dataesr/scientific-paragraphs-categorization huggingface dataset:
Randomization test for judge bias on compensation for wrongful dismissal:Note: The dependent variable in the first column is an indicator variable equal to one if the dismissal is deemed wrongful. The dependent variable in the second column is the judge pro-worker bias computed as defined in section 4. 2. Standard errors are displayed in parentheses. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales, and an indicator variable for economic dismissals. All independent variables are transformed to increase clarity of the table: variables are divided by 1000. Standard errors are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database.
From the initial to the final number of observations used to estimate the effect of judge bias on firm performance Note: The final sample is restricted to private firms, with less than 100 employees the year preceding the judgement, which go to Appeal courts for individual dismissals. Employment: headcounts on 31 December before the judgment year;Wages: gross monthly wage; Roa: Return on assets. In this table, the number of observations corresponds to the number of cases × the number of years in the sample. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database.
Judge pro-worker bias and firm performance 1 year after the judgment Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. The upper part of the table displays coefficient α 1 of equation (5) and the bottom part coefficients β 1 and β 2 of equation (6). Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database.
Judge pro-worker bias and firm performance 2 years after the judgment Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. The upper part of the table displays coefficient α 1 of equation (5) and the bottom part coefficients β 1 and β 2 of equation (6). Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database.
The variable of interest is the judge pro-worker bias computed as defined in section 4. 2. Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database.
2. Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. The table displays coefficient coefficients β 1 and β 2 of equation (6) for all firms in the top panel and for firms with less than 10 employees the year before the judgment. Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database.
Note: Each cell corresponds to one regression where the dependent variable is the total compensation for wrongful dismissal. Control variables included in column (2): indicator variable for economic dismissal,wage, seniority. The bottom and top fifth percentiles of judge bias are trimmed to account for the non-linearity of the relation between judge bias and the qualification of dismissal displayed on Figure 12. Court and year x sector fixed effects are used. Standard errors, clustered at the judge level, are in parenthesis. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: Appeal court rulings database.
000 -0. 000 (0. 000) (0. 000) Worker's salary -0. 000*** 0. 000 (0. 000) (0. 000) Economic dismissal 1. 116 -0. 025 (0. 683) (0. 030) Time between dismissal and Appeal Court 0. 001 -0. 000 (0. 001) (0. 000) Joint F-Test 0. 0000 0. 7458 Observations 4,948 4,948 Standard errors clustered at the judge level. Standard errors, clustered at the judge level, are in parenthesis. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: Appeal court rulings database.
The variable of interest is the judge pro-worker bias computed as defined in section 4. 2. Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database. Survival growth rate between t -1 and t + 3 within [t, t + 3] Employment Employment cdi Employment cdd Share cdi Sales Total amount -0. 005** -0. 011* -0. 013* 0. 000 -0. 006* -0. 016** (0. 002) (0. 006) (0. 007) (0. 015) (0.
The variable of interest is the judge pro-worker bias computed as defined in section 4. 2. Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database. (1) (2) (3) (4) (5) (6) Survival growth rate between t -1 and t + 3 within [t, t + 3] Employment Employment cdi Employment cdd Share cdi Sales Total amount -0. 008** -0. 008 -0. 013 -0. 006 -0. 008** -0. 012 (0. 004) (0. 008) (0.
2. Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. The table displays coefficient coefficients β 1 and β 2 of equation (6) for all firms in the top panel and for firms with less than 10 employees the year before the judgment. Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database. (1) (2) (3) (4) (5) growth rate between t-2 and t-1 Employment Employment cdi Employment cdd Share cdi Sales All firms Pro-worker bias × Low Roa -0.
005 -0. 013** -0. 047*** × Low Roe (0. 004) (0. 011) (0. 012) (0. 029) (0. 006) (0. 011) Pro-worker bias -0. 003 0. 010 0. 010 0. 008 -0. 001 -0. 002 × High Roe (0. 005) (0. 012) (0. 015) (0. 028) (0. 007) (0. 011) R 2 0. 044 0. 047 0. 046 0. 031 0. 039 0. 030 # obs 4447 4447 4084 4084 4084 4369 Small firms Pro-worker bias -0. 025*** -0. 062** -0. 087*** -0. 036 -0. 028** -0. 063*** × Low Roe (0. 007) (0. 021) (0. 020) (0. 038) (0. 010) (0. 017) Pro-worker bias -0. 003 0. 036 0. 038 0. 014 -0. 002 0. 019 × High Roe (0. 010) (0. 023) (0. 032) (0. 030) (0. 013) (0. 023) R 2 0. 054 0. 057 0. 061 0. 069 0. 061 0. 058 # obs 1887 1887 1735 1735 1735 1878 level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database.
000 1907. 000 2022. 000 Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database. Column (2) the symmetric growth rate between t -1 and t + 3 of firm's employment; in Column (3) the symmetric growth rate between t -1 and t + 3 of firm's employment in permanent contract -cdi; in Column (4) the symmetric growth rate between t -1 and t + 3 of firm's employment in temporary contract; in Column (5) the change between t -1 and t + 3 in the share of permanent jobs; in Column (6) the symmetric growth rate between t -1 and t + 3 of sales.
Sample We test the influence of the legal and institutional environment  (Shleifer and Vishny 2003)  in the choice of means of exchange in mergers and acquisitions in the industry of travel agencies and tour operators. Data was collected through the SDC Thomson Financial database. SDC provides authoritative, consistent, and global coverage of mergers and acquisitions worldwide in different industries. The sampling procedure follows identical previous studies (see, for example,  Faccio and Masulis 2005). A summary of the descriptive statistics of the dependent and independent variables in presented in Tables  1  and 3. We used the following screening criteria to extract the data: 1. Our data includes a sample of approximately 750 completed deals worldwide for the period 1990 through mid-August 2018; 2.
The target is a firm included in the U. S. Standard Industrial Classification (SIC) Codes 4724-Travel Agencies and 4725-Tour Operators; 3. There is no restriction on the choice of the target country or in the deal value; 4. The database includes public and private corporate transactions. Non-completed transactions, withdrawn transactions and transactions pending and subject to regulatory approval were not considered. The sample includes transactions involving all the public and private firms. Target firms included in the sample are as follows: 598 travel agencies (SIC 4724) and 119 tour operators (SIC 4725). The transactions involving 'cash only' are the most widespread in the industry. However, the transactions targeting operations in some countries include a significant amount of payments in shares only, surpassing 15 percent of the total (United States, Australia, South Korea, and Japan) (see Table  1 ).
Discussion Using a dataset of 750 completed deals worldwide involving travel agencies and tour operators, we find support for our hypothesis that firms use cash as a means to manage the constraints and opportunities in the acquirer's and target's countries. Previous studies on mergers and acquisitions demonstrated the importance of cash in both domestic and cross-border mergers and acquisitions  (Sankar and Leepsa 2018; Xie et al. 2017; Chhabra and Popli 2018). This means of exchange offers the advantage of maintaining the ownership structures unchanged. However, in the case of the transactions paid in cash, the shareholders of the target firm are immediately subject to a tax. Payments in cash provide a positive signal  (Ross 1977)  implying confidence of the acquirer on achieving post-acquisition performances  (Chhabra and Popli 2018).
However, we did not consider the opportunistic behavior of acquirers when facing less informed targets. Therefore, the dynamics of tourism development also requires an improvement of legal conditions and of the institutional to protect and attract investors for the industry. The principal limitation of our empirical study relies on the fact that we did not directly control for the size, equity control, or the financial constraints of the firms involved in the transactions. This limitation is due to the fact that many of the firms included in our database are private companies and, therefore, the financial data disclosed is limited. Furthermore, our logistics regression uses a dependent binary variable (i. e. , cash versus other sources of financing) and we do not test the determinants of the amount of 'cash' used in the operations.
Data and descriptive statistics Our dataset contains annual information on each local DH market. Information about prices was gathered from the Nils Holgersson annual price survey (NHS), which reports municipal specific list prices for a representative customer. 4 Between September and November each year, the firms announce the price that will be charged in the next year from the 1st of January. Fig. 1a  shows the average annual price for DH between 1996 and 2014, and Fig. 1b  shows the changes in price over the same time period. After 2000, prices increased steadily. By 2005, when the debate about regulation started (the beginning of the threat period), prices had increased by a total of 11%. By the peak of the debate at the end of 2008, prices were almost 20% higher than in 2000. It is interesting to note that after 2005, prices increased at a slower pace.
After 2009, price changes follow no clear pattern. Information on customer price complaints was collected directly from the DH board. Additional demand and supply characteristics were added from other sources. On the supply side, we have information on the different fuel types and quantities used to produce heat by each firm. The types and amounts of fuel used affect firms' cost levels. This information was collected from the Energy Markets Inspectorate and directly from the DH firms. We also observe labor cost for the period 2008-2014. On the demand side, our dataset contains municipal-specific information on the average income, the share of the population above the age of 65, the total number of inhabitants, and the share of detached dwellings (i. e. single family houses). These variables are collected from Statistics Sweden and the municipalities directly.
Additional covariates such as the electricity tax 5  and weather (number of heating degree days and amount of precipitation) were also gathered. Since they have no (electricity tax) or only very limited (weather) cross-sectional variation, including them into the analysis made no difference. Table  1  contains descriptive statistics on the subsample used to test the RTH. Due to availability of the data on the instrument, we only use the periods 2010-2013 (see Section 4 for details). The price is measured in SEK per MWh. The labor cost variable depicts the monthly labor cost per worker (in SEK, averaged over the year and across workers). The last variable in this table, "Unanticipated disruptions", is explained in Section 4. Finally, the table reveals that complaints are in general rare events, with a mean number of complaints per year and per market equal to 0.
19. Our panel dataset contains  50 nonzero firm-year complaints observations, exhibited in 28 firms (that is, in roughly 12% of all 238 firms in our sample). 12 Firms exhibited complaints in one year, 11 firms in 2 different years, 4 firms in 3 different years and 1 firm exhibited complaints in all 4 years of observation. Fig. 2a  displays a histogram of the counts of market-year number of complains and Fig. 2b  displays the absolute frequency of complaints per year (dark-colored) and the number of firms receiving complaints each year. Remark: the substitute. The only substitute of district heating is electricity-based heating. The electricity price paid by end-consumers consists of three parts -an electricity tax, the retail price and the local distribution price -where only the first component is observable.
Complaints as threat of regulation Customer complaints are used in many industries to discipline the behavior of firms. The Consumer Financial Protection Bureau in the US, for instance, maintains a database of customer complaints that are visible to firms, customers and politicians. The classical example for the self-regulatory power of customer complaints is discussed in  Olmstead and Rhode (1985)  in the context of the oil and gas industry. Two recent examples include: (i) proposed price increases by domestic waste firms in Ireland resulted in intense customer complaints and informal talks between the Government and the firms where the firms voluntarily agreed on a temporary price cap (The Journal, 2016), and (ii) following complaints about excessively high electricity prices in the UK and the opening of an investigation by the UK Competition Authority, several of the energy firms reduced their prices shortly ahead of the Authority's release of its provisional conclusions  (The Guardian, 2016).
We retrieved information on newspaper articles from Retriever, which is a database that contains all articles from regional and national newspapers in Sweden, and matched them with specific complaints made by customers. 7 Our results imply that roughly 44% of all complaints are also discussed in a newspaper article. In many of the articles, a complaint is used to demand a stricter regulation from the politics. An example is the following statement made by Per Forsling, energy expert at the Property Federation: "The new District Heating Act is toothless and it is necessary to review the rules in the district heating market"  (Swedish Radio Stockholm, 2009). Finally, although the reports of the DHB, as well as the negotiating process with the firms, are not public, there is occasional anecdotal evidence in newspaper articles that firms do respond to complaints.
Hypothesis: Are the results of the REDUCE-IT trial applicable to the French registry on acute ST-elevation and non-ST-elevation myocardial infarction (FAST-MI) population? Methods: Data were extracted from the FAST-MI 2010 and 2015 registries. We applied the REDUCE-IT enrolment criteria (triglycerides 150-500 mg/dL and LDL-C 40-100 mg/dL on statins) to the FAST-MI population in patients aged ≥45 years who had detailed lipid values postacute hospitalization, focusing on their clinical profile and cardiovascular prognosis. Results: Of the 3789 FAST-MI patients with a full lipid profile (median 11. 1 [IQR 7. 6-17. 4] months after hospitalization for myocardial infarction), 472 (12. 5%; 95% CI 11. 4-13. 5) met the eligibility criteria for REDUCE-IT (REDUCE-IT-like group). The cardiovascular event rate (all-cause death, nonfatal myocardial infarction, nonfatal stroke) was 36.
Finally, we conduct numerical experiments with a benchmark dataset from the literature and a real dataset from the Chinese OTA, to analyze the performance of the above methods and to derive managerial insights. The paper is structured as follows. Section 2 provides a literature review on the related TOP. Section 3 presents the compact formulation for the TOP-DC. Section 4 describes the two-index extended formulation and associated branch-and-price method, with a focus on the pricing sub-problem. Section 5 gives the one-index master reformulation, the associated branch-cut-and-price method and modification of the dominance rule for the subproblem. Section 6 describes the results of numerical experiments and detailed analysis. Section 7 gives concluding remarks.
Computational experiments We first use a benchmark dataset from the literature to test the performance of our BP and BCP methods. Then, we conduct experiments on a real-case study with data provided by a Chinese OTA (niding. net) based at Beijing who offers global travel products. Beyond the computational aspects, we analyze the obtained solutions and derive managerial insights. In particular, we analyze the trade-off between high diversity, which provides a richer offer for customers, and low diversity, which ensures to select less suppliers with better prices. Our implementation has been coded in C++ on Linux running on an Intel Core i7 with 64 GB RAM, with a time limit of 3600s. All algorithms use IBM ILOG Cplex 12. 9 as a LP-solver in multi-threads (8 threads).
Benchmark dataset tests Dataset description. The benchmark dataset we used are the TOP instances tested in  Chao et al. (1996b ), Boussier et al. (2007)  and  Keshtkaran et al. (2016). When s ≥ 1, there are no benchmark results as this are summarized in Tables 1 to 6. In the tables, columns BP, BCP and CF provide the results obtained from Branch-and-Price, Branch-Cut-and-Price, and the compact formulation solved by Cplex; subcolumns val (i. e. , optimal or best-known value), optgap (i. e. , (upperbound -optimal or best-known value)/upperbound ), rootgap (i. e. , (root node LP relaxation value -optimal or bestknown value)/root node LP relaxation value ), nodes (i. e. , number of branching nodes) and time (i. e. , computational time in seconds) for each instance. We also provide the Tour Details: #cities in the optimal/best-known solutions, including the total (i.
As we can observe, the larger s, the less pairs of tours violating the diversity constraints, and less cuts are generated, which favors BCP. Thirdly, we notice that the computational time increases with m and n for a given s, as more iterations are needed to solve the subproblems by dynamic programming. We also observe that the subproblems in the two-index formulations are solved more fastly but with a less tight bound, while that in the one-index formulation are slower but with a tighter bound. Therefore, the two methods are somewhat complementary. The Chinese OTA niding. net provided us with 1379 orders information of tour products sold in the last three years. These orders cover famous tourism destinations distributed among Europe, Asia and North/South America. To construct our dataset, the tour products were decomposed into several elementary components, including the visited cities and the relevant stay times and costs.
In particular, 40 European cities (see Figure  3. a) among them were selected to represent the nodes in our Europe dataset (EuroData). For the sake of triangular inequality requirement on travel times/costs in solving subproblems, only the direct flights between cities were considered in our EuroData, even though some close cities without flights may be connected by railways. Moreover, the attractiveness (score) of destinations are measured by the "nights spent in tourist accommodation" in all NUTS-2 regions  (EuroStat, 2016). The basic information of the selected European 40 cities are summarized in Table  7. After analyzing the orders information in detail, we found that a tourist spent on average around USD 2300 for a trip of around 10 days in Europe. In our test instances, the travel budget is restricted by c max ∈ {1500, 2300} USD and the travel time is limited by t max ∈ {10, 14} days.
Data Analysis and Results The SmartPLS Version 3. 0 software was used to analyse the data gathered from the survey. SmartPLS is one of the prominent software applications for Partial Least Squares Structural Equation Modeling (PLS-SEM). It has been deployed in many fields, such as behavioural sciences, marketing, business strategy and building information modelling  (Enegbuma et al. , 2015). Following the recommendations by other researchers  (Chin, 2010)  the bootstrapping method (500 subsamples) was used to determine the significance levels of loadings, weights, and path coefficients. The minimum sample size was identified at n=55 by using the G*Power calculator with the setting as follows: effect size: f 2 = 0. 15 (medium), α = 0. 05, number of predictors = 3 and a statistical power of 80%  (Kwong and Wong, 2013).