2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Statistical Analysis with Missing Data

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

Economics Of Discrimination

High-growth firms (HGFs) have attracted considerable attention recently, as academics and policymakers have increasingly recognized the highly skewed nature of many metrics of firm performance. A small number of HGFs drives a disproportionately large amount of job creation, while the average firm has a limited impact on the economy. This article explores the reasons for this increased interest, summarizes the existing literature, and highlights the methodological considerations that constrain and bias research. This special section draws attention to the importance of HGFs for future industrial performance, explores their unusual growth trajectories and strategies, and highlights the lack of persistence of high growth. Consequently, while HGFs are important for understanding the economy and developing public policy, they are unlikely to be useful vehicles for public policy given the difficulties involved in predicting which firms will grow, the lack of persistence in high growth levels, and the complex and often indirect relationship between firm capability, high growth, and macro-economic performance.

/pdf/high-growth-firms-introduction-to-the-special-section-3wwabrngu7.pdf

High-growth firms: Introduction to the special section

Most firms do not grow, and a small number of high-growth firms seem to create most new jobs. These firms have therefore received increasing attention among policymakers. The question is whether high-growth tends to persist? We investigate this question using firm-level data from Sweden during 1997–2008. We find that high-growth firms had declining growth rates in the previous 3-year period, and their probability of repeating high growth rates was very low. Thus, these are essentially “one-hit wonders,” and it is doubtful whether policymakers can improve economic outcomes by targeting them.

Are high-growth firms one-hit wonders? Evidence from Sweden

Prior studies have defined high-growth firms (HGFs) in terms of growth in firm employment or firm sales, and primarily analyzed their contribution to overall employment growth. In this paper we define HGFs using the commonly applied growth indicators (employment and sales), but also add definitions based on growth in value added and productivity. Our results indicate that HGFs in terms of employment are not the same firms as HGFs in terms of productivity, and that their economic contributions differ significantly. Economic policy promoting fast growth in employment may therefore come at the cost of reduced productivity growth. Although HGFs of different definitions may not be the same firms, young firms are more likely to be HGFs irrespective of definition. This suggests that economic policy should focus on the conditions for new firm formation and early growth of firms, rather than target a particular type of HGFs.

The Economic Contribution of High-Growth Firms: Do Policy Implications Depend on the Choice of Growth Indicator?

The purpose of this article is to investigate if the industry context matters for whether Gibrat’s law is rejected or not using a dataset that consists of all limited firms in five-digit NACE-industries in Sweden during 1998–2004. The results reject Gibrat’s law on an aggregate level, since small firms grow faster than large firms. However, Gibrat’s law is confirmed about as often as it is rejected when industry-specific regressions are estimated. It is also found that the industry context—e.g., minimum efficient scale, market concentration rate, and number of young firms in the industry—matters for whether Gibrat’s law is rejected or not.

https://ratio.se/app/uploads/2014/11/sod_ne_gibrat_158.pdf

When is Gibrat’s law a law?

We study employment and new hires among high-growth firms (HGFs) in the Swedish knowledge-intensive sectors 1999-2002. Using matched employer-employee data, we find that HGFs are more likely to emp ...

/pdf/whom-do-high-growth-firms-hire-21hbrt7n6v.pdf

Sven-Olov Daunfeldt

Papers

High-growth firms: Introduction to the special section

Are high-growth firms one-hit wonders? Evidence from Sweden

The Economic Contribution of High-Growth Firms: Do Policy Implications Depend on the Choice of Growth Indicator?

When is Gibrat’s law a law?

Whom do high-growth firms hire?