rafmacalaba/datause-encoder-data huggingface dataset:
words” in Spanish, Italian, and French as some of the project descriptions are in the donor’s language.

Our method of categorizing aid flows through “key words” also has some drawbacks, starting from omitted “key words” to projects that include more than one key word from different categories. Nonetheless, the gains outweigh the drawbacks; using detailed project information is a crucial element of our analysis as we set out to evaluate the effectiveness of aid on its main objective.

After employing our “key word” search process we are left with close to 41,000 aid flow observations. Most of the observations from the CRS dataset are dropped as they do not match any of the seven categories of interest. Most aid flows are either for disaster relief, humanitarian purposes (including health, water, and food), and/or for primary and secondary educational projects.<sup>6</sup> The distribution of all aid flows and the distributions of all firm perceptions amongst the different obstacles are presented in Figure 1.<sup>7</sup> The size of the bubbles describes the distribution of aid flows. We can clearly see that the _trade_ area has received more money than the other areas as 32.17% of all disbursements in the sample target this area. This is mainly driven by transportation related disbursements such as projects designed to build/improve roads and ports which require sizeable investments and are included under the trade category. The other two areas which aid targets more intensely are _rule of law_ and economic and political _stability_ .

> 6 We only include projects that target the education of the labor force such as vocational and professional training under our Labor category. Aid targeted for primary or secondary education is not included in our sample.

> 7 The distribution of firm perceptions and aid flows will change in each country, region, and year. Figure 1 is intended to display the general data and is not meant to be used to reach any conclusion about the relation between aid and firm perspectives.

11
**Table 1: Factor Decomposition** Continuous Variables Only.

||Factor I|Factor 2|Communality|
|---|---|---|---|
|Education|-0.78|0.24|0.66|
|Experience|0.88|---|0.78|
|Firm life|0.68|0.16|0.49|
|Hours|0.10|0.63|0.41|
|K/L|---|0.67|0.46|
|Earnings|---|0.75|0.56|
|Eigenvalue|1.93|1.43||
|% of variation|32.1|23.9||
|_Moments and mixing_|_parameter (p) offact_|_or distributions_||
|111|-0.89|-0.36||
|P2|0.45|1.02||
|at|0.56|0.69||
|**aF**<br>**2**|0.87|1.06||
|p|0.43|0.74||
|LR|-159.95**|-445.80**||

"*Statistically significant at the 5% level. Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.57 Table 2: Factor Decomposition Continuous Variables Only. Earnings Residual Replacing Earnings

||Factor I|Factor 2|Factor 3|Communality|
|---|---|---|---|---|
|Experience|0.88|--|--|0.78|
|Education|-0.79|0.22|0.10|0.68|
|Firm life|0.68|0.16|0.17|0.51|
|K/L|-0.12|0.75|0.15|0.59|
|Hours|--|0.74|-0.17|0.59|
|Residual|--|--|0.96|0.93|
|Eigenvalue|1.89|1.17|1.01||
|% of variation|31.5|19.6|16.9||
|_Moments and mixin_|_g parameter (p) of_|_factor distribut_|_ions_||
|**11L**|-0.89|-0.31|-0.97||
|R2|0.45|1.28|0.15||
|a,|0.56|0.67|1.42||
|a2|0.86|1.09|0.82||
|p|0.34|0.80|0.13||
|LR|-162.02**|-675.88**|-310.35**||

**Statistically significant at the 5% level.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = _0.55_ 26
**Table 2: Regression Results of Static Inequality augmented Inklaar Equations**

# **Dependent Variable: ln (PPP)**

||**Estimated P**<br>|**arameters**<br>|
|---|---|---|
|**Explanatory Variables**|**Year 2005**|**Year 2011**|
|**ln (GDP Deflator)**|0.405<br>(0.740)|Omitted due to<br>collinearity|
|**ln (GDPPC)**<br>|0.627***<br>(0.000)|0.227***<br>(0.000)|
|**ln (Exchange rate)**|0.357**<br>(0.016)|0.769***<br>(0.000)|
|**Inequality**|||
|**Low income countries**|4.200**<br>(0.045)|0.227<br>(0.577)|
|**Higher income countries**|1.709<br>(0.245)|-0.147<br>(0.619)|
|**Constant**<br>|- 6.607***<br>(0.000)|-2.373***<br>(0.000)|
|**R**<sup>**2**</sup>|0.9182|0.9856|
|**No. of observations**|107|101|
|**No. of countries**|107|101|
|**F-statistic for testing**��<br>��1|21.86***<br>(0.000)|38.59***<br>(0.000)|

Note: (1) Figures in parentheses are the p-values. **: significant at 5% level; ***: significant at 1% level.

- (2) **Low income countries** are those as specified by World Bank. **Higher income countries**

include lower middle, upper middle, high income non OECD and high income OECD countries.

38
# **I. Introduction**

Conflict affects education in several ways. It destroys infrastructure (Abdi, 1998), displaces and most tragically results in the deaths of students and teachers (Buckland, 2005), causes problems in harmonizing school calendars across war-affected regions (UNICEF, 2005) while schools remain closed for an indefinite period of time (Bruck, 1997), and has a damaging and pernicious socio-psychological impact on students (Sany, 2010). A cross-country analysis by Lai and Thyne (2007) showed that countries experiencing civil war suffer a decline in school enrolment by 1.6 to 3.2 percentage points. Evidence is growing at the subnational level that the outcomes are similar. Merrouche (2006) documented that exposure to landmines in Cambodia resulted in an average loss of .4 years of education. In a similar study, the mid-1990s genocide in Rwanda lowered the average level of educational attainment by .5 years (Akresh and de Walque, 2008). From the perspective of gender, Shemyakina (2006) finds that conflict makes no significant impact on male education rates in Tajikistan. However, females were 12.3 percentage points less likely to complete the mandatory secondary schooling compared to those who completed their education before the war broke out. A recent study, using household survey data between 2000 and 2008 from twenty-five conflict affected countries, finds that conflict leaves a legacy of fewer average years of education, decreased literacy rates and a smaller share of the population with formal schooling (UNESCO, 2010).

In this paper we estimate the average causal effect of civil war on education in Côte d‟Ivoire. In particular, we measure the effect of Ivoirian conflict, which reached its peak between 2002 and 2004, on years of education for individuals who were exposed to it in their schoolgoing age. The civil war in Côte d‟Ivoire broke out in September 2002 as a result of growing ethnic tensions and a failed attempted military coup. It divided the country into two: the rebelheld North and the government-controlled South and caused more than 3,000 deaths (World Bank, 2010). The war internally displaced more than 700,000 people and as many as 500,000 children were out of school between 2002 and 2004 (UNICEF, 2004). According to the Ministry of Education in Côte d‟Ivoire (2004), education in the North was affected more severely than education in the South. As per this report, almost 50 percent of the school-going aged children were out of school and only 20 percent of government-paid teachers stayed in their posts in the North since 2002. Moreover, the start of the 2005 school year was delayed in the North, and 2
|Lightbulbs|USD/bulb|0.6|43.2|kWh/bulb/year|124.8|25.0|
|---|---|---|---|---|---|---|
|||**Bui**|**lding Insu**|**lation**|||
|Plastic double-<br>glased<br>windows|USD/sq.m|2.0|4.0|kWh/sq.m/year||-15% from heating<br>consumption|
|Roof insulation|USD/sq.m|0|3.7|kWh/sq.m/year||-24% from heating<br>consumption|
|||||||-36% from heating|
|Wall Insulation|USD/sq.m|0.0|12.0|kWh/sq.m/year||consumption|

Sources: analysis of technologies sold on Georgian market for electric appliances, for building insulation measures – energy audits (Sustainable Development Centre Remissia 2014) Other technologies, such as refrigerators, cooling systems, etc. can be also considered for commercial sector, but they were not assessed here because there are no data available for the moment about types of the technologies that are used in commercial sector and their penetration.

The future penetration rates of efficient technologies and insulation measures in commercial sector are based on expert assessment. Theble A5 shows the assumptions on shares of efficient technologies in new stock in baseline and mitigation scenarios.

**Table A5. Share of energy efficient appliances in the new stock of appliances (%)**

|Technology | year and scenario|2035<br>baseline|2035<br>mitigation|Annual rate at which existing<br>appliances are replaced with<br>efficient appliance|
|---|---|---|---|
|Publiclights|5%|60%|2.50%|
|Lightbulbs|1%|87%|3.00%|
|Plastic double-glazed windows|80%|100%|5.00%|
|Roof insulation|0%|89%|2.00%|
|Wall Insulation|0%|89%|1.00%|

The emission factor for natural gas is 1.875 kg CO2/cub.m (National Inventory Report, 2015). For electricity, we used average grid emission factors. Grid emission factors will change overtime as electricity generation mix changes. Based on MARKAL-Georgia (MARKAL-Georgia, April 2015), the grid emission factors are as presented in table A.4.

33
# **Table 6: Comparison of citation counts for different Bank book series**

|Series|N|% cited|Median citations|Mean citations|Total citations|
|---|---|---|---|---|---|
|Policy Research Reports|18|89|59.5|129.9|2338|
|Latin America and Caribbean Studies|27|78|82|100.0|2700|
|Regional and Sectoral Studies|22|100|64.5|68.1|1498|
|World Bank Institute Development Studies|42|83|26|47.6|1998|
|World Development Reports|32|97|28|46.0|1473|
|Foreign Investment Advisory Service Occasional Papers|14|93|38.5|45.2|633|
|Trade and Development|28|89|27.5|44.1|1235|
|Stand alone (no series)|1,624|73|9|41.3|66991|
|Latin American Development Forum|22|77|17.5|34.5|760|
|Health, Nutrition, and Population|24|79|13.5|26.3|630|
|Directions in Development|167|78|6|23.2|3879|
|World Bank Institute Resources|57|82|5|19.5|1109|
|Law, Justice, and Development|10|100|8.5|14.4|144|
|Public Sector Governance and Accountability|12|92|10.5|13.2|158|
|Orientations in Development|13|77|4|9.3|121|
|Independent Evaluation Group Studies|111|50|0|8.3|921|
|Doing Business|7|71|4|8.0|56|
|World Development Indicators|14|||7.3|102|
|New Frontiers of Social Policy|6|67|1.5|4.3|26|
|Global Economic Prospects|13|38|0|2.7|35|
|Environment and Development|6|50|0.5|2.0|12|
|Country Studies|110|29|0|1.7|190|
|Agriculture and Rural Development|15|27|0|1.3|20|
|Global Development Finance|17|||0.9|15|
|Annual World Bank Conference on Development Economics|16|12|0|0.6|9|
|Global Monitoring Report|7|14|0|0.4|3|
|BerlinWorkshop|6|17|0|0.3|2|

Sources: Authors’ calculations from Google Scholar.
a lower risk premium.

Regarding bank lending, the OECD’s recommendation for T ̈urkiye is to reduce private sector debt OECD (2021). Nevertheless, we consider the counterfactual scenario in which the government implements reforms that gradually reduce constraints on green finance within five years. In particular, we assume that the share of capital which banks accept as collateral for lending to renewable energy firms increases by 20%-points. Figure 7 illustrates the results. Similar to the scenario of an improved rule of law, renewable energy investment and generation increase considerably. The GDP response demonstrates the importance of the financial accelerator mechanism forcefully. With a higher debt-capital ratio and borrowing constraints proportional to the value of capital, business activity becomes more pro-cyclical. As the value of the renewable capital stock decreases in the fiscal-consolidation phase, borrowing constraints tighten up, and GDP contracts considerably. On the flip side, the subsequent boom initiated by the renewable energy subsidy alleviates borrowing constraints, reinforcing the expansion of GDP.

# **5 Concluding remarks**

In conclusion, this paper examines the feasibility of T ̈urkiye’s net-zero emissions target by 2053, given the country’s structural deficits and recent climate-related risks. The country’s dependence on fossil fuel imports and the volatility of energy prices due to exchange rate fluctuations pose significant risks to energy security, while its primary export market, which is more carbon-intensive than its European counterpart, is also under threat from the European Union’s low-carbon agenda. Therefore, T ̈urkiye must implement effective climate-fiscal policy interventions that enable the country to grow along the 2053 net-zero pathway, taking into account significant feasibility constraints.

The analysis employs an empirical dynamic stochastic open-economy macro framework, allowing for the simulation of low-carbon transitions. The policy instruments evaluated include a carbon tax, a renewable energy subsidy, transfer payments, public infrastructure investment, a bad bank for stranded assets, and the phase-out of fossil fuel subsidies and public investment. The study also explores how the macroeconomic effects of the policy interventions differ in counterfactual scenarios of a fully credible commitment to net zero, an improved rule of law, and facilitated access to green bank finance.

The study finds that T ̈urkiye’s transition to net zero is feasible with a mix of policy instruments that address the country’s structural deficits. The model simulations show that a credible commitment to net zero, improved rule of law, and access to green bank finance would improve the effectiveness of climate-fiscal policy interventions in mobilizing private finance. Moreover, renewable energy subsidies and public infrastructure investments have a positive effect on economic growth, while the carbon tax and phase-out of fossil fuel subsidies contribute to the decarbonization of the economy.

Therefore, the study suggests that T ̈urkiye’s climate policy should focus on the imple28
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|---|---|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Afghanistan Rural Access Project|Mohammad Ajmal Askerzoy|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Mongolia Livestock and Agricultural MarketingProject|Sitaramachandra Machiraju|
|Ministryof Finance CapacityBuilding|Anzhela Prygozhyna|
|Ministryof Finance CapacityBuilding|Anzhela Prygozhyna|
|Ministryof Finance CapacityBuilding|Anzhela Prygozhyna|
|CapacityBuildingof Institute of Capital Markets(ICM)|Kiran Afzal|
|CapacityBuildingof Institute of Capital Markets(ICM)|Kiran Afzal|
|CapacityBuildingof Institute of Capital Markets(ICM)|Kiran Afzal|
|Development of labor statistics at the local level|Katherine M. Scott|
|Development of labor statistics at the local level|Katherine M. Scott|
|Development of labor statistics at the local level|Katherine M. Scott|
|Mexico Institutional Strengtheningof Congress - Phase II|Maria Guadalupe Toscano Nicolas|
|Mexico Institutional Strengtheningof Congress - Phase II|Maria Guadalupe Toscano Nicolas|
|Mexico Institutional Strengtheningof Congress - Phase II|Maria Guadalupe Toscano Nicolas|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
|Distribution EfficiencyProject|HungTien Van|
# Corridors without Borders in West Africa

Mathilde Lebrand<sup>*****</sup>

**Keywords:** regional corridors, trade facilitation, spatial general equilibrium, Africa **JEL Classification:R12, R13, R40, F15, O18, N17**

> *World Bank (E-mail: mlebrand@worldbank.org). I thank Michele Ruta for his helpful comments, Vivien Foster and Daniel Benitez for their guidance. This work benefited from discussions with Anca Dumitrescu and Aiga Stokenberga.
20 Children In Female-Headed Households Concern about the growth of female-headed households is partly motivated by the potential consequences of this household structure for the welfare of children. Poor households in Brazil, as in other countries, are more likely to contain more children, and as a result, minors are generally overrepresented in the bottom third of the income distribution. However, even though female-headed households with children are overrepresented among poor households in our case studies, this does not imply that children living in female-headed households are over-represented among poor children. Given that female-headed households have a lower number of children on average, it is possible that they are not. Table 14 presents a distribution of children across income class for each metropolitan area, among all minors and those living in a female-headed household. Table 15 shows the relative incidence of minor children living in female-headed households, indicating the degree of overrepresentation. Not only are children much more likely to live in poor households, but poor children are quite likely to live in female-headed households. Indeed, minor children in female-headed households are more overrepresented among the poor than are female-headed households as a whole.

Children in female-headed households tend to be older (Table 16). In all three cities, older children are overrepresented in all income groups. However, younger children in female-headed households are disproportionately concentrated in poor households, as the ratio between the average share in all income groups and the average share in the lowest income group is highest for the youngest childre-r.20 Are children in female-headed households worse off than other children in their income group? It is well known that poverty is hard on children (National Commission on Children, 1991; Wood, 1989). However, what we attempt to analyze is whether the femaleheaded household structure exerts a separable, independent effect on children, above and 20 This result was also verified by a logit analysis. See Barros, et al., (1993) for details.
# **Reducing Subsample Size**

Reducing the size of the subsample is expected to worsen the precision of the poverty estimates of both the Subsample Imputation and the Subsample Direct Estimation. Reducing the subsample size increases the sampling errors of poverty estimates from the Subsample Direct Estimation. Also, reducing the subsample size likely increases the SEs of poverty estimates from the Subsample Imputation, because the sample size that can be used to estimate consumption models is reduced. As a result, consumption models are more vulnerable to small sample bias, or the so-called “overfitting” problem. Restricting the number of variables is a typical way of minimizing the risk of small sample bias, but such a strategy tends to worsen the model’s fit.

However, reducing the size of the subsample is a cost-effective strategy for survey implementation. As discussed above, collection of consumption data is costly, particularly so in Bangladesh, where enumerators need to visit households seven times in two weeks during their stay in one village. Halving the size of the subsample with detailed consumption data will reduce survey implementation costs dramatically. This point is discussed more later in this section.

Table 4 shows that cumulative means from both the Subsample Imputation and the Subsample Direct Estimation remain close to those of the Full Sample Direct Estimation. It appears that the Subsample Direct Estimation slightly outperforms the Subsample Imputation in terms of the cumulative means as the size of the subsample declines. The SEs of the Subsample Direct Estimation deteriorate rapidly as the size of the subsample declines while SEs of the Subsample Imputation did not. For poverty estimates at the national level, the estimate from the Subsample Imputation remains reasonably accurate and its SE hardly increases. In this sense, if the government is interested in poverty rates at the national level only, reducing the subsample size to 320 households using the Subsample Imputation approach might be an option.

**Table 4: Comparing Results from First 30 Replications**

|Approach|Level/Area|320 Hou|seholds|640 Ho|useholds|
|---|---|---|---|---|---|
||National|39.1|(2.1)|39.4|(2.0)|
|Subsample Imputation|Rural|42.1|(3.1)|43.1|(2.5)|
||Urban|30.0|(3.6)|28.2|(3.1)|
||National|40.3|(3.5)|40.2|(2.7)|
|Subsample Direct Estimation|Rural|44.0|(4.3)|43.8|(3.2)|
||Urban|29.2|(5.9)|29.0|(4.5)|
||National||40.0|(1.1)||
|Full Sample Direct Estimation<sup>a</sup>|Rural||43.8|(1.3)||
||Urban||28.4|(1.9)||

_Source_ : Authors’ estimations using 2005 HIES data.

a. Full Sample Direct Estimation reports poverty rates by using household consumption per capita data from all households included in the 2005 HIES. So, the subsample size does not apply to the numbers. Estimates for 30 replications are available in appendix tables A2.2 and A2.3. Numbers in parentheses are SEs.

11
**Table 4: Labor force participation of HIV-positive patients. Probit and OLS regressions.**

||**Labor force**<br>**participation**|**Hours worked**|
|---|---|---|
|**HIV household**|-0.136*<br>(0.0801)|0.172<br>(1.506)|
|**Year 2008**|0.0864<br>(0.0664)|4.959***<br>(1.557)|
|**HIV*year 2008**|0.0569<br>(0.0867)|-1.437<br>(1.839)|
|**HIV patient in household**|-0.347***<br>(0.0786)|-5.323***<br>(1.578)|
|**HIV patient*year 2008**|0.171**<br>(0.0874)|3.708*<br>(1.899)|
|**Constant**|-3.394***<br>(0.295)|-27.18***<br>(4.871)|
|**N**|4782|4761|

Notes: Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1. Controls included but not shown: age, education, sex, marital status of the individual and the household head, status in the household of the individual, size of household, province dummies. Probit regression used for the labor force participation specification, OLS for the hours worked.

**Table 5: HIV status, household welfare indicators and attrition. OLS regressions with household fixed effects adding an interaction variable for attrition of the HIV patient.**

|**VARIABLES**|**Consumption**|**Income**|**Food**<br>**consumption**|
|---|---|---|---|
|**Year 2008**|-0.197***|-0.380***|-0.0529|
||(0.0585)|(0.123)|(0.0697)|
|**HIV household*year 2008**|0.212***|-0.0620|0.233***|
||(0.0713)|(0.153)|(0.0848)|
|**HIV household*year 2008*patient left household/died**|0.0466|0.465|-0.0834|
||(0.155)|(0.363)|(0.184)|
|**Constant**|6.707***|6.478***|6.017***|
||(0.0223)|(0.0485)|(0.0265)|
|**Observations**|1889|1615|1889|
|**R-squared**|0.014|0.051|0.017|

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

25