Emergence of Industries: A Review and Future Directions

This paper reviews the literature on the emergence of industries and the theoretical and methodological approaches employed. The analysis reveals that industry emergence can be depicted as a three‐stage process. In the first, initial stage, a disruption to the existing industrial order triggers the second, the co‐evolutionary stage, which includes four sub‐processes related to developments in technology, markets, activity networks and industry identity. The convergence of these sub‐processes leads to the third stage, a growth stage and the birth of a new industry. While these three stages and the four sub‐processes are well covered in the literature, the authors find that there is a lack of understanding in terms of the transitions between the stages, the interactions and interdependencies between sub‐processes and moderating factors of industry emergence. Future research can bridge these gaps by exploring the different origins and initial conditions of industries, the processes and interactions in the earliest stages of industry emergence, and the role of facilitating and managing industry emergence. This implies a shift in the research focus from the industries that have emerged to the nascent processes of emergence.     

Study Design in Causal Models

The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing‐data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions on whether a causal or observational relationship can be estimated from the collected incomplete data can be made directly from the graph. Causal models with design offer a systematic and unifying view to scientific inference and increase the clarity and speed of communication. Examples on the causal models for a case–control study, a nested case–control study, a clinical trial and a two‐stage case–cohort study are presented.     

The Effects of Education on the Duration of Unemployment

ABSTRACT This paper studies the relationship between the level of education and the probability of re-employment. Using a search theoretical model, it is shown that on the lowest levels additional education increases the probability of re-employment, but on the highest levels the relationship turns negative. Finnish microeconomic data on unemployed workers show that unemployed people who have 13–14 years of education have the highest re-employment probability.