rafmacalaba/datause-encoder-data huggingface dataset:
Much of this work developed as an alternative to econometric work that used latent class techniques to infer capabilities in the absence of direct measures (see for instance Martinetti (1994) and Krishnakumar and Ballon (2008)). However, some direct capability indicators are routinely collected in household surveys and, in this paper, we draw on data that ultimately derive from and extend such questions. More precisely, the 15 capability indicators used in this study are versions of a general quality of life measure developed in a sequence of papers by Anand et al (2009, 2011), Lorgelly et al (2015) and Simon et al (2013) for use with general populations and in health assessments. The Simon et al (2013) measure is in effect a short form version of the OCAP instrument developed by Anand et al (2009), which implemented the normative list proposed by Nussbaum (2000) justified on the basis of an Aristotelian approach to human flourishing.<sup>11,12</sup> The data used in this paper draw on questions in an Indian adaptation of the Simon et al (2013) version as well as pilot work in Uttar Pradesh and can be seen as a short form, capability based measure of life quality informed by theory and consultation.<sup>13</sup> In this program, most capability indicators could be positively impacted with three exceptions. First, additional involvement in agricultural activities and leaving the house more often than before could cause program members to report that health constraints

> 11 The list is useful because while it shares common features with several other such lists, it is relatively comprehensive and therefore provides a useful starting point for mapping out life quality of life issues without making any claims about what a state should do about them.

> 12 OCAP comprises some 50 items and so to produce a short form version, Lorgelly et al (2015) developed a consultation process based on focus group interviews. The refinement developed subsequently by Simon et al (2013) drew also on this shortened version produced by Lorgelly and colleagues and produced psychometric data for capability indicators in Vergunst et al (2017). 13 It is worth noting that this approach could be seen as an alternative to those studies that follow a tradition in development of measuring empowerment in terms of agency – see for example recent work by Alkire et al (2013). Our approach helps to identify the areas of life in which a person’s capabilities have changed and therefore offers a complement to measures of agency or empowerment that focus on delivering an overall judgement about whether someone is empowered or otherwise.

13
cially, these data provide dates associated with major decisions, including the date of offense and date of arrest. Combining these data with detailed daily temperature data allows us to measure the causal effect of heat on the probability of arrest for different types of crime.

Our approach offers several advances on the existing literature. Previous work on the impacts of heat on crime has been restricted by data limitations to focusing either on a wide geographic region with little detail on individual defendants or temporal resolution (for instance, Ranson (2014)), or to using detailed information for a smaller geography, generally a single city (for instance, Heilmann and Kahn (2019); Harries et al. (1984)).<sup>3</sup> Our data have the advantages of both of these approaches. We are able to cover the geographic scope of the second largest state in the United States, with 28 million residents living across an area that is larger than any country on the European continent aside from Russia. Despite this large geographic scope, we are not constrained to analyzing monthly or geographic aggregates of crime. Rather, we have individual-level crime data, including the address at which the defendant resided, on a daily scale. These two factors allow us to examine the impact of heat on crime across a variety of climates and where the entire study area is not subject to the same temperature shocks (as might be the case with data from an individual city).

The individual scale of our data also allows us to conduct richer analyses of adaptation than existing work. Much of existing work has relied on reported crime data (for instance, Ranson (2014); Heilmann and Kahn (2019)) and so uses information about the location of the crime (or report), but not the residence of the defendant.<sup>4</sup> This makes it difficult to link defendants to a specific built environment in order to examine how this built environment might mediate the impact of heat. Because we know the address of the defendant at the time they commit a crime, we are not subject to this limitation and conduct a variety of heterogeneity analyses to understand how income, poverty, and the age of the housing stock

> 3Blakeslee et al. (2018) is a notable exception to this, using detailed daily data for the entire Indian state of Karnataka.

> 4Levy et al. (2020) demonstrates that where someone commits a crime and where someone resides are often different areas. We thus believe that our approach does a better job capturing who is driven by heat to commit crimes than earlier studies.

4
# **Appendix 3 (continued)**

||Δ Accounts per<br>1,000 adults<br>in commercial<br>banks (logs)|Δ Loans per<br>1,000 adults in<br>commercial<br>banks<br>(logs)|Δ Value of<br>deposits /GDP<br>(logs)|Δ Value of<br>loans/ GDP<br>(logs)|Δ Branches per<br>100,000 adults<br>in commercial<br>banks<br>(logs)|Δ Branches per<br>squared km in<br>commercial<br>banks<br>(logs)|
|---|---|---|---|---|---|---|
|Low-fee accounts|-0.0216|-0.0436|0.2267*|0.1989|0.0557|0.0526|
|documents_~p|0.0254|-0.1167|0.106|-0.0364|0.1745|0.1837|
|deposit_mi~n|0.0155|-0.0482|0.0876|0.0951|0.2233|0.2365|
|deposit_an~e|0.1168|0.1718|0.2826|0.2878|0.3715*|0.4051*|
|Places to submit loan<br>application|-0.1578|0.0747|0.0165|0.1776|-0.2273|-0.2428|
|Minimum loan amount|0.2346|0.1365|0.2041|0.1135|0.4530*|0.5011*|
|Fees - consumer loan|0.6231*|0.1646|0.0937|0.0914|0.1335|0.1003|
|Days to process loan<br>application|0.0683|-0.4355*|-0.0385|-0.0605|0.2523|0.253|
|Lending rate|0.1348|0.2348|0.0972|-0.0236|0.1061|0.0888|
|Branch approval|-0.0661|-0.0777|0.0962|-0.0083|0.0583|0.0738|
|Tax incentives|-0.0996|-0.0679|0.1108|0.1483|-0.056|-0.0644|
|Agents (savings)|-0.0791|0.0518|-0.0677|0.0017|-0.035|-0.0428|
|Agents (credit)|-0.0646|0.0658|-0.0779|0.0056|-0.0019|-0.0014|
|Postal system (private<br>operator)|0.0599|-0.0801|-0.0462|0.0169|-0.0325|-0.0476|
|Capital account<br>openness|0.0183|0.0912|-0.0999|0.1042|0.039|0.0359|
|Deposit rate|0.052|0.1461|-0.2516*|-0.212|-0.0007|-0.0247|
|Credit legal rights|-0.0452|-0.2385|0.0595|0.0285|-0.057|-0.0453|
|Credit rank|0.1237|0.2646*|-0.0714|-0.0395|0.0734|0.0751|

55
# **How Successful is World Bank Lending** for Structural Adjustment?

by Patrick Conway*

# Table of Contents

|I.|Measuring Structural Adjustment|4|
|---|---|---|
|**II.**|**Evaluating a**Country's Economic**Performance**|6|
|III.|<br>Estimation of the Impact of Adjustment Lending|8|
|IV.|Conclus&ons|22|
|Bibli|ography|23|
|Appen|dix|25|

* This paper was prepared from research funded by World Bank research grant 675-32. I have benefited greatly from the comments of my co-researchers Jaime de Melo, Lant Pritchett, and Dani Rodrik, as well as from those of participants in seminars at the World Bank, North Carolina State University, and the University of Florida at Gainesville. Jeff Konz provided excellent research assistance.
<!-- Start of picture text --> Zimbabwe<br>Sierra Leone Timor-Leste<br>Philippines<br>Pakistan<br>Peru<br>United Kingdom<br>500 1000 2000 5000 10,000 20,000 50,000 100,000<br>GDP per capita (PPP, constant 2011)<br>1.9$ poverty line<br>10<br>1<br>0.1<br><!-- End of picture text --> Figure 6: Break-even _α_ ̃ in all countries as of early June 2020 (baseline, distributionneutral contraction, no-intervention scenario with saturation, extreme poverty threshold) This nding suggests that a country's optimal intervention is likely to dier as a function of its development level. For a given rate of infection and negative GDP shock, the relative sizes of the two sources of welfare consequences vary greatly with GDP per capita. On average, the more developed the country, the larger are the mortality costs and the smaller are the poverty costs. We have shown that these dierences are quantitatively large. This implies that best policy responses might be more targeted towards containing infections in developed countries and towards containing poverty in developing countries - even though we treat the value of a human life as identical across countries throughout.

Table 4 summarizes our main estimates for the four income categories of countries and for the world, under our dierent scenarios. These estimates are the building blocks ˆ ̃ behind our break-even _α_ and _α_ . The table reiterates the point that for LICs, PYs are on average of the same magnitude than LYs in all no-intervention scenarios, while for HICs LYs dominate in the no-intervention scenarios or if considering the frugal $1.90 poverty line. For the world as a whole, PYs dominate greatly in the current scenario but are surpassed by LYs in both no intervention scenarios. Table 4 also provides a sense of the additional increases in poverty likely to arise if the pandemic-induced recession is inequality-increasing, instead of distribution neutral. Applying a stylized "Argentina" growth incidence curve across all countries, as described above, adds 62 million povertyyears when the IPL is used for all countries, and 133 million PYs when the income-classspecic "WB classication" poverty lines are used.<sup>26</sup>

> 26These conclusions are qualitatively unchanged when using the downside growth scenario (results

21
<!-- Start of picture text --> 58<br>56.2<br>a 55.7<br>ae ee er as=—:= S47:~<br>a‘<br>3‘ao52 53.4 mS, .<br>u TM.<br>= 20.4<br>© oo = - -Unweighted .<br>—— Population weighted ‘. ~<br>~~<br>48 =<br>48.1<br>46<br>1993 1997 2001 2005 2009 2013<br><!-- End of picture text -->
# 1. Introduction

Poor sanitation has been identified as one of the major causes of malnutrition (Briend 1990; PrüssÜstün and Corvalan 2006; Humphrey 2009). Following up on the United Nations’ Sustainable Development Goals (SDGs), put forward in 2015, there is a global effort to end open defecation by 2030 by providing universal access to adequate and equitable sanitation and hygiene (United Nations 2016). In line with this effort, the Government of India launched the Clean India Mission for Villages (most commonly known as the Swachh Bharat Mission–Gramin; hereafter, SBMG) on October 2, 2014, with the aim to achieve universal sanitation coverage and eradicate the practice of open defecation in rural India by 2019. Completed on October 2, 2019, the SBMG emphasized a community-based approach with a focus on raising awareness and encouraging collective behavior change, and thus generating demand for toilets in rural areas. It also provided an incentive of Rs. 12,000 to eligible households for the construction and use of toilets (Government of India 2017).

Punjab, with a population of 27.7 million living in 22 districts, is one of the richest states of India. Even before the initiation of the SBMG, it had made great strides in implementing water supply, sanitation, and hygiene (WASH) reforms in rural areas, while delivering services appropriate to the specific needs of local communities. Punjab was perhaps the first state in India to provide safe water supply to all its 12,258 villages. According to the 2011 Census, rural sanitation coverage was impressive, at about 71.9 percent (the average across India was 32.7 percent), mainly because of state government efforts to prioritize sanitation.

Yet, despite this high coverage rate, before the initiation of the SBMG, only 1 percent of all Gram Panchayats (GPs; “village councils”) in the state had received the Government of India’s Nirmal Grama Puraskar (“Clean Village Award”) for the achievement of a 100 percent open defecation free (ODF) status. Though Punjab ranked 7th out of 28 states for toilet coverage, it ranked 19th for the number of GPs winning the award, and 25th for the percentage of total awards granted. Of 47 GPs in Punjab that won the “Clean Village Award” in the years 2009–11, only 17 percent were found to still be ODF in 2014 (World Bank 2016). So, by the time the SBMG was launched, it was not just toilet construction but also the promotion of toilet use, and the achievement and maintenance of ODF status, that were critical priorities in Punjab.

The World Bank `-` assisted Punjab Rural Water and Sanitation Sector Improvement Project (PRWSSIP) supported Punjab’s Department of Water Supply and Sanitation (DWSS) in implementing the SBMG across all villages in Punjab, with the objectives of 100 percent toilet coverage and 100 percent toilet use. Its main goal was to eradicate open defecation and facilitate the achievement and sustenance of ODF communities. An ODF community is one in which all human excreta are safely disposed and all 2
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32
DHS. In all cases, the urban coefficient on open defecation is greater than the whole-country coefficient and the rural coefficient is smaller. Hausman tests (reported under the open defecation coefficients in columns 5 through 7) verify that urban coefficients are larger than rural coefficients from the corresponding specifications.

# **2.2.4 Mechanism: A gradient that steepens with age**

Height-for-age _z_ scores are computed by age-in-months so that, in principle, the heights of children of different ages can be pooled and compared. If international reference charts were genetically or otherwise inappropriate for some countries, we might expect a consistent gap across children of different ages, analogous to a country fixed effect. However, stunting in India and elsewhere develops over time: children’s mean _z_ -scores fall relative to the norm until about 24 months of age, where they flatten out. This is consistent with early-life health deprivation causing a steepening “gradient” between health and economic status, more steeply negatively sloped as children age (Case et al., 2002). If the association between height and sanitation were indeed the unfolding result of accumulating exposure to fecal pathogens, then it is plausible that the association would become steeper over the first two years of life, at a rate that flattens out.

Figure 5 plots the coefficients from estimating the basic equation 1 separately for collapsed means of children in four age groups: 0-5 months, 6-11 months, 12-23 months, and 24-35 months. Thus, as in the rest of this section of the paper, each coefficient is computed in a regression with 140 country means, but now these height means only include children in – – subsets of the age range. The independent variable country-wide open defecation is the same in each regression.

Two conclusions are visible in the figure. First, the gradient indeed steepens in age, at a rate that flattens. Second, the mean height of Indian children in the 2005 NFHS-3 is plotted for reference. The curve has a similar shape to the age pattern of the coefficients. This suggests that a fixed exposure to open defecation could be scaled into a similar shape as the 13
highly profitable. For those already participating in international trade, it is opposite: they may, optimally, choose not to leave the market even though staying in this market is highly unprofitable in the short run.

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8
17 consumers or to farmers in particular income brackets (DeWulf, pp. 87-88, surveys the early literature; see also Meerman, 1979; Foxley et al., 1979; Bahl et al., 1986).19 These allocation rules assume that there is zero benefit shifting. In addition to the benefit shifting that reflects the relative elasticities of supply and demand, benefit shifting may adse due to interlinked markets and informal labor contracts, changing social sharing mechanisms, and migration effects.

Programs involving input subsidization, credit subsidization, technology subsidization, or output subsidization can all be analyzed as negative taxes. In developed country tax incidence studies, commodity subsidies are often believed to be at least partly passed forward to consumers. One argument against forward shifting to consumers of subsidies in developing countries is that in many cases the govemrnment maintains a fixed consumer price via export (import) controls or govenmment purchases (sales) (Meerman, p. 234). However, shifts in agricultural subsidies would require additional governnent policy changes to maintain fixed consumer prices (Sah and Stiglitz, 1987). Thus, the analyst fares a very complex issue.

In any event, even when subsidies do accrue to the agricultural sector, the subsidies may not accrue to intended low-income farmers. For exaample, traditional economic relations in agricultural markets are often characterized by interlinked markets and infomial labor contracts, whereby peasants, who may be the statutory beneficiaries of subsidies, are kept near subsistence through a variety of channels as landlords, moneylenders, and merchants appropriate any increases in surplus (Braverman and Stiglitz, 1984; Bhaduii, 1983; Bell, 1988). Thus, in many countries sizable fractions of the populaton remain indebted, landless, and near subsistence, even in the post-fisc equiibrium, raising doubts as to the dbuton effects of government subsidies.

Benefits from govenmment land reform and land settlement are typically treated differendy than other benefits. Meerman assumes that land settlement costs accrue to resettled households. Foxley assumes that land reform costs benefit agricultural labor. Both approaches encounter the well-known problem that cost may be a poor proxy for benefit (especially given the investment nature of such spending). In addition, while land reform often offers the only prospect of breaking the cycles of poverty inherent in an unequal distribution of land, critics observe that the
27 The observed poverty numbers were obtained using a deflator derived from the survey, as opposed to the official CPI. The rather good performance of the (full) asset model in predicting the observed changes in poverty based on the survey deflator provides some support to the use of these survey-based deflators in analyzing poverty in Kenya, and underscores the potential of asset based poverty prediction models in adjudicating such choices.

# **5 Preliminary Meta Analysis**

The SAE poverty prediction technique has been explored here in a range of settings including quite different degrees of poverty change (increase, stagnation, decline), different time intervals (1-10 years), different levels of poverty as well as a wide variety of geographic environments (rural versus urban, lowlands versus highlands) each subjected to different shocks and structural change. Application of different combinations of poverty predictors in each of these settings provides the beginning of a database to conduct multi-variate meta analysis on the importance of the different factors affecting the performance of the SAE poverty prediction methodology.

In particular, the (absolute value of the) deviation between observed and estimated poverty levels in the second period divided by the observed poverty measure in that period<sup>20</sup> , is taken as dependent variable. Using Ordinary Least Squares with error terms corrected for heteroskedasticity at the country level, this dependent variable is subsequently regressed on the characteristics of the consumption model (R-squared and sample size), spell characteristics (the direction of poverty change), the nature of the ~ _s_ 20 _s s_ To be precise, ( _W_ ( _ct_  _k_ )  _t_  _k_ /) _W_ ( _ct_  _k_ ) is taken as dependent variable.