rafmacalaba/datause-encoder-data huggingface dataset:
-15-

The regression results show that both the quantity and the cost of credit have a strong and significant impact on private sector capital formation. The real after-tax lending rate has a negative sign and is significantly different from zero: the t-statistic equals 4.17. The credit variable too is highly significant. The precision of the coefficients on capacity utilization and on the share of infrastructure investment in total public sector investment is low, although they have the right sign.

# (iii) Investment and Output Growth

The relation between investment and output growth is based on a simple production function approach. First, a measure of capacity output was derived by combining actual real GNP with the measure of capacity utilization used in the investment equation: YKA - Y/CPUTL TIhis is an imperfect measure, since CPUTL applies to manufacturing only, and it is used to derive aggregate capacity, not just capacity output in manufacturing. No better measure was available however. Also, reliable data on labor use are not available. So in the end the equation estimated simply links capacity output to last period's capacity output and the share of total fixed capital formation in GNP:

- (6a) log(YKA) 0.016 + 0.45 (INFT(-l)/Y(-l)

(0.20) (1.20) + 0.94 log(YKA(-l)) (28.3) R2 - 0.98, DW - 1.67, Sample Period 1970-1986, OLS This can be rewritten to yield an expression linking investment shares with the rate of output growth: ChhibberDisk/chhibber-Turkey Paper/ac:07-28-88:1c
available for three different base years (1999-00, 2004-05, and 2011-12) starting from the fiscal year 1999-2000 until the most recently available. Given the three different base years, we construct a spliced growth rate series. The spliced GDP series is constructed by using the real GDP growth rate as implied by the latest base year; for instance, if the 2011 growth rate is available under both 2004-05 and 2011-12 base year, we use growth rate based on the 2011-12 base year. We have data for approximately 550 districts (covering around 25 states and union territories) with each having, on average, 15 observations. The available district data accounts for, on average, more than 90 percent of the respective states’ GDP and based on 2015 levels, these states and union territories contribute 90 percent of India’s real GDP.

The real GDP growth rate has averaged 6.9 percent over 2000-2019 for the 550 districts covered in the sample with a standard deviation of 7.2 percent (Table 1). We winzorize our GDP growth data at the 1<sup>_st_</sup> and 99<sup>_th_</sup> percentile. This eliminates 34 observations where the absolute growth rate exceeds 50 percent.

# **3.2 Inversions**

To measure inversions we use data from the Modern-Era Retrospective analysis for Research and Applications (MERRA) database provided by NASA (Rienecker et al., 2011). This is a satellite based reanalysis product that provides data on temperature at each of 42 atmospheric levels on a 0.5<sup>_◦_</sup> _×_ 0.625<sup>_◦_</sup> grid.<sup>8</sup> We collect the daily aggregate mean of temperature for the first 19 levels of the atmosphere. This roughly corresponds to the levels beginning at the surface and extending 5km into the atmosphere.<sup>9</sup> Because MERRA indexes layers based on pressure levels, rather than consistent elevation thresholds we calculate surface temperature individually for each grid-point _×_ day based on the temperature reported at the lowest level for that grid-point _×_ day. We set this layer as the surface and calculate temperatures in the layers counting up from the surface layer.

We aggregate our grid-point _×_ day temperatures to the district level by taking the weighted average across grid-points for each day with weights defined as the total population in each grid-point’s unique catchment area.<sup>10</sup> We assign grid-points to the districts that contain them. For districts that do not contain any grid-points we use the nearest grid-point to the center of the district. We then calculate whether a district experiences an inversion on a given day based on the district _×_ day temperature at each atmospheric level.

We use two different measures of inversions. The first considers the difference between

> 8Data available here: `https://disc.gsfc.nasa.gov/datasets/M2I3NPASM_5.12.4/summary? keywords=M2I3NPASM`

> 9The actual height varies with surface elevation and day-to-day changes in surface pressure.

> 10 Catchment areas are the area around each grid-point that is closer to that grid-point than any other grid-point.

8
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# UNITED NATIONS **INTERNATIONAL** CHILDREN'S **FUND** (UNICEF)

**The UNICEF** Annual Report tor 1988 describes **the** distribution of S374 million in 1987 program expenditures by functional category. Within the broad definition of SM, all activities may be contributory, including water supply, formal and nonformal education, emergency relief, and project planning suppon. If SM content is limited to family planning (100 percent) and maternal and child care (50 percent), the total would be on the order of S9.5 million, or about 2.5 percent of the budget. This level would be increased to the extent that water, sanitation, health education, and disease control are considered directly contributory.

A policy of benefiting the mother is explicit within UNICEFs primary attention to child health. For the purpose of this review, it is assumed that approximately a third of all program expenditures directly or indirectly benefit the mother. This assumption is not statistically verifiable and should be examined.

For African countries south of the Sahara, the Bamako Initiative offers conditional provisions for inclusion of maternal health components, such as prenatal health, control of anemia, safer motherhood, child spacing, and traiiiing of traditional birth attendants. It is still premature to estimate the actual program emphasis of maternal health within the proposed S380 million 1989-1993 plan.

Total UNICEF expenditures have steadily increased since 1984. The outlook for the future is favorable.

UNICEF prepares periodic "Situation Analysis' reports for each UNICEF-cooperating country on the situation of children or the position of women and children. While these reports are often excellent with respect to technical data, there are only limited financial data and essentially no financial trends related to maternal health.

UNICEF Three United Nations Plaza New York, N.Y. 10017
before local courts. The second group of indicators relate to the complexity and cost of regulatory processes. These indicators measure the efficiency in achieving a regulatory goal, such as the number of procedures to obtain a building permit or the time taken to grant legal identity to a business. In particular, they focus on business start-up, construction permitting, property transfer, taxes and international trade logistics. Based on time-and-motion case studies from the perspective of the business, these indicators measure the procedures, time and cost required to complete a transaction in accordance with relevant regulations.

# _Distance to the regulatory frontier_

The distance to the regulatory frontier (DTF) score measures the average distance from the best regulatory performance. First, sub-indicator data is normalized between 0 and 100 thanks to the following formula: dtfi,k,t = 100 −(yi,k,t −besti)⁄(worsti −besti) where _worsti_ and _besti_ refer to the worst and best performances respectively recorded over time for across all countries for sub-indicator _i_ . _Yi,k,t_ refers to the performance of country _k_ on year _t_ for sub-indicator _i_ . In sub-indicators where outliers are found, countries performing worse than the 95<sup>th</sup> percentile are automatically assigned a 0. Finally, DTF scores at the sub-indicator level are averaged within each indicator. Indicator-level DTF scores are also averaged to obtain the overall DTF score.<sup>3</sup>

# _Outcome variables_

This study used two main outcome variables: GDP per capita growth and new firm entry. We use GDP per capita annual growth rate from the World Bank’s World Development Indicators (WDI). To better capture the historical trend and not bias our estimations with short-run fluctuations in growth rates we compute a 3-span moving average of the variable. We use new business entry density, defined as the number of newly registered corporations per 1,000 working-age people (those ages 15–64) collected by the World Bank with the support of the Kauffman Foundation.<sup>4</sup> Table 1 describes all the variables including the ones used as controls. Table 2 provides the summary statistics for all the variables included. The data spans over 8 to 11 years depending on the variable. For most of the variables included there is a complete panel.

Using the across time variation to do a first analysis of the data, we see that there is a clear convergence for most of the business regulation variables. Figure 1 shows the distributions using

> 3 For more information on the DTF measure see World Bank (2012). 4

> Data on business density is available at http://doingbusiness.org/data/exploretopics/entrepreneurship

5
level. Over a 24-hour period, levels of PM10 in biomass-using homes in Africa, Asia or Latin America can range from 300 to 3000 micrograms per cubic meter (μg/m3) with the peak as high as 10,000 μg/m3 during cooking time. These values are many folds higher than the standards set by the World Health Organization (WHO). WHO standards suggest that 24-hour mean concentration of PM10 should not exceed 50 μg/m3 (WHO, 2005).

The adverse health impacts of indoor air pollution from biomass include child pneumonia, other acute respiratory illnesses, chronic obstructive pulmonary disease (COPD) and lung cancer (Smith et al., 2004). According to the World Health Organization, around 3 billion people (or 40% of the current world population) still cook and heat their homes using solid fuels (i.e. wood, crop wastes, charcoal, coal and dung) in open fires and leaky stoves and each year more than 4 million deaths are caused by the household air pollutants emitted from cooking with solid fuels. Mainly women and young children are at risk, particularly in Sub-Saharan and South Asia regions (UNDP and WHO, 2009).

Many studies, published especially in medical or public health journals, have provided the linkages between HAP exposure and health impacts. While it is not possible to discuss all those studies here, we have presented a few examples of those studies.

Three recent meta-analyses (Kurmi et al., 2010; Hu et al., 2010; Po et al., 2011) find that exposure to smoke from burning biomass fuels for cooking and/or heating is associated with increased risk of COPD. There is also evidence of indoor air pollution impacts on child cognitive function, low birth weight, cervical cancer, adverse pregnancy outcomes, asthma, and tuberculosis (Velema et al., 2002; Pokhrel et al., 2010; Pope et al., 2010; Hosgood III et al., 2011; Dix-Cooper et al., 2012; Sumpter and Chandramohan, 2013; Trevor et al., 2013; Wong et al, 2013).

Epstein et al. (2013) examines the impact of cooking fuels on low birth weight (LBW < 2500 g), and neonatal mortality (death within 28 days of birth) using cross-sectional data from India's National Family Health Survey (NFHS-3) and finds that compared to households using cleaner fuels (in which the mean birth weight is 2901 g), the primary use of coal, kerosene, and biomass fuels is associated with significant decreases in mean birth weight (of −110 g for coal, −107 g for kerosene, and −78 g for biomass). It also finds risk of neonatal death strongly associated with household use of coal.

Adetona et al. (2013) finds that women in Trujillo, Peru who cooked exclusively with fuelwood or kerosene had higher exposure to polyaromatic hydrocarbons (PAH) compare to 19
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 by the Research Support Team
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links and 500 TB of raw data. By employing proprietary machine learning algorithms, Semrush analyzes this data to generate estimated metrics such as the number of visits, unique visitors, average session duration, etc. Furthermore, Semrush disaggregates these metrics by economies using users’ IP addresses, providing globally comparable data across both advanced and developing economies. For each website, Semrush also infers user profiles based on patterns of online behaviors. This dataset offers a unique opportunity to monitor a selected sample of generative AI websites’ traffic globally. Despite the inherent limitations of using website traffic as a proxy, it provides an effective means to monitor generative AI usage from a globally comparable perspective.

Utilizing Semrush data complemented with Google Trends data, this paper offers an in-depth analysis of generative AI usage by individuals across countries, focusing on both temporal trends and geographic disparities. We examine the adoption rates of various generative AI tools, identify key socioeconomic factors influencing usage, and explore the early impact of generative AI tools on traffic to other websites, people’s online activities, and broader implications. By highlighting outperforming and underperforming economies and surfacing factors shaping generative AI adoption, this study provides valuable insights into the global diffusion of generative AI technologies and their potential to drive transformative change. The paper is designed to provide descriptive insights that may prompt further questions and exploration. Our analysis seeks not to establish causal relationships but to highlight correlations and trends that could inform subsequent research. Several key findings have emerged from our analysis:

1. Rapid proliferation of generative AI tools and quick adoption. At least hundreds of generative AI tools exist as of March 2024. The top 40 most visited tools have nearly 3 billion monthly visits. Chatbots dominate the generative AI landscape due to their versatility and wider applicability, accounting for 95% of traffic among the top 40 tools. ChatGPT alone commands 82.5% of total traffic and boasts 500 million users per month, representing 12.5% of the global workforce. Impressively, it took only five months for ChatGPT to reach 500 million monthly unique users. However, traffic and users of several generative AI tools including ChatGPT have plateaued since mid-2023, hinting at market saturation and intensifying competition.

2. Generative AI user demographics skew toward young, educated males. Youth and male bias is most pronounced for video generation tools, while chatbot users are more highly educated than Google users. Generative AI tools are primarily used as productivity tools, as these tools are 4
2021b). Consumption data is available for 148 countries from 2000 to 2019, which restricts the time dimension of the combined dataset for this study.

Overall, the unbalanced panel data we use contains 133 countries over a 20-year period (2000–2019), totaling 2,465 observations. Table 1 shows the summary statistics of the variables for 2017.

**Table 1. Summary statistics for 2017 of the variables used in this study**

||**Mean**|**Standard deviation**|**Minimum**|**Maximum**|
|---|---|---|---|---|
|**PM2.5**|16.84|15.54|1.27|90.45|
|**Gasoline price**|0.99|0.38|0.11|1.92|
|**Diesel price**|0.83|0.45|0.00001|2.32|
|**Coal price**|3.68|1.55|0.21|22.64|
|**Gasoline consumption per capita**|192.20|234.01|2.81|1,245.44|
|**Diesel consumption per capita**|344.19|478.16|2.37|4,619.94|
|**Coal consumption per capita**|256.20|448.54|-2.41|2,513.82|
|**GDPper capita**|22,099.75|22,366.75|773.57|126,183.70|

# 3.2. Estimation methods

We model the relationship of PM2.5 concentrations, fuel prices, fuel consumption, and GDP per capita as follows: PM2.5i,t = αi + β1,ipi,t + β2,iqi,t + β3,iGDP/capi,t + θt + εi,t where PM2.5 is the annual average PM2.5 reading in the capital; α is the country-specific intercept; p is the fuel price in international $ (PPP); q is the quantity of fuel consumed per capita (in toe); GDP/cap is the GDP per capita in 2017 US$ (PPP); θ is the time trend; and ε is the error term for country i at time t. We use the CCE estimator to evaluate the model coefficients and outline our rationale for doing so in the remainder of the section.

Since time series econometrics offers a vast library of methods, we begin to narrow down potentially applicable estimators by first evaluating slope heterogeneity within the panel. Slope homogenous models, such as fixed effect models, assume that correlation coefficients in the model are similar between different panel members. In a setting with highly different units—as is the case with the country-level analysis conducted here—this assumption might not be appropriate. Using the adjusted delta test of Pesaran and Yamagata (2008) (table 2) to assess the heterogeneity among slope coefficients, we find that the p-value is 0.000 for all three fuels. This finding rejects the null hypothesis of slope homogeneity for the model, allowing us to exclude slope homogenous estimators and continue our search within estimators that account for slope heterogeneity.

8
Policy Research Working Paper 7461

# **Abstract**

This paper uses the night lights (satellite imagery from outer space) approach to estimate growth in and levels of subnational 2013 gross domestic product for 47 counties in Kenya and 30 districts in Rwanda. Estimating subnational gross domestic product is consequential for three reasons. First, there is strong policy interest in how growth can occur in different parts of countries, so that communities can share in national prosperity and not get left behind. Second, subnational entities want to understand how they stack up against their neighbors and competitors, and how much they contribute to national gross domestic product. Third, such information could help private investors to assess where to undertake investments. Using night lights has the advantage of seeing a new and more accurate estimation of informal activity, and being independent of official data. However, the approach may underestimate economic activity in sectors that are largely unlit notably agriculture. For Kenya, the results of the analysis affirm that Nairobi County is the largest contributor to national gross domestic product. However, at 13 percent, this contribution is lower than commonly thought. For Rwanda, the three districts of Kigali account for 40 percent of national gross domestic product, underscoring the lower scale of economic activity in the rest of the country. To get a composite picture of subnational economic activity, especially in the context of rapidly improving official statistics in Kenya and Rwanda, it is important to estimate subnational gross domestic product using standard approaches (production, expenditure, income).

This paper is a joint product of the Kenya and Rwanda Country Management Unit, Macroeconomics and Fiscal Management Global Practice Group, and Poverty Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at asanghi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team