rafmacalaba/datause-encoder-data huggingface dataset:
capital in infrastructure) affects directly the level of production in the private sector-and thus the marginal productivity of primary factors employed in that sector-whereas public investment in education has a direct impact on unskilled workers' decision to acquire skills (as in Jung and Thorbecke (2001)). This effect operates in addition, of course, to movements in relative wages across skill categories and the initial level of individual wealth, which acts as a constraining factor in the presence of restrictions in accessing the credit market. Our view is that this distinction is essential to understanding how the savings created by debt relief should be allocated by governments (or donor agencies) concerned with maximizing their poverty-reduction effects.

# **3** Structure **of** IMMPA

Given the type of policy issues that we wish to address, and the complexity of some of the channels highlighted above (regarding notably the structure of the labor market and the linkages between the real and financial sides of the economy), our strategy has been to specify our framework in a relatively parsimonious way by restricting its various building blocks to what we believe to be essential components. In this section we review these various building blocks and consider in turn the production side, employment, the demand side, external trade, sectoral and aggregate prices, income formation, the financial sector and asset allocation decisions, and the public sector. We specify behavioral functions for each of the six broad categories of agents included in IMMPA: households (disaggregated by levels, of skills and location into various types of rural and urban households), firms, the government, the central bank, commercial banks, and the rest of the world. We also discuss the poverty and income distribution indicators that we use as a basis for analyzing the effects of our various simulation experiments on the poor, as well as the links between the model's predictions and household expenditure surveys. Throughout the discussion, we often use "generic" forms to specify functional relationships; explicit functional forms (as well as variable names and definitions) are provided in Appendices A and B and their exact specification in the computer simulation program are provided in Chen et al. (2001)). Finally, as indicated earlier, we focus in this paper on the prototype IMMPA model that we have designed with low-income countries in mind, leaving for a companion paper (see Ag6nor, Fernandes, Haddad, and van der Mensbrugghe (2003)) the modified version that we have built 11
reports that in a 2006 survey of Colombian migrants in Ecuador, the snowballing procedure worked poorly, with less than one referral obtained per four interviewed households.

The third method used to sample immigrants or ethnic minorities makes use of the fact that immigrants often cluster at certain locations. Simple examples of this type of sampling carried out sampling at only one type of location. Examples include surveying Mexicans at border crossing points in the Encuesta sobre Migración en la Frontera Norte (EMIF) (Bustamente et al., 1997), and surveying Latina immigrant women at churches in the U.S. (Wasserman et al. 2005). However, by sampling at only one type of location, the survey is likely to miss many migrants. Better coverage of the population of interest can be achieved by surveying at multiple locations. An issue which arises here is that individuals can potentially be surveyed more than once, so the survey needs to account for multiple selection possibilities during analysis.

Sampling theory for multiple location samples are provided in Kalsbeek (1986) and Kalton (1991, 2001). The basic survey design involves sampling in both space and time. Primary sampling units are constructed as combinations of locations and time segments where surveying will take place at the location. Then some form of systematic sample is employed to select individuals visiting the location during the specified time period. Such an approach has been used to survey other rare populations, such as visitors to soup kitchens, African nomadic populations by surveying at watering holes, and homosexual men, by surveying at bars, dance clubs and street locations. Blangiardo (1993, cited in Groenewold and Bilsborrow, 2004) proposes a similar methodology for sampling migrants, which was used in the NIDI/Eurostat survey of Ghanaian and Egyptian immigrants in Italy. A listing of popular places, called aggregation points, where migrants tend to meet (such as mosques, health care facilities, telephone calling centers, shelters, and public squares) is made. At each location migrants surveyed are asked how often they visited any of the other aggregation points, allowing _ex-post_ selection probabilities to be calculated for each individual surveyed.

7

-  -
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<!-- Start of picture text --> N<br>for)<br>ne}<br>oO<br>co<br>3B2%0<br>9<br>,<br>ce}<br>Tt<br>©| 7 TL<br>PSU Municipality<br>ME Unconstrained =) Constrained<br><!-- End of picture text -->
Table 9: Impact of Heat Exposure on Standardized Scores

||Standardize|d Score|
|---|---|---|
||Quartiles 1-3|Quartile 4|
||(1)|(2)|
|T_>_33C|_−_0_._018|_−_0_._003|
||(0_._048)|(0_._006)|
|T 30-33C|_−_0_._008<sup>_∗∗∗_</sup>|_−_0_._003|
||(0_._002)|(0_._006)|
|T 27-30C|_−_0_._002<sup>_∗_</sup>|_−_0_._001|
||(0_._001)|(0_._006)|
|T 24-27C|_−_0_._003<sup>_∗∗∗_</sup>|_−_0_._007|
||(0_._001)|(0_._006)|
|T 21-24C|_−_0_._003<sup>_∗∗∗_</sup>|_−_0_._007|
||(0_._001)|(0_._007)|
|T_<_18C|_−_0_._003<sup>_∗_</sup>|_−_0_._017|
||(0_._001)|(0_._029)|
|Outcome Mean:|0.039|-0.133|
|No of Schools:|900|299|
|Mean Days T_>_30C:|3.886|107.428|
|Female Control|X|X|
|**Fixed Effects**|||
|Year|X|X|
|School|X|X|
|Stream|X|X|
|Observations|1,813,086|319,549|
|R<sup>2</sup>|0.252|0.219|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is the standardized test scores of students taking the 12th grade exams. We divide the the schools into quartiles based on the mean number of days that each school experienced temperatures above 30<sup>_◦_</sup> C; the sub-headers represent the subsample of quartile 1-3 and quartile 4. All regressors include the number of days in the temperature bin. All regressions control for the number of days in the precipitation bins. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

28
11 ground.<sup>15</sup> Indeed, ninety-three percent of the biogas users observed in the sample during the first round reported to be satisfied or very satisfied with their system (3% are not satisfied).

Where possible, the robustness of the preliminary findings based on bi-variate analysis is further explored using multi-variate analysis. In particular, a series of observed household characteristics and village-level fixed effects are included to help protect against potential estimation bias from household heterogeneity and program placement respectively. The former does only provide partial protection, as it does not control for unobserved heterogeneity. The latter on the other hand helps overcome potential estimation bias from program placement effects, which might follow from non-random selection of villages into the (subsidized) biogas service program. As many renewable energy studies have had to rely on relatively small, purposively selected samples from a limited number of villages, they could often not properly control for placement effects or the village characteristics more broadly. Our study adds value also in its relatively larger and more diverse sample.

# **_Environmental benefits_**

Bivariate comparison of energy use among biogas adopters and non-adopters suggests an important degree of fuel switching, with reported annual coal consumption almost 200 kg/per year lower among biogas adopting households than among non-adopting households (95 kg/year versus 290 kg/year). Adopters also tend to use less fuelwood and crop residues (respectively 157 kg and 347 kg/year less on average). These numbers are suggestive of substantial displacement of dirty fuels among biogas adopters. However, attributing causality to biogas adoption is complicated. Biogas users may for example also be richer and thus more likely to use other clean fuels even in the absence of biogas.

15 To be more conclusive and ensure external validity, randomized experiments on a national scale are necessary. Given political constraints and ethical reservations, these have proven impossible to conduct so far in China (and elsewhere). Follow up analysis will focus on difference-in-difference methods paired with propensity score matching exploiting the multiple rounds of the data to generate more definitive conclusions about the welfare effects of biogas. The current findings nonetheless provide useful pointers to motivate the debate and follow-up analytical efforts.
Figure A.3: Regional Level Protected Water Deprivation a) b) <!-- Start of picture text --> Urban Protected Water Source Deprivation - by<br>Geopolitical Zone - GHS<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE<br>Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS<br>Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW<br><!-- End of picture text --> <!-- Start of picture text --> Wave 2_SW<br><!-- End of picture text --> <!-- Start of picture text --> Wave 3_SW<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4.

Note: Individuals with missing age were dropped from the analysis. Estimates are based on children aged 0-17.

<!-- Start of picture text --> Figure A.4: State Level Protected Water Deprivation by Region<br>a) b) c)<br>Protected Water Deprivation - by Protected Water Deprivation - by Protected Water Deprivation - by<br>State in North Central - MICS State in North East - MICS State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Protected Water Deprivation - Protected Water Deprivation - by Protected Water Deprivation - by<br>by State in South East - MICS State in South South MICS State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Abia Anambra Ebonyi Enugu Imo Akwa IbomDelta BayelsaEdo Cross RiverRivers Jigawa Kaduna Kano Katsina<br>Kebbi Sokoto Zamfara<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis.

65
PovcalNet continues to rely on such data.<sup>10</sup> To obtain a consistent number of observations per country-year, we fit a parametric Lorenz curve to the quantile groups and build a data set of percentile groups throughout.<sup>11</sup> The Appendix includes a robustness check which uses the raw quantile data without fitting a parametric Lorenz curve.

Each percentile group is population-weighted in the analysis, so every person is assigned the average consumption expenditure of her percentile in the country-year distribution. Welfare is measured using per capita consumption expenditure (or income), expressed in 2011 PPPadjusted USD (see methodological details in the Appendix). Because we compare consumption levels across countries it is important to account for differences in purchasing power across countries using purchasing power parity (PPP) exchange rates. For the same reason, welfare aggregates could also be adjusted for spatial price differences within countries, as recently discussed by Ferreira et al. (2016) in the context of estimating global poverty. In the baseline results, we follow the approach adopted in PovcalNet (Chen and Ravallion, 2010) and adjust for spatial price differences within China and Indonesia (see Appendix), but not in other countries in the region.<sup>12</sup> In the Appendix, we present robustness checks with further withincountry spatial price adjustments between 2002 and 2012, as well as using the older set of 2005 PPP exchange rates.

> 10 The number of quantiles per country-year ranges from 9 to 200, with an average of 71 across all years (and 36 on average in 1988).

> 11 We fit a log-normal Lorenz curve to the quantiles and use it to generate a distribution of 10,000 points. We use the ‘ungroup’ Stata routine (Abdelkrim and Duclos, 2007) which implements the Shorrocks and Wan (2008) method. As described in more detail in Lakner et al. (2014), this is very similar to the approach used by PovcalNet in the measurement of global poverty. Fitting a parametric Lorenz curve is more robust than fitting a kernel density (Minoiu and Reddy, 2014).

> 12 We thus have a consistent spatial price adjustment over time. One reason why PovcalNet adjusts for spatial price differences in China is that rural and urban surveys were conducted separately until recently.

5
59 FIGlURE 2a

# NET TRANSFERS 1978-1982 **($B)**

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Policy Research Working Paper 7043

# **Abstract**

Obtaining consistent estimates on poverty over time as well as monitoring poverty trends on a timely basis is a priority concern for policy makers. However, these objectives are not readily achieved in practice when household consumption data are neither frequently collected, nor constructed using consistent and transparent criteria. This paper develops a formal framework for survey-to-survey poverty imputation in an attempt to overcome these obstacles, and to elevate the discussion of these methods beyond the largely ad-hoc efforts in the existing literature. The framework introduced here imposes few restrictive assumptions, works with simple variance formulas, provides guidance on the selection of control variables for model building, and can be generally applied to imputation either from one survey to another survey with the same design, or to another survey with a different design. Empirical results analyzing the Household Expenditure and Income Survey and the Unemployment and Employment Survey in Jordan are quite encouraging, with imputation-based poverty estimates closely tracking the direct estimates of poverty.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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 hdang@worldbank.org and planjouw@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
21 prevented hundreds of thousands of them from dying. The relatively inexpensive nature of immunization -- between $1.5 and $10 per fully immunized child'? -- may have shielded it from economic setbacks.

The socio-economic dimensions of child mortality declines have been analyzed by Samuel Preston.<sup>1</sup> 8 Preston, who believes that individual mortality rates are sensitive to private living standards, seeks to extend this observation to a national level so as to account for international differences in life expectancy. Some indication of the importance of private living standards for international mortality differenceg may be gained, according to him, by examining the importance of income distribution as a factor in those differences. Preston expects that mortality responds nonlinearly to individual income levels in which case the distribution of income within a nation should influence its aggregate level of mortality. Therefore, life expectancy of the population as a whole is a function of the mean national income and of the distribution of income.

The distribution of income seems, however, to be a more important variable than the level of national income per se, which implies that the mortality risks facing a family earning $10,000 a year or $100 per yaar are not strongly influenced by the prevailing level of national income in the nation in which they reside.

# 17UNICEF, Thg State of the World's Children (1990), processed.

"<sup>8</sup> Samuel H. Preston, "Causes and Consequences of Mortality Declines in Less Developed Countries during the Twentieth Century. " Richard Easterlin, "Population and Economic Change in Developing Countries" (Philadelphia, 1976). (Chicago and London: The University of Chicago Press, 1980) pp. 289-360.
# **Table 1. Illustration of Successive Stages of Data Processing**

## **_Albania_**

## **_Costa Rica_**

|Descriptive Statistic||_Original_<br>Data Set|_Truncated_<br>Data Set|_Right_<br>_Censored_<br>Data Set|_Final_<br>Data Set|
|---|---|---|---|---|---|
|Number of observations||3,389|3,376|3,303|2,709|
|Price (USD)|Median|97,619.05|97,619.05|97,619.05|89,285.71|
|Price (USD)|Mean|137,498.50|137,483.8|137,155.6|99,599.84|
|Square meter|Median|97,00|97,00|98,00|92.00|
|Square meter|Mean|116.73|115.84|116.98|92.40|
|Price per square meter|Median|1,046.57|1,047.62|1,047.62|1,035.87|
|Price per square meter|Mean|1,227.50|1,219.54|1,219.54|1,075.40|
|Price per square meter|IQR|508.32|505.95|505.95|423.66|
|Price per square meter|SD|2,886.30|2,804.12|2,804.12|326.74|
|Number of bedrooms|Mean|1.98|1.98|2.00|1.78|

|Descriptive Statistic||_Original_<br>Data Set|_Truncated_<br>Data Set|_Right_<br>_Censored_<br>Data Set|_Final_<br>Data Set|
|---|---|---|---|---|---|
|Number of observations||10,376|10,202|10,159|7,551|
|Price (USD)|Median|200,000.00|200,000.00|200,000.00|175,000|
|Price (USD)|Mean|293,751.60|295,211.70|295,126.70|185,570.10|
|Square meter|Median|181,00|181,00|181,00|150,00|
|Square meter|Mean|347.11|234.17|234.17|160.71|
|Price per square meter|Median|1,156.72|1,162.28|1,162.28|1,273.90|
|Price per square meter|Mean|2,744.14|1393,17|1393,17|1,138|
|Price per square meter|IQR|673.14|665.61|665.61|569.76|
|Price per square meter|SD|80,189.03|3,869.17|3,869.17|543.29|
|Number of bedrooms|Mean<sup>13</sup>|.|.|.|.|

## **_Morocco_**

## **_Morocco (cont’d)_**

|Descriptive Statistic|_Original_<br>Data Set|_Truncated_<br>Data Set|_Right_<br>_Censored_<br>Data Set|_Final_<br>Data Set|
|---|---|---|---|---|
|Number of observations|10,734|10,479|10,479|8,673|
|Price (USD)<br>Median|104,395.60|105,494.50|105,494.50|93,406.59|
|Price (USD)<br>Mean|271,361.30|234,233.80|234,233.80|117,836.70|
|Square meter<br>Median|95,00|95,00|95,00|88,00|
|Square meter<br>Mean|188,59|133.68|133.68|95.47|

|Descriptive Statistic||_Original_<br>Data Set|_Truncated_<br>Data Set|_Right_<br>_Censored_<br>Data Set|_Final_<br>Data Set|
|---|---|---|---|---|---|
|Price per square meter|Median|1,119.25|1,117.21|1,117.21|1,039.27|
|Price per square meter|Mean|3,071.55|1,677.39|1,677.39|1,178.07|
|Price per square meter|IQR|932.10|903.46|903.46|776.44|
|Price per square meter|SD|33,458.45|7,570.71|7,570.71|557.07|
|Number of bedrooms|Mean|2.65|2.64|2.64|2.44|

13 The number of bedrooms in Costa Rica is missing because it was not available consistently from scraped websites.

17
# <u>Introduction</u>

In a recent issue of the _Quaderni di Scienza Politica,_ Roberta Maffio published a comparative analysis of parliamentary oversight<sup>3</sup> in which she elaborates a conceptual mapping of the oversight tools, discusses the characteristics of oversight tools, and investigates whether the adoption of oversight tools by parliaments is related to the _model_ of democracy (i.e. majoritarian, consensual, or mixed)<sup>4</sup> .

The analysis between type of democracy and oversight potential was conducted in a sample of 24 countries. Twenty-one of these countries are the democracies investigated by Lijphart (1984),<sup>5</sup> while the other three are Spain, Portugal, and Greece. Maffio divided the 24 countries into three groups depending on whether their government was majoritarian, consensual, or mixed. She then estimated the oversight potential for each country, using the 1986 data of the Inter-Parliamentary Union (IPU)<sup>6</sup> .

Maffio concluded that there is no correlation between model of democracy and oversight potential. “There are some majoritarian democracies with strong oversight potential (such as Greece) and others with weak oversight potential (such as Ireland, New Zealand, United Kingdom).” Similarly, among consensual democracies, she found cases

> 3 Roberta Maffio, “ _Quis custodiet ipsos custodes?_ Il controllo parlamentare dell’ attivitá di governo in prospettiva comparata”, _Quaderni di Scienza Politica_ , vol. IX, n. 2, (Agosto) 2002, pp. 333-383.

> 4 Roberta Maffio, “ _Quis custodiet ipsos custodes?_ Il controllo parlamentare dell’ attivitá di governo in prospettiva comparata”, _op. cit._ , p. 359.

> 5 Arend Lijphart, _Democracies. Patterns of Majoritarian and Consensus Government in Twenty-One Countries_ , New Haven, Yale University Press, 1984.

> 6 Inter-Parliamentary Union, _Parliaments in the World: a Comparative Reference Compendium_ , Aldeshot, Gower, 1986.

1