rafmacalaba/data-use-mentions-extended huggingface dataset:
0.00)|(0.00)||(0.00)|(0.00)|
|Ln pop. density_t-_1||2.045***|1.917***||1.086***|0.853***|
|||(0.01)|(0.01)||(0.01)|(0.01)|
|Ln dist. to major port||-0.578***|-0.556***||||
|||(0.00)|(0.00)||||
|Constant|6.054***|11.093***|10.841***|8.115***|9.370***|9.238***|
||(0.00)|(0.06)|(0.06)|(0.00)|(0.02)|(0.02)|
|Country×yeardummies|No|Yes|Yes||||
|Observations|1,161,628|1,160,235|1,160,235|1,161,628|1,160,235|1,160,235|
|Number of groups||||290,407|290,130|290,130|
|R-squared|0.365|0.621|0.622|0.000|0.031|0.039|

Notes: This table presents estimates from OLS (columns 1, 2 and 3) and fixed effect (columns 4, 5 and 6) regressions of the logarithm of GDP at time _t_ on the natural logarithm of cropland area at time _t –_ 1 ( _Ln cropland t-_ 1). Controls included in the OLS regression (columns 2 and 3
temperature.<sup>7</sup> Finally, we conduct a placebo test by using within-sample randomization of temperature. Overall, the results of these exercises remain similar to our main findings.

We also offer further heterogeneity analysis across regions. We employ the temperature bin approach to consider the non-linearity of temperature effects and plot the results in Figure 3. It shows that rising temperature causes higher poverty (Panel A) and inequality (Panel B) in poorer regions such as Sub-Saharan Africa, Middle East and North Africa, and South Asia, but the effects are attenuated in the other richer regions. Furthermore, we also document negative effects of colder temperature on both outcomes, particularly in Europe and Central Asia. We further estimate temperature effects for each country. Plotting the results where each country’s marker is proportional to its real GDP per capital, Figure A5 (Appendix A) shows that countries bearing the largest effect of global warming (e.g., Uganda, Ghana, and Mozambique) also tend to be poorer or located in poorer regions.

Furthermore, we explore different country characteristics that might help mitigate temperature effects. Employing a democracy index that categorizes countries into democracies, authoritarian regimes, and hybrid regimes, we find that countries with democratic regimes appear to be less vulnerable to the impacts of global warming. Additionally, we observe that temperature effects are stronger in countries with higher shares of agriculture and are weaker in countries with higher shares of manufacturing. These findings suggest that institutions could play important roles in mitigating the effects of global warming on different countries and regions.<sup>8</sup>

> 7 These include using (i) log of temperature; (ii) temperature measured in degrees Fahrenheit; (iii) the temperature data from CRU; (iv) the number of days that temperature is above 28<sup>◦</sup> C; (v) dropping regions with temperature being above that level; and (vi) temperature shock, defined as the
metrics for individual websites and subpages, our study offers rich, timely insights into how emerging technologies are diffused and adopted globally, user characteristics and usage patterns.

Second, this study contributes to a strand of literature on the geography of technology diffusion, deepening the understanding of the country level factors that influence the adoption of generative AI. Previous research has broadly identified economic and infrastructural factors as influencers of technology adoption at the country level (Keller 2004; Comin and Hobijn 2010; Czernich, Falck, Kretschmer, and Woessmann 2011; Delera, Pietrobelli, Calza, and Lavopa 2022), but lacks specific analysis related to generative AI. This paper uses regression analysis to explore how demographics, economic development, industry structure, human capital, and digital infrastructure affect the adoption of various generative AI tools. Our findings highlight the risks of AI exacerbating the already growing digital divide and call for collaborate efforts to ensure the technology is accessible to all.

Finally, by combining website traffic data with Google Trends data, this study also sheds light on early impacts of generative AI tools on individuals’ online activities, and discusses the implications for public preferences, digital firms’ business model and competition dynamics.

The remainder of the paper is organized as follows. Section 2 introduces the website traffic data. Section 3 identifies a list of the most popular generative AI tools and shows the rapid adoption of generative AI by individuals. Section 4 analyzes user characteristics of different types of generative AI tools and usage patterns. Section 5 demonstrates the geographical disparities in generative AI usage and highlights the record-breaking global diffusion of generative AI. Section 6 delves into the socioeconomic and infrastructural factors that influence these geographic disparities in adoption. Section 7 briefly explores early impacts of generative AI tools on individuals’ online activities, and discusses the broader implications. Section 8 concludes and discusses avenues for future research.

# **2 Website Traffic Data to Monitor Generative AI Usage**

Thanks to advancements in digital analytics technologies, we are now able to monitor generative AI usage in real-time
answered questions related to decision-making in the household and agency.

In 2017, 14,371 15-49 year old women from 13,405 households were surveyed.<sup>8</sup> In addition to household- and individual-level characteristics, community characteristics can be retrieved using GPS coordinates of the cluster centroid. The DHS website provides access to topographical characteristics for all clusters.<sup>9</sup> The SPA data were collected between December 2017 and May 2018 on all 1,033 health centers across the country (IHE and ICF, 2018). This assessment made an inventory of all the facilities and equipment, surveyed the staff, observed visits for ANC and family planning services, as well as services for sick under-5-year-old children. Women who were observed attending ANC or family planning consultations and the families of sick children were also surveyed. All these survey tools allow the SPA to assess how much the services work and how satisfied service users are. In this paper we use data from facility inventory provided by the facility manager or most knowledgeable person on the infrastructure, supplies, staffing, and routine practices.<sup>10</sup> The quantitative data are analyzed using a multilevel, hierarchical model to investigate the neighborhood and individual effects, while controlling for the clustered nature of the data (Bafumi and Gelman, 2007; Gage and Calixte, 2006). Multilevel models allow one to look at

> 8The DHS uses a two-stage cluster sample design, where clusters are the enumerating areas provided by the IHSI based on a 2011 update of the 2003 population census. The sampling methods are described in IHE and ICF (2018) but in this study, weights are computed to ensure the representativeness of the evidence at the national, urban-rural, and department-levels.

> 9The cluster data are anonymized by displacing the cluster centroid by, on average, 0.8 km for urban clusters (maximum displacement of 2 km), and 2.1 km for rural clusters (maximum displacement of 5 km and of
undertaken, a travel time threshold of 75-minutes will be used. This threshold provides a cross-section where residents are able to use transit services for mobility, there is clear variation between the travel budget thresholds and no secondary inflection point with respect to travel time and travel budget threshold increases has been reached, which could potentially convolute results.

Figure 5 shows the accessibility spatial distribution in Rio de Janeiro and Kinshasa, with increasing travel budget thresholds. This analysis is further supported by baseline accessibility distributions per income decile, which are shown in Appendix C – Baseline Accessibility per Income Decile. At low travel budget limits, accessibility is concentrated around the economic centers (Centro and Gombe) of both cities. However, as the limits get higher, accessibility levels increase along key transit corridors away from the economic hubs. This is particularly concentrated along rail, metro and BRT corridors in Rio and along the N1 between Bon-Marche and Matete in Kinshsasa. Residents located at the peripheries of both cities generally have very low accessibility to economic opportunities.

14
The results suggest that the energy price collapse impacted the credit allocation of banks that were more exposed to energy-related sectors. Banks that were more exposed increased lending to firms in the affected sectors and contracted credit to firms in other sectors. If borrowers were not able to switch credit suppliers, the contraction in bank lending might have had a material impact on their real outcomes.

Using firm-year-level data for the sample of firms in non-energy sectors, we run the following specification: where the real outcome _yf,y_ of firm _f_ in year _y_ corresponds to one of the following variables: total lending, loans for working capital, loans for investment projects, total liabilities, assets, and revenue.<sup>13</sup> _AvgExposureEnergym,Aug14_ is a measure of exposure to the energy sector in August 2014 of a firm headquartered in municipality _m_ . _Posty_ is an indicator that the yearly observation is after 2014. _β_ is the coefficient of interest, as it measures the extent to which the real outcomes of firms in municipalities with more banks with greater ex-ante exposure were affected by the drop in energy prices. Finally, _γf_ and _γy_ are fixed effects at the firm and year levels, respectively, and _εf,y_ is the error term clustered at the municipality level.

The results of this exercise are displayed in Table 5. Starting with information from the credit registry of bank loans, we find that an increase of one standard deviation in the exposure of banks with which a firm has relations reduces total lending by 2.1 percent. Again, the impact is much larger for financing for investment projects. Total loans for working capital contract by 1.9 percent, and loans for investment contract by 12.4 percent. These results suggest that non-energy firms are unable to smooth the shock that their banks receive. We also find evidence that other firm outcomes (liabilities, assets, and sales) were negatively impacted by the municipality’s exposure to the energy sector. An increase of one standard deviation in a municipality
# **1. Introduction**

**This paper supplements our earlier report (World Bank, 2020), which estimates the number of foreign workers in Malaysia at 2.96 million to 3.26 million in 2017** (of which an estimated 40.8–49.3 percent are irregular foreign workers<sup>†</sup> ) **.** The report has contributed to the literature by spelling out ways to use existing administrative data, including remittance transaction data, in Malaysia to better estimate the total number of foreign workers, encompassing both regular and irregular workers. As an extension of that report, this paper explores another way to use the existing remittance transaction data compiled by the central bank, Bank Negara Malaysia (BNM), to estimate the foreign worker population.

**Malaysia has seen rising inflows of low-skilled foreign workers, supporting jobs of higher-skilled natives.** A rapid expansion of education levels over the past two decades resulted in Malaysians’ job preferences shifting to more skilled jobs, and unemployment rates stayed at a low level owing to rapid economic growth. Inflows of foreign workers, thus, have been responding to domestic labor demand to fill lowskilled, labor-intensive jobs in export-oriented agriculture, manufacturing and domestic service industries (World Bank, 2016). The presence of these workers has led to the creation of higher-skilled jobs for Malaysians and contributed to increased skills premia of Malaysian workers ( _ibid_ ).

**These foreign workers are temporary labor migrants,** primarily low-skilled, regulated under Malaysia’s work permit system. They enter the country with an employment contract and can stay with the same employer for up to 10 years. They are not allowed to change their employers within the country and must return to their home countries after termination of their contract. In principle, therefore, the Ministry of Home Affairs (MOHA), which manages the work permit system, monitors the number of total foreign workers by sector, nationality, and state.

*
of the same dataset.

In some cases it can be useful to define a separate dataset to describe the data in sufficient detail. For example, two measures of the same outcome, such as measures of crop productivity obtained through inperson audits and satellite imagery, may have large discrepancies in coverage and diverging numbers of observations. In cluster-randomized studies, data may be available both at the cluster and individual levels, and these datasets should be described separately if each contains primary outcome measures. Similarly, a full census in the study area followed by sampling a subset of the population for the interventions and endline data collection warrants defining separate datasets for each data collection round.

For each dataset, contributors are asked to define a short description or _name_ , then provide information on the types and number of _observational units_ , first in total and then, further below, per arm. This information is collected at the dataset level in order to allow for multiple units of observation, for example outcomes measured at the teacher and the student levels. The per-arm fields are optional to allow description of data collected prior to treatment assignment (e.g. a study population census). A unit counts as a _targeted_ observation unit if it was selected or intended for data collection. This may be an estimated number. A

> 12Conversely, a data file may contain several datasets (e.g. an excel file with several sheets).

17
initial search, we excluded 111 studies during this stage because (i) they were not published in peerreviewed journals or did not appear as part of a working paper series; (ii) they were not experimental or quasi-experimental studies; (iii) they assessed early childhood interventions that targeted children outside of the 3 to 6 year age range, (iv) they measured outcomes unrelated to

> 4 We did include a subset of effects from Baker et al., (2008) – specifically those restricted to 4-year-olds in the post-period survey – who were unlikely to have access to subsidized care before age 3 since the childcare expansion started with older children.

> 5 One implication of not including unpublished studies is the potential for publication bias—that is, a bias toward positive and significant results. On the other hand, Brodeur et al. (2020) find that randomized control trials exhibit less of this bias than methods like instrumental variables and differences-indifferences, and more than 70 percent of the studies we eventually included were randomized control trials.

12
# **I. Introduction**

A defining characteristic of labor markets in developing countries is the high proportion of workers who are self-employed or work in the informal sector. Despite a vast literature, there is still little consensus on the extent to which self-employed and informal sector workers are in those sectors because they are excluded from formal sector employment or because, based on pecuniary or non-pecuniary factors, they choose to be in those sectors. Earnings penalties for self-employment and informal employment are often interpreted as evidence of exclusion from higher-paid formal employment. Many studies have examined earnings differences between informal and formal employment and self-employment and wage employment for individual countries or for some regions of the world such as Latin America. However, there is very little comparative literature on how and why these earnings gaps differ across countries around the world.

This paper contributes to the ongoing discussion on self-employment, informality, labor market segmentation and earnings differentials. It uses data from 73 countries and multiple years from a comprehensive set of harmonized household surveys, the World Bank International Income Distribution Database (I2D2), to estimate the proportion and wage differentials of self-employed, informal, formal and salaried workers from around the world. The first contribution is to provide new comprehensive estimates of the proportion of workers who are non-professional own-account workers (interpreted broadly as a measure of unskilled self-employment), employers and own-account professionals (a measure of skilled self-employment), informal sector employees and formal sector employees. Our second major contribution is an estimate of the wage penalties or premiums for each of these groups in countries around the world. The estimated premiums/penalties for each country/year are from ordinary least squares estimates of wage equations and control for worker characteristics such as age, education, gender, as well as industry of work.

This study addresses the following eight questions: What proportion of workers fall into the following categories: non-professional own-account workers, employers and ownaccount professionals (which, for conciseness, we will also refer to as “employers and professionals”), informal employees
Croke et al. (2016) containing infected individuals, creates an apparent paradox, since if there are effects of deworming among those known to be infected then one would expect a smaller but still positive average effect of MDA in endemic populations. A simple exercise suggests that this hypothesis cannot be rejected using TMSDG's data. Under the assumption that MDA does not affect weight in uninfected children,<sup>12</sup> the expected average effect of MDA on weight is given by multiplying prevalence by the expected effect on infected children. Thus we calculate each study’s expected MDA treatment effect by multiplying the studyspecific STH prevalence by TMSDG’s random effects estimate for the impact of deworming on infected children, which is 0.75 kg.<sup>13</sup> We generate a standard error for this expected MDA treatment effect by multiplying the study-specific prevalence by TMSDG’s standard error of 0.26 kg. These hypothetical data represent MDA effects that would be reasonable to expect in each setting, with standard errors that reflect our uncertainty about the expected treatment effects.<sup>14</sup> We apply a random effects model to these expected MDA effects, which yields a point estimate of 0.20 kg and a confidence interval of (0.13 kg to 0.28 kg). We cannot reject that this expected average effect of MDA is different from TMSDG’s estimate of 0.08 kg (with p=0.23). Thus, TMSDG’s MDA results are consistent with their estimates of the effect of deworming on infected

> 12 This assumption may not hold if deworming creates epidemiological externalities and also affects weight, but it will hold under the null hypothesis that worms have no impact on weight.

> 13 For the expected effect on infected children, we use TMSDG’s random effects estimate of 0.75 kg (95% CI: 0.24, 1.26). Note that these calculations use estimated prevalence for
# **Understanding the Business Environment in South Asia: Evidence from Firm-Level Surveys**<sup>*****</sup>

Wendy Carlin<sup>1</sup> Mark Schaffer<sup>2</sup> JEL: D22, O17, O18, O43, O53 Key words: Firm Behavior, Formal and Informal Sectors, Business Environment, Constraints to Firm Growth, South Asia

> * We are very grateful to Paul Seabright for discussions on our related work which have influenced this paper.  Comments and feedback from Reema Nayar, Pradeep Mitra and their colleagues in the Human Development Department of the South Asia Region, and from the audience at a June 2011 seminar in the SAR Chief Economist Seminar Series, are gratefully acknowledged.  The usual caveat applies. 1 University College London and CEPR

> 2 Heriot-Watt University, CEPR and IZA
# **V Data**

The **data** section of the metadata schema describes the actual data available to users, arranged in one or more datasets. For data with restricted access, access modalities can be described in the External Resources section. Data that are not accessible to anyone but the original researchers should not be described.

More than one _dataset_ may be associated with a given RCT. For example, the contributors may have collected census or administrative data for a larger sample than the ultimate study population, or specific information on two different populations affected by the RCT (e.g. buyers and sellers of a good). Datasets are distinct from data files; a dataset may be broken up into several files and even stored in multiple locations, for instance restricted-access GPS data vs. publicly accessible de-identified survey responses.<sup>12</sup> In general, a set of records may constitute its own dataset if it contains information central to the study, such as a separate outcome measure or the sampling frame, and (i) consists of observational units from a distinct study population (or sample from the study population) or (ii) is based on an independent data source (e.g. with a specific mode of data collection).

What delineates a dataset is ultimately up to the metadata contributor, but we recommend keeping the number of datasets to the minimum needed to describe the data well for users; often the data associated with a given RCT can be characterized as a single dataset. For example, the same dataset may contain several rounds of data collection, and the metadata schema allows multiple “cycles” (including repeated cross-sections). Even data from different sources can often be treated as part of the same dataset.

In some cases it can be useful to define a separate dataset to describe the data in sufficient detail. For example, two measures of the same outcome, such as measures of crop productivity obtained through inperson audits and satellite imagery, may
to include the impact of organizational innovation on employment to also affect the relative or trend efficiency term.

since in β the impact of product and process innovation are confounded when both types of innovation are implemented simultaneously. In the following sections we propose an extension to Harrison et al. (2014) to further disentangle the impacts of product and process innovation on employment.

The last extension is related to the impact of process innovation. The way process innovation is defined in most innovation surveys using Oslo-manual guidelines includes any improvements on production or delivery methods, which can range substantially in their impact on efficiency and employment. Following the literature emphasizing the impact of automation on employment, we extend the model and decompose process innovation between innovations that imply some degree of automation in the production process and other types of process innovation.

# **3.1**

One problem when estimating equation (4) is the simultaneity introduced by the impact of innovation on prices. In practice we do not have firm level prices and so our estimates are based on nominal output (sales) growth, where g1 = y1 + π1 and g2 = y2 + π2y2. π1 measures the firm’s inflation rate for old products and π2 measures the price difference between the new product and the old product in period 1. Consequently, our estimation equation becomes: where we have netted out gli. The new unobserved error term is u = −π1 − βπ2g2 + v.<sup>3</sup> As a result, the sales growth rate due to new products, g2, is correlated with the error term and leads to a downward bias in β<sup>�</sup> OLS.

> 3An improvement could be to approximate the firm’s inflation rate for old products using an industry measure, π ̃1, in which case the error component would become E(π1 − π ̃1).

9