rafmacalaba/datause-encoder-data huggingface dataset:
# Control variables:

As discussed above, the variable that is, perhaps, most likely to influence the probability of postconflict justice as well as the likelihood of future conflict is the balance of forces after conflict, indicated by the type of conflict settlement. We created 2 dummy variables: victory (which includes both victory by sides A and B) and negotiated agreement or cease-fire.<sup>15</sup> The reference category is low activity and unclear terminations.

Another important control variable is the length of the conflict prior to the post-conflict peace period. This variable counts the number of days the conflict lasted, from the first day it reached the 25 annual battle-deaths threshold to the last day of violence.

To measure post-conflict regime type we rely on information from the Polity Project’s Dataset (Marshall 2003). We use the Polity2 variable, a democracy-autocracy scale where a county’s autocracy score is subtracted from its democracy score. It ranges from -10, perfectly autocratic, to +10, fully democratic. In the second part of the analysis, we seek to distinguish between democratic and non-democratic post-conflict societies. In doing so, we set the threshold for being democratic at 5. All ‘peace years’ with a polity score above 4.9 are considered to be democratic.

We believe type of conflict matters. Therefore we include a variable controlling for the incompatibility over which the conflict was fought. This is a dummy where the value 1 corresponds to conflicts over territory; the reference category is governmental conflicts. In this way we can differentiate secessionist wars and those conflicts for which the objective is to capture the state for ideological or narrower interests.

The intensity of conflict might also be important regarding post-conflict peace duration. The battle-deaths data are from Lacina & Gleditsch (2005), the variable is a natural logtransformed.

We have also controlled for economic variables. Our GDP per capita data is based on GDP data from Fearon & Laitin (2003), but has been updated by Lujala (2006) to 2002 using economic data from Penn World Tables 6.0.

> 15 About 20 conflict periods ended by independence for side B, thereby creating a new independent state. Most of these are colonial conflicts were the colony gained its independence, creating long peace periods with no risk of conflict reoccurrence. These 20 cases were therefore removed from the analysis, although they did not have strong effects on the final results.

13
effort to catch up will require major investments. And rural populations, which make up more than 60 percent of the population in low- and middle-income countries, have access rates about 30 percent lower than do urban populations (table 4). This discrepancy does not mean that investment requirements are significantly larger for rural areas, however, because the costs of delivering services are lower in rural areas.

**Table 3** Access of urban populations to infrastructure services, <u>by country income group</u>

||Electricity<br>(1997-01)|Water<br>(2000)|Sanitation<br>(2000)|Telecoms<br>(1999-02)|Transport<br>(1994-01)|
|---|---|---|---|---|---|
|Income<br>group|P|ercentage of urb|an population|Telephone<br>subscribers per 100<br>people|Percentage of<br>population within a<br>20 minutes walk of<br>public transport|
|Low|62.4<br>(24)|76.9<br>(56)|74.6<br>(55)|4.6<br>(65)|90<br>(7)|
|Lower middle|95.1<br>(8)|90.8<br>(40)|90.5<br>(37)|22.0<br>(52)|n.a.|
|Upper middle|n.a.|92.3<br>(23)|92.5<br>(22)|53.9<br>(36)|n.a.|

Note : Figures in parentheses indicate the number of countries for which data are available. Source: USAID Demographic and Health Surveys; World Bank, _World Development Indicators_ , various years; International Telecommunication Union; Roberts, 2003 .

**Table 4:** Access of rural populations to infrastructure services,

<u>by country income group</u>

||Electricity<br>(1997-01)|Water<br>(2000)|Sanitation<br>(2000)|Telecoms<br>(1997-02)|Transport<br>(1994-01)|
|---|---|---|---|---|---|
|Income group|Per|centage of rural po|pulation|Main lines per<br>100 people|Percentage of rural<br>population within 2 km of<br>an all-season road|
|Low|20.3<br>(24)|52.7<br>(54)|41.8<br>(52)|1.7<br>(55)|61<br>(21)|
|Lower middle|67.3<br>(8)|75.0<br>(40)|59.8<br>(37)|8.7<br>(43)|n.a.|
|Upper middle|n.a|76.4<br>(23)|81.3<br>(22)|22.5<br>(24)|n.a.|

Note : Figures in parentheses indicate the number of countries for which data are available.

Source: USAID Demographic and Health Surveys; World Bank, _World Development Indicators_ , various years; International Telecommunication Union; Roberts, 2003.

However humbling these figures are in terms of achievements thus far, these aggregate figures also hide major disparities between low-income slums and better-off areas within the same city. For example, in Mysore, India, 33 percent of households have water 12
|Youth Employment Support|Suleiman Namara|
|---|---|
|Youth Employment Support|Suleiman Namara|
|GRGC-3 Supplemental Credit|Cyrus P. Talati|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Health CommoditySecurityProject|Sangeeta Raja Jobanputra|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Ketsana EmergencyReconstruction and Rehabilitation Project|Lixin Gu|
|Latin American Dev. and Social Cohesion|Iamele P. Rigolini|
|Latin American Dev. and Social Cohesion|Iamele P. Rigolini|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Sustainable Production Systems and Biodiversity|Luz Berania Diaz Rios|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
|Second Land Administration Project(PRODEP II)|Enrique Pantoja|
- **Cashin, Paul, C. John McDermott, and Alasdair Scott.** 2002. “Booms and Slumps in World Commodity Prices.” _Journal of Development Economics_ , 69(1): 277–296.

- **Deaton, Angus.** 1999. “Commodity Prices and Growth in Africa.” _The Journal of Economic Perspectives_ , 13(3): 23–40.

- **Deaton, Angus, and Guy Laroque.** 1992. “On the Behaviour of Commodity Prices.” _The Review of Economic Studies_ , 59(1): 1–23.

- **Deaton, Angus, and Guy Laroque.** 1996. “Competitive Storage and Commodity Price Dynamics.” _Journal of Political Economy_ , 896–923.

- **Deb, Partha, Pravin K. Trivedi, and Panayotis Varangis.** 1996. “The Excess Co-Movement of Commodity Prices Reconsidered.” _Journal of Applied Econometrics_ , 11(3): 275–291.

- **Dixit, Avinash, and Robert Pindyck.** 1994. _Investment under Uncertainty._ Pricenton University Press.

- **Enders, Walter, and Matthew T. Holt.** 2012. “Sharp Breaks or Smooth Shifts? An Investigation of the Evolution of Primary Commodity Prices.” _American Journal of Agricultural Economics_ , 94(3): 659–673.

- **Engle, Robert F.** 2002. “Dynamic Conditional Correlation: a Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” _Journal of Business & Economic Statistics_ , 20(3): 339–350.

- **Engle, Robert F., and Kenneth F. Kroner.** 1995. “Multivariate Simultaneous Generalized Arch.” _Econometric Theory_ , 11(1): 122–150.

- **Frankel, Jeffrey A.** 2008. “The Effect of Monetary Policy on Real Commodity Prices.” In _Asset Prices and Monetary Policy_ . 291–333. University of Chicago Press.

- **Gallagher, Liam A., and Cian E. Twomey.** 1998. “Identifying the Source of Mean and Volatility Spillovers in Irish Equities: a Multivariate GARCH Analysis.” _Economic and Social Review_ , 29(4): 341–356.

- **Gardebroek, Cornelis, Manuel A. Hernandez, and Miguel Robles.** 2013. “Market Interdependence and Volatility Transmission among Major Crops.” Selected Paper prepared for presentation at the AAEA & CAES Joint Annual Meeting, Washington DC, August 4-6.

- **Gilbert, Christopher L.** 2010. “How to Understand High Food Prices.” _Journal of Agricultural Economics_ , 61: 398–425.

- **Gruber, Joseph, and Robert Vigfusson.** 2012. “Interest Rates and the Volatility and Correlation of Commodity Prices.” FRB International Finance Discussion Paper 1065.

13
# **TABLE 7. Impact of Nationalization on Water Quality**

<u>(count of abnormal tests of levels of microbiological elements)</u>

||Poiss<br>|on<br>|Negative<br>|Binomial<br>|
|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|
|Renationalized rovision|−0.651*|−0.642*|−0.650*|−0.665*|
|p|(0.357)|(0.371)|(0.355)|(0.390)|
|Edcation||0.367||0.466|
|u||(0.247)||(0.306)|
|(l) Hhld it i||−0.016||−0.145|
|og ouseo per capa ncome||(0.865)||(0.848)|
|Piitti||0.423||0.611|
|recpaon||(0.330)|<br>|(0.385)|
|Minimum temperature|<br>|−0.146<br>(0.118)|<br> <br>|−0.235<br>(0.165)|
|Average temperature|<br>|0.014<br> (0.146)|<br>|0.002<br> (0.149)|
|Individual effects|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Observations|182|182|182|182|
|Log likelihood|−101|−100|−101|−100|
|Sample||2004–2|009||

_Source:_ Authors’ estimation using URSEA, ECH and National Meteorology Office data.

_Note:_ All columns report estimates using a zero inflated model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city and year fixed effects. Vuong tests indicate that the zero inflated models are appropriate. Treatment Units (six cities): Maldonado, Pan de Azúcar, Piriápolis, Punta del Este, Punta Ballena, and San Carlos. Control units (26 cities): Artigas, Canelones, Colonia, Dolores, Durazno, Florida, Fray Bentos, La Paloma, La Paz, Las Piedras, Melo, Mercedes, Minas, Montevideo, Pando, Paysandú, Progreso, Rivera, Rocha, Salto, San José, Atlántida, Tacuarembó, Toledo, Treinta y Tres, and Trinidad.

* indicates statistical significance at the 10 percent level.

32
# **Table A15:  Global Integrity Index  (GII)**

**Data Provider Description**

**Website Data Source Type Respondents Frequency Coverage Public Access Description**

Global Integrity Nongovernmental organization located in Washington, United States, advocating integrity and accountability in government. www.globalintegrity.org Global Integrity Index Expert Assessment Local country experts and peer reviewers recruited by Global Integrity Every three years since 2003 Mostly developing country sample Yes The Global Integrity Index uses 290 indicators to assess the existence and effectiveness of anti-corruption mechanisms that promote public integrity. They typically pair an indication of the "in law'" existence of a particular institutions with an "in practice" assessment of its functioning. We use a simple average of the "in practice" components of each of the indicated GII indicators, in keeping with our practice of relying purely on perceptions-based data in the WGI.

|**Voice and Accountability**|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|Civil Society Organizations|X|X|X|X|..|..|..|..|
|Media|X|X|X|X|..|..|..|..|
|Public Access to Information|X|X|X|X|..|..|..|..|
|Voting & Citizen Participation|X|X|X|X|..|..|..|..|
|Election Integrity|X|X|X|X|..|..|..|..|
|Political Financing|X|X|X|X|..|..|..|..|
|**Political Stability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|Executive Accountability|X|X|X|X|..|..|..|..|
|Judicial Accountability|X|X|X|X|..|..|..|..|
|Rule of Law|X|X|X|X|..|..|..|..|
|Law Enforcement|X|X|X|X|..|..|..|..|
|**Control of Corruption**|||||||||
|Anti-Corruption Law|X|X|X|X|..|..|..|..|
|Anti-Corruption Agency|X|X|X|X|..|..|..|..|
|**Country coverage**|**41**|**25**|**25**|**25**|**..**|**..**|**..**|**..**|
|**Year of Publication**|**2006**|**2004**|**2004**|**2004**|**..**|**..**|**..**|**..**|

53
# **3. Capital flows between the European Union and Sub-Saharan Africa**

The EMU can potentially affect Sub-Saharan Africa countries through many different financial linkages: capital markets, foreign direct investment, interest rates, and portfolio diversification.

# _3.1._ **_Country risk ratings and access to international capital markets_**

The importance of the various financial channels depends on the degree of financial integration of the two regions. Similar studies for other regions use credit ratings from _Standard & Poors_ and _Moody's_ to assess domestic financial markets.<sup>6</sup> Few Sub-Saharan African countries are rated by either of these companies, however, because of poor performance (South Africa is an exception). _Euromoney_ and _The Institutional Investor_ measure a broader range of countries, including those in Sub-Saharan Africa, and rank them against each other in terms of risk (table 5). All Sub-Saharan African countries are ranked very low, except for Botswana, Mauritius, and South Africa. The general picture that emerges is of severely underdeveloped domestic capital markets (see table 5), with access to capital markets limited, according to _Euromoney,_ to the same three countries that are ranked relatively better in terms of risk ratings (Botswana, Mauritius, and South Africa).

# _3.2._ **_Foreign direct investment_**

The distribution of foreign direct investment (FDI) in Sub-Saharan Africa is very unequal: South Africa and Nigeria alone accounted for 68 percent of FDI in 1997 (chart 2). It is argued that one effect of the EMI would be to redirect European foreign investments back into the euro area as lower transaction costs make investments in the euro area more attractive. But investors will already have anticipated this decline in transaction costs earlier on, and FDI has continued to flourish in Sub-Saharan Africa in recent years (chart 2).

FDI in Sub-Saharan Africa is unlikely to be affected much by changes in competitiveness in Europe because the degree of direct competition is limited by the geographically

- 6 See Feldman and Temprano-Arroyo (1998) for countries in Eastern and Central Europe and the

10
-52 **-**

# ANEX 3 **BASIC EARNIGS FUNWCIONS**

# **Brazil 1989**

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.147|0.154|0.142|0.150|0.114|
|Experience (EX)|0.064|0.073|0.051|0.038|0.054|
|Experience-squared (EX<sup>2</sup>)|-0.0009|-0.0010|-0.0008|-0.0005|-0.0008|
|Log Hours (H) Worked|0.724|0.310|0.595|0.936|0.829|
|Constant Term|1.954|3.567|2.314|1.454|1.915|
|R<sup>2</sup>|0.29|0.30|0.29|0.26|0.26|
|N|108,527|69,773|38,754|24,690|17,428|
|MeanS||||||
|Earnings (Y)|6,969|7,151|6,640|9,979|7,746|
|S|5.3|5.0|5.9|4.1|8.2|
|EX|21|22|20|28|20|
|H|43|46|39|43|36|
|Var (Ln Y)|1.631|1.474|1.680|2.051|1.608|

Source: _Pesquisa Nacional por Amostra de Domicillos, CEPAL._ Notes: The earnings variable (Y) is in nuevos cruzados per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.
**Figure 6:  Rate of Increase per Year of Age in COVID-19 Mortality Rate** p g y

<!-- Start of picture text --> Afghanistan<br>Pakistan<br>Philippines<br>South Africa<br>Colombia<br>Peru<br>Brazil<br>Mexico<br>Malaysia<br>Argentina<br>Chile<br>Hungary<br>Portugal<br>Spain<br>Korea, Rep.<br>Italy<br>Japan<br>France<br>Canada<br>Germany<br>Netherlands<br>Sweden<br>Australia<br>Denmark<br>Norway<br>Switzerland<br>0 .05 .1 .15<br><!-- End of picture text --> Source: Author’s analysis. Countries are ranked by GDP per capita LMICs are shown in red and HMICs in blue.

**Figure 7: Rates of Increase per Year of Age in COVID-19 Mortality vs. GDP per Capita**

<!-- Start of picture text --> CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>SWE<br>ESP<br>KORITA DEU<br>HUN PRT NOR<br>FRA<br>JPN<br>CHL<br>ARG<br>BRA<br>COL MYS<br>ZAF<br>PER<br>MEX<br>5000 10000 50000 100000<br>GDP per capita, 2018<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Source: Author’s analysis, World Development Indicators for GDP per capita (current US$). GDP per capita is on a log scale. LMICs are shown in red and HMICs in blue.

11
barrier for SHS adoption is the large upfront cost. If micro-credit schemes are made available, SHS is likely to be an attractive option to many households in rural areas.

# 5.2.1 Cost comparison

In assessing affordability of SHS, it is instructive to compare the cost of different alternative energy options for lighting, including kerosene lamps, SHS and grid-electricity among nonelectrified households. The monthly cost of the kerosene lamps is the observed spending collected in the survey. But the monthly cost of SHS or grid-electricity needs to be estimated.

The cost of SHS is estimated based on the exiting micro-credit scheme implemented in rural Bangladesh under the RERED project. Under such scheme, households were provided with a loan for a period of 3-5 years, at an annual interest rate of 12%, plus a $50 cash subsidy. Households who received the loan were required to pay upfront a down payment at the 10% of the total cost of SHS they purchase.

The monthly cost of SHS is thus imputed using the compound interest rate method with two assumptions imposed. First, all households living in non-electrified villages are entitled to the micro-credit scheme and a cash subsidy. Second, the household choice of SHS depends on its level of income which is based on information from the case study of the RERED project.<sup>8</sup> The cost of the grid-electricity option among non-electrified households is predicted based on the grid-electricity demand model estimated using the subsample of households connected to gridelectricity.

Table 5 presents the estimated monthly cost of three lighting options. The results show that, on average, the imputed monthly cost of SHS is about 7 or 5 times the cost of monthly spending on kerosene with a cash subsidy of $50 or $90. For the bottom two income groups, the monthly SHS cost is about 3.5 times the kerosene cost, while it is about 6.4 times the kerosene spending for the top two income groups.

The comparison of SHS with grid-electricity shows that the average monthly cost of SHS is about 3.4 times the cost of grid-electricity. In reality, however, SHS is not directly comparable with grid-electricity because of significant differences in the quality of services provided from the two electrification technologies.

> 8 The case study of  the RERED project shows that poor households tended to purchase SHS  of lower capacity while better-off households opted for larger capacity SHS. Among the 320,000 SHSs installed by 2009, about 20% households chose a SHS of 40 Wp, 45% chose a SHS of 50 Wp, 20% in the range of 60-65 Wp and only 9% in the 85 Wp range. In estimating the monthly SHS cost,  we assume that the bottom two deciles purchase only SHS of 2030 Wp; the 3rd and 4th deciles purchase SHS of 40 Wp; 5th to 7th purchase SHS of 50 Wp, 8th and 9th for SHS 6065 Wp and the top deciles purchase SHS of 85 Wp and above. Battery costs are assumed to range from Tk 3,200 to Tk 11,400, depending on the size of the SHS (RERED report).  This amounts to about between 20-24% of a SHS price accordingly, which will be factored in the SHS prices.

10
# **Labor Drops:**

# **Experimental Evidence on the Return to Additional Labor in Microenterprises**<sup>**#**</sup>

Suresh de Mel, _University of Peradeniya_ David McKenzie, _World Bank_ Christopher Woodruff, _University of Oxford_ _JEL Classification codes:_ O12, O17, D22, J46, L26, C93 _Keywords:_ microenterprise, labor market frictions, field experiment.

> # Funding for this project was provided by the National Science Foundation (SES0820375), the World Bank, DfID, the Knowledge for Change Trust Fund, the Diagnostic Facility for Shared Growth Trust Fund, the Strategic Research Program Trust Fund, and the Templeton Foundation. Matthew Groh provided excellent research assistance. The surveys and interventions were carried out with aplomb by the Kandy Consulting Group, without whose assistance we would not have been able to undertake the project.