dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-country WMS data We build firm level measures of sales, employment, capital, profits and materials using accounting data extracted from Bureau Van Dijk's ORBIS. These are digitized versions of company accounts covering very large samples (close to the population in most of our countries) of private and publicly listed firms. In our baseline specifications we estimate in three-year (annualized) growth rates. We are able to build firm level measure of sales growth for at least one year for 1,330 out of the 2,351 firms with decentralization data in 2006.
U. S. MOPS data In addition to our decentralization data, we also use data from other Census and non-Census data sets to create our measures of performance (growth in sales, productivity, and profitability). We use establishment level data on sales, value-added and labor inputs from the ASM to create measures of growth and labor productivity. As described in more detail in Appendix A, we also combined the plant-level capital stock data from the Census of Manufactures with investment data from the ASM and applied the perpetual inventory method to construct annual capital stocks. Finally, for profitability we use profits as a percent of capital stock, with profits defined as sales less total salaries and wages, material costs, and rental expenses.
Measuring the Great Recession Our baseline measure of the intensity of impact of the Great Recession ("SHOCK") at an industry by country cell level comes from the UN COMTRADE database of world trade. This is an international database of six-digit product level information on all bilateral imports and exports between any given pairs of countries. We aggregate COMTRADE data from its original six-digit product 7 The full questionnaire is available on http://www. census. gov/mcd/mops/how the data are collected/MP-10002 16NOV10. pdf. 8 The ASM is a stratified randomly sampled rotating 4 year panel, so many plants are not included across panels, which accounts for over 90% of this drop in sample size level to three-digit US SIC-1987 level using the  Pierce and Schott (2010)  concordance. We deflate the industry and country specific export value series by a country and year specific CPI from the OECD to measure "real exports.
Summing this across all partner countries and then aggregating to the three digit industry level gives an IV for the export shock. The results from using this Bartik IV are very similar to those shown in Table  2. 24 Columns (  5 ) and (  6 ) of Table  2  repeat the specifications of columns (  3 ) and (  4 ) using the MOPS sample. Remarkably, although drawn from a distinct dataset, a single country (US) and di↵erent survey methodology, the results in this larger sample of plants are extremely similar to the ones reported using the cross country WMS data. The coe cients on the interaction terms are of the same sign, statistically significant and of a broadly comparable magnitude. The results discussed so far suggest the presence of a positive relationship between firm sales growth and decentralization in the industries most a↵ected by the Great Recession.
Columns (4) to (6) repeat the same specifications, this time using durability as an alternative industry level proxy for the Great Recession. The coe cient on the interaction between decentralization and product churn is positive and significant, and its inclusion reduces the magnitude of the coe cient on the interaction between decentralization and durability, driving it to insignificance. As discussed in the data section, we also used the change in stock market volatility in an industry by country cell as an alternative measure of turbulence. An advantage of this measure is that it is available for the WMS as well as MOPS, but a disadvantage is that it is constructed from firms listed on the stock market (in the same industry). Table  7  shows the results. In column (1) we reproduce the specification in column (2) of Table  2.
Changes in decentralization over time Recall that our identification assumption is that pre-recession decentralization is weakly exogenous and that there are some adjustment costs which mean that after the Great Recession shock firms do not immediately adopt the new optimal (more decentralized) organizational form. A corollary of our theory, however, is that firms will start moving to a more decentralized form (to the extent that they believe the shock is likely to be long-lasting). Hence, we should expect to see some increase in decentralization for firms more exposed to the shock. Table A11 examines this by using the change in decentralization as a dependent variable. This is a demanding specification, especially for WMS where the panel element of decentralization is limited (we have data in 2009 and 2010 for a sub-sample of the 2006 wave).
Conclusion When does decentralizing power from the central headquarters to plant managers increase firm growth? We examine the responses of a panel of 1,300 firms in 10 OECD countries (WMS), and 8,700 plants in the US (MOPS) to the Great Recession which reduced demand across industries and countries in heterogeneous ways. Using pre-recession data on decentralization we find that negative demand shocks hurt firm growth in centralized firms significantly more than in their decentralized counterparts. This is true whether we use industry by country export shocks, or exogenous predictors of these negative shocks like product durability. We formalized a simple model where the CEO considers decentralizing product-related decisions to the plant manager. The increased turbulence that comes with bad times makes the importance of the plant manager's local information more valuable and so means decentralized firms will perform relatively better in unexpected downturns.
Assuming the financial crisis was a common cross country negative shock, we can trace out the implied post shock growth performance depending on each nation's average decentralization. We calculate that greater decentralization in the US could account for about 16% of the U. S. superior GDP growth post 2012-2007 compared to the other OECD countries in our sample. We see our paper as a first attempt to unravel the relationship between growth and the internal organization of firms using micro data with observable measures of decentralization. Many papers have speculated on this issue without a systematic theory linked to rich survey data. There are many directions to take the research. First, we need to look at the ways in which, in the longer-run, firms change their organizational forms. For example, as the e↵ects of the Great Recession recede, how will the growth e↵ects and degree of decentralization change? Second, we would like to go deeper into the relation between the debt structure of companies (and so their bankruptcy risk) and the incentives for firms to change.
A. 2. 1 Firm-level Accounting Databases Our sampling frame was based on the Bureau van Dijk (BVD) ORBIS which is composed of the BVD Amadeus dataset for Europe (France, Germany, Greece, Italy, Poland, Portugal, and the United Kingdom); BVD Icarus for the United States, BVD Oriana for Japan. These databases all provide su cient information on companies to conduct a stratified telephone survey (company name, address, and a size indicator). These databases also typically have accounting information on employment, sales and assets. Apart from size, we did not insist on having accounting information to form the sampling population, however. Amadeus are constructed from a range of sources, primarily the National registries of companies (such as Companies House in the United Kingdom). Icarus is constructed from the Dun & Bradstreet database, which is a private database of over 5 million U.
S. trading locations built up from credit records, business telephone directories, and direct research. Oriana is constructed from the Teikoku Database in Japan. The full WMS consists of 34 countries but because we need decentralization data in 2006 we are restricted to the 12 countries surveyed in the 2006 wave. Because we wanted to focus on mature economies we dropped China and India which left us with 10 OECD countries (France, Great Britain, Germany, Greece, Italy, Japan, Poland, Portugal, Sweden and the US).
A. 2. 2 The Organizational Survey In every country the sampling frame for the organization survey was all firms with a manufacturing primary industry code with between 50 and 5,000 employees on average over the most recent three years of data. Interviewers were each given a randomly selected list of firms from the sampling frame. More details are available in  Bloom, Sadun and Van Reenen (2012)  where we compare the sampling frame with Census demographic data from each country and show that the sample is broadly representative of medium sized manufacturing firms. We also analyzed sample selectionthe response rate was 45% and respondents appear random with respect to company performance, although larger firms where slightly more likely to respond. We collected a detailed set of information on the interview process itself (number and type of prior contacts before obtaining the interviews, duration, local time-of-day, date and day-of-theweek), on the manager (gender, seniority, nationality, company and job tenure, internal and external employment experience, and location), and on the interviewer (we can include individual "analyst" fixed e↵ects, time-of-day, and subjective reliability score).
A. 2. 3 Firm-level variables Our firm accounting data on sales, employment, capital (fixed assets), profits and intermediate inputs came from BVD ORBIS. Whether the variable is reported depends on the accounting standards in di↵erent countries. Sales are deflated by a three digit industry producer price index. BVD has extensive information on ownership structure, so we can use this to identify whether the firm was part of a multinational enterprise. We also asked specific questions on the multinational status of the firm (whether it owned plants aboard and the country where the parent company is headquartered) to be able to distinguish domestic multinationals from foreign multinationals. We collected many other variables through our survey including information on plant size, skills, organization, etc.
as described in the main text. We also collected management practices data in the survey. These were scored following the methodology of  Bloom and Van Reenen (2007) , with practices grouped into four areas: operations (three practices), monitoring (five practices), targets (five practices), and incentives (five practices). The shop-floor operations section focuses on the introduction of lean manufacturing techniques, the documentation of processes improvements, and the rationale behind introductions of improvements. The monitoring section focuses on the tracking of performance of individuals, reviewing performance, and consequence management. The targets section examines the type of targets, the realism of the targets, the transparency of targets, and the range and interconnection of targets.
A. 3. 1 Sample Table  A2  shows how our sample is derived from the universe of U. S. business establishments. The U. S. Census Bureau data on decentralization comes from the 2010 Management and Organizational Practices Survey (MOPS), which was a supplement to the 2010 Annual Survey of Manufactures (ASM). The MOPS survey was sent to all ASM establishments in the ASM mail-out sample. Overall, 49,782 MOPS surveys were successfully delivered, and 37,177 responses were received, yielding a response rate of 78%. The MOPS contains 36 multiple choice questions, split into 3 modules: management practices (16 questions), organization (13 questions), and background characteristics (7 questions). Decentralization measures come from the "Organization" module of the MOPS. Only establishments with headquarters located o↵-site are instructed to answer questions in the organization module.
A. 3. 3 Product Churn Product churn is constructed using data come from the US Census Bureau's Census of Manufactures (CM). The CM asks establishments to list the dollar value of annual shipments by 10-digit product code. Establishments receive a list of all the product codes typically produced by establishments in their industry, along with corresponding descriptions of each code. We start by calculating the total number of 10-digit products by each establishment in a given year, as well as the number of added products and the number of dropped products for each establishment compared to the previous CM 5 years earlier. This of course restricts the sample to manufacturing establishments which were alive five years earlier. We further restrict the sample by dropping establishments producing fewer than 3 products in both Censuses.
Product churn at the establishment level is measured as the number of products added or dropped between the previous Census and the current Census, divided by the average number of products produced in both Censuses. That is, product churn for establishment i in year t is defined as: Product Churn i,t = Products Added i,t + Products Dropped i,t 0. 5 (# Products i,t + # Products i,t 5) Industry product churn in year t is the average establishment-level product churn amongst establishments within an industry (three digit US SIC-1987). To calculate industry-level change in product churn, we simply subtract product churn in 2007 (constructed from the product data in the 2002 and 2007 Censuses) from product churn in 2012.
A. 3. 4 ASM variables Directly from the ASM we obtain material inputs, shipments (deflated by a three digit price deflator) as our sales measure and the headcount of employees for labor. Real capital stocks are constructed using the perpetual inventory method, following the methodology in  Bloom et al. (2014). In particular, we combine detailed data on the book value of assets every 5 years from the CM with annual investment data from the ASM. We first convert CM capital stocks from book to market value using BEA fixed asset tables. We then deflate capital stocks and investment using industry-year price indices from the NBER-CES Manufacturing Industry Database. Finally, we apply the perpetual inventory method, using the formula Kt = (1 t)Kt 1 + It. This procedure is done separately for structures and for equipment.
| ASSUMPTIONS For data simulation, we made the following assumptions. Individuals have recruited into the breeding population and survived to a second breeding occasion: The shortest breeding trajectory includes two occasions. Once recruited, no individual skips breeding, but there is (Bernoulli) variability in breeding success. HPDH can be described by a bivariate normal distribution with possible correlation between individual survival and breeding success propensities. We simulated both survival and breeding trajectories to mimic data commonly used to investigate HPDH (e. g. ,  Cam, Link, Cooch, Monnat, & Danchin, 2002; Cam et al. , 2013). Only simulated data on reproductive life histories were subsequently analyzed, but survival allowed us to take into account that reproductive life span is a random variable.
| Data simulation We simulated life histories under several scenarios corresponding to different values for the set of 4 parameters ( , , repro , cor). We considered data-generating mechanisms with only HPDH ( = 0, ≠ 0, repro ≠ 0), only state dependence ( ≠ 0, = 0, repro = 0), and with none or both (Table  1 ). There were 7 × 4 × 4 × 5 = 560 different combinations of values for ( , , repro , cor). For each combination, 500 random datasets were simulated and analyzed (Appendix S2: Fig. S1-S2 ). In all simulation scenarios, the parameter μ was set to 0, corresponding to an average breeding success probability of 0. 5.
| Model fitting To keep the problem tractable, mean survival was kept constant throughout life in simulations. Furthermore, we only analyzed breeding trajectories: Observed reproductive life span was treated as data. In other words, although the true data-generating mechanism is a joint model of breeding success and survival, only data on breeding success were analyzed with probabilistic models (Table  2 ). Models were fitted with software R v. 3. 2. 3 (R Development Core Team, 2015) using the function glmer from the library lme4  (Bates, Maechler, Bolker, & Walker, 2013 ) on a HP Compaq LA2306x desktop (Intel (R), Xeon (R) CPU E5-2630, 2. 30 GHZ, 32 Go RAM). We specified 5 quadrature points for the adaptive Gauss-Hermite approximation to the log-likelihood for accurate random effect estimation  (Lesaffre & Spiessens, 2001).
For each dataset and fitted model, estimated parameters (̂ ,̂ ,̂ repro ) were stored to assess bias and to compute individual stochasticity and the scaled entropy. Entropy is a measure of randomness in transitions between breeding success and failure  (Tuljapurkar et al. , 2009). The scaled entropy varies between 0 and 1, with 1 corresponding to complete randomness in the succession of states in the trajectory. It has been argued that HPDH should decrease entropy  (Bonnet & Postma, 2016; Jenouvrier et al. , 2015; Tuljapurkar et al. , 2009)  and that entropy could thus be used to infer the presence of HPDH. We used estimated parameters (̂ ,̂ ) to compute the within-individual variance ̂ 2 within for the breeding trajectory 01. Since variances are additive, the total variance is the sum the rightmost terms in Equation  11.
The estimated within-individual variance term was compared to its true value for each simulation scenario to assess bias, that is whether 2 within = ̂ 2 within or not. Finally, the log-likelihood of each simulated dataset under each model (Table  2 ) was recorded to compute the Bayesian information criteria (BIC;  Link & Barker, 2009). BIC weights ̂ BIC were then calculated assuming that random individual effects count for 1 additional (variance) parameter. We used BIC rather than Akaike information criterion (AIC) because the later tends to favor overcomplex models in large data sets (chapter 7 in  Link & Barker, 2009). Results were qualitatively similar with AIC (Appendix S3). For legibility, only results for scenarios where there was no individual-level correlation in HPDH (cor = 0) are presented below.
Tile-plots of the average bias in estimated state dependence ̂ (across 500 simulated datasets) for each simulation scenario. True values of state dependence (e γ on the Odds-Ratio scale) and HPDH (σ repro ) are on the x-and y-axes, respectively. Each panel corresponds to one of the four models used to analyze data. Vertical black lines bracket scenarios in which  HPDH is the true data-generating mechanism. Because  HPDH excludes γ, estimates (̂ ) are by definition exactly 0. Horizontal black lines bracket scenarios in which  NTLH is the true datagenerating mechanism. At the intersection,  nil is the true data-generating mechanism. Everywhere else,  full is the true data-generating mechanism. Estimates from the true data-generating model should have no bias on average. Actual bias values (rounded to the nearest integer) are displayed on each tile.
Tile-plots of the average bias in estimated HPDH ̂ repro (across 500 simulated datasets) for each simulation scenario. True values of state dependence (e γ on the Odds-Ratio scale) and HPDH (σ repro ) are on the x-and y-axes, respectively. Each panel corresponds to one of the four models used to analyze data. Vertical black lines bracket scenarios in which  HPDH is the true data-generating mechanism. Horizontal black lines bracket scenarios in which  NTLH is the true data-generating mechanism. Because  NTLH excludes σ repro , estimates (̂ repro ) are by definition exactly 0. At the intersection,  nil is the true data-generating mechanism. Everywhere else,  full is the true data-generating mechanism. Estimates from the true data-generating model should have no bias on average. Actual bias values (rounded to the nearest integer) are displayed on each tile.
It is paramount to address the potential problem of model misspecification, and the resulting biased estimates, if reliable inferences on life-history evolution are to be drawn from such simulations. Because of estimation biases, the neutral model fits many longitudinal life-history datasets and mirrors the situation observed in community ecology, where neutral models were found to successfully predict species abundance distributions (SAD). It was later shown that predicting SAD did not provide robust support for the Neutral Theory of Biodiversity (Chave, 2008) : Both neutral and non-neutral models can predict the same pattern (Chave, Muller-Landau, & Levin, 2002).
Tile-plots of the average estimated Earth Mover Distance (across 500 simulated datasets) between the observed and predicted distribution of LRS for each simulation scenario. True values of state dependence (e γ on the Odds-Ratio scale) and HPDH (σ repro ) are on the xand y-axes, respectively. Each panel corresponds to one of the four models used to analyze data. Vertical black lines bracket scenarios in which heterogeneity ( HPDH ) is the true data-generating mechanism. Horizontal black lines bracket scenarios in which state dependence ( NTLH ) is the true data-generating mechanism. At the intersection,  nil is the true data-generating mechanism. Everywhere else,  full is the true datagenerating mechanism. The true data-generating model should have the smallest Earth Mover Distance. Actual values (rounded to the nearest integer) are displayed on each tile.
Tile-plots of the mean estimated ̂ BIC (across 500 simulated datasets) for each model and each simulation scenario. True values of state dependence (e γ on the Odds-Ratio scale) and HPDH (σ repro ) are on the x-and y-axes, respectively. Each panel corresponds to one of the four models used to analyze data. Vertical black lines bracket scenarios in which  HPDH is the true data-generating mechanism. Horizontal black lines bracket scenarios in which  NTLH is the true data-generating mechanism. At the intersection,  nil is the true data-generating mechanism. Everywhere else,  full is the true data-generating mechanism. The best predictive model, which would be selected for inference, has the largest weight. Actual values (rounded to one decimal) are displayed on each tile.
ACKNOWLEDGMENTS We are grateful to  Amélie Lescroël ,  Christophe Barbraud ,  Jessica Metcalf ,  Ulrich Steiner , and two anonymous reviewers for constructive comments. MA thanks  Christophe Guinet  and  Grégory Lenglin  for their support during the write-up of this manuscript and  Lubéron  for carrying out simulations. LMA thanks  Stéphanie Jenouvrier  and  Hal Caswell  for stimulating discussions on the role that different sources of individual heterogeneity play in shaping the evolution of life histories. EC was supported by the  "Laboratoire d'Excellence" LabEx TULIP  ( ANR-10-LABX-41 ).
1 Tile-plots of the average estimated Earth Mover Distance (across 500 simulated datasets) between the observed and predicted distribution of LRS for each simulation scenario. True values of state dependence (e γ on the Odds-Ratio scale) and HPDH (σ repro ) are on the xand y-axes, respectively. Each panel corresponds to one of the four models used to analyze data. Vertical black lines bracket scenarios in which heterogeneity ( HPDH ) is the true data-generating mechanism. Horizontal black lines bracket scenarios in which state dependence ( NTLH ) is the true data-generating mechanism. At the intersection,  nil is the true data-generating mechanism. Everywhere else,  full is the true datagenerating mechanism. The true data-generating model should have the smallest Earth Mover Distance. Actual values (rounded to the nearest integer) are displayed on each tile 0.