rafmacalaba/data-use-mentions-extended huggingface dataset:
an opportunity to study the impact of gender-based tax incentives on property ownership patterns and on tax compliance.

Two types of taxes related to property are covered in this paper. The first is the stamp duty (a property transfer tax), which is levied by state governments on the conveyance or sale deeds of properties. This onetime charge is levied concurrently with the transaction. The second is a recurrent property tax levied periodically, most often annually, by ULBs on properties that lie within their jurisdiction. Appendix 1 provides details on the main taxes related to immovable property in India.

> 1. Assam, Bihar, Delhi, Gujarat, Haryana, Maharashtra, Odisha, Punjab, Uttarakhand, and Uttar Pradesh offer stamp duty discounts.

> 2. India’s GDP in 2017 was US$2,651 billion, and in 2018 it was US$2,701 billion. Source: World Development Indicators, World Bank.

> 3. Source: Government Financial Statistics, IMF; https://data.imf.org/?sk=a0867067-d23c-4ebc-ad23-d3b015045405. 4. While projections vary, some sources estimate that India’s urban population in 2031 will reach 600 million. The UN World Cities Report 2016 projects that the country will have seven mega cities with populations above 10 million. The McKinsey Global Institute report “India’s Urban Awakening: Building Inclusive Cities, Sustaining Economic Growth” forecasts that India’s cities will need an estimated US$1.2 trillion in capital investment to meet projected infrastructure demands. Existing capital expenditure is about seven times below the required levels.

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patterns that inform their decisions whether to enter the labor market or to self-select into specific occupations. Self-selection has been found to have a significant impact on wage estimates and estimates of the pay gap (Belbo et al., 2003). Thus, we adopt Heckman’s (1979) method to correct for selection bias. However, the selection correction method is used only in the case of the mean decomposition analysis and not for the quantile decompositions discussed above. Selection correction within decomposition techniques, in general, can be problematic and is more complex in the context of quantile decompositions.<sup>8</sup> As a first step in applying the selection correction method proposed by Heckman, a probit model predicting labor force participation is estimated: In this equation, �� is a vector of instruments that includes number of children below age 6 in the household and number of shocks experienced in childhood. The predicted probabilities from this selection equation are then used to compute the selectivity term, which is added to the wage equation as an additional explanatory variable.

# **IV. Data**

The data for this study come from the <u>STEP Skill Measurement surveys. These data were</u> collected in 2012 and 2013 in seven countries—Armenia, Bolivia, Colombia, Georgia, Kenya, Ukraine, and Vietnam.<sup>9</sup> The household survey gathers information from urban adults between the ages of 15 and 64. The survey provides various measures of cognitive and noncognitive skills that are the focus of this paper (see Pierre et al. (2014) for a detailed description of the skills measures included in the STEP surveys). The STEP surveys provide a direct, objective measure of _reading proficiency skills,_<sup>10</sup> using an assessment scored on the same scale as the OECD’s PIAAC (Program for International Assessment of Adult Competencies). The scores on this assessment range from 0 to 500 and span six levels of
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> do not advance a new theory, this paper empirically explores one cause of cross-country differences in total factor productivity growth: differences in the level of banking sector development.

> Specifically, this paper examines whether the level of banking sector development exerts a

> causal impact on real per capita **GDP** growth, capital per capita growth, productivity per capita growth and private saving rates. For convenience, we refer to capital per capita growth, productivity per capita growth and private saving as the "sources of economic growth." Recent industry- and firm-level

> research suggests that the level of banking sector development has a large, causal impact on real per capita **GDP** growth [Rajan and Zingales **1998;** Demirgiig-Kunt and Maksimovic **1999].'** Past work,

> however, does not explore the channels via which banks affect economic growth. Thus, using a crosscountry dataset, we assess the causal impact of banks on capital accumulation, private saving rates and productivity growth and trace these effects through to overall per capita **GDP** growth.

This paper improves on the existing literature both in terms of econometric technique and data. First, while King and Levine (1993a) and Levine and Zervos **(1998)** empirically assess the connection between banking sector development and the sources of economic growth, they do not explicitly confront the issue of causality. We use two econometric techniques to control for the simultaneity bias that may arise from the joint determination of banking sector development and (i) capital accumulation, (ii) total factor productivity growth, and (iii) private saving rates. The first technique employs a pure cross-sectional, instrumental variable estimator, where data for **63** countries are averaged over the period **_1960-95._** The dependent variable is either real per capita **GDP** growth, real per capita capital stock growth, productivity growth, or private saving rates. Besides a
We then turn to the third and final dimension of child development covered in this study: socioemotional and behavioral development. The survey included instruments based on the Strengths and Difficulties Questionnaire (SDQ), in which mothers assess children’s behavioral and emotional problems along five dimensions: emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems and prosocial behavior problems.<sup>8</sup> While all five sub-scales are typically used when screening for disorders, broader subscales can be used for analytical purposes. This involves combining the emotional and peer subscales into an internalizing subscale and the conduct and hyperactivity subscales into an externalizing subscale (Goodman, Lamping, & Ploubidis, 2010). Internalizing behaviors are negative behaviors focused inward, and can include fearfulness, social withdrawal, and excessive thoughts, feelings and behaviors focusing on physical symptoms such as pain, weakness or shortness of breath (somatic complaints). In contrast, an externalizing behavior is directed outward toward others. Bullying, vandalism, and arson are examples of externalizing behaviors. Studies have found the Strengths and Difficulties Questionnaire (SDQ) to be a good predictor of children’s scores on the same instrument one year later (Hasan et al., 2013).

Figure 4 relates the count of difficulties with maternal education (completed years). In this case, there are some interesting differences in the strength of the gradient. Prosocial difficulties (considerate of others people feelings; sharing with other children; helpful if someone is hurt, upset or ill; kind to younger kids; volunteering to help others) are the most strongly associated with maternal education, followed by internalizing and externalizing difficulties, respectively. Illiterate mothers have kids with prosocial difficulties scores 0.23 standard-deviations (s.d.) higher than kids whose moms are literate. The corresponding numbers for internalizing and externalizing difficulties are 0.08 and -0.02 s.d., respectively. These scores were standardized using the age specific group within the full ECED sample. Appendix Table
seasonality) are included in the analysis (see Beegle et al 2016). Table 1 provides a list of the microdata used in this study.

We obtain monthly national CPI data disaggregated into food and non-food components from the respective NSOs. In terms of geographical coverage, all countries, except Nigeria and Rwanda, only collect prices in urban areas. In other words, the national CPIs in these countries reflect the prices paid by urban residents. Conversely, the national CPIs in Nigeria and Rwanda reflect both rural and urban prices. In terms of the availability of cross-sectional CPIs, regional CPI data are available for only four countries – Ethiopia, Ghana, Mozambique, and Uganda. These data allow us to estimate the CPI-bias based on equation (4). The remaining countries only have national CPIs, and we are therefore required to apply equation (10) for the CPI-bias estimation. Table 2 lists the CPI data used in this study.

Detailed expenditure data are aggregated into two broad groups: food expenditure and total expenditure. Food expenditure is defined as expenditure for the consumption of food, nonalcoholic beverages, alcoholic beverages, and tobacco from own-production, market purchase, and gifts. Total household expenditure includes spending on food and non-food expenditure, such as clothing, housing, household furnishings and equipment, health and personal care, education, transport, communications, recreation, and miscellaneous goods and services. We then merge the consumption data for each household with the monthly CPI during the period when the household was interviewed. This allows for temporal variations in inflation patterns.

We use several criteria to select our analysis sample. First, to minimize the effect of extreme outliers, which may indicate data quality problems, households with a food budget share of less than 2 percent or greater than 95 percent are excluded. Second, to render household demographics more homogeneous, we exclude households in which the household head is younger than 21 years of age or older than 75 years of age. These restrictions are similar to those used by Costa (2001
20 section III, holding constant factors that may be correlated with the timing of autonomization and unobservables influencing outcomes.

# _Individual-level analysis_

There are two major differences between our hospital-level and individual-level analyses: first, the hospital data are panel data, while the household survey data are a series of cross sections covering the years 2004, 2006 and 2008; second, in the hospital analysis our unit of analysis is the entity that has been autonomized while this is not the case in the individual analysis.

In the household survey data, we know the district that the household lives in. We construct from the hospital-level autonomy dataset a hospital autonomy dummy variable for the household‘s district for each of the three years covered. If the district has just one hospital – which is typically a district hospital but is sometimes a provincial hospital or a central hospital – we simply define autonomy as 1 when the hospital in question has been autonomized. If there are multiple hospitals in the district – this mostly happens when there is a district and a higher-level hospital in the same district, but sometimes we find multiple district hospitals in a district – we explore various options for defining the autonomy of the local hospital district – these are explained below in section V.

Although we know whether a visit or inpatient admission was to a district, provincial or central hospital, we do not try to link utilization to the autonomy status of the hospital used. This is in part because we know only the level of hospital used not the name of the hospital; we cannot be completely sure, for example, which provincial hospital was used. More importantly, however, we want to know whether the autonomy status of the hospitals in a person‘s district affects the likelihood of them making an outpatient visit and being
criteria. Students must pass five out of eight disciplinary subject areas and practical modules per semester. Otherwise they must repeat the semester. Students who fail three or fewer subject areas can enroll in the next semester, but they must attend and pass leveling courses during a time window to be determined by each school on a case-by-case basis. In addition, students must satisfactorily complete all their subject areas and modules—completed after 6 semesters when students show no delay—within, at most, 10 semesters after enrolling in EMS. Otherwise they lose the right to re-enroll and therefore the chance of having a high school diploma. Partly because of the strict promotion rules, grade repetition rates are high, 15.6 percent in 2015.<sup>3</sup> According to the 2009 Survey of EMS Dropouts (Encuesta Nacional de Deserción en la Educación Media Superior), subject repetition is the second most common reason—after financial constraints—mentioned as a reason for early dropout.<sup>4</sup> In 2016, 15.5 percent of enrolled students dropped out of upper secondary, on average. These average repetition rates translate into an ontime graduation rate of 60 of every 100 students enrolled in EMS. More than half of total dropouts in EMS take place during the first year, with a large share of them concentrated during the first semester. A dimension that shows a large correlation with dropouts is household income. In 2012, according to the “National Evaluation Council”, CONEVAL, 59.5% of young people between 15 to 18 years old, the standard age to be enrolled in upper secondary education, who were not enrolled in school were concentrated in the poorest 50%.

> 3 Students who fail three or more subjects for two consecutive semesters have to repeat the entire grade.

> 4 Jacob and Lefgren (2009) use a plausibly exogenous variation in retention generated by a test-based promotion policy in the United States to show that retaining low-achieving eighth grade students in elementary school substantially
# Appendix 2. Basic facts of HtM and non-HtM individuals

On top of questions FIN24, FIN37, and FIN38 (already discussed in the main text, sub-section 4.1), in this Appendix, we also used the following questions from Global Findex Data to help characterize basic relevant facts of HtM and non-HtM individuals:

- Question FIN25 (which is included in the ...nancial resilience section) asks the surveyed individual in case of responding positive to FIN24 “What would be the main source of money that you would use to come up with [1/20 of GNI per capita in local currency] within the next month?”The possible answers being one of the following: “savings”, “family, relatives, or friends”or “money from working”, “borrowing from a bank, employer, or private lender”, “selling assets”, or “some other source”. We use this question to show that while savings is the most important (yet hardly the single) mechanism to cope with a ...nancial shock in the developed world, it is more limited in emerging markets where “family, relatives, or friends” seem to be more relevant. We also show that selling assets is not very relevant either in developed or emerging markets supporting that illiquid wealth does not seem to be very relevant in dealing with a ...nancial shock.

- Question FIN19 (which is included in the borrowing section) asks the surveyed individual “[d]o you, by yourself or together with someone else, currently have a loan you took out from a bank or another type of formal ...nancial institution to purchase a home, apartment, or land?”, respectively. The possible answers being “yes” or “no”. We use this question to provide an insight regarding the so-called wealthy HtM individuals identi...ed in previous studies for developed countries. In particular, we distinguish HtM individuals into those having a property
population in each EU country (Sutherland and Figari 2013). Using the same platform, the models produce comparative results based on the same income concepts, joint modeling conventions and similar assumptions. The simulations performed by EUROMOD are static in the sense that they abstract from behavioral reactions of individuals, and changes in the population composition are not considered.

The underlying microdata used in the simulations comes from the 2019 European Union Statistics on Income and Living Conditions (EU-SILC), which contain income information for 2018 and labor market information for 2019. Our simulations are based on 2020 tax-benefit policies (as of June 30) taking already existing and COVID-specific policies into account. Market incomes and non-simulated tax-benefit instruments in the data are adjusted to 2020 levels using source specific updating factors.

The data is furthermore adjusted for COVID-related labor market changes using the EUROMOD Labour Market Adjustment (LMA) Add-on (Christl et al. 2022a) which is adapted for the purpose of this research. The tool was initially developed to nowcast poverty and inequality changes in Europe (Gasior and Rastrigina 2017) as microdata only becomes available with a time-lag of about two years and is regularly being used by EUROSTAT – the statistical office of the EU – for their flash estimates of poverty and inequality.<sup>2</sup> Adjusted for the COVID specific situation, the LMA add-on transitions individuals from work (employment and self-employment) to unemployment as well as well as from work to monetary compensation schemes – i.e. workers still employed, but temporarily absent from work or working reduced hours, due to the lockdown - if available in the respective country. Following Christl et al. (2022b), we exclude transitions from inactivity or unemployment to employment as such transitions played a very small role in 2020.

Individuals are randomly selected for a transition to a different labor market status based on transition shares calculated from administrative information and the European Labour Force Survey, both provided by EUROSTAT. Both types of transitions – to unemployment and a monetary compensation scheme - are based
networks: federal, state, and municipal. _Mais Educação_ has been one of the main federal policies to incentivize and expand the provision of full-time education and ultimately achieve the _Plano Nacional da Educação_ (PNE) targets.<sup>6</sup> _Mais Educação_ extends the school day by financing extra activities scheduled before or after the regular class hours. Public schools joining the program can choose the extracurricular activities to be carried out, but the pedagogic supervision-related activities are mandatory.<sup>7</sup> Although the criteria for selection of schools changed over time, de facto priority was given to schools in socially disadvantaged areas or schools with low learning achievements. _Mais Educação_ started in 2008 and gradually expanded from 1,380 schools in 2008 (corresponding to 2.1 percent of state schools<sup>8</sup> and 0.4 percent of municipal

> 2 As of 2013, 3,665 out of 5,570 Brazilian municipalities had schools enrolled in _Mais Educação._

> 3 According to the _Pesquisa Nacional de Amostra por Domicílios_ (PNAD), in 2006, 98 percent of children between ages 7 and 14 attended school.

> 4 In the fields of reading, math, and the sciences, Brazil ranked 59th in student achievement. Furthermore, compared to the 2009 PISA results, Brazil worsened its position across the three knowledge areas assessed.

> 5 The regular school day in Brazil lasts on average 4.5 hours (Brazilian School Census 2007).

> 6 PNE establishes that 50 percent of public schools and 25 percent of public school students should have a full-time education.

> 7 Schools could choose mathematics, reading and writing, history, geography, and sciences activities.

> 8 In 2008, only the state of São Paulo did not participate in _Mais Educação_ .

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<!-- Start of picture text --> FIGURE IV-1: INCOME TAX DISTRIBUTION BY DECILE<br>99.91%<br>100% 94.21%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Estimated tax Self-reported tax<br>Source: World Bank Group staff estimates by based on EPNH 2017 and DIAN<br>Note: The deciles were created using market income + per-capita pensions<br>% of tax paid by each decile<br><!-- End of picture text --> If the income tax reported by households in the survey was used, a total estimated value for this tax of 152 billion pesos would be obtained, which is equivalent to only 4.4 percent of the value obtained using the DIAN information and income from the survey ($ 2,881 billion). As shown in the chart, the percentage of tax paid for each decile of income has a similar distribution; but in the case of the tax estimated with the income from the survey and the information from the DIAN, almost all the tax is paid by the highest decile.

# **_b) Indirect taxes:_**

In the previous version of the CEQ, the Quality of Life Survey (ECV) 2010 was used. Unlike the National Household Budget Survey (ENPH), the ECV expenses module does not include the product-by-product information on expenditures. Rather, the products were aggregated into groups. Likewise, the place of purchase question is not available in said survey. This limitation led to rural zones being considered an informal market in the previous version of the CEQ. In other words, people who lived in rural areas were considered not to be paying
capital base to over $100 billion by 2030. As the decarbonization process will continue over a period of two, three or four decades – this pace will need to be sustained potentially over 3-4 decades. Transmission investment numbers are likely be in the order of multiple billions per year – a volume of investment that is unprecedented.

This begs the first question as to where would these dollars come from? Even for a massive entity like the Powergrid that has strong financials, sustaining a double-digit growth rate on its capital base over even a single decade is a significant challenge. The question over capital availability looms larger for the state transmission companies for the other half of the investment. Transmission, notwithstanding all the efforts to attract private investment, obstinately remains in the domain of public financing. To put this in perspective, the cumulative total private in transmission _across all developing countries_ was reported as $11 billion in 2015 (ESMAP, 2015). Reliance on public financing is a common story, and yet with the exception of Africa and probably the odd exception like Türkiye, the recent focus of the MDBs have been more on cutting edge renewables, storage, hydrogen, etc., rather than grid strengthening projects. This trend is beginning to change, as we discuss later in this section, and may be a part of the solution to scale up transmission.

Pivoting to a different example of an emerging market, it is clear that in countries like Indonesia RE at grand scale cannot happen without high-capacity AC/DC lines including inter-island cables being laid out first for islands like Sumatra to support the major load centers in the Java-Bali system. IEA’s Net Zero by 2050 roadmap (IEA, 2021) shows the extent of grid expansion needed globally to meet net zero which will require over 4 million km of transmission lines and over 4 TW of substation capacity to be built _each year_ over this decade and the number will increase to over 6 million km and 6 TW, over the next decade (Figure 2). This will
**Table C1: Estimated Elasticities of Substitution for Varieties in Tanzanian Imperfectly Competitive Goods Sectors**

|**Sector in our Model**|**Matching HS-3 Code from Broda et al estimates weighted elasticity of substitution**|
|---|---|
|Processed food|110,150,151,160,170,180 190,200,210<br>10|
|Beverages & tobaccoproducts|220,240<br>2|
|Textile & leatherproducts|510-630,650,420<br>4|
|Manufacture of basic & industrial chemicals|280-391<br>3|
|Manufacture of fertilizers &pesticides|310<br>2|
|Petroleum refineries|271<br>4|
|Glass & cement|680-702<br>5|
|Iron steel & metalproducts|720-831<br>7|
|Mining&quarrying|270,271<br>4|
|Grain milling<br>Source: Authors calculations based on estim|110<br>3<br>ates from Broda,Greenfield and Weinstein(2006).|

96