rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 1 shows the results of regressing GVA on electricity consumption and nighttime light intensity. Columns 1 and 2 show that electricity consumption and GVA move together closely both in levels and as deviations around a trend. In both cases the relationship is statistically significant at the one percent level. For each percentage point increase in electricity consumption, GVA grew 1.3 percentage points. This relationship is very similar to the one-to-one relationship Cicala (2020a) finds for the United States, including during the global financial crisis, and even closer to the elasticity of 1.4 that Chen et al. (2020) find in Europe.<sup>13</sup> Columns 3 and 4 replicate the regressions for nighttime light, showing that it also follows the evolution of GVA very closely.<sup>14</sup> However, GVA shares more of its variation with electricity consumption than with nighttime light intensity, as reflected in a lower R2 of the latter regression. This could be due to larger measurement errors in nighttime lights compared to electricity consumption. Since both move together with economic activity, they are related. Consequently, nighttime light turns out not to be significant when we include both electricity and light in the same regression (column 5). In columns 6 and 7, we demean the data by regressing GVA, electricity consumption and nighttime lights on year and quarter fixed effects. We then regress the residuals of GVA on the residuals of electricity and lights. This allows us to understand the relationships between the quarterly fluctuations of GVP, electricity and lights. Columns 6 and 7 confirm that both electricity and lights are able to track quarterly fluctuations, with both coefficients turning out to be significant at the one percent level. Finally, we regress electricity on lights to examine how strongly they are correlated in India. Columns 8 and 9 show a strong relationship both in levels and around a common trend. A 1 percent increase in light intensity is associated with a 1.2 percent increase in electricity consumption.

Electricity
2 inequality using NA data and combine it with the empirical observation that withinnational income distributions tend to follow a log-normal pattern. Then the only additional piece of information needed is a Gini coefficient or some other summary inequality statistic describing national income distributions. They are published in various compendia of the Gini coefficients such as WIDER, the Deininger-Squire database etc.<sup>9</sup> Under the assumption of lognormal distribution of income, the inequality statistics allow us to retrieve an estimate of the variance of each national distribution. Once we know the variance and the mean, and given the assumption of log-normality, we can estimate the entire distribution, that is, each fractile’s income. It is then a relatively simple task to combine these national distributions into a single world-wide income distribution, particularly so if one uses an exactly decomposable measure of inequality like the Theil coefficient or the variance of logs. This was precisely the approach followed by many early and some recent studies of global inequality (Berry, Bourguignon and Morrison, 1983; Grosh and Nafziger 1986; Quah 1999; Shultze 1998; Chotikapanich et al. 1997). At times this approach can be refined by using a bit more information than what is contained in a Gini or Theil index. Sala-i-Martin and Bhalla have used quintiles of income distribution to get a better handle on national distributions and thus a more precise estimate of global distribution. All these methods can be considered _tatonnements_ , groping for the global distribution.

These methods are quite ingenious given their rather minimal information requirements. But they are also very “costly” because it is often the numerous assumptions, piled up one upon another, that drive the results rather than the data. A lot of assumptions are made simultaneously (e.g. that each country’s distribution is lognormal; that GDP per capita gives the correct mean income and that its under- or overestimation compared to household surveys is constant across poor and rich alike) and it is quasi impossible to tell the impact which each of the
regulators;<sup>12</sup> Schenk and Thornton (2016), instead, indicate that TARP bailouts are likely to increase risktaking in “weak”institutions (low charter values and high leverage) and to decrease it in “strong” institutions (high charter values and low leverage). Finally, using a di¤-in-di¤ strategy, Berger et al. (2016b) also show that TARP contributed to the reduction in systemic risk and that such a reduction has been stronger for larger, safer banks, in states with better economic situations.

The crisis also o¤ered natural experiments to look into the pricing behavior of bank investors. Data on banks’sovereign debt holdings disaggregated by bond issuer are not generally available. However, the EBA released detailed bank-level information when it announced the results of its stress tests. And evidence suggests that this had an impact on banks’CDS spreads. The table below (from Dell’Ariccia et al. 2016) shows that banks that were found to hold larger amounts of distress sovereign bonds on their balance sheets experienced a sharper increase in CDS spreads and drop in stock returns. This evidence is not consistent with the notion that investors price risk at

> 12 Because it occurs in the same asset classes and thus does not a¤ect the CAR.

23
Base Shock*Dummy|||0.2930<br>(0.3806)|
|Sample Size|1215|1215|1215|

* ** *** _p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses.

Note: All regressions include a constant and a full set of country and year dummies. Standard errors are cluster-robust.

In sum, using quarterly narrative data on fiscal consolidations, I find that austerity programs which increase tax rates during bad times do not necessarily cause output losses, and can be potentially expansionary. This finding is robust to how one defines the critical debt level beyond which an anti-Keynesian effect is expected to kick in.

15
38 Table 9. Subsidized food as a source of calorie availability by expenditure quintile group for rural Egypt, 1997

|||Percent o<br>availability, b|f total daily<br>y per capit|per capita<br>a expenditu|calorie<br>re quintile||
|---|---|---|---|---|---|---|
|Subsidized food|Lowest<br>[11|Second<br>[21|Third<br>[3]|Fourth<br>[4]|Highest<br>[51|All|
|Baladi bread|15.6|13.5|13.2|14.6|13.3|13.9|
|Baladi wheat flour|16.4|13.0|11.5|11.2|9.8|12.0|
|Edible oil|2.7|2.6|2.8|3.0|2.7|2.8|
|Sugar|3.1|3.2|3.0|3.1|2.7|3.0|
|Percent of total daily per capita<br>calorie availability from subsidized food|37.9|32.4|30.5|31.8|28.5|31.6|
|Total daily per capita calorie availability|2,874|3,058|3,543|3,653|4,194|3,555|

Source: IFPRI Egypt Integrated Household Survey, 1997.

Notes: N = 1,274. Rural population is divided into quintile groups on the basis of total per capita fitted expenditures.
source of these emergency funds, with 64 percent of women relying on family and friends, compared to 42 percent of men. There are a number of informal financial institutions in Sudan, and Kevane and Stiansen highlight three: _sandug_ , _dayn_ , and _shayl._<sup>19</sup> Of these, _sandug_ are the only viable credit option for most women. _Sandug_ are monthly gatherings of small groups of women at which each woman contributes something from her savings to the group’s pool of savings enabling one woman—based on a rotating system—to borrow from the group’s savings for consumption or investment.<sup>20</sup> Microfinance is another option for some Sudanese women, and according to a study of microfinance’s role in supporting women entrepreneurs in urban Sudan, there are a variety of institutions that offer microfinance.<sup>21</sup> The lack of access to finance contributes to low levels of entrepreneurial activity among women. Data from the Enterprise Survey conducted in Sudan in 2014 show that only 3 percent of firms have a woman

> 19 _Dayn_ is a form of informal credit utilized by those who trade in the markets; for example, traders may maintain a ‘line of credit’ with a small shopkeeper who agrees to provide them with the staple items they need to prepare the food they sell. _Shayl_ is a form of ‘futures contract’ for those who engage in smallholder agriculture.

> 20 For example, the UN International Fund for Agricultural Development (IFAD) has helped women in Kordofan State who rely on _khatas_ , which are similar to _sandugs_ , channel their groups’ savings into the Agricultural Bank of Sudan’s microfinance program. This microfinance program had, as of 2017, worked with 1,800 women’s groups which comprised 30,000 members (IFAD 2019).

> 21 (Siddig, Siddig, and Hegazi 2014) The study highlights, for example, the Savings and Social Development Bank and the Youth Foundation
system. Handling return water presents technical and cost issues; releases may occur near the seabed, or take place at mid-water (Heffernan, 2019). Tailings plumes from dewatering operations contain fine particles under 10 μm, and may drift beyond mining sites (Washburn et al., 2019). Of particular concern to marine scientist is the cumulative effects of these impacts on ecosystem functioning and connectivity as multiple seabed mining sites are developed over time (Dunn et al., 2018).

The existing information about DSM impacts on the environment is poor. Acquiring information on deep seabed environments is costly and technically challenging. Exploratory leases have been used to complement other studies on marine ecosystems. Expert surveys have also been used to help assess the environmental effects (Clark et al., 2016; Washburn et al., 2019). In a recent survey of expert opinion, the authors state that a “.. striking result is ... the relative paucity of experts willing and able . . . to offer an opinion about the vulnerability of mineral-associated habitats to a variety of risk sources ...” (Washburn et al., 2019, pp 36). The authors suggest that “survey outcomes . . . underscore the need for risk assessment to progress from expert opinion with low certainty to data-rich and ecosystem-relevant scientific research assessments to yield much higher certainty” (Washburn et al., 2019, pp 25).<sup>8</sup>

> 8 The environmental impact of deep-sea mining might be weighed against terrestrial mining of low grade deposits of the same metals, an activity that disturbs habits, and produces waste slags and toxic leachates that can damage ground water and terrestrial ecosystems. However, more is known about the environmental effects of terrestrial mining than the effects of seabed mining. The lack of knowledge about marine biodiversity and ecosystem function, and the possible consequences of disrupting dynamically complex seabed ecosystems with little information about the effects, would suggest a cautious, precautionary stance is warranted in the case of
amounting to an increase of 14 percent (or 1.8 million individuals) in spite of their decline relative to the 15 to 24 year-old population. This trend of a declining share but rising number of ninis is driven by high population growth for this age group during the 1990s.

> 6 Although for most of the analysis in this report the latest year included is 2010, more household surveys became available as the study was being completed. To confirm the report’s findings, the key statistics presented here were updated to circa 2013, and all of the messages remain unchanged. For instance, the long term trend in the decline in the share of ninis remains reaching 19 percent in 2013, while the absolute number of ninis declines only marginally to less than 18 million in that same year. The most recent statistics are included in country-specific briefs and are available from <u>https://openknowledge.worldbank.org/handle/10986/22349.</u>

6
create three conict-exposed cohorts. Although in the important phase of adolescence height spurt, girls aged 9 to 15 (treatment 3) would have faced conict only in their third phase of height growth. While girls aged 4 to 8 (treatment 2) would have been exposed to conict in the second and third stages of growth, cohort 0 to 3 (treatment 1) would have been exposed to conict through the entire growing period (all three stages).<sup>7</sup> In the baseline specication, I dene conict intensity at the village level, hence, all ve cohorts from any given village would have been exposed to the same total amount of conict during their lifetimes. However, the exposure would have started at dierent times of their lives.

> 7These ve cohorts aged 0 to 3, 4 to 8, 9 to 15, 16 to 21, and 22 to 29 at the start of the conict in 1996 would become 20 to 23, 24 to 28, 29 to 35, 36 to 41, and 42 to 49 at the time of the NDHS 2016 survey.

13
The WGI draw exclusively on perceptions data on the quality of governance. Perceptions of governance matter because individuals and firms are likely to act on those perceptions. In addition, for hard-to-measure dimensions of governance such as corruption (which by definition leaves no “paper trail”) there often are few alternatives to relying on perceptions when comparing outcomes across countries. This is not to say that perceptions are the only thing that matters when measuring governance across countries. However, they can provide a valuable “reality check” relative to more objective or factual measures of specifics of the legal, regulatory and institutional environment, particularly in environments when there is likely to be a substantial gap between de jure rules and their de facto implementation.

In this paper we have also updated and expanded our earlier analysis on three key methodological issues relevant to the WGI: the possibility of correlated perceptions errors, the robustness of the WGI to alternative weights, and the possibility of global trends in governance that are not captured by our baseline methodology. Our updated analysis confirms our earlier findings that these issues are not major concerns for the WGI. Admittedly imperfect indirect evidence suggests that correlated measurement error across expert assessments may not be significant. While such correlations might indicate a different weighting scheme, we also find that the baseline WGI estimates do not change very much when compared with an alternative approach that imposes equal weights. Finally, our updated analysis continues not to show any evidence of substantial trends in global averages of governance. This in turn implies that our baseline WGI estimates which are rescaled to have zero mean in every year are informative about both relative and absolute movements in governance over time.

We conclude with a final caveat worth reiterating. While the WGI can be a useful resource for broad cross-country and over-time comparisons of various dimensions of governance, they are not a panacea for measurement – particularly when evaluating specific governance reforms in specific countries. In these cases, the WGI must be supplemented with more granular country-specific data that can shed light on the likely impacts of reforms to
<!-- Start of picture text --> Qatar: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>30<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text -->

# **B- Oil importers**

<!-- Start of picture text --> Jordon: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>30<br>20<br>10<br>0<br>-10<br><!-- End of picture text --> <!-- Start of picture text --> Lebanon: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>50<br>0<br>-50<br><!-- End of picture text --> <!-- Start of picture text --> Morocco: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>10<br>0<br>-10<br><!-- End of picture text --> <!-- Start of picture text --> Tunisia: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>20<br>10<br>
not be very closely integrated in the short term. Second, it is plausible that higher food prices were the leading factor behind the declining rural-urban wage gap, though there are other possible explanations such as accelerated out-migration from rural areas (Zhang et al. 2013).

> 15. The rural wage index places most of its weight on the agricultural wage series for Bangladesh while the urban series includes wages for construction, masons, carpenters, plumbers, painters, electricians, and brick-breaking.

15
<!-- Start of picture text --> IControls<br>∗<br>Zi = wj Zj ,<br>∑<br>j=1<br><!-- End of picture text --> <mark>then</mark> <!-- Start of picture text --> IControls<br>δit = Yit − wj∗Yjt<br>∑<br>j=1<br><!-- End of picture text --> <mark>is an unbiased estimator of</mark> _<mark>δ</mark> it_ <mark>. For further details on the SCM, we refer the reader to Abadie et al. (2010), as well as Abadie and Gardeazabal (2003).</mark> <mark>Note that in order to limit the risk of interpolation and overfitting, as well as to build controls using structurally similar countries, we restrict the pool from which the synthetic control is constructed on the basis of two variables: GDP per capita and institutional strength, proxied by the Polity V index. To be included in the donor pool for a given synthetic control study, the potential donor country has to fall within one quartile of the distribution of each of these two variables.</mark><sup>16</sup> <mark>To move from one individual event study to the average effect of sovereign defaults on the outcome variables across the vast sample of sovereign defaults in our data, we primarily rely on the aggregation methodology introduced by Cavallo et al. (2013). We extend that methodology in that rather than calculating the unweighted (normalized) average effect, we aim to further account for the fact that not all individual analyses bear the same informational value. Which stems from not all sovereigns that default necessarily having a suitable synthetic control constructable from the donor pool (Abadie et al. (2010, 2015)). To
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- Muralidharan, Karthik, Mauricio Romero and Kaspar W ̈uthrich. 2021. “Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments.” NBER Working Paper, No. 26562.

- National Bureau of Statistics, Ministry of Finance, Finance Office of Chief Government Statistician President’s Office, Economy and Development Planning. 2012. “Tanzania Population and Housing Census.”.

- Niolon, Phyllis Holditch, Alana M. Vivolo-Kantor, Allison J. Tracy, Natasha E. Latzman, Sarah Little, Todd D. andDeGue, Kyle M. Lang, Lianne Fuino Estefan, Sharon R. Ghazarian, Wendy Li KamWa McIntosh, Bruce Taylor, Linda L. Johnson, Henrietta Kuoh, Tessa Burton, Beverly Fortson, Elizabeth A. Mumford, Shannon C. Nelson, Hannah Joseph and Andra Teten Valle, Linda Anne andTharp. 2019. “An RCT of Dating Matters: Effects on Teen Dating Violence and Relationship Behaviors.” _American Journal of Preventive Medicine_ 57(1):13–23. **URL:** _https://doi.org/10.1016/j.amepre.2019.02.022_

- Nkata, Hamida, Raquel Teixeira and Henrique Barros. 2019. “A scoping review on sexual and reproductive health behaviors among Tanzanian adolescents.” _Public Health Reviews_ (4). **URL:** _https://doi.org/10.1186/s40985-019-0114-2_

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22

|Country,<br>La<br>economy, or<br>territory|stoublishe<br>P<br>Date|d<br>census<br>opulation<br>(1000)*|Source for poputation<br>(mid-1985)|Source forfertility<br>(1985-90, unless Indicated)|Source for mortality<br>(1985-90, unless irdicated)|
|---|---|---|---|---|---|
|Netherlands<br>Antilles|Feb 81|172J|Bank est.|Based on USBOC 1985|Based on USBOC 1985|
|Netherlands,<br>Th|e Feb 71|13060 J|Eurostat<br>1987|Eurostat<br>1987|Eurostat<br>1987|
|New Caledonia|Apr 83|145 F|USBOC 1985|Based on UNPVSR 1984<br>S:cial<br>Supplement|Based on UNPVSR 1984<br>Special<br>Supplement|
|New Zealand|Mar 86|3307 F|U.N. 1984 assessment|Based on UNPVSR 4/87|Based on UNPVSR 4/87|
|Nicaragua|Apr 71|1878 J|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(preLim.)|
|Niger|Nov 77|5098 F|Bank projection from census|Bank assessment of 1959-60<br>survey ard age data from<br>1977 census|U.N. 1988 revision (prelim.)|
|~igeria|Nov 63|55670 F|Bank projection from<br>official data|Official est., based on<br>U.N. 1980 assessment|U.N. 1988 revision (prelim.)|
|Niue|Sep 76|4