rafmacalaba/data-use-mentions-extended huggingface dataset:
method consists of two steps. The initial step relies on estimating a welfare model (Eq:1) using household survey data, and then obtaining generalized least square ( _GLS_ ) estimates for the model. Given the interrelatedness between households in a cluster, ordinary least squares is not the most efficient estimator. The second stage consists in utilizing the parameter estimates from the first stage and applying these to census data to obtain small area poverty and inequality measures.

Because the motivation is to implement the methodology into a Stata command, where possible, we also follow the methods implemented by PovMap (Zhao, 2006). It must be noted, however, that the methodology utilized by ELL is not necessarily the one followed by the PovMap software. Places where methodologies will be indicated in footnotes.

> 9This point is crucial, and the quality of a good poverty map is highly dependent on these two criteria being met. For further discussion and motivation refer to: Tarozzi and Deaton (2009).

> 10Note that the interrelatedness of _ηc_ across observations is already a violation of _OLS_ assumptions.

> 11Molina and Rao (2010) follow the naming convention of a nested error linear regression model. Regardless of its name, standard _OLS_ is invalid under this structure.

> 12Rao and Molina (2015).

4
# **1 Introduction**

The speed with which new technologies spread across nations is key for explaining crosscountry differences in economic performance (Comin and Hobijn, 2004, 2010; Comin and Mestieri, 2018). Multinational firms and firm-to-firm relationships through global value chains are believed to play an important role in this process of international technology diffusion (World Bank, 2020). However, understanding the role of firm-to-firm relationships in the cross-country spread of new technologies has proved difficult because of the lack of detailed panel data on both the adoption of new technologies across countries and firms, and on different types of international-firm-to-firm linkages.

In this paper, we leverage a rich combination of granular information on online job postings with data on several types of international firm-to-firm linkages to study the role of multinational firms and global value chains in the cross-country diffusion of new technologies. We examine the diffusion of 29 new technologies across 17 countries and 107,981 firms in the period 2014-2022. Following Bloom et al. (2021), we use the raw text of online job postings and earnings calls data to measure hiring patterns and firm discussions relating to frontier technologies, using this as a proxy for technology diffusion. We combine this information with detailed data on international firm-to-firm linkages, which includes information on buyer-supplier relationships, innovation partners and affiliates in multinational firm networks. To study the process of technology diffusion, we employ various measures of dispersion used in the literature, such as the Gini coefficient and the coefficient of variation, which allow us to document the intensity and spread of emerging technology jobs across countries, regions, and firms.

We first establish four stylized facts about international technology transfer. First, emerging technology hiring has become more dispersed across countries and regions over time. This finding is consistent with evidence on the US only by Bloom et al. (2021). Second, emerging technology jobs are highly concentrated in multinational firms and their supply chains. Around one third of all job postings related to these 29 frontier technologies in the 17 countries are
can support the acquisition of core and ambivalent green skills. To our knowledge, their methodology for coding the 38,000 unique skills terms into green and non-green is not publicly available.<sup>43</sup> For LinkedIn, green jobs are occupations that cannot be performed without extensive knowledge of green skills. Instead, Lightcast (formerly Burning Glass, BG) uses a combination of data sources, including O*NET classification, job titles, and job text searches.<sup>44</sup> Like the others, this approach presents advantages and disadvantages for its implementation in advanced and developing countries. The most salient advantage is the granularity and frequency of the data. However, it is difficult to infer from vacancy data if the job to be filled will require the skills to carry out tasks, or if green skills are simply desired or used as a signal for some other job need. Developing countries face the additional challenge that the use of online matching platforms is not yet widespread, and hence the data is usually biased towards large metropolitan areas and the high-skilled segment of the market.<sup>45</sup> The next section illustrates these challenges applied to the Indonesian and U.S. cases in more detail.

> 42 LinkedIn 2022 Green economy report 2022.

> https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/global-green-skills-report/globalgreen-skills-report-pdf/li-green-economy-report-2022-annex.pdf.

> 43 This team has unsuccessfully attempted to connect with LinkedIn to learn about the methodology.

> 44 Based on our communication with Lightcast.

> 45 See Granata, Posadas, and Testaverde (2021) for a discussion of biases applied to the Indonesian labor market.

15
monitoring private sector delivery); (iii) reducing the transaction cost in land markets by publishing valuations and using them as an objective benchmark for payment of compensation in case of compulsory land acquisition, for example, for infrastructure construction; and (iv) using the information generated by tax maps and evidence of property tax payment to open up a low-cost path toward recognition of landownership rights.<sup>12</sup>

# **3. Property taxation in the Ukrainian context**

As market-based valuation is impossible without a market for land and real estate, this section first describes Ukraine’s journey towards a market for agricultural land. Valuation methods have not yet caught up with these advances, resulting in a disconnect that risks distorting of private and public land use and services in ways that impede effective decentralization and reduce efficiency, equity, and resilience. By mandating recording of purchase prices, land market reforms have, however, provided the basis for a more flexible approach to property valuation and the use of such values in multiple contexts.

> Scartascini 2019). Appeals to civic duty had limited effect (delCarpio 2015) even if they included information about neighbors’ compliance levels, and making delinquency more visible increased compliance only for small debts (Perez-Truglia and Troiano 2018). 12 For example, in Peru, expected land value increments from titling even informal slum land, if adopted jointly with land taxation, are argued to allow implementation of a titling program that would be fully self-financing (Hawley _et al._ 2018).

8
Refugee and Asylum-seeker Work Rights dataset (RAWR), with partners at Asylum Access, a non-profit focused on refugees’ work rights.<sup>22</sup> The goal is to measure de facto labor market access, along with freedom of movement, access to public services, and the extent to which host communities benefit from refugee response programming, through a structured survey of practitioners in each country. Data collection is ongoing.

The other policy we evaluate is the existence of camps. These data come from the same UNHCR database as the sub-national populations, described above. We code camp presence in a region-year if there is the existence of a planned camp in the UNHCR data.<sup>23</sup> We again impute across unexplained discontinuities in camp existence and check the data against secondary sources. We measure camp presence as a binary variable, as we do not have a reliable measure of the proportion of the refugee population within a region-year living in the camps.

## **4.1.4 Additional Data**

We further supplement the main datasets with region-year variables from AidData’s GeoQuery (population, GDP based on nightlights, distance to the border and urban centers, and ACLED conflict data), country-year data from the World Bank (income ranking and population), and country-year data from the Polity5 project on institutions (Systemic Peace 2018).

# **4.2 Empirical Strategy**

Our overall question is how the arrival and presence of refugees affect host community attitudes. First, we address how refugees affect attitudes on average across locations. Then we examine the heterogeneity of the effects by host country policy, in terms of the _de jure_ right to work and the existence of camps.

> 22This project builds on Asylum Access’ earlier efforts to create global scorecards for refugees’ work rights, available here: `https://www.refugeeworkrights.org/scorecard/` .

> 23This excludes self-settled camps, transit centers, and collective centers, which
aminations for grades 5 and 7 and grade repetition for all grades. Primary schools in India may have only primary classes (grades 1 through 5), only upper-primary classes (grade 6 through 8), or both (grade 1 through 8). The data provide information about whether the school offers only primary classes, only upper-primary classes, or both. The school management categories in the data include (1) Department of Education, (2) Tribal/Social Welfare Department, (3) Local body, (4) Private Aided, (5) Private U- naided, (6) Others, and (7) Un-recognized. We construct three aggregate categories - government run schools (1 and 2), private schools (4 and 5) and others (3 , 6, and 7). In addition to these features, the data report ongoing incentive schemes in various schools to increase enrollment. Various schemes running in schools before MGNREGA provide free uniforms, textbooks, stationery, and attendance fellowships. One concern with the DISE data is that since schools self-report the data, there is measurement error in the data.<sup>13</sup> We address the implications of measurement error in the data for our results in subsequent sections.

The district level characteristics are from the Census of India 1991 and 2001. These include total population, population growth rate, percentage of female population, literacy rate, female literacy rate, percentage of the Scheduled Castes and Scheduled Tribes population, and percentage of working population. Agricultural wages for 1996-97 and total output per agricultural worker for 1990-93 come from the Planning Commission’s 2003 report.

Table 1 provides the summary statistics of outcome variables by phases of MGNREGA districts. Consistent with the roll-out criterion, the better-off Phase III districts have better educational outcomes. Conditional on being enrolled, 92 percent of the children pass exams in grade 5 in Phase III districts, whereas 88 percent do in Phase I districts.

> 13These data are collected using a district level administrative
|0.63|-5.71|-7.61|
|India<br>Source: Author cal|2462.08<br>culations based on ‘|1338.48<br>Our World in Da|1.84<br>ta‘(https://ourw|-27.87<br>orldindata.org/).|-37.16|

Source: Author calculations based on ‘Our World in Data‘ (https://ourworldindata.org/).

# _Modification 1: Consumption versus Production_

An important adjustment to the GCRI is to focus on consumption rather than production – a country should not avoid responsibility for the carbon it consumes by outsourcing production to another country. So, the proposal adds the carbon embedded in imported goods and subtracts the carbon embedded in exported goods from the carbon produced by a country. For a country like China, CO2 emissions based on production are 10 billion tons and fall to 9 billion tons when a consumption-based measure is used. In the United States, 5.2 billion tons of CO2 are “produced” and 5.6 billion tons are “consumed”. This exercise is carried out for all countries where these data 8
elasticities in comparable units. We observe that a one percent change in learning is associated with a 6.5 percent change in annual growth. In contrast, a one percent change in the other human capital measures is associated with between a 1.6 to 3.3 percent change in annual growth. Moreover, the R-squared for the learning measure is highest at .275 relative to non-learning human capital measures which range from .240 to .261. We further observe when we include variables in the same multivariate regression that the relationship between learning and growth remains statistically significant between 4.7 to 5.5 percent, whereas other human capital variables have a reduced and statistically insignificant association with growth. However, we observe the overall model fit improves when all measures are included with an R-squared that increases, although only slightly, from .275 to between .281 to .298.

To this end, we observe that our measure of human capital individually and jointly appears to have a stronger relationship with economic growth. This is likely because alterative human capital measures rely largely on years of schooling and might underestimate the role of human capital in economic development by omitting learning.<sup>10</sup> However, their use remains standard practice in part since these data have the broadest coverage. By constructing learning data across 164 countries we fill a key gap: broad coverage over nearly two decades and a measure of human capital with strong links to economic development.

# IV. CONCLUSION

To understand and track human capital formation, a critical ingredient for development, there is need for globally comparable measurement of learning. The growth of international standardized achievement tests is a significant step in this direction. However, many of the countries that participate in these tests are often already rich. This limits the ability to track, compare or understand education patterns in developing countries – the countries that might have the most potential to gain from human capital formation.

We bridge this gap, constructing a globally comparable database of 164 countries from 20002017
Andr ́ee To investigate this further, the current paper looks at confirmed cases and COVID-19 related hospital admissions in 355 municipalities in the Netherlands and uses regression techniques to investigate correlations between COVID-19 case data and particulate matter concentrations, controlling for a variety of demographic characteristics and data on health related pre-conditions. The analysis finds that PM2 _._ 5 is a highly significant predictor of both the number of confirmed COVID-19 cases and the number of related hospital admissions per 100,000 inhabitants.

The analysis suggests that the association between air pollution and case incidence is robust to proxies for worse respiratory health and symptom severity. The findings are also robust to other important control variables and different regression specifications that allow for spatial dependence, nonlinearity, alternative error distributions and outlier treatment. Results are obtained with ground-measurements and satellite-derived PM2 _._ 5. Analyzing COVID-19 data from alternative dates resulted in similar conclusions.

The remainder of this paper is organized as follows. Section 2 visually inspects several available confirmed case maps and discusses the spatial distribution. Section 3 introduces the data used for analysis. Section 4 presents regression results and discusses several of the estimates. Section 5 concludes.

# **2. Spatial Distribution of COVID-19: Country Examples**

Suggestive evidence that the spatial distribution of COVID-19 cases is not purely random and might be related to environmental factors can be found by exploring several maps of confirmed cases. A few easily accessible fine resolution maps are presented below, in particular for the Netherlands, Germany, Spain and Italy. The data for the Netherlands is taken from the Dutch National Institute for Public Health and Environment (RIVM).<sup>2</sup> The data for Germany is from the Robert Koch Institute.<sup>3</sup> The data for Italy can be viewed via a live dashboard,<sup>4</sup> and the raw data is well organized and available on a github page.<sup>5</sup> The Spanish data was taken from this link
10, ** p<0.05, *** p=<0.01.

Dependent variables: col. (1) and (4): a dummy equal to one if wife has savings, col. (2) and (5) a dummy equal to 1 if wife has savings entrusted to informal or formal institutions, col. (3) and (6) a dummy equal to 1 if wife has savings held at home.

Sample: Col. (1) to (3): monogamous unions in the two waves, below median household per capita expenditures. Col. (4) to (6), monogamous unions in the two waves, above median household per capita expenditures Fi: predicted failure probability estimated by a Cox survival model stratified by the polygamy status of the husband's father (see Table 1).

All regressions include union fixed-effects t Controls not shown: time (in months) between the two survey waves and its square Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents

* Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time

Data source: PSF Survey, waves 1 and 2? (2006-2007 and 2010-2012)
suggesting household adaptation to gradual warmer temperature over time (Dang, Nguyen, and Trinh, 2023). But this study also finds some evidence for more impacts on inequality in the longer term.

But more importantly, this framework helps highlight the fact that not all climate change impacts on poverty and inequality are expected to be mediated by economic growth (Hallegatte _et al._ , 2017), or any single sector alone. It would be important to investigate the final impacts of climate change on poverty and inequality, which are generated as a sum total through all these different channels. Furthermore, the existing literature has mostly focused on the impacts of temperature change on short-term poverty. Indeed, existing studies do not distinguish between various forms of poverty dynamics, such as chronic and transient poverty. We attempt to fill these gaps with new estimates on the global impacts of temperature change on both chronic and transient poverty.

# **2.2. Data and Empirical Models**

We review recent studies and pay special attention to data and empirical models in this section. We focus on questions whether studies use household consumption (income) surveys as the main sources of data, whether they use panel data, whether recent studies use big data (e.g., satellite data) more often, and whether studies analyze data that are more disaggregated than the country level. Regarding empirical models, we review studies that employ cross sectional models, panel data models, as well as non-parametric and simulation techniques.

# _2.2.1. Data_

#  Are household consumption (income) surveys the main sources of data?

While household surveys appear to be the standard data sources for climate change studies, it is useful to review this question and limitations with household surveys before we discuss the new data sources employed in recent studies. Indeed, existing studies examining the effects of 10
approach, however, is not viable for external sovereign debt, due to the many defaults in this market and because defaults come in different varieties and can affect bonds differently, resulting in heterogeneous outcomes (Meyer 2021). A rigorous calculation of total returns on external sovereign bonds thus requires pricing data as well as details on the fate of each bond in default, in particular on the timing and scope of missed payments and detailed restructuring terms. This type of bond-level default data is much harder to collect and was not readily available prior to this study.

The second main building block is our new archive of external default and restructuring events. We compute creditor losses (haircuts) bond-by-bond and deal-by-deal and combine the information on restructuring outcomes with our monthly bond price data. Moreover, we trace missed or partial bond payments at a monthly frequency. Because, as we establish here, coupons are the main driver of total returns in this market, it is important to measure interest payments accurately, especially during lengthy default spells. What the data reveal is that sovereigns in default on principal payments often continue to service coupons in full or in part, which pushes up investor returns. Moreover, coupon payments on the same bond can vary markedly over the course of a crisis. The granular repayment track record thus allows us to build monthly total return series on a bond-by-bond basis in a consistent manner, as well as representative country and global portfolios over long time spans.

Thirdly, we compile an extensive supplementary database that provides unique insights into the liquidity of this asset class. We gathered monthly, bond-level bid-ask spreads over two centuries, covering 62% of our pricing sample (in total 165,638 observations, see Figure B3).

Our results are hard to reconcile with seminal quantitative models of sovereign default. Until recently, this literature assumed risk-neutral investors and sovereign risk premia that solely reflect the expected losses from default (e.g., Aguiar and Gopinath 2006; Arellano 2008; Mendoza and Yue 2012). When investors are
how misaligned fertility preferences can prevent efficient information sharing between spouses, leading to suboptimal outcomes for women (Ashraf et al. 2020). Finally, our paper also contributes to the extensive literature on the drivers of IPV (Aizer 2010; Anderberg et al. 2016; Erten and Keskin 2018; Bhalotra et al. 2021).

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 explains the empirical strategy. Section 4 presents the main results and discusses the evidence on potential mechanisms. Section 5 concludes the paper.

# **2 Data**

We use data from the 2015–16 NFHS, or NFHS-4, a nationwide household survey of both men and women. The Woman’s Questionnaire in NFHS-4 was administered to all women aged 15–49 and included a domestic violence module administered to one randomly selected eligible woman per household from 15 percent of the households (IIPS 2017).<sup>5</sup> The NFHS is the only source of information on several dimensions of IPV experienced by women in India, including physical, sexual, and psychological violence by male partners.<sup>6</sup> The Man’s Questionnaire in NFHS-4 was administered to men aged 15-54 in a subsample of households selected for the women’s surveys.

Our NFHS estimation sample consists of 65,292 ever-married women and 70,923 men from 628 districts covering 30 state election constituencies. Appendix Table A.1 presents descriptive statistics for the female and male NFHS samples for our variables of interest. The NFHS data include binary variables on whether a woman reports having experienced various forms of violent acts at the hands of her husband. Following Anderson (2008) and Erten and Keskin (2018), we construct three indices to capture physical, sexual, and psychological violence by averaging the z-scores of the underlying IPV indicators over the previous 12 months.<sup>7</sup> Based on a respondent’s state of residence at the time of