dataesr/scientific-paragraphs-categorization huggingface dataset:
19 We follow Graetz and Michaels (2018) and calculate withincountry employment-weighted distributions of changes in robot adoption between 2005 and 2015. We use the Stata code that Graetz and Michaels (2018) made available at https:// dataverse. harvard. edu/dataset. xhtml?persistentId = doi:10. 7910/DVN/5JWBXU. Specifically, we denote robot adoption by RA ci,t = RS ci,t /EMP ci,t , i. e. the robot stock per thousand persons employed in industry i of country c. We denote ws c the weighted change in robot adoption of country c , which is the summation of changes in robot adoption by industry i weighted by their employment shares. The change in robot adoption net of the weighted change in robot adoption is ΔRA ci = (RA ci,t -RA ci,t-1 ) -ws c. We then calculate the percentile rank of the change in robot adoption ( ΔRA ci ) and use this variable in the regression analysis.
It is about a quarter of the workforce in advanced countries such as Denmark, the Netherlands, and the United States throughout the sample period. It is over 50% of total persons employed in industrializers such as the PRC, Turkey, and Poland. In most regression specifications, we control for changes in the investment to value added ratios, and (the natural logarithm of) value added. 21 Although robots are a visible and much discussed form of  20  After matching the datasets, we have data for 37 countries and 19 sectors, with missing data for a few country-industry pairs. High-income countries include the 'old' EU15 countries, western offshoots, and high-income East Asian countries, namely Australia, Austria, Belgium, Canada, Germany, Denmark, Spain, Finland, France, the United Kingdom, Greece, Ireland, Italy, Japan, South Korea, Malta, the Netherlands, Portugal, Sweden, Chinese Taipei, and the United States.
EMTEs are the others, namely Brazil, the PRC, Czech Republic, Estonia, Hungary, Indonesia, India, Lithuania, Latvia, Mexico, Poland, Romania, Russia, Slovakia, Slovenia, and Turkey. 21 This data is obtained from the WIOD 2016 release ( Timmer et al. 2015 ). The first control variable, investment to value added ratios may be subject to concerns about multi-collinearity as robots are part of physical capital investment. We explored the share of robot investment in overall investment by using turnover-based prices of robots for the US provided in IFR (2012). The number of robot times their unit price gives a rough approximation of nominal investment. Our estimates suggest that the share of robot investment in total investment is small, typically not exceeding 1 percent. The first differences of our data for robot adoption and investment to value added ratios are only loosely correlated, with a correlation coefficient of -0.
2 below). Using 'reaching and handling' tasks as an instrument gives similar results, although more prone to weak identification concerns (see Appendix Table  A2  ). An advantage of our dataset is the broad country coverage, including various emerging market and (post-) transition economies. In column (3) of Table  3  , we differentiate the relation between robot adoption and routine shares across high-income countries and EMTEs. 26 We do so by   27  The relationship between robot adoption and declining routine shares appears to mainly occur in high-income countries: for both, the OLS and 2SLS regressions, the negative overall coefficient estimate for robot adoption in column (  3 ) is almost equal in size to the positive interaction term with the EMTE dummy, indicating that the effect of robot adoption is essentially nullified in those countries.
28 Since technical constraints to robots replacing tasks are more likely to bind for firms in high-wage advanced countries, improvements in robot capabilities might account for the larger employment response in advanced countries compared to EMTEs. Additionally, our dataset allows us to further disaggregate routine and non-routine employment shares into manual and analytic taskintensive occupations. Results are reported in Table  4  , again with OLS results in panel A and 2SLS results in panel B. 29  We find that the negative relation between robot adoption and routine employment shares is exclusively driven by manual routine jobs: the estimates in column China from the sample and re-classified it as a non-EMTE. This did not alter the results (available upon request). 27 In the reported 2SLS regressions, we only instrument robot adoption but not the interaction.
Acknowledgment We thank the editor and two anonymous referees for stimulating comments and suggestions. We thank  Donald Jay Bertulfo  for excellent research assistantship. A previous version of this paper served as back-ground research for the Asian Development Outlook 2018. We thank all colleagues who offered help and advice, including  Abdul Abiad ,  Rana Hasan ,  Yasuyuki Sawada , and  Joseph E. Zveglich Jr. We also thank seminar attendees at the  Asian Development Bank , and conference participants at the  XXXI Villa Mondragone International Economic Seminar , the 57th Annual Meeting and Conference of the  Philippine Economic Society, the Asian and Australasian Society of Labour Economics 2019 Conference , and the 1st  LISER-IAB Conference on Digital Transformation and the Future of Work.
003  *  *  * (0. 00) (0. 00) (0. 00) (0. 00) Δ (natural logarithm of) value 0. 005 0. 002 0. 004 -0. 009 added (0. 01) (0. 00) (0. 00) (0. 01) R 2 0. 024 0. 003 0. 007 0. 031 Observations 700 700 700 700 Number of countries 37 37 37 37 Panel B: 2SLS (IV: Replaceable tasks) Percentile of changes in robot -0. 119  *  * -0. 003 -0. 032 0. 152  *  *  * adoption (0. 05) (0. 01) (0. 02) (0. 05) Δ Investment to value added ratio 0. 004  *  *  * 0. 001 -0. 001 -0. 004  *  *  * (0. 00) (0. 00) (0. 00) (0. 00) Δ (natural logarithm of) value 0. 012 0. 003 0. 006 -0. 019  *  * added (0. 01) (0. 00) (0. 01) (0. 01) R 2 -0. 020 0. 001 -0. 021 -0. 059 Observations 700 700 700 700 Number of countries 37 37 37 37 * p < 0. 1. * * * p < 0. 01 * * p < 0. 05 able decline in the sample to 277 observations because the EU KLEMS dataset does not report ICT investment by industry for many EMTEs.
We downloaded the quarterly US seasonally adjusted real GDP data from the Federal Reserve Bank of St. Louis 1  , and considered monitoring for changes near two recent recession periods 2. Two preprocessing steps were conducted before applying the tests. First, we consider the log of the real GDP, which is conventional. Second, in order to remove the deterministic trend, we estimate the trend line only within the training period and then extend the trend to the test period. After this, we are able to input the detrended series into our monitoring procedure and check whether the detector goes beyond the boundary at any point during the testing period. The monitoring procedure was carried out using the detector V (1) M and the quadratic spectral kernel K Q to estimate the long run variance parameter.
Acknowledgement We are grateful to the Editor ( Oliver Linton ), one anonymous Associate Editor and two Referees for their helpful insights and remarks, which have improved the manuscript significantly. The research of  Zhenya Liu  was supported by the  Fundamental Research Funds for the Central Universities , and the  Research Funds of Renmin University of China  [ 17XNQJ01 ]. The research of Gregory Rice was partially supported by the discovery grant from the  Natural Sciences and Engineering Research Council of Canada.
New datasets enable speculators to use new variables to forecast asset payoffs and should therefore push back the data frontier, i. e. , increase τ max. 3 In fact, the advent of big data in asset management is often described as a gold rush for this reason: Big data combined with new forecasting techniques (machine learning) enable asset managers to discover more precise predictors. 4 We refer to this dimension of data abundance as the "hidden gold nugget" effect. However, data abundance also creates "a needle in the haystack problem": It results in a proliferation of datasets and only a fraction of these datasets contains useful information for forecasting asset payoffs. Separating the wheat from the shaff can only be done through explorations, which is costly. To capture this dimension of data abundance, we assume that each exploration returns an informative predictor with probability α < 1.
In sum, we analyze the effect of data abundance on equilibrium outcomes by considering either an increase in τ max (the hidden gold nugget effect) or a decrease in α (the needle in the haystack problem). 5   As for greater computing power, it reduces the cost of exploring a new dataset. 6 Thus, we study the effect of greater computing power by considering the effect of a decrease in the cost of exploration, c, on equilibrium outcomes. In equilibrium, each speculator's optimal search strategy follows a stopping rule: She stops searching for a predictor after finding one whose quality (signal-to-noise ratio) exceeds an endogenous threshold, denoted τ * (we refer to such a predictor as being "satisficing"). This threshold is such that the speculator's expected utility of trading on a predictor of quality τ * is just equal to her expected utility of searching for another predictor.
S. (which allows investors to have internet access to electronic filings by firms) had a positive effects on measures of price efficiency. One possible reason, as argued by  Gao and Huang (2019) , is that the EDGAR system reduced the cost of accessing data (a component of exploration cost) for investors. 9 Given that technological progress has both enlarged the search space and reduced search costs, these implications of our model can explain why the empirical literature on the effect this progress on asset price informativeness reports conflicting results. See Section 5. 2 for a discussion. data frontier reduce the pairwise correlation in speculators' trades while a drop in the proportion of informative datasets (α) has the opposite effect.
In period 0, they just choose the type (quality) of the predictor whose realization they will observe at date 1. A predictor can be viewed as a particular combination of variables from various datasets (e. g. , past earnings, satellite images and consumer transactions data) that forecast the payoff of the asset. One exploration consists in testing the predicting power of a particular combination with prediction tools (e. g. , regression analysis or machine learning techniques). For instance, one can interpret each exploration as collecting various variables and running a regression of the asset payoff (e. g. , stock earnings) on these variables. The estimates of the coefficients of this regression can then be used to compute the predicted value of the regression, s θ , at date 1 after observing the realization of the variables used in this regression at this date.
as a combination of variables whose weights have been optimally chosen to minimize the predictor's forecasting error in-sample. In this interpretation, speculators can try to improve the quality of their predictors by trying new combinations (e. g. , by buying datasets containing new variables). As more datasets become available ("data abundance"), the number of possible combinations of variables that one can use to predict asset payoffs increases. This evolution has two consequences controlled by parameters θ and α in the model. First, it pushes back the "data frontier", i. e. , it increases the chance (at least weakly) of finding even more informative predictors than those existing before. We refer to this dimension of data abundance as the "hidden gold nugget effect. " For instance, by combining satellite images of parking lots at Walmart with credit card transactions data and more traditional accounting data, one might be able to find more informative predictors of future earnings for Walmart than using accounting data alone.
This dimension of data abundance is controlled by θ in our model: When θ decreases, the quality of the best predictor (the "hidden gold nugget"), denoted τ max ≡ τ (θ), improves. Second, the share of combinations that yield informative predictors might fall as the number of all possible combinations explodes. For instance, there are myriads of ways in which one could combine traffic data in large cities with other data to predict economic growth. However, a few are likely to be informative and discovering these combinations take time. We refer to this dimension of data abundance as the "needle in the haystack problem. " 14 It is controlled by α in our model: As α decreases, each round of exploration is less likely to be successful as if the share of informative predictors was falling. 15 inally, parameter c represents the cost of exploring a specific dataset to identify a predictor.
Greater computing power reduces this cost. For instance, with more powerful computers, one can explore more datasets in a fixed amount of time. So the time cost of data mining is smaller. Thus, we analyze the effect of progress in computing power by considering the effect of a decrease in c on the equilibrium. We focus on equilibria in which each speculator follows an optimal stopping rule θ * i. That is, speculator i stops searching for new predictors once she finds a predictor with type θ ≤ θ < θ * i (a predictor of sufficiently high quality in the feasible range). We denote by Λ(θ * i ; θ, α) the likelihood of this event (the probability of success) for speculator i in a given search round. That is: Λ(θ * i ; θ, α) ≡ αPr(θ ∈ [θ, θ * i ]) = α × (Φ(θ * i ) -Φ(θ)) (2) Thus, a decrease in θ raises the likelihood of finding a predictor in a given exploration, holding α constant.
This effect captures the idea that while data abundance might reduce the fraction of informative datasets, it increases the chance of finding a good predictor once one has identified an informative dataset. As the outcome of each exploration is random, the realized number of explorations varies across speculators (even if they use the same stopping rule). We denote by n i the realized number of search rounds for speculator i. This number follows a geometric distribution with parameter Λ(θ * i ; θ, α). Thus, the expected number of explorations for a given speculator (a measure of her search intensity) is: E[n i ] = Λ(θ * i ; θ, α) -1. ( 3 ) To simplify the exposition, we assume that speculators cannot "store" predictors that they turn down (i. e. , the search for predictors is without recall).
As a result, price informativeness increases. This indirect effect reduces the expected utility from trading on a satisficing predictor (the second term in bracket in eq. (  15 )) and therefore dampens the direct positive effect of a decrease in c on the value of searching for a better predictor after finding one. However, it is never strong enough to fully offset it. We now consider the effect of data abundance on speculators' optimal stopping rule. Remember that data abundance has two consequences in the model: (i) it pushes back the data frontier by raising the quality of the best predictor and (ii) it increases the risk for speculators of using datasets which, after exploration, proves to be useless (the needle in the haystack problem). Proposition 4. 1. A decrease in the fraction of informative datasets, α, always increases speculators' stopping rule, θ * , in equilibrium (∂θ * /∂α < 0).
18   The effect of pushing back the data frontier on speculators' stopping rule is more complex. Counterintuitively, it can lead speculators to trade on predictors of worse quality, even though the quality of the best predictor increases. The reason is as follows. On the one hand, pushing back the data frontier increases the chance of finding a satisficing predictor holding the search strategy, θ * constant (Λ(θ * ; θ, α) increases when θ goes down). This effect reduce the expected number of rounds required to find a predictor and therefore reduces the expected utility cost of searching for a new predictor after rejecting one. Therefore, it increases the continuation value of searching for a predictor (see eq. (  15 ). 18 Given this, one might be tempted to capture the needle in the haystack effect by just considering the effect of increasing c (on the ground that it becomes more costly to find good datasets).
Greater computing power induces speculators to be more demanding for the quality of their predictors (to put more effort in the search of good predictors) because it reduces the cost of exploring new data to obtain a predictor (see Proposition 3). Thus, speculators obtain signals of higher quality on average. Hence, on average, they trade more aggressively on their signals, their aggregate demand for an asset becomes more informative and, for this reason, price informativeness increases (see eq. (  11 )). Proposition 7. 1. In equilibrium, an improvement in the quality of the most informative predictor (a decrease in θ) raises the average quality of speculators' predictors and therefore price informativeness. 2. In equilibrium, a decrease in the proportion of informative datasets (a decrease in α) reduces the average quality of speculators' predictors and therefore price informativeness.
2. Other things equal, the dispersion of speculators' expected profit increases when the data frontier is pushed back for θ small enough (d Var[π(θ)]/dθ < 0 for θ sufficiently close to zero). 3. Other things equal, the dispersion of speculators' expected trading profit increases when the fraction of informative datasets decreases (α decreases). To understand the first part of the proposition, suppose that c = 0. In this case, all speculators search for a predictor until they find one with the highest possible quality, θ * = θ. As a result, all speculators trade on predictors of the same quality (Var[τ (θ) | θ < θ < θ * ] = 0) and therefore the dispersion of expected trading profits is nil, as can be seen by inspection of the expression for Var[π(θ)] (eq. (  27 )). Now consider a small increase in c starting from the situation in which c = 0.
Thus, if new datasets open the possibility that the quality of the best predictor improves, each speculator will individually find optimal to use these datasets, if she expects others not to do so. But then the equilibrium stopping rule must shift from θ * (θ 0 ) to θ * (θ 1 ). 23 hus, data abundance can be "excessive" from speculators' viewpoint in the sense that they would be better off if the data frontier could not be improved. We now show that speculators' average investment in search is also excessive in the sense that, holding all exogenous parameters constant, they would be better off if they could commit to use a less demanding stopping rule (and therefore predictors of lower quality on average). To see this, let assume that speculators can collectively choose a stopping rule, θ r and commit to this choice.
In this way, our model can generate predictions about the effects of data abundance and computing power on the distribution of asset managers' skills (precisions of their signals), the distribution of their trading profits or the correlations in their holdings. Moreover, asset price informativeness is determined by speculators' optimal data mining strategy because this strategy determines the average quality of their signals and thereby the informativeness of their aggregate demand. The main message of our model is that the effects of data abundance and greater computing power are not the same. For instance, greater computing power always induces speculators to be more demanding for the minimal quality of their predictors while this is not necessarily the case for data abundance. As a result, positive shocks to computing power improve and homogenize predictors' quality across speculators and, for this reason, improve price informativeness.
Marenzi (2017)  estimates that asset managers have spent more than four billion in alternative data in 2017 (see also "Asset managers double spending in new data in hunt for edge", Financial Times, May 9, 2018. Abis (2018)  finds that quantitative funds (using computer-driven models to analyze large datasets) have quadrupled in size from 1999 to 2015 and that their growth has been more than twofold that of discretionary funds. Moreover, Grennan and Michaely (2019)  find that about 87% of the FinTechs in their sample(190 FinTechs)  specialize in producing investment signals using artificial intelligence.
Sample selection To create our international sample, we rely on the Sustainalytics database. Specifically, we include in our sample all banks covered by Sustainalytics for which the necessary financial variables are available in the Datastream database. This requirement reduces the number of banks to 184. Overall, our final sample comprises 184 banks from 41 countries from 2009 to 2015, yielding an unbalanced panel of 937 firm-year observations. Table  1  reports our sample distribution across country.
Bank efficiency Following previous literature  (Avkiran, 2009; Tone and Tsutsui, 2009) , we use the DEA Solver Pro software to obtain efficiency scores. Efficiency values range from 0 to 100%. A score of 100% implies that the bank is efficient and is located on the efficient frontier. A score lower than 100% denotes inefficiency, i. e. , that the bank must decrease its inputs and/or increase its outputs. Specifically, efficiency is a microeconomic measure of productivity that evaluates the production process taking into account the volume of inputs and outputs. The concept of Pareto-Koopmans efficiency states "a production is fully efficient, if and only if, it is impossible to improve any input and output without reducing any other inputs or outputs"  (Cooper et al. , 2006). A firm is considered efficient when it lies on the production frontier where it is unable to increase the output level produced for a given input level, or to reduce the level of resources consumed to produce a given quantity of output.
Corporate social responsibility CSR data come from the Sustainalytics 2  database. Sustainalytics is a provider of environmental, social and governance assesment for responsible investment all over the globe. For each firm analyzed, Sustainalytics generates a profile of the organization's CSR and compiles these profiles in a stepwise approach. First, it scrutinizes relevant organizational information from multiple sources such as financial accounts, organizational documentation, media reports and interviews with stakeholders. This results in a preliminary report on a firm's degree of sustainability, which is then sent to the firm for verification and correction. The changes made by the firm are then checked and verified again by Sustainalytics.
Diffusion of knowledge-revealing practices (KRP). To capture the shared logic in the ecosystem for openness, we focus on the extent of knowledge-revealing behavior in the country. In the process of collaborative knowledge development, new scientific and technological knowledge is developed, and making it public through publication reflects the disclosure willingness of the parties involved in developing that knowledge. As publications are mainly an output of academics, we obtained data on public-private co-publications at the country level from the EC's 2010 Innovation Union Scoreboard (now known as the European Innovation Scoreboard). The variable diffusion of KRP provides an indication of how widespread KRP are in a national context. It is a continuous variable that indicates the number of public-private co-authored research publications per million of the population of research articles.