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Germany’s job market is in a deep crisis. Economists are in general agreement that unemployment in Germany is not the result of economic cycles but is, instead, rooted in structural causes. The original German model of social market economy () emphasized the principle of subsidiarity and thus self-help. However, the development of unions and employer associations, and the resulting actions of political parties interested in the patronage of such entities, have changed the original German model into one of an all-embracing welfare state that guarantees individuals their acquired standard of living. This evolution is partly due to Germany’s history of being a protected industrial society where labor agreements could be negotiated between employers and employees that promised noncompetitive wages regardless of the employees’ productivity, a situation particularly true for unskilled and semi-skilled employees. Employers profited from conflict-free relations with their employees and thus from relatively few strike days. Individuals who remained unemployed due to non-market-clearing wages and excessive individual reservation wages did not suffer much, if at all, due to the elaborate welfare state that guaranteed them a high standard of living. In fact, there was very little incentive to seek full- or even part-time employment.

Does new business formation cause economic growth? Much recent research, initiated by Fritsch and Mueller (2004), has been devoted to this question (for an overview of this literature, see Fritsch 2007). Most studies find that long-run (supply-side) effects of new business formation are more pronounced than the direct short-run effects. Fritsch and Mueller (2004) enumerate four categories of these supply-side effects: securing efficiency, acceleration of structural change, amplified innovation, and greater variety of products. All research on this topic to date has in common that it analyzes the short-run and long-run relationship between new business formation and economic development by means of distributed lag models.

Globalization has had an enormous impact on traditional industrial structures — one could even go so far as to say a “shattering” impact. Increasing competition has led to a greater variety of products at low price-cost margins and sellers’ markets are rapidly evolving into buyers’ markets. Today, consumers expect increasingly customized products so that mass production capability is not necessarily the advantage it once was (Piore and Sable 1984). This is especially true for the automotive industry (Womack et al. 1990), where statistics show that producing two cars that are exactly the same color and have exactly the same equipment options happens about as often as a blue moon.

There is little doubt that new business formation plays an important role in the process of economic development (Fritsch and Mueller 2004; van Stel and Storey 2004; Carree and Thurik 2003). Each new business or market entry represents a challenge to the incumbents and, consequently, may generate significant incentives for improvements. The determinants of new business formation have been investigated theoretically and empirically in a number of ways. Most empirical studies in this field are cross-sectional analyses of different industries or regions. Longitudinal analyses of new business formation processes are rather rare. A severe shortcoming of these analyses is that most of them are limited to only one category of influence — industry, space or time — and tend to neglect other factors. The types of influences that are accounted for is mainly due to the approach chosen. For example, cross-sectional analyses limited to the industry level can only investigate the role of industry characteristics (e.g., minimum efficient size, capital intensity) but not regional determinants such as population density or workforce qualifications. Without accounting for the regional dimension, however, in the case of such industry-level studies, reliable results cannot be attained if the importance of a certain factor, such as innovation conditions, varies significantly across regions. Additionally, if certain regional conditions stimulate new business formation in some industries but deter start-ups in other industries, the effect of space on the formation of new businesses cannot be adequately assessed by means of an interregional approach that does not account for different industries.

Setting up a firm can be an arduous task. Entering a market and competing successfully is subject to severe uncertainty and requires diverse qualifications that are rarely contained in one single person. As a result, a considerable proportion of new firms leave the market relatively soon after entering; thus, in some industries or regions only a minority of the entrants is able to survive for a longer period of time.

It has been said that the great thing about starting your own business is that you get to decide which 24 hours of the day you will work. Maybe it is owner exhaustion that leads to such a high failure rate among new businesses! Joking aside, though, the subject of new business failure has generated extensive empirical research using econometric methods of survival time analysis, covering numerous countries as well as varying time periods. Nevertheless, it is worth revisiting this topic for at least two reasons: First, while industry characteristics are broadly taken into account in micro-econometric survival time analyses, the same is not true of the regional dimension. One main reason for this deficit may be the lack of adequate data for considering the regional dimension. However, if the regional dimension of the data is not considered, estimates may be inefficient and the standard errors may be estimated wrongly due to regional dependency in the error terms.