dataesr/scientific-paragraphs-categorization huggingface dataset:
The second data set includes specific, additional information from the manufacturing survey, including detailed information on outputs and inputs and raw materials at the firmproduct level. Specifically, the data set reports the annual value, quantity, and export value of each product that a firm produces. It also documents the annual value and quantity of each product that a firm purchases domestically or imports as an input material. 3 The third data set contains customs trade information including value, quantity, HS product code, and import country of origin, as well as export destination countries for each Indonesian firm in a given year. The customs trade data set is essential for identifying direct imports of automation equipment at the firm level. We match these three data sets using a unique firm identifier provided by BPS.
The same pattern holds for almost all types of automation equipment and is broadly consistent with the pattern we observe in the UN Comtrade data at the national level. This suggests that the matched firm-product level data set provides a good representative sample for an overview of automation in Indonesia. 4  Turning to the types of automation equipment included in Table  9. 1, we see that weaving and knitting machines and other textile dedicated machinery together accounted for 66% of the total value of automation equipment imports in 2008, but was only 35% in 2012. This decline can be explained by the overall contraction of the textile and apparel industries in Indonesia over the same period. Whereas, import shares for both industrial robots and numerically controlled machines increased significantly from 2008 to 2012.
It shows that importing automation equipment is associated with an increase of 0. 499 log points in product-level market share. We also estimate how changes in the product-level price vary with automation status. Columns 3 and 4 show that the price increase is associated with importing automation equipment, but the coefficients are not statistically significant. Columns 5 and 6 indicate that automators also see a larger increaseof 0. 478 and 0. 604 log pointsin the inferred quality of their products. To summarize, we identify the associations between changes in various firm-level outcomes and automation status. Consistent with the empirical findings of studies that use firm-level data from other countries, we find that importing automation equipment is associated with larger increases in output and employment.
Conclusions We conduct an empirical analysis of automation in Indonesia, with a particular focus on how firm-level automation is associated with different firm-level outcomes. We measure direct imports of automation equipment by Indonesian firms and describe patterns of automation by these firms. Combining data on firm-level imports of automation equipment with other micro-level information, we find that automators produce more outputs, hire more workers, and have higher productivity. They also pay higher wages, have lower labor shares, and use capital more intensively. We find further that automators produce a larger variety of outputs, engage more actively in exports and imports, and produce higher-quality goods. Using a firm-level long-difference specification, we find that automators see larger increases in outputs, employment of production workers, export shares, and inferred product quality.
Ruiqi Sun and Daniel Trefler The transformative potential of artificial intelligence (AI) is apparent from our daily use of smartphones. We log in using AI-enabled facial recognition, issue commands with AI-enabled speech recognition, conduct AI-enabled internet searches, buy from stores pushing AI-enabled recommendations, and receive goods shipped with AI-enabled logistics systems. Not only has AI enabled the creation of new services, it has improved on existing services and disrupted older services in a familiar process of creative destruction  (Schumpeter, 1942). All of these changes can be seen in the palm of our hand and are meticulously tracked by corporations. This chapter uses big data on international App downloads and AI patents to track how AI is changing the pattern of trade in services, the variety of services available in each country, and the process of creative destruction.
By implication, the paper shows how government subsidies in the AI sphere can improve competitiveness. More tangential to our interests here,  Bailey, Gupta, Hillenbrand, Kuchler, Richmond, and Stroebel (2020)  use Facebook data to construct bilateral social connections between countries and show that these are a more powerful determinant of bilateral trade flows in goods than are traditional determinants such as distance and borders. Though tangential to our main results, we include their bilateral social connections measure and find that it impacts App-based service trade as well. 1  This review, even if missing some citations from the rapidly growing AI literature, clearly demonstrates that the literature on AI and trade is very small. This is research output and (  2 ) an App category's AI intensity.
These three results demonstrate that AI deployment in the mobile App space has already had tangible effects on trade, product variety, creative destruction, and welfare. One might wonder whether our conclusions are the result of a spurious correlation between AI patenting and other unobservables. To examine this, we consider non-AI patents and find that their effects are modest and their inclusion in the analysis does not affect our results. Section 1 provides background on mobile Apps and AI. Section 2 describes the database. Section 3 uses bilateral gravity equations to estimate the impact of AI deployment on trade. Section 4 estimates the impact of AI deployment on the extensive margin, that is, on the number of Apps/varieties. Section 5 examines the impact of AI deployment on entry, exit, creative destruction and welfare.
Mobile Application Data Our primary database is the App download data purchased from SensorTower. SensorTower is the largest and most reliable company providing App-level metadata. The data track App-level downloads by user country from 2014 to 2020 for the Apps available in the Apple App Store and Google Play, which are the biggest application marketplaces for the iOS and Android operating systems. 5 Each App in the Apple App Store and Google Play has a unique, time-invariant product ID and is accompanied by the name of the developer, the name of the selling publisher (App terminology for 'firm'), and the selling publisher's website. Sensor-Tower consolidates App IDs to deal with the fact that an App may have different IDs in different countries, e. g. , TikTok in the US and Douyin in China.
To deal with this we interact the 19 App groups with the 19 2-digit NACE industries to define 292 App categories at the level of App group × NACE industry. 7  There are billions of Apps in the App Store and Google Play, many of which have no downloads or just one or two. It is not computationally feasible to deal with terabytes of such data. We therefore initially restrict the sample by selecting the 1,000 most downloaded unified firms. Over our 2014-2020 sample period these unified firms had 223 billion downloads of 82,850 Apps. We are using download data whereas revenue data would be better. To show that the two are correlated, we divide our Table  10. 3 subsample into two bins, Notes: Groups are ordered by downloads with the most downloaded group on top. Some firms have patents in multiple categories.
Linkage to Patent and Financial Data Our core analysis is about the impact of AI on a variety of trade and welfare outcomes. We will be measuring AI using patent data. We use SensorTower's unified firm names to link with the Bureau Van Dijk Orbis Intellectual Property database. This provides us with patent and financial data. We were unable to reliably match firm names across the two data sets using machine learning tools. We therefore select the largest 1,000 unified firms in the world (as measured by global downloads) and then find by hand their global ultimate owners in the Orbis database. We match 834 of the 1,000 firms. Unlike many studies, there is no linkage error here. 8  To investigate the representativeness of our sample we also looked at the 100,000 unified firms with the most downloads-these are not matched to patent and financial data-and call this the 'full sample'.
AI Patent Data To estimate the impact of AI on trade and welfare we need to be precise about what we mean by AI and how we measure it. We use AI-related patents as the basis of our measure. From the Orbis data we know the 10,144,089 patents assigned to our 834 firms. We categorize each of these patents as AI or non-AI patents following the  WIPO (2018 WIPO ( , 2019) )  methodology. For each patent we check if it meets one of three criteria. 1. The main and/or minor CPC codes are on a list of CPC codes that WIPO uses to identify specific AI technologies. For example, CPC subclass G10L-015 is speech recognition. 2. The title and/or abstract contains a phrase that is on a keyword list that WIPO uses to identify specific AI technologies. The list includes phrases such as 'machine learning' and 'neural network' along with extensions of these phrases such as 'neural networks' and 'neural-network'.
Summary Statistics Table  10. 5 reports summary statistics of our data. Each observation is uniquely identified by an App category (292), an exporter (53), an importer (84) and a year (2014-2020). We have 469,879 observations with positive levels of downloads. There are several points to note about the sample size. First, we do not work at the firm level and this requires an explanation. We do not know whether any given App uses AI so we cannot work at the level of a firm's Apps. What we do know is the extent to which AI is deployed in an App category in an exporter country. So we must aggregate up from firms to App-categories and exporters. Second, we exclude zero downloads, but return to this later using PPML. Third, Google Play is banned in China, so we only have Apple App Store data for Chinese downloads.
The coefficient on contiguity is a little smaller than in Head, and the coefficient on common language is just a little larger. We also include dummies for whether m and x were ever in a colonial relationship and whether they are in the same regional trade agreement. These covariates are less significant and much smaller than in Head and Mayer. The importer GDP and exporter GDP coefficients are very small, but this is not surprising given that they do not vary much over our period 2014-2020 and so are largely soaked up by the fixed effects. We do not include the populations of either m or x because these are also largely soaked up by fixed effects. In column 2, we use our subsample matched with patent and financial data to estimate equation 1. The coefficients on the gravity covariates do not change, which provides evidence that our sample is representative in dimensions familiar to trade economists.
The comparative advantage logic of Heckscher-Ohlin (HO) provides such a shock. The cost of AI deployment by producers of product c in country x is low if (1) the country is abundant in AI and (2) the product is AI intensive. We measure a country's AI abundance using the number of AI conference papers presented by scholars from exporter country x in year t. Denote this by ConfPaper xt. This is a commonly used measure of a country's AI capacity. See for example  Goldfarb and Trefler (2019a). Data are from  Zhang et al. (2021). We measure the AI intensity of a product or App category as the sum of all single-category-firm AI patents for firms in category c divided by the sum of all single-category-firm assets for firms in category c. This is calculated at the global level, meaning we sum across firms in all countries.
AI and Creative Destruction in the Global Economy: A First Look In this subsection we look at the raw data on AI and creative destruction, that is, on how the download shares of new and exiting Apps are impacted by AI. Consider an App in App category c that is downloaded by country m. The App is 'new' in year t if it was downloaded in t, but not t -1. The App is 'exiting' in year t if it was downloaded in t -1 and t, but not t + 1. The App is 'continuing' in year t if it was downloaded in t -1, t and t + 1. The denominator is total downloads for cmt (including downloads of domestically produced Apps). Since these shares are calculated using data for t -1 and/or t + 1 we drop the y k cmt for the first and last years and work with t = 2015,. , 2019. We are interested in how AI impacts the entry of Apps (y n cmt ) and the exit of Apps (y e cmt ).
The Welfare Gains from AI: New Empirics We saw in equation (  4 ) that the welfare gains from AI deployment can be expressed as DW t = (l t /l no AI t ) -1/(s-1) where λ t equals one minus the share of new Apps in total downloads and l no AI t is its counterfactual value in a world with no AI deployment. Before explaining how we estimates ΔW t we make two observations. The first is the major caveat that we are using download data whereas the welfare calculation should be based on expenditure data. Second, this is a welfare calculation for the mobile App category. It ignores all other goods. To estimate ΔW t , we first need an empirical counterpart to λ t. From equations (  3 ) and (  5 ), it is natural to equate λ t with 1y n cmt , that is, with one minus the new downloads share for category-c Apps downloaded by users in country m in year t.
Conclusions Artificial Intelligence is a powerful new technology, yet almost nothing is known empirically about this process, partly because impacts on goods trade have likely been minimal and partly because researchers have failed to look where the action is most obviousin the palms of our hands. We observed that mobile Apps provide a large and growing collection of services that billions of people use daily and whose international dimension is captured by mobile App downloads. We developed a new database of App downloads and the AI deployed in those Apps. Using an IV strategy to estimate the impacts of AI deployment we presented three results: 1. Bilateral Trade: AI deployment increased App downloads by a factor of six. This is the first systematic evidence of the impact of AI on trade. 2.
Notes 1 In the late 1990s, the textile and apparel industries accounted for about 16% of the manufacturing value added in Indonesia. This figure dropped to about 7% during the 2008-2012 sample period. 2 We observe Indonesia's exports and imports of automation equipment from 2000 to 2019, by type and by Harmonized System 6-digit product code, based on the UN Comtrade WITS database.
who consider the impact of robots on Spanish multinationals during 1990-2016. 2 More specifically, AI intensity is measured as total global AI patents associated with the App category. AI expertise is measured as the number of papers presented at AI conferences by researchers affiliated with the country's universities and other research institutions. Data on AI expertise are from Zhang, Mishra, Brynjolfsson, Etchemendy, Ganguli, Grosz, Lyons, Manyika, Niebles, Sellitto et al. (2021). 3 https://www. statista. com/statistics/617136/digital-population-worldwide. 4 https://www. statista. com/statistics/269025/worldwide-mobile-app-revenueforecast. 5 This database does not cover downloads from the remaining application marketplaces. The largest of these are Huawei App Gallery, Xiaomi App Store, Amazon App Store, and Samsung Galaxy Store.
Once collected, this material was analysed with a view to, first, identifying which interdependencies had been selected and promoted for governance; and second, how these choices were structuring governing practices. Methodological tools of geographical mental mapping coupled with NVivo 11 software were used to help us in this work. Geographical mental mapping was used in interviews to map actors' spatial visions of those interdependencies which mattered most to them. NVivo 11 enabled us to code actors' selection and promotion of interdependency and categorize governing practices. As we have explained, a central line of enquiry running throughout was to understand mutual influences of ecological transition and territorialization. Consequently, in a first instance when analysing our data, we specifically examined actor political usages of territorial interdependency shaping their ecological transition governing strategy.
For HiFi data at 30x coverage the smoothing algorithm takes about 15 minutes to run on 16 cores. SFS extraction is the most computationally intensive step and takes about 45 minutes on 16 threads for the CHM13 HiFi data. Finally the SV calling steps is fast and take less than 8 minutes. Overall the runtime of the SVDSS pipeline is less than 2 hours for a high-coverage HiFi sample on 16 cores.
However it is possible artifacts accumulated in the cell-line and actual heterozygosities in the genome may result in heterozygous SVs being reported. We built the FMD index for v1. 1 of the CHM13 assembly and extracted SFS from smoothed CHM13 PacBio HiFi data against this index. We then passed the SFS through the SVDSS pipeline for SV discovery. Our pipeline discovers a total of 102 SVs. For comparison, we repeated the above experiment with the other tools pbsv, cuteSV, SVIM, debreak and sniffles. Table  2  includes a summary of the result. We calculate the baseline False Discovery Rate for each tool as the number of calls it makes against T2T divided by the number of calls it makes against GRCh38. SVDSS has the lowest number of calls against the T2T assembly and also has the lowest baseline error rate.
Equations An overview of Pathfinder is presented in Fig. 1. The model is composed of a climate module, of three separate modules for the carbon cycle (ocean, land without land use and land permafrost), and of two additional modules describing global impacts: sea level rise (SLR) and surface ocean acidification. We do not emulate cycles of other non-CO 2 gases. Mathematically, the model is driven by prescribing time series of any combination of two of four variables: global mean surface temperature (GMST) anomaly (T ), global atmospheric CO 2 concentration (C), global non-CO 2 effective radiative forcing (R x ), and global anthropogenic emissions of CO 2 (E CO 2 ). The model can therefore be run in the traditional emission-driven and concentration-driven modes but also in a temperature-driven mode (in terms of code, implemented as separate versions of the model).
Sea level rise Global SLR has been implemented in Pathfinder as a variable of interest to model climate change impacts. In this version, it is firstly a proof of concept, modelled in a simple yet sensible manner. The total sea level rise (H tot ) is the sum of contributions from thermal expansion (H thx ), the Greenland ice sheet (GIS; H gis ), the Antarctic ice sheet (AIS; H ais ), and glaciers (H gla ): H tot = H thx + H gis + H ais + H gla. (7) The thermal expansion contribution scales linearly with the OHC  (Goodwin et al. , 2017; Fox-Kemper et al. , 2021) : H thx = thx U ohc , ( 8 ) where thx is the scaling factor of the thermosteric contribution to SLR. Note, however, that the thermal capacity of the climate module does not match that of the real-world ocean  (Geoffroy et al. , 2013b) , and so this equation cannot describe equilibrium SLR over millennial timescales.
Land carbon The land carbon module of Pathfinder is a simplified version of the one in OSCAR  (Gasser et al. , 2017 (Gasser et al. , , 2020)). It is shrunk down to four global carbon pools: vegetation, litter, and active and passive soil (see Fig. 3 ). All terrestrial biomes are lumped together, and there is therefore no accounting for the impact of land use change on the land carbon cycle in this version of Pathfinder. This is an extreme assumptionalthough very common in SCMs -motivated by simplicity, and it implies that CO 2 emissions from fossil fuel burning and land use change are assumed to behave in the exact same way, in spite of their not doing so in reality  (Gitz and Ciais, 2003; Gasser and Ciais, 2013). The vegetation carbon pool (C v ) results from the balance between net primary productivity (NPP; F npp ), emission from wildfires (E fire ), emission from harvest and grazing (E harv ), and loss of carbon from biomass mortality (F mort ): dC v dt = F npp -E fire -E harv -F mort.
We note this is also the default solving scheme for regular simulations with the model, although the value of n t can be altered and alternative schemes are available. The Bayesian procedure itself is implemented using the Python computer language and specifically the PyMC3 package  (Salvatier et al. , 2016). The solving of Eq. (  47 ) and its normalization are done using the package's full-rank automatic differentiation variational inference (ADVI) algorithm  (Kucukelbir et al. , 2017) , with 100 000 iterations (and default algorithm options). The choice of variational inference instead of Markov chain Monte Carlo is motivated by the significant size of our model  (Blei et al. , 2017)  and the speed of ADVI. An additional strength of the full-rank version of the ADVI algorithm is its ability to generate correlated posterior distributions even if the prior ones are uncorrelated.
Permafrost carbon The permafrost module's parameters are recalibrated using the same algorithm as used by  Gasser et al. (2018)  but adapted to the global formulation of Pathfinder. First, the algorithm is run once to obtain a set of parameters reproducing the behaviour of the global average of five permafrost models (with data from UVic (MacDougall, 2021) added to the four original models). This gives the values of the structural parameters (i. e. α lst , γ rt1 , γ rt2 , κ rt , a min , all α th,j , all τ th,j , ν thaw , and ν froz ). Second, the algorithm is run five additional times for each of the five permafrost models separately, with the structural parameters established in the first step, to obtain prior distributions of the remaining parameters (i. e. C fr0 , κ a , γ a , and κ τ th ).
2× CO 2 Pathfinder unconstrained Pathfinder constrained CMIP6 AR6 ECS (K) 4. 1 ± 1. 3 3. 3 ± 0. 7 3. 7 ± 1. 1 3. 0 (2. 0, 4. 5) TCR (K) 2. 2 ± 0. 5 1. 9 ± 0. 3 2. 0 ± 0. 4 1. 8 (1. 4, 2. 2) TCRE (K EgC -1 ) 2. 20 ± 0. 63 1. 65 ± 0. 32 1. 77 ± 0. 37 1. 65 (1. 0, 2. 3) β ocean (PgC ppm -1 ) 0. 81 ± 0. 10 0. 87 ± 0. 11 0. 91 ± 0. 09 γ ocean (PgC K -1 ) -12. 9 ± 5. 4 -12. 5 ± 6. 0 -8. 6 ± 2. 9 β land (PgC ppm -1 ) 1. 05 ± 0. 5 1. 26 ± 0. 30 1. 22 ± 0. 40 γ land (PgC K -1 ) -33. 2 ± 26. 6 -25. 3 ± 24. 2 -34. 1 ± 38. 4  tions to historical SLR reported in AR6 do not match that total SLR  (Fox-Kemper et al. , 2021, Table 9. 5) , which likely has some impact on the consistency between our constraints. Finally, although the LIA relaxation contribution is not altered by the calibration, as its central value remains 50 % too high, it is the likely source of the strong correlations found among the parameters of this module because it straightforwardly links the individual SLR contributions together.
, 2021, Table 4. 2) and of carbon pools to  Liddicoat et al. (2021)  (since this was not directly reported in the AR6). Be it on short-, mid-or long-term scales, Pathfinder's projections of GMST are very much in line with the one assessed by the IPCC in the AR6 based on multiple lines of evidence  (Lee et al. , 2021, Table 4. 5). The only significant difference is a smaller uncertainty range in our projections for the longerterm periods. Although this is the result of the efficiency of the Bayesian calibration, one might wonder whether the cli-mate module is over-constrained (or equivalently, too limited in its number of parameters). The ocean carbon storage appears to be overestimated by nario consistent with the 2 • C target), it is underestimated in ssp119 (consistent with the 1. 5 • C target) and increasingly overestimated in higher warming scenarios.