rafmacalaba/datause-encoder-data huggingface dataset:
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE five years of the concession, the volume of freight almost tripled from 300 million to 800 million tonnes annually.

**Table 4 Railway indicators for Côte d’Ivoire and selected other countries, 2000–05**

|**Railway**|**TRANSRAIL**|**SITARAIL**|**GRC**|**NRC**|**OCBN**|
|---|---|---|---|---|---|
|**Country**|**Mali/Senegal**|**Burkina**<br>**Faso/ Côte**<br>**d'Ivoire**|**Ghana**|**Nigeria**|**Benin**|
|Concessioned (1) / State run (0)|1|1|0|0|0|
|Freight Traffic Density (1000 tonne-km/km)|318|494|242|15|148|
|Productivity||||||
|Labor (1,000 traffic units per employee)|na|481|84|37|40|
|Carriage (1,000 passenger-km per carriage)|na|862|416|737|900|
|Wagon (1,000 net tonne-km per wagon)|804|1020|458|59|74|
|Locomotive (million traffic units per locomotive)|40|35|7|13|3|
|Tariffs (Aver. Unit Tariff)||||||
|Passenger (US cents/passenger-km)|2.2|3.3|2.4|na|2.0|
|Freight (US cents/tonne-km)|3.3|5.5|4.4|na|5.8|

Source: Bullock 2009, derived from AICD railways database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

# **Challenges**

The crisis that erupted in 2002 severely affected rail freight traffic volumes, and by 2003 traffic volumes had fallen to 100 million tons (figure 5). Following special security measures, traffic volumes recovered quickly even before the peace accord of 2007, and since 2006 they have exceeded their earlier peak, which was reached in 2001.

More recently, theSitarail concession has entered into a dispute with the authorities, due to the failure of the concessionaire to finance anticipated track rehabilitation. As a result, only half of the planned five year investment program of $12.4 million has been delivered. This problem has been widespread across Sub-Saharan African rail concessions. Due to the relatively low volume of freight traffic and competition from the road sector, it is rarely possible for rail networks to earn sufficient revenue to finance track rehabilitation. In the case of Sitarail, it is likely that the original forecasts made at the time of the award of the concession overestimated the likely freight traffic flows and underestimated the extent of the investment need. More recently, spending needs for the concession for 2008 to 2020 have been estimated to be $132 million for track rehabilitation and $99 million for rolling stock—a total of $231 million. This is roughly three times the revenue of the concession in 2009 and can therefore only be met with public finance.

12
Table 1: Summary of activities in the<sup>PHC and non-PHr villages</sup>

|Period|PHC treatm<br>(17 villages:<br>PRE-INTERVE|ent villages<br>children= 2616)<br>NTION PERIOD|Non-PHC<br>controls<br>(56villages:<br>children= 1795)|
|---|---|---|---|
|June 1988 to<br>July 1989|Census followed<br>births, deaths a<br>questionnaires|by routine collect<br>nd migration with<br>for all children dy|ion of data<sup>on</sup><br>verbal autopsy<br>ing<sup>under age 6</sup>|
||INTERVEN|TION PERIOD||
|July 1989 to<br>June 1990<br>_<br>_<br>_<br>_<br>_ _<br>_<br>_ _<br>_<br>_ _<br>_<br>|Treated bed-<br>nets plus 1/4<br>strength<br>Maloprim for<br>half of all<br>children 6<br>months to 5<br>_<br>y e a r s_<br>POST-INTERVE|Treated bed-<br>nets and<br>placebo for the<br>other half of<br>all children 6<br>months to 5<br>years<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_ _<br>NTION<br>PERIOD|Mortality and<br>morbidity<br>surveillance<br>only<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|
|May-June 1990|New census with<br>and deaths plus<br>demographic sur<br>all women of re<br>analysis.|up-dating of all r<br>a single round ret<br>vey with a full bir<br>productive age. Ver|ecords of births<br>rospective<br>th history for<br>bal autopsy|

The total population of all the villages averaged<sup>21,157</sup> individuals, comprising mostly Mandinka-speakers<sup>with some Fula</sup> as well as other smaller ethnic groups.<sup>In this area of flat</sup> Sudan savanna, with mangrove swamps at the<sup>river's edge, the</sup> rainfall is close to 1000 mm. per year: in<sup>1988, it was 1051 mm</sup> and in 1989 it was 887 mm. The rainy season<sup>from June to October</sup> is the most important season for the transmission<sup>of malaria by</sup> the AnoRheles gambiae complex and most malaria<sup>deaths are</sup> concentrated in September, October and November<sup>of each year. In</sup> this paper, only the effects of interventions<sup>on the mortality of</sup> young children are discussed. Full details<sup>of the conduct of the</sup> intervention, the dipping of the bed-nets and<sup>the</sup> 4
_(Table C.4 continued.)_

|Recreation|Expenditures on recreation, leisure and entertainment such as social<br>activities, books, newspaper, magazines, theatre, concerts, movies,<br>CD, VCR, DVD, etc. Item is winsorized at a fraction of 0.01 in each<br>tail. Two variables are used capturing expenditures in Taka and as a<br>share in household income.|
|---|---|
|Domestic work|Time spent on domestic work on last working/non-workingday.|
|Leisure|Time spent on leisure on last working/non-workingday.|
|Sleep|Time spent on sleepon last working/non-workingday.|
|Work|Time spent on work on last workingday.|
|Economically active|Dummy variable indicating whether respondent works as a paid em-<br>ployee, grows fruit, vegetables or tends animal that household mem-<br>bers eat,or runs their own business.|
|Slapped or beaten by<br>husband<br>(5<br>months,<br>Dummy)|Dummy variable indicating whether woman was beaten or slapped<br>by the other respondent at least once. Two variables capture different<br>recallperiods: a)ever,b)in the last 5 months.|
|Hiding<br>money<br>(Dummy)|Dummy variable indicating whether woman chooses private option<br>in any choice between receiving money privately or publicly in the<br>game.|
|Controlling<br>money<br>(Taka)|Highest amount in Taka that woman is willing to give up to have<br>money paid to herself instead of her husband (ignoring multiple<br>switching).|
|Above<br>median<br>initial<br>decision-making|Dummy variable indicating whether respondent’s decision-making<br>index is above the median at factory baseline. Only available for the<br>promotion sample.|
|Starts earnings more|Dummy variable indicating that the respondent earns more than the<br>spouse during household surveys, but did not earn more than the<br>spouse at factorybaseline. Onlyavailable for thepromotion sample.|
|Earns more|Dummy variable indicating that the respondent earns more than the<br>spouse duringhousehold surveys.|
|placebo|Placebo treatment coded as a dummy variable indicating whether a<br>respondent in the selected group was in the top half of the ranking,<br>equal to zero if in the bottom half. Only available for the selected<br>group.|

71
# **INTRODUCTION**

Education and migration are two strategies widely believed to improve living standards for populations living in rural areas. A vast literature demonstrates that educated rural households are better able to avail of new technologies, that consumption growth is higher among households who migrate, and that secondary schooling and migration (whether international or within-country) increase earnings.<sup>2</sup> However, two data-related issues have made it harder to assess the returns to specific skills for migrants versus non-migrants and the ability of children to acquire these skills through schooling in the first place. First, despite an extensive literature in the United States that demonstrates the importance of cognitive and socioemotional skills for labor market outcomes (Heckman, 2007), these skills have proven notoriously difficult to measure in low-income countries (Laajaj and Macours, 2021; Valerio et al., 2016). Second, long-term panels with information on skills, schooling, and earnings in low-income countries are extremely rare, although we expect more will become available in the next decade.

We address both of these gaps in this paper. We focus on the measurement of cognitive and socioemotional (SEM) skills and assess their reliability and validity relative to comparable research from the existing literature in low- and middle-income countries. To allow for potential links between migration and skills, we study children who grew up in rural Pakistani villages and were first surveyed in 2003 when they were between the ages of 5 and 15 and then re-surveyed between 2017 and 2018, regardless of where they were living at that time. At this point, 38% had migrated from their native homes, so migration appears as a potentially endogenous response to the skills that respondents have acquired, which in turn may lead to differential returns to these skills. We further corroborate our findings using data from a similar sample from Cambodia, which shares key features with the data from Pakistan.

Our results show that more schooling is associated with higher cognitive skills and, to a smaller extent, greater SEM skills. Labor earnings are correlated with years of schooling, and cognitive and SEM skills conditional on years of schooling, with the size of the associations varying with migration from the

> 2 See Foster and Rosenzweig (1996) on the relationship between returns to schooling and technological change in an agrarian economy; Duflo, Dupas, and Kremer (2021) on the experimentally estimated returns to secondary education in Ghana; McKenzie, Stillman, and Gibson (2010) on the returns to international migration between Tonga and New Zealand; Bryan, Chowdhury, and Mobarak (2014) on the experimental returns to seasonal migration in Bangladesh and Beegle, Weerdt, and Dercon (2011) on consumption growth among migrant and non-migrant households in the Kagera region of Tanzania.

2
# Referenaces

Ballard, Shoven, and Walley, "General Equilibrium Computations of the Marginal Welfare Costs of Taxes in the United States," American Economic Review, 1985.

Baron, "Noncooperative Regulation of a Nonlocalized Externality," Hand Journal of Economics, 1985.

Bator, "The Anatomy of Market Failure," Ouarterly Journal of Economics, 1958.

Baumol and Oates, The Theory of Environmental Policy, 1975.

Bohm and Russell, "Comparative Analysis of Alternative Policy Instruments," in Kneese, ed., Handbook of Environmental and Resource Economics, 1985.

Branigan, "Bracing for Pollution Disaster," The Washington Post, November 28, 1988.

Choucri, Brown, and Haas, "Dimensions of National Security: The Case of Egypt," in Brown, ed., In the U.S. Interest: Resources. Growth. and Security in the Developing World, 1990.

Coase, "The Problem of Social Cost," Journal of Law and Economics, 1960.

Dasgupta, Hammond, and Maskin, "on Imperfect Information and Pollution Control, " Review of Economic Studies, 1980.

Eskeland and Jimenez, "Pollution and the Choice of Economic Policy instruments in Developing Countries: A Research Proposal," mimeo, The World Bank, October 18, 1990.

Eskeland and Jimenez, "Policy Instruments for Pollution Control in Developing Countries," mimeo, The World Bank, Public Economics Division, Country Economics Department, March 1991a.

Eskeland and Jimenez, "Curbing Pollution in Developing Countries," Finance and Development, 1991b.

Farrell, "Information and the Coase Theorem," Journal _of_ Economic Perspectives, 1987.

Gordon, "The Economic Theory of a Common-Property Resource," Journal of Political Economy, 1954.

Hahn, "Economic Prescriptions for Environmental Problems," Journal of Economic Perspectives, 1989.

72
depicts a negative and significant correlation between the CCI and per capita GDP. An increase of 1 standard deviation (1.23) in log GDP per capita is associated with a decline in the CCI of about 0.10.<sup>12</sup>

**Figure 5: Cyclicality of Currency and GDP Per Capita**

<!-- Start of picture text --> CCI = 0.84 - 0.08*Log(GDP per capita)<br>(3.15) (2.89)<br>IDN KOR<br>URY<br>UKR TUR<br>PHL THA MYSARG MEX NZL ISL<br>ROM SWE<br>IRN COL BRACHL SVKCZE FIN<br>IND PER RUS POLHUN PRTGRCESP AUS<br>GBR<br>GEO ZAF HRV SVNISR DNK<br>CHN MAR MLT ITACAN NOR<br>BOL BLZMKD CRI CYP FRADEUBELJPNAUTUSA LUX<br>BGR LVA HKG<br>LTU NLD<br>IRLCHE<br>EST<br>6 7 8 9 10 11<br>Log GDP per Capita<br>1<br>.5<br>CCI<br>0<br>-.5<br><!-- End of picture text --> Note: CCI is the correlation between the HP filtered NEER and HP filtered real GDP. GDP per capita is at US$ 2005 prices. The t- statistics are reported in parentheses.

# **3.2 CCI and the Policy Stance**

Next, we look at the way the degree of cyclicality of a currency relates to the choice of the broad policy framework, such as the exchange rate regime, and the degree of openness of the capital account. On the relationship between currency cyclicality and exchange rate regimes, one would expect that, by adopting a peg, a country reduces the volatility of exchange rate and thus the degree of currency cyclicality. This is what we observe in the data as well. Using Reinhart and Rogoff’s “coarse” classification of _de facto_ exchange rate regime (which takes values from 1 for “strongly fixed” to 6 for “strongly flexible” regimes), we find a positive relationship between the flexibility of the exchange rate regime and the degree of procyclicality of the currency,<sup>13</sup> as depicted in Figure 6.

In addition, we find that the exchange rate regime conditions the impact of capital flows on the cyclicality of currency. Countries with flexible exchange rate regime (e.g. those above the median) have a positive and significant correlation between the cyclicality of capital flows and that of

> 12 On the other hand, we do not find a robust correlation between the CCI and the economic size of a country, measured as a country’s share of world GDP.

> 13 More precisely, one should expect a positive relation between the flexibility of the exchange rate regime and the absolute value of the degree of cyclicality of a currency. The result in figure 6 is driven by the fact that countries with procyclical currencies outnumber those with countercyclical ones in our sample.

10
The main policy performance indicators we use in our analysis are the Freedom House Index (Freedom House, 2007), trade openness (imports plus exports over GDP) and ethnic fractionalization (Fearon and Laitin, 2003). The Freedom House Index assigns an annual score for civil and political freedom on a scale from 1 (most free) to 7 (least free) to each country. We add both scores to get an overall democracy index.<sup>16</sup> High Freedom House scores are generally associated with "good" institutions such as property rights, individual liberties, free information flows and low corruption. Such institutions may increase the potential of affected countries to deal with disasters themselves, but may also facilitate and encourage the provision of foreign emergency aid.

Higher fractionalization is generally associated with higher risk of internal conflicts, lower provision of public goods and higher inequality levels (Easterly and Levine, 1997; Garcia-Montalvo and Reynal-Querol, 2005). Fractionalization likely decreases the population’s capacity to deal with external shocks especially in the case of minority groups, but also limits the degree to which foreign aid can reach its targets. We also control for trade openness in our specifications, since open countries are generally more integrated into the international financial markets, and should thus be more able to smooth negative shocks relative to less open countries. On the other hand, open economies may have the better infrastructure for foreign aid transfers, making emergency aid particularly efficient in open economies.

# **4.4 Bilateral Relations and Strategic Factors**

We define bilateral relations as broadly as possible to test the degree to which economic, historical and political ties shape the allocation of aid after natural disasters. The two most frequently used bilateral measures are distance and prior colonial status. The geographical distance variable we use measures the distance between the capital of the donor and the capital of the recipient (Gleditsch and Ward, 2001). Although distance is commonly used as a proxy for bilateral trade, distance may also capture the relative cost of providing help, especially if aid is provided in kind as it is often the case after natural disasters. We also add a control for oil exporting countries to capture the potential strategic relevance of recipients, and Gartzke’s affinity index measuring bilateral political alignment as the correlation of historical voting patterns in the United Nations General Assembly (Gartzke, 2002).

A value of 1 of this index implies that the donor and the recipient always voted the same way, while a value of -1 implies that the two countries never agreed. Both of these measures are intended to capture donors’ strategic and political objectives in the aid process.

# **4.5 Other Controls**

To control for the total number of disasters in a given year and other exogenous shocks to donor’s budget constraints, we include year fixed effects in all of our specifications. In the panel regressions, we also allow for donor fixed effects to control for differences in the average likelihoods of giving.

9
# **APPENDIX 1: METHODOLOGY FOR THE DATA COLLECTION ON THE CAPITAL COST DATA FROM CLEAN DEVELOPMENT MECHANISM PROJECT ACTIVITY**<sup>13</sup>

Reporting project capital cost data is not required in for CDM registration, but the information is sometimes used to demonstrate additionality. One consequence is that the way the information is presented in official CDM documents is not standardized and some interpretation is required.

Of those PDDs that contain capital cost information, it is often reported as “Capital costs” or “fixed costs” for the project, and as a single number either in host country currency units or USD. From what is reported, capital cost in the CDM PDDs generally includes: (1) procurement of any plant and/or machinery dedicated to the realization of the CDM project, (2) construction and civil works, and (3) engineering consultation (non-ongoing). In some cases, the following were included in the capital costs: (1) Costs incurred for the validation, registration, and verification of the project as a CDM project, (2) Contingency and margin money for working capital, (3) interest during construction, and (4) licenses.

Unfortunately, it was not possible to disaggregate the costs as only one (all inclusive) number was reported. In some cases, project participants reported all costs, including variable costs in a table. For these cases, capital cost had to be reconstituted into a single number in order to record it into the dataset.<sup>14</sup>

# **_Methods in obtaining capital cost data_**

As stated above, information on project costs is sometimes used in the demonstration of “additionality” of the CDM project. The section of the PDD where participants prove the additionality of the project is section B.5.

In each of the PDDs, this section was thoroughly reviewed to determine if capital cost data was included. In addition, the entire PDD was searched using key words such as USD, $, investment, cost, capital, and currency acronym for the host country (ex: for projects in china, key words included, CNY, RMB, yuan).

> 13 We thank Stephen Seres for helping with this Annex.

> 14 Most PDDs provided one single capital cost value which generally included construction, equipment, and engineering costs.  Where detailed tables were included, only the values for construction, equipment, and engineering costs were summed to produce a single value for the dataset with hopes at maintaining comparability.

35
# 4.3 Caseload

The SDI indicates that, on average, a health care provider in these countries attends roughly 13.3 outpatients per day (see **Table 4** ). Caseload ranges from a low of 2.8 patients per provider per day in Nigeria to a high of 23.2 in Mozambique. On average, providers in public facilities attend about 14.0 patients per day, while those in private facilities attend approximately 11.5 patients daily (though this trend varies by country). Rural facilities have higher caseload on average, again with some variation by country. Among public facilities, almost half (46.7%) have a caseload below 5 patients per provider per day. Table 4 also includes results on the percent of facilities with fewer than 5 patients per provider per day, which varies from 14.0% of facilities in Mozambique to 86.0% in Nigeria. In six of the nine countries, public facilities are less likely than private facilities to have fewer than 5 patients per provider per day. Variance decomposition shows that 72.0% of the variance is attributable to within-country differences, while 28.0% is attributable to between-country differences, as shown in **Appendix Table A8** .

**Figure 5** shows the distribution of caseload by rural and urban. In general, caseload is slightly higher at rural facilities, but this is not evident in all countries. Madagascar has a larger share of facilities in rural areas with a caseload below 5 patients per provider per day. Nigeria’s distribution shows that the majority of their facilities have fewer than 5 patients per provider per day, regardless of urban or rural location. Some countries, such as Mozambique and Kenya, show an even distribution suggesting that both urban and rural facilities see a highly variable number of patients.

_Figure 5. Distribution of caseload by country and rural/urban_ The caseload data have a strong left-model skewedness. To provide a clear visualization of the center of the distribution, the x-range is restricted to facilities with 1-20 patients per provider per day.
# **Appendix Figure 2. Average Share of Counterparty Countries, by Source Country per Year and Block**

<!-- Start of picture text --> A. Bank Loans and Deposits B. Portfolio Investment<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>C. Foreign Direct Investment<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>North-North North-South South-North South-South<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Note: This figure shows, for each investment type and block, the average share of destination countries reported by each source country in all possible destination countries per year. A country is considered to have data as a source to a specific country in a specific year if the value of international investment positions is nonmissing.