rafmacalaba/datause-encoder-data huggingface dataset:
sector, by region, and by gender. In some cases, education seems to have a healthy impact on earnings, comparable to or larger than that found for wage employees encountered in the same household survey. In other cases, the impact is not statistically different from zero. Thirty-two of the 83 coefficients estimated are statistically significant at the 10 percent significance level or better (26 of 83 at the five percent level) so we can feel confident that most of the "significant" positive results are not just chance findings.

Most of the significant coefficients come from the equations for all of the self-employment sectors together. When we look at these equations, three conclusions emerge. First, there are no discernible educational effects on earnings in rural areas. The activities in which both men and women participate in the countryside are presumably for the most part traditional employments, for which schooling is rarely relevant. In many cases -- notably in textile production but probably also in food production and in some retail trade -- the activity is an adjunct to farming, adding value to some agricultural product. Second, post-secondary education always has a fairly high and significant payoff in urban areas, for both men and women. Women's returns are systematically (though not always significantly) higher than men's, perhaps because higher education is still much less frequent among women. Post-secondary schooling is so rare within any one subsector that we cannot test for its effect, and the earnings equations for trade, manufacturing, and services can only distinguish primary from all post-primary education. Third, again within urban areas only, men appear to get a 33
**Figure 3.1  Urea use per/ha and land size, 2007**

<!-- Start of picture text --> 0 1 2 3<br>Land size (ha)<br>600<br>400<br>200<br>0<br><!-- End of picture text --> _Source_ : World Bank staff calculations.

**Table 3.1  Rice yields  and urea/SP36 use by** **<u>quintiles of land size, 2007</u>**

|Quintile|Rice Yields<br>(kg/ha)|Urea<br>Use<br>(kg/ha)|Intensity<br>Urea/Yields|SP36<br>Use<br>(kg/ha)|Average<br>Land Size<br>(ha)|
|---|---|---|---|---|---|
|1|4,836|343|7.1%|164|0.12|
|2|4,596|291|6.4%|151|0.25|
|3|4,447|268|6.1%|136|0.41|
|4|4,053|224|5.6%|118|0.73|
|5|3,490|170|5.0%|88|1.97|
|Average/Total|4,283|259|6.0%|132|0.7|

_Source_ : World Bank staff calculations.

The data on fertilizer consumption in Indonesia show that farmers use more urea than recommended by the MoA. While the optimal levels of urea required per hectare vary according to soil quality across islands in Indonesia, the MoA recommends using 200-250 kg/ha of urea and 50-100 kg/ha for SP36 fertilizer.<sup>4</sup> However, as Table 2.1 illustrates, all farmers in quintiles 1-3 used more than the suggested amount of fertilizer. In the case of SP36, only the larger farmers in quintile 5 consumed within the recommended range.

# _Benefit Incidence Analysis_

There is no evidence of targeting subsidized urea. When looking at the incidence of benefits by land-size in 2003, there were subtle differences that favored the smaller farmers (those in land-size quintiles 1-3). Yet, while the coverage of the subsidy program increased in 2007 the targeting was ineffective and these differences disappeared. (See Figures 3.2 and 3.3, which show that all quintiles benefitted equally

> 4 These recommended level does not include blends with organic fertilizer.   MoA Decree No. 01/Kpts/SR.130/I/2006  (Lampiran Keputusan Menteri Pertanian).  The lower range is an average  for off-Java provinces, while the  higher range applies for Java.

**8**
Lewis and Lockheed A second explanation for poor performance, relevant to team work and classroom interaction, can be found in literature regarding expectations and social interaction (Berger, Fisk, Norman and Zelditch 1977; Webster and Hysom 1998; Ridgeway 1997). Marginalized groups have both lower social status and are subject to generalized, diffuse expectations about their competencies and abilities; these expectations affect social interaction and cannot easily be reduced (Ridgeway 1997). Sociologists distinguish between such status-based beliefs and simple discrimination or group identity effects “by the fact that those in the disadvantaged group overcome in-group bias and concede that the other group is more socially worthy” (Ridgeway and others 1998, p338). For example, in Yemen “most of the Akhdam have internalized their low status and do not try to better themselves, find real jobs or seek an education” (Worth, 2008). Marginalized children, particularly girls, suffer from these expectations.

Expectations lead to differences in opportunities for interaction in school classrooms, and hence affect children’s opportunity to learn in two ways. First, marginalized children in heterogeneous classrooms talk less. Educators have long recognized that learning is enhanced through verbal interaction with classroom peers and teachers (Piaget 1926, Rogoff 1990, Vygotzky 1962). Girls, ethnic sub-groups and the poor remain comparatively silent in class, constrained by status-based expectations regarding their own competencies relative to the competencies of others in the class (Cohen 1982). In US classrooms, “low status” students talk less than “high status” students in elementary and middle school classrooms, and black children exercise less influence over group tasks than do white children (Cohen and Lotan 1995; Cohen, Lockheed and Lohman 1972). In Israel, children from North African backgrounds exercise less influence over group tasks than do children from European backgrounds (Sharan and Schachar, 1988).

Second, teachers often hold performance expectations based on student social status. For example, in Brazil, Ligea de Oliveira Barbose (2004) found differences in teacher expectations for the academic performance of 4<sup>th</sup> grade children on the basis of gender, ethnicity and household wealth in a study of 48 primary schools in Minas Gerais. In this case, teachers’ expectations were higher for girls, but lower for colored students and students from poorer households. After taking into account the actual performance of the students, only teacher expectations in favor of girls remained statistically significant. In China, teacher expectations for primary students are strong predictors of whether children subsequently enroll in secondary school, but mother’s aspirations and teachers’ expectations were equally important in explaining children’s aspirations for their own schooling (Hannum and Adams 2007).

14
developing world suggests that effects are harder to identify than for savings. For example, one of the best-known studies of micro-credit finds no significant impact on income and consumption among poor households in Hyderabad, India (Banerjee et al., 2013), though multiple studies show modest impact on investment.<sup>xiii</sup> Critically, recent research has identified differential effects across different borrower groups, with entrepreneurial types increasing their investment when gaining access to external finance while non-entrepreneurial types increase consumption.

A key impediment to broader extension of credit in Africa is the lack of reliable methods for personal identification. Individuals who lack collateral and credit histories, which characterizes a large share of the African population, struggle to overcome informational asymmetries that make it almost impossible to access credit from formal sources. Establishing collateral and credit registries could help, but these can only function if people can be accurately identified. A field experiment among paprika farmers in Malawi tested whether biometric identification methods can improve the functioning of credit markets in a country where identity theft is common (Giné et al., 2013). Applicants for agricultural input loans from a state-owned bank were randomly assigned to a treatment group in which a fingerprint was collected from each member as part of the loan application, or to a control group in which no fingerprint was taken. Both treatment and control groups attended a training session on the importance of a credit history in ensuring future access to credit. For the subgroup of farmers identified as having high ex-ante default risk, fingerprinting led to a forty percent increase in repayment rates.<sup>xiv</sup> As in other parts of the developing world, micro-insurance products could offer benefits in Africa, especially in agricultural areas. For example, rainfall insurance, which pays a set amount when rainfall falls below (or surpasses) a pre-determined threshold could be useful to African farmers.<sup>xv</sup> Yet, empirical studies of micro-insurance are rare. In Ghana, 29
Page 12 Taking the current income distribution of slum/challi dwellers, it is possible to define roughly what housing price each income group could afford. Based on price data collected for different housing typologies in Ahmedabad, we analyze affordability based on a range of price / income ratio (PI ratios). At the very low end of the income distribution, less than INR 7,000 per month, households can hardly afford any expenditure on housing except for a very low rent for a small hut in an existing slum. The 2011 households’ income distribution shows that about 8% of Ahmedabad population (or about 24% of the total slum/challi population) has a monthly income below INR 7,000 for whom it is difficult to afford a decent low standard home. For the other 76% of households in slums/challis, it should be possible to find housing solutions much like low-cost ‘vernacular’ housing solutions that already exist in large quantities in Ahmedabad. As we discuss in Section IV below, this involves investing more in basic infrastructure services in poor neighborhoods and introducing more flexibility in standards for formal housing as currently defined in the General Development Control Regulations (GDCR).

The analysis uses a price-to-income (PI) ratio between 2 and 3.5 for monthly income above INR 7,000 to calculate the range of price that slum dwellers could afford on the current housing market (Figure II.3). Individual households vary in their capacity to save and borrow from banks or relatives, hence the range of PI ratio for the same income groups.

For instance, for a land cost of INR 16,000/m2 and a construction cost of INR 8,000/m2, a dwelling of 12 m2 would cost INR 1.7 lacks if the same land use standards as much of the vernacular housing stock in Ahmedabad (84% coverage, 3 floors). The same 12 m2 dwelling on 3 floors would cost 2.4 lacks or 38% more if GDCR standards were used. In fact the GDCR standards penalize low buildings as they force using a large amount of land on the ground for large streets, open space and parking.
sample live in Oromia Regional State and almost all non-Orthodox Christians live in SNNP.

Respondents in general are middle-aged (average age 42 years) males (90%). The average respondent is poorly educated, with 45% having a maximum of primary education and 38% being illiterate. Average household size in the sample is 4.9 adult equivalents, with a mean of 3 children under 15 years old. Households live at an average of 2,214 meters, but as low as 1,434 and as high as 3,150 meters. The sample can therefore reasonably be characterized as a highland sample.

Almost all respondents report being farmers (91%) and own an average of 1.8 hectares of land, with a maximum of 10.5 hectares. As is the case for the country as a whole, in the sample irrigation is unusual, only 8% of respondents have any irrigated land. Farmers both cultivate and raise animals, with virtually all households having animals, with an average of 5 tropical livestock units (TLU).<sup>2</sup> Only 7% of households had less than the equivalent of one cow and 10% had more than 9.5.

# **4. Methodology**

# **_Choice Experiment Surveys_**

CE surveys are based on Lancaster's (1966) consumer theory and are used to elicit preferences for environmental goods and policies (Boxall et al. 1996, Louviere et al. 2000). Lancaster (1966) proposed that consumers obtain utility from the characteristics of goods rather than the good itself. Therefore, CEs can be considered the analog of hedonic analysis for stated preference valuation methods and they allow the researcher to gain a detailed understanding of the respondents’ preferences for the policy or scenario being analyzed. In a typical CE survey, the respondent repeatedly chooses the best bundle/choice from several hypothetical bundles/choices. The attribute values appearing in each bundle/choice are identified using 8
Policy Research Working Paper 8974

# **Abstract**

Does financial compensation for providing environmental conservation, improve the food security of the rural poor in the drylands of Sub-Saharan Africa? This paper explores this question using data from a randomized controlled trial of a large scale reforestation implemented by the Government of Burkina Faso. Members of communities located around selected protected forests were invited to plant indigenous tree species on degraded areas, and to take care of their maintenance. The financial compensation they would receive depended on the number of trees still alive a year later. The vast majority of the community members participating in the project were farmers, and the timing of the payments coincided with the lean season, when most farmers were at risk of food insecurity. Compared with the control group, the project’s participants’ households reported 12 percent higher food consumption expenditures, and a reduction in moderate and severe food insecurity by 35 percent to 60 percent. The transfers received were spent mostly on cereals, meat, and pulses, with no evidence of increased consumption of temptation goods.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gadjognon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The first round of CCT data was collected in May and June 2013.The second round CCT and the improved stove use satisfaction survey were undertaken during October and November 2013. A total of 25 fieldworkers (5 supervisors and 20 enumerators) hired by the Environmental Economics Policy Forum for Ethiopia (EEPFE) at the Ethiopian Development Research Institute (EDRI), which is one of the most important federal government policy research institutes in Ethiopia, were trained to assemble, set up and use the _Mirt_ stove, as well as conduct the CCT. All had prior experience in field survey research and spoke the dominant local languages.<sup>2</sup> Five teams consisting of 1 supervisor and 4 enumerators were deployed, each covering 7 villages, except one that covered 8. A brief questionnaire was prepared and used for the stove use satisfaction survey (see Appendix C). T he German international aid agency GIZ, an important stakeholder in household energy and stove dissemination, provided a half-day training on CCT procedures and implementation to the fieldworkers.

We use a standard CCT implementation protocol discussed in Bailis et al., (2007). This protocol was chosen, because it allows us to compare traditional and improved stove fuel consumption and cooking time under field experiment conditions. The details of the measurement protocol are available at http://www.aprovecho.org/lab/pubs/testing. Our adaptation of the protocol for Ethiopian conditions is provided in Appendix A.

The CCT involved purchasing _teff_ , which is the essential ingredient for baking _injera_ . This was purchased either in flour or grain form. If it was in grain form it had to be ground and the flour provided to participants three days before the CCT so that they could prepare the dough. Participants were provided homogenous and equal quantities of all ingredients, which included eight kilograms of _teff_ flour; 4kg for baking on the traditional three-stone stove and 4kg for baking on the _Mirt_ stove. These amounts were derived from focus groups conducted prior to beginning the research and are believed to be typical of the _injera_ dough cooked by a typical family at one time.

The team also supplied fuelwood for cooking that was similar in type across households. These resources not surprisingly differed across regions, but the CCT protocol adjusts for wood species and moisture content, which was measured using

> 2 Ethiopia is a multi-lingual country with over 80 languages. The fieldwork required that the enumerators are able to speak the dominant local/official languages in the study regions, i.e., Amharic and Oromiffa.

10 | P a g e
# Mapping state capture risks across North Macedonia

In this subsection, we examine the public procurement network in North Macedonia spanning from 2011 to 2022. Our primary objective is to conduct an empirical evaluation of state capture patterns present in the complete contracting network. By utilizing this approach, we aim to gain a comprehensive understanding of the extent and degree of state capture risks across various grouping variables, including geographical regions, time periods, and economic sectors.

The first step in the process is to condense the contract dataset into bi-modal graph data, which is achieved by calculating the average CRI score for each unique pair of buyers and suppliers. This aggregation process involves analyzing a total of 267,295 contracts which ultimately results in 92,195 distinct relationships represented as ties between nodes in the graph. The contracting network analyzed in this study has a total value of 512 billion MKD, which is distributed across 45 CPV sectors. The median procurement authority has 393 contracts, with a total value of 304 million MKD and a CRI score of 0.48. The median supplier in the network has 50 contracts, with a total value of 40 million MKD and a CRI score of 0.44. On average, a tie between a procuring authority and a supplier in the network represents 2.9 contracts, with a total value of 5.5 million MKD and a CRI score of 0.45. However, it is worth noting that the distribution is heavily skewed towards larger organizations, as the median value of a tie is only 500,000 MKD.

For the network representations that follow, our focus is solely on organizations with more than one tie. This allows us to capture contracting patterns across organizations, as isolated nodes offer limited insights in this regard. Additionally, we focus on the main component of the network to present a comprehensive analysis. As a result of this filtering process, we reduce the number of ties under investigation by 22%. In Figure 23 we present the complete contracting network in North Macedonia between 2011 and 2022.<sup>8</sup> Each link in the figure denotes a relationship between a buyer and supplier, with the color coding representing the average CRI between the two parties. It is clear that the low and high corruption risk ties are clustered together in the peripheral regions of the network. This clustering of high corruption risk ties suggests a higher likelihood of misconduct in those areas. In contrast, the center of the network features contracting activity with mixed corruption risk scores, depicted by the presence of white ties.

> 8 To improve the clarity of the figure, we have chosen to display only the ties between the nodes, omitting the nodes.

37
characteristics (size, composition, housing) as well as on individual characteristics like labor earning, sector of employment, hours worked, education, age, gender, marital status, etc. The EPH is a repeated cross-section and is usually collected in May and October in each year. In this paper, we pool individual data from 1974 to 2001. In Table A3, we report the different historical EPH surveys that we use and the sample sizes. For the evolution of the skill premium, see Figure 2 in the text.

22
tional Monetary Fund (IMF), Locational Bank Statistics (LBS) and Consolidated Bank Statistics (CBS) from Bank for International Settlements (BIS), International Debt Securities (IDS) Statistics from BIS, Quarterly External Debt Statistics (QEDS) of IMF and World Bank (WB), and Debt Reporting System (DRS) data of WB.<sup>11</sup> The cornerstone of our dataset is the Balance of Payments (BOP) data produced by the IMF, which is the most comprehensive source of international capital flow data across countries. The BOP data, which is reported to the IMF by country statistical offices, captures capital flows into and out of a given country. The accompanying stock measures of external assets and liabilities are captured in the IMF’s International Investment Position (IIP) data. Capital flows are measured as asset flows (outflows), liability flows (inflows), and net flows (inflows - outflows). We focus on the financial account portion of the data and the latest (6th) version of the balance of payments manual (BPM6). More details on the BOP data, along with its different presentations and versions, are given in Appendix A.2.<sup>12</sup> In theory, each type of capital flow can be disaggregated by sector (borrower and lender type). In practice, however, the coverage of such disaggregated information in the BOP tends to be for countries and earlier To be abso- sparse, especially EM/developing years. lutely clear, capital flow types (asset classes) are generally very well reported in aggregate terms in the BOP data, and the reporting of the sectoral breakdowns has improved in recent years. Nevertheless, for most emerging/developing countries and years before 2005 the reporting of the data by sector is much less exhaustive.

> 11It should be noted that, even though combining different data sources to complement BOP/IIP statistics is rarely done at the global level, this is exactly what many country-level BOP/IIP compilers do on a regular basis (e.g. many country BOP/IIP compilers use the BIS IBS data series on banks’ cross-border deposit liabilities to the residents of their respective countries in order to enhance their BOP/IIP compilation).

> 12See the 6th Edition Balance of Payments Manual (BPM6) Appendix 8 for more details on the differences between the previous edition (BPM5) and BPM6.

11