Data and Methodology. The data set of all pitchers that have pitched in major league games dating back to the year 2000 were grouped together, separated into starters and relievers, and then randomly sampled. In order to make sure that the entire body of work of pitchers who meet the threshold is taken into account, the entirety of a pitcher’s career amongst the measurable data set is being counted as long as the pitcher meets the threshold for at least one season. For example, if pitcher A threw 55 innings of work and made less than 10 starts in 2005 only, but pitched from 2001-2006 in the major leagues, all years from 2001-2006 would be included in the sample. The cutoff being used to cut down the large sample size of pitchers is the threshold of 100 innings and at least 10 games started in the same season to be considered a starter. For relievers, the threshold is less than 10 games started and more than 50 innings pitched in the same season. These thresholds were used because they are good indicators of throwing a ‘complete’ season. The need for a complete season is important in this study, especially in the valuation portion, because it provides a baseline of what a pitcher is capable of given a full body of work. Once the overall population is broken down into that smaller subset, 550 pitchers will be randomly selected. Those 550 pitchers will have their entire career in the subset for testing and their videos and pictures will be examined for the mechanical flaw. This is only one of a myriad of variables that go into a pitcher’s throwing motion. This study will consider many different variables, including differing interpretations of the variable of overall health and workload. As mentioned in the previous section, days on the Injured List will be primarily used as the dependent variable when discussing health, as it is the most efficient way available to measure how long a player is injured in-season. To best treat the sample in order to create an econometric model that may be able to show the injury risk amongst pitchers, knee collapse will be used as a binary variable. The methodology for coding this variable into the data set was to manually watch video from every pitcher that qualifies for the sample size and xxxx their mechanics as 0 or 1. Any knee collapse that was considered ‘minimal’ still qualified as 0, anything that was deemed to be more than minimal was coded 1. The collapse needed to be fairly obvious to the eye in order to be coded as 1. To achieve the mos...
Data and Methodology. 0.0.Xxxx sources and sample We start our sample with all French listed firms available in Worldscope database during 1998–2007. Following prior literature, we exclude financial firms (SIC codes 6000–6999), regulated utilities (SIC codes 4900–4999), and widely held firms (i.e., firms with no major shareholder owning more than 10% of total control rights). We discard firms that are headquartered in the French Overseas Departments and Territories because considerable distance between these regions and the Paris region may distort the location analysis and remove those with missing financial or governance data. The screening process results in a total of 710 firms making 4,111 firm–year observations. Data on ownership structure is manually gathered from firms’ annual reports whereas financial data and ZIP codes for firms’ headquarters are retrieved from the Worldscope database. Data on latitudes and longitudes of the location of the firms’ headquarters are collected from Maps of World.2
Data and Methodology. 3.3.1 Data This study employs two household-level datasets obtained from the UK Labour Force Service, originally collected in 2010 and 2016. The data-clearing process has filtered out individuals who are outside of working age (16 to 64) or have significant health issues or potentially require working visas. The data-clearing process also excludes the observations with incomplete sociodemographic information such as gender, education, nationality, employment, etc. The effectiveness of the datasets used in this study can be attributed to the large size of observations and sufficient sociodemographic information, thereby ensuring a high level of diversification in observations. However, these datasets only capture the sociodemo- graphic information and employment status of individuals at a specific point in time. This limitation has restricted our ability to track the evolution of individuals’ employ- ment throughout continuous time periods. Moreover, as is a common issue with census data, our datasets lack detailed wage-related information, such as gross monthly pay or hourly rate. This constraint makes it difficult for this paper to investigate the impacts of wage rigidity.
Data and Methodology. An outline of the analytical work follows:
Data and Methodology. Budgetary data for both revenues and expenditures have been obtained from the UK Office for National Statistics. The particular database employed in this paper is ‘Country and Regional Public Sector Finances’, covering the period from 2000 to 2019. In addition, the counterfactuals used to compare the simulated results with the actual income of devolved Scottish institutions have been obtained directly from the Budgetary Bills of Scotland’s govern- ment, as published in the country’s Official Gazette. Due to data limitations, I have made certain modifications to the original methodology, to better adapt it to the information available. Although the 71. Xxxxxx, “Xxxxx tax autonomy on Corporate Income Tax and European harmonization”. 72. Xxxxxxx, “El Concierto Económico de 1981”.
Data and Methodology. Data Description In this research, secondary data from certain sources are used. Import data is retrieved from the United Nations Commodity Trade Statistic Database, Department of Economic and Social Affairs. GDP data is from World Development Indicators, a database of the World Bank. Data on the distance between the capitals (or importers) and trading partners (exporters) are collected from
Data and Methodology. The paper considers the entire population of all network agreements signed under the new legislation framework defined by the Italian government with law n. 122/2010. At December 2012 this population includes 647 agreements recorded in the official Chamber of Commerce registers, at provincial and national level (Unioncamere), characterized by the following dimensions (Tab. 1). We can note that over 20% business entities present the typical organizational structure of entrepreneurial and small enterprises (individual businesses and partnership). In addition some recent studies [22] performed on the agreements formalized until December 2011 confirmed the small and medium size of the companies involved in the contracts, employing less than 10 people on 39.2% of the cases, from 10 to 19 employees on 19.4%, from 20 to 49 on 14.4% and over 50 for 12.0% of the total. Financial indicators of these companies complete the analysis and highlight the high presence of SME’s, presenting the following median values: 2,708,000 € of total asset, 2,275,000 € of turnover and EBITDA margin at 7.9%. The data base reported for each contract the following records: name of the network, strategic goals identified, date of set-up, names of the partners involved, VAT number and the statistic code identifying the sectors and sub-sectors where the members operate. Tab. 1. Population of “enterpirses’ network agreements” N. % - “Enterprises network agreements” Involving: - Regions 647 20 - Entities 3.360 100.0% of which: - limited companies 2.275 67.7% - partnership 437 13.0% - individual business 350 10.4% - mutual entities 228 6.8% - foundations and association 10 0.3% - others 60 1.8%
Data and Methodology. We have assembled a small collection of cases like those in (1)–(4) for both English and Finnish, using a moderately large corpus of everyday British and American English conversation as well as the Finnish Conversation Data Archive (located at the University of Helsinki). Currently there are approximately 54 exemplars in our collection, 27 for each language. The forms used in each exemplar have been tracked in tables like those shown above. For each division-of-labor case we have carried out a close analysis of the sequential and interactional context in which the structure is found using the methods of Conversation Analysis (see, e.g., Sidnell & Xxxxxxx 2013). At the same time we have analyzed the linguistic forms encountered using the methods of Interactional Linguistics (see, e.g., Xxxxxx-Xxxxxx & Xxxxxxx 3 The combining element ja ‘and’ is also documented in Finnish: see ex. (9) below. 2001). Our aim has been to understand what the division-of-labor structure is doing – why and when it is used – and how it is formed in the two languages, English and Finnish. We also wished to learn what similarities and differences there are between division-of-labor structures in the two languages in order to come to an appreciation of the language-independent and the language-specific dimensions of this phenomenon. In the following we first explore the activity contexts in which division- of-labor structures occur and propose what we believe is their rationale (section 3). Next, we explore the linguistic forms used to promote a division of labor in the two languages and point out the recurrent features of the division-of-labor patterns documented, analyzing some of the similarities and differences between English and Finnish variants of the practice (section 4). In conclusion, we single out the specific and unique characteristics of the division-of-labor practice as a combination of two clauses and two actions (section 5). Activity contexts and rationale for dividing the labor in talk-in-interaction
Data and Methodology. We use econometric estimates from De Cian and Xxx Xxxx (2017); the authors analyse per capita demand for three different final energy carriers associated with heating and cooling (electricity, petroleum products, and natural gas, in four economic sectors (agriculture, industry, residential, and commercial) for tropical and temperate countries as a function of per capita gross domestic product (GDP) and exposure to hot (>27.5 °C) and cold (<12.5 °C) days. We use the long-run temperature elasticities in combination with future changes in temperatures under two warming scenarios for the EU. We utilize population projections from the Shared Socioeconomic Pathways-2 (SSP2) to construct two baselines for global energy demand in 2050 and 2070 and compare them to a scenario without climate-change impacts. De Cian and Xxx Xxxx (2019), using data from 204 countries for 1970–2014, estimates elasticities and temperature semi-elasticities of sectoral energy demand. The relationship between energy demand, weather, income, and prices as a dynamic adjustment process using an Error- Correction Model (ECM)51. The authors find that temperature change impacts energy demand in a majority of energy carrier, sector, region combinations. Demand for energy increases heterogeneously with hot days across energy carriers and sectors. The paper also shows that extreme cold weather could reduce energy demand, especially in industry and agriculture. We combine these estimates with future projections of temperature. Our temperature projections are simulations of two representative concentration pathway scenarios (RCPs; van Xxxxxx et al., 2011) indicative of a high-warming scenario (RCP8.5) in which climate change is unabated and moderate-warming (RCP4.5)52 scenario in which mitigation policies are pursued. We use four different regional climate models (KNMI RACMO22E, IPSL-CM5A-MR, MPI-ESM-LR, and CNRM-CM5) to compute a multi-model mean. These bias-corrected and downscaled RCMs are part of the EURO-CORDEX53 climate simulations and are available at a spatial resolution of 12 KM. We define current and future climate as the mean of temperature between 1986-2005 (historical), 2030-2050, and 2050-2070, respectively. The climate data used as the input is the difference in the hot (>27.5 °C) and cold (<12.5 °C) bins between the historical and future periods.
Data and Methodology
