I analyse a model that explicitly incorporates local interactions and allows agents to exchange information about job openings within their social networks. Agents are more likely to be employed if their social contacts are also employed. The model generates a stationary distribution of unemployment that exhibits positive spatial correlations. I estimate the model via an indirect inference procedure, using Census Tract data for Chicago. I find a significantly positive amount of social interactions across neighbouring tracts. The local spillovers are stronger for areas with less educated workers and higher fractions of minorities. Furthermore, they are shaped by ethnic dividing lines and neighbourhood boundaries.

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We develop a framework where mismatch between vacancies and job seekers across sectors translates into higher unemployment by lowering the aggregate job-finding rate. We use this framework to measure the contribution of mismatch to the recent rise in U.S. unemployment by exploiting two sources of cross-sectional data on vacancies, JOLTS and HWOL, a new database covering the universe of online U.S. job advertisements. Mismatch across industries and occupations explains at most 1/3 of the total observed increase in the unemployment rate, whereas geographical mismatch plays no apparent role. The share of the rise in unemployment explained by occupational mismatch is increasing in the education level.

To examine if the theory of planned behavior (TPB) predicts smoking behavior, 35 data sets (N = 267,977) have been synthesized, containing 219 effect sizes between the model variables, using a meta-analytic structural equation modeling approach (MASEM). Consistent with the TPB’s predictions, 1) smoking behavior was related to smoking intentions (weighted mean r = 0.30), 2) intentions were based on attitudes (weighted mean r = 0.16), and subjective norms (weighted mean r = 0.20). Consistent with TPB’s hypotheses, perceived behavioral control was related to smoking intentions (weighted mean r = −0.24) and behaviors (weighted mean r = −0.20) and it contributes significantly to cigarette consumption. The strength of the associations, however, was influenced by the characteristics of the studies and participants.