rafmacalaba/datause-encoder-data huggingface dataset:
# 1. **Introduction**

Since the creation of the Emergency Social Fund (ESF) in Bolivia in late 1986, social funds have been established in dozens of countries, often with support from multilateral organizations and international donors. Social funds like the ESF were originally put in place to mitigate the social costs of structural adjustment programs (for example, Newman, Jorgensen and Pradhan, 1991; Jorgensen, Grosh, and Schachter, 1992). Since then, they have been proposed, amongst other things, as a safety net for the poorest poor; a means of generating employment and transferring income; an efficient mechanism to construct small-scale infrastructure, especially in outlying, traditionally neglected areas; and a way of building on (or generating) local social capital by involving communities in project choice, preparation, and operation and maintenance (for example, World Bank, 1997; IDB, 1997).

This paper analyzes the targeting and impact of investments made by one social fund-the Peruvian Social Fund (FONCODES) -between 1992 and 1998. Specifically, the paper looks at the investments FONCODES made on education. In so doing, the paper aims to answer two questions. First, who received FONCODES transfers? This is, essentially, a question of targeting. FONCODES aims to transfer resources, including investments in education, to poor areas and to poor households within those areas. The paper evaluates the extent to which it has been successful in doing so. Second, did FONCODES transfers improve educational outcomes? This is, essentially, a question about the impact of investments in school facilities on measures such as school attendance rates, the likelihood of being at an appropriate grade level for a given age, and the average amount of time it takes children to get to school.

There are at least two reasons to focus on expenditures made by FONCODES on education, rather than on total FONCODES expenditures. First, education is one of the two biggest sectors in the FONCODES portfolio, accounting for about one-quarter of total FONCODES expenditures made between 1992 and 1998. Moreover, as Table 1.1 shows, the bulk of these expenditures on education was made in the first few years of FONCODES operations, so that measurable changes in outcomes such as school attendance have had some time to take place (although the full benefits of investments in education may not be realized for years). An analysis of FONCODES investments in education therefore deserves attention in its own right. Second, it is possible to use available data sources to construct credible outcome measures in education; this is not easily done in other sectors, such as agriculture, transportation, or even health.<sup>2</sup>

> 2 Initial inspection of the data on health in the 1994 and 1997 Peru LSMS, for example, suggests that infant mortality went up during the period. This seems quite implausible given other changes which took place in Peru between 1994 and 1997, and is likely driven by the very small number of reported deaths in both

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estimates of returns to education in urban India, please see the first two columns of Table T4.I. We focus on the returns to education at secondary (10 years of schooling) and higher secondary (12 years of schooling) levels, given that the average education in urban India in our data set is 9.93 years for the sons, and 9.43 years for the daughters.<sup>25</sup> Since _R_<sup>_cd_</sup> _> R_<sup>_cs_</sup> , inequality (17) is satisfied even if we assume that _δ_<sup> ̃</sup> 2<sup>_dσd_=</sup><sup>_δ_ ̃</sup> 2<sup>_sσs_.Clearly,thislastequalityis</sup> consistent with no gender bias by the parents.

Turning to the intercept estimates from the investment equation, the theory implies the following relation: An immediate observation is that it is not possible to satisfy (18) if there are no gender biases (i.e, if _δ_<sup> ̃</sup> 1<sup>_d_=</sup><sup>_δ_ ̃</sup> 1<sup>_s_,</sup><sup>_δ_ ̃</sup> 2<sup>_d_=</sup><sup>_δ_ ̃</sup> 2<sup>_s_,and</sup><sup>_σd_=</sup><sup>_σs_),because</sup><sup>_Rcd> Rcs_.</sup> The fact that there is no significant difference in the intercepts implies that urban India corresponds to the case depicted in Figure F1.IU in section 2.3 above. An inspection of Figure F1.IU shows that the returns to education must be smaller for the curve with higher ( _φ_<sup> ̃</sup><sup>_j_</sup> _unexpected_<sup>_′′_</sup> _inmath_ ) and ( _q_<sup>_j_</sup> ). Since the returns to education is smaller for the sons (see Table T4.I), the higher curve must refer to the sons, implying that the parents have systematically higher ability estimates for the sons, and/or the girls face substantial hurdles in the schools. It is important to note that these conclusions hold irrespective of the value of _δ_ 5.

The above analysis of the mechanisms of gender bias in the observed educational investment in urban India is based on the assumption that the parents are aware of the fact that returns to education are, in fact, higher for the daughters. One might wonder what are the implications of the alternative assumption that, despite the evidence, the parents believe that the returns from education are lower for daughters. If _R_<sup>_cd_</sup> _< R_<sup>_cs_</sup> , then an

> non-market sources are even higher. Thus the expected total returns for women are likely to be higher than that suggested by the estimates of labor market returns.

> 25Most of the estimates of returns to education in India are based on NSS data, which do not provide years of schooling information, only the highest completed level (primary, secondary etc).

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POLICY RESEARCH WORKING PAPER 2334

# Summary findings

The practice of safe sex by commercial sex workers is considered central to preventing the transmission of AIDS in developing countries. Rao, Gupta, and Jana estimate the compensating differential for condom use among sex workers in Calcutta, based on results from a survey conducted in 1993.

If, as suggested by anecdotal evidence, this loss in income is large, it would indicate the existence of strong disincentives for practicing safe sex.

To identify the relationship between condom use and the average price per sex act, they follow an instrumental variable approach, exploiting an intervention program focused on providing information about the AIDS virus and about safe sex practices. The program, instituted in 1992, was not systematically administered.

Using this method, they found that sex workers who always use condoms face a loss of 79 percent in the average earnings per sex act.

- - This paper a product of Poverty and Human Resources, Development Research Group is part of a larger effort in the group to understand the behavior underlying HIV/AIDS transmission. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-4733902, fax 202-522-1153, email address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Vijayendra Rao may be contacted at vrao@worldbank.org. May 2000. (18 pages)

_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 necessanily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
the Government of Ethiopia, working closely with Wageningen UR, CGIAR, and other partners, has experimented with seed market liberalization to allow firms to market seed directly to farmers rather than through the state’s rural administrative apparatus. This Direct Seed Marketing (DSM) approach aimed to increase and sustain the supply of improved varieties and quality seed to farmers while rationalizing the use of public resources allocated to the distribution of seed to fragmented, spatially dispersed, resource-poor smallholders that make up the vast majority of farmers (MoA/ATA 2016).

Since DSM’s launch in 2011, several governmental entities—the Agricultural Transformation Agency (ATA), the Ministry of Agriculture (MoA), the regional bureaus of agriculture, development projects such as the Integrated Seed Sector Development (ISSD) initiative—have promoted and closely monitored its implementation. Benson, Spielman, and Kasa (2014) conducted an operational evaluation of the pilot DSM program and found encouraging results, ultimately recommending that the government stay the course and advance the seed sector reform process. Mekonen et al. (2019) conducted a descriptive analysis of DSM’s key performance indicators, including seed availability, quality, sufficiency, price competitiveness, supply timeliness, and accountability. Their analysis documented mixed results, including differences in performance across regions and crops.

However, to date, there has been no rigorous assessment of DSM’s impact on the amount of fresh seed purchased by farmers, a first-order performance indicator. Nor has there been any assessment of DSM’s impact on key outcomes, such as changes in farm-level productivity. The availability of three rounds of household-level panel data from DSM and non-DSM _woredas_ (districts) in 2012 (the beginning of DSM’s pilot phase), 2016, and 2019 (after the program had expanded to more than two hundred districts) provides an opportunity to fill this gap in the evaluative literature on DSM.

This study exploits the staggered scale-up of the DSM approach over time, across districts, and in crop coverage. Using a quasi-experimental difference-in-differences econometric approach, the study finds that DSM led to a 14-percentage-point increase in the proportion of farmers purchasing maize seed, a 45 percent increase in the quantity of maize seed purchased per hectare, and an 18 percent increase in maize yield. However, the effects of DSM on seed purchase and yields are not statistically significant for wheat. The results suggest caution in scaling the DSM approach, 3
2007; Beck et al, 2008a; Brown et al, 2009; Liberti and Mian, 2010; Love et al, 2013; among others).

While cross-country evidence on the drivers of bank financing for SMEs is extensive, detailed information for Sub-Saharan Africa remains limited. We use new data from bank surveys for a total of 62 commercial banks in four countries to analyze the extent, drivers, and obstacles to bank involvement with SMEs. The data collected through bank questionnaires is complemented with qualitative information obtained through interviews with bank officials. The surveys are comparable to previous studies analyzing the determinants of bank financing for SMEs in other regions (World Bank, 2007a; World Bank, 2007b; Beck et al., 2008b; Stephanou and Rodriguez, 2008; De la Torre et al., 2010; Rocha et al., 2010). While bank-by-bank data is only available for four countries – Kenya, Nigeria, Rwanda, and South Africa – aggregate data was collected and bank interviews were also held in Tanzania.

The analysis shows that the share of SME lending in the overall loan portfolios of banks varies between 5 and 20 percent, with banks in Kenya, Rwanda, and Tanzania being more involved with SMEs in terms of the share of their loan book going to SMEs than banks in South Africa and Nigeria. Based on the data collected as well as on the information collected in the indepth interviews, important contributing factors are the structure and size of the economy and the extent of government borrowing, the degree of innovation as regards SME lending, and the state of the financial sector infrastructure and enabling environment including Government support programs.

The evidence suggests that competition, especially as introduced by innovators, is important to encourage banks to venture into the SME space and to move them out of their comfort zone in countries like Nigeria where high interest rates on Government securities are a disincentive to intensify lending to SMEs. Providing a conducive lending environment and encouraging competition through allowing banks to innovate, for instance through agency or mobile banking, seems to be an important role to play for Government. Equally important is to ensure that an effective credit bureau is in operation, and that the securitization and realization of (movable) collateral is efficient. Nevertheless, while a conducive legal and regulatory framework is necessary to support SME lending over time, it is not sufficient and growth can happen even if the enabling environment is still developing, as is the case in Kenya.

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Note that _m_ corresponds to the minimum number of richest individuals that the government should target for collecting enough revenues to fill the poverty gap. A straightforward index of income lever can therefore be defined as the proportion of taxed individuals on the total number of non-poor individuals: the highest this proportion, the heavier the income lever: Consider the following example. Let us assume that society _A_ has a sum of poverty gaps _G_<sup>_P_</sup> _A_<sup>=7andthattheincomedistributionamongrichindividualsis(6,7,8,9,10,11).The</sup> following matrix shows the stepwise procedure.

|steps||indiv|idua|l inc|omes||_G_<sup>_P_ </sup>-Revenues|
|---|---|---|---|---|---|---|---|
||_y_1|_y_2|_y_3|_y_4|_y_5|_y_6||
|0|6|7|8|9|10|11|7|
|1|6|7|8|9|10|10|7-(11-10) =5|
|2|6|7|8|9|9|9|7- [(11-10) + 2(10-9)] =4|
|3|6|7|8|8|8|8|7- [(11-10) + 2(10-9) +3(9-8)]|
|4|6|7|7|7|7|7|7- [(11-10) + 2(10-9) +3(9-8) + 4(8-7)]=-3|

The recursive process in the example ends at step 4: the first four richest individuals are taxed out of 5 non-poor individuals: _IL_<sup>_progB_</sup> _n_<sup>1</sup> =<sup><u>4</u></sup> 5<sup>.</sup> Note that this procedure mimics what it is done in Medeiros (2006) in order to define endogenously an affluence line, which can be considered by itself as an indicator of income lever: the highest the affluence line, the smaller the income lever. In particular, given _m_ as defined in equation (15), the affluence line _aN_ can be defined as: In the example given above, for instance, the affluence line _aA_ would be 7+<sup><u>3</u></sup> 4<sup>= 7</sup><sup>_._75.</sup> Instead of using the affluence line, we may think of using the ratio between the poverty line and the affluence line, so that the index becomes scale invariant: 18
## 3. Impact of migration on the destination countries in the South

Unlike commonly believed, around half of the official international migration from the South is to other developing countries rather than wealthier countries in the North (Ratha & Shaw 2007). And official statistics likely underreport South-South migration, especially between contiguous countries because of lack of border controls; almost 80 percent of the South-South migration is estimated to take place between countries with contiguous borders.

# **_3.1 Economic implications in the destination country_**

Even though the channels transmitting welfare impacts of migration on the destination countries are well known in the literature, there is very limited amount of empirical evidence from the South quantifying the impacts. Thus, a brief overview of the evidence from the North is presented in box 1. Migration of people, just as international trade, benefits both the sending country and the receiving country (van der Mensbrugghe & Roland-Holst 2009). The welfare gain for the destination country is due to the fact that immigration increases the supply of labor, which increases employment, production and thus GDP (Ortega and Peri 2009). Immigration has also been found to increase the productivity of the receiving economies through the contribution of migrants to innovation (see box 1). Another way in which immigration increases productivity is that immigrants free up the local workforce to move to higher productivity occupations.

Despite the benefits of immigration, public and the policymakers at the destination country usually believe that immigration can become an economic burden _,_ as immigration is feared to lead to loss of jobs, heavy burden on public services, social tension and increased criminality (UNDP 2009, p. 70) despite the evidence to the contrary.<sup>13</sup> The main channels for negative economic outcome for the destination countries are increased job competition that allegedly brings down the wages for the locals, and the increased fiscal burden for caring for a growing population of immigrants.

> 13 As noted by Hanson (2009), the economic benefits for allowing freer immigration to destination countries ―may simply not be worth the political hassle‖.

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# References: Recent Studies Using Firm-Level Survevs

Claudio Frischtak. _Adjustment and Constrained Response. Malawi at the Threshold of Sustained Growth_ (Industry and Energy Department Working Paper Industry Series Paper No. 41, October 1990) Ahmed Ghalal. _Egypt. Private Sector Regulatory and Incentives Study_ (Unpublished, CECPS, 1991) Brian Levy. _Obstacles to Developing Small and Medium-Sized Enterprises: An Empirical Assessment_ (Policy Research and External Affairs Working Paper 588, World Bank, 1991) Ron Parker and William Steel _Small Enterprises under Adjustment in Senegal_ (IED: Draft, 1992) Enrique Rueda-Sabater and Andrew Stone. _C6te d'Ivoire: Private Sector Dynamics and Constraints_ (CECPS, 1992) Mark Schankerman and Andrew Stone. _Shackles or Shillings? Constraints to Private Sector Development in Kenya_ (Unpublished, CECPS and AFTIE, 1992) Andrew Stone, Brian Levy and Ricardo Paredes. _Public Institutions and Private Transactions: A Comparative Study of the Legal and Regulatory Environment for Business Transactions in Brazil and Chile_ (World Bank, Country Economics Department Policy Research Working Paper Series, No. 891, April 1992) William F. Steel and Leila M. Webster. _Small Enterprises underAdjustment in Ghana_ (World Bank Technical Paper Number 138, Industry and Finance Series, 1991) The author would like to acknowledge the contribution of Brian Levy to his understanding of this field; to Mary Shirley, William Steel, Alice Hill and Junaid Ahmad for helpful comments on earlier drafts; and to Polly Means for the graphic on page 15.

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<!-- Start of picture text --> Figure 5<br>INVESTMENT RATIOS IN EEC AND OTHER OECD COUNTRIES<br>29<br>27 +<br>25<br>23 N<br>21<br>19<br>1950 1955 1960 1965 1970 1975 1980 1985<br><!-- End of picture text -->

* EEC-6 C1950-72); EEC-9 (1973-80); EEC-10<sup>**_(1961-85);_**EEC-12(1986-88)</sup> + OECD COUNTRIES LEFT.

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Adjusting for this suggests that the estimates of the share of the variance of growth in social welfare due to growth in incomes reported in Table 2-Table 4 are likely to be lower bounds.

# **4.4  More General Social Welfare Functions and Generalized Lorenz Dominance**

In all of our results thus far, we have relied on specific social welfare functions in order to be able to explicitly measure the contribution of inequality changes to growth in social welfare. In particular, the benefit of this approach is that it allows us to express inequality changes in terms of growth in average incomes, which in turn leads to straightforward variance decompositions. Although we have considered a variety of common social welfare functions, a drawback of this approach is that our conclusions do depend on the specific functional form of the selected social welfare function. And this in turn raises the possibility that there might be other social welfare functions for which the contribution of growth to improvements in social welfare is much smaller (or larger) than for the ones we have considered.

To partially address this concern, we draw on the concept of generalized Lorenz dominance to determine the _direction_ , although not the magnitude, of welfare changes for a much broader class of social welfare functions than those considered here, over the same set of spells. Shorrocks (1983) shows that as long as the social welfare function is increasing and concave in incomes (i.e. the social welfare function prefers more income to less, and less inequality to more), then social welfare is unambiguously higher if and only if the growth rate of cumulative average income of each percentile of the population is positive. In our case where we focus on decile grouped data, this corresponds to the case where growth in average incomes of the first decile, the first two deciles, etc. all the way up to growth in overall average incomes, are all positive over a given spell. If this is true, then social welfare will have improved over this spell for any social welfare function that satisfies the minimum requirements of being increasing and concave in incomes.

To implement this, we divide our sample of spells into those with positive average growth rates and those with negative average growth rates. In the first group, we count the number of spells where the final distribution in each spell generalized-Lorenz-dominates the initial distribution. In the second group, we do the opposite, counting up the number of spells where the initial distribution (before the negative average growth experience) generalized-Lorenz-dominates the final distribution. In both cases, these correspond to cases where positive (negative) growth unambiguously raised (lowered) social welfare for any increasing and concave social welfare function.

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by 3.6% in nominal tenns and 12.1% in real terms.<sup>7</sup> However, as Reyes et al. acknowledge, even these before-after comparisons are problematic for the Philippines due to non-comparability of the income and consumption modules across the two surveys (see below for details). The before-after comparisons also run into the problem of a misspecified counterfactual. Even for a systemic shock, "before" estimates may not be a good approximation of the estimates "in the absence of a shock".

A different approach has been used for Thailand and Korea (Kakwani 1998, Kakwani and Prescott 1999) where the counterfactual level of an indicator of interest is constructed by obtaining a predicted value from past trends of the indicator up to the crisis. Thus, if _y,_ is the value of, say, the poverty indicator in the crisis period t, and y, is its predicted value based on past trends, then a crisis index for the poverty indicator is defined as ((y, _/yt) - 1)_ and it measures the percentage change in poverty due to the crisis. This approach, though unimplementable for the Philippines for lack of comparable post-crisis distributional data, is also methodologically problematic on two counts. First, it is not clear over what period should one estimate trends prior to the crisis, particularly so for a country such as the Philippines which has had a checkered history of booms and busts (Lim 1998). In the end, the choice of the estimation period often becomes an arbitrary expedient of data availability. Second, since the counterfactual is constructed using unconditional trends, the approach attributes a 100% of the departure from trend to the crisis, thus making no allowance for changes in other non-crisis determinants of living standards.

7There were some studies done before the APIS data became available in 1999. For instance, Reyes and Mandap ( 1999) used an existing CGE model to simulate the likely impact of the crisis on incomes. They found that the crisis would lead to a fall in average incomes of all deciles and an increase in the Gini ratio. No attempt was made to separate the El Nifno from the financial crisis effects, nor were there any simulated effects on consumption. There were also studies undertaken by the World Bank and UNDP, and by Lim (June 1998). These studies were done shortly after the financial crisis and in the midst of the El Niflo drought, and had to rely on secondary data to explore anticipated rather than actual effects.

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