rafmacalaba/datause-encoder-data huggingface dataset:
32 ability to pay.<sup>28</sup> Moreover, the quantity and quality of care<sup>received may differ from utilization</sup> or the number of trips made to physicians or hospitals per se. Nevertheless,<sup>efforts to determine</sup> benefit incidence based on household utilization data for individual<sup>programs can be much more</sup> informative than simply allocating the benefits of the health care system<sup>as a whole by household</sup> income, by household size, or as a general good.

Benefits from curative health care expenditures are typicaUy pro-poor<sup>in percentage terms</sup> (less so in dollar terms) (see Table 4). When researchers are<sup>able to distinguish among types of</sup> care, inpatient care is less pro-poor than outpatient care, which<sup>is, in turn, less pro-poor than</sup> preventive care. However, the incidence results vary greatly among countries.<sup>Riboud examines</sup> 1986 data for Costa Rica reported by Sauma and Trejos (1990) that suggest<sup>health expenditures</sup> in that country are significantly pro-poor. Hanuner et al. (1992) fmd that<sup>health expenditures in</sup> Malaysia favor the poor more in 1989 than in 1974. In contrast, Meesook reports<sup>1978 data for</sup> Indonesia suggesting that government health care benefits are significantly<sup>pro-rich in that</sup> country.2<sup>9</sup> van de Walle fmds that the health care subsidy in Indonesia remains<sup>pro-rich in</sup> 1987, although the poor have captured more of the subsidy by 1987. The health care<sup>subsidy</sup> gains accrue to the urban poor in Indonesia, and the rural poor do not<sup>gain between the time</sup> periods. Selowsky and Meesook also report benefit. incidence separately<sup>by region, though in</sup> both cases the separate incidence results for each region are roughly the same as the total<sup>results.</sup> Benefit incidence analyses of health programs can provide important<sup>insights into</sup> proposed policy changes. Two policies of recent interest are: moving<sup>resources from curative</sup> to preventive care, and increasing user fees. With respect to the former, the<sup>benefits of increased</sup> prevention programs would likely be concentrated more heavily on the poor than<sup>the rich, since</sup> higher-income households already have higher rates of inoculation and other<sup>preventive measures,</sup> and the marginal benefits would likely accrue to the poor. At the same<sup>time, publicly-funded</sup> curative care tends to be less pro-poor and reductions in curative care expenditures<sup>would affect</sup> the poor less than the rich. With respect to increasing user fees, relatively<sup>high utilization by the</sup> poor of aU health services suggests that the poor will pay a substantial<sup>portion of such fees (or</sup> reduce their consumption of public health). Thus, it is critical to specify<sup>how the user fees would</sup>
**Table D.12:** Health outcomes (z-scored)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Health outcomes index**(SUR)||0.021<br>(0.023)<br>[0.322]|0.055<br>(0.025)<sup>_∗∗_</sup><br>[0.094]<sup>_∗_</sup>|_−_0.010<br>(0.027)<br>[0.463]|5053|
|(1) U5 Child death per HH|0.000<br>(1.000)|_−_0.047<br>(0.025)<sup>_∗_</sup>|_−_0.075<br>(0.027)<sup>_∗∗∗_</sup>|_−_0.019<br>(0.030)|5053|
|||[0.544]|[0.039]<sup>_∗∗_</sup>|[1.000]||
|(2) Maternal death per HH|0.000|0.000|0.017|_−_0.017|5053|
||(1.000)|(0.026)|(0.031)|(0.028)||
|||[1.000]|[0.462]|[1.000]||
|(3) Illness/injury in HH|0.000|_−_0.005|_−_0.018|0.007|5053|
||(1.000)|(0.033)|(0.037)|(0.039)||
|||[1.000]|[0.462]|[1.000]||
|(4) Child weight for length|0.000|0.079|0.093|0.065|1991|
||(1.000)|(0.048)|(0.055)<sup>_∗_</sup>|(0.055)||
|||[0.544]|[0.252]|[1.000]||
|(5) Vaccine completion index (Under 2)|0.000|0.012|0.118|_−_0.082|1457|
||(1.000)|(0.059)|(0.072)|(0.064)||
|||[1.000]|[0.252]|[1.000]||
|(6) Child birth complication index|0.000|_−_0.026|_−_0.126|0.061|856|
||(1.000)|(0.077)|(0.086)|(0.087)||
|||[1.000]|[0.277]|[1.000]||
|(7) Child illness index|0.000|0.024|0.031|0.018|4993|
||(1.000)|(0.102)|(0.115)|(0.116)||
|||[1.000]|[0.513]|[1.000]||

_Notes:_ Variables are control-group normalized at endline (z-scored). Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets.Maternal death is defined as death relating to either pregnancy complications or childbirth. The Pentavalent Vaccination targets diphtheria, tetanus, whooping cough, hepatitis B, and haemophilus influenza type B. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A23
# PREFACE

Population projections for all countries are prepared annually by the Population and Human Resources Depanment of the World Bank They are published first in summary form<sup>in</sup> the Bank's _World Development Report_ and later in greater detail as technical notes or<sup>working</sup> papers and, in alternate years, as a separate book This set of projections was prepared for _World Development Report 1989._ It appears for the second time in the Policy, Planning, and Research (PPR) Working Papers series.<sup>The</sup> tables will also be published shortly, somewhat differently organized, in book form.

As was done last year, separate working papers cover each oi the Bank's four regions: Africa (sub-Saharan); Asia (and Oceania); Europe, the Middle East, and Nlorth Africa<sup>(including</sup> the U.S.S.R.); and Latin America and the Caribbean (and Northern America). Combined,<sup>these</sup> papers cover all countries of the world.

The data format of these papers generally follows the previous edition and is consistent in the demographic indicators provided, except for minor changes, with the series of preceding reports.

We intend these papers to provide a convenient and up-to-date reference on the likely demographic future of each country, to facilitate the Bank's work of assisting them in planning<sup>more</sup> productive futures. We welcome comments on their utility, and how this might be enhanced.

Anthony R. Measham Chief Population, Health and Nutrition Division
fast manner even in fragile areas.<sup>14</sup> A similar ex‐post simulation for South Sudan (data not shown) indicates that the rapid consumption methodology can also be applied at the country level with large intra‐country consumption variation.<sup>15</sup> Further research can help further refining the methodology and estimation techniques. A better understanding of the relationship between the number of items in the core module and the number of optional modules with the accuracy of the resulting estimates can help to further optimize the methodology. Also the algorithm for the assignment of items to modules was designed _ad hoc_ and can certainly be further improved. The estimation techniques can be optimized utilizing different techniques and more appropriate welfare models, for example including locational random effects. Finally, ultimate validation of the rapid consumption methodology should come from a parallel implementation of a full consumption survey and the rapid consumption methodology to directly compare estimates.

> 14 Costs for implementing a rapid consumption survey are lower than conducting a full consumption survey due to the reduced face‐to‐face time allowing enumerators to conduct more interviews per day.

> 15 Ongoing field work employs the rapid consumption methodology currently in South Sudan to update poverty numbers.

18
Policy Research Working Paper 6728

# **Abstract**

This paper offers an axiomatic characterization of two classes of poverty measures that are sensitive to inequality of opportunity—one a strict subset of the other. The proposed indices are sensitive not only to income shortfalls from the poverty line, but also to differences in the opportunities faced by people with different predetermined characteristics, such as race or family background. Dominance conditions are established for each class of measures and a sub-family of scalar indices, based on a rank-dependent aggregation of type-specific poverty levels, is also introduced. In empirical analysis using household survey data from eighteen European countries in 2005, substantial differences in country rankings based on standard Foster-Greer-Thorbecke indices and on the new opportunity-sensitive indices are found. Cross-country differences in opportunity-sensitive poverty are decomposed into a level effect, a distribution effect, and a population composition effect.

This paper is a product of the Office of the Chief Economist, 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 authors may be contacted at fferreira@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
# **Figure 9 Fiscal volatility, growth, and fiscal frameworks**

# **A. EMDE annual per capita growth**

<!-- Start of picture text --> Percentage points<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>EMDE growth With AE Total<br>policies<br><!-- End of picture text -->

# **C. Types of fiscal rules in commodity exporters**

<!-- Start of picture text --> Number Revenue rules<br>Expenditure rules<br>100<br>Debt rules<br>80 Budget balance rules<br>60<br>40<br>20<br>0<br>1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text -->

# **B. Fiscal rules and SWFs**

<!-- Start of picture text --> Number Commodity importers<br>80<br>Commodity exporters<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 2010 2020 1990 2010 2020<br>Fiscal rules SWFs<br><!-- End of picture text -->

# **D. Share of fiscal rules, by type of commodity exporter**

<!-- Start of picture text --> Percent<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Agricultural Energy Metal<br>exporters exporters exporters<br><!-- End of picture text -->

# **E. Assets under management by SWFs in commodity exporters, by region, 2022**

<!-- Start of picture text --> US$, billions<br>3,500<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>MNA AEs ECA EAP SSA LAC<br><!-- End of picture text --> _Sources_ : Davoodi et al. (2022); International Monetary Fund; Sovereign Wealth Fund Institute.

38
Our field experience and interactions with the survey teams in both settings, as well as the quantitative evidence on the reasons for not obtaining GPS-based plot areas, suggest that the missing data are overwhelmingly due to the aforementioned distance criteria for the selection of plots for GPS-based area measurement. Refusal (and physical accessibility) constitute a near-negligible share of reasons for the lack of GPS-based area measures in each setting, in line with the high response rates observed in rural East African settings.

Tables 1 and 2 present summary statistics based on the UNPS and TZNPS, respectively, and report the results from the tests of average differences by GPS-based plot area measurement status. Given the focus of the empirical application on agricultural productivity, we focus on cultivated plots in both samples. A number of noteworthy findings emerge. First, the number of plots lacking a GPS-based area measure is relatively large, with 1,519 and 759 missing data points accounting for 35 and 18 percent of the plot samples in Uganda and Tanzania, respectively. Second, the GPS-based and farmer-reported plot area estimates of average plot area are similar, and just over 2 acres in both countries. Average farmer-reported area is slightly larger for plots that were not measured, but the differences are not statistically significant. Third, several important plot- and householdlevel characteristics, which are expected to be associated with productivity-related outcomes, display statistically significant differences by GPS-based plot area measurement status. Taken together, these observations highlight the importance of systematically addressing missingness in GPS-based plot areas, if such GPS data are to be used in a robust fashion.

Plots without a GPS-based area measure are clearly non-random picks and they differ from their GPS-measured counterparts in similar ways in both Uganda and Tanzania. In line with the expectations informed by the field work protocols, non-measured plots tend to be further away from the household location. They are additionally more likely to be rented and to lack desirable features in terms of soil quality and slope. Plots with a GPS-based area measure originate, on average, from households that are headed by older individuals who are more likely to report agriculture as their primary occupation. In Uganda, heads of households associated with plots with a GPS-based area measure are more likely to be female, while their counterparts in Tanzania have, on average, fewer years of education. Lastly, non-measured plots in both settings are more likely to stem from (intact) mover original and split-off households, as opposed to non-mover households re-interviewed in the TZNPS 2010/11 and the UNPS 2009/10. Given the additional workload associated with tracking these households in their new locations, and the possibility that households moving into distant areas might still keep their plots in their original locations, which in turn may not be GPS-measured in accordance with the survey protocol, this finding is also in line with our expectations.

10
Table 4: Total production at time _t_ + 1

||(1)|(2)|(3)|
|---|---|---|---|
|_Panel A: First Stage_||||
|||payment =|1|
|Below trigger|0.245<sup>_∗∗∗_</sup>|0.235<sup>_∗∗∗_</sup>|0.239<sup>_∗∗∗_</sup>|
||(0.0590)|(0.0768)|(0.0649)|
|_Panel B: Reduced form_|∆|log ha sowed|(all)|
|Below trigger|0.0704<sup>_∗∗∗_</sup>|0.0663<sup>_∗∗∗_</sup>|0.0479<sup>_∗_</sup>|
||(0.0231)|(0.0227)|(0.0240)|
|_Panel C: 2SLS_||||
||∆|log ha sowed|(all)|
|�<br>% ha receiving payment|0.288<sup>_∗∗_</sup>|0.282<sup>_∗_</sup>|0.200<sup>_∗_</sup>|
||(0.117)|(0.146)|(0.121)|
|Bandwidth|50|70|all|
|Polynomial|linear|linear|quadratic|
|N|1503|1947|3329|

_∗ ∗∗_ Note. Standard errors are clustered at the state level. Asterisks indicate statistical significance: _p <_ 0 _._ 10, _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Observations are at the municipality-year-level. Hectares sowed are defined as the total hectares growing any rainfed agricultural crop as reported in SAGARPA production data. Panel A shows results from the OLS estimation of the first stage, which corresponds to estimating equation 1. Panel B shows results from the OLS estimation of the reduced form, which corresponds to estimating equation 2, and panel C displays 2SLS estimates. Column 1 is estimated with a linear polynomial on a sample of observations within the optimal bandwidth of 50 mm. Column 2 is estimated with a linear polynomial on a sample of observations within an alternative bandwidth of 70 mm. Column 3 is estimated using a quadratic polynomial on the entire sample.

28
# **Introduction**

The informal sector constitutes a very large portion of employment in many developing economies, often greater than 90% of workers. Moreover, many new enterprises that are formed are in the informal sector, creating persistence (e.g., Schoar 2009, Ardagna and Lusardi 2008). Informal sectors are associated with high poverty rates, poor jobs, and gender discrimination (e.g., OECD 2009, Kanbur 2011), and evidence for India suggests that the productivity growth for the informal sector is not keeping pace with the formal sector (Kathuria et al. 2010). Simple cross-sectional plots like Figures 1 and 2 highlight the importance of understanding the origins of the informal sector, its economic consequences, and its relationship to the formal sector.

To realize sustained development, many policy makers and business leaders want to encourage the informal-to-formal sector transition of workers (e.g., NCEUS 2009, Unni 2005). A number of studies focus on issues like property rights, business registration procedures, and financial access that are important for this transition, often with specific application to whether entrepreneurs choose to enter the formal economy or not (e.g., de Soto 1989, Bruhn 2011). These studies have been very influential in the design of policies to aid regional economic growth and development. Addressing these issues at the local level is one of the most pressing challenges for regional planners in many developing economies.

We consider these informal-to-formal sector transitions within India. As we define in greater detail in Section 2, our data allow us to consider unorganized and organized sectors of the Indian economy for manufacturing and services. Establishments in the unorganized sector in India are unregistered, do not pay taxes, and are generally outside the purview of the state, so this division closely parallels common discussions and definitions of informal and formal sectors. Our paper starts by outlining some key facts about India’s unorganized sector. We then undertake several empirical analyses to identify traits of state-industries where transitions occur.

The Indian data exhibit four key facts about the unorganized sector:

1. _The unorganized sector is very large_ – This fact is fairly well known, but worth repeating. For the manufacturing sector in 2005, 99% of establishments and 81% of employment are in the unorganized sector. The estimated size of the unorganized sector for services depends upon definitions, as discussed below, and ranges from 74% to 90% of services employment in 2006. Even if viewed from an establishment size distribution perspective, these shares are much larger than in the United States, for example. Moreover, Figure 1 shows that India’s unorganized sector is large for its stage of development.

2. _The unorganized sector is extremely persistent_ – India’s economy has undergone amazing changes during the past 20 years, but the share of the unorganized sector has remained stubbornly persistent. The employment share in the unorganized sector for manufacturing in 2
WPS3502 Access to Capital in Rural Thailand: An Estimated Model of Formal vs. Informal Credit

## Xavier Gin ́e<sup>_∗_</sup> The World Bank

#### **Abstract**

The aim of this paper is to understand the mechanism underlying access to credit. We focus on two important aspects of rural credit markets in Thailand. First, moneylenders and other informal lenders coexist with formal lending institutions such as government or commercial banks, and more recently, micro-lending institutions. Second, potential borrowers presumably face sizable transaction costs obtaining external credit. We develop and estimate a model based on limited enforcement and transaction costs that provides a unified view of these facts. The results show that the limited ability of banks to enforce contracts, more than transaction costs, is crucial in understanding the observed diversity of lenders.

### JEL Code: O12

Keywords: Credit Constraints, Transaction Costs, Maximum Likelihood.

# **1 Introduction**

Most productive activities entail a time lag between the time when inputs are acquired and the time when output is obtained. For this reason, when self-financing is not possible, the inputs must be purchased using credit from financial institutions or informal

> _∗_ Email: xgine@worldbank.org. This paper is a revised version of my PhD thesis. At different stages, I benefitted from the valuable comments of Chris Ahlin, Orazio Attanasio, Paco Buera, Pedro Carneiro, Maitreesh Ghatak, Joe Kaboski, Stefan Klonner, Robert Townsend, Chris Udry, Ivan Werning, Luigi Zingales and many workshop participants. All errors are my own.

1
**Table 15: Number of Full-time employees (% of respondents)**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|Less than 10|13|18|-|11|
|Between 10 and 100|67|52|58|59|
|Between 100 and 500|7|18|21|15|
|500 and more|13|12|21|15|
|Total|100|100|100|100|
|_Shanghai_|||||
|Less than 10|5|-|-|2|
|Between 10 and 100|25|55|67|45|
|Between 100 and 500|25|44|27|29|
|500 and more|45|1|6|22|
|Total|100|100|100|100|

**Table 16: Age of Company as of 2003**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|Less than 5 years old|47|55|47|50|
|Between 5 to 10 years old|27|20|20|22|
|Between 10 to 20|13|20|27|20|
|More than 20 years old|13|5|6|8|
|Total|100|100|100|100|
|_Shanghai_|||||
|Less than 5 years old|43|56|35|42|
|Between 5 to 10 years old|33|44|45|40|
|Between 10 to 20|24|-|20|18|
|More than 20 years old|-|-|-|-|
|Total|100|100|100|100|

# **Location Choice of Firms**

This section highlights the factors that a firm considers when it makes the decision to locate its operations in a particular location. During the interviews, we inquired about the factors at the neighborhood level and on a larger geographical scale concerning regional differences between the two cities.

Forty-eight firms in Beijing and 40 firms in Shanghai responded to the inquiry on their initial location decisions. The majority of respondent firms in Beijing did not consider other locations apart form Beijing when making their initial location decision, while much larger 29