rafmacalaba/data-use-mentions-extended huggingface dataset:
deaths(6.9%)|Predicted values from Poisson regression<br>model|
|June 1-August 31 (3 months)<br>2003—Netherlands|1400deaths|Numberofdegrees above 72°F multiplied<br>bytheestimated numberofexcessdeaths<br>perdegree (25-35 excess deaths)|
|June 1—August 23<br>2003—Baden-Wuerttemberg,<br>Germany|1410deaths|Calculations based on mortality ofpast<br>five years|
|August 1-24<br>2003—Belgium<br>August 4-13|1297 deathsforagegroupolderthan 65|| Average ofdeaths forsame period inyears<br>1985-2002|
|2003—England and Wales|2091 (17%). Mortality in London|Average ofdeaths forsame period in years|
||region:616deaths(42%excess)|1998to2002|

<!-- Start of picture text --> Heat wave event Baseline measure<br>1976—London, UK 9.7% increase England and Wales and 31-day moving average of daily mortality<br>15.4% Greater London in same year<br>1981—Portugal Predicted values<br>in Portugal, 406 in Lisbon (month of<br>July)<br>1983—Rome, Italy 35% increase in deaths in July 83 in Compared with deaths in same month in<br>1987—Athens, Greece ‘Time trend regression adjusted<br><!-- End of picture text --> <!-- Start of picture text --> 1991—Portugal 997 excess deaths Predicted values<br>July 12-21<br>1995—London, UK 11.2% (768) in England and Wales, 31-day moving average of daily mortality<br>23% (184) Greater London in previous two years<br>July 30-August 3<br>1994—Netherlands 24.4% increase, 1057 (95% C1913, 31-day moving average of previous 2 years<br>1201)<br>
The rest of the paper is organized as follows. Section 2 describes the data used in the analysis. Section 3 discusses the empirical strategy we follow. The results are summarized in section 4. Finally, section 5 concludes.

# **2. Data**

We use data from three main sources, covering January 2013 to June 2016. The first data set, which we refer to as the loan-level data, consists of the universe of commercial loans in Mexico, which we obtained from regulatory reports sent monthly by every commercial bank to the bank regulator. The reports are mandatory, updated electronically, and include detailed characteristics of all new and continuing commercial loans. All loans, regardless of their size, are reported. Each loan has an identifier of the issuing bank, as well as the borrower’s identifier, location, sector, and number of employees. The data set includes information on the interest rate, outstanding amount, type of financing (i.e., whether the loan is for working capital or investment purposes), and start and end dates (maturity) of each loan. Given that some borrowers have more than one loan issued by the same bank at a given point in time, we adopt a similar approach as La Porta et al. (2003) and aggregate individual loans at the _firm-bank-month_ level. We then report loan characteristics, such as the interest rate, fraction of the loan covered by collateral, and maturity at origination, using a weighted average by loan value. This approach puts greater weight on larger loans, ensuring that our results are economically meaningful.

Our second data source is Orbis, a _firm-year_ -level data set compiled by Bureau van Dijk, which contains information on the balance sheets and income statements of a large set of Mexican firms. The data set reports information on assets and revenues of firms as well as their total and bank-specific liabilities by type of financing. As shown by Morais et al. (2019), this sample of firms is representative
<br>**international**<br>**remittances**<br>**(7)**|
|---|---|---|---|---|---|---|---|
|Poverty||||||||
|headcount|58.03|57.34|56.69|56.19|(-1.19)|(-2.31)|(-3.18)|
|(percent)||||||||
|Poverty gap<br>(percent)|24.60|23.90|23.17|22.62|(-2.85)|(-5.82)|(-8.05)|
|Squared<br>poverty gap|14.06|13.47|12.15|11.69|(-4.20)|(-13.59)|(-16.86)|
|(percent)<br>Gini|0.484|0.484|0.477|0.476|--|(-1.45)|(-1.66)|
|coefficient||||||||
|Observed||||||||
|mean per<br>capita||||||||
|household<br>expenditure<br>(including<br>remittances)<br>(quetzals)<br>N|7,362.76<br>7,276|7,563.40<br>7,276|7,556.22<br>7,276|7,721.43<br>7,276|+2.72|+2.63|+4.87|

Notes: All
.233***|-0.31|-0.10|-.207***|-0.46|-0.24|-.227***|
|<br>Avg. return to capital|1.18|1.00|.188***|1.23|1.01|.214***|1.14|0.97|.172***|
|Capital distortion|0.80|0.75|0.049|0.80|0.69|0.114|0.80|0.85|-0.051|

Sources: World Bank Enterprise Surveys, World Value Surveys and World Development Indicators. Notes: Calculations using survey weights. See Table A.1 in the Appendix for details on variables definition and section 2 for details on the classification of countries as More or Less traditional.
obstacles to growth, and performance measures. The survey is designed to be representative of a country’s private non-agricultural economy and only registered firms with at least five employees are included in the sample.

The data consist of a random sample of 12,000 firms across 27 developing countries in different regions stratified by firm size, location, and sector. The survey year ranges between 2007 and 2009. This sample of ES data is selected on the basis of consistent sampling methodology. Surveys for previous years do not have the same methodology. Police data is obtained from the United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems. This data set consists of an unbalanced panel covering years between 2003 and 2008 for both developing and developed economies. Due to the unbalanced nature of the data set, about 39 countries match between the ES firm-level data and the UN country-level police data. Accounting for issues such as missing questions and incorrect data, the sample size reduces to a cross-section of 27 developing economies. To account for firm-specific unobservables, we use panel data for 644 firms in a subset of 12 Eastern European economies for 2004 and 2007.<sup>2</sup> We would caution that the firms sampled in 2004 do not use the same sampling methodology as the firms in 2007, and data are only available for Eastern European economies. Details of countries in the sample and their respective survey years can be found in the first column of table A3 in the appendix.

# _2.1 Dependent variable_

The dependent variable used is losses due to crime as a percentage of annual sales of a firm. This variable is derived from the survey question: “In fiscal year [insert fiscal year], what are the

> 2 Panel data are not available for the other countries

8
<!-- Start of picture text --> Post-transfer poverty ($1.25), % of populattion People out of poverty due to SP, at $1.25, % of population<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>-40%<br>Source: Authors based on ASPIRE, See Annex<br>Bulgaria Kosovo Belarus Romania Ukraine Latvia Serbia Uruguay Mauritius Poland Malaysia Bosnia and Herzegovina Kazakhstan Pakistan Azerbaijan Egypt, Arab Rep. Macedonia, FYR Turkey Thailand Armenia Argentina Chile Dominican Republic Nepal Mongolia Bangladesh Ghana Peru Ecuador Kyrgyz Republic Mexico Yemen, Rep. Brazil Paraguay Panama El Salvador Bhutan West Bank and Gaza Sri Lanka Georgia Bolivia Cambodia Malawi Philippines Nicaragua Guatemala Vietnam Afghanistan Lao PDR Kenya Timor-Leste Rwanda<br><!-- End of picture text --> There are several countries where absolute poverty measured by $ 1.25 a day standard is low or even zero, and hence the impact of social protection cannot be assessed. For this reason, the annexes give estimates of poverty impact not only for $1.25/day line, but also for a relative line with a cutoff of the bottom quintile.

The poverty headcount, while informative about the scale of poverty, does not characterize the nature of poverty: how deep and severe it might be, how far an average poor person is from the poverty line. The most intuitive measure for this is the total poverty gap- or the sum of the gaps between income and the poverty line for the poor in country.<sup>33</sup> Using again the international poverty line of $1.25 and a simulation of social protection impacts we can see in Figure 4 that the impact of social transfers on this poverty measure is even greater than what we saw for the
availability as a measure of the reliability of electricity supply, we find that outages have a negative effect on economic outcomes and that the effects of gaining access to electricity become stronger after controlling for outages. For example, per capita income decreases by 0.5 percent for each additional hour of outages. And while electrification combined with a 24‐hour‐a‐day power supply is associated with a 17 percent increase in income, electrification alone (with outages not controlled for) is associated with only an 11 percent increase. The aggregate benefit of obtaining reliable electricity in rural India is estimated to be US$11 billion a year, with US$4.7 billion from increasing the access rate and US$6.5 billion from improving the reliability of existing supply.

Several studies have examined the benefits of rural electrification in India. For example, van de Walle et al. (2015) estimate the long‐run effects of electrification on household consumption in rural India based on data for 1981–98; Burlig and Preonas (2016) investigate the effect of India’s national rural electrification program on labor force participation, living standards, and other village‐wide outcomes using census data for 2001 and 2011; and Khandker et al. (2014) estimate the benefits of electrification projects in rural India based on cross‐sectional data for 2005. A study by Chakravorty, Pelli, and Marchand (2014) is one exception that examines the effects of reliability on the benefits of electrification. Also relying on IHDS data, the authors find that a grid connection and a higher quality of electricity increased nonfarm income by 28 percent during 1994–2005, while a grid connection alone increased nonfarm income by 9 percent.

Our study differs from the one by Chakravorty, Pelli, and Marchand (2014) in several ways. First, we analyze the effects of rural electrification based on a more recent two‐round panel survey conducted in 2005 and 2012. In addition to providing updated evidence, the survey data for
and clarifications may be useful when completing all the templates related to fiscal data (see templates 9 and 10).

## **The functional classification of expenditures or COFOG**

Using the GFSM 2001 as the starting point for this exercise provides a methodological platform that is well used and known across countries and has substantive advantages. The use of the GFSM 2001 functional classification allows for examining expenditure trends over time, regardless of country-specific institutional arrangements or restructuring. Another advantage is the unambiguous definition of the sectoral scope.

The functional classification of expenditures, also known as the classification of functions of government (COFOG), is a detailed classification of the functions, or socioeconomic objectives, that 35
# **Figure 19 Average and Median Export Value per Firm**

<!-- Start of picture text --> Average Export Value per Firm (million USD) Median Export Value per Firm (million USD)<br>6 0.08<br>0.07<br>5<br>0.06<br>4<br>0.05<br>3 0.04<br>0.03<br>2<br>0.02<br>1<br>0.01<br>0 0.00<br>2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

Like in most countries worldwide, not only do top exporters in Pakistan account for a large proportion of export values, on average, they also export more products to more destination countries – see Table 6 . The total number of firms, destinations, sectors and products in the database are summarized by each year in the first few rows. The data is then broken down by averages for all firms, the top 1%, 5%, 10% and bottom 80% of exporters. On average, the top 1% firms export between 21 and 50 products and to 20 destinations, while the corresponding figures for the lowest 80% of firms are 2-4 products and 1-2 destinations. Exports are highly skewed towards particular countries – for instance, 5 countries (US, UAE, UK, Germany and China) account for about half of total export values in 2003, and 20 countries account for over 80% of total export values.

26
For all three types of sampling, when multiple households are found living in the same building or tent, a single household is randomly selected.

# **5.2. Assets**

Based on the June survey, an asset index is calculated using a wider set of preearthquake dichotomous variables, namely some durable goods not declared in the February survey and access to basic utilities. Table 14 reports both weights and contributions to inertia. Weights have signs consistent with interpretation of the first component as an asset-poverty index. In directly affected areas, contribution to inertia of lighting appears to be particularly high (26.7%). Water source also contributes in a large extent to inertia (18.9%). Having tools or material for fishery, agricultural production and handicraft contributes to 12.2% of the inertia explained by the first component of the analysis. Other items contribute to less than 10% of inertia each.

> 29 This section is an excerpt from Echevin (2011).

37
_Figure 23: Decomposition of consumption change in real terms compared to the level of 2014, %_ <!-- Start of picture text --> 6 3<br>inkind<br>4 2<br>2<br>1<br>other food<br>0<br>0<br>-2<br>fruits and<br>-1<br>-4 vegetables<br>-6 -2<br>diary<br>-8 -3 products<br>-4 meat and<br>fish<br>average Poorest Deciles 2-3 Deciles 4-7 Deciles 8-9 Richest<br>-5<br>decile decile<br>2015 2016 2017 staples<br>food other consumables durables<br>Poorest decile<br>utilities other services total<br>2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> Total consumption by deciles<br><!-- End of picture text --> <!-- Start of picture text --> (b) Food consumption by the 1st<br>decile<br><!-- End of picture text --> Note. See Methodological notes for description of the procedure applied to estimate real consumption. Source: Authors’ estimates based on HSS data.

Across different types of households, the most severe cuts in food expenditures during the recession were observed among households with two or more children (Figure 24). It should be noted, that these households have the lowest level of per capita expenditures on food among the analyzed social groups, in part because of the economies of scale in these households and the significant role of in‐kind income from subsistence farming. This is worrisome, as it may be indicative of worsening nutrition in households with children, although
and for panel and cross-sectional data.

If we were to interpret the coefficient in Column (3) of Table 1 as causal – how economically significant is the relationship? The standard deviation of the debt variable is 0.6 which would imply that an increase of one standard deviation in conflict debt is associated with an increase of 1 percentage point (0.6*1.767). But the standard deviation is a bad measure of the burden on some countries with the maximum being at 5 which would imply an increase of almost 9 percentage points of the poverty rate.

One simple way to understand the dynamic relationship found in the regression is to simulate conflict debt for a conflict and plot the implied divergence in the poverty rate over time. In Figure 3 we show the implied increase of the poverty rate with a conflict of five years duration - symbolized by a series of five 1s on the X-axis. With the start of the conflict the poverty rate increases dramatically and to over 7 percentage points at the end of the five years of the conflict duration. It then falls gradually as the conflict debt falls but is still about 1 percentage points higher ten years after the end of the conflict. This kind of relationship leads to an image of catching up seen in Figure 2 but is also consistent with persistent divergence if a country enters the so-called violence trap, i.e. a repeated cycle of peace and violence. This is shown in a Figure 4. Divergence here is much more persistent. Poverty rates in countries like Afghanistan, the Democratic Republic of Congo or Somalia could therefore be explained by such a pattern.

13
all teachers employed in public schools. Health care is largely the responsibility of the central government. Data on military employment are taken from the Intemational Institute for Strategic Studies: The Military Balance Survey of 1995-96. Data include conscripts (189,200), but exclude personnel in paramilitary units, i.e., Federal Border Guard (24,500), under the authority of the Ministry of Interior, and the Coast Guard.

Germany Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Greece Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1993.

Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1990. Public education and health are solely the responsibility of the central government. Military employment data include conscripts (114,000), but exclude personnel in paramilitary units, i.e., Gendarmerie (26,500) and the Coast Guard and Customs (4,000). Data on wages in manufacturing is taken from the Intemational Labor Office's Yearbook of Labor Statistics 1995 and refers to 1994.

Ireland Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1991. Public education is the responsibility of the central government. University education is subsidized by the central government, but funds are channeled through the departrnent of education to an autonomous Higher Education authority. Vocational education is managed at the county level. Health services are under the responsibility of health boards which administer them on a regional basis and are responsible for the implementation of a large element of govemment health policy. Most of their expenditures are financed however by the central government.

# Italy

Paid employment in non-agricultural activities
rate of female labor participation implied that our working samples include a lower percentage of women.

As previously suggested, the sample selection of ENCEL 2017 and the restrictions derived from survey collection imply limitations to the representativeness of the results of this study. Therefore, despite the similarities between our working samples and the originally selected sample, we consider that the results of this study are valid only for the subpopulation analyzed in this study and may only be considered as indicative (but not representative) of the beneficiary population of PROSPERA in rural areas. Furthermore, it is reasonable to believe that the results may underestimate the characteristics of occupational mobility of youths, due to the fact that the analysis did not include all of those individuals from the original sample of ENCEL who have migrated from their locality of origin.<sup>27</sup>

# B. _Characteristics of the study group_

Our first approach to characterization is to compare our study sample to similar youths at a national level. Table 1 shows some of the educational and labor characteristics of our study group (ENCEL 2017),<sup>28</sup> as well as of occupied youths in the same age range at the national level and in rural areas of the country (ENIGH 2016). For context, to interpret these data, it is important to emphasize that, while the sample of ENIGH 2016 (national and rural) includes youths that reside in localities of all degrees of marginality, the youths in our sample currently live predominately in localities with high and very high levels of marginalization (8.5% and 72.6%, respectively), while only the remaining 18.9% live in localities with a lower degree of marginalization.<sup>29</sup> When comparing the characteristics of both groups, there are some observed differences between the two that point towards a more unfavorable situation for youths in our study’s sample, both in the area of education, as well as in their employment and labor outcomes.
coupd’état|0.207**(0.094)|0.217***(0.061)|
||**Interaction**|**-0.388***(0.116)**|**-0.113(0.157)**|
||Ross oil rentsper capita(observations 4988)|0.00015**(0.00007)|0.00014***(0.00005)|
|Model 29|Risk of coupd’état|0.200**(0.096)|0.202***(0.065)|
||**Interaction**|**-0.00008 (0.00007)**|**-0.00005 ****<br>**(0.00002)**|
|Coup d’état history in<br>past 5years|Quantitative measures of resources endowments|||
||Fearon and Laitin oil export dummy (observations 6767)|0.866***(0.277)|0.879***(0.206)|
|Model 30|Couphistory|0.528*(0.269)|0.636***(0.194)|
||**Interaction**|**-0.987*(0.612)**|**-0.964**(0.441)**|
||Humphreys oilproduction(observations 5061**) **|1.660**(0.818)|1.566***(0.507)|
|Model 31|Couphistory|0.131(0.283)|0.303(0.200)|
||**Interaction**|**-1.542(1.751)**
“adoptive” family, actually increases the magnitude and the significance of these coefficients. It is probably due to the fact that ignoring the very strong link between type of relationship in the new family and schooling was obfuscating the actual mechanism of intergenerational transmission of human capital.

An additional way to control for non-random placement is to include the schooling level of the biological parents: if biological children from educated parents are placed in other families with high education levels, controlling for the biological parents’ education will correct the bias. Since the EICV contains this information for most children—see the reduction is the sample size and therefore in power—this strategy is followed in column 6. This approach also suggests a very intuitive comparison between the nature and nurture components of the intergenerational transmission of human capital.<sup>9</sup> In panel A, both coefficients on the schooling of “adoptive” parents remain significant and their magnitude is comparable with column 5. In panel B, however, significance is only achieved for the male head of the household. What is striking about both panels is the strongly positive and significant coefficient on the education of the biological mother. Also very surprising the coefficient on the biological father’s education; although positive, it is not significant<sup>10</sup> .

It might be argued that including children identified as domestic employees of the household in the samples was not appropriate since it is clear that the family has no particular incentives to improve their schooling outcomes. Columns 7 and 8 acknowledge this by repeating columns 5 and 6 with samples from which all children employed as domestics workers have been removed. There is no changes in the significance of the coefficient on the education of “adoptive” parents, except that the coefficient for the male with the highest education in panel A is now only significant at the 10 percent confidence level, but the magnitude of most coefficients increases quite substantially. This is a sign that including domestic employees in the regressions might indeed have obfuscated some
**Appendix 6.  Comparison of the November 2017 WFP Survey to the 2014 Household Budget Survey**

||2014 Hous<br>_________<br>|ehold Budget<br>___________<br>|Survey<br>_______|Novemb<br>__________<br>|er 2017 WFP Su<br>____________<br>|rvey<br>______|
|---|---|---|---|---|---|---|
||Lower|Upper||Lower|Upper||
|Variable|Bound-<br>95% CI|Bound-<br>95% CI|Mean|Bound- 95%<br>CI|Bound- 95%<br>CI|Mean|
|Own House|0.776|0.880|0.828|0.459|0.570|0.515|
|Rent House|0.070|0.165|0.118|0.224|0.373|0.299|
|HH Size|6.73|7.51|7.12|9.50|10.53|10.02|
|Share relying on Food<br>Coping|0.040|0.123|0.082|0.851|0.901|0.876|
|Number of Days Eating<br>Staples Last Week|6.99|7.00|7.00|5.92|6.11|6.01|
|Number of Days Eating<br>Pulses Last Week|3.38|4.21|3.80|2.74|3.32|3.03|
|Number of Days Eating<br>Vegetables Last Week|3.91|5.03|4.47|2.11|2.67|2.39