rafmacalaba/data-use-mentions-extended huggingface dataset:
al. (2018), “Understanding interpersonal violence: the impact of temperatures in mexico.” Technical report, Grantham Research Institute on Climate Change and the Environment.

- Colmer, Jonathan and Jennifer L Doleac (2022), “Access to guns in the heat of the moment: More restrictive gun laws mitigate the effect of temperature on violence.” _Available at SSRN 4195573_ .

- Correia, Sergio, Paulo Guimar ̃aes, and Thomas Zylkin (2019), “Ppmlhdfe: Fast poisson estimation with high-dimensional fixed effects.” _arXiv preprint arXiv:1903.01690_ .

- Davis, Lucas W and Paul J Gertler (2015), “Contribution of air conditioning adoption to future energy use under global warming.” _Proceedings of the National Academy of Sciences_ , 112, 5962– 5967.

- Denissen, Jaap JA, Ligaya Butalid, Lars Penke, and Marcel AG Van Aken (2008), “The effects of weather on daily mood: a multilevel approach.” _Emotion_ , 8, 662.

- Department of Public Safety, Texas (2019), “Seventeenth report examining reporting compliance to the texas computerized criminal history system.” Technical report.

- Donohue, John J, Samuel V Cai, Matthew V Bondy, and Philip J Cook (2022), “More guns, more unintended consequences: The effects of right-to-carry on criminal behavior and policing in us cities.” Working Paper 30190, National Bureau of Economic Research, URL `http://www.nber.org/papers/w30190` .

38
# **5. Results**

## **5.1 Summary Statistics and Balance**

We start by providing an overview of the study sample and examining balance across the treatment and control groups in table 3. Panel A of Table 3 focuses on demographic information on jobseekers from the FEI database. Overall, in line with the objectives of the program, nearly 80 percent of beneficiaries were women, more than half were above the age of 30, more than 70 percent had a secondary school education or less, and 43 percent had been unemployed for over a year. As can be seen from the last column of this table, there are no differences in the average characteristics across control and treatment groups.

Panel B examines jobseeker characteristics collected through the baseline survey. Roughly half the sample is married, and the average household size is 3.36. About 60 percent of beneficiaries live in households with monthly income below 900 KM. For comparison, the average household monthly consumption in the country in 2015/16 was 1,419 KM and in 2021/22 was 1,778 showing that program beneficiaries came from relatively poor households. Of the beneficiaries 29 percent had some vocational training. Once again, there are no significant differences between the control and treatment groups for any of these characteristics. Finally, the baseline survey included a standard battery of questions to characterize each of the jobseeker’s “Big 5” personality traits - openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. The table reports standardized averages of each of these traits and again the treatment and control groups are (on average) identical. Panel C shows the distribution of the job counselor’s assessment of applicant employability. Of the jobseekers, 78 percent are categorized as “ _employable_ ”, whereas 11 percent each are categorized as “ _highly employable_ ” and “ _less employable_ ”. Again, these distributions are identical across the treatment and control arms.

## **5.2 Differences in Services Provided to
.011)|(0.013)|(0.015)|(0.0093)|(0.0088)|
|Other group mean education level|0.013|0.013|0.0028|0.0018|−0.0044|−0.00096|
||(0.0095)|(0.011)|(0.013)|(0.015)|(0.0098)|(0.0080)|
|Observations|1362|1362|1362|1362|1110|1110|
|_R_<sup>2</sup>|0.849|0.851|0.848|0.850|0.841|0.845|
|HH controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Agr. controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Group controls|No|Yes|No|Yes|No|Yes|
|Controls for other group|No|Yes|No|Yes|No|Yes|

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ :<sup>*</sup> _p <_ 0.1,<sup>**</sup> _p <_ 0.05,<sup>***</sup> _p <_ 0.01. Robust standard errors clustered at the village level are reported in parentheses. The coefficients on the household controls (demographic and farm characteristics) are reported in table S1.1 in the supplementary appendix S1. Group controls and controls for other groups include the number of individuals in each group (in log) and the mean value for the demographic characteristics in each group.

supplementary appendix S1. The specification used is the baseline specification with state dummies
# **3. Data**

Data on publicly listed firms are obtained from the Financial Statements Analysis of Companies (Non-Financial) Listed at Pakistan Stock Exchange. The data set contains balance sheet, income statement, and export flow data for 410 publicly listed firms in Pakistan over the period 2012-2017.<sup>7</sup> We exclude from the analysis firms that are either in the coke and petroleum sector or are state-owned companies. Of the remaining 322 companies, 262 companies are observed throughout the period, while 46 companies exited at various points during the period and only 14 entered the sample after 2012. Firm-level data are matched to the Orbis data set from Bureau van Dijk (BvD) to obtain information on foreign ownership for most firms (98 percent), and data on the number of employees for about 68 percent of firms. All nominal variables are deflated using sectoral deflators provided by the Pakistan Bureau of Statistics. We define foreign ownership by the presence of at least one direct foreign shareholder as reported by BvD.<sup>8</sup> We construct a measure of innovation by considering the presence of intangible assets, which include patents, copyrights, trademarks, exploration accounts, and knowledge accounts. The innovation ratio is defined as the ratio of intangible assets over total assets. Tariffs are obtained from the UN TRAINS database, while FDI inflows are obtained from the State Bank of Pakistan.

Table 2 – Descriptive statistics

|Sector|Average<br>sales|Average<br>wages|Employees|Share of<br>exporters|Share of<br>foreign<br>owned|Share of<br>innovators|Obs|Firms|
|---|---|---|---|---|---|---|---|---|
|Beverages and tobacco|13.87|8.86|215.30|0.20|0.25|0
**_Pct. rec. any_**<br>**_bank loan at_**<br>**_startup_**|12.6|34.2***|6.6|12.2|10.7|12.3|3.6|16.5**|

_Source: Enterprise Surveys, 1990s. Regional Program on Enterprise Development, World Bank_ *** Differences are significant at 99% confidence level; ** Significant at 95% confidence level; *Significant at 90%; Table 4 shows the educational attainment of entrepreneurs managing SMEs in these four African countries. Differences are evident between ethnic minority entrepreneurs and indigenous-African entrepreneurs, particularly in the level of higher education attained. Many more ethnic-minority entrepreneurs have university degrees and secondary school educations.

**Table 4: Highest Educational Attainment of Entrepreneurs (percent)**

||**Primary**|**Secondary**|**Technical Ed.**|**Univ**|
|---|---|---|---|---|
|**_African_**|36|28|24|12|
|**_Asian_**|12|38|15|35|
|**_European_**|2|32|33|33|
|**_Kenya_**|28|36|14|22|
|**_Zambia_**|13|31|33|23|
|**_Zimbabwe_**|16|26|32|26|
|**_Tanzania_**|39|32|12|17|

_Source: Enterprise Surveys 1990s, Regional Program on Enterprise Development, World Bank_ 14
**Policy Research Working Paper Series**

|Title|**Author**|**Date**|Contact<br>**for**paper|
|---|---|---|---|
|WPS1 979 Banking on Crises: Expensive<br>Lessons from Recent Financial<br>Crises|Gerard Caprio, Jr.|September 1998|P. Sintim-Aboagye<br>38526|
|WPS1980 The Effect of Household Wealth<br>on Educational Attainment:<br>Demographic and Health Survey<br>Evidence|Deon Filmer<br>Lant Pritchett|September 1998|S. Fallon<br>38009|
|WPS1981 Evaluating Public Expenditures<br>When Governments Must Rely<br>on Distortionary Taxation|James E. Anderson<br>Will Martin|September 1998|L. Tabada<br>36896|
|WPS1982 Analyzing Financial Sectors in<br>Transition: With Special Reference<br>to the Former Soviet Union|Alan Roe<br>Paul Siegelbaum<br>Tim King|September 1998|D. Cortijo<br>84005|
|WPS1983 Pension Reform in Small Developing<br>Countries|Thomas Charles Glaessn<br>Salvador Valdes-Prieto|er September 1998|M. Navarro<br>84722|
|WPS1984 NAFTA, Capital Mobility, and<br>Mexico's Financial System|Thomas Charles Glaessn<br>Daniel Oks|er September 1998|M. Navarro<br>84722|
|WPS1985 The Optimality of Being Efficient:<br>Designing Auctions|Lawrence M. Ausubel<br>Peter Cramton|September 1998|S. Vivas<br>82809|
|WPS1986 Putting Auction Theory to Work:<br>The Simultaneous Ascending Auction|Paul Milgrom<br>|September 1998|S. Vivas<br>82809|
|WPS1987 Political Economy and Political<br>Risks of Institutional Reform in<br>the Water Sector|Ariel Dinar<
**Figure 1. Female labor force participation rates in Egypt (percentages), ages 12-64, 19772021**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025<br>Year<br>LFS -urban LFS-rural LFS-Total<br>ELMPS-urban ELMPS-rural ELMPS-total<br>Sp. Round of LFS - urban Sp. Round of LFS - rural Sp. Roumnd of LFS - total<br>Percentage<br><!-- End of picture text --> Note: The numerator of the participation rate is the economically active female population aged 12 to 64 and the denominator is the manpower basis, which includes all the population 6 to 64 minus those who are permanently disabled, plus and those 65 and older who are employed. The definition of the economically active population from 1977 to 1984 is limited to market work and those seeking such work. The definition in the special round of the LFS in October 1988 includes subsistence work. From 1990 to 2005, the definition is not explicit about the inclusion of subsistence work. The definition used in the LFS from 2007 to 2021 and that used in all rounds of the ELMPS is limited to market work. The LFS was not carried out in 1986, the year of the population census. In 1987, the design of the LFS was changed to be collected on a quarterly basis rather than in a single annual round. However, no LFS bulletin is available for 1987 or the first half of 1988.

Source: Authors’ calculations using data from CAPMAS Annual Bulletin of the Labor Force Survey for LFS series from 1977 to 2005 (CAPMAS, n.d.), from the LFS public use microdata (OAMDI 2023) for LFS series from 2007 to 2021, and from
Table 2: List of Analyzed Countries

|Region|Country|
|---|---|
|Sub-Saharan Africa (16)|Angola, Burkina Faso, Chad, Côte d’Ivoire, Ethiopia<br>Gabon, Ghana, Guinea, Guinea-Bissau, Lesotho,<br>Mauritania,<br>Malawi,<br>Niger,<br>Senegal,<br>Tanzania,<br>Uganda|
|Additional countries||
|North Africa and the Middle East (1)|Egypt, Arab Rep.*|
|South Asia (1)|Bangladesh|
|East Asia and Pacifc (1)|Vietnam|
|Latin America and the Caribbean (1)|Colombia|

Note: *The Arab Republic of Egypt only has information about poverty status and urban classifications at the individual level, other information such as consumption expenditures, age, sex, and education is not currently available.

Africa (Arab Republic of Egypt, Bangladesh, Vietnam, and Colombia) in Appendix C. For six of the 16 SSA countries in our study, we were able to access household-level GPS coordinates, specifically for Ethiopia, Gabon, Lesotho, Mauritania, Malawi, and Tanzania. In the remaining countries, GPS coordinates are available for enumeration areas (EA) or administrative units.

Table 3 summarizes the household-level data pooled across 16 Sub-Saharan African countries.<sup>18</sup> Each HBS dataset contains valuable information on household per capita consumption expenditures, which serves as a crucial factor in estimating poverty rates.<sup>19</sup> The spatially deflated per capita consumption expenditures exhibited a right-skewed distribution, with a mean of $3.99 (in 2017 PPP terms) and a standard deviation of 8.13. To ensure consistency and comparability, we utilized the consumption expenditure measures prepared by the World Bank for global poverty monitoring. However, we made certain adjustments through the reclassification of urban areas and the update of spatial deflators, as explained in Section
the same language is spoken in both countries, and a dummy variable valued 1 when the two countries share the same colonial past—these variables come from the CEPII distance database (Mayer and Zignago 2011).

Two additional variables, suggested by Melitz and Toubal (2014), help in controlling for cultural similarities between country pairs: first, an index of language similarity. People whose languages share common roots will likely share similar cultural backgrounds. To compute this index, one single language is assigned to every country, and using information on the classification of languages provided by the Ethnologue Project, a language similarity index based on the distance between branches in this classification is computed.<sup>14</sup> Then the number of branches that coincide between each pair of languages are added together and the result is divided by the sum of branches of each of the two languages (in order to take into account the fact that the granularity of branches may not be the same across languages). As a result, an index between 0 and 1 is obtained, where 0 means complete dissimilarity and 1 means that these two languages are almost the same in linguistic terms.<sup>15</sup> Second, the religious heritage of countries is a critical element of their culture and identity (Guiso et al 2009). Countries with similar religious roots, culturally closer, are likely to interact more (for an application in the trade literature, see, among others, Melitz and Toubal 2014).

14. For example, the linguistic classification of Portuguese, Swedish, and Danish, from the largest, most inclusive grouping to the smallest, is: Indo-European, Italic/Romance, Italo-Western, Western, GalloIberian, Ibero-Romance, West Iberian, Portuguese-Galician (Portuguese); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Swedish (Swedish); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Danish-Riksmal, Danish (Danish). www.ethnologue.com, accessed May 20, 2014
of higher cereal prices will be uniformly negative.

To compare per capita expenditures levels across rural and urban households, we adjust for cost-of-living difference using a new set of poverty lines produced for NSS61 by the World Bank. Panel (a) of Figure 11 highlights the rural-urban wealth disparity, showing an increasing urban representation in higher per capita expenditure percentiles. This fact largely explains the shape of the national CCIC in panel (b), which is merely a weighted average of the corresponding urban and rural curves displayed alongside. Given that rural households are made worse off relative to urban households more or less uniformly along the whole range of the wealth distribution and that the urban weight increases with wealth, the national CCIC must become increasing steep as well.

# **5.5 Digression: Economic growth and Engel’s Law**

First-order comparative statics implies that the welfare cost of a price increase is proportional to (minus) the initial net consumption bundle, an approximation which is probably quite accurate for a change in cereal prices of just ten percent.<sup>31</sup> Climate change, however, will not increase cereal prices overnight, so the question arises: What is the right ”initial” net consumption bundle? So far, we have used the value of _p_ ( _y − x_ ) _/c_ observed in 2004-05. But, as a result of (distribution-neutral) economic growth,<sup>32</sup> household incomes will also rise over the next three decades and, by Engel’s law, drive the food share, _px/c,_ downward. Consequently, the farther off in the future the cereal price increases actually take place, the lower the (instantaneous) welfare cost is likely to be.

Suppose, then, we take the other extreme position and assume that cereal prices rise overnight, not in 2005, but only in 2040. In other words, we replace the 2005 cereal share with the predicted cereal share in 2040. Assuming that
# **VI.A.3 Re-weighted Estimates**

_Table 9_ shows the results from re-weighting the observations in the sample in order to replicate the results that would have been obtained without attrition. Given that attrition occurred in different forms, we produce three different comparisons. The method employed for the reweighting is the inverse probability weighting (DiNardo et al., 1996). As a first step, a logit model of attrition with respect to a group of variables is estimated. All the observations are employed at this stage since baseline explanatory variables from the 1997 ENCASEH are used to predict attrition. Based on the results of this logit, a weighting factor is calculated to be used with the analysis sample from the previous estimations.

Table 9: Re-weighted estimates of long-term effects on durable good’s accumulation.

||(1)<br>Transport|(2)<br>Entert eq|(3)<br>Kitchen sup|(4)<br>Basic serv|(5)<br>IT items|(6)<br>IT serv|(7)<br>ln(impcons)|
|---|---|---|---|---|---|---|---|
|**_Panel A: Original Ho_**|**_useholds_**|||||||
|_1. Analysis Sample_||||||||
|Treatment|-0.020|0.028|-0.029|-0.002|-0.011|0.026|-0.002|
||(0.022)|(0.042)|(0.047)|(0.046)|(0.027)|(0.036)|(0.010)|
|Observations|4,494|4,494|4,494|
threshold value in group A) and children from households who are ineligible (i.e., with household discriminant scores just above the threshold value in group C).

Our analysis is limited to the school attendance and work activities of boys and girls between 12 and 16 years of age. We focus on this age group for the primary reason that the programs’ impact is more likely to be found among children transitioning from primary to junior high school.<sup>15</sup> As noted earlier, we also exclude from our sample households in the treatment villages (group C) that were later reclassified by PROGRESA as beneficiaries, as well as all children that reside in another place outside the household for the purposes of studying or working. The adult member responding to the interviewers is less likely to have accurate information about their school attendance or work activities.

School attendance is defined according to those who respond that the child attends school. This question is identical over the different rounds of analysis. Our definition of working is defined to include all children who report that they worked over the previous week (whether paid or unpaid). There is also a follow-up question to capture individuals who may engage in informal activities that the respondent may not have initially considered as work. This question asks about participation in a) selling a product, b) helping in family business c) making products to sell, d) washing, cooking or ironing and e) working in agriculture activities or caring for animals. We also include as working individuals who respond that they engage in any of these activities.

All households were initially surveyed in October/November 1997, and based on the first survey the eligibility status of households was determined. Based on PROGRESA’s between the A and B groups of households.

15 An additional reason for not including 17 year old boys and girls in our analysis is the fact that in round 3, the evaluation survey collected information on the school attendance and 13
owned commercial enterprises. Although the number of studies is quite limited, some of them involve thousands of observations, which increases their reliability. With the exception of C6te d'Ivoire, where the wage premium is negative, public sector employees thus appear to make around 10 to 30 percent more than their private sector fellows.<sup>10</sup> Taking non-wage benefits into account, these studies imply that working for the public sector entails significant rents. Moreover, the comparison in Table 9 is mainly based on wages. It therefore involves formal sector jobs, be these public or private. It was shown above that wages in the private, formal sector of the economy were much higher than other labor earnings. The results reported in Table 9 imply that public sector wages are even higher. This conclusion, together with the finding that wages in the private formal sector follow closely the fluctuation of public sector wages, suggests that government pay policies are one of the main driving forces of wage misalignment in CFA countries.

- c) Product market distortions

Another potential culprit for wage misalignment are the rents created by barriers to competition in product market. If they had some monopoly power, firms could behave as price makers rather than price takers. The prices they would charge for their products would usually involve some mark-up over their unit costs, including labor costs, with the mark-up ratio being larger the higher their monopoly power. Therefore, a stable relationship between wages and prices,

- 10 The Ivorian result was surprising enough to prompt van der Gaag _et al._ (1989) to use data on moon-lighting to check that the estimated earnings differentials made sense. They found that moon-lighting was more prevalent among public sector workers than among those in the private sector. They also found that the wage disadvantage of public sector employees was an important determinant of moon-lighting. This latter result can be seen as an additional confirmation that the wage premium is negative in C6te d'Ivoire.

46
through cash grants.

- The **Great Lakes Displaced Persons and Border Communities Program** is a regional program under preparation to target IDPs, refugee and host populations in the Democratic Republic of Congo (DRC) and Zambia with investments in socio-economic services, livelihood support, land access and social cohesion.

- The **Development Response to Displacement Impacts Project** in the Horn of Africa will help improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees in target areas of Ethiopia, Uganda and Djibouti. The project is the first phase of an expanded program to include other countries affected by forced displacement.

# **C. Historical trends and patterns of forced displacement: Insights from available global data**

This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR’s published data on asylum-seekers, refugees and IDPs.<sup>41</sup> Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i.e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation.

Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR’s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as