dataesr/scientific-paragraphs-categorization huggingface dataset:
control over Polish firms on the part of CEE centres in 2013 reveals that CEEC-based owners of Polish firms' capital are mainly from Czechia, Slovakia and Hungary, which are all adjacent to, or located not far from, Poland's borders. In contrast, no direct ownership from Romania or Bulgaria was registered in the database as of 2013. An interesting fact is that foreign CEEC-based investors located most capital (600 million euros) in Polish firms based in Kraków, as opposed to in Warsaw, as might have been expected (Fig. 2). The latter fact reflects the Slovak firm Slovnaft a. s. (headquartered in Bratislava) being the Central and Eastern European holder control over Polish economic management centres in 2013 owner of the Kraków-based Slovnaft-Polska S. A. Slovnaft a. s specialises in the manufacture of refined petroleum products, while the subsidiary in Kraków engages in the retail sale of automotive fuel.
In turn, the Hungarian firm Egis Gyogyszergyar rt. headquartered in Budapest specialises in the manufacture of basic pharmaceutical products and pharmaceutical preparations. As of 2013 it was owner of Egis Polska Dystrybucja Sp. z o. o. , with its seat in Warsaw, and operating in the wholesaling of pharmaceutical goods. The Croatian firm Podravka Prehrambena Industrija d. d. from the food-product sector had a 100% capital stake in Podravka Polska Sp. z o. o. , which is located in Kostrzyn nad Odrą (west-central Poland). Holder CEE firms Polish firms Warsaw Links CEE-PL CEE Cracow Prague Bratislava Budapest Ljubljana Strength indicator in millions of euros* 600 300 100 0 Km 150 *= annual revenues of CEE firms x percentage of foreign ownership of capital/100 Source: ORBIS database, BVD, 2013 Zdanowska, N.
control over Romanian firms on the part of CEE centres in 2013 Warsaw Holder CEE firms Romanian firms Warsaw Links CEE-RO CEE Prague Bratislava Bratislava Budapest Ljubljana Budapest Zagreb Bucharest Bucharest Zagreb 1 000 Strength indicator in millions of euros* Strength indicator in billions of euros Sofia Sofia 500 = annual revenues of CEE firms x percentage of foreign ownership 100 *= annual revenues of CEE firms x of capital/100 50 0 0 Km 100 Km 150 percentage of foreign ownership of capital/100 25 Source: ORBIS database, BVD, 2013 Zdanowska, N. , 2016 0,5 Figure 4. Central and Eastern European holder control over Romanian economic management centres in 2013 Source: ORBIS database, BVD, 2013 Zdanowska, N. , 2016 the largest investor (owning Carrefour Polska now invested (with Volkswagen International Sp.
Allowing the time effects to vary across demographic groups is important, since in the UK TV market advertisers purchase expected "impacts" by time and demographic groupindicating advertising is targeted at specific demographic groups  (Crawford et al. (2012) ). In addition to this rich individual variation we exploit the institutional set up of the UK TV market, which has a number of features that are useful for our identification strategy. Specifically we use data on all potato chip adverts (around 150,000) that aired on TV over a two year period. These data include details on what brand was advertised, the time the advert aired and what channel it was shown on. We combine this with information on the TV shows, channels and times of day that individual consumers report watching TV to construct an individual specific, time varying measure of exposure to the advertising of each brand.
This provides us with a large amount of variation in exposure to advertising across consumers, brands and time. We describe these data and this variation in detail in Section 4. 1. The UK institutional set up means that there is variation in advertising regulations across UK TV channels. There are four large public service broadcasters -the BBC, ITV1, Channel 4 (C4) and Channel 5 (C5) -which face some requirements over the programs that they air. The BBC is funded by an annual television license fee and is not allowed to air adverts. ITV1, C4 and C5 do not receive license fee income and can air adverts, but have some requirements regarding the programs they air. These public broadcasters have relatively large audience shares -BBC1 has viewing figures of around 20%, ITV around 16%, BBC2 and C4 around 7% and C5 around 5%.
A third identification issue is how we identify the distribution of unobserved heterogeneity (which we model as random coefficients). We use data that are at the micro level and that are longitudinal so that we observe each individual making repeated choices. Micro data has been shown to be particularly useful in identifying and estimating substitution patterns (see  Berry and Haile (2010) ,  Berry et al. (2004) ). We specify the distribution of random coefficients conditional on demographic group, assuming a parametric form, and we estimate the parameters that characterize the distribution. There is cross-sectional variation in choice situations (e. g. two consumers will face different advertising states and, if they are observed in different markets, different price vectors). There is also within consumer variation in choice situations across time.
Advertising data We use advertising data collected by AC Nielsen. The data contain aggregate advertising expenditure across all platforms (cinema, internet, billboards, press, radio and TV) and detailed disaggregate information for TV advertising. In the potato chip market, in common with other junk food markets, TV advertising is by far the most important form of advertising. Over 2009-2010 the annual expenditure on TV advertising on the products that we consider was £19. 1m, while annual expenditure on advertising in magazines and newspapers was £2. 3m, on outdoor billboards £1. 9m, in cinema £0. 6m, on radio £0. 5m and on the internet £0. 2m. Given the dominance of TV advertising, and the rich TV advertising data we have access to, we focus on its effect on demand. The common effects of non-TV advertising will be absorbed in the brand-time effects that we include in the model.
Household level advertising exposure We combine the data on household viewing behaviour with the detailed data on individual adverts to create a household specific measure of exposure to advertising. Variation in TV viewing behaviour creates considerable variation in the timing and extent of exposure an individual household has to adverts of a specific brand. As argued in Section 2. 4, this leads to cross household variation in advertising exposure that is plausibly unrelated to idiosyncratic shocks to potato chip products, conditional on all the controls in our demand model. Denote by T bskct the duration of time that an advert for brand b is shown during show s on day and time slot k on channel c during week t. From the viewing data we construct an indicator variable of whether household i was likely to be watching channel c on day and time slot k during show s, w iskc.
Purchase data The purchase data are from the Kantar Worldpanel for the period June 2009 to October 2010. Our data are unusual in that we have information on households' purchases for food at home and individuals' purchases for food on-the-go. For each household we observe all food purchases made and brought into the home (we refer to these as "food at home" purchases). We also have information from a sample of individuals drawn from these households that record all food purchases made for consumption "on-the-go" (we refer to these as "food on-the-go" purchases) during the same period. Food at home purchases are by definition made for future consumption (the product has to be taken back home to be recorded), while food on-the-go purchases are made for immediate consumption. Individuals participating in the on-the-go panel include both adults and children aged 13 or older.
Product definition Purchase data is at the UPC or barcode level, containing information on purchases of over 1800 unique potato chip UPCs. We aggregate these UPCs into 37 products over which we estimate demand. We define a potato chip product as a brand-pack size combination (the products are listed in Table  4. 2); in terms of product definition the main form of aggregation is across different flavours. In the UK potato chip market, price and advertising does not vary across flavours and variation in nutrients across flavour within brand is minimal (and far out weighed by variation across brands). For instance, the brand Pringles has 78 separate UPCs. Of these, four UPCs (original flavour, salt and vinegar, sour cream and onion, and barbecue) account for over 55% of Pringles' transactions.
Prices Our data contain prices for each transaction, a transaction is the purchase of an individual UPC (or barcode). These transaction level prices are well measured in our data. As explained in Section 4. 2. 1, we aggregate UPCs into 37 products. In estimation we use the price of each product measured in pounds sterling (£s), the average of these prices across weeks are shown in Table  4. 2. We measure these product prices as the mean across transactions for the UPCs that comprise the product in that week; these transaction prices can vary within a week due to within week price changes and some differences in pricing across stores. As outlined in Section 2. 4, we follow an identification strategy suggested by  Bajari and Benkard (2005) ; we include brand-time effects in the model and identify the effect of price on demand by exploiting differential time series variation in product level prices within brand across different pack sizes (i.
The authors gratefully acknowledge financial support from the  European Research Council (ERC)  under  ERC-2009-AdG-249529  and  ERC-2015-AdG-694822 , from the  Economic and Social Research Council (ESRC)  under the Centre for the Microeconomic Analysis of Public Policy (CPP), grant number  RES-544-28-0001  and under the  Open Research Area (ORA)  grant number  ES/I012222/1  and from  ANR  under  Open Research Area (ORA)  grant number  ANR-10-ORAR-009-01.
We ran 3000 iterations of the algorithm with p = 50 particles, leading to an acceptance ratio of approximately 60% for the particle moves and the both datasets. All random-walk Metropolis-Hastings steps are Gaussians, with amplitudes chosen to achieve approximately 25% acceptance rates. All the statistics were computed using only the 2000 last samples provided by the algorithm. Figures  2  and 4  show the marginals of the posterior mean estimates of Wigner distributions for our two examples. We represented the true marginals in dashed lines, as well as the posterior credible bands provided by the algorithm, which we computed by retaining the 95% samples with the smaller sup-norm distance from the posterior mean estimator of the marginals. Compared to other classical methods in this area, our estimate is non linear, preventing easy computations.
Although we do not know the coverage by country, we know that the average market coverage of the sample in the EU is 87%. The sample is representative both for the smaller independent sellers and for the large chain-stores. 5 GfK uses a "point of sales tracking" technology, which reports which products are sold, when, where and for how much, both at online and offline outlets on a monthly (or sometimes weekly) basis. The data was collected directly from the electronic point of sales systems from retailers and resellers. Sales were tracked at the individual For portable computers we further reduced the number of observations by aggregating sales for similar products. There is a large number of portable computers offered by the same manufacturer with slight differences in model names, which we aggregate up to the series name.
To simplify the estimation of the BLP model, we limit the number of months to the second, fifth, eight and eleventh month of each year. The estimation results are not influenced by this stock keeping unit level and coded with a full set of features using a cohesive international methodology to allow for accurate comparison both within and across European markets. Any brand or model which was found to be sold in the covered countries is tracked, unless the brand is exclusive, in which case it is still audited but with a label which hides its exact origin. Sales volumes and turnover per item were gathered at the same time as the model specification information. The price of the item was calculated as turnover divided by units sold. choice. The final data set thus consists of 17,645 observations for portable computers, where the observation is a product (series name) sold online or offline in a particular country, during 13 different months.
Among these the most relevant are transport costs for the physical delivery of goods, regulatory barriers to cross-border trade and persistent language barriers to cross-border online shopping, which hamper the full geographical integration of online markets in the EU. In this paper we estimate a differentiated products demand model to analyze some of the effects related to the introduction of e-commerce. We use a rich dataset on prices, quantities and characteristics of three different consumer electronics products: digital cameras, portable media players and portable computers in several European countries. In this setting, we ask three questions. First, we are interested in determining whether the introduction of e-commerce increases total sales or, on the contrary, only crowds out sales from traditional channels.
The literature has historically faced an empirical challenge in identifying these effects since regulatory capital requirements were set at 8% for all banks from the 1990s. To deal with this homogeneity, economists have used regulatory actions where supervisors force banks to change their leverage ratios  (Peek and Rosengren, 1995; Aiyar, Calomiris and Wiedalek, 2012)  but such evidence may be hard to interpret since targeted banks are not randomly chosen. In this paper, we add to this literature by exploiting the intrinsic 3 heterogeneity of the Basel II regulatory framework, which has been in place since the mid-2000s, and make capital requirements vary across both banks and firms. We exploit a large loan-level dataset which provides us with information on loan and borrower characteristics and in particular, capital requirements.
Data In this study, we use three different datasets. The period under review is 2008-2011. The first sample is a panel of "active" bank-firm linkages, corresponding to banks lending a positive amount to firms. It is drawn from a large loan-level sample gathered by the French bank supervisor (the Autorité de Contrôle Prudentiel et de Résolution). This sample allows us to study the effect of capital requirements on the "intensive margin" of lending (i. e. the decision of how much to lend conditional on lending at all). The second sample is a panel of 7 To fix ideas, one can assume that Σ = 𝜎 2. 𝐼 to simplify the formula. In this case: 𝑥 = 𝑥 𝑀 -𝑤. 𝑥 𝑀 ′𝑤 -𝐸 𝑤′𝑤 This formula shows that it is the collinearity between 𝑥 𝑀 and 𝑤 that makes the constraint more binding. active and inactive linkages, i.
3. a. i. The loan-level survey Our starting point is a large sample of loans extended by French banks to French firms over the 2008-2011 period. This dataset is built using the results of a confidential survey, conducted in 2012 by the French prudential authority. This survey required the six largest banking groups in France to provide detailed information on all their corporate loan exposures along with firm and bank identifiers that make it possible to match bank loans and firm-level data. This survey collects information on capital requirements of each loan. Its coverage is also very wide, as the six banking groups surveyed account for about 80% of all corporate loans in the country. The sampling design of this survey is the following: using an ancillary dataset (Centrale des Risques), the French prudential authority drew up the list of all firms to which at least one bank subsidiary has an exposure of at least EUR 25,000 over the 2008-2011 period.
This list was then sent to all six banking groups, which were asked to report all of their exposures to each of these firms over the entire 2008-2011 period. This leads to slightly more than three millions exposures of bank subsidiaries to firms. In the rest of this paper, we will refer to bank subsidiaries as "banks". We keep four variables from this loan survey. The first one is loan size. We construct it using the exposure at default (EAD), which is a regulatory quantity equal to the amount outstanding for most non-defaulted loans. Credit lines are treated differently: their EAD is equal to the size of the credit line, multiplied by a regulatory ratio (known as the credit conversion factor). EAD for undrawn lines is thus smaller than the commitment. There are 2,854,608 loans reported in the database among which 27% have an on-balance sheet and an off-balance sheet EAD, 72% have just an on-balance sheet EAD and 18% have just an offbalance sheet EAD.
But, in aggregate, our restriction to corporate loans is not too restrictive. According to COREP data (the French equivalent of the US Call Reports), the corporate portfolio accounts for about 70% of the total credit exposure of the French banks to the non-financial corporate sector. In our data (which are skewed towards larger, hence more corporate, loans), the corporate portfolio represents only 45% of the loans, but as much as 89% of the aggregate volume of exposures. Secondly, we also exclude from the sample a few banks (subsidiaries) that do not consistently report information over time. We end up with 83 different "banks", each one of them being a subsidiary of one of the six large groups on which our loan-level data is constructed. Finally, we merge this dataset with firm-level accounting and credit rating information available from the Banque de France (Centrale des Bilans).
Such information is updated annually. Accounting information comes from the tax forms that firms have to fill in and provides us with detailed data on firms' balance sheet and income statement. Credit ratings are awarded by a specific department of the Banque de France. After these manipulations, we end up with 414,895 observations. This panel is unbalanced as each bank may not be actively lending to all of its clients every year over the period under review. Table  1 , Panel A, provides summary statistics for the EAD and CR. The mean EAD is EUR 1. 95 million and the mean capital requirement is 7%. This sample only contains bankfirm-year linkages that are "active" in the sense that the firm borrows a positive amount from the bank. Hence, this sample only allows us to investigate the impact on the "intensive margin" of lending, i.
Newly created bank-firm-year linkages correspond to "potential" bankfirm relationships where firms are not actually borrowing from banks, so we set the loan amount to zero. More importantly, we do not observe the capital requirements when the bank does not actually lend to the firm, so we need to impute them. We present our imputation approach in the following section. Overall, the implicit assumption that we make in constructing this "extensive margin" sample is that a firm can only borrow from banks that have been observed to lend at least once over the 2008-2011 period. Finally, we remove the same firm-bank-year observations as in the intensive margin sample: all "retail" loans and loans extended by a few banks that were not consistently reporting loans over time. We then merge the data with accounting and rating data from the Banque de France.
3. c. Firm-level sample Last, we collapse the "extensive margin" dataset previously constructed into a firm-level dataset. The objective is to measure the impact of heightened capital requirements on firmlevel decisions and outcomes such as capital structure and investment. We start from the exposure-level dataset described in Section 2. a. For all observations corresponding to the same firm, we then take the average of all variables, with the exception of the exposure at default that we sum across all banks. Firm-level accounting variables are not affected by this procedure, as they are by definition the same across all firm-bank linkages corresponding to the same firm-year. Note that we keep the information related to the lender before collapsing. We create a dummy that takes the value of one when a firm borrows from a given bank (there are therefore 83 such dummies).
4. b. Firm-level Equation After the bank-firm level analysis, we turn to corporate outcomes. The objective is to understand to what extent the effect of capital requirements on lending can affect the firms' decisions, especially the investment decisions. For this purpose, we use the firm level dataset described in the previous section. We then regress: 𝑌 𝑓,𝑡 = 𝛽 • 𝐾 𝑓,𝑡 + 𝛿 𝑏,𝑡 + 𝛾. 𝑋 𝑓,𝑡 + 𝜀 𝑏,𝑡 (6) where 𝑌 𝑓,𝑡 can be any corporate outcome (total liabilities, turnover, fixed assets) and 𝐾 𝑓,𝑡 is the average capital requirement across all the lenders of a given firm. 𝑋 𝑓,𝑡 are firm-level controls such as rating and size dummies. Rating dummies are critical because capital requirements are imputed using actual and potential lenders' identities and firm ratings. Their inclusion ensures that 𝛽 is identified because different firms face different sets of actual and potential lenders who face different capital requirements.
The findings were then projected onto the entire portfolio. Publicly available data support the idea that internal models were broadly correct for all six banking groups in our data (see  ECB, 2014, p. 150). At banking group level, this audit led to a downward revision of equity ratios between 10 and 30 basis points, to be compared to equity ratios in the vicinity of 10%. The downward bias in CRs was thus present, but of an order of magnitude of 1 to 3%. However, these numbers are aggregated at banking group level and may thus conceal a large degree of heterogeneity in asset composition and across portfolios. We have access to the PD and the LGD at portfolio level for the six banking groups in our dataset, both as stated by banks and as estimated by the AQR. We plug these two parameters into the Basel II formula to obtain a CR for each loan portfolio from the two viewpoints, that of the bank and that of the independent auditor.
We first check that there is enough variability in the data to identify the effect that we are interested in, by reproducing the analysis of Table  2  in Table  A. 2. In Table  A. 2, we show that, even in the standard approach, banks assign significantly different capital requirements to firms that are in the same Banque de France rating category. Clearly, the heterogeneity across banks is much smaller since the mapping from Banque de France ratings to capital requirements is exogenously imposed by the supervisor. However, as illustrated by F-tests ran on bank dummies, differences in CRs remain (see Table  A. 2) even within Banque de France rating categories. For all banks following the standard approach, the mapping from borrower ratings to capital requirements is the same, but borrower ratings may be different.
The second sample includes both intensive and extensive margins of lending, i. e. the propensity to lend at all and the amount lent conditional on lending. The other difference between the two datasets is that the balanced panel has only imputed CRs. The use of imputed capital requirements in the balanced panel still leaves us with a lot of identifying power. As we noted in Section 3. b. , imputed CRs have much less variability than true ones. But the model remains identified, even in the presence of firm-year fixed effects.