rafmacalaba/datause-encoder-data huggingface dataset:
Table A5: <u>Specification of variables — Impacts of hosting refugees on household welfare and poverty</u>

||||Househo|lds<br>Consumption||Wealth|
|---|---|---|---|---|---|---|
||Consumption|Wealth|Consumption|poverty|Wealth|poverty|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Ln (Refugee Intensity)|-0.189***<br>(0.070)|0.257<br>(0.248)|||||
|IHS Refugee Intensity|||-0.189***|0.179***|0.257|-0.007|
||||(0.070)|(0.051)|(0.248)|(0.045)|
|Constant|10.418***|-3.704**|10.550***|-1.020***|-3.882**|0.656*|
||(0.410)|(1.725)|(0.445)|(0.365)|(1.868)|(0.356)|
|Round FEs|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|
|R-squared|0.068|0.190|0.068|0.043|0.190|0.115|
|# of EAs|433|433|433|433|433|433|
|N|12937|13522|12937|12937|13522|13522|

_Notes:_ This table provides estimates of the impacts of logarithm of (the first two columns) or inverse hyperbolic sine (IHS) of (the last four columns) refugee intensity on IHS of total household consumption expenditure per capita (columns (1) and (3)), a hybrid hyperbolic sine and its inverse transformation of the wealth score (columns (2) and (5)), a binary measure of household consumption poverty status (column (4)), and a binary measure of household wealth poverty status (column (6)). Household sample weights from ESS2 applied. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

55
# Governing the Commons?

Water and Power in Pakistan’s Indus Basin Hanan G. Jacoby<sup>_∗_</sup> Ghazala Mansuri<sup>_†_</sup>

**Keywords:** Bureaucracy, rent-seeking, corruption, common property resource, irrigation

**JEL codes:** D73, P48, Q15, Q25

> _∗_ World Bank (hjacoby@worldbank.org), 1818 H St NW, Washington DC 20433. Funding for this project was provided by the World Bank’s Knowledge for Change Program, its Strategic Research Program, and its Research Support Budget. The project would not have been possible without the support of the Punjab Irrigation Department. The authors are particularly grateful to Mr. Habibullah Bodla at the PMIU for providing access to the discharge data and to Mr. Afzal Toor at PIDA for facilitating the irrigation outlet survey. The team also thanks Mr. Saif Anjum (ex-Secretary Irrigation) for his consistent support. The views expressed herein are those of the authors, and do not necessarily reflect the opinions of the World Bank, its executive directors, or the countries they represent.

> _†_ World Bank (gmansuri@worldbank.org).
12

# **Table 1: Estimates of elasticities of substitution in previous studies**

|**Inputs**|**Estim**<br>|**ates**<br>|**Study**|
|---|---|---|---|
||**Allen Elasticity**|**Full Elasticity**||
|Capital and natural<br>resourceinputs|-0.82<br>(0.47;1.08)<sup>a</sup>|-<br>-|Parks (1971)<br>Moroney andTrapani(1981)|
|Labor and natural<br>resourceinputs|0.90<br>(0.63 to1.33)<sup>b</sup>|-<br>-|Parks (1971)<br>Moroney andTrapani(1981)|
|Capital and Labor|0.12<br>1.09<br>1.01<br>(.06 : 0.39)<br>(0.60 : 0.95)<br>0.88<br>-|-<br>(0.56 : 0.74)*<br>-<br>(0.17 : 0.19)*<br>-<br>-<br>0.82**|Parks (1971)<br>Hudson and Jorgenson (1974)<br>Brendt and Wood (1979)<br>Griffin and Gregory (1976)<br>Moroney and Trapani (1981)<br>Prywes (1986)<br>Kemfert (1998)|
|Capital and Energy|-1.39<br>-3.22<br>1.03: 1.07<br>-1.35<br>2.17<br>-|(-0.09 : 0.24) *<br>0.26**<br>(0.33 : 0.92)*<br>-<br>0.87**<br>0.65**|Hudson and Jorgenson (1974)<br>Brendt and Wood (1979)<br>Griffin and Gregory (1976)<br>Prywes (1986)<br>Chang (1994)<br>Kemfert (1998)|
|Labor and Energy|2.16<br>0.65<br>(0.84 : 0.87)<br>0.88<br>0.35|-<br>-<br>-<br>-<br>-<br>0.42**|Hudson and Jorgenson (1974)<br>Brendt and Wood (1975)<br>Griffin and Gregory (1976)<br>Prywes (1986)<br>Chang (1994)<br>Kemfert (1998)|
|Labor and Capital<br>‘nest’ and Energy|-<br>-|0.40**<br>0.42**<br>0.50**|Manne and Richels (1992)<br>Chang (1994)<br>Kemfert (1998)|

The figures are rounded off to the nearest hundredth.

Unless stated otherwise, the elasticities above are calculated for the aggregate of the industry. Energy – the variable refers to total energy consumption a The given Allen elasticities are statistically significant only for two industries: primary aluminum and blast furnaces/basic steel, respectively.

b A range is given to cover the estimated elasticities for 4 of the 6 industries studied: primary aluminum, blast furnaces/basic steel, storage batteries and hydraulic cement.

(*) The full elasticity was calculated by Kang and Brown (1981)

- (**) The full elasticity was calculated by the authors

Study focus and estimation method: Parks (1971): Swedish manufacturing industry; Generalized Leontief Function Moroney and Trapani (1981): Mineral-intensive manufacturing industries; Translog cost model Hudson and Jorgenson (1974): U.S. manufacturing sector; Translog cost function Berndt and Wood (1979): U.S. manufacturing sector; Translog cost function Griffin and Gregory (1976): U.S. and U.K. manufacturing sectors, respectively; Translog cost function Prywes (1986): Manufacturing sector; Nested CES estimation. Manne and Richels (1992): Manufacturing sector; Nested CES Chang (1994): Taiwan manufacturing industry (aggregate); Nested CES and Allen elasticities of substitution Kemfert (1998): German manufacturing industry (aggregate); Nested CES
assistance), with pensioners in the middle; consistent with the life-cycle model under the assumption of a steeper expected income profile. However, no significant difference emerges between employed and unemployed heads of household, furthermore, while savings rates for households headed by individuals employed in either the private or the public sector exceed savings in the excluded references group of households headed by individuals employed in the mixed sector, no significant difference emerges between the categories of public versus private employment. Overall, the income factor most relevant to savings thus appears to be the split between gainful employment and other income sources, with much more muted differences between types of employment. To the extent that a lack of gainful employment signals decreasing income expectations, these findings are consistent with the results for Russia obtained by Gregory, Mokhtari and Schrettl (1999).

The level of education attained by the head of household provides a second indication of the likely net benefit from transition. In comparison with market economies, the monetary return to education was negligible in planned economies; the earnings of university graduates were generally comparable with, and occasionally below the earnings of factory workers. The transition is thus likely to lead to a relative income gain and a steeper income profile of households headed by individuals with tertiary education, suggesting, in terms of consumption smoothing motives, a lower propensity to save. The regression results are consistent with this view: with one (statistically insignificant) exception, households headed by individuals with no tertiary education on average saved a higher income fraction compared to the excluded group of households headed by individuals with tertiary education.

The evidence on consumption smoothing and risk-aversion presented here is of course conditional on the correct identification of factors likely to be associated with the slope of the lifetime income profile and income uncertainty. Conditional on these assumptions, the results do suggest that at least some of the factors associated with upward tilting lifetime incomes and lower risk are associated with reduced savings rates. Perhaps most puzzling is the apparent irrelevance of the sector of employment for savings decisions, despite the likely future contraction of the public relative to the private sector. The muted support is consistent with prior findings for developing market economies [Gupta (1987), Campbell and Deaton (1989), and Schmidt-Hebbel, Webb and Corsetti (1992), inter alia] which, following quite different methodologies, find at most muted support for smoothing.

# **5. Conclusion**

There are few instances of radical economic change comparable to the transition of eastern Europe from plan to market, providing a valuable opportunity to study economic behavior in the presence of first order changes in circumstances. Using three matching surveys for Bulgaria, Hungary and Poland, we explored the savings responses of household to the transition experience.
_Toilet construction._ Toilet construction was measured using households’ self-reported ownership of toilets, and toilet technology as observed by the survey team. In line with the Joint Monitoring Programme (JMP) for Water Supply and Sanitation, coordinated by the World Health Organization and United Nations Children’s Fund, we have defined toilets in three categories. _Safely managed_ toilets are those involving piped sewer systems, septic tanks (with or without soakage pits), and leach pit latrines. _Basic_ facilities include safely managed toilets as well as pit latrines with a slab. _Limited_ facilities are improved toilets shared among several households. We also included data on toilets under construction.

_Toilet use_ . Toilet use was measured based on the self-reported use of survey respondents. Respondents were asked whether some members of their household practiced open defecation. So, our measurement of open defecation implies open defecation by any member of a household.

_Hygiene awareness and hygienic behavior._ Hygiene awareness was measured using respondents’ correct identification of the critical times when handwashing is required, the benefits of using soap, and the risks of defecating in the open. Hygienic behavior was measured using the following: (i) percentage of critical times that the respondent washes hands; (ii) percentage of critical times soap is used; (iii) whether the respondent washes hands with soap after defecation and before eating; (iv) the availability of soap in the household; (v) the treatment of water for drinking; and (vi) the disposal of child feces (in a toilet). Similar hygiene awareness and behavior outcomes were measured among children aged 6 to 14 years.

# 4.6 Design Validation

We compared the baseline characteristics of the three treatment arms and the control arm to check for any imbalances in the observed characteristics. Since we had randomized the selection of GPs as well as of households, we expect any imbalances in baseline characteristics to be purely due to chance. We did not test for significance in these baseline differences. As has been argued by various authors (Senn 2013; De Boer et al. 2015), significance tests are not suitable for detecting meaningful differences in baseline characteristics that can lead to confounding. Confounding occurs when certain differences in baseline characteristics can predict the outcomes. For covariates that are highly correlated with the outcomes, even a small difference that is statistically insignificant can be far more important than large significant differences in covariates that are otherwise uncorrelated with the outcomes. So, following Where _w = T, C_ 16
World Bank's population division. Explanations of methodology and assumptions are discussed in a series of World Bank Working Papers covering each region and published by the Population and Human Resources Department in 1989. The methodology produces a convergence of demographic distributions among most countries by the year 2150, with about 25% of the population over age 65 and 25% under age 20. Further inforrnation on the methodology and assumptions is available from the authors, Bulatao and Bos of the World Bank (see Bibliography).

The World Bank's population division produces a STARS version of its population projections which is publicly available on computer diskette. These data were used to calculate variables that were used in the study. The projections cover 180 countries between 1985 and 2150 by five-year cohorts and by gender. The STARS data set which has been produced in conjunction with the book contains both projections and historical demographic data. These data can easily be transferred into most spreadsheet software.

Specifically, the report focuses on seven demographic indicators, including the percentage of the population over 60, 65 and 75, the ratio of old to working age persons and the ratio of old women to old men. The inverse of the dependency ratio, the support ratio, is also presented. These ratios appear several times in the book. For example, Figure 4.11 compares the old age dependency ratio to the ratio of pensioners to active contributors (see Section III.E below). In most cases, the old age dependency ratio mentioned in the text was defined as the ratio of persons over 60 years old to persons aged 20-59. This ratio was chosen over other possible combinations based on the observed age distributions of contributors and pensioners in most public pension schemes around the world. The tables in Annex Appendix I show demographic indicators for all countries along with regional weighted and unweighted averages.

2
Table 3: Correlates with Payment Behavior

||Ever<br>made a<br>payment|Proportion<br>of invoices<br>paid|Proportion<br>of loan<br>outstand-<br>ing|Connection<br>loan fully<br>repaid|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Property owner resides in the compound|0.034**|-0.029*|0.080***|-0.054***|
||(0.014)|(0.017)|(0.026)|(0.015)|
||r0.017s|r0.087s|r0.002s|r0.000s|
|Compound received water last week|0.169***|0.191***|-0.276***|0.125***|
||(0.012)|(0.015)|(0.023)|(0.013)|
||r0.000s|r0.000s|r0.000s|r0.000s|
|Property owner knows how to pay the bill|0.215***|0.224***|-0.204***|0.090***|
||(0.015)|(0.018)|(0.027)|(0.016)|
||r0.000s|r0.000s|r0.000s|r0.000s|
|Compound has tenants|0.006|-0.005|-0.032|0.013|
||(0.013)|(0.015)|(0.023)|(0.014)|
||r0.661s|r0.744s|r0.157s|r0.342s|
|Property owner pays water bills|-0.004|0.020|-0.034|0.002|
||(0.018)|(0.022)|(0.034)|(0.020)|
||r0.827s|r0.378s|r0.309s|r0.911s|
|Years with a water and/or sewerage connection|0.208***|0.127***|-0.050***|0.116***|
||(0.006)|(0.008)|(0.011)|(0.007)|
||r0.000s|r0.000s|r0.000s|r0.000s|
|Property owner knows how to read the meter|0.012|-0.006|-0.014|0.011|
||(0.014)|(0.017)|(0.026)|(0.015)|
||r0.410s|r0.727s|r0.577s|r0.466s|
|Property owner knows how to check balance|0.004|0.032|-0.056*|0.045***|
||(0.016)|(0.020)|(0.030)|(0.018)|
||r0.818s|r0.108s|r0.059s|r0.010s|
|Observations|4175|4164|4175|4175|
|R-squared|0.432|0.237|0.148|0.209|

*** ** * _Notes_ . Standard errors in parentheses; P-value in brackets. p<0.01, p<0.05, p<0.1. Payment outcomes are derived from the billing data and compound outcomes come from the property owner listing exercise in August/September 2018. Settlement fixed effects included.

32
The Gini coefficient is a metric that captures the extent to which the situation in a given urban area departs from the perfect equality situation (where every individual has an accessibility level equal to the average of the urban area). Mathematically it is equal to the area between the 45 degree line and the Lorenz curve for accessibility (see section 5 for a graphical illustration). The higher the Gini coefficient the more unequally distributed the level of accessibility within the urban area.

The Gini coefficients for accessibility Gare calculated with the following formula: Where the localized accessibility indicators are sorted in increasing order so that A� �A���. The first part of the numerator displays the cumulative sum of total accessibilities opportunities, where k∈�1, ... , I� and I represents the index of the pixel with the highest localized accessibility.

# **3.2 Geographic Frame of Analysis**

In order to have a comparable frame of analysis between metropolitan areas, regardless of different definitions of administrative boundaries, we established a uniform boundary for the metropolitan area. The boundaries of the analysis for each region were defined as all areas with an accessibility of above zero – that is, locations that fall inside a 1-hour travel time access to at least a single employment opportunity or proxy thereof. This defined the study area of our analysis.

As opposed to relying on administrative areas, and extending beyond the geographic distribution of opportunities/jobs within the analysis, peri-urban areas are included in the analysis based only on access to employment opportunities identified within the urban area, and not in outlying areas themselves. Thus, areas at the outer edges of the analysis may not account for access to nearby employment opportunities that fall outside the core area analyzed.

# **4 DATA**

Based on the methodology previously described, this work required three main sources of data inputs: (i) the public transportation network in order to calculate travel times between OD pairs, (ii) the spatial distribution of employment opportunities and (iii) the spatial distribution of population. Below is a description of the data sources used.

# **4.1 Public Transportation Networks**

Transport data were based on available GTFS feeds and shapefiles. In order to calculate accessibility, first a digital representation of the public transport network is created - either the General Transit Feed Specifications (GTFS) which is the standard data used for planning routes or a Geographic Information System (GIS) shapefile with the information on public transportation travel speeds and frequencies. General Transit Feed Specification (GTFS) is a free and open template for entering data related to basic transit system services, such as routes, stops, fares, and schedules. The information on routes may also be digitally recorded as a GIS shapefile. This shapefile must include each route that is served in the city. Additional to the routes the file must contain information about the frequency and speed, at different times of day, particularly the am peak.

7
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|Basic Education Project|Patricia Alvarez|
|---|---|
|Basic Education Project|Patricia Alvarez|
|Basic Education Project|Patricia Alvarez|
|Basic Education Project|Patricia Alvarez|
|Basic Education Project|Patricia Alvarez|
|Basic Education Project|Patricia Alvarez|
|Basic Education Project|Patricia Alvarez|
|Basic Education Project|Patricia Alvarez|
|Basic Education Project|Patricia Alvarez|
|LR-RRSP4 Budget Support|Errol George Graham|
|LR-RRSP4 Budget Support|Errol George Graham|
|LR-RRSP4 Budget Support|Errol George Graham|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change<br>|Bontje Marie Zaengerling<br>|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Cities and Climate Change|Bontje Marie Zaengerling|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|Water Management and Development Project|Carmen Rosa Yee-Batista|
|JSDF Grant for EmergencyCommunityCash for Work Project|Eli Weiss|
|JSDF Grant for EmergencyCommunityCash for Work Project|Eli Weiss|
|2nd. Programmatic Debt and Fiscal SustainabilityDevelopment P|olicSona Varma|
|2nd. Programmatic Debt and Fiscal SustainabilityDevelopment P|olicSona Varma|
four to four and a half months, and the remaining 27 percent for periods of two and a half to three and a half months.

# **3. SURVEY DATA AND DETERMINANTS OF MIGRATION**

# **3.1 Survey design**

One of the objectives of the RSE is to improve development outcomes within the sending countries. In order to evaluate the impact of the RSE program on Vanuatu, the World Bank partnered with the New Zealand Department of Labor and the University of Waikato, New Zealand to design a research study. The aim of the study is to survey households and individuals in Vanuatu before RSE workers leave for New Zealand, survey these same households once RSE workers have been in New Zealand for three to six months, and then survey the households again upon return of the workers. The study aims to survey participants in the RSE, individuals and households wishing to participate in the RSE who have not been selected, and individuals not wanting to apply for the RSE. At the village or community level, the survey also aimed to cover both communities with households participating in the RSE, and communities where no household had an RSE worker. In addition to a household survey, up to three community leaders (chiefs, church leaders, and teachers or youth representatives) in each community were administered a short community survey. This paper is based on the baseline survey, conducted between 9 October 2007 and 2 April 2008.

Several factors complicated the design of the survey. The most important was that approvals of ATRs to recruit in Vanuatu occurred on a rolling basis, as did their recruitment of workers. Once workers were selected for recruitment, there was often only

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