Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe issue whether regional development can be associated with population driving employment changes or employment driving population changes (do 'jobs follow people' or 'people follow jobs'?) has recently attracted considerable interest. Much of the interest herein stems from alleged inconsistencies in the empirical evidence, which naturally raises questions as for the reasons why.Arguably, the nature of causality differs across space as well as time, while speculations have been rife about a number of methodological issues that may play a crucial role in shaping the research outcomes. In this paper a preliminary attempt is described in clarifying these matters, by focusing on an articulate literature of 37 so-called 'Carlino-Mills studies'. Specifically, a statistically supported literature review, referred to as 'meta-analysis', is provided in which the study results are evaluated and systematically related to a variety of underlying study characteristics. By listing 308 study results reported in this literature, it is revealed that the empirical evidence is conform popular belief highly inconclusive, albeit that most results point towards 'jobs follow people'. The findings of the meta-regression analysis indicate that the spatial characteristics of the data, model specification, and variables measurement in particular affect the research outcomes that indicate the jobs-people direction of causality. No evidence is found that the examination of data referring to a particular time period, population and/ or employment group make much of a difference.

This article reports about a metaregression analysis of empirical results generated using data for the northern Netherlands (1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002) in order to investigate the ambiguity in results in the population-employment interaction literature. Specifically, the analysis deals with the issue whether ''jobs follow people'' or ''people follow jobs.'' The article starts with introducing the basics of quasi-experimental meta-analysis and with identifying some advantages of using quasi-experimental meta-analysis as compared with the standard meta-analysis approach. Two subsequent sections document the selection of the population-employment interaction model and salient characteristics of the data set as well as the setup of the primary analyses. A total of 4,050 quasi-experimental empirical results for the jobs-people direction of causality are generated using different specifications and estimators for a spatial econometric interaction model. The subsequent metaregression analysis reveals that the empirical results are largely shaped by the spatial, temporal, and employment characteristics of the data sampling. The results also appear much more sensitive to different measurements of the model's key variables when compared with alternative specifications of the spatial weights matrix. The main determinant driving empirical results about jobs-people causality are differences in model specification and estimation, as revealed by an inherent bias in parameter estimates and misguided inferences for some of the commonly used specifications. Finally, suggestions for future research are identified.

This article summarizes a spatial econometric analysis of local population and employment growth in the Netherlands, with specific reference to impacts of gender and space. The simultaneous equations model used distinguishes between population- and gender-specific employment groups, and includes autoregressive and cross-regressive spatial lags to detect relations both within and among these groups. Spatial weights matrices reflecting different bands of travel times are used to calculate the spatial lags and to gauge the spatial nature of these relations. The empirical results show that although population–employment interaction is more localized for women's employment, no gender difference exists in the direction of interaction. Employment growth for both men and women is more influenced by population growth than vice versa. The interaction within employment groups is even more important than population growth. Women's, and especially men's, local employment growth mostly benefits from the same employment growth in neighboring locations. Finally, interaction between these groups is practically absent, although men's employment growth may have a negative impact on women's employment growth within small geographic areas. In summary, the results confirm the crucial roles of gender and space, and offer important insights into possible relations within and among subgroups of jobs and people.