dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and Descriptive Statistics The present paper draws upon two similar firm-level databases, the Enquête Annuelle d'Entreprise (EAE) and Micro. 3, respectively for France and Italy. 7 A unique feature of the two databases is that they report, besides standard accounting information, the value of the acquisition of tangible and intangible assets in each year. The EAE databank is collected by the statistical department of the French Ministry of Industry (SESSI) and provided by the French Statistical Office (INSEE). The EAE contains longitudinal data on a virtually exhaustive panel of French manufacturing firms located on the national territory with 20 employees or more, over the period  1989-2007. Micro. 3 is based on the census of Italian firms yearly conducted by the Italian Statistical Office (ISTAT) for the period 1989-2006.
It contains information on firms above 20 employees. In both samples firms are classified according to their sector of principal activity. Our study focuses on the manufacturing industry i. e. sector 171 to 361 in the NACE rev 1. 1 classification. Both the Micro. 3 and the French database are quite representative of the two economies, and, more in particular, they account for a large share of their respective manufacturing industries: 40% in terms of employees and around 60% when considering value added, see  Grazzi et al. (2009). Furthermore, both databases underwent a change in the data collection process over the years. The criteria for inclusion have changed over time as well as the definition of variables. The latter change in particular reflects the necessary assimilation of the European Union's directives and regulations.
8 More precisely, some variables such as the capital stock were not available for the whole French sample before 1996. 9 Instead in the Italian database after 1997 the census of the whole population of firms only concerns companies with more than 100 employees, while in the range of employment 20-99, ISTAT directly monitors a "rotating sample" which varies every five years. In order to increase the coverage of firms in that range Micro. 3 resorts, from 1998 onward, to data from the financial statement that limited liability firms have to disclose, in accordance with Italian law. 10 However, standard data from company accounts do not include observed investment, hence this work does not benefit from the increased number of observations that is available from this other source. As a result, and in view of having a more homogeneous cross-country database we drop the first years from respectively Micro.
3 and EAE and focus on the period  1996 -2006 (2007 for France) for France). Further, given our interest in tracking the performance of firms over time, we consider only firms reporting data for more than three years. 11  For what concerns the definition of the variables employed in the empirical analysis, the investment rate (Inv. rate) is the ratio of investment (flow variable) over tangible fixed assets (stock variable), in particular we consider investment in year t over the stock of tangible assets at the end of the previous year, (I t /K t-1 ). As a proxy for firm level investment we employ the acquisition of tangible fixed assets, as reported by the firm. 12 Due to small differences in variable definitions, the Italian database reports the value of acquisitions of machineries and equipment only, whether for France it also includes land and property.
Note, however, that over the period 1989-1995 the investment variable for the French dataset is broken down into its main components and in those years the share of land and property 7 Both databanks have been made available to the authors under the mandatory condition of censorship of any individual information. Micro. 3 database has been developed through a collaboration between the Italian Statistical Office (ISTAT) and members of the Laboratory of Economics and Management of Scuola Superiore Sant'Anna, Pisa. More detailed information on development of the database Micro. 3 are in  Grazzi et al. (2009). 8 Over the last thirty years the process of harmonization of accounting standards has brought about relevant changes to the national legislation: 78/660/EEC on the annual accounts of certain types of companies, 83/349/EEC on consolidated accounts, 86/635/EEC on the annual accounts and consolidated accounts of banks and other financial institutions and 91/674/EEC.
Indeed, the variable is reported as the yearly cost of rents in the French database whereas in the Italian one it is the total indebtment in the year of the investment. over total investment is in between 14% and 18%. Further, the two investment variables for Italy and France, although bearing such slight differences in the definition, display comparable statistical properties and trend over time as shown in Figure  1  below. In addition to the value of investment and capital stock, the French dataset also includes information on the number of plants making up the firm. We employ this variable to enrich the empirical analyses as the setting up of a new plant allows to identify episodes of investment that are associated with expansion and capacity building rather than replacement investment.
In what follows we will refer to this spike measure as the linear rule. Figures  4  and 5  show the dependency of investment rate on firm size for the first and second moments, respectively. Smaller firms, in terms of capital stock, on average tend to display higher investment rates (Figure  4 ) and also exhibit higher variability (Figure  5 ) than bigger firms. The negative relation of firm size and investment rates hints at a violation of Gibrat's law when one is considering capital as the proxy for firm size. 20 A higher variability of investment rates for smaller firms is much coherent with the literature that finds analogous evidence for the relationship between firm size, in terms of sales or number of employees, and growth rates  (Stanley et al. , 1996). As it is apparent from Figure  4  there exists a non-linear relationship between the capital stock and investment rates both in the French and Italian databases.
According to this criterion the kernel rule is the best measure. In France (Italy) 13. 45% (13. 14%) of observations are classified as spikes and they account for 34. 67% (41. 50%) percent of total investment. The main reason for introducing the kernel rule was to appropriately control for the non-linear relation that exists between size and investment rates, as shown by Figure  4. As a final check, Table  4  compares how, and to what extent, the spike measures proposed in the literature and the Note: "Small" lnK < 6, "Medium" 6 ≤ lnK < 9 and "Large" lnK ≥ 9. kernel rule suffer such dependency on size. The table reports how the observations identified as spikes according to the four definitions are distributed among three size classes, and how this compares to the distribution of the whole dataset.
Results are not shown here but they confirm such proposition. 25 This is due to comparability issue, indeed both the French and Italian databases provide with the same set of variables to compute RoS, while the cash flow measure is computed differently in the two countries. However, in both samples, the cash flow and RoS variables are much correlated as suggested by a Spearman's rho coefficient around 0. 9. same specifications on the four macro industrial sectors according to the Pavitt taxonomy. Finally, we evaluate the accuracy of the different specifications by means of the Brier Score  (Brier, 1950)  which measures the average squared deviation between the predicted probabilities to observe a spike given the estimated coefficients and the actual data. 26 Thus a lower score provides evidence of a better performance of the model.
To date, the only evidence on this relation reports an increase in investment for firms preparing to export  (López, 2009)  however nothing was said about different patterns of investment for firms already having the status of exporters. Regression results also suggest that having had an investment spike in the previous years increases the probability of having a spike in year t. These patterns, which hold both for France and Italy, suggest that large investment projects are likely to span over more than one fiscal year. Such "multiyear spikes" are quite frequent in the data, representing nearly 44% and 33% of spikes in the French and Italian datasets, respectively. As a robustness check we have also run the same regressions without the multi-year spike observations. Results, which are not shown here, do not change in a relevant manner.
Also notice that in both countries the effect of past spikes is always positive and significant, however its magnitude decreases over time: having had a spike three years ago is around one third as important in explaining today's spike as compared to having had a spike in the past year. Overall, regression results are much coherent for France and Italy, conferring a wider breadth to our findings. As mentioned earlier, for the French database the number of plants per firms is also available, and we use it as an alternative proxy for the size of the firm in columns (iii) and (iv) of Table  5. 28  This second model specification shows that results are robust to the inclusion of this alternative size proxy. The only noticeable difference concerns the effect of firms' export status that becomes positive and significant when considering the number of plants as a proxy for firm size.
Still, if productivity growth had no significant impact in the French database as a whole, a negative relation emerges for the science-based sector, Table  12. Besides, in this particular French sector, the probability to invest is strongly associated with a past increase in sales (with a coefficient that is much higher than for other sectors), but not with an increase in the number of employees. Finally, although the export dummy turns out not to be significant in explaining investment spikes, the sectoral analysis uncovers some differences in the French dataset as the export dummy is significant and positive in the scale-intensive sector, but significant and negative in the supplier-dominated sector. To sum up, the analysis of the determinants of investment spike suggests a high degree of similarity for the two countries.
Notice however that we define expansionary episodes while Licandro et al. (  2004 ) identified expansionary firms. As previously mentioned, the number of plants is available only in the French dataset. A thorough assessment of the link between productivity growth and investment spikes, requires to study the dynamics of the interrelation between the adjustment episodes and other firm level variables over time. In order to properly account for such dynamics, it is possible to rely on a methodology proposed by  Sakellaris (2004)  using US data and employed (with some modifications) also by  Nilsen et al. (2009)  on Norwegian data and  Asphjell et al. (2010)  on Dutch data. Building on such approach, we investigate the impact of investment spikes on seven performance variables. They include total sales 29  (Sales), the number of employees (Empl) and labor productivity (Prod).
We also consider a further specification of the model that enables to identify the effects of strictly expansionary events from non-expansionary ones. Using the number of plants, available in the French database, we construct a dummy, DP lant i,t , which takes value 1 if the firm has increased its number of plants between t -1 and t. 30 Those capture expansionary episodes, and as such they allow to study the effect of setting up a new plant on firm performance. We thus estimate the following model 31 X i,t = βD i,t + λDP lant i,t + γ 1 DBef ore i,t + γ 2 DLeast i + v i + ǫ i,t (3) Results are presented in Tables  6 to 9. For each table we report regression results for both specifications presented above: the first set of results refers to the estimation of equation 2 and shows the effect of past investment spikes on firm performance in France (column i) and in Italy (column iii); the second set of results (equation 3) additionally investigates the effect of reporting an increase in the number of plants on French firms' performance (column ii).
Trade data sources The data used in this study come from Chinese Customs and provide export flows aggregated by province, year, product, and destination country over the 1997-2007 period. 1  We reaggregate the original eight-digit-level data into HS4-level data (more than 1,200 product lines). An interesting feature of this dataset is that it allows us to identify whether the export flows emanate from domestic or foreign firms 2  and whether they correspond to processing trade or to ordinary trade. 3 The processing trade includes all trade flows by firms operating in the assembly sector; that is, firms that import and process inputs in China and then re-export the final products abroad. Firms engaged in this type of activity might be less embedded in their local environment and might consequently generate fewer (and possibly benefit less from) externalities.
Explained variable: creation of new export linkages The creation of a new export transaction is measured by a dummy that takes the value 1 if the domestic firms in province i begin exporting product k to country j at time t + 1 and 0 otherwise. A specific database is constructed that incorporates the set of alternatives faced by each province. For a given province, these alternatives are defined as the product-country pairs for which at least one export start is observed over the 1997-2007 period. For these province-product-country triads, the dataset is originally a balanced panel from 1997 to 2007 covering 211 countries and 1213 HS4 products. The dataset includes 1,050,516 observations each year, resulting in a total of 11,551,716 (province/product/country/year) observations over the 1997-2007 period.
Approximately 11% of the observations from the entire database correspond to domestic starts, that is, to provinces where domestic firms do not export product k to country j at time t but do export k to j at time t + 1. These domestic starts are the trade flows to be explained. As in  Koenig et al. (2010) , ceasing and continuing export flows are not included in the study. Given the time span, for a given province-product-country triad, several starts might be observed. For example, the subsequent export statuses 00011001111 become. 001. 01. in our sample, with 1 denoting positive exports, 0 denoting no exports, and. denoting a missing value. By definition, all of the observations are missing for 1997, the first year in the sample, because the export statuses in 1996 are not observed. Continuing export flows (a 1 preceded by another 1) and ceasing export flows (a 0 preceded by a 1) are also coded as a dot because they are excluded from the analysis.
First, following the gravity literature, the demand-side determinants of new export linkages are controlled for by the destination country's import value, defined at the four-digit product level taken from the BACI world trade dataset 5 and the GDP per capita of the importing country. 6 Second, supplyside determinants are taken into account by introducing proxies for provincial and Chinese comparative advantages and export intensity. In the absence of firm-level data, these controls are crucial to account for the time-varying ability of different provinces to export different products to different countries. Hence, the log of the province total export sales, the provinceproduct export sales, and the China-product export sales in year t are introduced. Because the regression also includes year fixed effects, which account for the evolution of total Chinese exports, controlling for these variables amounts to introducing the elements of a Balassa index of "revealed comparative advantage" at the province-product level.
Descriptive statistics Province-product-country triads for which at least one export start is observed over the period are used in the estimation. For these province-product-country triads, the observations originally constitute a balanced panel from 1997 to 2007, covering 220 countries and 1213 HS4 products. As reported in Table  1 , the data include 1,050,516 observations each year, resulting in a total of 11,551,716 (province/product/country/year) observations over the 1997-2007 period. Approximately 32% of the observations correspond to strictly positive export flows from domestic firms. As emphasized in Table  2 , 1,268,768 observations of the 11,551,716 observations of the entire database correspond to domestic starts, that is, to provinces where domestic firms do not export product k to country j at time t but do export at time t + 1.
We do not have firm-level data, but we believe that province/firm-type/trade-type/product/destination country data are suitable for the investigation of micro-phenomena such as export spillovers. The information that we have is very detailed. Feenstra and Hanson (2005)  argue, for example, that their city/firm-type/tradetype/product/destination country dataset approaches the precision of a firm-level dataset. Moreover, with firm-level data, we would have information on the overall size or productivity of the firm, but we would lack information on the firm/product-specific ability. Finally, we have over four million observations for our regressions. For the analysis of the determinants of entry on export markets, firm-product-destination country data would hardly be.
Measurement of civic capital As mentioned earlier, we measure the stock of civic capital in a given province by average electoral turnout in referenda held between 1946 and 1987, the number of blood donations (per 1000 inhabitants), and the number of volunteers in non profit organizations (standardized again by 100,000 inhabitants). The choice of the proxies for civic capital is governed by the following reasoning. First, all activities are associated with a personal cost which often exceeds the mere opportunity cost of time devoted to these activities. 16 Second,  13  Remember that information about ownership is provided for the last year of each wave. Let us consider an example to clarify the data structure. If a firm is classified as foreign owned in 2000, but was not so in 1997, we call this a start event.
Results reveal that they are not driven by this province. 38 The straight way to tackle this issue would be to add to the growth equations (  3 ) or (4) a firm-specific term ν i , and then to proceed with standard panel data methods. This amounts to adding a term equal to ν i t in equation (  2 ), getting what is sometimes called a random trend model. We abstain to follow this route provided the low number of firms with two observations in terms of growth rates (these are the firms which are surveyed in all three waves). 39 The results in terms of the ∆F O variable do not show any significant difference. 40 Similarly to provincial population, we average gross domestic product for each province over the period 1995-2003. Finally, in columns (5) we measure firm performance by labor productivity, defined as value added per worker.
We analyze TFP changes and ownership changes that occur over a period of three years only (the lapse of time between two consecutive waves). We cannot exclude that the productivity implications of foreign acquisitions may be different when affiliates are observed over a longer period of time. Another important issue that deserves more attention in the future is to understand whether the nationality of the investor plays a role, in conjunction with civic capital, in shaping the performance of foreign affiliates. Results such as those in Benfratello & Sembenelli (2006)   highlights that the nationality of the investor can make a difference. Unfortunately, the UniCredit data set does not allow to retrive the nationality of the foreign investor. This would be an interesting piece of information, since one could investigate if and how the cultural distance between the investing country and the recipient country interact with civic capital to influence the performance of foreign firms.
Measures of civic capital Blood donations: The number of blood donations per 1000 inhabitants, disaggregated by province. The data are collected from the health authorities of Italian regions. In each region, regional health authorities collect data on blood donations and subsequently send this information to the Superior Institute of Health (Istituto Superiore di Sanità) which, in turn, maintains a National and Regional Registry of Blood and Plasma. Provincial data on blood donations are not available for Apulia and Lazio. For the provinces of these two regions we take the total regional value. Data refer to the year 2002 and the source is Cartocci  (2007) on data from the Superior Institute of Health. Volunteers: It is the number of volunteers in non-profit organizations per 100,000 inhabitants.
The two aims of the paper are pursued with the estimation of a two-step Generalized Methods of Moments dynamic regression  [18]  and  [19]  and a Panel Threshold Regression Model  [20]  respectively. Both empirical approaches use bank-level balance sheet information contained in the quarterly Consolidated Report of Condition and Income (Call report) collected from the Federal Deposit and Insurance Corporation (FDIC) database (https://www2. fdic. gov/call_tfr_rpts/). The results show that net interoffice accounts increase when global banks reduce their borrowings from both the private sector (via sight deposits) and repo markets. This effect is more pronounced during the period 2007-2010 and for the largest 15 global banks. As expected, the effect of the federal funds rate on net interoffice accounts ceases to be significant during the crisis.
Acknowledgments I would like to thank three anonymous referees as well as  Thomas Vieten  for discussing this paper at the  World Finance Conference  ( Rio de Janeiro ,  2012 ) and the participants at the  International IFABS 2012 Conference (Valencia)  and at the 2011  Fourth International DMM (University of Montpellier ). Also, I am extremely grateful to  Yannis Kalantzis  for his discussion at the internal seminar at the  Banque de France  and to  Loriana Pelizzon  for the very useful remarks.
Background: This study compares annual ambulatory care expenditures per patient with type 2 diabetes mellitus (T2DM) in France according to treatment phase and renal function status. Methods: Records from patients with T2DM were extracted from a health insurance database. Patients were classified in subgroups, by treatment phase: oral/GLP1 monotherapy, double therapy, triple therapy or insulin therapy, and according to renal function status (identified using pharmacy, lab and consultation claims). Annual ambulatory expenditures were estimated from the national insurance perspective by year (from 2005 to 2010) and subgroup. Results: The number of patients ranged from 9,682 to 11,772 between 2005 and 2010. The average annual expenditure per individual in 2010 ranged from €3,017 (standard deviation: €3,829) for monotherapy to €3,609 ± €3,801 for triple therapy, and €7,398 ± €5,487 with insulin (adjusted ratio insulin therapy/monotherapy: 2.
Study design and data source We performed a retrospective cross-sectional analysis using data from the 'Echantillon Generaliste de Beneficiaires' (EGB) database. The EGB database contains records of reimbursements made by the national health insurance for ambulatory care, for a sample of 530,000 subjects, representative of the general population affiliated with the French health insurance system. The sample is obtained by 1/97th random sampling with control for distribution of age and gender. This anonymized database includes patient demographics, health plan enrolment information, and reimbursed amounts for outpatient procedures, laboratory tests, physician visits and outpatient drug dispensing claims, along with specialty of the prescriber. It contains neither direct clinical information nor results of the lab tests.