What makes the Difference between Unsuccessful and Successful Firms in the German Mechanical Engineering Industry?

Against a background of rising costs and increasing competition, it is besoming more and more difficult for the small and medium-sized firms of the German mechanical engineering industry to be economically successful. The thesis that rapidly changing markets, products and production processes cause serious economic problems for these firms is, however, a proposition on an average trend. A substantial number of firms are not only capable of coping with these conditions and challenges, but are even able to expand their business activities, including employment. We may hypothesize that their product and market strategies as well as their internal mode of operation and organization differs significantly from those firms doing economically less well. In order to test the significance of factors which could lead to different levels of success, operationalized with data of the NIFA panel the method of static microsimulation is applied using the program MICSIM. This particular method offers the possibility of reweighting the information contained in micro datasets according to restrictions given by aggregated data (i.e. marginal distributions). The latter will be chosen in such a way that the number of firms with properties (strategies), hypothetically leading to success in terms of lower excess capacity, are 'artificially', increased in the sample. The research goal is to find out whether such hypothetical strategies are supported by the data. The basic finding that certain complex strategies are more often successful demonstrates that unidimensional approaches to modernize production are of less value. Only in those strategies wehere organization of production, technical equipment, degree of vertical integration, products and customers are part of an intergrated innovational strategy, is success most likely to be fuelled.economic succes, NIFA PANEL, microsimulation, engineering

Sampling, Stratification, Expansion and Results of a Business Survey in the German Business-related Services Sector

No other area of the German economy has developed so emphatically in the past ten years as has that of business-related services. Regardless of its growing overall economic importance, official statistics fail to provide economic researchers and economic policy with current data on the business-realted service sector. In such a situation where quantitative information about certain sectors is lacking, data obtained from business surveys give important information on the state of economy. The outcome of such surveys crucially depends on the expansion factors attached to the responses of individual firms. In this paper it is shown how a robust method of calculating expansion factors can be obtained using known auxiliary totals from the population. Robust in this sense means that the expanded data of the ZEW/Creditreform business survey are insensitive to changes in the sample design while the non-expanded data are not

What makes the Difference between Unsuccessful and Successful Firms in the German Mechanical Engineering Industry?

Against a background of rising costs and increasing competition, it is besoming more and more difficult for the small and medium-sized firms of the German mechanical engineering industry to be economically successful. The thesis that rapidly changing markets, products and production processes cause serious economic problems for these firms is, however, a proposition on an average trend. A substantial number of firms are not only capable of coping with these conditions and challenges, but are even able to expand their business activities, including employment. We may hypothesize that their product and market strategies as well as their internal mode of operation and organization differs significantly from those firms doing economically less well. In order to test the significance of factors which could lead to different levels of success, operationalized with data of the NIFA panel the method of static microsimulation is applied using the program MICSIM. This particular method offers the possibility of reweighting the information contained in micro datasets according to restrictions given by aggregated data (i.e. marginal distributions). The latter will be chosen in such a way that the number of firms with properties (strategies), hypothetically leading to success in terms of lower excess capacity, are 'artificially', increased in the sample. The research goal is to find out whether such hypothetical strategies are supported by the data. The basic finding that certain complex strategies are more often successful demonstrates that unidimensional approaches to modernize production are of less value. Only in those strategies wehere organization of production, technical equipment, degree of vertical integration, products and customers are part of an intergrated innovational strategy, is success most likely to be fuelled

The ZEW - Creditreform business survey in the business-related services sector : sampling frame, stratification, expansion and results

No other area of the German economy has developed so emphatically in the past ten years as has that business-related services. Regardless of its growing overall economic importance, official statistics fail to provide economic researchers and economic policy with current data on the business-related service sector. In such a situation where quantitative information about certain sectors is lacking, data obtained from business survey give important information on the state of the economy. The outcome of such surveys crucially depends on the expansion factors attached to the responses of individual firms. In this paper it is shown how a robust method of calculating expansions factors can be obtained using known auxiliary totals from the population. Robust in the sense means that the expanded data of the ZEW/Creditreform business survey are insensitive to changes in the sample design while the non-expanded data are not. --expansion factors,business-related services

Data of exercise-induced arterial hypertension in triathletes

<p>Anthropometry parameters, training habits, echocardiography and spirometry data of 51 healthy male triathletes who completed an Ironman 70.3 or an Ironman full distance race are shown.<br>D = Ironman Distance, LD Long distance / MD = Ironman 70.3, A= Age, G= gender, We = Weight, H = Height, BSA = body surface area, %BF = %body fat, Tts = Training time swim, Tds= Training distance swim, Ttb= Training time bike, Tdb= Training distance bike, Ttr= Training time run, Tdr= Training distance run, Ttt= Total training time, Ts= Triathlon since, sbT= Sport before Triathlon, sbTs= Sport before triathlon since, HRmax= Heart Rate at exertion, HRVAT= Heart rate at ventilator anaerobic threshold, HRRCP= Heart rate at respiratory compensation point, HRLAM= Heart rate at Lactate threshold 4,0mmol/l (Mader), HRLAD= Heart rate at Lactate threshold according to Dickhuth, HRLAI= Heart rate at first nonlinear increase of blood lactate, Abs VO2max= Maximum oxygen uptake L/min, Abs. VO2VAT= Oxygen uptake at ventilatory anaerobic threshold L/min, Abs. VO2RCP= Oxygen uptake at respiratory compensation point L/min, Rel VO2max= Maximum oxygen uptake relative to body weight mlL/min/kg, Rel VO2VAT= Oxygen uptake at ventilator anaerobic threshold relative to body weight mlL/min/kg, Rel VO2RCP= Oxygen uptake at respiratory compensation point relative to body weight mlL/min/kg, %VO2maxAT= Oxygen uptake at ventilator anaerobic threshold as percentage of maximum oxygen uptake, %VO2maxRCP= Oxygen uptake at respiratory compensation point as percentage of maximum oxygen uptake, VEmax= Maximum minute ventilation, O2HFmax= Maximum O2 Pulse, RERmax= Maximum Respiratory Exchange Ratio, BLCmax= Blood lactate concentration at exertion, Wmax= Maximum ergometer performance (Watt), WAT= Ergometer performance at ventilator anaerobic threshold, WRCP= Ergometer performance at respiratory compensation point, WLAM= Ergometer performance at Lactate threshold 4,0mmol/l (Mader), WLAD= Ergometer performance at Lactate threshold according to Dickhuth, WLAI= Ergometer performance at first nonlinear increase of blood lactate, Wmax/kg= Maximum ergometer performance in relation to body weight (Watt/kg), BPsRest= Systolic blood pressure at rest, BPdRest= Diastolic blood pressure at rest, BPsVAT= Systolic blood pressure at ventilator anaerobic threshold, BPdVAT= Diastolic blood pressure at ventilator anaerobic threshold, BPsRCP= Systolic blood pressure at respiratory compensation point, BPdRCP= Diastolic blood pressure at respiratory compensation point, BPsWmax= Systolic blood pressure at exertion, BPdWmax= Diastolic blood pressure at exertion, Ao= Aortic root dimension, LA= Left atrial diameter, IVSd= Inter-ventricular septum in diastole, LVIDd= Left ventricular internal diameter in diastole, LVPWd= Left ventricular posterior wall in diastole, %IVS= Percentage of thickening of the inter-ventricular septum form diastole to systole, LVIDs= Left ventricular internal diameter in systole, %FS= Fractional shortening, LVPWs= Left ventricular posterior wall in systole, LV Mass (ASE) = left ventricular mass according to the ASE recommended formula, LVEDV MOD A4c ml= Left ventricular end-diastolic volume calculated according to the Simpson method in apical 4 chamber view, LVESV MOD A4c ml= Left ventricular end-systolic volume calculated according to the Simpson method in apical 4 chamber view, LVEF MOD A4c= Left ventricular ejection fraction calculated according to the Simpson method in apical 4 chamber view, SV Mod A4C ml= Stroke volume calculated according to the Simpson method in apical 4 chamber view, EF Biplan= Left ventricular ejection fraction calculated according to the Simpson method in apical 4 and 2 chamber view, LVEDV MOD BP ml= Left ventricular end-diastolic volume calculated according to the Simpson method in apical 4 and 2 chamber view, LVESV MOD BP ml= Left ventricular end-systolic volume calculated according to the Simpson method in apical 4 and 2 chamber view, MV E Max m/s= Mitral valvular E-Wave m/s, MV A Max m/s= Mitral valvular A-Wave m/s, MV E/A Ratio= Mitral valvular E/A Ratio, LVOT Vmax= Left ventricular outflow tract maximum velocity in PW-Doppler, HR Rest= Heart frequency at rest, RWT= Relative wall thickness, RV parasternal= Right ventricular diameter in the parasternal long axis, RVDA = Right venrticular area in diastole, RVSA= Right ventricular area in systole, RV FAC= Area change fraction RVSAx100/RVDA.</p