dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, our dataset combines data on employment from the European Labour Force Survey (EU-LFS) with occupation-level measures of "automatability" as in Acemoglu and  Autor (2011)  and "offshorability" as in  Blinder and Krueger (2013). We find that over the period of observation, sectors with higher initial automatability experienced a differential increase in selectivity. By contrast, we find that sectors with higher initial offshorability experienced a differential decrease in selectivity. We argue that these findings are consistent with the "paradox of automation" and CBTC in the case of automation, while they are inconsistent with something similar happening in the case of offshoring. We spell out our argument through a growth model that, beyond productivity and substitution effects, features search frictions in the labor market and two-sided heterogeneity of horizontally differentiated skills and tasks.
It therefore implies that automation and offshoring have opposite effects on employment due to their opposite effects on selectivity. While automation reduces employment by raising selectivity, offshoring increases employment by reducing selectivity. The model also predicts that more selectivity is associated with more wage inequality between ideal and less-than-ideal matches as the surplus of the former increases relative to the surplus of the latter. The rest of the chapter is organized as follows. Section 2 offers some anecdotal examples of the "paradox of automation" and discusses survey evidence on specialization trends in occupations. Section 3 introduces the dataset and describes the empirical analysis. Section 4 presents the model, discusses the conditions on assortativity needed to make it consistent with the empirical findings of Section 3, and studies its implications for employment and wage inequality under those conditions.
The table reports coefficients of estimating  (2). The dependent variable is our proxy for selectivity. Δln(SSO) is the log change of the Sectoral Selectivity of an Occupation calculated as the Herfindahl index of occupational employment shares across industries in a country. In Column 1, the dataset is aggregated at the country × occupation level. It is aggregated at the country × sector × occupation level in columns 2 to 4. RTI is routine-task intensity as in  Acemoglu and Autor (2011)  and Offshor. measures the offshorability of an occupation as in  Blinder and Krueger (2013). Standard errors in parentheses are clustered at the occupation level in column 1 and at the country × occupation level in columns 2 to 4. *** p < 0. 01, ** p < 0. 05, * p < 0. 1.
A Data Description We use the annual files of the European Labour Force Survey (EULFS) made available by Eurostat. This survey combines labour force surveys conducted at the national level in European countries. It has the advantage to provide harmonized information on basic labour markets variables. Our final database corresponds to country × industry × occupation × year cells. The information on the sector is based on the broad NACE sectors (21 sectors in the NACE Rev. 2 classification) and the information on the occupation is based on the 3-digits ISCO-88 classification. The EULFS is used to derive the number of employed and unemployed workers in each cell by collapsing individual observations using the provided weighting coefficients. We also use the EULFS to compute the unemployment duration in each cell.
Construction of the variables We keep the employed people as defined by the ILO criteria and derived by Eurostat. It is less common to compute unemployment at the sector × occupation level since workers can be mobile across sectors and occupations. We define unemployment in a given sector and a given occupation as the number of unemployed people who had this precise occupation in this precise sector. This measure corresponds to the true and unobservable unemployment rate at the sector × occupation level if workers do not move across sectors and occupations. Dataset selection We restrict our dataset to the 13 following countries: Austria, Belgium, Germany, Denmark, Spain, France, United Kingdom, Greece, Ireland, Italy, Luxembourg, Netherlands and Portugal. This group of countries corresponds to all countries that provided data at least from 1995.
29 Finally, our data contains information, virtually complete, at the cell level for 92 occupations, in 16 sectors. Table  4. A1 sums up the coverage of our database relative to official statistics. According to official Eurostat statistics, we cover around 70% of the employment in each country, except for Luxembourg for which we only cover 58. 5% of the employment. This is due to the fact that Luxembourg is a small country with a large institutional sector driven by the presence of some European institutions. Our coverage of unemployment is a bit less precise, going from 36. 2% of official unemployment numbers in Italy to 69. 6% in Denmark. This is principally due to the lack of precise reporting of the last job for unemployed people and to dropped industries. Especially the coverage is very low for Portugal in 1995 (around 10%).
A task-based model with a GVC structure enables us to address a rich set of questions regarding globalization, technology adoption, and income inequality. In Section 2, we set up a model with robots but not AI and evaluate the effects of past technological progress and globalization. Section 3 presents our unique dataset that includes five production factors: two labor skill groups, robot installation, input-output tables, and bilateral trade for 17 ISIC 2-digit industries and 50 countries. We develop a novel method to use the data and the model's structure, notably the gravity equation of robot trade, in estimating two critical parameters: robot income shares in low-skilled task production in each industry and each country and elasticities of substitution between robots and low-skilled labor in each industry.
Equilibrium Conditions for Counterfactual Analyses Following  Dekle et al. (2008)  and  Caliendo and Parro (2015) , we consider a system of equilibrium conditions for changes in variables. We denote the counterfactual change in variable x by xt = x ' t =x t , where x t is the value of variable x in equilibrium at time t while x ' t is its value in equilibrium of the counterfactual economy. We relegate to the Appendix the description of the system of equilibrium We construct a unique dataset for our quantitative general equilibrium analysis. The dataset includes information about bilateral trade in goods and robots, input-output tables, country-industry-year-level output and input allocations, and country-year-level factor prices for: 50 countries and the rest of the world (Table  5. 1); 13 good industries and four service industries, which closely follow the 2-digit ISIC (International Standard Industrial Classification) codes; five production factors; and four years  (2004, 2007, 2011, 2014).
This section explains the data on robots and labor skill groups. The data sources for other variables are conventional in the literature: the Global Trade Analysis Project (GTAP) database version 10, the Penn World Table, and the International Labour Organization (ILO) stat database. Details on the data construction are relegated to the Online Appendix. Robot Stocks, Robot trade, and Robot Prices: the International Federation of Robotics (IFR) database reports the physical quantity of newly installed industrial robots for 71 countries and 18 industries since 1993. The IFR follows the ISO 8373 definition of robotics, mentioned in the Introduction, and has been used in the literature, e. g. ,  Graetz and Michaels (2018)  and  Acemoglu and Restrepo (2020). Following the procedure in  Graetz and Michaels (2018) , we construct robot stocks using the perpetual inventory method with the depreciation rate δ R = 0.
1. We collect bilateral trade in industrial robots from the UN Comtrade database, which contains the category of the 6-digit harmonized system code 847950, "industrial robots for multiple uses". We construct time-series data on the price for robots in Japan, unit robot price in Japan in 2014 calculated from the IFR data, and the time-series robot price indices with the benchmark of the 2015 price collected from the Bank of Japan (BoJ) Corporate Goods Price Index database. The BoJ monthly surveys prices of industrial robots and reports quality-adjusted price indices since 1990, which to our knowledge is the only data on quality-adjusted robot prices in our sample period. Labor Skill Groups: following Weingarden and Tsigas (2010), we aggregate 10 occupations in the International Standard Classification of Occupations (ISCO-08) from the ILO database into two skill categories.
Using the robot price in Japan P R bt , we can now calculate robot price P R nt for each country from  (13). Figure  5. 3 compares the estimated robot prices with the unit prices in the IFR database in the four countries, Germany, Japan, Korea, and the USA. The estimated robot prices are reasonably close to the actual ones, except in Germany. 7 Finally, we calculate the robot rental rate w Rnt from  (7) , where we collect the real interest rate r nt from the World Development Indicators and choose a depreciation rate of δ R = 0. 1, following  Graetz and Michaels (2018). Robot Income Shares: based on the robot-stock data and estimates of the robot rental rates, we calculate the robot income share in the income from low-skilled tasks, v nst ¼ w Rnt R s nt / w Rnt R s ns þ w Lnt L s nt ( ) and report it for selected countries in 2014 in Table 5.
14  Trade Effect We estimate changes in trade costs between 1993 and 2014, following  Novy (2013) d s nit ¼ d s ni1993 d s ni2004 ¼ X s ii1993 X s nn1993 X s ni2004 X s in2004 X s ni1993 X s in1993 X s ii2004 X s nn2004 ( ) 1/ð2y s Þ. ð16Þ With the estimates of θ s in Table  5. 2, we estimate trade costs from the data on international and domestic trade: the CEPII TradeProd database (De Sousa,  Mayer, and Zignago, 2012)  and the International Trade and Production Database for Estimation  (Borchert, Larch, Shikher, and Yotov, 2020). 15  Robot Effect We calibrate the progress in robot-related technology from 1993 to 2014 by matching predicted values of two target variables in the model with the data. The first target is robot density, which is defined as the stock of industrial robots per 1000 workers.
AI This section extends our analysis of robots to AI. We assume here that AI can perform the high-skilled labor's tasks H nst and reformulate the production function (3) in the same manner as robots substitute for the low-skilled workers. Unlike robots that are a special sort of capital, we treat AI as a service to which user firms subscribe. Thus, we directly calculate each country's expenditure on AI to derive the share of AI in high-skilled tasks. The difficulty, though, is that we do not have data on such expenditures, specifically on AI. AI trades are recorded in the GTAP database as part of trades in the communication sector, which includes postal services, motion pictures, and others. To find an appropriate share of AI in the communication sector, we make a bold assumption that the world average share of AI in the most AI installing Industry, namely "All other non-manufacturing branches" (IFR industry 14), equals 0.
002, the world average share of robots in the most robot-installing industry. Using the World Input-Output Database (WIOD) Release 2016, we calculate the trade shares of "computer programming, consultancy, and the related activities; information service activities" in those in the communication sector. When only 1. 5% of those shares are related to AI, the world average share of AI in the most AI installing Industry equals 0. 002. We estimate the elasticities of substitution between AI and the high-skilled labor in 17 IFR industries, in the same manner as we have done with robots. Table  5. 10 shows the results. In every industry, the estimated elasticity is smaller than that of robots and low-skilled labor, suggesting that the high-skilled labor is less substitutable by AI than the low-skilled labor is by robots.
Felten et al. (2018)  assess which occupations are heavily affected by AI advances. TC ni includes the logarithm of distance, dummy variables for contiguity, common language, ever-colonial relationship, and a dummy indicating international trade (i. e. , i 6 ¼ n) as opposed to intranational trade. These variables are from the CEPII datasets. We also estimated  (10)  by the Poisson pseudo maximum likelihood (PPML) and found the PPML estimates slightly greater than those in Table  5. 2. Our estimates of trade elasticities are robust to the removal of observations with small trade shares. The Online Appendix shows the comparison of our estimates with those of some other studies:  Caliendo and Parro (2015) ,  Giri et al. (2021), and Shapiro (2016). It also shows the results of the same counterfactual analysis as presented in Section 4.
15  We connect the two datasets in the year 2000. Equation (  16 ) cannot be applied if either X s nit or X s int is zero or missing. In such cases, we choose the median trade costs for other products between the same pair of countries. 16 Our model allows countries to have different factor endowments as well as different production technologies. Therefore, part of the heterogeneity of this impact of trade across countries can be explained by the traditional Stolper-Samuelson effect. The effect of trade liberalization on factor prices is complex (see, for example,  Grossman and Rossi-Hansberg (2008); and Feenstra (2015) , Chapter 4). It is beyond the scope of the chapter to identify the main cause of each specific result as to the effect of trade on real wage rates. 17 In terms of the number of workers, 465,418 workers in Japan and 165,852 workers in the United States, for example, would be relocated.
Data We estimate gravity service regressions using data released since Head, Mayer, and Ries (2009)henceforth HMRwrote their study. We use trade flows for 12 service sectors in 2005-2019, as reported by the OECD-WTO Balanced Trade in Services (BaTIS) dataset, and flows for 11 service sectors in 1995-2006, as reported in the previous BaTIS edition  (Fortanier et al. 2017; Liberatore and Wettstein 2021). In sharp contrast to the data employed by HMR, these services trade data sets report very few zeros. 5 Following HMR, we include log physical distance, differences in time zones, and dummy variables indicating a colonial relationship, shared language, and a common legal system for each pair of countries. 6  While one might combine estimates of the frictions impeding international services trade with proxies for latent occupation-level demand and supply for teleworkable tasks, here we confine ourselves to discussing the direct effects associated with the estimated elasticities of the gravity regression.
Supporting the workers who construct machine-learning algorithms and design their implementation on dedicated computer hardware are specialists who create and manage large databases, provide expertise in relevant domains (e. g. , oncologists and radiologists for the use of AI to detect cancer), and develop and market AI products, among other tasks. In order to focus on jobs that are core to innovations in AI, I target the first group of occupations and not the second. I define the universe of potential AI-related occupations as those in STEM, using the STEM definition in  Hanson & Slaughter (2018). They take the Census Bureau categorization of STEM jobs and remove those in which a relatively high fraction of workers lack a college degree (e. g. , lab technicians, computer support staff, drafters).
For instance, the two most exposed occupations to virtual reality are computer hardware engineers and fine artists. One may imagine that the technology represents a strongly positive shock for engineers but an ambiguous shock for fine artists (e. g. , positive for those working in digital media and negative for those working in non-digital media). Other work focuses on identifying occupations exposed to the job-displacing impacts of AI. The measure in  Felten et al. (2019)  combines the Electronic Frontier Foundation AI Progress Measurement dataset, which tracks technological progress across categories of AI activities (e. g. , image recognition), with crowdsourced assessments of how well these categories apply to O*NET ability scales (e. g. , depth perception), to measure the vulnerability of occupations to AI, based on the importance of each ability scale to an occupation.
These categories are dropped in V. 2, which contains broad AI titles. 6 The excluded occupations, based on their 2000 Census codes, are: computer and information systems managers, engineering managers, financial specialists (all other), computer support specialists, database administrators, network and computer systems administrators, engineering technicians, computer operators, data entry keyers, and computer control programmers and operators. Among life and physical scientists, I exclude agricultural and food scientists, chemists and materials scientists, conservation and forestry scientists, and surveyors, cartographers, and mapping scientists. 7 In unreported results, I perform analysis for V. 0 and V. 1 occupations and obtain similar results. 8 Note that the 30 job titles designated to be AI-related exclude many other titles within the five occupation codes that appear to be support roles, e.
On the one hand, robotization makes Northern producers more competitive at home (i. e. p n,n,i s is larger), which implies that some varieties that were previously imported from the South are now sourced domestically. On the other hand, robotization leads to an expansion in the scale of production, which raises demand for first-stage varieties sourced from the South (i. e. Q m,i     including  Grossman and Rossi-Hansberg (2008) ,  Eaton and Kortum (2002) ,  Acemoglu and Restrepo (2020) ,  Caliendo and Parro (2015) ,  Lee and Yi (2018) , and  Artuc and McLaren (2015). 3 Offshoring in the model takes place through imports of intermediate inputs, which embody tasks performed abroad. This allows us to calibrate all trade flows, production functions and labor shares using the World Input-Output Database.
e. , substitution effects from increased robot use by Northern intermediates producers). We use the World Input Output Database (WIOD) to calibrate international trade, production functions and labor shares. This setup is suitable for illuminating the relevant dynamics without losing tractability. If we were to aggregate within groups, rather than averaging to create representative countries, the North would account for more than 50% of world GDP, and the bulk of world trade would occur within the North. This setting would underrepresent the importance of North-South trade, and trade as share of GDP would be very small. To avoid this aggregation bias, we instead construct representative countries in the North and the South. Considering a relatively large group of other developed countries is also important to allow for the possibility that competitiveness gains associated with robot adoption in the North translate into higher demand for its finalgoods exports.
In contrast, for firms that had already offshored some production, there was also a negative effect on offshoringas robots allowed some previously-offshored production to be automated domestically. In the Spanish case they find that the effect for the former group dominates, with robotization in the full sample having a net positive impact on imports from lower-income countries. 27 Source: The World Bank's Exporter Dynamics Database, which covers all trade transactions except those of oil and arms. We use data from importer-exporters in Bangladesh, Chile, China, Colombia, Ecuador, Egypt, Mexico, Peru, Romania and South Africa, which contain 10,312 separate robot purchases by 4646 distinct firms. 28  We also include industry-year and country-year fixed effects and cluster at the firm level.
The International Federation of Robotics (IFR) maintains a database that records annual installations of industrial robots. In 2019, China, Japan, the United States, South Korea, and Germany were the top five installers of industrial robots. The IFR database also reports robot density as measured by the number of industrial robots in operation for every 10,000 workers. Using this ratio as a measure of automation intensity, the countries with the highest levels of automation intensity in 2019 were mostly developed countries such as Japan (364 robots per 10,000 workers) and Germany (346 robots per 10,000 workers). Other countries with high automation intensity by this measure were Singapore, South Korea, and China. However, IFR does not have data on domestically produced robots in Indonesia.
We therefore focus on imports and exports of automation equipment to Indonesia using UN Comtrade, an international database of trade statistics maintained by the United Nations (the classification of automation equipment is based on Harmonized System 6-digit codes). The data show that Indonesia ran a massive trade deficit in automation equipment from 2000 to 2019. Moreover, the data reveal interesting patterns. For example, imports of the type of automation equipment used in the textile and apparel industries, such as weaving and knitting machines and other textile machinery, declined as a share of all automation equipment imports. This is consistent with a slowdown in these industries, which had been darlings of the Indonesian economy in the 1990s. The data also show significant increases in imports of automation equipment that is intensively used in high-tech industries such as motor vehicles and metals.
g. ,  Koch, Manuylov and Smolka (2021)  using Spanish data; Humlum (2019) using Danish data; Acemoglu,  Lelarge and Restrepo (2020)  using French data). We use a firm-product level approach, employing three data sets from the most disaggregated micro-level database of Indonesian manufacturing firms. The database, which compiles results from an annual survey of about 35,000 firms, includes raw materials and product outputs at the firm level, and exports and imports at the firm level. Merging these three main data sets leaves us with a total sample of 118,570 firms for the period 2008-2012. Merging the three data sets allows us to identify each manufacturing firm's direct imports of automation equipment. Because the firm-level import data are consistent with nationally aggregated import data, we conclude that the firm-level data are fairly representative.
We refer to equipment with these product codes as 'automation equipment'. We can then identify the quantity and value of automation equipment purchased from abroad by a country or a manufacturing firm. Using UN Comtrade data from the World Bank's World Integrated Trade Solution (WITS) database, we observe annual exports and imports of automation equipment to Indonesia, by value, for the 2000-2019 period (Figure  9. 1). Since Indonesia is not a major producer of automation equipment, we can see that 278 Lili Yan Ing and Rui  Zhang 278  substitute for production workers in developed economies, we find that Indonesian automators employ more workers compared to non-automators. In particular, we document a relative increase in the number of production workers compared to non-production workers.
In 2019, these two types of equipment accounted for around 10% of automation equipment imports. Last, imports of automatic machine tools have been relatively stable during this period, accounting for around 20% of imported automation equipment in most years. 2  We next examine firm-level production and trade data from Statistics Indonesia (BPS) to identify automators. As a first step, we combined three main micro-level data sets. The first data set is from an annual survey of large and medium-sized manufacturing firms in Indonesia. The survey includes around 35,000 firms annually and records firm-level production and financial information, including information on gross production output, value added, number of production and non-production workers, wages, capital, domestic and foreign equity shares, materials usage, and export and import shares.