rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4580

# **Abstract**

There is an extensive literature on violent conflicts such as the 1994 Rwandan genocide, but few papers examine the profiles of victims and perpetrators, or more broadly the micro-level dynamics of widespread violence. This paper studies the demographic consequences of the Rwandan genocide and how the excess mortality due to the conflict was distributed in the population. Data collected by the 2000 Demographic and Health Survey indicate that although there were more deaths across the entire population, adult males were the most likely to die. Using the characteristics of the survey respondent as a proxy for the socio-economic status of the family dead, the results also show that individuals with an urban or more educated background were more likely to die. Over and above the human tragedies, a long-term cost of the genocide is the country’s loss of productive skills.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to understand the consequences of conflict. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ddewalque@worldbank.org and philip. verwimp@gmail.com.

_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
gives a detailed description of the methodology by which these hypotheses are tested. The results are presented in Section VI, and its implications are discussed in Section VII **.**

# **II. Data and construction of variables**

To perform the firm-level analysis we assemble a dataset that includes annual accounting data for a set of publicly traded firms in Germany, France, Italy, Japan and the UK, since 1997 until 2011. The data comes from the Worldscope database. In accordance with the financial literature (Bates, Kahlen and Stulz, 2009, Custodio, Laureano and Ferreira, forthcoming), our database includes only industrial firms. The selection of industrial firms was carried out according to the variable "General Industry Classification", which differentiates between manufacturing, services, transport, banking, insurance and other financial activities.

Table 1 summarizes the accounting definition of the main variables used in the study. Table 2 provides descriptive statistics of the sample for each country presenting the mean, median, 25th and 75th percentiles, standard deviation and number of observations (firm-years) of each of the six variables listed above. Table 3, meanwhile, shows the number of firms by country and year for which data are available for the key variable ES.

# **III. Excess savings (ES) and their applications over time**

Figure 1 illustrates the evolution of the variables of interest in the period from 1997 to 2011. Each panel of the figure shows the time series of the median and size-weighted mean (mean weighted by total assets) of the variables of interest. We used both weighted mean and median measures to illustrate that the trends detected are representative of the aggregate trends (captured by the size-weighted mean) and that these aggregate trends are not driven by outliers (the median is less sensitive to the behavior of outliers). Unless otherwise noted, the description that follows refers to the evolution of the size-weighted average.

Figure 1 displays the growing trend in excess savings for the entire sample. It is also evident that the ES fluctuates with the business cycle, and these fluctuations can be explained mainly by the cyclical variations in gross capital formation, which are greater than the cyclical variations of gross savings. The amplitude of the fluctuations in the ES is always greater for the size-weighted mean than for the median. This suggests that the ES of big companies 4
# What Are the Effects of Expanding a Social Pension Program on Extreme Poverty and Labor Supply? Evidence from Mexico’s Pension Program for the Elderly

## Clemente Avila-Parra<sup>†</sup>

_The World Bank_ David Escamilla-Guerrero<sup>‡</sup> _London School of Economics_ Keywords: Non-contributory pensions, poverty, labor supply, elderly (JEL I38, J2, O15)

> *We are grateful to Margaret Grosh for her guidance in our research and analytical approach. We are thankful to William Wiseman, Concha Steta, and Francesca Lamanna for their helpful comments on early discussions of this paper, and for their encouragement and support. We are particularly grateful to Pablo Gottret, Rafael Rofman, Gonzalo Reyes, Carolina Romero, and Ignacio Apella for their thoughtful comments and suggestions for improvements. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

> †Social Protection and Jobs Global Practice, Av. Insurgentes Sur 1605 - Floor 24, Mexico City, 03900. (Tel: +52(55)54804274. e-mail: cavilaparra@worldbank.org).

> ‡Economic History Department, Houghton Street, London, WC2A 2AE. (Tel: +44(0)7477456964. e-mail: D.EscamillaGuerrero@lse.ac.uk).
Nelissen, Jan. 1987. "The Redistributive Impact of the General Old Age Pensions Act on Lifetime Income in the Netherlands." _European Economic Review_ 31(7): 1419- 1441.

Netherlands, Ministry of Social Affairs and Employment. 1990. _Social Security in the Netherlands._ Deventer: Kluwer Law and Taxation Publishers.

New Zealand Department of Social Welfare Annual Report, 1993.

Nordic Countries Statistical Secretariat. Various Years. _Social Security in the Nordic Countries._ Stockholm: Nordic Statistical Secretariat.

Nordic Social Statistical Committee "Social Security in the Nordic Countries: Scope, Expenditure and Financing". 1993. NOSOSO.

OECD. Japan. OECD Economic Surveys. 1986 November.

*. 1988. Ageing Population: The Social Policy Implications. Paris.

*. 1988. Reforming Public Pensions. Paris: OECD.

- *. 1989/1992-93. Portugal. OECD Economic Surveys.

*. 1989/1992-93. Spain. OECD Economic Surveys.

-. 1990. OECD Economic Survey: Belgium-Luxembourg.

- -. 1991. January. Belgium-Luxembourg. OECD Economic Surveys.

-. 1993. Unpublished data provided by the Directorate for Education, Employment, Labour and Social Affairs, OECD Paris.

Office Federal des Assurances Sociales. Departement Federal de l'Int6rieur. Switzerland 1993. "Assurances Sociales en Suisse".

Oliveira, Francisco, da T. Henriques, M.H. and Beltrao, K.I. 1987. "The Brazilian Social Security System: Coverage and Constraints." _International Social Security Review;_ 1987; 4: pp.<sup>3 73</sup> -<sup>3 85</sup> .

PAHO (Pan American Health Organization). 1989b. _A Profile of the Elderly in Argentina._ Washington D.C.

-. 1989c. _A Profile of the Elderly in Guyana._ Washington D.C.

- -. 1989d. _A Profile of the Elderly in Trinidad and Tobago._ Washington D.C.

- -. 1990a. _A Profile of the Elderly in Chile._ Washington, D.C.

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winner of the global flirting with PPI during the 1990s. It leads in all categories. Particularly impressive is the per capita figure. East Asia has done quite well in terms of total investment levels and number of projects but per capita, the numbers are clearly not as impressive. As expected, South Asia and Sub-Saharan Africa, the two globally poorest regions have been the main losers. Although the Middle East appears to be at the bottom of the distribution of the investment payoffs of reform, its per capita payoff is twice as large as for Africa and two an a half times larger than for South Asia. Eastern Europe has done somewhat surprisingly well per capita.

Table 3: Selected Indicators of Regional distribution of PPPI

||Total investment<br>commitment (US$ billion)|Number of<br>Projects|Average Project Size<br>(in US$ million)|Accumulated Investment<br>commitment per capita as of<br>2002 (in US$)|
|---|---|---|---|---|
|East Asia|182|687|265|99|
|Eastern Europe|100|589|169|211|
|Latin & Central<br>America|368|978|376|694|
|Middle East|24|64|312|79|
|South Asia|42|195|213|29|
|Sub-Saharan Africa|26|213|122|38|
|Total|741|2726|272|141|

Source: Author Calculations from World Bank PPI database and World Development Indicators Looking at sectors the story of the winners and losers is also quite clear as can be seen from Table 4. In terms of investment commitments and average project size, the telecoms sector is the clear winner of the PPPI experience so far. In terms of the number of projects, however, telecoms lag both the energy and the transport sector. The clear loser of the experience is the water and sewerage sector. Things did not work out as planned. Its investment figures are very significantly lower than for any other sector. It represents only 5% of the total investment commitments generated between 1984 and 2002, in spite of the huge needs identified in the context of the Millenium Development Goals debate.

Table 4: <u>Sectoral Distribution of PPPI (1984-2002)</u>

||Energy|Telecoms|Transport|Water and<br>Sewerage|Total|
|---|---|---|---|---|---|
|Total Investment Levels<br>(US$ billion)|242|332|129|39|741|
|TotalNumberof Projects|1071|680|730|245|2726|
|Average Project Size<br>(US$million)|226|448|176|159|272|

Source: Author Calculations from World Bank PPI database The most interesting assessment may be in terms of the payoffs to the actors. The list of actors in the PPI game is relatively long and longer than recognized by casual observers. Most typically looked at are the users, the taxpayers, the workers and the operators. It is however useful to add the bankers, one of the driving forces behind most of the transactions. It is also useful to isolate the owners from the operators and within that group to distinguish between foreign and local owners. In view of their importance in the decision to reform and in 13
**Graph 1 North American GDP**

<!-- Start of picture text --> 100.00%<br>90.00%<br>80.00%<br>70.00%<br>60.00% -<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00% __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>0.00%<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998<br>|- Canada Mexico 'United States<br><!-- End of picture text -->

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Other coefficients of interest have expected signs. The probability of employment increases with age. On average, men and women from the younger birth cohorts (1970-1985) are less likely to be employed, which is consistent with a relatively large proportion of this group reporting that they are still in school. An increase in education has a significant and positive effect on employment for men and women, and the estimated coefficient is greater for women. Married men/women are more/less likely to work. Russian women are significantly more likely to have had a job in the last 14 days. Women from larger households are significantly less likely to hold a job.

The household’s composition has a strong negative effect on the employment of women, while an increase in the number of dependents (children age 0 to 15 and elderly age 65 and above) increases chances of employment for men. Non-wage income<sup>20</sup> that includes old-age pensions and scholarships has a negative impact on employment of males (significant at 5%), but no significant effect on the employment of women.

Access to land is positively correlated with rural residence. Access to land increases employment of males and females. The coefficients are almost twice as high for women as compared to men in the households that have access to own and rental land of "11-20" and "21 and above" sotkas (Col. 2, 3, 5 and 6).<sup>21</sup>

# **Robustness checks**

In Tables 6-8 I test whether the results on the interaction term that are reported above could be attributed to the added worker effect at a household-level, in particular, whether migration or unemployment of other household members had a significant impact on the labor force participation of men and women.

> 20 Non-wage income is calculated as in Lokshin and Glinskaya (2009: page 493, footnote 9): “Nonwage income is defined as the sum of all government and private transfers, such as, pensions and scholarships, that are exogenous to household migration and labor force participation decisions; it excludes interhousehold transfers, donations, and other private transfers that may respond to the household’s migration and labor supply decisions.

> 21 Sotka is “a Russian name for the _are_ , a metric unit of area equal to 100 square meters. This unit is commonly used to state the areas of small tracts of land. One sotka is approximately 1076.4 square feet, 119.60 square yards, or 0.02471 acre.” <u>http://www.unc.edu/~rowlett/units/dictS.html (Accessed: October 11, 2010.)</u>

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Table 7b: Trade, Legal Origins and Institutional Quality, Relative Explanatory Power, Average Trade Partner Dep. Var.: log of average trade flow **_with average trade partner_**

||(1)|(2)<br>(3)|(4)|(5)|
|---|---|---|---|---|
|A.|1995-99|1984-99<br>1984-99|1984-99|1995-99|
|Different-ness in Legal Origin|-0.64|-0.36<br>-0.36|-0.2|-1.23|
||[3.43]|[3.28]<br>[3.26]|[1.78]|[7.12]|
|Bureaucratic Quality|0.33|0.33<br>0.34|||
||[5.61]|[12.21]<br>[16.02]|||
|Control of Corruption|-0.05|0.01|0.17||
||[1.25]|[0.39]|[9.95]||
|Protection of Property Rights|0.22|||0.36|
||[3.97]|||[8.28]|
|Observations|539|1790<br>1790|1790|638|
|R^2|0.82|0.79<br>0.79|0.77|0.8|
|B.||Beta Coeffici|ents||
|Different Legal Origin|-0.079|-0.042<br>-0.041|-0.023|-0.166|
|Bureaucratic Quality|0.185|0.223<br>0.228|||
|Control of Corruption|-0.032|0.006|0.133||
|Protection of Property Rights|0.124|||0.197|

Notes: All variables are averages per reporting country vis-à-vis its trading partners. Panel A reports OLS estimates. t-values in brackets. The following variables are included in the estimation but their coefficients are not reported: log distance, log product of GDP, log product of land area, no. of landlocked, common border, common border, common language, currency union. A constant and year dummies are not reported as well. Panel B reports beta coefficients estimated for the same specification as panel A.

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regional fixed-effects, we find that a one unit decrease in the RSQ index –in this case corresponding to a one standard deviation decrease in precipitations– is associated with a 3.2 points loss in the Food Consumption Score (column 4). The estimated coefficient is significant at a 1 percent level and both the statistical significance and magnitude of the coefficient are robust to the exclusion of group B controls (column 3), of regional fixed-effects (column 2) and when excluding all types of controls (column 1). To better understand the magnitude of the estimated impact we carry out two complementary analyses. First, we simply estimate a log-linear version of the model so that we can interpret the estimated coefficient in terms of a relative change in food consumption. We find that a one standard deviation decrease in precipitations is associated with a 6.4 percent loss in the FCS (see Table C.2 in the appendix). Second, we apply the estimation procedure of the prediction model described in section 3 to calculate that a hypothetically uniform one standard deviation decrease from the mean in precipitations would lead to a 7.3 percentage points (p.p.) increase in the national food insecurity rate in rural areas, from 34% to 41.3%.<sup>26</sup> In 2020, this would roughly represent an additional 150,000 people in a situation of food insecurity compared to the baseline scenario.

Table 1: Pooled OLS regression of food consumption on 5-month z-rain.

||_Dep. variable_: Food|Consumption Sc|ore|
|---|---|---|---|
||(1)<br>(2)|(3)|(4)|
|5-month z-rain|3_._183<sup>_∗∗∗_</sup><br>3_._386<sup>_∗∗∗_</sup>|2_._894<sup>_∗∗∗_</sup>|3_._194<sup>_∗∗∗_</sup>|
||(0_._442)<br>(0_._440)|(0_._445)|(0_._497)|
|Constant|53_._478<sup>_∗∗∗_</sup><br>42_._223<sup>_∗∗∗_</sup>|38_._098<sup>_∗∗∗_</sup>|35_._085<sup>_∗∗∗_</sup>|
||(1_._029)<br>(2_._594)|(4_._392)|(4_._383)|
|Controls-A|No<br>Yes|Yes|Yes|
|Controls-B|No<br>Yes|No|Yes|
|region FE|No<br>No|Yes|Yes|
|Observations|10,966<br>10,924|10,925|10,924|
|Adjusted R<sup>2</sup>|0.035<br>0.127|0.125|0.170|
|_Note:_||_∗_p_<_0.1; _∗∗_p_<_0.|05; _∗∗∗_p_<_0.01|

> 26To arrive at this result, we first subtract the estimated total effect of the RSQ (i.e. the product of the RSQ index value with the estimated marginal effect 3.194) to our historical FCS values to obtain a nationally representative synthetic sample of baseline food consumption values. Those represent the estimated food consumption level for an RSQ scenario in which precipitation levels are equal to their historical mean. Then, we infer the FCS values for a scenario where precipitations are one standard deviation below their historical mean by subtracting 3.194 to baseline FCS values. Accounting for sampling weights, we find that 41.3% of households have an FCS below the food insecurity line (42) in this scenario against 34% in the baseline RSQ scenario corresponding to average precipitations.

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tables overlap, but they seem close and consistent in their method of construction. Miguel et al. (2004, 730–31, and table C1) seem to use primarily the union of the X, I, O, and F data at intensities 1–3 (corresponding to the concepts in row 2 of table 1), but they also present some results based on intensity 3 (row 1 of table 1). In a study of the onset of civil war, Gleditsch (2007, 300–01) seems to do the same but considers only states in which conflict has erupted to have had an onset of civil war, seemingly excluding our F coding. Buhaug and Gleditsch’s (2008, 223) study of civil war onset also seems to use the union of X, I, and O data for intensities 1–3. Earlier quantitative studies using different data also code a civil war by a battle-death count, usually of at least 1,000 per year (Licklider 1995), or more than 1,000 battle-related deaths in any given year (Wallensteen and Sollenberg 1999, 595). These rules are close to those used for row 1 in table 1. Thus, the most recent studies seem to favor the lower threshold of row 2, but the earlier studies favored a higher threshold, similar to that of row 1. As argued below, both approaches have serious shortcomings.

Deaths related to a conflict are difficult to estimate, a point well made by Cramer (2007, table 1). Furthermore, “battle” is a strange modifier of deaths when most of the violence that is considered part of civil wars in these data sets does not involve set-piece battles, making it difficult to know what a battlefield death is. This modifier is especially problematic because these episodes often involve civilian deaths. These deaths hardly ever seem to occur on a battlefield, and certainly not in a case like Sierra Leone, where terrorizing civilians in their villages and homes was a prevalent strategy during the conflict. Similar situations occur when civilians in camps for refugees or internally displaced persons are targets, as in Darfur, CongoKinshasa, Burundi, or Rwanda. Some researchers employ a count of any death associated with large-scale violence (whether civilian or battle deaths) to identify a civil war. Doyle and 9
Table 4: Determinants <u>of Earnings by Level and by Sector</u>

||Public||Privat|e|
|---|---|---|---|---|
|1998|Estimated coefficient|Standard error|Estimated coefficient|Standard error|
|Primary complete|0.143|0.054|0.178|0.025|
|Secondary incomplete|0.323|0.056|0.354|0.027|
|Secondary complete|0.524|0.055|0.595|0.028|
|Tertiary incomplete|0.728|0.060|0.934|0.033|
|Tertiary complete|0.978|0.055|1.311|0.031|
|Experience|0.037|0.003|0.041|0.002|
|Experience-squared|-0.001|0.001|-0.001|0.000|
|Constant<br>|0.145|0.059|-0.245|0.030|
|R<sup>2</sup>|0.241||0.232||
|N|3,667||10,778||
|2002|Estimated coefficient|Standard error|Estimated coefficient|Standard error|
|Primary complete|0.237|0.047|0.190|0.037|
|Secondary incomplete|0.423|0.050|0.351|0.040|
|Secondary complete|0.674|0.048|0.622|0.040|
|Tertiary incomplete|0.837|0.055|0.954|0.047|
|Tertiary complete|1.185|0.049|1.394|0.045|
|Experience|0.034|0.003|0.038|0.003|
|Experience-squared|-0.001|0.001|-0.001|0.001|
|Constant|-0.158|0.055|-0.443|0.044|
|R<sup>2</sup>|0.321||0.225||
|N|3,085||6,178||
|Source: EPH|||||

The results are in line with what is found in most other developing countries, including the studies reviewed by Heywood and Wei (2004), and those reported in Mehta and Villarreal (2003, 2004) for Mexico, although Mora (2003) finds evidence of screening in the Colombian labor market. There is more evidence of screening in higher-income, developed countries (see also Ferrer and Riddell 2002; Sanmartin 2001; Jaeger and Page 1996; Hungerford and Solon 1987), such as Canada, Spain and the United States. However, Chevalier and others (2004), using changes in the compulsory school leaving age in the United Kingdom, find no support for the screening hypothesis.

# **Conclusions**

The returns to schooling in Argentina increased over the last decade. The premium to completing each level of schooling shows that there has not been much change in the premium to

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survey data to form panel. Finally, DD method is applied to the panel. To be more specific, we apply FE regression on this matched panel sample to control for biases due to unobservable characteristics (assuming they are time-invariant). We report the results of all three techniques discussed above which give us a broader perspective on the extent and nature of impacts of electrification on household welfare.

# **6. Findings: Benefits of rural electrification**

# <u>i) Benefits of commune electrification</u>

As we have seen before, all sample communes have been connected to a grid by 2005, making no comparison group available at the commune level in the follow-up survey. So a true DD method cannot be implemented to estimate the benefits of commune-level access to grid electrification. But we still can make use of the data to see commune-level effects for differences in the years of electrification exposure.

Two types of outcomes are considered: economic (income and expenditure) and educational (school participation and completed schooling years). Unfortunately, our data, particularly that of 2002, collected information only on cash income and expenditure. So we use cash income and expenditure, instead of total income and expenditure, in our analysis.<sup>8,9</sup> Summary statistics are presented in Table 5 for the outcome variables by commune-level grid connectivity in 2002 and 2005. Outcomes of treated and comparison communes in 2002 clearly

> 8The major source of households’ non-cash income and expenditure is their farm production which the households consume themselves. Unlike the 2002 survey, 2005 survey collected information on non-cash consumption that the households produce, and they account for only 13 and 11 percent of total income and expenditure. So if we assume similar pattern for 2002, we can infer that the trends in income and expenditure benefits from electrification are also similar. However, in cases where households switch between non-cash and cash intensive activities between the survey years, benefits in cash income and expenditure due to electrification may not reflect well the patterns in total income and expenditure.

> 9 The monetary figures of 2005 throughout this paper have been adjusted by inflation rate with base year being 2002.

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