idiosyncratic error term. We measure the probability of participation a number of different ways: (a) as an indicator variable for any participation in an individual institution, (b) as the degree of participation within a particular institution, and (c) as the degree of participation among a range of potential institutions. In each case the null hypothesis is that: (a) 3,>0, (b) 32>0, that is participation should be an increasing function of both firm size and firm age.

Table 3 shows the rate of registration with the federal treasury, the rate of registration of firms' paid workers with the social security administration (IMSS), and the rate of enumeration in the 1989 Census by firm size and by firm age. As predicted, there is a very strong positive relationship between participation and firm size/revenue. The relationship between participation and firm age is also positive, though much weaker. Table 4 reports the results from fitting probit regressions for the seven different types of institutions. In each case the coefficients on both firm size and age are positive and significant at better than the 1 percent level of confidence. (The standard errors were corrected for arbitrary forms of heteroskedasticity.) The estimated changes in the probability of participation for a unit change in each regressor are reported in Table 4.A. The relationship between size and participation is very strong: each point increase in log revenue corresponds to, for example, a 20 percent greater rate of tax compliance, a 23 percent greater rate of social security compliance, and an 1 1 percent greater rate of business guild registration. '" The relationship between age and participation is more marginally significant: a ten year difference in age increases participation in the various institutions by 2 to 4 percent.<sup>20</sup> One potential concern is that the benefits of participation undoubtedly vary by industry. Given systematic differences