rafmacalaba/datause-encoder-data huggingface dataset:
well as at least nine months of repayment history for each loan<sup>9</sup> . The information contained in each loan application can be grouped into the following categories, corresponding to the sections of the Lender’s standard application format: (1) basic client information including a detailed description of the client’s business, (2) documents and verification (3) balance sheet and (4) income statement. In addition, participants in the experiment had access to three types of background checks for each applicant: (5) a site visit report on the applicant’s business, (6) a site visit report on the applicant’s residence and (7) a credit bureau report, available for 66% of all applicants. We limit our attention to uncollateralized small business loans to self-employed individuals, with a ticket size between Rs 150,000 (US$ 3,000) and Rs 500,000 (US$ 10,000).<sup>10</sup> Loan file summary statistics are reported in Table III. We consider only term loans to new borrowers, many of whom are first-time applicants for a formal sector loan.<sup>11</sup> The median loan in our database has a tenure of 36 months, a ticket size of Rs 283,214 (US$ 6,383) and a monthly installment of Rs 9,228. (US$ 208).

To rule out vintage effects and ensure consistency in the initial screening standards applied to loans used in the experiment, we restrict our sample to loans originated in 2009 Q1 and 2009 Q2. Based on the Lender’s proprietary data on the repayment history of each loan, we then classified credit files into performing and non-performing loans. Following the standard definition, we classify a loan as delinquent if it has missed two monthly payments and remains 60+ days overdue, and as performing otherwise. To calculate profitability to

> 9More than 90% of all defaults occur during the first five months of a loan’s tenure, so that our default measure allows for a relatively precise measurement of loan quality.

> 10While none of the loans in the experiment carried any collateral security, borrowers faced strong incentives for repayment. First, the Lender routinely offers follow-up loans at reduced interest rates to clients with a good repayment history. Second, borrowers who default on their loans are reported to India’s main credit bureau, implying a credible threat of future exclusion from bank credit. Loans that remain unsettled for 90+ days are classified as non-performing (NPA), reported to the credit bureau, and referred to the Lender’s collections department. A small fraction of loans in the overdue category are restructured in negotiation between clients and the Lender. To rule out selection bias, our sample excludes repeat borrowers and restructured loans.

> 11Since none of the loans in our sample are collateralized, they are priced at an annual interest rate of between 15 and 30 per cent. We control for the variation in interest rates by including loan fixed effects.

13
Policy Research Working Paper 6057

# **Abstract**

Substantial declines in infant and under-5 mortality have taken place in recent years in many countries in Sub-Saharan Africa. Kenya’s infant mortality rate has fallen by 7.6 percent per year, the fastest rate of decline among the 20 countries in the region for which recent Demographic and Health Survey data is available. Kenya’s rate of postneonatal deaths per 1,000 live births fell by more than half over a five-year period, dropping from 47 to 22, as measured using data from the 2003 and 2008-09 Demographic and Health Surveys. Among the possible causes of the decline are various targeted new public health initiatives and improved access to water and sanitation. A Oaxaca-Blinder decomposition using Demographic and Health Survey data shows that the increased ownership of insecticide-treated bednets in endemic malaria zones explains 39 percent of the decline in postneonatal mortality and 58 percent of the decline in infant mortality. Changes in other observable candidate factors do not explain substantial portions of the decline. The portion of the decline not explained may be associated with generalized trends such as the overall improvement in living standards that has taken place with economic growth. The widespread ownership of insecticide-treated bednets in areas of Kenya where malaria is rare suggests that better targeting of insecticidetreated bednet provision programs could improve the cost-effectiveness of such programs.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. 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 author may be contacted at gdemombynes@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
**Table 8. ECD Anthropometric Outcomes OLS Regression Results by Outcome and Round**

||**Height-**<br>|**for-age (**<br>|**z-score)**<br>|**Weight**<br>|**-for-age (**<br>|**z-score)**<br>|**Weight-f**<br>|**or-height**<br>|**(z-score)**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|
|**INDH**|˗0.325|˗0.060|1.331**|˗0.344|0.252|0.403|˗0.101|0.054|0.130|
||(0.374)|(1.056)|(0.601)|(0.265)|(0.572)|(0.878)|(0.413)|(1.119)|(0.695)|
|**Community poverty rate**|0.095|˗0.057|˗0.273|0.059|˗0.121|˗0.115|˗0.046|0.180|˗0.049|
||(0.137)|(0.296)|(0.207)|(0.094)|(0.195)|(0.306)|(0.121)|(0.312)|(0.227)|
|**Constant**|˗4.475|˗0.344|5.208|˗1.827|4.314|3.765|2.582|˗2.894|4.188|
||(3.940)|(8.247)|(5.854)|(2.706)|(5.558)|(8.618)|(3.435)|(8.675)|(6.369)|
|**N (Observations)**|470|249|236|526|298|273|465|214|208|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

# 5.5.2 RDD Results

Turning now to the RDD results on ECD, the estimates for each year and across different bandwidths are presented in Table 9 and the RDD graphs in Figure 6 and Figure 7. As with the economic outcomes, the figures show results with a bandwidth of 50 percent. Other bandwidths have relatively smoother results. As with economic outcomes, there are substantial fluctuations over the range of poverty rates with differences at the discontinuity being largely comparable to fluctuations. Results are almost all insignificant, a mix of negative and positive estimates that vary substantially by bandwidth. While in 2011 there are positive prenatal and delivery effects that are significant at some bandwidths, in 2013 these have become negative effects significant at some bandwidths. Results are positive and significant for weight-for-height in 2008 but sensitive to bandwidth and likely spurious. Overall, the RDD results for ECD confirm the lack of an INDH program impact on ECD outcomes.

25
E. Impact on Central Europe potential output of Next Generation EU (NGEU) reforms, as described in World Bank (2022i). Orange whiskers show min-max range. Sample includes Bulgaria, Poland, and Romania. F. Scores for transition quality, which measures each economy’s performance against that of comparator economies in EBRD regions, as presented in EBRD (2021). Scores range from 1 to 10 (10 = standards of a sustainable market economy).
reported by the husband himself. In terms of the model, _yw_ corresponds to the income<sup></sup> _y_ reported by the migrant to the recipient, and<sup>_y_</sup> _h_<sup>corresponds to his actual income</sup><sup>_y_.</sup> Figure 1. Migrants’ Earnings Residuals Against Reported Earnings Ratio _Source_ : Authors’ analysis based on data from QSKF Our theoretical predictions describe implications for underreporting of income by the recipient in a number of cases. According to prediction 1, the migrant with levels of income  above the remittance threshold _r_ is more likely to take advantage of the recipient’s unawareness of his earnings and to underreport or conceal a positive income shock. The graphical examination of our data finds strong support for this prediction (econometric tests are presented in the next subsection). Absent panel data, we measure income shocks as outcomes that are unobserved by the remittance recipient. Figure 1 plots residuals from the Mincer wage regression of the migrant’s earnings and shows that positive income shocks (i.e., those that are not anticipated by the spouse based on observable characteristics of the migrant) is associated 18
**Table 22: Sectoral Migration in Ghana, 19897-88 (in percent).**

|**Sectors**|**1987**||**1988**||**1991**||
|---|---|---|---|---|---|---|
|From|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|
|Rural|33.46|11.56|34.68|10.97|58.37|30.24|
|Urban|32.21|22.77|31.91|22.44|6.63|4.76|
|All|65.67|34.33|66.58|33.42|65.00|35.00|
|Sample size|4948|2587|4359|3660|3765|6991|

**Table** 23: **Regional Migration in Ghana, 1987-91 (in percent).**

|**Regions**||**1987**||**1988**||**1991**|
|---|---|---|---|---|---|---|
||**Within**|**Between**|**Within**|**Between**|**Within**|**Between**|
|**Western**|53.78|46.22|48.03|51.97|36.46|63.54|
|**Central**|50.86|49.14|55.16|44.84|42.37|57.63|
|**Gr.Accra**|39.82|60.18|35.28|64.72|15.38|84.62|
|**Eastern**|60.46|39.54|62.78|37.22|52.52|47.48|
|**Volta**|61.50|38.50|60.43|39.57|43.97|56.03|
|**Ashanti**|61.72|38.28|60.41|39.59|47.97|52.03|
|**Brong-Ahafo**|54.45|45.55|61.01|38.99|47.17|52.83|
|**Northern**|76.91|23.09|73.76|26.24|50.00|50.00|
|**U/West**|72.21|27.79|58.04|41.96|26.76|73.24|
|**U/East**|54.25|45.75|42.74|57.26|4.89|95.11|

**Source: Authors' own calculations using GLSS 1,2,3.**

**Table 24: Major reasons for migration, 1987-91.(in** percent)

|**Reasons for Migration**|**1987**|**1988**|**1991**|
|---|---|---|---|
|Moving with family|54.35|56.15|43.3|
|Work-related|14.84|14.01|19.6|
|Marriage|13.54|10.44|23.1|
|school|7.02|6.54|6.1|
|Adventure|3.65|2.71||
|Others|6.37|10.15|7.8|

**Source: Authors' own** calculations using GLSS 1,2,3.

Since most of the migration is towards the high growth regions-Ashanti, Greater Accra and Western, it would be interesting to look at the characteristics of the migrant workers- their educational levels, age, occupation and kind of industry in which they find 35
# 4.2  How can respondent selection affect the measurement of inequality?

Given the emphasis on self-reported asset ownership in the LSMS+, a natural question might arise as to how different individual-level data on wealth might be if collected instead by one representative per household (i.e., who would report as a proxy for others). Malawi provides a unique setting to make this comparison, where the LSMS+ supported Malawi IHPS 2016 was conducted concurrently with another national survey, the Malawi Fourth Integrated Household Survey (IHS4) 2016/17.<sup>17</sup> Both surveys were implemented by the Malawi National Statistical Office, relying on identical household and agricultural questionnaires and field teams that covered both IHPS and IHS4 EAs.<sup>18</sup> Unlike the IHPS, the IHS4 did not include an individual questionnaire that included LSMS+ assets modules, and a “most knowledgeable” member was to report on other members’ land ownership and valuation (see Kilic, Moylan and Koolwal, 2021, who compare statistics on men’s and women’s agricultural land ownership across the two surveys).<sup>19</sup> Both surveys used identical household and agriculture questionnaires and followed the same protocol for administering interview questions. The two national survey samples were also similarly distributed across interview months and survey strata (see Kilic, van den Broeck, Koolwal and Moylan, 2020, for a detailed discussion).

17 The data, questionnaires and basic information document for the IHS4 2016/17 can be accessed here: <u>https://microdata.worldbank.org/index.php/catalog/2936 . As with the IHPS 2016, the IHS4 2016/17 was also implemented with</u> technical and financial assistance from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), using the Surveys Solutions Computer-Assisted Personal Interviewing (CAPI) platform.

> 18 On average, field teams spent approximately 3.4 days in an IHS4 EA, with one enumerator visiting each household. The same field teams spent an average of 4.5 days in an IHPS EA, which involved interviewing, if possible, simultaneously, up to four adults in each household.

> 19 Only residential and non-residential land were covered in the IHS4.

35
# **2. Data**

A new and unique data set on SME financing volumes across the world is collected through the _Financial Access 2010_ survey in January-April of 2010 by CGAP/World Bank Group.<sup>8</sup> The survey annually collects data from the main financial regulators such as central banks or bank supervisory agencies in 140+ countries. One of the objectives of the 2010 survey was to identify the feasibility of collecting consistent cross-country data on SMEs from financial regulators. To achieve this objective, the survey asked about existing institutional arrangements for collecting SME data and about SME definitions used by the authorities. It also requested the most recent statistics on the volume and number of SME loans, as well as the number of SMEs with outstanding loans. The data collected is as of the end of 2009.

As is the case with most data collection endeavors, the survey methodology has some limitations. First, the survey collects information only on regulated financial institutions, leaving out nonregulated providers of financial services. This practice is likely to understate the scale of SME financing, as lending to SMEs is often done by non-regulated credit providers. Second, even though the main financial regulator was asked to provide data on all regulated financial institutions, in cases where some financial institutions are regulated by secondary regulators, the data are rarely available. As a result, the data understate the true scale of overall SME lending.

The survey asks regulators if they monitor the level of lending to SMEs by regulated financial institutions through regular or irregular reporting, through periodic surveys of financial institutions, through collecting estimates from credit registries and/or by using any other method. In addition, in the cases where a different institution than the financial regulator is responsible for monitoring SME lending, regulators were asked to identify this institution.

Out of 74 countries that reported that they collect data on SME finance, nearly 80 percent – 59 countries – stated they collected information on a regular basis. Financial regulators in middle and low-income countries are more likely to require regular reporting of SME statistics. Out of

> 8 The complete _Financial Access_ database is available online at http://www.cgap.org/financialindicators.

6
<mark>Valentinyi, A. and Herrendorf, B., 2008. “Measuring factor income shares at the sectoral level.” Review of Economic Dynamics, 11(4), pp.820-835.</mark> <mark>Venables, A.J., 2017. “Incorporating wider economic impacts within cost-benefit appraisal.” ITF Roundtable Reports Quantifying the Socio-economic Benefits of Transport, 109.</mark> <mark>World Bank. 2019. Belt and Road Economics. Washington, DC: World Bank.</mark> 38
questions that are included in the full sample period. Moreover, given the large number of questions, the assessment does not consider compliance question‐by‐question. Rather, it focuses on in what clusters / compliance points are firms more likely to adopt compliance requirements, overall and within the 26 critical issues. Table 4 gives the number of compliance questions per cluster, the number of questions that appear consistently throughout the sample period, and the number of public disclosure questions. It shows that the compliance questions are a minority of the total questions in the sample.

**Table 4: Number of compliance questions per cluster**

|**Compliance cluster**|**Total questions**|**Public disclosure**<br>**questions**|**Questions asked**<br>**consistently**|
|---|---|---|---|
|Child Labor|7|2|6|
|Compensation|40|4|24|
|Contracts and Human Resources|31|2|15|
|Discrimination|56|3|35|
|Forced Labor|18|4|9|
|Freedom of Association and Collective Bargaining|33|5|18|
|Occupational Safety and Health|83|7|43|
|Working Time|26|0|11|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Data from the Compliance Synthesis Report are then merged with firm‐level information collected from Better Work Vietnam’s Registration Document. These data are used as control variables in the assessment, including firm size, firm age, and information on buyer relationships. Table A.4 lists the variables names and their definitions available in the Registration Document.<sup>7</sup>

# Descriptive statistics

Table 5 shows the mean compliance across all questions and factories without controlling for any other potentially relevant variables. The mean compliance in Table 5 is therefore the simple arithmetic average of the 0/1 compliance variable taken over all questions, all factories and all years. Since 0 indicates compliance (and 1 non‐compliance), higher numbers in Table 5 indicate lower average compliance. Overall, firms are found to be non‐compliant in about 9 percent of all compliance questions in the Compliance Synthesis Report.

**Table 5: Finding of non‐compliance**

|**Finding**|**Frequency**|**Percent**|
|---|---|---|
|No evidence of non‐compliance found|219,154|91.01|
|Evidence of non‐compliance found|21,650|8.99|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Table 6 shows the average non‐compliance rates over time for all compliance questions, as well as for compliance questions that are related and non‐related to public disclosure (“critical issue groups”). It also presents the number of firms that are available in the sample for each year. Overall, compliance tends to be lower for disclosure questions than non‐disclosure questions, across the sample years. On average, 7 We also explored the possibility of merging additional firm‐level data from Better Work Vietnam’s Impact Assessment Survey. The firm identifier needed to merge the data sets is currently not available, though we are exploring the possibility of accessing the appropriate identifier.

14
of a large body of literature since then, surprisingly very little is known about the e¤ects of ...scal policy on economic activity and on the short-versus long-run destabilising behaviour of ...scal policy in emerging market countries. This is notwithstanding the fact that during the height of the crisis the largest ...scal stimulus packages were enacted in emerging market economies.<sup>1</sup> Malaysia, for instance, passed a stimulus package of around 8.6 percent of GDP in 2009.

Emerging market economic cycles tend to be more volatile than developed countries’ whilst being more susceptible to large shocks such as ...nancial crises ...scal policy could play a central role in dampening economic ‡uctuations and promoting long-run growth.<sup>2</sup> Aghion, Angeletos, Banerjee and Manova (2010), for example, show that the ability of ...rms to ...nance investment falls during an economic downturn due to a fall in pro...ts, which acts as a constraint on their borrowings. Indeed, the ...nancial accelerator principle implies the credit worthiness of the borrower may change depending upon the stage of the economic cycle. In the presence of ...nancial frictions the government, through undertaking counter-cyclical ...scal policy and pushing out the demand for goods and services for these ...rms, ...scal policy could have a positive impact on long-term investment and productivity.

As well as the habitual undulations in the economic cycle, it is particularly the role of ...scal policy during crisis times that should be of relevance to emerging market economies. As noted in Perotti (2005b), in the aftermath of the ...nancial crises of the late 1990s, many emerging markets were perceived as sacri...cing longrun growth in order to show signs of ...scal discipline. Since the crisis, a¤ected economies - including Malaysia - have endured a downward shift in their long-run growth trajectory.

> 1See Federal Reserve Bank of Dallas (2009).

> 2See Aguiar and Gopinath (2007), which discuss the characteristics of economic ‡uctuations in emerging market countries.

2
## **2.3 Re-classification and Poverty Profile**

Unless all individuals across the distribution consume the same amount of FAFH, the shift from a distribution without FAFH to one where all consumption is accounted for will result in a reordering of individuals. If this re-ordering is big enough, the identity of those individuals falling at the lower end of the distribution changes. This has two important implications. On the one hand, it affects the identification of the poor population leading to an important re-classification of the poor. On the other hand, it changes the composition, and therefore the characterization of the poor population.

By definition, the poverty (and inequality) indices do not capture these changes because they are constructed in a way that preserves the anonymity of the individuals (symmetry property). While that is desirable for poverty indices, identifying the poor and understanding their characteristics is essential to poverty analysis.

There are no strong a priori predictions as to how the profile would change, aside from the fact that those likely to leave poverty are individuals who eat more outside and those who fall into poverty are individuals more likely to eat at home. Therefore, correlates with eating out such as household composition, education, and labor market outcomes are likely going to be different across those who transition from one poverty status to another. To the extent those differences are strong enough they will have a significant impact on the profile of the poor. We empirically test these hypotheses below.

# **3. Setting and Data**

We analyze the impact of FAFH on poverty and inequality for the case of Peru. Peru is a middle-income country that has experienced sustained GDP growth over the last decade. Poverty has been steadily declining over time, reaching in 2013 23.9 percent and 4.7 percent for moderate and extreme rates respectively. Likewise, consumption inequality has been falling, with a Gini coefficient for 2013 of 35.

## **3.1 Consumption Data and the National Household Survey in Peru (ENAHO)**

For this analysis, we use the National Household Survey - ENAHO, a multi-topic household survey that has been collected annually since 1995, and is the main source of information to monitor the living standards of the population in the country. For annual statistics, it is representative up to 13