Start‐up Motivations and Growth Intentions of Minority Nascent Entrepreneurs

Firm growth is widely considered to be a measure of success for entrepreneurial businesses. Data indicate that there are systematic differences between minority and nonminority‐owned firms with respect to growth. Black entrepreneurs are 50 percent more likely to engage in start‐up activities than white entrepreneurs, however, black‐owned firms are smaller and less profitable than their white‐owned counterparts. Following the effort–performance–outcome–logic of expectancy theory and using data from the Panel Study of Entrepreneurial Dynamics (PSED), our paper investigates the differences between black and white entrepreneurs' motivations to start and intentions to grow a new venture. Findings indicate that there are significant differences in motivations between black and white entrepreneurs both in starting and in their intentions to grow the new venture. Implications for future research are discussed.     

Recent Progress on Endogeneity in Choice Modeling

We describe recent progress in several areas related to endogeneity, including: choice set formation and attention to attributes; interactions among decision-makers; respondents' strategic behavior in answering stated preference choices; models of multiple discrete/continuous choice; distributions of willingness-to-pay; and methods for handling traditionally endogenous explanatory variables.     

The Multiple Effects of Business Planning on New Venture Performance

We investigate the multiple effects of writing a business plan prior to start‐up on new venture performance. We argue that the impact of business plans depends on the purpose for and circumstances in which they are being used. We offer an empirical methodology which can account for these multiple effects while disentangling real impact effects from selection effects. We apply this to English data where we find that business plans promote employment growth. This is found to be due to the impact of the plan and not selection effects.     

APPLICATION OF PANEL DATA MODELS IN BENCHMARKING ANALYSIS OF THE ELECTRICITY DISTRIBUTION SECTOR*

ABSTRACT ** : This paper explores the application of several panel data models in measuring productive efficiency of the electricity distribution sector. Stochastic Frontier Analysis has been used to estimate the cost‐efficiency of 59 distribution utilities operating over a nine‐year period in Switzerland. The estimated coefficients and inefficiency scores are compared across three different panel data models. The results indicate that individual efficiency estimates are sensitive to the econometric specification of unobserved firm‐specific heterogeneity. This paper shows that alternative panel models such as the ‘true’ random effects model proposed by Greene (2005) could be used to explore the possible impacts of unobserved firm‐specific factors on efficiency estimates. When these factors are specified as a separate stochastic term, the efficiency estimates are substantially higher suggesting that conventional models could confound efficiency differences with other unobserved variations among companies. On the other hand, refined specification of unobserved heterogeneity might lead to an underestimation of inefficiencies by mistaking potential persistent inefficiencies as external factors. Given that specification of inefficiency and heterogeneity relies on non‐testable assumptions, there is no conclusive evidence in favour of one or the other specification. However, this paper argues that alternative panel data models along with conventional estimators can be used to obtain approximate lower and upper bounds for companies' efficiency scores.     

Understanding the Theoretical and Empirical Content of Critiques of U.S. Job Creation Research

Although the hypothesis that small firms create the majority of net new jobs is widely believed, a number of researchers have attacked this using empirical data. Since these attacks have been presented as corrections of past methodological errors, their authors may influence policy makers. This paper argues that the substance of these attacks is a difference in theoretical perspectives even though the researchers claim to focus on methodological differences. This paper explains the two underlying economic theories and then reviews the job creation research to demonstrate the relationship between theory and methodology.The theory discussion and methodological results demonstrates that all methodologies used to calculate small firms share of net new jobs are incorrect for analyzing the dynamics of capitalism. The appropriate methodology for understanding economic growth is analysis of new employment created over time by cohorts of newly formed firms. Recognition of this fact, combined with describing the empirical research supporting this approach, reinforces the use of economic development policies designed to promote entrepreneurship.     

Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies

The persistence of increasingly high government subsidies in Switzerland’s railroads has led the federal and cantonal authorities to discussing the possibility of high-powered incentive contracts such as those based on cost efficiency benchmarking. Railways are however, characterized by a high degree of unobserved heterogeneity that could bias the efficiency estimates. This paper examines the performance of several panel data models to measure cost efficiency in network industries. The unobserved firm-specific effects and the resulting biases are studied through a comparative study of several stochastic frontier models, applied to a panel of 50 railway companies operating over a 13-year period.* The authors wish to thank Michael Crew and two anonymous referees for their helpful suggestions. Aurelio Fetz provided an excellent assistance, which is gratefully acknowledged. Any remaining errors are the responsibility of the authors.This revised version was published in June 2005 with corrections in the author affiliations.