rafmacalaba/datause-encoder-data huggingface dataset:
-23-

individual farmers with the farmer's cooperative serving as guarantor. Loans were multi-year and repayment could be either in cash or in produce. As there was no institutional equivalent to the coffee marketing cooperatives for food crops, a different system of guaranteeing maize loans needed to be devised. Both Plan PAR and Plan MIDENO proposed providing credit to informal and formal groups. In this case it was the group, rather than the individuals, who received the loan and all members were jointly liable. As the program was experimental, only single-season loans were made at first.

9.31 The experiment has been successful, and very popular. Since its inception in the 1985 crop year, the number of groups participating has grown steadily from 23 to 60 in 1986, to 207 in 1987, and to 363 in 1988. The number of maize packages distributed through group loans has risen from 728 in 1985 to over 5000 in 1988. But perhaps most importontly, the repayment rate has been remarkably good: "almcst 100 percent" in 1985 (MIDENO. 1986: 12), over 98 percent in 1986 (MIDENO, 1987b: 9) and it is estimated that similar rates will prevail in 1987 (MIDENtO, 1988b: 7; personal interview) As a iesult of these enco-.raging resuslts, the group loan program was expanded in 1987 to include multi-year credit lines for group potato storage and group-manageds mechanized corn mills. 2.32 It should be noted that although there was no specific targeting of - food crop loans to women's groups, this is, in fact, what happened. Table 6 - which shows loans disbursed in 1986 -- illustrates the importance of women in the groups receiving loans. About half the groups that received loans throughout the province were comprised solely of women or had a single male (defined as "women-dominated groups" in the table) and women outnumbered men as members of mixed-gender groups. Overall, more than 90 percent of all group members were women and there were no male-only groups.

Table 6: GrOUp Food Croy **Loans** bZ GCnder. 1987

||||Groups||Membe|rs<br>in**All**<br>**G**|**roups**|
|---|---|---|---|---|---|---|---|
|Division|Distribution<br>Center|Total<br>**Number**|**_No._**of Women<br>Dominated*|<br>Percentage|**Total**<br>**Number**|Number of<br>**Women**|<br>Percentage<br>ofWomen|
|**Bui**|Kumbo|63|32 (6)|50.8|2877|2651|**92.1**|
|Donga-M-ntung|**Mbiyeh**|45|36 (a)|80.0|2491|2374|**95.3**|
|Menchum|Njinikom|21|14 (3)|66.7|1143|1102|**96.4**|
|**Me:Zm**|Nkven|**40**|7 (2)|17.5|1639|1349|**82.3**|
||**Santa**|37|11 (3)|29.7|1283|1061|**82.7**|
|Mezam Subtotal||77|**18**(5)|**23.4**|2922|2410|**82.5**|
|Momo|Nyon|**1**|0 (0)|**0.0**|45|42|**93.3**|
|TOTALS||**207**|100|**48.3**|9478|8579|**90.5**|

Source: FONADER, B-menda Note: * Women **dominated** groups **are** those with 0 or 1 **_male_ members. Nubers in** parentheses **indlcate** the number of **groups with** 1 **male mb-er.**

# AdaRtive Research

2.33 In a region with as much agro-ecological diversity as the North West Province, the ability to take recommendations developed by research stations and
to education corresponding to the higher _θ_<sup>ˆ</sup> 0 curve must be lower. As discussed in some detail above, returns to education are lower for sons in both India and China which implies that the upper curve must refer to the sons. This implies that if the estimated intercepts of the investment equation do not vary across gender, then the evidence would imply that the parents underestimate a girl’s cognitive ability, and/or the girls face significant genderspecific constraints in school.

# **(3.4) Discussion**

Many existing studies on intergenerational educational mobility in developing countries share two features. First, all of the published empirical studies on intergenerational educational mobility we are aware of use a linear intergenerational persistence equation. Second, the implicit theoretical model assumes that the primary source of intergenerational link in educational attainment is financial investment by parents in children’s schooling, with special emphasis on the roles of credit constraint and returns to education. Although a primary focus of Becker-Tomes (1979, 1986) model was the implications of optimal parental financial investment on children’s education for intergenerational transmission of inequality, they also emphasized the link across generations due to factors such as cultural capital of a family. Following Becker et al. (2015, 2018), the cultural capital is represented by the direct effect of father’s education on son’s schooling.

The theoretical analysis above clarifies the assumptions implicit in the linear CEF used almost universally in the empirical literature on intergenerational educational mobility. A linear intergenerational persistence equation implies that _ψ_ 2<sup>_j_=</sup><sup>_θ_</sup> 1<sup>_j_</sup> � _δ_ 5<sup>_j−δ_</sup> 2<sup>_jθ_</sup> 1<sup>_j_</sup> � _− δ_ 4<sup>_j_= 0.We</sup> have _ψ_ 2<sup>_j_= 0 when there are no diminishing returns or complementarity in the human capital</sup> production function; i.e., _δ_ 5<sup>_j_=</sup><sup>_δ_</sup> 2<sup>_j_=</sup><sup>_δ_</sup> 4<sup>_j_=0.Wecallthisthe“linearmodel”,astheCEF</sup> is _necessarily_ linear in this case. It is easy to check that in the linear model, parental bias against girls irrespective of the form it takes does not affect relative mobility (as measured by intergenerational regression coefficient IGRC), its effects are captured by the intercept of the linear CEF alone. However, we can also have a linear CEF as a special case of the more general quadratic model; this happens when, as a matter of chance, the convexity due to complementarity approximately cancels out the diminishing returns. When estimating 13
across-days preference spillovers. In particular, we provide evidence that recent earnings are negatively correlated with participation, which is suggestive of the existence of a weekly target on catches. Finally, Stafford (2015) looks at fishing in a highly regulated environment such as Florida, while we look at fishing in a developing country with few and weakly enforced regulations.

Other recent contributions to the literature are Goldberg (2016) and Dupas et al. (2015). These papers implement field experiments to study labor supply decisions also in developing countries. Our context is however different from theirs. Subjects in these experiments had limited access to credit and insurance markets and the study period lasted only several weeks. In addition, Goldberg (2016) cannot study the impact of consecutive days of work nor the impact of recent earnings because individuals are only offered work once a week. In our context, temporary increases in earnings are uncorrelated with effort, but in Dupas et al. (2015) higher expected earnings come from a larger expected number of trips rather than an increase in the hourly rate, and thus higher expected earnings are always associated with higher expected effort.

Our paper contributes to this literature by providing evidence that the standard neoclassical model is statistically rejected, since recent earnings are negatively correlated with labor participation. However, the low relative magnitude of these effects suggests that the neoclassical model is still a good approximation of the labor supply behavior of boat owners in coastal southern India.

The rest of the paper is structured as follows. Section 2 describes the institutional environment and data used. Section 3 describes the conceptual framework and empirical strategy. Section 4 discusses the results, Section 5 reports robustness checks and finally Section 6 provides some discussion and concluding remarks.

# **2 Background and Data**

We use a sample of 249 boat owners from seven villages along the coast of the southern region of the Gulf of Bengal in the state of Tamil Nadu. Figure 1 illustrates the exact location of these villages.<sup>11</sup> Boat owners belong to a Catholic fishing community, which following the precepts of their religion, usually work from Monday to Saturday and rest on Sunday to attend mass. On a typical night, boat owners go to sea around 1 A.M. together with a crew of three or four laborers. Before leaving, each boat owner decides the location and the type of fishing nets to 1129 boat owners are located in the village of Idindakarai, 24 in Kootapanai, 15 in Manapad, 50 in Patnam, 41 in Periyathalai, 27 in Thomayar Puram, and 63 in Uvari.

5
<!-- Start of picture text --> We begin with an illustration of the mapping algorithm that we have used for this exercise. Figure 1<br>range includes areas in Argentina, Chile, Bolivia and Peru. Figure 1(a) presents an expert range map for<br>Lagidium viscacia developed by Burgin et al. (2020). Figure 1(b) shows the locations of the GBIF occurrence<br>reports used for our mapping exercise. The two figures show that reported sightings of this species include<br>the Burgin map but extends its northern and southern boundary areas to incorporate these sightings.<br>The mapping algorithm is a byproduct of recent research on machine-based pattern recognition, cluster<br>analysis and image processing. Formally, it addresses a problem in computational geometry: efficient<br>bounding of a spatial set, given a subset of points that are actually observed. Sets with irregular shapes<br>are poorly represented by traditional algorithms that draw simple convex hulls.<br>continuous non-convex boundaries for efficient representation. This powerful property has motivated<br>rapid adoption of alphahull for species range analysis in the recent professional literature (Kass et al. 2022;<br>Of the 610,694 species in our database, alphahull successfully estimates occurrence maps for 92.9%<br>occurrence reports into spatial clusters and draw a convex hull around each cluster. Overall, our algorithms<br>estimate occurrence maps for more than 560,000 terrestrial species and more than 41,000 marine species.<br>To our knowledge, this is the largest set of species maps that have been estimated from open-source data.<br>Our estimation algorithms are designed to support expansion of GBIF species maps with the continuing<br>As an illustration, Figure 1(c) displays the algorithm’s application to GBIF occurrence reports for Lagidium<br>viscacia. The alphahull boundary follows the curvilinear north-south orientation ofthe point set, widening<br>Burgin et al., while extending it to incorporate new occurrence information.<br><!-- End of picture text --> <!-- Start of picture text --> This algorithm is a function in the R programming language.<br>Two other powerful features of alphahull are not illustrated here, but play an important role in the production of<br>The second is production of separate bounded areas for point clusters that are distant from one another.<br><!-- End of picture text -->
|Northern Corridor Transport Improvement Project|Josphat O. Sasia|
|---|---|
|Northern Corridor Transport Improvement Project|Josphat O. Sasia|
|Northern Corridor Transport Improvement Project|Josphat O. Sasia|
|Northern Corridor Transport Improvement Project|Josphat O. Sasia|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|Beira RailwayProject|HenryDes Longchamps Deville|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|NWFP CommunityInfrastructure Project II(NWFP CIP2)|Kevin John Crockford|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|Vilcanota ValleyRehabilitation and Management Project|Ellen Hamilton|
|VN-Second Payment System and Bank Modernization Project|NancyChen|
|VN-Second Payment System and Bank Modernization Project|NancyChen|
|VN-Second Payment System and Bank Modernization Project|NancyChen|
|VN-Second Payment System and Bank Modernization Project|NancyChen|
|VN-Second Payment System and Bank Modernization Project|NancyChen|
countries. I then drop 12 countries for which there are remaining gaps in the government spending data. This results in a final sample of 29 countries reported in Table 1. Given that I am focusing on the most aid‐dependent countries in the world, it is not very surprising that the majority of countries in my sample are located in Sub‐Saharan Africa. The only four exceptions are Bolivia, Morocco, Tunisia, and Jordan. For these 29 countries, World Bank spending is large not only as a share of GDP, but also as a share of total government spending. The time‐averaged share of World Bank‐financed spending in total spending ranges from a low of 3.3 percent in Jordan to a high of 18.5 percent in Uganda, and averages 9.3 percent.

Merely restricting attention to countries that are large recipients of World Bank loans on average is however not enough to ensure that fluctuations in disbursements on World Bank loans are large – it could for example be the case that countries receive a large but steady flow of project approvals, leading to a large but stable flow of disbursements. Fortunately for my purposes, this is not the case, as disbursements on World Bank loans fluctuate significantly over time. Table 2 documents summary statistics on the magnitude of fluctuations in the four measures of disbursements on World Bank projects, ∆��� , as well as total government spending, ∆�� and output growth, ∆�� , pooling all country‐year observations in my sample. Fluctuations in total disbursements are quite substantial, with a standard deviation of 1.4 percent of GDP, and of 1.1 percent of GDP for disbursements on previously‐ approved projects. By way of comparison, the standard deviation of GDP growth rates is 3.9 percent in this sample, and the standard deviation of changes in total government spending is 2.9 percent.

# **4.  Estimates of the Government Spending Multiplier**

# **4.1  Basic Results**

My benchmark estimates of the government spending multiplier are reported in Table 3. The top panel reports reduced‐form regressions of growth on changes in the four measures of disbursements on World Bank loans: (1) total disbursements, (2) disbursements on previously‐approved projects (excluding disbursements on projects approved in the same year), (3) disbursements on previously‐approved projects (excluding disbursements on projects approved in the same year _and_ the previous year), and (4) predicted disbursements. The estimated slope coefficients in the reduced‐form regressions are small in absolute value, ranging from ‐0.24 to 0.14, depending on the measure of 13
# **Table 7: Distribution of Benefit-Cost Ratios of Various Selected Interventions in** **<u>Ghana, Ghana Priorities</u>**

|||<br>|Quantile<br>|||
|---|---|---|---|---|---|
||0.10<br>(1)|0.25<br>(2)|0.50<br>(3)|0.75<br>(4)|0.90<br>(5)|
|BCRs, all interventions|1.2|1.7|3.3|8.0|34.0|
|BCRs, w/o health and nutrition|1.2|1.6|2.5|5.1|9.0|
|interventions||||||

_Source:_ Information obtained from the Ghana Priorities project. See <u>https://www.copenhagenconsensus.com/sites/default/files/ghana_outcome_doc.pdf</u> (Accessed January 28, 2022).

_Note_ : BCR = benefit-cost ratio.

# **5 Conclusion**

We have estimated benefit-cost ratios for spending on public schooling in Ghana. The measures of public schooling we look at are the quantity of schooling in terms of years of schooling completed, as well as gains in test scores from selected interventions in public schools. Although imprecise, we refer to these interventions as quality improvements. Our motivation is to consider both the value for money that Ghana gets for this spending and to compare the benefit-cost ratios to similar ratios for a wide variety of public spending options. In relation to quality improvements, we have limited our analysis to studies that have good prospects for identifying the impact of a particular type of spending on test scores. There are few of these, covering only variations of remedial teaching methods in primary school and school feeding. Clearly, interventions that yield quality improvements involve much more than this, an argument for further experimental studies on what produces good schooling outcomes in Ghana. For the quantity of schooling, there is only one as yet incomplete experimental study, so we resort to a standard approach to the problem, Mincer labor earnings regressions.

In general, we find that the benefit-cost ratios are greater than one, so public spending on schooling in Ghana provides good value for money. We also find that several different types of remedial teaching methods have considerably higher benefit-cost ratios than does providing an additional year of schooling at any level. This suggests that the returns to improving the teaching of students already in school may be higher than getting more students in school, an uncomfortable policy dilemma, particularly in light of the fact that children not currently in 36
health surveys and learning-outcome surveys. The tracking of environmental indicators requires improvements both at the national and local level if progress is to be made. It will also be important to put in place a mechanism for collecting and analyzing qualitative information. Innovative approaches are needed to assess progress in areas defined as priorites by Government but that are not currently addressed through household surveys or administrative reporting systems. This includes information related to vulnerability, governance, domestic violence and the local level business climate. Outcome indicators are not yet well integrated into a framework that clearly demonstrates the hierarchical links between inputs, outputs, outcomes and impact and more research is needed to assess what inputs (policies and public spending) are needed to reach the defined outcomes. It will also be essential to link the analytical work to the policy-making process, which will require better networking and interaction among ministries research institutes and universities. And, lastly, strengthening provincial level capacity to analyse trends and causes of poverty will be essential given the substantial decision-making autonomy that exists at the provincial level.
Policy Research Working Paper 6025

# **Abstract**

This paper provides the first analysis of the Global Financial Inclusion (Global Findex) Database, a new set of indicators that measure how adults in 148 economies save, borrow, make payments, and manage risk. The data show that 50 percent of adults worldwide have an account at a formal financial institution, though account penetration varies widely across regions, income groups and individual characteristics. In addition, 22 percent of adults report having saved at a formal financial institution in the past 12 months, and 9 percent report having taken out a new loan from a bank, credit union or microfinance institution in the past year. Although half of adults around the world remain unbanked, at least 35 percent of them report barriers to account use that might be addressed by public policy. Among the most commonly reported barriers are high cost, physical distance, and lack of proper documentation, though there are significant differences across regions and individual characteristics.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at Ademirguckunt@worldbank.org and lklapper@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