rafmacalaba/data-use-mentions-extended huggingface dataset:
For instance, Colombia has 23 regional poverty lines, whereas Chile, Ecuador, Haiti, and Nicaragua have only one national poverty line. Finally, there are some extreme values of national poverty lines that could substantially increase the mean value of all poverty lines in LAC. For instance, Montevideo-Uruguay’s moderate poverty lines is $14.9 per person per day, which is considerably higher than the rest of the moderate poverty lines in LAC.

Since the number of subnational poverty lines varies by country and the extreme values of official poverty lines and size of the population of the subnational regions play an important role (according to Figure 3), we take into consideration these factors when estimating regional extreme and moderate poverty lines. Therefore, since the _mean_ is more sensitive to outliers, we use the _median_ of subnational urban poverty lines for some specifications to account for extreme values. In addition, we also consider other specifications that weight all poverty lines by the number of individuals residing in their corresponding urban subnational regions to account for the size of the population of each region and the number of poverty lines by countries.

Table 3 presents a number of specifications and the corresponding regional comparable extreme and moderate poverty lines in LAC. The first and second columns present the mean of extreme and moderate poverty lines, while the last two columns show the corresponding median values. The first two rows consider the mean and median of all subnational poverty lines shown in Figure 3, while the following two rows are based on national averages of subnational poverty lines shown in Table 2. The first and third row show the mean and median of official poverty lines weighted by the population of _subnational_ regions within each country, while the second and fourth rows show the unweighted mean and median poverty lines. The last two rows present the mean and median of the underlying poverty lines used to estimate the currently-used $2.5 and $4 per person per day poverty lines updated to 2011 PPPs using national CPIs, both unweighted and weighted by the _national_ population
citation data to locate innovations across states. The study concludes that innovative activity tends to cluster more in industries where knowledge spillovers play a decisive role, thus confirming the hypothesis that the ability to receive knowledge externalities is influenced by the distance from the knowledge source, which in turn generates geographic concentration of production.

Branstetter (2001) provided new estimates of the relative impact of domestic and international knowledge spillovers on innovation and productivity at the firm level. His study uses micro data on publicly traded high-technology manufacturing firms in the United States and Japan during the period 1977-1989. The results provide strong evidence on intra-national knowledge spillovers. There is limited evidence that Japanese companies, for example, benefit positively from research undertaken by American firms, and there is no evidence that American companies benefit positively from research conducted by Japanese companies. In fact, when the last effect is statistically distinguishable from zero, it is negative. This seems to be an important result within the literature on spillovers. We will discuss more extensively the mechanisms for negative knowledge spillovers in our results section.

Similarly, Griffith et al. (2006) investigates how much firms in the United Kingdom benefit from U.S. R&D with panel data. The authors found that U.K. firms benefit significantly from U.S. R&D. In contrast, the opposite flow of knowledge seems not to generate such benefits for U.S. firms. The evidence supports the hypothesis of international spillovers with strong idiosyncratic components. They also find that industries in the UK whose productivity lags behind with respect to the U.S. benefit the most; however surprising this result might be, it highlights the importance of controlling for technological distance between firms.

Botazzi and Peri (2003) discuss plausible impacts of existing knowledge on the generation of new ideas. They argue that they can be positive, negative or zero due to two opposing effects. First, existing ideas can broaden the base of useful knowledge available to generate new innovations in the future. Alternatively,
financial stability risks per se. Moreover, their typically limited size and interconnections with the commercial banking system means their failure has no significant impact on financial stability. Ensuring the safety and soundness of CGSs is essentially a matter of public policy effectiveness, given that many SMEs depend on credit guarantees for financing working capital and investment. A financially sound and healthy CGS gives lenders the confidence to leverage it to extend lending to small businesses. Ensuring the financial sustainability of CGSs is also a fiscal imperative, given the contingent liability they represent for the government.

**In particular, this paper focuses on a sample of European CGSs.** Europe as a whole is heavily exposed to transition risks, which may have been exacerbated by the recent energy crisis, linked to the war in Ukraine, and the potential this poses for an associated delayed phase out of fossil fuels. The region is also significantly exposed to physical risks, especially heatwaves, droughts, floods, and sea level rise, with significant between- and within-country variation. At the same time, Europe is a frontrunner in climate policies, with ambitious targets in terms of greenhouse gas (GHG) emission reductions and a well-established yet evolving policy framework. Europe is also a region where CGSs are a historically important component of the financial system, facilitating access to finance for millions of small businesses that are relatively dependent on bank financing for their operations. CGSs in Europe can play an important role in supporting green investment by SMEs, both on the mitigation and adaptation fronts.

**This paper assesses the vulnerability of European CGSs to both transition risks and physical risks.** The paper is based on unique sectoral and spatial data from 29 European CGSs, accounting for EUR 278 billion or 84 percent of the total outstanding guarantee volume of the European Association of Guarantee Institutions’ (AECM) members as of end-2020. We then link CGSs’ guarantee portfolio data to a set of sectorally and spatially disaggregated transition and physical risk vulnerability indicators, which allows us to identify the portfolio shares
> For example, in France, one of the high-income countries with higher insurance penetration, a recent survey on the preparedness for climate change shows that physical risks appear to be modest in the short term as these risks are covered by insurance companies (ACPR 2019).

Our empirical results are in line with the findings of similar studies. Directly comparable with our outcomes, an IMF report shows that natural disasters contribute to a deterioration in NPLs in Sub-Saharan Africa, which is up to 1 percentage point in the case of storms (IMF 2016). The Central Bank of Mexico undertook a more granular analysis than the one carried out in our paper by analyzing the effect of the exposure to unusual extreme heat events on the delinquency rate of small, micro, and medium enterprises in the Mexican agriculture sector (Central Bank of Mexico 2020). This analysis finds evidence of an increase in NPLs of 1 percentage point following a 20 days of unusual extreme heat wave during a given trimester. In a developed country-setting, Noth and Schüwer (2018) document that natural disasters significantly weaken the stability of US banks with business activities in regions affected by natural disasters, reflected, among others, in higher NPLs. Moreover, Gallagher and Hartley (2017) show evidence that four quarters after the Katrina Hurricane hit New Orleans, there is a 2.5 percentage point increase in the share of residents in flooded areas with at least one NPL account.

> 32 Measured as life and non-life insurance premiums as a percentage of the GDP, the depth of the LAC insurance market is above most regions at 1.8 percent of the GDP, only slightly surpassed by the East Asia and Pacific region. Nonetheless, the LAC insurance market is shallower when compared with high-income countries, where the insurance premiums represent 4.9 percent of the GDP. In addition, there is significant heterogeneity across LAC economies, where the indicator varies from 1 percent in Paraguay to 4.5 percent of GDP in Chile.

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## **Generators can mitigate short‐term losses but reduce other investments**

Using a set of Enterprise Survey observations restricted to eight Sub‐Saharan African countries in 2007, Oseni and Pollitt (2015) show unmitigated losses due to outages range between US$ 2‐24 per kWh for firms possessing generators and US$ 1.5‐32 per kWh for firms without generators. These figures, however, do not reveal all differences between the two sets of firms as those owning generators are also more likely to be exposed to outages and more reliant on electricity. And while generators can prove to be effective in mitigating short term losses, they are also linked to lower longer‐term productivity due to higher marginal costs, thus limiting investments into other input factors (Mensah, 2016). Furthermore, scarcity in electricity supply has been found to increase production cost via spending on alternative inputs and even lead to the outsourcing of production to firms with reliable electricity supply in a large panel of Chinese firms (Fisher‐Vanden, Mansur and Wang, 2015).

## **The high cost of generators changes industry composition in power‐intensive sectors and disadvantages small firms**

Aside from increased operational costs, unreliable power networks can drastically increase the initial investments required to start a business. In Nigeria, small firms have to spend between 10 and 30 percent of their start‐up cost on technologies for self‐generation (Adenikinju, 2003, 2008). In industries that require a large amount of electricity for production, small firms might be unable to finance such investments and thus be shut out of the market, leading to overall efficiency losses. Indeed, Alby et al. (2013) use Enterprise Survey data for 77 countries to show that energy‐intensive sectors (e.g. chemical and textile industries) have a significantly lower share of small firms in countries with frequent outages. In these circumstances, large firms are more likely to be able to mobilize sufficient funds for investing in generators
Table 1: Baseline cluster characteristics by experimental condition

||Treatment<br>_Mean_<br>(1)|Control<br>_Mean_<br>(2)|Normalized<br>Diference<br>(3)|_T-test_<br>(_p-value)_<br>(4)|
|---|---|---|---|---|
|**Cluster covariates**|||||
|N. of clusters|39|40|||
|Population (Million)|2.756|2.629|0.077|0.629|
|Share living in solid houses|0.502|0.494|0.078|0.623|
|Share with university degree|0.598|0.593|0.063|0.693|
|Share unemployed|0.439|0.446|-0.103|0.519|
|Share with malaria in last 2 weeks|0.017|0.016|0.072|0.652|
|Share with malaria in last 5 years|0.190|0.184|0.051|0.750|
|Average click-through rate (%)|1.830|1.847|-0.048|0.764|
|Average cost per mille (USD)|0.764|0.758|0.050|0.756|
|Average ad cost per survey started (USD)|0.252|0.241|0.072|0.650|
|Average ad cost per survey completed (USD)|0.820|0.858|-0.052|0.744|

Notes: The table reports clusters’ characteristics measured at baseline, by experimental condition. Population numbers come from census data. The normalized difference is the difference between the two means divided
**Table 2:** Descriptive statistics ERA5 and CHIRPS weather data

||Mean|SD|Min|Max|
|---|---|---|---|---|
|**Temperature**|||||
|Average temperature|22.59|2.29|15.93|28.52|
|Historical average temperature|22.00|1.96|16.45|27.37|
|Z-score average temperature|0.68|1.25|-2.24|4.42|
|Average maximum temperature|28.58|2.13|18.96|33.76|
|Historical average maximum temperature|27.90|1.89|21.92|32.50|
|Z-score average maximum temperature|0.64|1.04|-2.00|3.87|
|**Heat days**|||||
|Number of days above 30°C|38.76|14.43|0.00|81.00|
|Historical number of days above 30°C|34.54|12.10|0.00|74.50|
|Number of days above 35°C|8.80|6.68|0.00|39.00|
|Historical number of days above 35°C|5.63|3.37|0.00|23.52|
|**Precipitation**|||||
|Average precipitation|4.36|1.81|0.63|10.80|
|Historical average precipitation|4.15|1.13|1.27|6.01|
|Z-score average precipitation|0.14|0.95|-2.46|4.11|
|Total precipitation|392.40|163.27
the share attained in 2021 in 2023. While lower input prices led profits to recover to US$397/ha in 2023, this was only 54% of the 2021 level.

Price data in appendix table A3 reinforce this narrative by illustrating that (i) output prices for main crops almost halved in 2022; (ii) with unit transport cost increasing by 48% over pre-war levels in 2022, prices for imported fertilizers more than doubled and those for other inputs increased markedly directly after the invasion; (iii) prices for locally produced services or inputs increased markedly in 2022 but largely returned to within 10% of their pre-war levels by 2023; and (iv) output prices for all crops except soybean declined further from their 2022 levels in 2023, resulting in a modest rebound of farm profitability.

Panel B of table 2 uses survey data to support the notion that price changes are the main channel through which the war affected agricultural outcomes in our sample. About 8% of respondents suffered direct damage to structures or land through the war. The number of family members working full-time on farms which had remained unchanged between 2021 and 2022 increased by about 30% from 2022 to 2023, from 1.5 to 2, and 0.72 to 0.89, with a 24% increase in part-time workers from 0.72 to 0.89, though the number of permanent employees did not change. The share of those receiving public support fluctuated between 32% and 40% with only marginal fluctuations in the amount received per household. While respondents’ subjective rating of their situation had dropped sharply from 2021 to 2022, it improved slightly since then.

Despite the drop in profitability, data on land prices and intentions to expand operated area suggest that fundamentals in agriculture remain favorable as compared to the alternatives: lease prices remained largely unchanged, partly because contracts are long term and may be difficult to renegotiate. Although the share declined slightly, from 85% in 2022 to 79% in 2023, more than three
</sup> The key players providing digital payment services differ in developed and developing countries. Developed countries have been using modern cashless payment methods by means of credit and debit cards since the 1950s. Banks are at the center of this payment system. Acquiring banks host the merchant’s account to receive payment. Issuing banks supply consumers with credit or debit cards to make purchases. When a customer orders a product or service online, the payment instructions and information are encrypted and sent to the acquiring banks through the facilitation of payment service providers. Acquiring banks then transmit the information through card networks such as Visa and Mastercard to the issuing bank. Once verified, the issuing bank transfers money through card networks to the acquiring bank’s merchant account.

Under this credit/debit card payment model, developed countries are in general more efficient in settling online payment transactions because of their well-established formal financial systems (figure 7). Among the 21 countries covered under the Digital Business Indicators project, a domestic online payment is settled within a day in most countries. However, in the Kyrgyz Republic it can take up to a week and five days in Malaysia. For cross-border payments, the settlement period ranges from three days in Indonesia and Vietnam to 10 days in Colombia and the Kyrgyz Republic.<sup>71</sup> A few factors affect the time it takes for merchants to receive payments. Having a subaccount with the payment service provider makes the process faster. Other factors include the delay in the authorization process among the issuing bank, acquiring bank, and payment service providers.

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work, and this is even more common at small and medium-sized companies. Individuals in administrative and support services, real estate and retail industries report the highest generative AI adoption. Reported generative AI usage is the highest in India, Thailand, Indonesia and China (above 90%).

The contribution of this paper is threefold. First, to the best of our knowledge, this is the first paper that uses website traffic data to offer real-time and granular insights into the usage of various generative AI tools across countries. While there is a growing body of research on AI adoption in advanced countries (Acemoglu 2024; Babina, Fedyk, He, and Hodson 2024; McElheran, Li, Brynjolfsson, et al. 2024; Goldfarb, Taska, and Teodoridis 2023), evidence in developing countries remains scarce. Many existing studies only offer a snapshot of AI adoption instead of dynamic trends. There is also a lack of cross-country comparisons of AI adoption. This paper addresses this gap by innovatively leveraging website traffic data obtained from Semrush. This type of data is particularly valuable in a rapidly evolving research domain like AI. By harnessing granular traffic 6
We use household-level total expenditure (as a proxy for consumption) and total income using the Centre for Monitoring Indian Economy’s Consumer Pyramids Household Survey (CPHS). These data are collected through stratified multi-stage surveys of 2011 census district classification in India, covering 28 states and union territories and 514 districts.<sup>7</sup> The CPHS is conducted every four months with every household surveyed in every round. The latest CPHS wave was conducted during June 2020. The monthly time-series is created by seeking data on income and expenses from households for each of the four months preceding the month of the survey. By putting together all the data from multiple surveys, we create a monthly time-series of income and expenses of households. We correct for response bias in CPHS using weights for non-response, which was a concern during the lockdown period.

All other data are either from Covid19India.org, the 2011 Census, the SHRUG data set (Asher et al. 2019), or the Reserve Bank of India Database on the Indian Economy. We aggregate daily district-level information on infections from Covid19India.org which collates information from the central and state governments, and verifies it against media reports, to monthly data. Since the 2011 district boundaries used by the Census and the SHRUG database are different from the 2020 boundaries used for the zone classification, we convert information from these sources using official orders for rearranging district boundaries and 2011 data on population in the relevant sub-districts. From the Census, we compute the average age for each district. From the SHRUG database, which consolidates village and urban ward characteristics from a large number of official sources (Asher et al. 2019), we obtain the population density and the fraction of workers in the services sector. We also use quarter-end outstanding aggregate credit for scheduled commercial banks from the Reserve Bank of India’s Quarterly Statistics on Deposits and Credit of Scheduled Commercial Banks (Database on Indian Economy). We use per-capita credit to measure access to finance.

Appendix Table 1 describes
appear in each choice set. We follow standard practice in the choice modeling literature (Adamowicz et al. 1997, Adamowicz et al. 1998, Louviere et al. 2000) and create an efficient experimental design that allows both main effects and interaction effects to be estimated. The designs for the choice experiments were generated following Kuhfeld (2010)<sup>9</sup> and achieve a 100% D-efficiency (Huber and Zwerina 1996, Carlsson and Martinsson 2003).

The experimental design yielded 30 unique choice profiles for CF households and 60 unique choice profiles for non-CF households (Kuhfeld 2010, Vermeulen et al. 2008). We create a block design giving five unique experiments for CFs and 10 unique experiments for non-CFs, where each experiment has six choice questions. Carlsson et al. (2010) test for learning and ordering effects in CE surveys and show that dropping the first choice question can decrease the error variance of estimates. We therefore duplicate the first choice set, yielding a total of seven choices made by respondents, and drop the first choice before conducting the analyses to account for possible learning effects.

> 9 The experiment design was conducted using the SAS experiment design macro (Kuhfeld 2010).

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empirical investigations have branched out into a number of directions, including ...rm-level analysis, new approaches to trade in tasks and o¤shoring, and, to a limited degree, implicit contracts.

# 3.2 Empirical Work on Heterogeneous Firms and Bargaining.

In part because the literature on ...rm heterogeneity is so new, and in part because the data demands for testing these theories are quite high, not many studies are available in this area. To take ...rm and/or worker heterogeneity into account properly, information at both the ...rm and individual employee level is typically required, suggesting the need for matched employeeemployer datasets. In this section, we review several recent papers that have succeeded in contributing to this literature.

The pioneering work on trade and income inequality with heterogeneous ...rms actually predates the theory. Bernard and Jensen (1997) study the Annual Survey of Manufactures from the US Census Bureau to decompose the large rise in average skilled wage premia that occurred over the 1980’s. They show that a substantial fraction of the increase occurred between plants, in other words, by intra-industry shifts in the allocation of workers from plants with lower skill premia to ...rms with higher premia. This between-plant e¤ect is larger than the within-plant e¤ect (which is merely a rise in the skill premium for any one ...rm over time). Indeed, by some measures, the between-plant e¤ect is completely dominant (Table 5). Further, it occurs entirely among ...rms that export, and vanishes when the sample is restricted to ...rms with only domestic sales. This was an early indicator that trade might cause an increase in wage inequality through within-industry e¤ects, a mechanism very di¤erent from HeckscherOhlin, and alerted the ...eld that ...rm heterogeneity may have something important to do with the e¤ect of trade on inequality.

More recently
Downward Trend and Cycles**

Figure S1 presents both the 365 day rolling average of NO2 VCDs for the US, along with the cyclical component. There are two points worth emphasizing. First, the US is one of the developed nations in the sample with a distinctly downward trend in NO2 (See Table 1, Panel B). In all the analyses, we include country-specific time trends. Thus, we are considering just the cyclical component of the data, which has no trend, and is an indicator of cyclical economic activity rather than trend growth. Second, in US cyclical NO2 data, the mild 2019 “manufacturing recession” is evident. This is part of the reason why world NO2 concentrations were already below trend before 2020 (Figure 2).

## **A.3 World Geographic Heterogeneity**

Figure S2 shows the clear correspondence between global economic activity and NO2 as of yearend 2019. The figure is also suggestive that it may be the case that some developing countries are subject to more pollution for the same amount of output (e.g. China and India). This is another reason why controlling for country-specific trends is important.

S1