J.C. Haltiwanger

Bio: J.C. Haltiwanger is an academic researcher. The author has contributed to research in topics: Respondent. The author has an hindex of 1, co-authored 1 publications receiving 136 citations.

The Creation and Analysis of Employer-Employee Matched Data

Abstract: These papers were presented at the International Symposium of Linked Employer-Employee Data, Washington DC, May 1998, to address the creation and analysis of such matched data in an environment that safeguards respondent confidentiality, and looks at the analysis of such linked data, related econometric issues, and creating large-scale linked data.

The impact of training on productivity and wages: Evidence from British panel data

Abstract: It is standard in the literature on training to use wages as a sufficient statistic for productivity. This paper examines the effects of work-related training on direct measures of productivity. Using a new panel of British industries 1983–96 and a variety of estimation techniques we find that work-related training is associated with significantly higher productivity. A 1% point increase in training is associated with an increase in value added per hour of about 0.6% and an increase in hourly wages of about 0.3%. We also show evidence using individual-level data sets that is suggestive of training externalities.

A Behavioral Account of the Labor Market: The Role of Fairness Concerns

Abstract: In this paper, we argue that important labor market phenomena can be better understood if one takes (a) the inherent incompleteness and relational nature of most employment contracts and (b) the existence of reference-dependent fairness concerns among a substantial share of the population into account. Theory shows and experiments confirm that, even if fairness concerns were to exert only weak effects in one-shot interactions, repeated interactions greatly magnify the relevance of such concerns on economic outcomes. We also review evidence from laboratory and field experiments examining the role of wages and fairness on effort, derive predictions from our approach for entry-level wages and incumbent workers' wages, confront these predictions with the evidence, and show that reference-dependent fairness concerns may have important consequences for the effects of economic policies such as minimum wage laws.

Longitudinal analyses of the effects of trade unions

Abstract: But union members are different from nonmembers in unobserved ways, biasing your estimates. Y ou should ... make a selectivity bias correction ... simultaneously determine union status and economic outcomes ... develop an unobservables model ... USE LONGITUDINAL DATA. (Archetypical comment on virtually any study of the economic effects of umomsm, or suitably modified, on any other empirical subject.)

The job satisfaction-productivity nexus: A study using matched survey and register data

Abstract: This paper examines the role of job satisfaction in the determination of establishment-level productivity. The matched data contain both information on job satisfaction from the ECHP (European Community Household Panel) and information on establishment productivity from longitudinal register data that can be linked to the ECHP. The estimates for the effect of a one point increase in the establishment average level of employee job satisfaction, on a scale 1-6, on productivity vary depending on the specification of the model. The preferred estimate, based on the IV estimation that uses satisfaction with housing conditions as an instrument for job satisfaction, shows that the effect on value added per hours worked is ~20% in the manufacturing sector. The economic size of this effect is modest, because the observations are bunched towards the higher end of the satisfaction scale making it very difficult to increase the average level of job satisfaction in the establishment by one point.

High wage workers and low wage firms: negative assortative matching or limited mobility bias?

Abstract: Summary. In the empirical literature on assortative matching using linked employer–employee data, unobserved worker quality appears to be negatively correlated with unobserved firm quality. We show that this can be caused by standard estimation error. We develop formulae that show that the estimated correlation is biased downwards if there is true positive assortative matching and when any conditioning covariates are uncorrelated with the firm and worker fixed effects. We show that this bias is bigger the fewer movers there are in the data, which is ‘limited mobility bias’. This result applies to any two-way (or higher) error components model that is estimated by fixed effects methods. We apply these bias corrections to a large German linked employer–employee data set. We find that, although the biases can be considerable, they are not sufficiently large to remove the negative correlation entirely.