rafmacalaba/datause-encoder-data huggingface dataset:
# Dependent variable:

To be able to investigate post-conflict peace duration, two conflict-related variables were added to the existing dataset: A duration variable measuring the length of the post-conflict peace period. This variable is measured in days, and represents the number of days from the first day of peace to the first day when the conflict again crosses the 25 battle-related deaths threshold. If the conflict does not re-occur, the end date is set to December 31, 2003, which is the last date in the dataset. An additional event variable (censor) indicates whether or not the ‘peace year’ ends in another conflict. The censoring variable is coded 1 if the ‘peace year’ ends in resumed conflict (failure) and zero if it did not.

# Independent variables:

Our main independent variables reflect to what extent post-conflict societies deal with crimes committed during the conflict. The main sources for this information are _Keesing’s Record of World Events, Library of Congress Country Studies_ , _Minorities at Risk Assessments and Chronologies_ , and the _Peace Agreements and Truth Commissions Digital Collection from the United States Institute for Peace_ . Other sources were used additionally. See Binningsbø (2005) for detailed narratives and references. Altogether, the dataset provides indicators of 4 types of PCJ: trials, purges, truth commissions, and reparations to victims, and 2 indicators associated with abstaining from post-conflict justice: amnesty and exile.<sup>13</sup> The detailed information in the Binningsbø, Elster & Gates dataset allows us to create additional variables based on various combinations in the data material. We run our analysis on an aggregation of each of these types of post-conflict justice. For the purposes of our analysis, we created a dummy variable indicating whether or not a particular type of transitional justice was employed.<sup>14</sup> We also include two variables indicating whether or not states have abstained from PCJ through exile or amnesty.

> 13 In the Binningsbø, Elster, & Gates (2005) dataset, all variables are measured due to which side of the conflict is the subject of justice, i.e. the government side (side A) or the opposition side (side B), or both, defined by the situation when the conflict started. We do not designate them as such so as to capture as much variance as possible. 14 In addition to looking at the different types of post-conflict justice separately, coded 1 if there has been postconflict justice and 0 otherwise; we created an additive index measuring the degree of post-conflict justice, based on how many types of justice have been conducted in the post-conflict society. Since there are four types of post-conflict justice, this variable will have values from 0 to 4, where 0 is no efforts toward justice and 4 if evidence of all 4 types is found. We also created an additive index of all recorded PCJ-measures, regardless of whether they were postconflict justice or abstaining from justice efforts.

12
Table 5. Militarization and Roeder’s measure of fractionalization, 1988–2002

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Military<br>Expend.|Military<br>Expend.|Military<br>Pers.|Military<br>Pers.|Arms<br>Imports|Arms<br>Imports|
|Ethlinfrac (Roeder)|-1.612**|-1.634**|-0.940**|-0.978**|-2.300*|-2.306*|
||(2.20)|(2.21)|(2.05)|(2.05)|(1.71)|(1.78)|
|GNI p.c. (ln)|-0.241|-0.252|0.086|0.074|-0.541|-0.509|
||(0.98)|(1.02)|(0.87)|(0.72)|(1.35)|(1.28)|
|Economic growth|1.282**|1.271**|0.137|0.112|-0.457|-0.379|
||(2.07)|(2.07)|(0.22)|(0.18)|(0.19)|(0.16)|
|Polity|-0.038**|-0.037**|-0.012*|-0.011*|-0.097|-0.102|
||(2.18)|(2.16)|(1.83)|(1.74)|(1.51)|(1.58)|
|Government expenditures|0.158***|0.157***|0.025**|0.025**|0.150**|0.153***|
||(4.51)|(4.55)|(2.06)|(2.03)|(2.46)|(2.61)|
|Population (ln)|-0.024|-0.033|-0.140**|-0.154**|0.367|0.383|
||(0.21)|(0.29)|(2.18)|(2.13)|(1.52)|(1.61)|
|Contiguous militarization|0.130**|0.126**|0.574**|0.562**|0.120|0.129*|
||(2.54)|(2.50)|(2.22)|(2.15)|(1.63)|(1.81)|
|Peace years (civil war)|-0.020***|-0.020***|-0.004*|-0.004**|-0.022*|-0.022*|
||(3.60)|(3.60)|(1.75)|(2.00)|(1.72)|(1.74)|
|Peace years (intern. conflict)|0.007|0.007|-0.009|-0.009|-0.034**|-0.030**|
||(0.74)|(0.75)|(1.50)|(1.48)|(2.26)|(2.17)|
|Civil war|0.642***|0.642***|0.062|0.060|2.389**|2.403***|
||(3.24)|(3.27)|(0.88)|(0.87)|(2.57)|(2.62)|
|International conflict|0.753|0.753|0.607*|0.612*|3.249**|3.176**|
||(1.23)|(1.24)|(1.76)|(1.77)|(2.17)|(2.16)|
|Observations|1589|1589|1393|1393|1406|1406|
|Countries|132|132|139|139|140|140|

Notes: Absolute t- and z-statistics in brackets. Constant and year-specific time-dummies included, but coefficients not reported. *, **, *** significant at .1, .05 and .01 level, respectively.

25
we employ the following formula: The first part of the formula calculates the benefit at the household level by taking the point estimate for the increase in log income per capita reported in column 4 of table 5 (0.852) and multiplying it by the average per capita income (5,935 MXN).<sup>13</sup> This quantity is, in turn, multiplied by the number of affected individuals per municipality. To calculate the number of affected individuals, we take the number of procampo beneficiaries per municipality (942.96) and multiply this by 45%, since on average 45% of the hectares in a treated municipality receive payment. This gives us a total benefit of 2.15 M MXN per municipality, which is multiplied by the number of payouts we observe (998) to obtain a total benefit of 2.12 B MXN. Given the uncertainty in our estimates, we follow the same procedure using the 5% confidence interval for our _βincome_ and obtain a range of 359.40 M to 3.88 B MXN for the estimated benefits. This compares to a program cost of 2.35 B MXN. We conclude that our estimates for the impact of CADENA are too imprecise to definitely determine whether the program is cost-effective. However, for a significant range of estimates, the benefits exceed the costs. Moreover, this exercise accounts only for a portion of the benefits of the CADENA program since we are not able to causally identify the risk management effect.

# **6 Conclusion**

This paper contributes to the literature on index insurance by exploring the effects of insurance payment on ex-post investment decisions and coping mechanisms. This analysis is valuable because it is carried out in the context of a large weather index insurance program with almost national coverage. In contrast to much of the existing evidence on index insurance, we are also able to observe effects several years after the start of coverage. There exist some data issues that negatively impact the precision and robustness of our results. These issues include, lack of payment data at the weather station level, missing data from some weather stations, and limited overlap between the set of insured municipalities and the set of municipalities represented in household expenditure and income surveys in any given year. Nevertheless, we believe that this analysis provides suggestive

> 13As mentioned earlier, this is per capita income for the three months prior to the ENIGH survey.

13
-9-

Table 4 (acu'3d

||RER|ATRh(.1)|I()!<br>GDPD-2|CaicIly<br>) Utilllntim|T|C<br>r|No.<br>foba.|R2|S.H.R|D.W|P-stadmic|
|---|---|---|---|---|---|---|---|---|---|---|---|
|(11)LmsIntnlmai Trade Adluelera||||||||||||
|Cioabla|4.07|4213|-1.07|||0.22|9|0.59|0.004|2.02|4.91|
||PO.9)|<br>P0.94)|<br>AM22|||_GA?)_||||||
||4.07|4.17|-1.72|_016_||0.24|9|0.63|0.0)4|2.46|4.45|
||(2.26)|<br>(1.61)|t0.41)|(1.37)||(4.23)||||||
||0.01|4214|-1.43||400.|0.30|9|0.69|O.C0|42.49|5.49|
||(2.03)|<br>(2.32)|(0tn)||(1A7)<br>1|t5.17)||||||
|Cbs dilrafe|4.010|<br>.0.24|0.23|||0.25|9|.0.034|<br>D.02|2.00|0.91|
||.PAD|)<br>(2. 15)|P.24)|||PP-S|M|||||
||-0.22<br>(0.93)|-a10<br> <br>(P8U4)|_O.6s_<br> <br>(1.22)|0.u<br>(4.16)'||0.24<br>(I.-C)|9|0.55|0.01|1.99|3.42|
||-0.21<br>(2.2z)|472<br>a59|3.59<br>(2.26r||0.02<br>(2.4r|0.35<br>(1.33)|9|0.35|.m|L99|Z.09|
|Januta|-0.06|0.18|2.50|||40.19|9|.0.11|0.03|2.118|<br>0.74|
||(0.37)|_047)_|(1.L3)|||0.52)||||||
||10.B|0.004|1.97|1.10||4.09|9|0.35|0.03|2.17|2.07|
||0.01)|P02°)|(1.39)|(3-M7||0.31)||||||
||0.01|04|2.14||0.04|.0.30|9|.0.37|0.06|2.35|0.46|
||0.02)|to05)|_BS)_||(0.24)|(0.44)||||||
|Pakrun|0.06|0.04|.4.15|||0.01|9|4.42|0.01|2.57|0.21|
||0.64<br>3|O34)|(1.07)|||p.O)||||||
||0.04|0.03|-5.44|40.59||0.01|9|_-0.67_|0.01|2.10|0.20|
||P54)|(P261|P.72)|(0.35)||(0-06)||||||
||-.OS|0.07|0.0||40.O04|<br>0.14|9|0.25|0.02|2.76|_1.67_|
||(0@9|(0P)|(O."5)||_C2M)_|P0.52)||||||

Eqa (logGDPGDP'-k DPt-1)- t0 + 3l sgRERE+ a2 ATRh-I + d ILIGDPR-I + *4 CUt + aS T Notes:

1. S- rnq Nl.

2. AotlLte valtu orf z;dst inL _psaren and_ I so_ signiane 5% keve.

3. EalesS by OS eai _anwml_ dat; HencrladaateuyCanhmas Cot tflaeW e Mbix to eatimate ittica.

4. R2 a4ured kr degroe t*edef I.

5. S.Et Swhrd Eirer of Regrssiorn 6 D.W.: Dubin-Wason atitle.

Definidn of wiables:

1. Real GDP (in 2910 $US): EuSD Natual Aon.

2. RER Real EFd Ra (1930-1; a fall fRER isdqr at: _IMP._ 3. ATJ : Aver Tariff Ram (S). vmter bl lkWn duties ealeted loS ofCIF waltd of ipom; lntda*ies: wk CEMs and IFS documnents; vba of tin: IFs.

4. IIGDP(t-1): Real pea dofmfa i_estorm as a ratio of real GDP or **ime** prvo4 year: Wodd Baivc Debas (tNeo Acna).

5. _CUV_ Capaity ulidaion is tbe diiercte between ata 2ogGDP ad fitned walhs of legGDP. whe fitn valh s ae esinautd by L4GDP - c+Ttne Trend.
Ministry of Social Affairs and Health of Finland. 1990. Social welfare in Finland. Helsinki.

Ministry of Social Affairs and Employment(SZW). Netherlands. Social Security in the Netherlands; 1990.

Mitchell, B.R. 1982. _International Historical Statistics: Asia and Africa._ New York: New York University Press.

-. 1983. _International Historical Statistics: The Americas and Australasia_ Detroit, Michigan: Gale Research Company.

-. 1990. _International Historical Statistics: Europe 1750-1988._ 3rd edition New York: Stockton Press.

Mitchell, Deborah (Australian National University). Research School of Social Sciences. Income Security for Old Age: Evidence for Eight OECD Countries. Unpublished.

Mitchell, Olivia. 1982. "Fringe Benefits and Labor Mobility." _Journal of Human Resources (17):_ 286-298.

Moffitt, Robert. 1984. "Trends in Social Security Wealth by Cohort." In Marylin Moon, ed., _Economic Transfers in the United States._ Chicago: National Bureau of Economic Research.

Ministerie van Sociale Zaken en Werkgelegenheid. 1990. "A Short Survey of Social Security", The Hague. MSZW.

Mujica R., Patricio documentos de trabajo. 1993 May. Sistemas de seguridad social: la experiencia chilena. Washington, D.C.: BID. Banco Interamericano de Desarrollo. Departamento de Desarrollo Economico y Social.

Mulosi, Samuel. 1990. "Economic Aspects of Social Security in Africa. International Social Security Association Tenth African Regional Conference" (December).

Murray, Christopher, Govindaraj, R. and Chellaraj, G. 1993. "Global Domestic Expenditures on Health." Harvard University, Center for Population and Development Studies, Cambridge, Mass.

National Social Insurance Board. Social Insurance Statistics, Facts 1989. Sweden.

National Statistical Coordination Board. 1990. Philippines. Philippine Statistical Yearbook; 1990. Chapter 12. Social Services.

National Statistical Office. 1987. Malawi Statistical Yearbook. Malawi. Tables 15.1 and 15.3.

National University of Singapore. Report of the Central Provident Fund study group. The Singapore Economic Review. 1986 April; XXXI(1) 135
|Furukawa<br>(2014)*|Student|Uganda|Education|Solar lamps increased reported study time, but nonetheless lowered<br>student test scores, perhaps due to inappropriate usage.|
|---|---|---|---|---|
|Aevarsdottir et<br>al (2017)+|Household|Tanzania|Consumption/<br>Jobs|Solar lamps, reduce expenditure on lighting and phone charging, and lead<br>to more employment and greater household consumption and well-being.|
|Aklin et al (2017)|Household|India|Welfare|Access to off-grid solar power decreases black market kerosene<br>expenditures, without systematically affecting other outcomes.|
|Ratledge et al<br>(2022)|Village|Uganda|Welfare|Grid access improves village-level asset wealth in rural Uganda by up to<br>0.15 standard deviations.|

Source: Compiled by the authors.

Note: Findings column is based on the main finding of the paper. * represents peer-reviewed papers. Plus identifies papers following the instrumental variable approach.

As a part of a field experiment in rural India, randomly chosen households were offered to set up a solar microgrid if at least 10 households within the area subscribed at a price that corresponds to about 2 percent of the monthly baseline household expenditure. The electrification rates for the households offered solar power under the experiment increased around 30 percent faster than other households. However, the study was not able to identify any consistent impacts of electrification, such as the use of lighting for study or power for the creation of new businesses (Aklin et al, 2017).

# **4.2 Reliability of Supply**

Based on World Bank Enterprise Surveys conducted between 2010 and 2020, 8.4 percent of the firms in the world chose electricity as the biggest obstacle affecting their businesses. The main reason for this is the unreliability of power supply, with outages taking place 29 times on average per month in South Asia and eight times per month in Sub-Saharan Africa.<sup>4</sup> Despite the negative impacts of electricity shortages on growth and productivity, empirical evidence remains limited. However, the emerging literature supports the positive contribution of a reliable electricity supply to firm productivity (Grainger and Zhang, 2019; Fisher-Vanden et al, 2015) and competitiveness (Mensah, 2018), as well as to household income (Samad and Zhang, 2017).

One of the first studies to address this issue was Fisher-Vanden et al (2015), which examined how firms responded to severe power shortages in China in the early 2000s. The research found that firms in regions with greater shortages decreased factor shares of electricity for production, while substituting other factors of production, but without any evidence of an increase in selfgeneration of electricity. An overall decrease in other non-electrical sources of energy indicates that these primary energy sources are complementary inputs in producing the intermediate products that have been outsourced in response to electricity shortages. The unit production

> 4 https://www.enterprisesurveys.org/en/data/

21
spread” option for each phase. The insurance policy’s premium payment was calculated based on projected payouts using historical rainfall data, plus a 17.5% governmentmandated surtax.

All farmers in the study were administered a household socioeconomic survey in September 2006. The survey covered income, education, assets, income-generating activities (including detailed information on crop production and crop choice), measures of risk aversion, and knowledge about financial products such as credit and insurance.

After the completion of the survey, an orientation meeting was held in each of the 32 localities in October 2006 where NASFAM field officers explained the loan product being offered (insured or uninsured) to the study farmers. Farmers then had two weeks to decide whether to take up the loan, which required a deposit of 12.5% of the loan amount at the local NASFAM field office. Seeds and fertilizer were then delivered to prespecified collection points near the club meeting place, and planting occurred with the beginning of the rains in November.

Summary statistics from the baseline survey are presented in Table 1, and variable definitions are provided in Appendix B.

# **<u>3. Empirical re</u>**

# **<u>sults</u>**

In what follows, the “treatment group” refers to farmers who were offered the insured loan, and the “control group” refers to farmers offered the uninsured loan. Randomization of treatment should ensure that treatment and control groups have similar baseline characteristics on average. To check this, Table 2 presents means of several key farmer and household characteristics for the treatment and control groups, as well as the _p_ -value of the F-test that the difference in means is statistically significantly different from zero.

For nearly all the variables presented (gender of the respondent, female headship of the respondent’s household, household income, respondent’s age, land ownership, risk tolerance, having experienced a drop in income due to drought, trust in the insurance company and an index of housing quality constructed from indicators for various household amenities), the difference in means is not statistically different from zero. The
In related work, Hanson and Woodruff (2003) also estimate the overall impact of migration on education in Mexico. They use the 2000 Mexican census, and look at the impact on number of school grades completed of 10 to 15 year-olds. Their main finding is that migration to the U.S. is associated with more years of completed education for 13 to 15 year-old girls, but only for those whose mothers have three years or less of education. We employ a large demographic survey instead of the Census, allowing us to consider a broader measure of household migration experience. We obtain an insignificant effect of migration on education for 12 to 15 year-olds girls with poorly educated mothers, and can not reject positive effects of similar magnitudes to those they find. However, our work builds on their findings in three important respects. Most fundamentally, we consider 16 to 18 year-olds, who are at the age when migration for work starts to become a possibility, especially for males, and who are also at the age when they may be entrusted with household responsibilities which take the place of schooling. That is, this is precisely the age range at which many of the other channels through which migration affects education start to manifest themselves.

Secondly, Hanson and Woodruff (2003) note that school attendance is high amongst their sample, with 82.5 percent of 10-15 year-olds attending school. Nevertheless, they use two-stage least squares for estimation, which does not account for this high rate of right-censoring. Once we do this, insignificant 2SLS results for 12 to 15 year-old males become significant. Finally, the survey we use enables examination of what children are doing when they are not in school, enabling investigation of the channels through which migration is affecting schooling.

The remainder of this paper is organized as follows. Section 2 presents the demographic survey data used for the empirical analysis and contrasts it to the 2000 5
workers as would a typical five-years or younger plant, the surviving manufacturing plants in India and Mexico roughly double its size over the same life-span. This divergence in plant dynamics could potentially lower aggregate manufacturing productivity on the order of 25 percent in India and Mexico compared to the U.S. over the long term. Such a substantial divergence in aggregate productivity over the long term could translate into dramatic differences in per capita GDP across countries.

Ayyagari et al. (2013), however, argue that the findings of Hsieh and Klenow (2014) on India are driven by informal firms, as the latter study combines both the informal and formal in their analysis. Ayyagari et al. (2013) undertook separate analysis for formal and informal plants, as informal firms tend to be quite different from formal firms (La Porta and Shleifer, 2008). In doing so, they found that the average 40-year-old plant in the formal sector in India is two to four times the size of plants less than five years of age, both when compared contemporaneously and when comparing older and younger firms within the same cohort. The evidence of flat life-cycle appears to be the case only in the informal sector in India, where older firms in the unorganized manufacturing sector tend to employ fewer people than firms younger than 5 years old. Despite the divergence of findings of India between these two studies, the lower bound estimates (i.e., doubling of the size by older plants in the formal sector) of Ayaggari et al. (2013) is in line with that of Hsieh and Klenow (2014).

Using WBES data from 120 developing countries, Ayyagari et al. also find that the upwardsloping firm size-age profile is pervasive across the formal sector in developing countries. They found that the average plant 40 years old and older to be smaller than plants younger than five years in less than 10 percent of the countries studied. While the average middle-aged firm, between 10 and 19 years of age, is only twice the size of the average plant under the age of 5 years, the average plant that is 40 years and older is 4.65 times the size of the average plant under the age of 5 years, in terms of employment. These cross-country estimates, however, could be skewed upward due to survivorship bias in the data as discussed above.

Thus, there are at least two worrisome issues for the policy makers. First, what to do if firm growth tends to occur in the formal sector while a significant share (if not the majority) of the 7
to assess the recent historical export performance in Eastern Europe. Figure 4 shows exports in 5 countries using preliminary estimates in Marer _et. al._ [1992] from 1986 to 1990, extrapolated to 1992 using data from the IMF _International Firance Statistics._ These figures are very tentative and should be used with care, specially the data points of 1991-92. Nevertheless, it is evident that - there was almost a total collapse of exports in Romania and a huge drop in Bulgaria; a smaller decline is registered in the former Czechoslovakia registered; and slightly beter performance were reported in Poland and Hungary.

<!-- Start of picture text --> 150 ________ __<br>x -- o- Bu-ad 4uba<br>E : Forme rCzechoslovakbiav|<br>- aL ~~50 - - Hungaiy __ is ><br>Lb _- Poland \ _<br>; s- ~~~~~~Romania\ C ~ ~ ~ ~ ~ ~ ~ ~<br>19e6 19i7 1988 1989 4M9 t99t ts.92<br>Year<br><!-- End of picture text -->

'ilgure 4: E xports in Easten Europe (in current U.S. _dollas)_ Figure 5 displays the shares of CMEA trade in exports. The shares are generally and rapidly declining. The two countries baving difficulties switching away faom CMEA trade, Romania arxi Bulgaria, are also the ones experiencing the most difficulties in their total export performance. 'Me share of CMEA exports still fcll drmatically in the other countries: from 53% to about 5% in the case of Poland, 589% to 15% in Hungary, and 60% to 22% in the forFer Czechoslovakiar

**_Go._** _h_ **_Uncererinn gevenue_** _dc_