dataesr/scientific-paragraphs-categorization huggingface dataset:
While 'flexible standard costing' relies on Schmalenbach's definition of cost, and therefore does not question the dual ledger accounting model, Riebel suggested that costs should be cash-based  (Riebel, 1959) , which implies an abandonment of imputed costs. Nevertheless, the complexity of Riebel's approach has probably prevented the "Relative Einzelkosten-und Deckungsbeitragsrechnung" from gaining much relevance in German companies  (Weber and Weißenberger, 1997). Instead, full costing and flexible standard costing were the most common cost accounting systems in Germany through the second half of the 20 th century  (Christensen and Wagenhofer, 1997)  and contributed to institutionalizing the dual ledger accounting approach. In summary, our reading of the literature suggests that the German economic context gave rise to prudent financial accounting rules, which served as a coercive force for the dual ledger accounting approach, given that the perceived inappropriateness of HGB data for managerial decision-making and control initiated the establishment of a second database.
The complexity was further reinforced by the dual ledger accounting approach, which was usually implemented by the German parent companies and imposed on their subsidiaries. That is, the performance measures which were employed for management evaluation in the subsidiaries were decoupled from the financial accounting database  (Haller, 1997; Ziegler, 1994). This practice was found to confuse employees in the foreign divisions who were unfamiliar with the German dual ledger accounting approach (e. g. Schweitzer and Ziolkowski, 1999; Wagenhofer, 2008). Since the strict distinction between costs and expenses is a peculiarity of the Germanic accounting approach, employees in foreign subsidiaries were disturbed to see their per- formance measured with what they perceived as "strange data" that was not compliant with the financial accounting rules (e.
g. ,  Hoke, 2001; Haring and Pranter, 2005; Müller, 2006)  it also seems to have been for a consequence of normative and mimetic pressures, particularly reinforced by consultancies  (Jones and Luther, 2005). These developments encouraged some practitioners to establish a fully integrated system from which, they maintained, all information for managerial decision-making could be derived from the financial accounting system  (Hahn and Nicklas, 1999; Damberger et al. , 2002). Indeed, surveys of the 200 companies with highest revenues in Germany  (Horváth and Arnaout, 1997)  and of the Top 500 companies by revenue in Germany and Austria  (Haring and Prantner, 2005)  indicated the intention to abandon any use of imputed cost and therefore to render a second database unnecessary. Consequently, this approach would have eliminated the need for a second accounting ledger and thus would have made the traditional German imputed cost accounting obsolete.
In summary, the globalization and the corresponding economic necessity to raise more capital constitute economic pressures for an integration of the previously separated databases that were amplified by the internalization of financial accounting. The higher appropriateness of IFRS data for management accounting purposes served as a coercive force towards an integration of accounting systems. Eventually, these economic and coercive pressures coincided with a mimetic pressure to adopt management accounting approaches designed in countries with primarily equity financed companies partially to provide the investors' information demands. Our conclu-sion that such measures are not implemented for economic reasons only, is reinforced by empirical findings that EVA is not more highly associated with stock returns than with 'conventional' financial accounting metrics  (Ismail, 2006; Palliam, 2006).
At the same time, several arguments suggest that some kind of inertia may be achieved with regard to the partial integration system instead of an increasing separation. First, in a number of large companies, the partial integration of accounting systems was accompanied by the implementation of enterprise resource planning systems that draw on a single -rather than the previously dual -accounting database(s) (e. g. ,  Hebeler, 2006; Lochner et al. , 2007; Weide, 2009). The corresponding investments in, often customized, information systems architecture may constitute a major economic obstacle to a future reversion to a dual ledger approach. Second, a declining volume of bank lending has reinforced the trend for German companies to issue equity and seek to attract foreign shareholders  (Hardie and Howarth, 2009; Gronwald and Nasev, 2013).
Data This research uses data from the database Technology Innovation Panel (PITEC), including information since 2003, and aims to provide ad ditional information to databases on innovation activities in Spanish firms. This data panel builds on the Community Innovation Survey (CIS), which covers the time span between 2008 and 2010. The Spanish gross sample consists of 31,636 firms. The data gathering process consid ered the organizational innovation variable to collect the questionnaires in those firms. This measure leaves a final sample of n = 10,163 firms ob servations. Thus, for this study the researchers consider the study of  Greckhamer et al. (2013) , who apply fsQCA to large data samples. These are the definitions of variables: Organizational innovation, variable reflecting the implementation of organizational innovation during the period considered.
DATA AND SAMPLE Our initial sample selection was based on observations for all firms in the Execucomp database for the period from 1993 to 2001. 4 A turnover event was identified if there was a change in a firm's CEO in a particular year based on Execucomp. The Factiva news database was then searched to obtain information on the exact announcement date of the succession and on whether it was a forced or a voluntary turnover  (Jenter and Kanaan, 2015; Parrino, 1997). 5,6 The data was then merged with the NBER patents file assembled by  Hall, Jaffe, and Trajtenberg (2001) , which was used to measure firm-specific knowledge. Board and CEO duality data were collected from Compact Disclosure. CEO compensation and CEO ownership data were extracted from the Execucomp, firm diversification data from Compustat's industrial segment file, and 4 Execucomp data began in 1992, and NBER patent citation data, which is the source of data for our key measure of firm-specific knowledge, was available between 1976 and 2006.
Finally, the cases classified as forced can be reclassified if the reports convincingly explain the departure as due to reasons that are unrelated to the firm's activities. This careful classification scheme is necessary since CEOs are rarely openly fired from their positions. CEO turnovers caused by mergers and spin-offs are excluded from the analysis. 6 We thank Professors Jenter and Kanaan for sharing their forced CEO turnover data. Firm-specific Knowledge Assets and Employment Arrangements 1883 analyst coverage information from IEBS database. Finally, Compustat and CRSP databases were used to construct accounting and stock return variables respectively. After merging the major data sets and deleting observations with missing values for the key variables, the final panel data set contained 972 firms and 4,390 firm-year observations for a sample period of 1993 through 2001.
Measurements Restricted stock was defined as the value of restricted stock granted to the CEO during the year divided by the total annual compensation. CEO dismissal was a dummy variable coded as 1 if there was a forced turnover in a firm's CEO in a particular year according to the classification procedure described earlier. Otherwise, this variable was coded as 0. Firm-specific knowledge was measured using patents and their citations. Patents represent an important component of a firm's knowledge assets and patent citations provide direct evidence of the path of knowledge flow and spillovers. Each patent's citations are typically evaluated carefully by patent examiners in order to avoid inappropriate citations and the exclusion of relevant citations. The data enable the tabulation of both citations that are made within the same firm (self-citations) and those made by other firms (external citations).
Firm-specific Hypothesis 2a suggests that firm-specific knowledge may negatively affect the performance-CEO dismissal sensitivity. The test results for this hypothesis are shown in Table  4. Specifically, Model (2) reported that the interaction between firm-specific knowledge and stock return-based performance measure was positive and significant, suggesting that although market performance was negatively related to CEO dismissal (i. e. , poor performance increases the likelihood of CEO dismissal), such an effect was weakened if the firm had high level of 7 The analysis is implemented using STATA ado program prchange. firm-specific knowledge. This was in line with our argument. In addition, when industry-adjusted ROA was used as an alternative performance measure in Models (  3 ) and (  4 ), we also found similar results supporting Hypothesis 2a.
Among the 42 studies surveyed by  Frederick et al. (2002) , 34 use experimental methods. A typical approach is for individuals to be offered a menu of (real or hypothetical) choices between a quantity of money now and a different quantity of money at some point in the future. Respondents' choices are used to estimate a discount rate. Our paper fits into a much smaller literature that estimates discount rates using field data on aspects of behaviour and a lifecycle model of consumption and saving. A typical way to estimate preference parameters in such models, though not the one that we will take, has been to solve numerically the intertemporal optimisation problem that the agents in a particular population are assumed to face. Estimates of parameters such as the discount rate are chosen such that the model's predictions are close, in some metric and according to some data, to those seen in reality.
Data Our data come from the English Longitudinal Study of Ageing (ELSA). ELSA is a panel survey that is representative of the English population aged 50 and over. It started in 2002, and individuals have been re-interviewed every 2 years since thenour main results use data from the first three waves. The purpose and form of the survey is similar to the Health and Retirement Study (HRS) in the US and the Survey of Health, Ageing and Retirement in Europe (SHARE) in 20 European countries. The first wave was conducted between April 2002 and March 2003 and sampled 12,099 individuals (of whom 11,391 were core sample members; the remainder was individuals aged under 50 who were the partners of core sample members). There are 7894 benefit units (i. e. a single person or couple along with any dependent children) where each member of the couple is a sample member.
Our sample is drawn from these benefit units. While ELSA contains questions on some components of expenditure (food, domestic fuel and clothing) it does not, unfortunately, contain data on total expenditure, which, approximating consumption, is needed to estimate the discount rate. In fact, there is no nationally representative longitudinal survey that collects total expenditure in the UK and such data is rare internationally. 2 This lack of comprehensive longitudinal data on expenditure has proved something of an obstacle to bringing Euler equations to data. The literature has either relied on aggregate data, or, following  Browning et al. (1985) , has used repeated cross-sections to form a quasi-panel of birth cohort-level average expenditure. An alternative approach was suggested by  Skinner (1987)  and refined by  Blundell et al.
Calculating consumption using longitudinal data on assets and income We use longitudinal data on assets and income along with the budget constraint to calculate consumption between two waves. Equation 1 can be re-arranged to get the value of consumption in period t as follows: p t c t = e t + d t + j r j t p j t X j t + j p j t X j t -p j t+1 X j t+1 (7) The timing convention and how it relates to the data deserves some discussion. In ELSA, interviews take place approximately every two years. So to be precise, • Flow variables representing consumption c t , non-capital income e t , transfers d t and the asset yield, including any capital gain or loss, r j t , are measured over the entire two year period; • Stock variables X j t represent holdings of assets at the beginning of the period (i.
Comparing consumption in ELSA and in the EFS In this section, we compare the distribution of consumption estimated in the manner described above with that estimated using the Expenditure and Food Survey (EFS). 4 The EFS is the UK's household budget survey, and is used to calculate the commodity-weights of the UK's inflation indices (the Consumer Prices Index and the Retail Prices Index). The data is collected annually, throughout the year and the survey is designed to be nationally-representative. Respondents are asked to record all purchases over a 2-week period in a diary and also to complete a questionnaire that seeks information on infrequently-purchased items. The combination of the diary and the questionnaire allows a comprehensive measure of consumption to be calculated. Figure  2  shows the cumulative distribution function and the probability density function of total consumption in both surveys.
Conclusion This paper puts forward a method for estimating individual discount rates using field data. We build consumption panel data from the intertemporal budget constraint and panel data on income and wealth. This household-level panel data is used, with the Euler equation, to estimate discount rates for groups defined by socio-economic characteristics. We show, unsurprisingly, that there is substantial heterogeneity in discounting in our sample which is drawn from a population of older households. But surprisingly, we find that, among this older population, households with less education and lower numerical ability exhibit greater patience than, respectively, those with more education and greater numerical ability. This result, which is robust to differential housing wealth shocks, differential mortality and differential incidence of liquidity constraints is somewhat puzzling as it is the opposite to that found in investigations of time preference for younger households.
A. 1. 2 Data on education and numerical ability We outline here how our measures of education and numerical ability are defined. We categorise individuals into one of three education groups on the basis of the highest qualification that they have. We consider those who have a third-level degree or higher to have a 'high' level of education. Those who have A-levels (British school-leaving exams, taken at age 18) or equivalent but no university degree are in the 'mid' education group. All others are in the 'low' group. Our measure of consumption, and therefore our estimates of discount rates, are at the household (formally benefit unit 11  ) level. We need an education measure, therefore, at the household level. We take the education of a household containing a couple to be the greater of the two levels of education held by the adults in that couple.
A. 2. 2 Imputing missing income For each month between the waves where we do not have income data, we interpolate linearly between the two income observations that we have. We carry out this procedure separately for each category of income (employment income, self-employment income, private pension income, state pension income, benefit income, asset income and other income). We vary the procedure in two cases. The first of these is when respondents do not report some category of their income exactly in one (but not both) of the waves (i. e. they perhaps only give bounds). In these cases we assume that income has been equal over the period to the value in the year for which we have full information. The second case where we vary the procedure is when it comes to state pension income. Here we use the data on the age of the respondents and the state pension age to establish when their state pension payments started.
As a final comparison of our generated data on consumption with that in the EFS we make use of the fact that data on food spending is recorded in ELSA. In Fig. 8 , we plot the relationship between food spending and total spending in both surveys (again trimming the bottom and top 10 % of consumption). The relationships shown are estimated using locally-weighted regressions. Food spending is estimated as greater in ELSA than in the EFS; 13 the slope of the relationship between it and total spending 13 One potential reason for this is the different methods by which the data on food spending is gathered. The ELSA data is taken from respondents' responses to a question that asks how much they spend on food in a typical month. The EFS data is taken from respondents' spending diary entries. is, however, similar in both surveys the point at which total consumption is equal to approximately £10,000.
Acknowledgments Co-funding is acknowledged from the  European Research Council  (reference ERC-2010-AdG-269440-WSCWTBDS) and the  Economic and Social Research Council  (Centre for Microeconomic Analysis of Public Policy reference  RES-544-28-5001 ). Thanks to  James Banks ,  Richard Blundell ,  James Browne ,  Rowena Crawford ,  Mariacristina De Nardi , two anonymous referees and to seminar participants at the  Institute for Fiscal Studies , the  Paris School of Economics  and the  Economic and Social Research Institute of Ireland  for valuable comments. Any errors are our own.
Biomimicry design tools came to life with the aim of solving these problems helping designers along the phases of the NPD process. However, only few contributions investigate applications of such tools in real-world NPD processes, which could prove the tools' effectiveness  (Fu et al. , 2014; De Pauw et al. , 2014). This paper attempts to start filling this gap by carrying out an exploratory study. The aim is collecting data from design practitioners asking them if they are familiar with existing biomimicry tools, in order to understand the underlying motivations of their choices in terms of tools awareness. Also, the study wants to assess whether -and to what extent -designers rely on these biomimicry tools within the NPD process. To this end, new indicators are built mainly to measure the tools awareness which is based on the distance between how the existing literature assesses these tools and the perception of our surveyed respondents.
Biomimicry Tools in NPD Phases Phases 1 and 2: Problem Definition and Reframing Going through Phase 1 and Phase 2 is a necessary condition to formulate a specific problem and find the functions that better solve it. Figure  5  shows which tools are used by designers and researchers during these phases. The sum of the percentages is more than 100%; this means that some respondents use more than one tool in these phases. It emerges that beyond using AskNature, respondents prefer to go for other solutions (e. g. , outsourcing). The most used tool in these phases is AskNature; this tool can inspire, suggest and link functions to the natural phenomena that best perform them. It is worth noting that 44% of the respondents used other tools not related to biomimicry. In fact, these phases are still in the company domain and are simply the translation of the problem into the functional language.
Procedure In order to collect data among employees with different job status and from a range of organizations, participants were recruited in two ways: using our personal and professional networks; and, for employees located in the Paris region, by in-person contact. The former were directly contacted by e-mail, while the latter were given a flyer advertising our survey. In both cases, an electronic link was provided to access the online survey. All respondents were required to review legal information and provide their consent before participating in the survey. This research is not invasive, is anonymous and voluntary. Using a snowball sampling strategy, potential participants were encouraged to forward this link to their acquaintances. A similar approach was applied by  Steger et al. (2013)  to increase their pool of participants.
Predicting Turnover Intentions To examine the predictors of turnover intentions, two hierarchical multiple regression analyses were performed. For the first model, a three step hierarchical multiple regression was conducted, in which demographic data was entered at step 1, adverse working conditions at step 2, and meaning of work at step 3. For the second model, demographic data was also entered in the first step of the equation while meaning of work were included in the second step. Regression results are presented in Table  2. For Model 1, after entering gender, age, education level (i. e. , graduate degree level vs. non-graduate degree level), types of occupation (i. e. , administrative or managerial positions vs. non-administrative or managerial positions), contract status (i. e. , permanent position vs.
Procedure and Sample A cross-sectional survey (self-administered questionnaire) was developed to collect data for the study. Several members of the Securities and Exchange Commission of Pakistan were interviewed in order to identify which firms the survey should include. Based on these interviews, we selected five multinational corporations that run vigorous, high-profile CSR campaigns, making them visible to the public (external stakeholders). We used interviews for this purpose because there is no published data available on Pakistan in the context of this study. Having asked each firm's human resources department for permission to administer the survey, we sent them a copy of the questionnaire (in English), accompanied by a cover letter explaining the purpose of the study and assuring all respondents they would remain anonymous.
Data Analysis The data was analyzed and the hypotheses tested using SPSS 21 and AMOS 21. The data screening stage incorporated missing value analysis, multivariate outliers, normality, descriptive 1 We used items related to two dimensions which were CSR towards social and nonsocial stakeholders, and CSR toward employees. As per the conceptualization of CSR i. e. , CSR is a set of initiatives of an organization that go beyond the notion of profit-making or compliance with the law  (McGuire, 1963; Davis, 1973; McWilliams et al. , 2006; Aguilera et al. , 2007; De Roeck et al. , 2014) , we have excluded CSR directed toward customers and government. statistics, multicollinearity, homoscedasticity and correlation analyses. Table  1  summarizes the descriptive statistics, indicating moderate correlation among the variables.
RESULTS The structural regression model is tested using AMOS 21; the multiple regression analysis employs SPSS 21. The results of structural regression model show a good fit to the data (CMIN/df = 1. 65; RMSEA = 0. 04, TLI = 0. 96, CFI = 0. 96). Hypothesis 1 concerns the mediating effect of perceived external prestige between external CSR and organizational identification. However, the model is characterized by multi-mediation, which AMOS 21 cannot test directly. For this purpose, we use the phantom model technique  (Macho and Ledermann, 2011)  along with 5000 bootstrapping samples  (Preacher and Hayes, 2008) , which enables us to determine the specific indirect effects and their significance levels. The results of structural regression model show that external CSR has a positive relationship with perceived external prestige (unstandardized estimate = 0.