rafmacalaba/data-use-mentions-extended huggingface dataset:
survey data until a new survey becomes available. Specifically, we use the survey data of 1999 for years 1996 to 2001, the survey data of 2002 for years 2002 to 2004, and the survey data of 2005 for years 2005 to 2009.

We consider three groups of bank regulation/institutional variables. The first group of regulatory variables is related to state policies that enable or restrict competition. Entry barrier index, _entry_bar,_ measures bank entry requirements, which is constructed based on eight questions in the Barth, Caprio, and Levine surveys regarding legal submissions required to obtain a banking license in a given country. Application denied, _ap_denied_ , is the percentage of applications to set up a bank which were denied in the past five years. Government ownership, _gov_own_ , measures the fraction of banks that are 50% or more owned by the government. The second group of variables measure bank regulation and supervision. Activity restrictions index, _activity_restriction_ , measures the degree to which the national regulatory authorities allow banks to engage in securities, insurance, and real estate businesses. Capital stringency index, _capital_stringency_ , measures the amount of capital a bank must maintain. Supervisory power index, _supervisory_power_ , indicates whether the supervisory authorities have the power and the authority to take specific preventive and corrective actions. Diversification index, _diversification_index_ , captures whether there are explicit, verifiable, quantifiable guidelines for bank asset diversification and whether banks are allowed to make loans outside of national borders. Deposit insurance coverage ratio, _covratio,_ is the amount of deposit insurance coverage divided by deposits per capita. It is set to 1 if a country offers full coverage. We obtain this variable from Demirguc-Kunt, Kane, and Laeven (2008). Since the data ends in year 2003 and most countries did not change their deposit insurance coverage till the recent financial crises, we use the deposit insurance coverage in 2003 for years 2003 to 2007.<sup>9</sup> Finally, we also use data on the investor protection index ( _investor_protection_ ), depth
<br>China, Burma,<br>Namibia,<br>Thailand,<br>Botswana,<br>Laos,<br>Zambia,<br>Cambodia,<br>Zimbabwe,<br>and Vietnam<br>and<br>Mozambique,<br>Snowmelt No No<br><!-- End of picture text -->

**Table 2: Selected river basins have a range of physical and socio-economic characteristics**

|Mekong|Zambezi|
|---|---|
|-1.0|-12.6|

**Table 3: Palmer et al. predict the relative change (%) of discharge in selected river basins from 1960-1990 (km**<sup>**3**</sup> **yr**<sup>**-1**</sup> **) compared to 2050s (km**<sup>**3**</sup> **yr**<sup>**-1**</sup> **)**

> 12 The selection is based on greater or equal to 50,000 Km<sup>2</sup>

> 13 Population statistics based on Landscan 2000 estimates summarized by basin (TFDD, 2008).

32
Figure 4: Distribution of Total Seasonal Rainfall <!-- Start of picture text --> Ethiopia (n = 10,674) Malawi (n = 8,897)<br>.0025 .004<br>.002<br>.003<br>.0015<br>.002<br>.001<br>.001<br>.0005<br>0 0<br>0 2000 4000 6000 8000 500 1000 1500 2000 2500<br>Niger (n = 3,913) Nigeria (n = 9,145)<br>.008 .002<br>.006 .0015<br>.004 .001<br>.002 .0005<br>0 0<br>0 200 400 600 800 0 1000 2000 3000<br>Tanzania (n = 9,916) Uganda (n = 11,692)<br>.002 .004<br>.0015 .003<br>.001 .002<br>.0005 .001<br>0 0<br>0 2000 4000 6000 0 1000 2000 3000 4000<br>Total Season Rainfall (mm) Total Season Rainfall (mm)<br>ARC2 CHIRPS CPC ERA5 MERRA-2 TAMSAT<br>Density<br>Density<br>35<br>Density<br><!-- End of picture text --> _Note_ : The figure presents the distribution of total seasonal rainfall pooled across all years, disaggregated by Earth observation source. Each distribution (Earth observation product) in each panel is constructed using all household-year observations ( _n_ ). Variation in distributions do not come from variation in the household data that is paired with the Earth observation data. Rather, variation in distributions within a panel is solely due to differences in the value of precipitation reported by the Earth observation source.
br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; Rozenberg and Fay (2019); World Bank. Note: AEs = advanced economies; EMDEs = emerging market and developing economies; SSA = Sub-Saharan Africa.

A. Average annual cost of investment in the “preferred scenario”, 2015-30 (Rozenberg and Fay 2019). B. Median; bars indicate interquartile range. Logistics Performance Index (LPI) measures the performance of trade logistics and is a weighted average of country scores on six key dimensions: customs performance, infrastructure quality, ease of arranging shipments, logistics services quality, consignments tracking and tracing, and timeliness of shipments. A higher value indicates better performance; for example, Germany’s LPI (top performer) is 4.2. C. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 47 SSA economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 47 SSA economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 47 SSA economies. Latest data available during 2011-15.

D. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 29 SSA economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 44 SSA economies. Latest data available during 2011-15.
**Figure 2:** Electoral and Bureaucratic Turnover

<!-- Start of picture text --> (a) New village head in survey data (b) Tenure of village head (years)<br><!-- End of picture text --> <!-- Start of picture text -->
-.2 -.1 0 .1 .2 -.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger Margin of victory of best-ranked challenger<br>(c)  % New appointments (d)  Bureaucrat parent served in village gov.<br>-.2 -.1 0 .1 .2 -.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger Margin of victory of best-ranked challenger<br>6<br>0.5<br>4<br>0.2<br>-0.1 1<br>New village head in survey data Tenure of village head (years) -2<br>-0.3<br>-0.6 -4<br>0.7 0.3<br>0.4 0.1<br>0.1 -0.1<br>Bureaucratic turnover<br>-0.2 -0.3<br>Parent served in village government<br>-0.5 -0.5<br><!-- End of picture text -->

_Notes_ : Panel (a) looks at the probability that the village head in our survey sample is a different individual from the incumbent candidate competing in the most recent village election. Panel (b) looks at the number of years in office of the village head in our sample. Panel (c) looks at the village-level fraction of bureaucrats (excluding the village head) who began in their current position since the last election. Panel
1<br>.8 0<br>.6<br>-.1<br>CCDF<br>.4 Difference<br>.2 -.2<br>0<br>-.3<br>.5<br>.4<br>.3<br>.2<br>Social Sustainability Index .1<br>0<br><!-- End of picture text -->

# **C. Sensitivity Analysis: Number of indicators per dimension, 3 instead of 4.**

_C1. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Quality of employment indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C1.1. FOSD: Counting Vector

<!-- Start of picture text --> C1.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.2<br>1<br>.8 .15<br>.6<br>CCDF .1<br>.4 Difference<br>.05<br>.2<br>0<br>0<br><!-- End of picture text --> C1.3. SOSD: Social Sustainability Index C1.4. Rank correlations 46
Table 3: Evolution of productivity

||**Labor productivity (sales**|**per worker in 2009 US$)**<br>|**Total factor**|**productivity**<br>|
|---|---|---|---|---|
||Mean|Median|Mean|Median|
|**2006**|40,834.38<br>[37,144.52; 44,524.23]|30,115.93<br>[27,224.56; 32,333.8]|2.34<br>[2.22; 2.46]|2.22<br>[2.04; 2.33]|
|**2010**|61,021.07<br>[40,803.56; 81,238.57]|40,045.05<br>[35,577.81; 45,986.21]|2.76<br>[2.59; 2.93]|2.54<br>[2.38; 2.72]|
|**2017**|50,029.06<br>[42,930.46; 57,127.66]|41,843.06<br>[38,414.61; 46,530.6]|2.43<br>[2.26; 2.59]|2.31<br>[2.13; 2.43]|

Notes: Survey weights are applied in the computation of the data presented above. Lower and upper bound 95 percent confidence intervals are presented in brackets.

Turning next to establishments’ management practices, the data from the 2017 Enterprise Surveys show that, based on the average management score, establishments in Colombia
(1994), recognizes that farmers will vary the mix of activities to choose the one yielding the highest return on any given parcel of land. As a consequence, they focus on the impact of climate on land values<sup>ii</sup> , not yields. By associating climatic variation in a cross-section of data with variation in land values, researchers aim to estimate the long run economic value of climate and hence the impact of changes in climate, _once adaptation to the new climate has taken place_ .

The Ricardian approach has been applied in a wide range of circumstances, including developed and developing countries. Mendelsohn (2009) offers a summary of the findings with individual farm data and with district level data. His studies of Brazil and India suggest that agriculture in both countries is sensitive to even modest warming. However, within these countries, there is great heterogeneity in the estimated impacts. For example he finds that “the wet eastern region of India would mildly benefit from warming whereas the dry western region of India would suffer large damages. The southeastern region of Brazil would benefit whereas the Amazonian and northeastern region of Brazil would be hurt.” (pp. 7-8) Mendelsohn, Nordhaus, and Shaw (1994) and Mendelsohn et al. (2007) have also examined the impact of increased inter-annual variance in climate on land values. They find that the impact depends on the timing of these climatic shocks. If the increased variance is in the spring or summer, it tends to reduce land values, as farmers cannot adapt by changing planting decisions. However, they find that heightened winter climatic variance can actually boost the economic value of land, as farmers can take advantage of these changes by planting different crops and adjusting the growing season.

Analysis at the farm level in Africa shows that warming hurts dry land farming, but benefits irrigated agriculture. These studies also show that temperature affects the choice of cropping vs.

12
other aspects related to the quality of care, including the duration of consultations, whether providers explained the different elements, and whether they encouraged the women to ask questions. However, the presence of observers during the consultations might lead to providers performing better than usual due to observer or Hawthorne effects (Leonard and Masatu 2010). Observer or Hawthorne effects could induce attenuation bias in the estimated socioeconomic gradients if increased provider efforts were more concentrated among poor women. If this was the case, the direct observation results would be a lower bound for the true wealth‐quality gradient and its correlates.

To facilitate comparability, we restrict our analysis of the direct observation and exit interview data to women who were receiving the first consultation of their pregnancy. These consultations are more comparable to each other, because their content does not depend on previous consultations and include procedures that should be conducted regardless of women’s medical and fertility histories. As poorer women attend fewer consultations during their pregnancies, there is also most variation in wealth among women attending a first consultation.

Table C.1, in appendix C, presents the basic sample characteristics. The first panel provides the characteristics of women in the household survey. We restricted the analysis to women who had a live birth in the two years preceding the survey. Because most of the analysis is of the information on content of care, the table is further restricted to the 82 percent of the women who attended at least one antenatal consultation during their recent pregnancy. The second panel presents the characteristics of the sample of pregnant women who completed exit interviews after receiving the first antenatal consultation of their pregnancy. The two samples are overall similar, although women in the household sample are slightly older on average and slightly less likely to have attended secondary school.

Following the methodology originally proposed by Filmer and Pritchett (2001), we use principal component analysis to create a wealth index, using a list of 32 household assets collected in the household survey and exit interviews. For the household sample, the
WPS3818

# **The Use of Willingness to Pay Experiments: Estimating demand for piped water connections in Sri Lanka**

**_Subhrendu K. Pattanayak, Caroline van den Berg, Jui‐Chen Yang, and George Van Houtven_**<sup>**_1_**</sup>

## Abstract

_This paper shows how Willingness to Pay surveys can be used to gauge household demand for improved network water and sanitation services when a private sector transaction is considered. We do this by presenting a case‐ study from Sri Lanka, where we surveyed approximately 1,800 households in 2003. Using multivariate regression, we show that a complex combination of factors drives demand for service improvements. While poverty and costs are found to be key determinants of demand, we also find that location, self‐provision, and perceptions matter as well, and that sub‐sets of these factors matter differently for sub‐samples of the population. To evaluate the policy implications of the demand analysis, we use the model to estimate uptake rates of improved service under various scenarios – demand in sub‐groups, the institutional decision to rely on private sector provision, and various financial incentives targeted to the poor. The simulations show that in this particular environment in Sri Lanka, demand for piped water services is low, and that it is unlikely that under the present circumstances the goal of nearly universal piped water coverage is going to be achieved. Policy instruments, such as subsidization of connection fees, could be used to increase demand for piped water, but it is unclear whether the benefits of the use of such policies would outweigh the costs._

**JEL classification** : **H4** Publicly Provided Goods; **R2** Household Analysis; **O1** Economic Development; **O2** Development Planning and Policy

## World Bank Policy Research Working Paper 3818, January 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of
Henriksen, & Foshee, 1998; Smith, 2011). Some dimensions of parenting are not applicable at all ages (e.g. inductive reasoning is not applicable in infancy), and specific parenting behaviors may be appropriate at some ages but not others (e.g. leaving the child alone in their room may be an appropriate discipline strategy for a preschooler, but not for an infant). As a result, both the broader parenting constructs and the specific items used to assess them needed to be mapped against the ages of intended use. We therefore standardized our measures to the age of the child when estimating econometric models.

We present descriptive statistics on these measures in Panel A of Table A6. These figures indicate that there is variation on these practices within our sample, and also that the caregivers we study are over-represented in the bottom of the distribution of these scores. When we examine differences by literacy, we see that women who can read are substantially more likely to show warmth and consistency in their parenting, and less likely to show hostility.

# **2.3.2 Activities**

Caregivers were also asked to complete an inventory asking whether or not the child took part in a series of typical play and stimulation activities in the past week. This is akin to the inventory of activities employed by time-use surveys in Attanasio et al. (2020) and Andrabi et al. (2012). The descriptive statistics are presented in Panel B of Table A6. Across the board we see high level of engagement in stimulating activities, but also substantial engagement in household chores. We also looked at differences between children of literate and of illiterate mothers, and found that, as expected, the former are more likely to be engaged in reading and music-playing (13 p.p.), but also in every other activity (albeit with smaller differences across groups).

# **2.3.3 Investments in human capital**

In addition we use information from the section
>1<br>11|33% (32%)|
|secondary|Unemployed|6<br>4<br>7<br>**24**<br>59|16% (10%)|
||Out of labor force|4<br>3<br>4<br>12<br>**77**|43% (49%)|
||Private infor|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to women who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by educational levels. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

13
# 1 Introduction

Designing voluntary programs that induce behavioral change can be fraught with problems of selfselection: households who would have sent their children to school without incentives are compensated for enrollment (De Janvry & Sadoulet 2006), investors who were already building lowincome housing receive tax benefits intended to induce them to do so (Sinai & Waldfogel 2005, Gyourko 2009), energy efficiency programs enroll individuals who would already have adopted energy-efficient technologies (Joskow & Marron 1993, Boomhower & Davis 2014), or producers who did not intend to deforest are paid for forest conservation efforts (Alix-Garcia et al. 2008, Ferraro 2008). While conditional cash transfer programs (CCTs) usually benefit the voluntary recipients, they often fall short on the behavior-change goals, and thus can result in inefficient allocation of scarce resources. One of the important lessons from this literature is that effective targeting requires being able to predict which potential beneficiaries are most likely to engage in the targeted behavior (truancy, pollution, deforestation, etc.). Yet obtaining the information necessary to identify where transfers are most effective and determining the appropriate levels of compensation can be immensely difficult and expensive in practice. Practical strategies that successfully allow policy-makers to do this are scarce (Johnson & Lipscomb 2017, Allcott & Kessler 2018).

In this paper, we study CCT design in the context of a major environmental cash transfer program in Mexico. We propose that adding a simple stated preference referendum-style willingness to accept (WTA) question to occasional surveys of potential recipients could be used to improve program additionality and cost-effectiveness as well as to generate estimates of program social cost. Specifically, we illustrate two strategies to refine program design for conditional cash transfers: re-targeting based on the WTA estimates and a constructed risk index using observable geographic covariates, and changing the program’s schedule of payments based on geographic zones and WTA estimates.

The conditional cash transfer we study is focused on reducing deforestation. Tropical deforestation threatens biodiversity, carbon storage, watershed function and rural livelihoods, and policymakers continue to struggle
Policy Research Working Paper 9628

# **Abstract**

This paper examines the welfare effects of international trade on workers in a new dynamic general equilibrium discrete choice model of labor mobility, where the workers’ choice set of jobs is endogenous. The analysis exploits differential exposure of sectors and regions to destination-specific demand shocks to estimate the impacts of exports on wages, employment, and labor mobility, using employer-employee panel data for Brazil. It then employs the same empirical strategy to estimate structural parameters and the different components of changes in model-implied worker welfare. Counterfactual simulations show that the endogenous number of job options significantly magnifies the welfare effects of trade shocks.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at eartuc@worldbank.org, pbastos@worldbank.org, and eunhee11@umd.edu.

_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