rafmacalaba/data-use-mentions-extended huggingface dataset:
|Table 1. C<br>LF|ase 1 Resilience Indicator Data|||
|---|---|---|---|
|o<br>Indicators|Relevant Data|Data Source|Content|
||Traffic volume per min under theordinarystate (x�)|Design traffic<br>volume|13.68 pcu/min<br>(19,700 pcu/day)|
||Traffic volume per minwithoutthe project under thedisrupted<br>state (x�)|Simulation|6.84 pcu/min|
|Economic|Travel time between representative OD (Asago – Toyooka)without<br>the project under theordinarystate (t<sup>�����</sup>)|Route search<br>system|50 mins|
|Loss|Travel time between representative OD (Asago– Toyooka)withthe<br>project under theordinarystate (t<sup>����</sup>)|Route search<br>system|42 mins|
||Travel time between representative OD (Asago – Toyooka)without<br>the project under thedisruptedstate (t<sup>�����</sup>)|Route search<br>system|79 mins|
||Travel time between representative OD (Asago – Toyooka)withthe<br>project under thedisruptedstate (t<sup>����</sup>)|Route search<br>system|42 mins|
||Population of coverage area (Tajima Area) (Pop��<br>����)|Population<br>statistics|180,607|
||The percentage ratio of population who can access major hospitals<br>in 30 minuteswithoutthe project under theordinarystate|Route search<br>system|45.1%|
|Saving<br>Lives|The percentage of population who can access major hospitals in<br>
investment quality that controls for project manager characteristics. We do this by using two approaches: (i) by including a TTL dummy variable and (ii) by controlling for TTL characteristics, including years of experience and educational attainment.<sup>15</sup> While controlling for TTL characteristics leads to a substantial loss of observations, we find that the baseline PIQ index is highly correlated with the version built using regressions with TTL fixed effects or characteristics (see Annex Table A1 for estimation results and Annex Figure A1 for the correlation between the two indexes).

One possible caveat with our index is that it focuses on World Bank-financed investment projects. There are two potential issues here.

First, it is likely that World Bank monitoring improves the performance of public investment. However, this should not be an issue because we build our index by holding constant World Bank inputs and focus on a residual element which is associated with country-specific project performance. As long as the influence of World Bank monitoring is uncorrelated with countryspecific variables which affect public investment performance, our index remains a valid measure of country-specific public investment quality.

> 13 Note that we only retain country-years with at least 8 observations of project outcomes in a given period for robustness.

> 14 This seems the optimal threshold given the tradeoff between the number of country-years for which the index can be estimated, and the stability of the index based on a sensitivity analysis with alternative thresholds. 15 Project data on TTLs are obtained from the WB Operations Portal and from Limodio (2021) and Bulman et al. (2015). In all cases, we remove identifying information and numerically code TTL names.

8
||268,403|27,136|29,365|45,490|
|---|---|---|---|---|
|United Rep. of Tanzania|265,184|680,862|109,286|102,099|
|Zimbabwe|190,950|4,127|4,435|6,389|

|**Top 10**|**by asylum, b**<br>**1990**|**ased on 20**<br>**2000**|**13**<br>**2010**|**2013**|
|---|---|---|---|---|
|Kenya|14,249|206,106|402,905|534,938|
|Chad||17,692|347,939|434,479|
|Ethiopia|773,764|197,959|154,295|433,936|
|Uganda|145,718|236,622|135,801|220,555|
|Sudan*|1,031,050|414,928|178,308|159,857|
|Cameroon|49,876|43,680|104,275|114,753|
|Dem. Rep. of the Congo|416,435|332,509|166,336|113,362|
|United Rep. of Tanzania|265,184|680,862|109,286|102,099|
|Mauritania|60,000|350|26,717|92,767|
|Rwanda|23,601|28,398|55,398|73,349|

Note: Authors’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.* denotes that the data
surveys, public opinion surveys and different “big data” approaches are some of the alternatives discussed.

The paper is organized as follows. Section 2 discusses the importance of having regularly conducted HBSs<sup>3</sup> to measure monetary poverty. Section 3 describes the evolution of the statistical capacity of MENA. Section 4 summarizes the availability of HBSs in the region and compares data availability to that in other regions. Section 5 presents potential ways to address gaps in data availability and innovative data approaches to supplement existing estimates of monetary poverty. And Section 6 concludes.

# 2. Importance of household budget surveys in measuring and understanding monetary poverty

“Household budget surveys” (HBSs) is a generic term covering a broad category of surveys that might also be referred to as “family expenditure surveys” or “income and expenditure surveys”, among others. The common goal of these surveys is to collect information about household “budgets” reflected in expenditure, consumption or/and income. Many surveys add other modules to the core consumption/expenditure section, to collect information on education, labor market, nutrition, and housing conditions. The most common practice is to use consumption, expenditure, and income to derive a money-metric measure to calculate poverty rates (Dang et al. 2019).<sup>4</sup> National governments and international organizations use HBSs to inform poverty monitoring systems which track progress towards achieving regional/global poverty targets and which help formulate operational policies.

Having comparable and up-to-date household budget surveys is crucial for monitoring global and regional poverty trends. To measure extreme poverty at the international poverty line (USD1.90 2011 purchasing power parity (PPP) exchange rates), the World Bank uses either current consumption/income distributions from the most recent surveys or updates old distributions using GDP per capita or private consumption growth rates.<sup>5</sup> In addition, the World Bank uses consumer prices indexes (CPI) and PPP exchange rates to account for variations in
mainly fall into two categories; 12 in of 30 country surveys are from the LSMS collection included in the World Bank Microdata Library. The other 18 country surveys are from other multi-topic household surveys, most of which are used as the data sources for PovcalNet<sup>3</sup> , an online analysis tool for global poverty monitoring, covering more than 1,500 household surveys spanning 1967 – 2018 and 166 economies (138 economies after 2010). Since these surveys are included in the regional collections and are tagged “LSMS” in the Microdata Library searching tool, in this report they will be labelled as “LSMS-tagged” surveys<sup>4</sup> .

> 2 LSMS program website: https://www.worldbank.org/en/programs/lsms. The data of 129 LSMS studies can be downloaded by external users through https://microdata.worldbank.org/index.php/catalog/lsms.

> 3 PovcalNet program website: http://iresearch.worldbank.org/PovcalNet/home.aspx.

> 4 Six “LSMS-tagged” countries surveys used to be supported by the LSMS program before 2010.

4
use to determine whether to pay for WSS, program designers can create policies to increase demand for WSS.

The use of conjoint methods is not new to infrastructure service industries.<sup>2</sup> Service attribute quantification has been common among the electricity industry for some time, with information on the value of reliability to customers used to design appropriate services and tariffs (Eto et al., 2001). In service delivery programs, we can think of conjoint analysis as a tool to determine what combination of attributes consumers will most likely purchase. Hensher et al. (2004) assess the value that households in Canberra, Australia, place on drinking and wastewater attributes. Respondents to their surveys valued attributes of WSS such as reliability of water services in addition to cost. They find evidence that customers place value on operational procedures within the water industry.

Estimation of demand for WSS and evaluation of differences in household preferences across sociodemographic groups typically requires at least three types of data that vary across the sample: (a) access and quantity of WSS, (b) price, and (c) socio-demographics. Given the variety of situations and scenarios that are considered for policy design and evaluation, researchers have used both stated preference (SP) and revealed preference (RP) methods to assess demand and understand preferences. SP approaches use data from surveys that describe potential scenarios to estimate the _ex-ante_ demand for goods and services such as WSS. The contingent valuation and conjoint analysis methods are examples of SP approaches. RP approaches use behavioral data, often from surveys, to estimate _ex-post_ demand for WSS. The hedonic price, averting and coping models are examples of RP approaches. In the case of WSS services, consumer data (RP data) typically does not contain enough variation across households to estimate the value of individual service attributes. Instead, water policy analysts can use stated preference methodologies such as conjoint surveys to understand the importance of service delivery attributes and cost to consumers of WSS and the extent to which consumers are
general, while in columns 5 and 6, we observe a 9 percentage point reduction in the likelihood of being severely food insecure. All reported impacts are significant at the 1% level, or lower. These results are illustrated graphically in Figure 8. Relative to the control group, these estimates amount to 35 and 60 percent reduction, respectively, in food insecurity and severe food insecurity, as a result of the FIP PES transfers.

The results on the HFIAS are even stronger when we consider the Household Hunger Scale as a food security indicator instead of the HFIAS. As shown in Table A.11 in the appendix, participation in the PES scheme has reduced the proportion of households who experienced hunger during the months prior to the survey by almost 70%.

# _5.4. Robustness Analyses_

The results presented above suggest significant and positive impacts of Burkina Faso’s FIP PES scheme on households’ food security outcomes. In this section we perform a series of robustness checks to ensure that those results were not driven by the choice of estimation strategies.

# _5.4.1. Randomized Inference Tests_

The results presented so far are derived from regression analysis, with inferences based on asymptotic theory and distributional assumptions. In this section, we use a randomized inference approach to ensure that our main results are robust to relaxing those assumptions (Athey and Imbens, 2017; Imbens and Wooldridge, 2009). In Table 7, we summarize the p-values for 21
Activities|8.45|11.92|9.1|14.5||
|<br>Market Research, Business &||||||
|Management Consultancy Activities|43.09|31.00|26.8|30.0||
|Engineering services|10.30|12.84|14.2|12.1||
|Architectural, quantity surveying,||||||
|building & appraisal services|7.21|10.12|6.0|8.5||
|_Total KIBS sector_|_100.0_|_100.0_|_100.0_|_100.0_||

_Note_ : KIBS data includes the following industries: IT and related services, legal activities, market research, business & management consultancy activities, engineering services, architectural, quantity surveying, building & appraisal services.

Singapore Department of Statistics (DOS). (2001). _Economic Surveys Series 2001: Architectural, Engineering & Technical Services._ Singapore: DOS.

Singapore Department of Statistics (DOS). (2001). _Economic Surveys Series 2001: IT & Related Services._ Singapore: DOS.

Singapore Department of Statistics (DOS). (2001). _Economic Surveys Series 2001: Professional Services._ Singapore: DOS.

15
**_Table 6. Relationship between Task Team Leader (TTL) turnover and IEG outcome rating_**

||**De**|**pendent variable: I**|**EG 1-6 outcome rat**|**ing**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Dummy for preparation TTL change|-0.053|||-0.018|
|in the 1<sup>st</sup>or 2<sup>nd</sup>ISR|(0.044)|||(0.045)|
|TTL turnover during first half (Number of||-0.412***||-0.309*|
|TTLs/Number of ISRs)||(0.156)||(0.166)|
|TTL turnover during second half (Number of|||-0.579***|-0.575***|
|TTLs/Number of ISRs)|||(0.171)|(0.166)|
||2.066***|2.240***|2.471***|2.603***|
|Constant|(0.330)|(0.331)|(0.365)|(0.354)|
|Observations|2,763|2,762|2,757|2,752|
|Adjusted R-squared|0.105|0.107|0.108|0.110|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **
Policy Research Working Paper 10175

# **Abstract**

Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the precision and accuracy of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that a household-level model outperforms other common small area estimation methods.

However, poverty estimates in 2015 derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve on existing poverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly, even for small subgroups.

This paper is a product of the Development Data 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 dnewhouse@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
and .04 for facades; the doubling time �2 is: between 8.3 and 27.5 years for roof-mounted systems and between 17.8 and 75.7 years for facades. A study by Fthenakis and Alsema (2006), based on more recent Life Cycle Inventory data from 12 European and US photovoltaic companies, estimated the payback times for ribbon, multi-, and mono-Si technologies to be 1.7, 2.2, and 2.7 years respectively. The corresponding doubling times then are estimated to be 9.11, 13.07, and 18.20 years.

The EROI estimates for nuclear power have been equally disparate. As with the other generating technologies, one of the main issues has been de...ning the appropriate boundaries of analysis. For nuclear lifecycles employing the gaseous di¤usion technology, fuel enrichment is the key energy input–more than half the lifetime total. Centrifuge technology reduces enrichment energy consumption to 1/12 of its di¤usion-based value and correspondingly E reduces h, the O&M component of EROI; down to a value closer to<sup>theinitial</sup> Pnp'T<sup>;</sup> emplacement component. During the past two decades, nuclear power plants have also achieved increasingly higher capacity factors. In the United States, for example, the average capacity factor ' for plants in operation in 1980 was 56.3 percent. By 1990, it had risen to 66 percent, and in 2007 to 91.8 percent. Similar developments occurred in other countries. In Russia, capacity factors increased from 56% in 1998 to 75% in 2007 (NEI, 2008). Most of today’s nuclear plants were originally designed for 30 or 40-year operating lives. However, improvements in system components and upgrading of structures have extended e¤ective plant lifetimes. Re‡ecting these technical improvements, many of the currently operating reactors in the United States are expected to be granted
In this section, we use the estimated model to investigate the GE effects of Sri Lanka’s fertilizer import ban (and of other potential alternative policies). Through counterfactual exercises, we demonstrate how government policies like import restrictions limit fertilizer access and how fertilizer subsidies in turn mediate the distributional impacts of such limitations. The counterfactuals

> 65Landholdings are defined as the total amount of land owned by a household (excluding land with housing built on top) and are measured in ha. The standard deviation is computed using only households with positive landholdings. We use 2016 HIES data because landholding data was not available in the more recent 2019 HIES. See Appendix C.C for more details on the estimation of the land distribution parameters.

> 66Alternatively, inversion consists of recovering unobserved from observed variables using the model’s structure.

> 67We include in the analysis the eight crops ( _K_ = 8) for which there is data on fertilizer intensity and district-level producer prices, namely: cinnamon, cloves, groundnut, maize, onions, potatoes, rice, and tea.
the two parties (PRI and PAN) in power during our study period had large plans for infrastructure development, we also examine whether they similarly resorted to _machine politics_ (Dixit and Londregan, 1996; Cox and McCubbins, 1986) using the distribution of federal highway spending. We find no differential effect of political bias in highway investment between the PRI and PAN.<sup>4</sup> This is consistent with the idea that any party will make use of the it has to local allocations as just possibility influence public spending a tool to strengthen its political position. Overall, our findings indicate that in addition to electoral competition, legislative stability—a rather understudied topic in the political economy literature applied to developing countries—is an important determinant of the geographic distribution of public capital.

To our knowledge, we make four contributions to the literature. First, to overcome misreporting issues inherent to road expenditure data that most studies rely on, we combine election outcomes with actual physical investments at a politically-relevant geographic scale. The granularity of our data allows us to consider heterogeneity effects based on primary funding sources (federal vs. non-federal) for different types of roads. Second, our quasi-experimental approach makes it possible to causally infer the impacts of political incentives on road construction projects. Our paper is one of the rare studies, if not the first, to establish a causal relationship between tangible highway construction and political outcomes.<sup>5</sup> Third, we offer new insights into the debate about the institutional and political environment in which political favoritism thrives. While the development literature acknowledges the role of democracy (Hodler and Raschky, 2014; Burgess et al., 2015) and electoral competition (Brollo and Nannicini, 2012; Asher and Novosad, 2017), we provide strong evidence that a divided government is a significant

> 4 The Institutional Revolutionary Party (PRI) and the National Action Party are two major political parties in Mexico. The PRI held the executive power uninterruptedly from its creation in 1929
Figure 8: Average Outcomes by Group <!-- Start of picture text --> Health insurance Number of disabilities<br>Teen pregnancy [14-19] Average of standardized outcomes<br>.8<br>.4<br>.6<br>.3<br>.4 .2<br>.2 .1<br>0 0<br>Querandí Rankulche Sanavirón Comechingón Charrúa Tehuelche Ona Pampa Maimará Diaguita-Calchaquí Mapuche Huarpe Atacama Guaraní Otros Omaguaca Lule Kolla Quechua Tupí Guaraní Toba-Qom Mocoví Chané Ava Guaraní Aymara Tapiete Tonocoté Vilela Chorote Wichí Mbyá Guaraní Pilagá Chulupí Omaguaca Maimará Chorote Kolla Tapiete Wichí Pilagá Diaguita-Calchaquí Lule Chulupí Tupí Guaraní Vilela Ava Guaraní Mapuche Tonocoté Tehuelche Guaraní Toba-Qom Atacama Huarpe Charrúa Ona Quechua Mocoví Querandí Sanavirón Comechingón Rankulche Pampa Aymara Chané Mbyá Guaraní Otros<br>.5 .2<br>.4 0<br>.3 -.2<br>.2<br>-.4<br>.1<br>-.6<br>0<br>Mbyá Guaraní Wichí Vilela Pilagá Chulupí Chorote Tonocoté Toba-Qom Lule Tupí Guaraní Ona Ava Guaraní Mocoví Otros Tapiete Kolla Querandí Mapuche Omaguaca Atacama Rankulche Guaraní Chané Quechua Sanavirón Aymara Diaguita-Calchaquí Tehuelche Maimará Comechingón Huarpe Charrúa Pampa Querandí Comechingón Charrúa Rankulche Pampa Sanavirón Tehuelche Otros Ona Huarpe Maimará Diaguita-Calchaquí Mapuche Guaraní Atacama Quechua Tupí Guaraní Aymara Kolla Omaguaca Lule Chané Mocoví Toba-Qom Tapiete Ava Guaraní Chulupí Vilela Tonocoté Wichí Chorote Mbyá Guaraní Pilagá<br><!-- End of picture text --> _Sources:_ REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad ́ıstica y Censos, 2010). _Notes:_ Solid horizontal lines show average for Natives, dashed horizontal lines show the average for non-Natives, and the dotted horizontal lines show the first and ninth decile by department.
Table A.2: Distribution of In-Kind rewards

||Proportion of<br>teachers|Proportion of<br>annual salary|
|---|---|---|
|None|36%|0%|
|Rice & radio|18%|4%|
|Rice & radio & cellphone|10%|8%|
|Rice & TV & cellphone|11%|32%|
|Rice & TV & cellphone & generator|25%|49%|

Table A.3: Quality of Implementation (teacher data)

||(1)|(2)<br>**Midlin**|(3)<br>**e**|(4)|(5)|(6)<br>**Endli**|(7)<br>**ne**|(8)|
|---|---|---|---|---|---|---|---|---|
||Control|In-Kind|Recog.|Obs|Control|In-Kind|Recog.|Obs|
|School participates|0.967|0.972|0.964|1055|0.788|0.918|0.832|1100|
|_Which teachers targeted?_|||||||||
|Only grade 2|0.032|0.051|0.065|999|0.104|0.111|0.115|861|
|Only grade 3|0.388|0.369|0.376|1016|0.296|0.157|0.254|861|
|Only grade 4|0.386|0.367|0.387|1023|0.052|0.071|0.062|861|
|Grades 3
for differences in local governance efficacy, province-specific variation in relationships with the central government,<sup>35</sup> or differences in conflict and insecurity that are present throughout the survey year.

> 34 The NRVA does not collect detailed income data, but rather focuses on detailed consumption data primarily because it is generally assumed that consumption is more accurately measured than income in poorer countries. Total household consumption (Afghani per month) consists of expenditures on food, non-food, durables and rent, following guidelines in Deaton and Zaidi (2002).

> 35 As one candidate example, the World Bank (2008) identifies wide variation across provinces in the distribution of public expenditures from central funds as a key public finance concern. Comparisons of per capita, public expenditures by province produces unusually large (and unexplained) variation with a six-fold difference between provinces receiving the highest and lowest levels of per capita public expenditures.

13