rafmacalaba/data-use-mentions-extended huggingface dataset:
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" significant penetration in this group of households. Nearly 32 percent of these very poor households had electricity in their homes. Very few had in-house water taps (6 percent), sewer connections (3 percent), or telephones (3 percent).

Telephone coverage remains at 3 or 4 percent among households in the first five 5-plercent quantiles (i.e., the lowest 25 percent of the sample households). Only when the median income proxy reaches US$120 per household per month does telephone coverage begin to rise. Coverage of electricity and of in-house water taps, on the other hand, begin to rise immediately and increases sharply from 5-percent quantile to 5-percent quantile. By the tenth 5-percent quantile (i.e., rnedian income proxy = US$225 per household per month), 66 percent of the sample households had electricity, and 33 percent had in-house water taps. Above US$225 per household per month, use of electricity and in-house taps continues to rise, but at a slower rate (Figure 1). Nearly all of the households in the wealthiest 5-percent quantile (US$1300 per household per month) had electricity, 88 percent had in-house water taps, and 72 percent had telephones.

Electricity was the most widespread of these three services at all income levels, and telephone service was the least common. In Figure 1 the coverage lines for these three sectors never cross, and the slope of the three lines is remarkably similar among households with incomes (as approximated by the consumption aggregate) above US$250 per month.

Figure I does show one puzzling result. One would generally expect more households (and particularly more poor households) to have modem water services than advanced sanitation solutions, but coverage of in-house water taps and sewer connections appear to be virtually identical up to US$300 per household per month. In fact, there are two shortcomings in the LSMS data used for this analysis
Policy Research Working Paper 10870

# **Abstract**

Leveraging unconventional data, including website traffic data and Google Trends, this paper unveils the real-time usage patterns of generative artificial intelligence tools by individuals across countries. The paper also examines country-level factors driving the uptake and early impacts of generative artificial intelligence on online activities. As of March 2024, the top 40 generative artificial intelligence tools attract nearly 3 billion visits per month from hundreds of millions of users. ChatGPT alone commanded 82.5 percent of the traffic, yet reaching only one-eightieth of Google’s monthly visits. Generative artificial intelligence users skew young, highly educated, and male, particularly for video generation tools, with usage patterns strongly indicating productivity-related activities. Generative artificial intelligence has achieved unprecedentedly rapid global diffusion, reaching almost all economies worldwide within 16 months of ChatGPT’s release. Middle-income economies have disproportionately high adoption of generative artificial intelligence relative to their economic scale, now contribute more than 50 percent of global traffic, while low-income economies contribute less than 1 percent. Regression analysis reveals that income level, share of youth population, digital infrastructure, specialization in high-skill tradable services, English proficiency, and human capital are strongly correlated with higher uptake of generative artificial intelligence. The paper also documents disruptions in online traffic patterns and emphasizes the need for targeted investments in digital infrastructure and skills development to harness the full potential of artificial intelligence.

This paper is a product of the Digital Development Global Practice. 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 yanliu@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
Source: Argentina (Rossignolo 2017); Brazil (Higgins and Pereira 2017); Chile (Martinez-Aguilar et al. 2016); Croatia (Inchauste and Rubil 2015); Mexico (Scott 2013); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018); US (Higgins et al. 2018). Turkey: Own estimates based on Turkey 2016 HBS. Note: Marginal contribution to equality is the difference between the Gini coefficient without the particular fiscal intervention and the Gini coefficient of all income components together. There is no marginal contribution calculated for Argentina. USA results are preliminary.

**Figure 9. Progressivity and redistributive effect of transfers in Turkey, 2016**

**a. Direct transfers in Turkey, 2016**

<!-- Start of picture text --> AGI<br>Agricultural... Disability... Unemployment... Widow transfer...<br>Scholarship Transfers Birth support for... Sickness Transfers Near Cash Transfers Disability Benefit Family Transfers Old Age Transfers Home Care Benefit All direct transfers... All direct transfers...<br><!-- End of picture text -->

- **b. Direct transfers in selected countries (excluding contributory)**

26
</mark> 2018). _Gender Differences in Poverty and Household Composition through the Life-Cycle: A Global Perspective_ . Policy Research Working Papers 8360. Washington, D.C: The World Bank.

- Munoz Boudet, A. M., Bhatt, A., Azcona, G., Yoo, J., & Beegle, K. (2021). _A global view of poverty, gender, and household composition_ . Policy Research Working Papers 9553. Washington, D.C: The World Bank.

- Nasri, Khaled (2020). Social Safety Nets in Tunisia: Do Benefits Reach the Poor and Vulnerable households at the Regional Level? GLO Discussion Paper, No. 440, Global Labor Organization (GLO), Essen

- NAF 2020. “Assessing Gender Equity in National Aid Fund Programs”. Jordan National Aid Fund <u>https://naf.gov.jo/EN/List/Statistics_and_Data</u>

- Nassar, Heba. (1997). “Feminization of Poverty.” The Egyptian Human Development Report; Research Papers Series, UNDP.

- <mark>Newhouse, D., Becerra, P. S., & Evans, M. (2017). New global estimates of child poverty and their sensitivity to alternative equivalence scales.</mark> _<mark>Economics Letters</mark>_ <mark>,</mark> _<mark>157</mark>_ <mark>, 125-128.</mark>

- OECD. (2018). A Broken Social Elevator? How to Promote Social Mobility. Paris: OECD Publishing. https://doi.org/10.1787/9789264301085-en

- <mark>Palestinian Central Bureau of Statistics (PCBS) and World Bank. (2018). “Measuring poverty in West Bank and Gaza: Methodology review using PECS 2016.” Technical report.</mark>

31
# The Insights and Illusions of Consumption Measurements<sup>_∗_</sup>

Erich Battistin<sup>_†_</sup> Michele De Nadai<sup>_‡_</sup> University of Maryland, CEPR, FBK-IRVAPP and IZA University of New South Wales Nandini Krishnan<sup>_§_</sup> World Bank JEL Classification: C81, D31, D63, E21, I32 Keywords: Household Consumption, Household Expenditure Surveys, Inequality, Poverty and Household Well-Being, Modes of Data Collection

> _∗_ This version Thursday 21st May, 2020. The manuscript benefited from comments by Kathleen Beegle, Leah Bevis, Joachim De Weerdt, Beatriz Godoy, Arthur Lewbel, Juan Munoz, Sergio Olivieri, Thomas Pave Sohnesen and Tara Vishwanath as well as seminar participants at the CEPR conference on New Consumption Data (September 2019), NEUDC (October 2019) and the IARIW-World Bank conference on “New Approaches to Defining and Measuring Poverty in a Growing World”(November 2019). The views expressed here are those of the authors alone.

> _†_ Department of Agricultural and Resource Economics, 7998 Regents Drive, Symons Hall, College Park, MD 20742, United States of America. Contact: ebattist@umd.edu.

> _‡_ UNSW Business School UNSW Sydney, NSW 2052, Australia. Contact: m.denadai@unsw.edu.au.

> _§_ Poverty and Equity Global Practice, World Bank, 1818 H Street, NW Washington, DC 20433, United States of America. Contact: nkrishnan@worldbank.org.
# **1.1 Features of Taxation in Africa**

Tax collection in Africa is low, but similar to other regions at a similar income level (Figure 1). In 2018, the most recent year with wide data coverage, Sub-Saharan African countries collected 14 percent in taxes as a share of GDP (UNU-WIDER Government Revenue Dataset, 2021). This continent-wide average masks significant variation across countries. High- and upper-middle-income countries like Seychelles, Namibia and South Africa have rates as high as 28 - 33 percent whereas low-income countries like Chad, Democratic Republic of Congo and Ethiopia have rates as low as 7 percent. These numbers have remained stagnant over the last 3 decades, with African countries collecting an average of 12 - 15 percent of GDP as taxes from 1990 to 2020. Both the South Asia region and the Middle East and North Africa region have similarly low rates as Sub-Saharan Africa of about 14 percent. In contrast, the Europe and Central Asia region has the highest rate of 32 percent (UNU-WIDER Government Revenue Dataset, 2021). Across all countries, on average, higher income countries collect a higher share of GDP as taxes.

The composition of taxes in Africa is also roughly similar to that of countries at similar income levels. Figure 2 presents evidence on the reliance of countries on different tax instruments: taxes on consumption, personal income, corporations, payroll, trade and property. As in almost all regions, the greatest component of taxes in Sub-Saharan Africa is consumption taxes (49 percent), which consist of sales tax, value added taxes and excise taxes. Some striking differences of African countries, even compared to countries at a similar income level, are that African countries collect a minimal amount in payroll taxes, possibly a reflection of the low share of the economy employed in the formal sector.<sup>1</sup> In contrast, African countries collect more trade taxes than countries at a similar income level.

With regards to tax rates, the tax rate on income,
<!-- Start of picture text --> Figure 12. Cost-recovery capabilities of residential effective tariffs at 100 kWh/month consumption<br>a. Total cost recovery b. Operational cost recovery<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>level of total cost recovery level of operational cost recovery<br>percentage of countries percentage of countries<br>cost cost<br>0%- 30% 30%-50% 50%-80% 80%-99% recovery 0%- 30% 30%-50% 50%-80% 80%-99% recovery<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database <!-- Start of picture text --> Figure 13. Household billing as a share of total power billing<br>a. Composition of annual billing per type of customer b. Residential share of billing and consumption<br>80<br>100%<br>90%<br>80%<br>70%<br>60% 60<br>50%<br>40%<br>30%<br>20% 40<br>10%<br>0%<br>20<br>0<br>0 20 40 60 80<br>High voltage industrial<br>Medium voltage commercial Share of residential bill<br>Low voltage residential<br>% of amount billed<br>Chad Mali Ghana Senegal Niger Tanzania Nigeria Kenya Ethiopia Rwanda<br>Burkina Faso Cameroon Cape Verde Congo, Rep. Congo DRC Cote d'Ivoire Mozambique Zimbabwe South Africa Share
Zhengzhou, the capital of Henan Province, which we have chosen because its population, income and air pollution level are representative for China's large cities.<sup>2</sup> 3Zhengzhou's industry pours approximately 45,000 tons of S02 into the atmosphere every year, contributing to an ambient S02 concentration of 90 ug/m<sup>3</sup> . At this level of air pollution, Xu's dose-response function predicts that over 400 people are dying annually from S02related pollution. Thousands more are undoubtedly suffering from serious respiratory illness.

Because Zhengzhou is a representative large city, we use it to develop an estimate of the optimum air pollution levy for urban China. The optimum levy should be set at the point where the incremental benefit of abatement is equal to its incremental cost. To identify this point, we simulate pollution control alternatives by reducing Zhengzhou's S02 emissions in 10% increments until 90% abatement is reached. We follow the method used for Beijing to estimate reductions in atmospheric S02 concentration and mortality from respiratory disease. We value mortality reductions at $8,000 per 'statistical life,' yielding the marginal benefit (MB) schedule in Figure 29.

To estimate the marginal cost (MC) schedule, we turn to our cross-provincial econometric results for the S02-intensity of industrial production. The levy response elasticity in this equation (which is always estimated with the appropriate sign and high statistical significance) registers the overall result when enterprise managers weigh incrementa **l** evy payments for pollution against the incremental cost of abatement. At the equilibrium level of pollution intensity, the effective levy rate should be just equal to the incremental cost of abatement for industry as a whole, whether the abatement is achieved by process change or installation of end-of-pipe treatment. Solving the estimated S02 intensity equation 'in reverse' for the effective levy rate (or, in equilibrium, the incremental cost of abatement), we obtain an expression for incremental abatement cost as a function of
present, teachers are devoting significant amounts of time to non-teaching and learning activities, such as classroom management (Abadzi 2009; Bruns and Luque 2015). The cumulative result is that students are receiving only a fraction of the scheduled teaching time as actual learning time: across 7 Sub-Saharan African countries, whereas scheduled teaching time was on average 5 hours and 27 minutes per school day—actual teaching time was on average 2 hours and 46 minutes (Bold et al. 2017).

In order to shed light on the measurement of these issues and their implications, we administered four different classroom observation instruments—from the Service Delivery Indicators, the Stallings Observation System, the Classroom Assessment Scoring System, and the recently developed _Teach_ classroom observation instrument—in about 100 schools across four regions of Tanzania. The research design is such that various combinations of tools were administered to various combinations of teachers so these data can be used to explore the commonalities and differences in the behaviors and practices captured by each tool, the internal properties of the tools (for example how stable they are across enumerators, or how various indicators relate to one-another), and how variables collected by the various tools compare to each other. In addition to insights on the tools themselves, we leverage measures of teacher and student 2
Policy Research Working Paper 9977

# **Abstract**

Recent literature suggests that the positive impact of population density on wages, the canonical measure of agglomeration effects, is multiples higher in developing countries than in advanced economies. This poses an urban productivity puzzle because on-the-ground observations do not suggest that cities in developing countries function especially well or are conducive to enhanced productivity. This paper uses manufacturing censuses from four countries at differing levels of income that allow separating plant output quantity from prices. It shows that higher wage elasticities with respect to density are due to higher marginal costs, and agglomeration elasticities of efficiency, physical total factor productivity, are in fact far lower in developing countries. Further, congestion costs decrease with country income. Both are consistent with often low rates of structural transformation that make cities in developing countries so-called “sterile agglomerations,” which are populous but not efficient.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Office of the Chief Economist, Latin America and the Caribbean Region. 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 agrover1@worldbank.org and wmaloney@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
constraints do matter for these households.

**Type of savings.** The higher savings in FG families take the form of higher per capita formal saving in financial institutions. As Table 11 shows, we do not find a significant difference in the annual flow of jewelry saving (in precious stones and metals) among FG and FB families.<sup>39</sup> Similarly, there are no significant differences in the flow of saving in livestock (although the coefficient is positive) and saving in durable goods. One may be concerned that the lack of significant effects on these flow measures is due to the fact that purchases of durable goods are relatively infrequent. Therefore, we also examine if using the _stock_ versions of the saving variables in Table 11 yields different results. The interaction coefficients remain insignificant, however (Table A.11). This pattern of saving behavior is consistent with greater access to the banking system in rural India and the less liquid nature of jewelry relative to cash savings in bank accounts during our study period.

**Magnitudes.** Back-of-the-envelope calculations suggest that FG families accumulate about INR 82,817 per daughter by the time she is 15 years old, the median age at marriage in our sample.<sup>40</sup> According to 2005 IHDS data, an average bridal family spends INR 75,777 to INR 98,498 on a wedding in rural India. Thus, an average household appears to be able to save for the bulk of the wedding expenditure in advance. Any remaining shortfall is potentially met through borrowing during or close to the year of marriage.

The average daily wage for agricultural casual labor in our data is INR 61 and, for nonagricultural casual labor, it is INR 91. Assuming that fathers are able to earn these wages for the extra days worked, their income increases by INR 610 to INR 910 each year relative to their income before the birth of their first child and relative to FB families. Although the increase in
results in bias because this truncation tends to remove observations with smaller errors, resulting in errors with positive expected value.

Heteroscedasticity can have substantial implications for Tobit estimators since the value affects the realization of the CDF in equation (4) for each of the limit values, x . The resulting ii σσbias increases with the degree of heteroscedasticity and the share of limit observations (see Arabmazar and Schmidt 1981) and is quite different from bias due to the combination of heteroscedasticity and nonlinearity identified by SST (2006).

Gravity models are frequently estimated on flows of aid, investment (Chitu, Eichengreen and Mehl 2014), migration (Artuc et al 2014) or trade data with missing values of the dependent variable. When these are mapped to zeros, the resulting sample merges data from two distinct regimes—the stochastic relationship between _x_ and _y_ and the deterministic relationship between _x_ and the zero observations. This can be expected to reduce the coefficient on _x_ estimated with non-limited-dependent estimators like Nonlinear Least Squares (NLS) or PPML. With limiteddependent variable estimators, the impact of the missing data on coefficient estimates depends firstly on whether these missing values would have exceeded the threshold (bias) or not (no bias). Figure 1 shows that the direction of the bias from mapping nonzero trade values to zero will depend on their values—mapping the square at ( _x_ = -1.1, _y_ =4.4) to the threshold biases the estimated slope upwards, while mapping the square at (8, 13) to the threshold biases it down. This suggests that missing trade for relatively small flows may bias gravity model coefficients up while missing trade for large flows may bias them down in absolute value.

8
# Table 2: Regression results from baseline specification

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐TF|P Ackerberg_et al_.‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.357***||||||
||(0.000)||||||
|Entryt‐2||0.015|||‐0.085|‐0.080|
|||(0.835)|||(0.283)|(0.313)|
|Conductt‐2|||‐0.198***||‐0.217***|‐0.221***|
||||(0.000)||(0.000)|(0.000)|
|FDI Overallt‐2||||0.085*||‐0.074
The objective is to calculate, for household interviewed in round 2, the change in log income between the two survey rounds: ii (2) where and are respectively the first and second round log per capita incomes of household ii ii2 ii1 ΔΔyy = yy −yy surveyed in round 2. Therefore, the log per capita household income in round 1 for household is ii1 ii2 yy yy ii unknown and must be estimated. These changes would be easily calculated with panel data since all ii households are interviewed in both rounds (i.e., is observed for every household interviewed in both rounds and does not require to be estimated). Given panel data is costly and scarce in most countries in ii ΔΔyy ii the region, synthetic panels allow for predicting the first round of “unobserved” incomes of households surveyed in the second. As noted above, to avoid making assumptions about the behavior of the residuals, in this approach the unobserved log income is not obtained directly from an econometric prediction, but through statistical matching between household heads from the two surveys. Thus, the predicted income from model (1) is one of the variables on which the closeness between observations is measured. More specifically, and are predicted using LASSO regression as follows: ii1 ii2 i yy L L yy L L i (3) TT LLjj LLjj 2 yy� = αα� + ββ̂ XX jj = 1, 2 where ( L L L L ) = argmin ) �∑ i i � �1�, is the penalization TT NNjj TT parameter chosen through cross-validation, and αα� LLjj, ββ̂ LLjj (ααjj,ββjj ii=1�yy �−αα1 is the ii −ββlii1XX norm of the coefficient vector + λλ�ββii λλ . ii ii This method estimates the portion of log income that is assumed to be time-invariant, at least when �ββ ββ using cross-sectional surveys close in time. Although this assumption does not always hold, there are several reasons why it may be reasonable to