rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9252

# **Abstract**

The COVID-19 pandemic has closed schools around the world, forcing school systems and students to quickly attempt remote learning. A rapid response phone survey of over 1,500 high school students aged 14 to 18 in Ecuador was conducted to learn how students spend their time during the period of quarantine, examine their access to remote learning, and measure their mental health status. The data show that 59 percent of students have both an internet connection at home and a computer or tablet, 74 percent are engaging in some online or telelearning, and 86 percent have done some schoolwork on the last weekday. Detailed time-use data show most students have established similar daily routines around education, although gender and wealth differences emerge in time spent working and on household tasks. Closure of schools and social isolation are the two main problems students say they face, and while the majority are mostly happy, 16 percent have mental health scores that indicate depression.

This paper is a product of the Development Research 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 dmckenzie@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
&D data are from firm R&D surveys conducted by the National Science Foundation (NSF).

But several intangible expenditures are not elicited in any firm survey in the US. In some cases, CHS use data on the revenue of the industry which produces the intangible asset to measure the expenditure on that asset. For example, spending on new architectural and engineering designs is estimated as half of the revenue of the architectural and engineering design industry as reported in SAS. Similarly, outlays on 8
The main reason for this is that, as currently defined, the fiscal impoverishment indicator of the authors is based on a given poverty line, whereas shared prosperity is determined by following a distribution of income within a country. As a result, the bottom 40 percent of a population may be below a chosen poverty line in one country, while in another population, only a fraction of the bottom 40 percent will be below the same line.

However, the measures proposed in this paper can be used to assess the relationship between fiscal impoverishment and shared prosperity among different socioeconomic groups within a country as well as in comparing a group of countries. Indeed, these measures consider the experience of every single individual in an economy, and they allow researchers to compare fiscal losses among specific population subgroups, especially different quantiles (such as the bottom 40 percent), both within a country and among a group of countries.

# **6. Conclusion**

In this paper, the main focus has been on measuring the various dimensions of the fiscal losses and gains of a given fiscal system. We are proposing a family of measures that can accurately assess the extent to which a population is fiscally impoverished or fiscally enriched after the intervention of a fiscal system. This set of indicators addresses the incidence, intensity, and severity of fiscal losses and gains. An empirical illustration using the 2014 household consumption data in Niger, and contrasting the conclusions drawn based on current measures used in the literature with the new measures proposed here provides evidence of the benefits of these new measures.

The findings reveal that the incidence and the intensity of fiscal losses in Niger were 33.2 percent and 0.014, respectively; whereas those of fiscal gains were 66.8 percent (incidence) and 0.126 (intensity) in Niger. This suggests that overall the fiscal system is improving the welfare of the Nigerien population. Comparing the experience of the prefiscal poor to that of the prefiscal nonpoor, we found that the poor are more favored
introduced in 2016 achieved remarkable success by bringing previously undisclosed foreign and domestic assets into tax compliance, amounting to a cumulative total of 21% of GDP and standing out as one of the most successful tax amnesty initiatives globally. The extensive scale of Argentina’s asset disclosures provides an opportunity to utilize straightforward methodologies to unpack the effects of changes in tax enforcement. Third, Argentines are required to report their domestic and foreign assets annually to the tax authority due to the existence of a gross wealth tax on individuals and firms, enabling us to track declared income and wealth over time. Specifically, we use two decades of detailed tax tabulations and employ the generalized Pareto interpolation method to characterize the distributions of income and wealth.

We begin by leveraging the disclosures made under the 2016 amnesty to shed light on the prevalence, nature, and distribution of tax evasion. We find substantial offshore evasion: about 255,000 Argentines admitted to concealing assets under the program, and strikingly, more than 80% of these assets were hidden abroad, mainly in the US and tax havens or low-tax jurisdictions like Uruguay, Switzerland, and the British Virgin Islands. Regarding the types of assets disclosed, over half of the disclosed foreign assets consisted of stocks, one-quarter were held in bank accounts and various currencies, and one-tenth were real estate properties. Furthermore, our analysis reveals that offshore tax evasion is predominantly concentrated among the wealthiest individuals, especially within the top 0.1% of the wealth distribution.

Next, we examine impacts on tax compliance up to five years later. We find that the asset disclosures resulted in enduring increases in reported wealth. The number of Argentines declaring foreign assets in their wealth tax returns tripled after 2016, and the value of reported foreign assets quadrupled from Arg$250 billion in 2015 (4.3% of GDP) to Arg$1 trillion in 2016 (16.5% of GDP) and reached Arg$1.25 trillion in 2019 (20.7% of GDP), bringing the
Phyto-sanitary standards mean for fresh fruits and vegetables that they must be free of pests and diseases when entering the US. Exporters must meet a complex set of regulations, compiled in the Fresh Fruit and Vegetables Import Requirements (FAVIR) online database. Compliance with these regulations is overseen and administered by the US Department of Agriculture’s Animal and Plant Health Inspection Service (APHIS).<sup>4</sup> In this paper we focus on US sanitary measures. Food products can be the source of numerous foodborne illnesses (due to pathogens, toxins, and chemicals). All food products must be unadulterated (not bear or contain any poisonous or deleterious substances), be fit for consumption, and not contaminated or decaying, in order to be allowed for consumption in the U.S. At the federal level, there are three agencies involved in the oversight of food and food ingredients safety: the US Department of Agriculture’s Food Safety and Inspection Service (FSIS), the Food and Drug Administration (FDA), and the Environmental Protection Agency (EPA). FSIS ensures the safety of imported meats, poultry, and processed egg products. FDA covers all other products. EPA licenses pesticide products and monitors pesticide residues in products.

The FDA enforces the Federal Food, Drug and Cosmetics Act (FD&C) as well as other laws designed to protect consumer health, welfare, and safety. Under Sec. 801 of FD&C, products are subject to inspection when imported. Imported food products are expected to meet the same standards as domestic products, i.e. they must be pure, wholesome, safe to eat, and produced under sanitary conditions. Food imports must also contain informative and truthful labeling in English.<sup>5</sup> Another important requirement is that since 1997, producers must follow FDA's good agricultural practices (GAP) for the control and management of microbial food safety. Likewise, since 1995 fish products imports must meet hazard analysis
%|8.9%|Debt service costs.|
|India – Odisha|91%|90%|94%|1.7%|n.a.|
|India – Rajasthan|45%|38%|57%|**16.0%**|Debt service costs.|
|Kenya|90%|80%|101%|9.0%|Fuel mix (hydro availability), capital costs.|
|Morocco<sup>a</sup>|84%|n.a.|n.a.|n.a.|Fuel prices.|
|Pakistan|80%|66%|97%|**17.0%**|Fuel prices (major factor), tariff increases (minor).|
|Peru|98%|93%|102%|3.6%|n.a.|
|Philippines|98%|95%|100%|1.7%|Fuel prices.|
|Senegal|70%|55%|87%|**16.0%**|Fuel prices.|
|Tajikistan|67%|52%|83%|**18.7%**|FEX rate.|
|Tanzania|68%|56%|90%|**22.6%**|Fuel mix (hydro availability), FEX rate, fuel prices.|
|Uganda|92%|81%|110%|**12.2%**|Capital costs, fuel prices, FEX rate.|
|Ukraine|106%|93%|115%|7.1%|FEX rate, fuel prices.|
|Vietnam|89%|86%|91%|2.1%|n.a.|

_Notes:_<sup>a</sup> Data for Morocco is only available for one
<!-- Start of picture text --> Predicted probabilities of stunting during infancy (< 6 months of age) for<br>select conditions<br><!-- End of picture text --> <!-- Start of picture text --> Predicted. baseline,risk —o—8.8<br>Pre-natal environment:. Inadequate —o——_16.1<br>Pre-natal environment:. Adequate —_o—5.9<br>Post-natal health care environment:: . 6.36<br>Inadequate<br>j<br>Post-natal- health care environment:: 11.36<br>Adequate<br>Post-natal water/sanitation environment: 138<br>Inadequate<br>Post-natal water/sanitationitati environment:i . 7.96<br>Adequate<br>0 10 20 30 40%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 2a (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
3

# **List of Abbreviations**

|ANC|Antenatal care|
|---|---|
|ARI|Acute respiratory infection|
|BCG|Bacillus Calmette–Guérin|
|BMI|Body Mass Index|
|CATA|Catastrophic (health) expenditures|
|CPI|Consumer price index|
|CWIQ|Core Welfare Indicators Questionnaire|
|DDH|World Bank Development Data Hub|
|DHS|Demographic and Health Survey|
|E123|Enquêtes 1-2-3|
|EAPPOV|East Asia & Pacific harmonized household survey collection|
|ECAPOV|Europe & Central Asia harmonized household survey collection|
|ECHP|European Community Household Panel|
|EHIS|European Health Interview Survey|
|EUROSTAT-HBS|Eurostat Household Budget Survey|
|FP|Financial protection|
|HBS|Household Budget Survey|
|HEFPI|Health Equity and Financial Protection Indicators|
|HEIDE|Household Expenditure and Income Data for Transitional Economies|
|HIES|Household Income & Expenditure Survey|
|ICP|International Comparison Program|
|IFG|Impaired fasting glycaemia|
|IFLS|Indonesia Family Life Survey|
|IMPOV|Impoverishing (health) expenditures|
|IMR|Infant mortality rate|
|IP|Inpatient|
|ISSP|International Social Survey Program|
|ITN|Insecticide treated bed net|
|LAM|Lactational amenorrhea method|
|LCUs|Local currency units|
|LIS|Luxembourg Income Study|
|LMIC|Low and middle-income country|
|LSMS|Living Standards Measurement Study|
|LWS|Luxembourg Wealth Study|
|MCH|Maternal and child health|
|MCSS|Multi-Country Survey Study on Health and Responsiveness|
|MDGs|Millennium Development Goals|
|MICS|Multiple Indicator Cluster Survey|
|MMR|Measles-Mumps-Rubella|
|MNAPOV|Middle East & North Africa harmonized household survey collection|
|NCD|Noncommunicable disease|
|OECD|Organization for Economic Co-operation and Development|
|ORS|Oral Rehydration Salts|
|Min. balance|50,000|27.9%|14.0%|13.2%|6.9%|5.9%|5.4%|2.2%|
|Registration book|29,082|16.2%|8.1%|7.7%|4.0%|3.4%|3.2%|1.3%|
|Monthly admin. Fee|4,808|2.7%|1.3%|1.3%|0.7%|0.6%|0.5%|0.2%|
|Debit card|5,563||1.6%|1.5%|0.8%|0.7%|0.6%|0.2%|
|processing||3.1%|||||||
|Other monthly fees|5,500|3.1%|1.5%|1.5%|0.8%|0.7%|0.6%|0.2%|
|**TOTAL**|**94,953**|53.0%|**26.5%**|**25.0%**|**13.1%**|**11.2%**|**10.3%**|**4.1%**|
|**Excl. Min. balance**|44,953|25.1%|12.6%|11.9%|6.2%|5.3%|4.9%|1.9%|

_Source: Superintendencia Bancaria; Encuesta de Calidad de Vida 2003_ 13. **Real interest rates for small savings amounts are frequently negative** . The average
the estimation of sibling correlation in equation (6), the family and individual components need to be estimated. The available literature on sibling correlations relies on two alternative estimation methods. Mazumder (2006, 2011) uses the Restricted Maximum Likelihood (REML) method which has better small sample properties under the normality assumption. Bjorklund et al. (2010) instead utilize a mixed effects model to estimate the family and individual components. The procedure in Bjorklund et al. (2010) can be implemented as a two-step procedure similar to the method employed also by Solon et al (1991) and Bjorklund et al (2002). The weakness of this procedure is that its small sample properties are not well understood. We implemented both procedures. Given the large size of the samples used in this paper (more than 34,000 in 1993 and 38,000 in 2006), both procedures produce nearly identical parameter estimates, and for the sake of brevity, we report estimates from the procedure suggested by Bjorklund et al. (2010). The estimates using Restricted Maximum Likelihood are available from the authors. The estimates of sibling correlations presented in this paper are from the mixed effects model using Stata GLLAMM procedure. As noted before, all standard errors are corrected for clustering at the family level.<sup>22</sup>

# **(3) Data and Empirical Issues**

The data for our analysis come from the National Family Health Survey (NFHS), 1992/93 and 2006. The NFHS is a large-scale and nationally representative survey of nearly all of Indian states.<sup>23</sup> The main target group for this survey is women in their reproductive years. While both surveys followed similar sampling methodology, the surveys differ somewhat in terms of sample size and questionnaires. The NFHS 2006 used three separate questionnaires to interview 109,041 households, 124,385 unmarried and ever married women between 15 to 49 years of age and 74,369 unmarried and ever married men
# **Table 1: Variable definitions and summary statistics**

This table provides a brief description of the variables and the summary statistics.

# **Panel A. variable definitions**

|Variable|Variable Description|
|---|---|
|_Privatized_|A dummy variable indicating the privatization of the firm:_Privatized_equals 1<br>if its state share falls below 50% or it exits from the database.|
|_Age_|The age of the city party secretary.|
|_College_|The dummy variable equals 1 if the city party secretary has college education<br>and 0 otherwise.|
|_Central Experience_|The dummy variable indicating the city party secretary having experience<br>serving in the central government.|
|_Provincial Experience_|The dummy variable indicating the city party secretary having experience<br>serving in the provincial government.|
|_Labor prod_|The labor productivity of the firm calculated as ln (sales/employment).|
|_Leverage_|The leverage ratio of the firm, calculated as total debt/total asset.|
|_GDP Per capita_|GDP per capita (of thousand RMB in the 1990 constant price) of the city.|
|_GDP Growth_|The real growth rate of GDP (%) of the city.|
|_Government activism_|The city-level average “_percent of the officials in the department who facilitate_<br>_the development of the company_” in city. Data is from the World Bank<br>Enterprises Survey for China (2005). We obtain the city average from firm-<br>level data.|
|_TFP_OP_|Total factor productivity (TFP) of the firm calculated using the Olley-Pakes<br>method (see Appendix C for details).|
|_TFP_OLS_|TFP of the firm calculated using the OLS method (see Appendix C for details).|
|_TFP_IN_|TFP of the firm calculated using the index number method (see Appendix C for<br>details)
This is not uncommon in social programs.12 Naturally it constrains the scope for pro-poor geographic targeting. We will see how much so later.

In the second stage, FEE households are identified within the selected UP's. Widows, day-laborers, low income professionals,'<sup>3</sup> landless or near landless farmers, and households with school-aged children not covered by other targeted programs are officially eligible to receive the program. 14 The program relies heavily on community involvement in the selection of the households. The selection is typically done by the School Management Committee (SMC); this is composed of teachers, local representatives, parents, education specialists and donors to the school. The food is distributed by the SMC (or sometimes by the UP or a local NGO). Each participating household is entitled to receive 15kg per month for each child enrolled in school, up to 20kg for more than one child.

The empirical analysis will be based on the Household Expenditure Survey (HES) collected in 1995-96 by the Bangladesh Bureau of Statistics, following well-established and credible survey practices, with support from international agencies including the World Bank. The household questionnaire contains extensive information on household expenditures, and has specific questions on household participation in FFE. A comprehensive consumption aggregate can be formed from the data, including imputed values of consumption in kind, valued at local market prices. We deduct the imputed value of the FEE transfer from the consumption aggregate. A simple random sample of households was drawn from each primary sampling unit (PSU) and a detailed community survey was done. The PSU is the 12 See, for example, the discussion of the political economy of program placement for an Argentinean program in Ravallion (1999c).

**13** Defined as fishermen, weavers, cobblers, potters, blacksmiths, etc.

14 In particular, the Vulnerable Group Development and Rural Maintenance Program are government sponsored programs distributing food to the poor that are not compatible with the FFE program. The household questionnaire accounts
**Table 2: NEET Rates for 15- to 24-Year-Olds, by Region**

|**Region**|**NEET**|**rates, latest y**|**ears**|**Female share**|
|---|---|---|---|---|
||**Total**|**Male**|**Female**||
|**World**|**21.8**|**9.8**|**34.4**|**76.9**|
|**Developing countries**|12.1|8.0|16.0|66.1|
|**Emerging countries**|25.2|9.6|41.8|80.3|
|**Developed countries**|13.1|11.3|14.9|55.7|
|**_Northern Africa_**|26.1|16.7|36.0|67.6|
|**_Sub-Saharan Africa_**|15.5|11.2|19.0|61.4|
|**_Latin America and the Caribbean_**|19.4|11.9|27.0|68.6|
|**_Northern America_**|16.3|14.1|18.6|55.8|
|**_Arab States_**|18.2|9.9|27.1|71.8|
|**_Eastern Asia_**|3.7|2.8|4.7|61.8|
|**_South-Eastern Asia and Pacific_**|18.0|13.4|22.6|61.5|
|**_Southern Asia_*
[21] [22] ,, _China's Energy Science and Technology Development Outlook for the 21st Century_ , _Electric Power Supervision Annual Report 2010_<sup>[23]</sup> , and other literature on technical parameters. and economic analysis of power production.

# **B3. End-use energy demand module**

The demand for end-use energy services in our TIMES model is classified into four major final demand sectors: buildings, industrial, transportation and agricultural. End-use energy demands are projected based on the relationships between energy demand and corresponding drivers. The relationships are established based on statistical analysis (e.g., regression analysis) using historical data. The end-use demand forecasts differ across the final demand sectors. The transportation sector is divided into different modes: road, rail, air and water. Each mode has two types of transportation services: passenger and freight. The industrial sector is divided into sub-sectors and technologies. The building sector is divided into: urban residential buildings, rural residential buildings, general public buildings and large public buildings. In each type of building, end-use energy demand is divided into heating, refrigerating, lighting, cooking, hot water and electronic equipment.

In the base year (2015), the model is calibrated with detailed data. The data for sectoral and sub-sectoral energy consumption are compiled from Energy Balance and the Industrial Statistical Yearbook. Table B3 shows data used in the base year by industrial sector category.

**Table B3** Energy consumption of each sub-sector of the industrial sector in the base year

|(Mtce)|
|---|

||Total|Coal|Oil|Gas|Electricity|Heat|
|---|---|---|---|---|---|---|
|Chemical|229.23|93.86|47.60|28.00|38.65|<br>21.12|
|Non‐metal|
**Romania**

<!-- Start of picture text --> 35.0<br>25.0<br>15.0<br>5.0<br>-5.0<br>-15.0<br>-25.0<br>-35.0<br>-45.0<br>location vulnerable households household structurehousinghealth head of the household individual characteristics<br>rural areas towns and suburbs hh with pensioner hh with disability recipient hh with +5 members hh with children single-elderly hh hh with unemployed female headed hh number of >64 number of 18-64 number of <18 crowded house unmet medical needs student inactive retired unemployed tertiary educ. secondary educ. home ownership age male<br><!-- End of picture text --> Note: Average marginal effects from the Probit regressions expressed as percentage points. See Annex A4 for further details on the regression analysis. See Table A4.1 to Table A4.4 for regressions results. Households with social aid cannot be identified as a vulnerable group in the Romania EU-SILC. Source: Own estimates based on 2022 EU-SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

# **The unconditional analysis (descriptive results reported in Table 6) gives us a considerably**

**good approximation of the profile of the AROPE.** The profile of the AROPE population in terms of their individual and socio-economic characteristics (gender, education, employment status, home property, and location) is the same under the unconditional analysis (Table 6) and under the conditional analysis (regression results reported in Table A4.1 to Table A4.4 and Figure 3). Further, the conclusions about which type of households are more vulnerable to being multidimensionally poor across all countries are the same, except for female-headed households, which  were identified as more likely to be AROPE in the unconditional analysis in all countries,