rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Underwriting and Loan Servicing**

Prospective borrowers must have a business or employment to be considered for a loan. Unit personnel visit the work site of each prospective borrower before issuing the loan. At the same time, the loan officer checks on the prospective borrower's character by talking to the local village chief and/or neighbors. Most BKK managers have found that neighbors provide more accurate character references than village chiefs. If the borrower is married, his or her spouse must also sign the promissory note. Loans are disbursed 1 day to 2 weeks after the loan application is received. Borrowers must come to the unit twice to obtain a loan - once to complete the application and once to collect the funds, Collections procedures vary across BKK units. Units serving villages a considerable distance from their facility send out field staff to collect installments. Units serving villages that have relatively easy access to their facility require that borrowers make payments at the unit. However, even these latter units send staff to visit borrowers' villages if payments are more than 2 to 7 days late. There is no penalty for late payments. Nevertheless, previous repayment performance is taken into account when determining whether to issue additional loans. Units give new borrowers small loans and allow subsequent loans to rise in value as the borrower proves repayment reliability.

# **Program Performance**

This section reviews program performance as measured by sustainability and outreach. Sustainability is measured by the program's arrears rate, profitability, and the size of the subsidy required to sustain operations. Outreach is measured by the volume of annual lending and savings activities (scope), and the population it serves (depth of market penetration).

_Sustainability._ According to data supplied by the BPD, for the 34 BKKs in existence before 1993, the volume of loans in arrears by more than 3 months was equal to 26 percent of the volume of outstanding loans in 1995. Because units do not write off loans, it is probably more meaningful to simulate
by this age-specific population. Instead, we control for trends in district-specific total population.<sup>15</sup> There are three concerns that may confound the interpretation of our results. First, we may be spuriously attributing to MGNREGA the effects of other government programs aimed at influencing enrollment. The Government of India introduced two programs in the early 2000s to promote direct enrollment in schools. The first program, the Sarva Shiksha Abhiyan (SSA), was intended to provide universal access to elementary education. The second program was the Mid-day meal which provided cooked meals for children in attendance at schools. However, both programs were launched much earlier than the MGNREGA. The second concern might be that the increase in private schools driven by growth in the private school market, independent of the program, affects our results. We see significant declines in the enrollment in the private schools. Hence, an increase in number of schools cannot be causing this decline. Further, our estimates are robust to including state- specific trends, and school type by year fixed effects. Therefore, different trajectories of growth across states is not generating our results. In Appendix Figure S1 (in the supplementary online appendix, available with this article at the World Bank Economic Review website), we also show that phase wise trends in expansion of schools both for private and public schools were similar. Thus, an independent increase in demand for private schools is unlikely to be driving our results.

Finally, if MGNREGA attracts migrants into districts, then the results could be driven by changes in population. Across district migration in India is very low (Topolova, 2010). Further, if migration were responsible for the changes in enrollment, then we would expect similar sized effects for primary and upper primary grades and individual classes within these grades. In Tables 2 and 3, the size of the effect is much larger in primary school with no effect discerned in upper primary school. It seems implausible that households with children only in specific age groups would
of definitions developed by the organization producing the data source. In many cases, the experts providing the assessments are based in the country or region they assess. These data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where 6
disconnection notices with clear instructions on how to pay or appeal for a financial hardship exemption, (3) allowing a reasonable period of time for property owners to pay or appeal, and (4) disconnecting services if they did not pay at least some of their outstanding bill in a timely fashion or apply for a financial hardship exemption.

The intervention was implemented in a context where water and sanitation charges were affordable, i.e., significantly below the 3% (water) and 5% (water and sanitation) thresholds of monthly income for service affordability set by the United Nations (United Nations, 2010). In addition, alternative water sources were available through utility-run public kiosks and private vendors, which were the primary sources of water prior to the 2014 expansion of utility piped water. During the intervention rollout, the utility maintained a transparent and convenient process through which property owners could delay payment and disconnection in the case of distress.<sup>5</sup> Using administrative data from the utility’s electronic billing and payment system, we find that contract enforcement significantly increased both the likelihood of property owners making a payment, and the overall amount paid. However, most of the change in payment behavior took place shortly after enforcement took place, with no evidence of further increases over time. This could be because the enforcement intervention was implemented as a one-time policy and not as a permanent change. We also find no evidence of spillover effects in the form of improved payment behavior among control property owners with compounds in treatment clusters. The face-to-face engagement intervention had a precisely estimated null In addition to the observed payment behavior, we did not find evidence that tenants were negatively affected nine months after implementation of the enforcement intervention. Using survey data, we find that water and sanitation service connections were not meaningfully different between treatment and control compounds. This is because most property owners whose service was disconnected were reconnected after agreeing to a payment plan. During service interruptions tenants had access to water from kiosks operated
distance decreases the probability that an MNE sets up a subsidiary in a country, but increases the size of the FDI flow if the MNE makes an investment. Moreover, FDI in the previous year, the rate of return on equity and the number of employees positively and significantly affect the probability of investment, whereas they do not affect the size of the investments. Age has a significant negative effect on both the intensive and extensive margin, whereas geographic diversification increases the probability of an MNE making an investment, while decreasing the size of FDI flow.

We also estimate the Heckman model separately for resource and non-resource FDI decisions.<sup>13</sup> The estimates for non-resource-related FDI flows (Column 3) are similar to those of total FDI flows. This is not surprising; in the firm-level data set 88% of the observations concern non-resource FDI flows. However, the estimates for the resource sector differ (Column 2). Political conflict, the type of conflict and the diversification moderator are no longer significant. In line with our hypotheses, the effect sizes are also considerable smaller than the effects on non-resource-related FDI flows. However, the number of MNEs in the resource-related FDI regression is small and therefore, the estimates of the coefficients are relatively imprecise. This concern in combination with the sample selection effect, explained above, lowers our confidence in the Heckman estimates for the resource sector.

# 6. THE CASE OF HYDROCARBONS: THE ROLE OF ECONOMIC RENTS AND GEOGRAPHIC CONSTRAINTS ON LOCATION CHOICE

This section examines the factors behind the insensitivity of resource-related FDI to political violence by focusing on the two mechanisms discussed in the theory section: the size of the economic rents and the geographic constraints on location choice. We focus on the oil and gas industry because no data on rents and location choice are available for other natural resource industries. The BP Statistical Review of World Energy provides a large data set containing information on global oil, gas reserves and prices, obtained from government sources and published data. As a proxy for
modest gains in the poverty impacts over other policy-relevant alternatives. Indeed, in a number of cases and depending on the country and the nature of its poverty profile, simpler state-contingent targeting methods or even a “basic-income scheme” (in which everyone is covered) dominate in certain policyrelevant cases, such as when one allows for plausible lags in PMT implementation. However, none of these methods can be considered to perform especially well. Prevailing methods do not reliably reach the poorest. The costs of each method in practice may then be decisive in the choice.

The following section describes the PMT method that we assess, while Section 3 describes the measures we use in assessing econometric targeting. Section 4 studies the basic version of PMT, while Section 5 turns to various extensions and revisions to that version. For stylized transfer programs, Section 6 compares the poverty impacts of econometric targeting to those of less methodologically sophisticated methods, including un-targeted (universal) transfers and simple demographic “scorecard” methods. Section 7 presents our results for (informationally-feasible) differentiated transfers, including optimal transfer schemes for poverty reduction with a given budget but limited information. Section 8 uses the panel surveys to introduce lags in implementation. Section 9 offers some cross-country comparisons of the performance of econometric targeting; here we ask how much the impacts of PMT on poverty for a given budget are explicable in terms of the alternative targeting measures and the predictive ability of the PMT regressions. Section 10 concludes.

# **2. Econometric targeting**

Quite generally, we can think of any PMT as some weighted function of a vector of covariates _xijt_ . The specific form of this function that has become popular and that we focus on uses householdconsumption regression coefficients as the weights. We can write the following empirical regression function for the consumption of household _i_ in country _j_ at date _t_ on a vector of covariates _xijt_ using a survey sample of size _N jt_ : The PMT is then based on: The most common method in practice for estimating  _jt_ and  _jt_ in
access to compound-level piped water and sewer services between 2014 and 2018 (World Bank, 2019).

The model to finance the last mile connection costs relied on the assumption that property owners would repay their loans and pay the monthly service fees. During the course of the program, however, it became clear that that the utility faced critical nonpayment issues. In 2016, 40% of connected property owners had yet to make a single service payment or loan installment even though the average property owner had already been connected for 1.3 years. Moreover, the average share of bills and loans paid fell from above 65% in 2014 to below 50% in 2018 (Figure 1).<sup>14</sup> Service quality deteriorated substantially over this time period, including a city-wide rationing program that started in early 2017. Based on data collected from a panel survey of 587 households from one of the settlements, the proportion of compounds that received water from their water point in the week prior to the survey fell from 95% to 40% between 2014 and 2018.<sup>15</sup>

# **3.2 Compounds and Residents**

Characteristics of property owners and tenants living in one such informal settlement, Kayole Soweto, are presented in Table 1 for owners and Table 2 for tenants.<sup>16</sup> In Kayole Soweto, almost all property owners are sole owners of their compounds with self-reported sales’ values of about US$ 20,000 on average. Property owners are 51 years old, are 60% male, 54% have completed secondary education or higher, and make US$ 551 in income per month on average from all sources.

Property owners rent out space in 86% of the compounds and make the compound their principal residence in about half. On average, the rental compounds have 7.7 distinct rental rooms, and generate US$ 115 per month in total rent per compound, or US$ 83 in income after deducting
>Gross Secondary Enrollment<br>72 0<br><!-- End of picture text -->

**Figure 9. Efficiency Frontier Shift Over Time**

The detailed comparison between observed input-output combinations in different time periods distinguishes whether variations in the levels of input utilization or output production levels are due to changes in efficiency or changes in technology. This testing is possible with observed levels of inputs and outputs, and are based on the concept of a Malmquist Index (Fare, Grosskpof, Norris and Zhang, 1994). This method has been used to study productivity change in the OECD economies, as well as productivity in agriculture across the world (Coelli and Rao, 2003; Nin, Arndt, and Preckel, 2003). Appendix D describes details of the method that uses some of the efficiency scores calculated in previous sections, and the index that will facilitate the analysis of productivity change through time.

> 14 Scores for individual countries are available at the PRMED website indicated in footnote 1.

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/sup> The

> 36For highly persistent shocks, some dierences arise as relative prices move. Specically, persistent shocks allow for substantial movement in relative prices in the full NK model in the long run, which result in lower multipliers than predicted by the simple NK model with rigid prices and wages. In the NC model with persistent shocks, regional prices adjust so empirical measured multipliers using national price deators are higher than from actual multipliers (using regional deators)see Section 5.

> 37As is common in models with hand-to-mouth households and sticky wages, (i) forwardlooking nominal wage setting only takes into account the Ricardian household's labor-leisure rst order condition, and (ii) the labor supply of the two households move proportionately. This shuts down the wealth eects for hand-to-mouth households receiving transfers studied in Giambattista and Pennings (2017).

> 38I also abstract from capital, and so assume output is produced using only labor with constant returns (equivalent to _a_ = 1 in NS). The simple NK model is the limit of the full NK model when prices and wages become perfectly sticky ( _θw, θp →_ 1 ).

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Africa, among firms that are credit constrained, and among firms that perceive access to finance as an obstacle. Our analysis shows that firm size and age are significantly correlated with firm growth. Distinguishing between different types of ownership, we find that firms tend to have higher growth if they are an exporter, are part of entities with multiple establishments, are foreign owned, or are privately owned.

The paper is organized as follows. The next section reviews the literature and shows how this paper relates to it. Section 3 presents an overview of the data and describes the sample used. Section 4 examines the most binding constraint of the business environment. Section 5 examines the effect of access to finance on employment growth in the full sample and by region, while section 6 looks at determinants of access to finance. Section 7 investigates differences in the effect of financial access variables on employment growth, and section 8 looks at the relationship between firm size and financial constraints. The last section concludes.

# **2. Literature Review**

Understanding the firm growth process is important for designing appropriate policies for job creation and pro-poor growth. Many studies are devoted to understanding the determinants of firm growth, especially employment growth. Most of these studies focus on the manufacturing sector and large firms (Evans 1987a, 1987b; Hall 1987; Dunne, Roberts, and Samuelson 1989). They find that firm age and firm size are important in the analysis of firm growth.

## **2.1 Effect of the Business Environment on Firm Growth**

A number of recent studies use World Business Environment Survey data (a firm-level data set covering 4,000 firms across 54 countries) to study the effect of the business environment on firm growth. Using subjective, firm-level data on the business environment, some of these studies show the importance of finance, corruption, and property rights (Batra, Kaufmann, and Stone 2003; Ayyagari, Demirgüç-Kunt, and Maksimovic 2006). Others examine the relationship between the business environment and
Standardized Precipitation Index (SPI) from the AFDM system. The SPI is calculated using data from the bias-corrected TMPA and hybrid observational reanalysis,<sup>5</sup> including data from 1950 to 2015. The SPI-1 is defined across districts and over time following an aggregation and standardization process analogous to the one employed for the rainfall and windspeed Z-scores. For ease of interpretation, the SPI district-level Z-scores are multiplied by negative -1 to invert the scale.<sup>6</sup> The graph at the bottom of Figure 2 shows the distribution of the SPI during the period of reference.

# **3. Results**

# _3.1 Household consumption and poverty status_

Evidence available in the literature illustrates the high levels of income and consumption variability in risky agricultural contexts (Dercon 2002). We first focus on the effects of exposure to each of the three weather shocks on food and total per capita consumption, a traditional measure of household monetary welfare. Since subsistence farming is widespread across Mozambique, the variable used to measure household food consumption includes not only the actual expenditures incurred in purchasing food items, both for food eaten at home and outside, but also selfconsumption. Consumption from gifts and wages in-kind is also included in the calculation of the consumption aggregate. Consumption levels are valued using price data obtained from surveys of local markets that were fielded around the same time as the household survey. As noted above, all specifications include districts fixed effects, year fixed effects, agricultural growing cycle fixed effects, gender fixed effects (for individual-level regressions), and control variables such as age and urban status.

The first set of results of the triple difference estimation (equation 1) is presented in Table 1. The coefficients are expressed as the effect on the dependent variable of a change of one standard deviation in the shock intensity measure (Z-score). One interesting pattern in the findings is that the three shocks pushed exposed households to cut back on their food consumption – all the point

> 5
**Table 5: Types of Employment as a Percentage of the Sample Population GHS 1999 - 2006 (in percent, weighted)**

||**1999**|**2004**|**2006**|
|---|---|---|---|
|Family agriculture|30.8|36.6|37.8|
|Non-agriculture self-employed|24.1|25.8|22.9|
|Non-agriculture unpaid family work|0|0.08|0.06|
|Wage employment|15.0|10.4|10.0|
|Apprenticeship|2.1|1.1|1.9|
|Unemployed|1.7|2.4|1.9|
|Not in the labor force|26.4|23.7|25.5|

_Source: Francis Teal / Luke Haywood NLSS 2003-2004 and GHS 1999-2004_ 17. From 1999 to 2006, the most important structural changes that have occurred in Nigeria’s labor force have been a shift _into_ agriculture employment and _out of_ wage employment: the proportion of the sample population aged 15 to 65 (excluding those in full-time education) with wage jobs _declined_ over this period (from 15 percent in 1999 to 10 percent in 2006). The same is also true for those classified as non-agriculture self-employed (their share of the population fell from 24 to 22.9 percent). The category that saw a major increase in this share of the population was family agriculture, which rose from 30.8 to 37.8 percent.<sup>4</sup> 18. Table 6 provides further insights into the development of wage employment since 1999: Wage employment in parastatals, ministries and public companies has declined, while employment in the private sector and others (including NGOs, international organizations and associations) has risen.

> 4 It is important to note that this finding does not
distinction between the severity of controls<sup>and the different types of transactions contributing to</sup> capital flows. Table 1 provides preliminary information<sup>on such a measure for a sample of 96</sup> countries in 1997.

This measure referred to as the Financial<sup>Openness Index is constructed using</sup> disaggregated measures of capital controls based<sup>on the classification and information contained</sup> in the IMF Annual Report on Exchange Arrangements<sup>and Exchange Restrictions (AEAER), and</sup> draws on the coding methodology developed by Quinn<sup>and Toyoda (1997). The measure is a</sup> composite index of our coding of rules, regulations,<sup>and administrative procedures affecting</sup> capital flows (both inflows and outflows) for a<sup>total of 27 individual transactions in the current</sup> and capital accounts of the balance of payments<sup>for each country in the sample. Thus out of 96</sup> countries in the sample, it is interesting to note<sup>that 46 can be classified, as of 1997, as open and</sup> 10 as semi-open, with both of these categories<sup>including emerging market economies in Latin</sup> America and Eastern Europe.The Financial Openness<sup>Index has a correlation of 0.51 with the</sup> length of time that a country has accepted the conditions<sup>of IMF's Article VIII.28</sup>

> 28Correlation is calculated between the Financial Openness Index and the logarithm<sup>of the number of years since a country has</sup> assumed RMF's Article VIII.

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