dataesr/scientific-paragraphs-categorization huggingface dataset:
21 Neighbourhoods were defined through discussion with local community groups, and a 250 m buffer was placed around the edge of the community to minimise edge effect issues. The data collection method was piloted in each setting through surveying smaller areas close to where data collection would take place. The tool was tested on each outlet types (number of occasions for each depending on prevalence in an area) and further amendments were made to the tool. Data collection occurred between September and December 2017. Trained researchers recorded any food outlet or advert observed within each study site. We recorded the outlet type using a classification we developed during a project partners workshop (a full list of outlets including descriptions can be found in online supplementary appendix B).
statistical analysis Descriptive statistics were used to explore the characteristics of our samples. Latent class analysis was used to identify groups within our data to characterise the food environment. Latent class analysis is useful for identifying subgroups within categorical data that are otherwise unknown. 32 An exploratory approach was used in the absence of an a priori understanding of what groups to expect. We included the outlet type, items being sold, advert type and items being advertised in the analytical models; although this represents a large number of variables, we had no prior justification for dropping variables in this exploratory analysis. The number of latent classes were evaluated through assessing model fit. Analyses were completed using R statistical software (V. 3.
Funding This work was supported by two funders. The 'Dietary Transitions in Ghana' project was funded by a grant from the  Drivers of Food Choice (DFC)   Competitive Grants Programme  [grant number  OPP1110043 ] which is funded by the  Bill and Melinda Gates Foundation  and the  Department for International Development (DFID) , and managed by the  University of South Carolina Arnold School of Public Health, USA. DFC supports new research on understanding food choice among the poor in low/middle-income countries, strengthening country-level leadership in nutrition and fostering a global community of food-choice researchers. The  TACLED  project was funded by a  Global Challenges Research Fund (GCRF) Foundation  Award led by the  MRC  [grant number  MR/P025153/1 ], and supported by  AHRC ,  BBSRC ,  ESRC  and  NERC , with the aim of improving the health and prosperity of low/middle-income countries.
Data availability statement All data are openly available. Data for Jamestown and Ho Dome (Accra and Ho respectively, Ghana) can be accessed via https:// doi. org/ 10. 23708/ QYHL8C https:// doi. org/ 10. 23708/ QYHL8C. Data for Makadara (Nairobi, Kenya) can be accessed via https:// doi. org/ 10. 23708/ G76QVS. All analytical code can be viewed at https:// github. com/ markagreen/ Food_ environment_ ghana_ kenya.
As a consequence, availability has been overlooked in most survey-based and observational studies of organic consumption. In this paper, we use a large scanner dataset of food purchases in France, provided by Kantar®, which provides detailed records (quantities, price, type of retail store, etc. ) of purchases of 237 food products by an unbalanced panel of 12,453 French households between 2011 and 2016, along with socio-demographic information about households (age, income, département of residence, household size, etc. ). This dataset enables to decompose household food purchases at a fine level of disaggregation in terms of products. Importantly, it informs about the labels (organic, PDO, PGI, label rouge), if any, attached to each purchased product. In order to identify regularity, we use two complementary approaches.
Distribution of quality-food consumption We first investigate regularity by analyzing the distribution of the quality consumption rate of a given product, i. e. the ratio of purchases of the quality version of the product over the total number of purchases during the time span the household remains observed in our dataset. Individuals who almost never buy the quality version of the product are characterized by a quality consumption rate close to 0. They are said to be 'conventional regulars'; those who mostly purchase the quality version (i. e. , quality consumption rate close to 1) are said to be 'quality regulars'. The first part of our investigation is organized around the three following hypotheses:  For a given product, consumers are regulars: they either purchase mostly its conventional version or its quality version, but not a mix of both (H1);  Quality regular consumption is consistent across the food basket: a consumer who is a quality regular for a product is a quality regular for an important part of his food basket (H2);  The total turnover of quality products purchased by regulars is substantial (H3).
Data This paper uses French data from Kantar WorldPanel which contains food-at-home purchases of French households. For the analysis, we use the number of organic, quality and conventional products purchased, filtering out households who bought less than three times the considered products. To define the product families (level 1, 23 families), the classification from (Boizot-Szantai et al. , 2017) is reused, with more detailed groups for fruits and vegetables. We also created a more in-depth classification (level 2, with 237 categories/products), which differentiate each fruit and vegetable (carrots, potatoes…) and types of meat (pork, beef…). In this panel data, we also have socio-economic information on 12,453 yearly active households, surveyed one or several years from 2011 to 2016 (5 years on average) whose descriptive statistics are presented below (Table  1 ).
Acknowledgments We are thankful to  Stéphan Marette ,  Céline Bonnet ,  Christine Boizot-Szantai ,  Burkhard Schaer ,  Sophie Legras ,  Georges Giraud  and  Louis-Georges Soler  for their fruitful discussions and comments on the manuscripts. We are also grateful to ALISS and especially  Nicolas Guinet  for facilitating the access to the panel data and providing support to its analysis.
Data collection and study sample To acquire a comprehensive dataset to test our hypotheses, we select the high technology industry in China as the context of this research. We focus on information and communications technology (ICT) firms that provide IT and telecom hardware, software and services for several reasons. Firstly, because ICT products often have short shelf lives, ICT firms tend to focus more on innovative activities in developing their new products  (Balkin and Gomez-Mejia, 1984). Secondly, ICT firms are often eager to commercialise their innovation and build relationships across the supply chain  (Brown and Eisenhardt, 1995). Thirdly, as the Chinese market presents both political and economic challenges as well as less developed institutional and legal regulations, leading to a highly uncertain and very competitive environment, the allocation of limited resources is of particular importance to ICT firms' innovation strategies.
Names and addresses of private independent firms were purchased from a managerial consulting company. To secure access to the participating companies, a trained research assistant was employed to contact the CEOs and managers by sending out email invitations to arrange the survey, which consisted of a hard copy self-administered questionnaire and an accompanying covering letter. To encourage participation, each respondent was also offered a summary of the study's results. Confidentiality was assured to all respondents. We aimed at collecting data from the CEOs as the key informant. Using a combination of onsite visits and email correspondence, we gained access to collect data from the right respondents. Our presence onsite ensured the correct use and understanding of the questionnaire and assisted us in getting higher response rates.
We restricted the recall timeframe to three years to reduce other possible biases associated with the data collection process  (Miller, Cardinal, and Glick, 1997). In total, we received 234 completed and valid questionnaires with a response rate of 23. 4% (234/1000). Among the key informants, 55. 3% were male, of which 17. 1%, 56. 4% and 26. 5% had completed secondary education, a bachelor's degree and a postgraduate degree (24. 4% were Master's and the rest were PhD's), respectively. 26. 5% of the participants were under 30 years of age, 53. 4% were between 30 and 40 years of age, 17. 1% were between 40 and 50 years of age and the rest were over 51 years of age. Table  1  provides a detailed sample profile. < Insert Table  1  About Here >.
This study analyses empirically the effects of counterfeiting on the economic and innovation performance of firms, exploiting a novel and unique dataset that integrates information on custom seizures worldwide during years 2011-2013 with financial accounting data, patent and trademark data for a sample of digital technology companies. We apply diff-in-diff models using a large control sample of non-affected firms. Results provide robust evidence that counterfeiting is associated to a negative impact on operative margins of affected companies, relative to the control sample. The analysis does not find conclusive evidence on the effects of counterfeiting on the innovation activities of affected companies. We find no evidence in support of the hypothesis that counterfeiting could also exert an indirect positive effect on the sales of genuine goods.
, 2007). The empirical evidence concerning the implications of counterfeits at present is scant, limited in scope and breadth, and inconclusive in its results  (Feinberg and Rousslang, 1990; Staake et al. , 2009; Qian, 2008 Qian, , 2014a;; Candelin-Palmqvist et al. , 2012; Qian et al. , 2015). Indeed, the lack of data that descends from the illegal nature of counterfeiting has impeded accomplishing large-scale, longitudinal and multi-industry analyses. The few empirical investigations that exist have attempted to investigate the implications of counterfeits only at the aggregate industry or economy level and not at the level of single companies, due to the lack of micro-level data on counterfeits. This paper is aimed at addressing this gap. It builds an original firmlevel and longitudinal dataset of digital technology companies affected by counterfeiting and on comparable digital technology companies notaffected by counterfeiting.
Digital technology companies are defined as companies that produce and/or commercialize at least one physical product that incorporates a digital technology, excluding the merchandising related to the company brands. The database integrates different sets of firm-level information: on the international trade of counterfeit goods from the OECD-EUIPO (2016) database, on financial accounting data from Orbis-Bureau van Dijk (combined with Datastream), on patent data from Clarivate-Thomson Reuters, and on trademarks from the WIPO. The data are longitudinal (annual). Economic and IPRs activities of companies are observed in the years 2009-2015 and counterfeiting activities are observed annually in the years 2011-2013. The database enables unprecedented empirical analyses on the counterfeiting and on the performance of companies affected by counterfeiting.
The paper is organized as follows. In Section 2, we provide a review of the empirical and theoretical studies that have investigated the phenomenon of counterfeiting, and we derive a set of research hypotheses. In Section 3, we illustrate the characteristics of the dataset and the methods adopted for the collection and integration of data. In the same section, we also provide summary statistics on the extent of the global counterfeiting activity affecting digital technology companies. In Section 4, we report the empirical analyses that aim at estimating the effects of counterfeiting on economic and innovation performance. In Section 5, we summarize and discuss the main findings.
For example,  Conner and Rumelt (1991)  maintain that, although software piracy generally harms both software firms (reducing profits) and customers (increasing prices), firms and customers could gain a positive network externality when pirate software enables a more widespread adoption of a product (see also  Givon et al. , 1995, and Shi et al. , 2016). 2 Under these circumstances it is possible that the externality effect in the long term generates an increase in the demand, particularly in the case of luxury goods and in brand-related business ventures  (Nia and Zaichkowsky, 2000; Bekir et al. , 2013; Li and Yi, 2017). The overall effect on social welfare depends upon the values of the relevant market parameters and remains an open problem to be answered by empirical investigation. Indeed, few studies have 2 The classical example is the market of operating systems and related software, in which it is possible that pirated software availability has indirectly contributed to consolidate the use of Microsoft Windows products  (Qian, 2014b)  investigated the effect of counterfeiting empirically or assessed the response of original producers to the challenge posed by counterfeited products.
Construction of the firm-level database In order to investigate the research questions outlined in the previous section, we developed an original firm-level database. Because we are interested at both the economic and innovative implications of counterfeiting, we chose to focus on digital technology companies, i. e. a group of companies characterized by a strong need to invest in technological innovation, and increasingly subjected to counterfeiting (OECD, 2017). 5 In this paper, we defined as digital technology companies those companies producing and/or commercializing at least one physical product that incorporates a digital technology, excluding the merchandising related to the company brands. This definition includes companies that produce and/or commercialize consumer electronics (e. g.
, cell phones, computer equipment, and smart watches), electronic components (e. g. , sensors, microchips, displays, and remote controlling), audiovisua content stored on physical digital support (e. g. , producers of music, films, and digital animation movies), and complex products that incorporate physical digital components (e. g. , automotive companies producing sensors for assisted driving). Excluded by the definition are companies that produce and/or commercialize only nonphysical products and services (e. g. , e-commerce companies) and companies whose only physical digital product is merchandizing (e. g. , football clubs that commercialize a digital watch with the name of the team). Our database integrates and combines four sets of data: counterfeiting data, economic/financial data, patent data and trademark data.
To assemble these diverse sets, we combined information from multiple sources and organized the information in a relational database in which the primary key was a single and uniquely identified company. Below, we describe the steps followed to retrieve and combine each pool of data. Counterfeiting data. We used the OECD-EUIPO database (OECD, 2017) as a source of information concerning counterfeiting. The OECD-EUIPO database contains information about the number and value of seizures registered by customs offices in 92 economies around the world (including all the EU countries, US, Japan, and Korea among others), in the years 2011, 2012, and 2013. With over one-half million seizures, it is, at present, the most comprehensive and reliable information that exists on counterfeiting activities.
We accessed the OECD-EUIPO database 6 and retrieved information about the names and incorporation countries of companies affected by counterfeiting, along with the estimated value of the goods seized, the country of origin and destination of the seized goods, a short textual description of the goods seized and the category of the goods based on the Harmonized System (HS) taxonomy. 7 A first and lengthy process concerned the identification of the digital-technology companies in the database, because these belong to different industries. We used a natural language processing algorithm supported by manual expert check in order to identify keywords describing digital technologies. 8 We obtained 91 terms (words and word combinations) related to digital technologies, which we used to identify 10 HS categories that included digital technology goods in the textual description of the goods seized in the OECD-EUIPO database.
9 This produced a list of 737 company names of potentially digital companies further searched in the Orbis-Bureau van Dijk® database. In total, 657 companies of those in the initial list of 737 were matched, equivalent to 89. 1%. The matching of OECD-EUIPO with Orbis-Bureau van Dijk enabled us to retrieve additional information on the companies and to perform a final manual screening of the companies for compliance to our definition of digital technology companies. The screening entailed looking at the portfolio of products made by each company, as available in websites and advertising and was particularly selective. It resulted in a list of 260 companies that complied with our definition of digital technology companies. For these companies, we accessed information concerning counterfeit activities in the period 2011-2013.
Economic and financial data. We retrieved from Orbis-Bureau van Dijk full records on the 260 companies identified above and for a control sample. Specifically, we collected economic and financial information from 2008 to 2015 (from Income Statements and from Balance Sheets), and legal entity name, country, and dimensional category. Because the incidence of missing financial information was considerable, economic and financial information was integrated by means of the database EIKON Datastream (Thomson/Reuters®). To create a control sample of digital-technology companies likely not affected by counterfeiting, we retrieved all companies listed in Orbis-Bureau van Dijk that shared the same combination of 4-digit NACE code, geographical location and dimensional category of the 260 targeted companies.
Overall, the search resulted in a control sample of approximately 29,000 companies. Patent data. We retrieved information about patent applications from Clarivate Analytics (®Thomson Reuters), which provides information on patent filings on a global scale. For the present study, only those patents filed at the EPO, the USPTO, the JPO, or through the PCT procedure were considered. Given the timeframe of the data on seizures  (2011) (2012) (2013) , the priority year of target patents was restricted to the interval between 2009 and 2015. The retrieved patent records were consolidated at the level of INPADOC patent families to avoid the duplication of single inventions extended to multiple patent offices. Because collecting full patent information for more than 29,000 companies would have been impossible, patent information was collected only for the 260 digital companies affected by counterfeiting plus a set of matching sample companies not affected by counterfeiting.
The matching procedure is described in detail in Section 4. Trademark data. Information were retrieved from the WIPO Global Brand Database that includes applications and registrations of trademarks from 58 world authorities and nearly 40 million records. The search strategy resembles the one used for patents. 10 5 The digital technology companies are also ideal to be investigated because they have a global supply chain, with a considerable part of the production taking place in Asia and because they are a B2C market. 6 The data on counterfeiting used in the analyses were accessed exclusively on the OECD premises. 7 HS is a multipurpose international product nomenclature developed by the World Customs Organization (WCO) to classify traded products. This classification is organized into 96 chapters, or 2-digit classes, describing broad categories of goods (e.
g. , HS 85-Electrical machinery and equipment and parts thereof). The 96 HS chapters are further subdivided into headings (4-digit classes) and subheadings (6-digit classes), for approximately 5. 000 fine-grained categories. The OECD-EUIPO database contained information at the 2-digit level. 8 Publications of the EC digital transformation monitor written in English were used to support the research. These included i) "Uptake of digital solutions in the healthcare industry"; ii) "The disruptive nature of 3D printing", and iii) "Autonomous cars -the future of the automotive industry"  (Digital Transformation Monitor, 2017a , 2017b , and 2017c) , plus publications related to robotics and to Internet of Things (European Commission, 2016a; Friess, 2016) 9 Collectively, we tagged all HS classes from HS84 to HS92 plus the class HS37 as potentially including digital-technology companies, resulting in 73,650 seizures associated with 737 potentially digital-technology companies.
Likelihood of being targeted by counterfeiting Prior to testing our hypotheses, we wanted to gain a deeper understanding of the firm-level characteristics affecting the likelihood of being targeted by counterfeiters. To this end, we run a set of Logit models on the dataset including both digital firms subject to counterfeiting and a control sample of firms not targeted. The dependent variable is dichotomous and takes the value one for the firms that had been affected by at least one counterfeiting case in the years 2011-2013, and zero otherwise. We included in our model as explanatory variables the company size (SIZE), computed as the logarithm of the operating revenues. 11 In addition, we included firm profitability, as measured by return on assets (ROA), i. e. , the ratio of earnings before interest and taxes (EBIT) to total fixed assets; the amount of intangible assets (INTANG); and the growth rate of the business (GROWTH), measured as the growth rate of operating revenues over a two-year period before the window of observation of counterfeits.
Counterfeiting and innovation activity In order to test the effect of counterfeiting on the innovation performance of digital technology companies (H3), we used three outcome variables: the book value of the intangible assets, 14 the annual number of new patent family filed, and the annual number of new trademarks applications. The retrieval of patent and trademark information for the entire sample, including more than 29,000 companies, would have required the application of complex automated procedures with expected low accuracy. We therefore opted to use a different methodological approach. We first identify a sample of comparable non-treated companies with a one-to-one matching procedure and we then searched manually for data on patents and trademarks of the matching sample. This enabled us to carefully control for company name variations and avoid instances of false positive results.
Discussion and conclusions The study presented is the first large-scale analysis of the effects of counterfeiting using company-level data. The analyses have been possible thanks to the creation of a novel and unique database developed by combining the OECD-EUIPO data on international trade of counterfeits with firm-level data on financial accounting data and patent and trademark activities. In the paper, we have investigated the firm-level correlation between counterfeiting and various indicators of economic and innovation performance, by means of difference-in-difference estimates. The analysis focused on digital technology companies that have been targeted by counterfeit during the years 2011-2013. The results indicated that counterfeiting activities were targeting specifically larger companies and having a high propensity to innovate, as proxied by larger volumes of intangible assets.
Acknowledgements This study benefited from the financial support of the  European Commission ,  Joint Research center (JRC), Seville (SP) , c/Inca Garcilaso, 3. 41092. The data on counterfeiting activities used in the analyses belong to the OECD-EUIPO database and were accessed exclusively at the OECD premises in Paris. The authors wish to thank  Florence Mouradian  for support and supervision in data access. The.