rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.000|0.001|

Note: OLS regression of vaccination status on dummies for treatment group 1 (positive demand) and treatment group 2 (negative demand). Base category is standard phrasing. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

# **4.4 Misreporting in administrative data**

So far, our analysis has found that errors of representation in the survey data explain part but not all of the gap between survey estimates and administrative figures. We next turn to possible sources of error in the administrative data. While we cannot experimentally probe the administrative data in the same way as the survey data, we present indicative evidence pointing to some weaknesses and inaccuracies in administrative data.

> 19 This pattern would also be consistent with self-reported information providing higher estimates of vaccine uptake than proxy-reported information.

24
if (i) the production functions of the formal and informal sectors allowed larger productivity in the formal sector than in the informal one (i.e., yformal = (Lformal)<sup>�form al</sup> , yinformal = (Linformal)<sup>�inform al</sup> , and �informal < �formal < 1), as it is frequently assumed based on ...rm level data evidence, and/or (ii) if there existed a segmentation in the labor market where formal workers were paid more than informal ones (i.e., �formal = yformal � wLformal", where 1 > " > 0), as empirical evidence strongly suggests.

# 3 Computation of tax multiplier and standard error

The derivation of equation (7) is as follows. Recall that the multiplier for time horizon h is typically de...ned as �Yh=�Rh, where R is the real VAT revenue, Y is the real output, �Yh � Yt+h � Yt, and �Rh � Rt+h � Rt. Notice that �Yh=�Rh = (�Yh=Y )= (�Rh=Y ). From equation (6), �Yh=Y = �h�t. Therefore, �Yh=�Rh = �h�t= (�Rh=Y ). Since R � I � Y , then �Rh � Y � �Ih + I � �Yh. Further, given that e � I=t is taken as a constant, then �Ih = e � �th. Hence, �Yh=�Rh = �h�t= (e � �th + I � �h � �t). Since �th � �h � �t, then �Yh=�Rh = �h= (e � �h + �h � I).

The derivation of equation (8
." _World Bank Policy Research Working Paper_ (6841).

Rosenbaum, Paul R, and Donald B Rubin. 1983. "The central role of the propensity score in observational studies for causal effects." _Biometrika_ 70 (1):41-55.

Sanghi, Sunita, A Srija, and Shirke Shrinivas Vijay. 2015. "Decline in rural female labour force participation in India: A relook into the causes." _Vikalpa_ 40 (3):255-268.

Sanyal, Paromita, Vijayendra Rao, and Shruti Majumdar. 2015. "Recasting culture to undo gender: A sociological analysis of Jeevika in Rural Bihar, India." Sen, Amartya. 2001. "The many faces of gender inequality." _New republic_ :35-39.

Sherwood, Ben, Lan Wang, and Xiao‐Hua Zhou. 2013. "Weighted quantile regression for analyzing health care cost data with missing covariates." _Statistics in medicine_ 32 (28):4967-4979.

Titus, Marvin A. 2007. "Detecting selection bias, using propensity score matching, and estimating treatment effects: An application to the private returns to a master’s degree." _Research in Higher Education_ 48 (4):487-521. World Bank. 2011. India - National Rural Livelihoods Project. Washington D.C.: The World Bank.

World Bank. 2018. India - National Rural Economic Transformation Project : additional financing (English). Washington D.C. : The World Bank.

26
# 3 Data and Methodology

## 3.1 PovcalNet

To predict poverty in 2030, we rely on the surveys used in PovcalNet, which contains the World Bank’s official country-level, regional and global estimates of poverty. Most of the data in PovcalNet come from the Global Monitoring Database (GMD), which is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty. PovcalNet contains more than 1,500 surveys from 164 countries covering 97% of the world’s population. The data available in PovcalNet are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption. By relying on the PovcalNet database, we ensure consistency with the official numbers used by the World Bank and United Nations for monitoring poverty, inequality and related goals.

For 119 of the countries, housing 64% of the world’s population, micro data are available. For an additional 35 countries, or 14% of the world’s population, mostly comprising the high-income world, grouped data of 400 bins are available. For the purposes of these projections, we treat the bins as microdata. Finally, for China and nine other countries constituting about 20% of the world’s population, only decile or ventile shares and the overall mean are available. This concerns Algeria, China, Guyana, St. Lucia, Macedonia, Suriname, Turkmenistan, Trinidad & Tobago, Venezuela and Zimbabwe. For these countries, based on the decile shares and means reported in PovcalNet, we use a lognormal Lorenz curve to generate a distribution of 10,000 points for each country. The use of a parametric Lorenz curve is very similar to what is done in PovcalNet to calculate poverty when micro data are not directly available.<sup>5</sup>

### 3.2 Growth scenarios

Our starting point is the latest available survey in each country or, if the latest available survey
About 25% of the Bachelor-students and 50% of the Master-students reports having a job on the side. Only 10% of the students receive a grant. Ownership of a house by the student’s family is 68% for Bachelorstudents, somewhat higher than for Master-students (61%).

Approximately 35% of the Bachelor-students are enrolled in an Area I subject (Humanities and Social Sciences), 40% in an Area II subject (Economics and Administration), and 24% in an Area III subject (Engineering and Natural Sciences). For Master-students the distribution between the three subject areas is 33%, 47%, and 20%, respectively. Tuition fees are approximately 5,100 pesos per month (US$ 510). The Economic Need Factor (ENF) for Bachelor-students is about 0.15, and for Master-students 0.16. Recall that this ENF is used to calculate the amount of credit for which the student is eligible. On average, a student receives a loan covering 44% of the tuition fee.

# _4.2 SOFES survey_

In the Summer of 2003, SOFES conducted a large-scale survey among students and graduates in Mexico. The survey was designed for this impact study. Four groups were surveyed: students with a SOFES credit, students without a SOFES credit, graduates with a SOFES credit, and graduates without a SOFES credit. Three universities participated in the survey: _Universidad Tecnologica de México_ , _Instituto Tecnológico de Monterrey_ , and _Centro Universitario Grupo Sol_ . These three universities form a small but fairly representative sub-sample of the more than 40 universities offering SOFES loans, and the outcomes must therefore be treated with care.

Appendix Table 2 summarizes the information collected from the survey.<sup>10</sup> The table shows summary statistics of the SOFES survey for students without SOFES credit (i.e. the control group), and SOFES-recipients (the treated group). The survey is organized into seven blocks:

- (1) General information

- (2)
# **III    Urban Air Pollution**

# **_3.1 Particulate Matter_**

There is substantial research evidence from around the world that outdoor urban air pollution has significant negative impacts on public health and results in premature deaths, chronic bronchitis, and respiratory disorders. A comprehensive review of such studies is provided in Ostro (1994), Ostro et al. (2004). The air pollutant that has shown the strongest association with these health endpoints is particulate matter and other secondary particles with similar characteristics of less than 10 microns in diameter (PM10)<sup>7</sup> . Research in the United States in the 1990s and most recently by Pope et al (2002) provides strong evidence that it is particulates of less than 2.5 microns (PM2.5) that have the largest health effects. Other gaseous pollutants (SO2, NOx, CO, and ozone) are generally not thought to be as damaging as fine particulates. However, SO2 and NOx may have important health consequences because they can react with other substances in the atmosphere to form secondary particulates. In particular, the evidence implicates sulfates formed from SO2, but is much less certain about nitrates, formed from NOx.

The focus of this study therefore is the health effects of all fine particulates (PM10 and PM2.5). This requires data on who is exposed, the health impacts of that exposure and the value attached to those impacts.

Given data limitations we can only estimate impacts for the urban populations and in fact only for a part of that population. Only major cities have TSP and PM10 monitoring data. In this study we focus on cities with a population of 100,000 and above only. Since the baseline population is from the 2001 census there are many cities that have achieved a population of 100,000 since 2001 and have not been included in the study. This can be updated in the future. .

Pollution data for all cities, where available, was taken from the
deviation from baseline)<br><!-- End of picture text --> Notes: Workplace mobility is Google measure of work-related mobility index. See text for sources.

Table 1 more formally tests this relationship by estimating Table 1 more formally tests this relationship by estimating M M M M i 0+ 1C C i −1 2 A A C 3 N i −1 i (1) ii−1 ii ii = ββ ββ CC + ββ CCAACCMMCC C CC + ββ NN + μμ + vv + εε Where mobility is the Google measure, Cases is the log incidence, Aggregate Cases is the national analogue, NPI are non-pharmaceutical intervention(s), and ui are subnational (state) fixed effects that also effectively put cases in per capita terms, and vt, time fixed effects. There are clear issues of bi-directional causality here.

5
While academics and international organizations have adopted various methodologies to measure multidimensional poverty, as discussed earlier, this study follows the approach taken by UNICEF (2011) to measure severe deprivations of children. This approach is based on operational definitions developed by Gordon et al. (2003a) and relies on the absolute poverty dimensions identified during the World Summit for Social Development in Copenhagen. Given the data available, this analysis distinguishes between three severe deprivations in the areas of food, health and education with information available at the individual child level, and four severe deprivations concerning water, sanitation, shelter and information with only household level information available for each child. See Table 1. While Gordon et al. (2003a) suggest an eighth deprivation, considering the access to basic services, defined as distance to school and health care services >20km, this study restricts itself to the beforementioned seven deprivations. Reasons for this being that there is no such information available from the MICS and GHS datasets, used in this analysis. The datasets only include distance to school at individual level, whereas distance to health care services is available at community level only. However, in both cases, it is defined in time used, rather than kilometers, thus requiring a conversion. Moreover, this deprivation has been criticized as access does not inform about quality of access.

Besides the seven severe deprivations, this study however analyses a few additional, even stricter but milder deprivation definitions relating to education, water and sanitation. The milder educational deprivation counts every child of school going age who is not currently in school as schooling deprived. For water deprivation, the study further analyses protected water deprivation, counting additionally those with only access to unprotected wells/springs and unprotected tap water in any season as deprived.<sup>5</sup> The improved sanitation deprivation definition additionally considers those deprived, who only have access to open pit latrines, latrines without slaps or toilets hanging over the water of any sort. The latter definitions for protected water and improved
# **Figure C5.3** _:_ **Evolution of the premium on industry-level Suharto connectedness –– broad connections**

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enrollment impacts are largest for children who experience a rain shock while in utero, and while these effects diminish in magnitude for shocks experienced during the first two years of life, they are still significant and economically meaningful. We find no relationship between rainfall shocks and ability for shocks experienced after age two or before the child was in utero.

The remainder of the paper is organized as follows. Section 2 describes the survey and rainfall data used in the analysis. Section 3 presents our empirical identification strategy and Section 4 presents the main results as well as robustness tests. Section 5 concludes.

# **2. Nahouri Cash Transfer Pilot Program (NCTPP) Survey and Rainfall Data**

# _2.1 Nahouri Cash Transfer Pilot Program_

The Burkina Faso Nahouri Cash Transfer Pilot Program (NCTPP) survey was conducted in June 2008 in Nahouri province in southern Burkina Faso, approximately 100 miles from the capital and bordering Ghana. The survey was the baseline for an ongoing project evaluating social protection strategies in Burkina Faso. Households were randomly selected from a village-level census conducted by our project team immediately prior to the survey in the 75 rural villages of Nahouri that each has a primary school. Households in this region are predominantly rain-fed subsistence farmers growing sorghum and groundnuts and have mean annual per capita expenditures of around $90.

Our primary analysis focuses on school-aged children 5 to 15 who are biological children of the household head in households with multiple children in this age range and with varying enrollment. There are 2,862 children in 893 different households. As shown in Table 1, 53 percent of this child sample is male and the average age is 9.3 years old. These children live in households with an average of 9.29 individuals, including a head of household, 1.53 wives, 3.97 biological children of the household head ages 5 to 15, 1.25 biological children of the household 6
demonstrates the absence of a link between increased number of judges and efficiency of courts in Peru.

Smith et al (2021) analyze whether performance measurement improves efficiency, by reviewing performance measurement systems in Australian state-level jurisdictions. The overall conclusion is that performance measurement, at least in the judiciary, does not lead to better efficiency. In contrast, de Figueiredo, Lahav and Siegelman (2020) use the complete record of U.S. federal civil cases between 1980 and 2017 to document that judges close substantially more cases and decide more motions in the week immediately before they report their outputs as part of semi-annual evaluation cycles. Chemin et al (2023) find even stronger supportive evidence of performance measurement improving court effectiveness in the case of Kenya.

Efficiency of the court system is related to the workings of existing ADR mechanisms, as these have the potential to reduce the caseload of courts. Court level data of conciliation activities in French civil magistrate courts between 2010 and 2017 show that efficiency is positively related to factors that foster the demand for ADR settlement. More efficient courts are more prone to involving conciliation in the ADR (Belarouci 2021). A study using data on 8,038 trials provides mixed support for ADR programs being able to reduce time or cost of proceedings but indicates that participation in an ADR program correlates with an increased likelihood of out-of-court settlement (Heise 2010).

In contrast, Genn et al (2007) find that out-of-court mediation is associated with similar delays and additional costs in London, the United Kingdom. They study the impact of ADR programs, using a quasi-compulsory mediation program which ran in the Central London County Court between April 2004 and March 2005. During the ARM pilot, 1,232 defended civil cases were randomly referred to mediation, of which 82% were personal injury cases. By the end of the evaluation (10 months after termination of the pilot), only 14% of the cases originally referred to mediation had been settled. Also, the study found that the additional cost of cases
of changing gender barriers. In the model, individuals differ by gender and ability, while occupation-sector pairs differ in wage rates per unit of human capital, returns to ability, and costs or barriers that men and women face when working in them. Individuals receive idiosyncratic preference shocks across occupation-sector pairs and then choose their occupation and sector. We model the option of not participating in the labor force as an additional choice, which we call the “home sector” (Hsieh et al., 2019; Ngai and Petrongolo, 2017). On the demand side, consumers have non-homothetic preferences, allowing them to shift consumption away from agriculture and toward services as their – income rises a key driver of structural transformation emphasized in the literature.<sup>4</sup> Wage rates per human capital unit in each occupation-sector and prices in each sector are endogenously determined in equilibrium.

Following Hsieh et al. (2019), we define two types of gender barriers: first, “gender norms” which capture the excess utility costs that women incur relative to men when working in a given occupation-sector pair; and second, female “wage discrimination”, which captures differences in remuneration that women receive _per human capital unit_ relative to men. In the absence of distortions, an efficient allocation of talent ensures that high-ability workers sort into sectors and occupations that offer the highest returns to their ability. However, gender barriers distort these choices, shifting workers away from the jobs where they have a comparative advantage. For instance, high-ability women may opt out of the labor market or work in clerical jobs with low returns to their ability rather than pursuing professional or managerial jobs with higher returns. Such distortions therefore lead to a misallocation of talent, reducing aggregate productivity and output.

– We estimate the model to a core sample of six large economies India, Indonesia, Brazil, Mexico, Canada and the United States – for which we have employment and hourly wage data spanning four to five decades. Gender barriers in each occupation-sector and country-year
Myles and Naylor (1996) state that the model developed by Gordon is a step forward but lies outside the mainstream of the social custom literature because psychic costs depend on the extent of evasion. They see no reason why such a relation should hold. They argue that if the psychic cost is due to the shame at prosecution then the extent of evasion is irrelevant. If it is due to the fear of detection, then it should be dependent on the detection probability rather than the extent of evasion. Based on the social custom literature where it is accepted that once a social custom is broken, all utility from it is lost, Myles and Naylor suggest a model in which a social custom utility is derived when taxes are paid honestly, but is lost when evasion is undertaken. In their model, taxpayers face a choice between evading or not. If a taxpayer chooses evasion, the standard model of tax evasion becomes operative. Myles and Naylor combine social customs and social conformity with the standard model which represents tax evasion as a choice with risk. Since then, further studies have also modeled this puzzle of tax compliance (see, e.g., Schnellenbach, 2010).

# _2. Measurement of Tax Morale_

Several studies have used the World Values Survey (WVS) to generate a proxy for tax morale (see Torgler, 2007a). The World Values Survey is a worldwide investigation of socio-cultural and political change collecting comparative data on values and belief systems among people around the world. WVS builds on the European Values Surveys (EVS), first carried out in 1981-1984. These surveys have assessed the basic values and beliefs of people around the world and have been carried out in about 80 societies representing over 80 per cent of the world‘s population. This large data set therefore permits cross-country comparison of people‘s tax morale that is based on representative national samples The WVS/EVS is particularly advantageous to this review because it covers a large set of Central Eastern and Former Soviet Union countries. Not
within households exhibits uniform patterns across all asset classes. Nuances of ownership, including exclusive versus joint ownership, vary substantially across countries, and it appears more common that households fall in either only exclusively-owned arrangements or only jointly-owned arrangements across asset classes, as opposed to combinations of exclusive and joint ownership.<sup>12</sup>

# 3.2 Constructing individual-level wealth measures and addressing issues related to measurement error

Following the UN guidelines that were discussed in Section 2.2, we use <u>respondents’ self-reported valuations of each asset they own, based on the potential sales value, to construct aggregate valuations of</u> their wealth. Data on asset valuation, however, can be subject to different forms of measurement error, that might affect an overall understanding of inequality within populations. There are two measurement issues with the valuation data that we address in the paper: (1) A key measurement challenge is that in Ethiopia and Tanzania, there was a substantial number of respondents who did not report values for specific assets, such as land and livestock (see Appendix Table A4). In Ethiopia, for example, 43 percent of men and 56 percent of women who were residential landowners did not report a potential sales value. In Tanzania these shares were 19 percent of men and 48 percent of women. Appendix Table A4 shows that among those with missing valuations, women were generally more likely to have missing data, and the most common reason among men and women was not knowing the value of the asset. In Malawi and Cambodia, on the other hand, the shares of respondents with missing valuations across asset classes was much lower — 5 percent or less among landowners in Malawi, and on average 15 percent in Cambodia.

> 12 In Cambodia, a high share of households owning bicycles (28 percent) and motorcycles (62 percent) are listed in column (5) since the survey did not distinguish across exclusive versus joint ownership, but joint ownership is likely given the country’