rafmacalaba/datause-encoder-data huggingface dataset:
| POLICY RESEARCH WORKING PAPER 2798

# Abstract

Filmer empirically explores the relationship between household poverty and the incidence and treatment of fever-as an indicator of malaria-among children in Sub-Saharan Africa. He uses household Demographic and Health Survey data collected in the 1990s from 22 countries in which malaria is prevalent.

The analysis reveals a positive, but weak, association between reported fever and poverty. The geographic association becomes insignificant, however, after controlling for the mother's education. There is some evidence that higher levels of wealth in other households in the cluster in which the household lives are associated with lower levels of reported fever in Eastern and Southern Africa. Poverty and the type of care sought for an episode of fever are significantly associated: wealthier households are substantially more likely to seek care in the modern health sector. In Central and Western Africa those from richer households are more likely to seek care from all types of sources: government hospitals, lowerlevel public facilities such as health clinics, as well as private sources. In Eastern and Southern Africa the rich are primarily more likely to seek care from private facilities. In both regions there is substantial use of private facilities-use that increases with wealth. Like the incidence of fever, treatment-seeking behavior is strongly associated with the level of wealth in the cluster in which the child lives.

This paper-a product of Public Services, Development Research Grou4p-is part of a larger effort in the group to understand the relationship between poverty and health. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hedy Sladovich, mail stop MC3-3 11, telephone 202-473-7698, fax 202-522-1154, email address hsladovich@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dfilmer@worldbank.org. March 2002. (37 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of idie.as about development issues. An objective of the series is toget 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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Research Advisory Staff
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# **Introduction**

Health systems are not just about improving health. Good ones also ensure that people are protected from the financial consequences of illness and death, or at least from the financial consequences associated with the use of medical care. Anecdotal evidence suggests health systems often perform badly in this respect, apparently with devastating consequences for households, especially poor ones and near-poor ones. The World Bank’s 50-country participatory poverty study known as _Voices of the Poor_<sup>1</sup> found that poor health and illness are universally dreaded as a source of destitution, partly because of the costs of health care but also the income lost due to illness. _Voices of the Poor_ documents the case of a 26 year-old Vietnamese man who, as a result of the large health care costs necessitated by his daughter’s severe illness, has moved from being the richest man in his community to being one of the poorest.<sup>2</sup> Also recorded was the case of a 30year-old Indian mother of four who has been forced to sell the family’s home and land, and has to walk 10 kilometers a day transporting wood on her head in order to finance the cost of her diabetic husband’s medical care.<sup>2</sup> How can one measure the success with which a health system protects people against the financial consequences of ill health? What do systems that do well in this regard have in common? And how far do health system reforms improve people’s financial protection vis-à-vis health expenses? This paper provides an overview of the methods and issues arising in each case, and presents empirical work in the area of financial protection in health, including the impacts of government policy. The paper also reviews a recent critique of the methods used to measure financial protection.
# **Table 6:  Comparison of estimated wage ratios of observably equivalent workers to Hall and Jones growth accounting estimates of the relative marginal product of human capital adjusted labor for 37 countries**

|Row||Median|Average|Avg.<br>without 5<br>outliers|
|---|---|---|---|---|
|I|Bilateral,regressionswitheducationcategories|3.11|4.39|4.04|
|II|Bilat. Mincer regressions geometric avg.ofyears, 9-12|2.92|4.10|3.79|
|III|Ratios of marginal product of human capital equivalent<br>labor, US to country|3.07|3.69|3.66|
|IV|Ratio ofUnited States _A_ to country _A_|2.44|2.71|2.86|
|V|Ratio of row I (preferred_Ro_estimate) to row III (growth<br>accounting estimate)|1.01|1.19|1.11|
|VI|Proportion of cross-national difference in human capital<br>adjustedlabordue to differencesin_A_ (ratioIVtoIII)|0.80|0.74|0.78|

Sources: Author’s calculations and Hall and Jones (1998) (Table 7), Hall and Jones (1999).

A second important implication of these results is that the large majority of the crossnational gap in marginal products of workers with equivalent human capital is due to productivity differences, not physical capital. This has consequences for the expected welfare gains of migration for people in the destination country: If the differences are mostly productivity (“ _A_ ”) and country-specific “ _A_ ” is a pure public good, then there is little “factor shallowing” effect of reducing wages for all existing workers by lowering the capital-labor ratio with labor inflows.

In fact, row IV of table 5 shows that the typical ratio of factor productivities (A) is 2.44, which is roughly 80 percent of the observed wage (or marginal product) ratio. Caselli (2005) reviews the literature on growth decompositions and shows that, in the standard models, it is typical for physical and human capital differences to account for less than 50 percent of differences in per-worker output. As Easterly (2004) points out, in “productivity world,” factors move to higher productivity locations, as opposed to “factor world,” in which places with scarce factors attract more factors. In “productivity world” the gains to movers are not offset by losses to existing residents.

# **4 Comparison with other literature**

The preceding section gives us the tools to plausibly estimate what fraction of the wage gaps we measure can be attributed to wage discrimination. We can then compare wage discrimination at the border to other forms of wage discrimination that have been observed in different labor markets. We go on to compare international price gaps for labor to those in goods and factor markets. Finally, we compare the potential gains from migration to other forms of poverty reduction policies or programs.

# 4.1 _Comparison with other forms of wage discrimination_

The diverse evidence reviewed in the previous section is notably consistent: Each piece suggests that the measured ratios (3) in Table 2 overstate the true ratio by a factor of 29
Table A.2: Productivity Decompositions and Policy Changes: Cost Shares Productivity

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.055|-.045|-.010|
||(.025)<sup>_∗∗_</sup>|(.028)|(.016)|
|Input Tariff|-.553|-.542|-.011|
||(.099)<sup>_∗∗∗_</sup>|(.107)<sup>_∗∗∗_</sup>|(.059)|
|FDI Reform|-.001|.023|-.024|
||(.010)|(.010)<sup>_∗∗_</sup>|(.008)<sup>_∗∗∗_</sup>|
|Delicensed|-.002|.003|-.004|
||(.015)|(.016)|(.011)|
|Obs.|17074|17074|17074|
|_R_<sup>2</sup>|.193|.158|.013|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. TFP is estimated using a chain-linked index number method, where input shares are calculated as the share of input cost in total cost.

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Net foreign direct investment inflows to Turkey increased to 3 percent of GDP, on average, in 2005-2008. As envisaged by some authors (Basar and Tosunoglu, 2006), the signaling caused by the official start of European Union (EU) accession negotiations in 2005 seems to have significantly helped increase FDI inflows. This is line with other emerging markets, as in Alfaro, Kalemli-Ozcan, and Volosivych (2008), who show that the most important determinant of FDI is a country’s institutional quality. It is also worth mentioning the modifications in the legal framework for foreign direct investment introduced in 2003, as well as increased macroeconomic and political stability as other contributing factors.

In the aftermath of the global crisis, FDI inflows to Turkey have dwindled, declining from 2.1 percent of GDP in 2010 to 1.6 percent in 2014, and in spite of the fact that the economy rebounded quickly from the slowdown in growth experienced in 2009. It is worth noting that this is partly due to the lower inflows to the financial sector, while FDI into utilities and manufacturing increased significantly (Table 2). It has been also argued that lower FDI inflows may be affected by slower than expected progress in the adoption by Turkey of the _acquis communautaire_ , allegedly seen as a proxy of institutional development, coupled with diminishing prospects of EU membership (Sánchez-Martín, Escribano, and de Arce, 2015). Concretely, the European Commission (2015), when relating the status of the Turkey’s membership process, explicitly mentions a deterioration of rule of law, accentuated in 2014, as one of the main challenges going forward. This could potentially hamper FDI inflows in the future.

**Table 2. FDI flows to Turkey by sector, as percentage of total**

||**2002-2004**|**2005-2008**|**2009-2013**|
|---|---|---|---|
|Primarysector(includingmining)|3.0%|1.2%|2.4%|
|Secondarysector|29.2%|17.2%|24.7%|
|Electricity, gas and water supply|8.6%|2.7%|24.7%|
|Construction|0.6%|1.5%|5.1%|
|Transport and communications|18.0%|21.1%|4.6%|
|Financial intermediation|15.6%|47.7%|26.3%|
|Other services|25.1%|8.5%|12.2%|
|Yearly average FDI flows in the period<br>(US$million)|905|11863|9921|
|Source: authors’ calculations based on Central B<br>data|ank of Turkey a|nd Ministry of t|he Treasury|

Why is foreign direct investment attraction so vital for developing economies? Contrary to other capital flows of short term nature, foreign direct investment is believed to be a stable source of 3
which the funds are distributed to families, distributing 2/3 of the funds immediately and the remaining funds at the time the students enroll in school. The third treatment, called the “tertiary treatment,” provides children with the same lower monthly subsidy as the savings treatment, but also pays a large subsidy that incentives both graduation and matriculation to an institution of higher education.

To allocate these treatments, we use an over-subscription model rather than the basic geographic allocation strategy used in most previous studies.<sup>3</sup> We staged a large recruitment drive and randomly allocated about 10,000 treatments to about 17,000 registered children. This model allows us to randomize at the child-level, generating variation within schools, families, and networks of friends. By pairing this randomization with detailed information on children’s siblings and friends, we are able to disentangle how these opportunities change the allocation of work in the household and the activities of the recipients’ peers.

Our research further makes two methodological improvements to previous studies of education-based CCTs. First, we collect attendance data through a series of school visits in order to avoid the self-reporting bias associated with the survey data used in most other studies of CCT models. Concerns about self-reporting biases are particularly important in this context. While subjects’ responses on these surveys have no implications for their participation or standing in the program, the subjects have been conditioned to value attendance and understand that their receipt of the transfers is determined by their rates of attendance. This could lead to a general upward bias in the reporting of attendance and could also lead to a differential bias by those most involved with the program – the treatment families. Second, we directly map students’ friendship networks, allowing us to assess directly the influences of peers on students’ attendance rates. Both of these methodological improvements prove to be important for our empirical analysis.

Our main findings are as follows. Taken together, all of the cash incentive treatments generate significant changes in the behavior of students directly treated by the program. Students are more likely to attend school (2.8 percentage points), more likely to remain enrolled (2.6 percentage points), more likely to matriculate to the next grade (1.6 percentage points), more

> 3 One exception is the system that the national Colombian government used to allocate school vouchers in the Program de Ampliacion de Cobertura de la Educacion Secunderia (PACES) program (see Angrist et al, 2002 and Angrist et al 2006).  While our use of this allocation strategy was a practical solution for conducting a student-level randomization, the intra-family and intra-friendship network variation enabled by this strategy have direct policy implications for this and other allocation mechanisms that partially families or groups of friends.

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to inclusion in commercial lending? Finally, what are the implementation conditions needed to promote additionality and effective targeting of MSME finance operations?

To answer these questions, we use administrative loan-level data from the KUR program itself, which includes information on over 8 million debtors between 2015 and 2020. Using the program database as a sampling frame, we complement the administrative data with surveys of a representative sample of 1,402 KUR borrowers as well as qualitative interviews with separate samples of 100 KUR borrowers and 100 nonborrowers. In sum, these data sources provide a rich and novel set of insights into the profiles, perspectives, and experiences of KUR borrowers since 2015 and shed light on the ways in which the publicly supported scheme plays out in supporting MSMEs on the ground.

Our main and novel finding is that the subsidized credit provided through KUR enhances financial inclusion by reaching previously excluded micro and small businesses; however, it does not serve as a stepping- stone to unsubsidized commercial lending. Prior to receiving a subsidized KUR loan for the first time, only 11 percent of borrowers had ever received a commercial loan from a bank. Although successful at reaching first-time borrowers, the longer-term impacts on their financial inclusion are uncertain because KUR borrowers do not move into unsubsidized commercial lending. Approximately half of KUR borrowers went on to receive KUR loans multiple times within a 5-year period, and only 3 percent of firms who received KUR financing graduated to other forms of commercial lending. Until recent regulatory changes, firms had little incentive to choose commercial sources of financing over KUR’s below-market interest rates, and banks preferred to encourage repeat lending to minimize risk.

We also identify key design features of the KUR scheme which pose challenges for inclusivity and consumer protection and hold important lessons for the design of public MSME finance support schemes. First, we find that although the KUR program targets firms with insufficient collateral, in practice, 96% of KUR borrowers submit collateral for loans, with collateral worth an average of more than four times the amount of their loan. De facto collateral requirements impede the program from reaching the target audience and hinder women’s access to KUR. Second, we find that the program’s implementation model through financial institutions has advantages in connecting borrowers to the financial sector, but also leads to heterogeneity in program communication and implementation, creating confusion for borrowers about their loan terms and conditions.

Although the study provides several policy-relevant findings, it relies on descriptive analysis complemented by qualitative insights. Due to data limitations, it was not possible to construct a valid counterfactual group

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encourage leaders to advocate publicly the selection of women contact farmers;

* emphasize selection of women farmers **in** training sessions for extension ages.;s.

- Increase the number of female contact groups. Women's groups, and to a lesser extent mixed groups, provide a cost-effective way of channelling and providing feedback on extension services, and offer women the means of overcoming social and educational disadvantages. Groups can be multi-functional, or can focus on a single activity: extension, credit, communal crop or livestock production, labor exchange.

- Monitor extension/farmer contacts by gender and set targets. Regular reporting with data disaggregated by gender will increase the awareness of extension agents and encourage them to address the issues of women farmers.

- Identify wgays to deliver extension-services to woMen ta overcome their constraints-cf time. mobility and lower educational levels.

Suggested approaches are:

- mobile training courses to overcome difficulties of leaving the

- village or family to attend residential training;

- use of mass media -- radio and video cassettes are promising adjuncts to face-to-face extension. They are an efrective, low cost way of transmitting agricultural information to farmers, and are particularly useful with illiterates. Local dialects should be used, and radio messages should precede visits from extension agents. Videocassettes can also be useful training tools, especially for low resource farmers. Viewing by farmers' groups is particularly cost-effective.

(4) Appoint Subject Matter Specialists (SMS's) with special resDonsibility for rural women. SMS's can serve particularly useful functions. They can diagnose women's activities and constraints, stimulate apprcpriate research and technology, develop recommendations, organize women's contact groups and broaden the technical training of extension agents to cover women's activities.

(5) Improve the approDriateness of the messages and technology for women farmers. An extension system is only as good as the technology offered. To develop messages and technology to better meet the needs of women farmers in Africa, the following are recommended:

- Identify the information and technology needs of women farmers by collecting information on women's farming activities to ideritify the needs, constraints and objectives of women farmers. Gather such information from three main sources: baseline socioeconomic
Our data show that household wealth and the outcomes of interest for this paper are strongly correlated. In Table 6 we report results for different outcomes measured in the 2009 round on respondents’ age and an indicator for household wealth above the median in the baseline survey. Household wealth is defined by an asset index using principle component analysis. Wealth is associated with a slower transition to adulthood. Relative to their poorer counterparts, young men and women of higher wealth are significantly more likely to report attending school in the endline survey. They are also less likely to be married, to ever be pregnant or to report ever having sex. The wealthier young women are also less likely to report being sexually active.

These trends observed in our data are consistent with findings in other studies. In a similar setting in southern Malawi, cash transfers that increase income have been shown to significantly delay school exit, marriage and fertility onset of young women in Malawi and to reduce prevalence of sexually transmitted diseases (Baird, McIntosh, and Özler 2012, Baird et al. 2012). In addition, several studies suggest that variation in income is linked to engaging in transactional sex -- sexual relationships motivated by material support to the female partner from the man (e.g. Burke, Gong and Jones 2012, Kohler and Thornton 2012 and Robinson and Yeh 2011).

Given this background, we could expect that one’s response to VCT may differ depending on household wealth. To that end, we complement our study with an ex-post analysis of heterogeneous effects of testing by household’s wealth. The original randomization in the test offer was a simple randomization with no regard to wealth status at baseline.

To explore heterogeneity in response to testing by household’s wealth, we perform the mean-comparison tests separately for individuals whose household wealth index in the baseline 15
Table F.1. Impacts of PEP on Socioeconomic Well-being

||Well-being<br>(Index)|Consumption<br>per Capita<br>(log)|Labor<br>Income (log)|Employed|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|1[PEPi = 1]|1.738***|0.469***|0.233***|0.178|
||(0.557)|(0.153)|(0.090)|(0.213)|
|q-values|[0.005]|[0.005]|[0.007]|[0.113]|
|Obs. left|338|538|252|425|
|Obs. right|612|1,210|594|832|
|Observations|3,385|3,762|1,791|3,386|
|Mean values (Non-RAMV refugees)|0.000|1.373|0.351|0.561|
|Outcome Level|Individual|Household|Individual|Individual|

_Notes:_ Dependent variables: (i) Well-being (Index) is constructed using the outcome variables of columns (ii)–(iv) using the methodology of Kling et al. (2007); (ii) Annual consumption (log) is the logarithm of annual consumption per capita in million COP; (iii) Labor Income (log) is the logarithm of the monthly labor income that includes wage, extra pay, and revenue from independent work in million COP; (iv) Employed is a dummy [=1] if reported as employed, and has a wage (includes independents and family workers). All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

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