rafmacalaba/datause-encoder-data huggingface dataset:
|Second EnergySector Project|Peter Johansen|
|---|---|
|Second EnergySector Project|Peter Johansen|
|Second EnergySector Project|Peter Johansen|
|Bosnia and Herzegovina Employment Support Program|MireyOvadiya|
|Bosnia and Herzegovina Employment Support Program|MireyOvadiya|
|Bosnia and Herzegovina Employment Support Program|MireyOvadiya|
|Bosnia and Herzegovina Employment Support Program|MireyOvadiya|
|Bosnia and Herzegovina Employment Support Program|MireyOvadiya|
|Bosnia and Herzegovina Employment Support Program|MireyOvadiya|
|Bosnia and Herzegovina Employment Support Program|MireyOvadiya|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|ICT Sector Support in Somalia Phase II|TimothyJohn Charles Kelly|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|Ebola EmergencyResponse Project|Opope Oyaka Tshivuila Matala|
|School Sector Reform Program Additional Financing|DilipParajuli|
|School Sector Reform Program Additional Financing|DilipParajuli|
|Somalia Towage Services in Berbera Port|Suhail Kassim|
|Somalia Towage Services in Berbera Port|Suhail Kassim|
|Somalia Towage Services in Berbera Port|Suhail Kassim|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
|TransformingHealth Systems for Universal Care|Jane Chuma|
and such factors are in effect given free rein under weak institutional regimes.<sup>3</sup> Those authors use World Economic Forum data to construct an index of the strength of institutions that support trade, focusing on contract enforcement and the existence of impartial and transparent government policies. Thus, while the concept of transparency is crucial to their work, their empirical development of its scope is much broader than ours: whereas they consider the general transparency of a country’s governance structure, we focus on the trading environment itself to develop a multi-dimensional measure of transparency that is as closely related as possible to the processes of exporting and importing.

Levchenko (2007) complements the Anderson and Marcouiller (2002) approach by embedding cross-country institutional differences affecting contract enforceability in a general equilibrium model of trade. Using import share data for the USA, he shows empirically that higher institutional quality in the exporting country is associated with stronger trade flows in complex products, which are argued to be institutionally intensive due to the need to contract for intermediate goods. He measures institutional quality using the rule of law component of the World Bank’s _World Governance Indicators_ dataset.

By contrast, de Groot et al. (2004) take a much broader approach to examining institutions and trade, including all of the _World Governance Indicators_ in their measure of institutional quality. In addition to rule of law, they also take account of voice and accountability, political stability, government effectiveness, regulatory quality, and control of corruption. Using a gravity model, they find that both institutional quality and the existence of similar institutions in trading partners are positively associated with bilateral trade.

Finally, Francois and Manchin (2007) measure institutional quality through the lens of economic freedom, focusing on aspects such as the size of government, freedom of trade, protection of property rights, and business regulation. They find that strong institutions in this sense are associated with increased trade at both the intensive and extensive margins.

> 3 Ranjan and Lee (2007) report similar findings using more detailed data on contract enforcement. They also find evidence that the link between contract enforcement and trade is stronger for differentiated goods than for homogeneous ones.

3
18 come to meet data-availability requirements for one or more models, parameters can be estimated and tested for reliability by using a sample of loan applications that have been screened and approved at designated banks by their own loan officers. Finally, in countries and banks for which statistical analysis can approve at least one model for transfer to their competitive environment, significant differences in estimates of marginal risks and of the average risks weights attached to different classes of loans can be used by World Bank or BIS analysts in cross-country analyses of asset risks and used by country supervisors as the basis for empirically based country-specific and bank-specific estimates of default probabilities.

When and as they are validated, credit-scoring models provide an empirical method for answering a number of tough supervisory questions. First, for what classes of assets (if any) does it make sense to set low or zero risk weights in a given country or class of countries? Second, how much safer or riskicer on average are a bank's clams on a developing county's government and public enterprises than its loans to private enterprises? Third, what minimum ratio of capital to assets may safely be deemed prudent for specific developing countries?

A scoring model can also be thought of as streamlining bank file structures and even automating the loan decision. In this conception, computer software treats infornation collected in loan applications as survey data and formulates a preliminary estimate of the bank's optimal loan response. Of course, final decision processes must include procedures for varying this response to make use of intuitive informnation that loan officers develop in ways that are not yet objecively reproducible. In straightforward cases, credit evaluation time may be reduced to a few hours. One U.S. software vendor, Appro Systems in Louisiana, claims that its software can produce a preliminary loan approval-or-denial decision in minutes.

It would be a mistake to dismiss loan-analysis software as too sophisticated for use by developing countries. In some developing countries, automated teller machines (ATMs) and applications of banking software are proliferating rapidly (Kraus, 1992). In fact, Fiserv Inc. has already contracted with four countries in the Northem tier of Latin America (Mexico, Honduras, Colombia, and Venezuela) for Spanish-language versions of its banking software (Sullivan, 1993).
placement.<sup>3</sup> They argue that infrastructure placement can be presumed exogenous in the equations measuring women’s work or school attendance, once these geographical characteristics have been included as additional explanatory variables.

We here adopt a novel approach, by building a (pseudo) panel of clusters/communities based on their spatial GPS coordinates. The use of panel data specific techniques will then allow us to account for cluster/community-specific unobserved effects and hence to control for the endogeneity of infrastructure placement.

In order to control for potential endogeneity bias due to unobserved individual/household characteristics, we propose to estimate school attendance at the cluster/community level, by calculating community averages from household data, along the lines of Dinkelman (2009) and Koolwal and van de Walle (2013). As argued by these authors, under the assumption that endogeneity arises from individual choices within communities, access to water can be treated as exogenous if community averages are used instead of household data.

# **4. Data and descriptive statistics**

The Republic of Ghana (hereafter only Ghana) is a country in West Africa, on the Gulf of Guinea. Situated at only a few degrees northern latitude, it enjoys a warm climate throughout the year. Ghana is divided into ten administrative regions, and has a population of about 24 million (a map of the ten regions is provided in Appendix A). Gross National Income (GNI) was assessed at USD 1,190 per capita in 2009 (World Bank, 2011).<sup>4</sup> Agriculture contributed about one-third of the country’s gross domestic product. Although Ghana has been classified by the World Bank as a middle-income country, it struggles with a water deficit and widespread lack of sanitation. Access to an improved water source rose from only about half of the population in 1990 to 82% in 2008.<sup>5</sup> By contrast, access to sanitation has increased from 7% to only 13% in nearly 20 years, one of the lowest rates in Africa (World Health Organization/UNICEF (2010)).

> 3 Among other factors, these authors consider access to roads, schools, banks, health centres and markets; price levels for food and other important commodities; male and female daily agricultural and non-agricultural wage rates. 4 The average GNI per capita in Sub-Saharan Africa was USD 1,126 in 2009. These figures were obtained from the document _Ghana at a glance_ published by the World Bank in 2011 and available at http://devdata.worldbank.org/AAG/gha_aag.pdf.

> 5 “Access to an improved water source” refers to the percentage of the population with reasonable access to water from an improved source such as a household connection, public standpipe, borehole, protected well or spring, and rainwater collection. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. “Reasonable access is defined as the availability of at least 20 litres per person per day from a source within one kilometre of the dwelling”; see http://data.worldbank.org/indicator/SH.H2O.SAFE.ZS.

7
The Salvadoran banking sector became one of the deepest in Central America<sup>6</sup> by the end of the 1990s (see Figure 1). However, despite the financial reforms undertaken during this decade, financial inclusion remained low in El Salvador. Based on a survey of rural households, conducted by FUSADES (Salvadoran Foundation for Economic and Social Development), 8.7 percent of households had a deposit account as of 1995 and 6.8 percent had a loan from a formal financial institution.

# **III. Data and empirical methodology**

The household-level data we use in this study come from the _National Rural Household Survey_ carried out by FUSADES in 1996, 1998, 2000, and 2002. All the information gathered by each survey refers to the previous calendar year. The survey uses a questionnaire adapted from the World Bank’s Living Standards Measurement Survey and covers a stratified, nationally representative, random sample of rural households. The four waves contain information for about 937 households dispersed across the 14 departments that make up El Salvador. Within our sample, 719 households are present in more than 1 wave and 451 households have data for the four waves, allowing us to construct a panel.

The survey includes data on demographic characteristics, education, employment, economic activities, wealth, and income of households. With respect to migration, the survey has information on the number of members per household that have migrated to other countries, their kinship relationship or role in the household (i.e. head of household, spouse, son), the time they have been abroad, and the year they came back to El Salvador in case they returned. Moreover, data on whether the individuals that migrated sent remittances and the amount sent are also available.

The survey also contains information on households’ use of financial services offered by formal financial institutions (i.e., commercial banks, cooperatives, credit unions, and other financial institutions), development institutions (i.e., NGOs), and informal sources (i.e. stores, friends, employers). In this study, we focus on formal financial institutions in order to analyze the impact of remittances on access to financial services such as loans, savings, and checking

> 6 Central America includes the following countries: Guatemala, Honduras, El Salvador, Nicaragua, and Costa Rica.

6
Another issue that has to be considered when one uses a review of existing impact evaluations on education-related interventions to draw policy conclusions relates to the political economy. Even if rigorous evidence exists that convincingly shows a certain type of interventions has positive impacts on specific education-related outcome variables, it might still not be worth putting a lot of effort in promoting this type of interventions if it is clear at the outset that politically influential actors will strongly oppose it. At the very least, one should be conscious about political realities on the ground and address or incorporate these into any policy recommendations. For instance, while rigorous and robust evidence exist on the benefits of supplementing traditional teacher contracts by more flexible and performance-based contracts, such reforms tend to face strong opposition, in particular from politically well-connected teachers unions. Recognizing these political economy realities, Pritchett and Murgai (2007) and Muralidharan (2013) suggest integrating contract and regular teachers into a career ladder with bonuses, pay raises, and promotion to regular civil-service rank based on performance over time.

These considerations illustrate that evidence from impact evaluations should not be blindly trusted. Rather, it should be seen as one – important – building block of informed decision making. At the same time, one should not overlook the huge potential offered by the relatively plentiful and highquality education-related impact evaluations that have been conducted in South Asia. During the last ten years, these impact evaluations have created more knowledge on the costs and effects of a diverse set of often innovative interventions than was ever available before. Moreover, South Asia is well ahead of other regions in terms of the number of education-related impact evaluations and the average quality of these impact evaluations is quite impressive with most of them paying close attention to common methodological pitfalls and biases. Today, the region enjoys a unique opportunity to benefit from the knowledge that has been created over the last ten years and to harness this knowledge for evidence-based policy making. If used appropriately, the rigorous impact evaluations of educationrelated interventions can help South Asian economies in mastering the formidable policy challenges they face in terms of strengthening their education systems. In particular, they can provide a sound basis for reforms that improve the quality of education and provide learning that reaches even the most disadvantaged groups.

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# **3. The Data**

We examine the linkages between poverty, MPA activities, and household choice of fishing gear using household survey data collected at six sites along the coast of mainland Tanzania and Zanzibar: five with existing MPAs (Tanga, Mafia Island, Menai Bay, JozaniChwaka Bay, and Misali Island) and one where a new MPA has been proposed (Kilwa). Each site has ecologically important and diverse marine and coastal habitats which coastal communities depend on for their livelihoods, including coral reefs, mangrove forests, and seagrass beds. Four of the MPAs are legally designated protected areas and one MPA is protected by community based management and locally approved resource management plans. These MPAs were established to protect resources and halt resource overexploitation and destructive fishing practices and all are involved in a number of management actions such as community-based planning, environmental monitoring, enforcement of fisheries regulations, and coral and mangrove conservation activities. These MPAs are also involved in various community development activities such as environmental education, alternative livelihood development, ecotourism, micro loans, and receive external financial and technical assistance.<sup>5</sup> Four villages in each site of the six study sites were selected in consultation with local and national level authorities, and MPA project staff. Criteria in the selection of villages included the proximity to the protected area, community involvement in conservation, and importance of coastal and marine resources. One village outside the MPA was selected at each site for comparison purposes. A total of 749 households were surveyed in 24 villages. Random sampling at the village level was used to pick households for the survey, to ensure a diversity of households in the sample and avoid systematic bias.

The primary instruments for data collection were household and focus group surveys. Both surveys were developed following a review of guidelines on socioeconomic assessment of MPAs and coral reef management. Prior to the field work, the survey questionnaires were tested to improve or eliminate questions that were confusing or difficult for respondents to answer. The household questionnaires covered information on: household composition and characteristics, economic activities, physical assets, expenditures, food security, education and health, markets, credits and savings, social capital, knowledge and participation in MPA activities, and

> 5 Technical and management assistance includes: IUCN in Tanga Region, WWF on Mafia Island and Menai Bay, and CARE Tanzania in Jozani-Chwaka Bay and on Pemba Island.

12
> <sup>14</sup>

legislation, therefore tracking whether formal institutions exist that potentially impose constraints on arbitrary behavior by the executive branch.<sup>3</sup> The _checks_ variable captures the two ingredients identified by many as essential for secure property rights: elections and checks on the executive branch. However, unlike the _Executive Constraints_ Polity IV measure, _checks_ captures only the formal constraints on the executive that theory predicts should protect property rights, not whether those formal constraints are in practice binding. It therefore constitutes a better test of theories of the role of institutions.

The _DPI_ also contains two variables assessing the competitiveness of elections, the Legislative and Executive Indices of Electoral Competitiveness ( _LIEC_ , _EIEC_ ). The executive index, _EIEC_ , is used here. This reaches its highest score (7) when multiple parties can and do compete for executive election and no party gets more than 75 percent of the vote. A six means that one party receives more than 75 percent of the vote; a five that only one party ran for office though others could have and so on until one, indicating no elections were held. Since most scholars would agree that only the most competitive category of _EIEC_ is a reasonable approximation to elections, a dummy variable is used in the regressions here, equaling one when _EIEC_ is seven and zero otherwise.

The remaining two democracy variables capture distortions in the market for political office. The first distortion is the lack of credibility of pre-electoral political promises. The evidence in Keefer (2003) indicates that the performance of democracies

> 3 Beginning from a value of one (meaning that there is only one veto player and no checks and balances), this variable increments by one if countries have potentially competitive elections of the executive; by one in presidential systems if the legislature and presidency are controlled by different parties; in parliamentary systems, the value is incremented by the number of parties in the government coalition whose departure would cause the government to lose a majority; and in all systems by  one for each party supporting the government in the legislature whose with an ideological stance strongly differing from that of the executive’s party (see the _DPI_ codebook for more details).
entrants each year (World Bank 2003). Nonetheless, there are encouraging signs on the poverty front. Poverty incidence is heading back to the pre-crisis level (see table 3.2). Still, the majority of the population of this middle-income country live below the international poverty line of 2 dollars a day. It is thus understandable that the government is committed to formulating a comprehensive poverty reduction strategy along the lines of the PRSP<sup>13</sup> framework.

# **_3.3.  The Role of Inequality_**

Poverty indices are computed on the basis of a distribution of living standards that is fully characterized by the mean and the degree of inequality (as represented by a Lorenz curve). It is therefore reasonable to think of _a poverty measure as a function of these two factors_ . As noted in the introduction, the same growth rate can bring about different levels of poverty reduction. The explanation of this fact can be traced to factors underpinning both the initial level of inequality and its change over time. We now address the counterfactual question of what would have been the poverty outcomes in Indonesia within the three periods considered had inequality not changed within each period. We first briefly describe changes in inequality, and then answer the question based on the Shapley decomposition of poverty outcomes (Shorrocks 1999).

Table 3.3 shows extended Gini coefficients for the last decade. This indicator of inequality is analogous to the extended concentration coefficient defined above. When the aversion parameter is equal to 2, the extended Gini is equal to the ordinary Gini index. Focusing on this case, we note from table 3.3 that the Gini coefficient increased

> 13 Poverty Reduction Strategy Paper.

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