The determinants of dismissals, quits and layoffs: a multinomial logit approach.

• Author: Campbell, Carl M., III

1. Introduction

   In efficiency wage models, workers' productivity depends positively on the wage, giving firms an incentive to pay wages above their market-clearing level. Two models in this literature are the shirking model of Shapiro and Stiglitz [12] and Bowles [1] and the turnover cost model of Stiglitz [13], Schlicht [11], and Salop [10]. In the shirking model, a higher wage and a higher unemployment rate raise the cost of losing one's job, thereby discouraging workers from shirking and increasing their effort. In the turnover cost model, a higher wage reduces quits and thus lowers the firm's cost of hiring and training new workers. Given the role of separations in efficiency wage models, it is important to understand the determinants of separations. This study uses data on individual workers to examine the determinants of dismissals, quits, and layoffs within the first six months of a hire, paying particular attention to dismissals because of the prominence of the shirking model in the efficiency wage literature. The purposes of this study are to test whether higher unemployment lowers dismissals, as predicted by the shirking model, and to examine the effect of firm variables related to the costs of monitoring and shirking on the probability of a dismissal. In addition, this study provides insight into the effects of firm characteristics and worker characteristics on dismissals, quits, and layoffs.(1)

2. Results

   The equations for dismissals, quits, and layoffs(2) were estimated with data from the Employment Opportunity Pilot Project (EOPP) survey of employers, which was conducted in the spring of 1980. 5302 firms in 28 geographic locations (in 11 states(3)) and 63 2-digit SIC industries were interviewed. The survey included questions concerning the personal characteristics of the last worker hired by the firm between 1 January 1978 and 1 October 1979. Firms were also asked whether this worker was still employed at the firm, and if not, whether the separation was a quit, a layoff, a discharge, or an induced resignation. A worker was considered to be dismissed if he or she were discharged or induced to resign. Table I lists the variables' names, descriptions, means, and standard deviations.

   In the shirking model,(4) workers' effort depends on the probability of finding another job if dismissed, which, in turn, depends on the unemployment rate. Two unemployment rates are considered: the unemployment rate in the local labor market and the unemployment rate in the 2-digit SIC industry.(5) Dismissals should also depend negatively on the cost of monitoring since firms with high monitoring costs would be expected to monitor their workers less intensely. This cost cannot be measured directly, so it was proxied by the firm's size since Bulow and Summers [2] and Rebitzer and Robinson [9] suggest that monitoring costs are likely to be higher at large firms. In addition, dismissals should depend positively on the capital-labor ratio since firms with high capital-labor ratios will suffer the greatest losses if their workers shirk and waste the services of the capital with which they work or damage it through their carelessness.(6)

   These variables may also affect the probability of a quit or a layoff. High unemployment is likely to reduce quits, since fewer workers should quit if jobs are scarce. On the other hand, high unemployment may raise layoffs, since firms are most likely to lay off workers during recessions. Firm size is likely to raise the cost of hiring and training workers, since part of the cost of being trained involves learning the way the organization operates and forming working relationships with co-workers across the organization. Thus, larger firms have a greater incentive to retain workers during economic downturns and to take actions to discourage workers from quitting. [TABULAR DATA FOR TABLE I OMITTED] Firms with a high capital-labor ratio also have an incentive to retain workers, since inexperienced workers would be the most likely to damage or misuse the capital with which they work. Thus, we would expect quits and layoffs to depend negatively on the capital-labor ratio.

   The firm's unionization rate is included in the regressions, since regulations in unionized firms may make dismissing workers more difficult and since unions can generally negotiate a high wage for their workers, making these workers less likely to shirk or to quit. In addition, according to Freeman [6], unions provide a voice for workers' grievances, reducing the probability that they will quit if dissatisfied with their jobs. Furthermore, unions tend to make wages more rigid, so that highly unionized firms may be more likely to lay off workers in response to a reduction in demand.

   Several regional variables are included in the regressions. The first is the ratio of state Unemployment Insurance (UI) benefits to wages in that state.(7) In addition, a dummy variable was created for Washington since a firm's UI tax in that state depends on the change in its payroll from year-to-year and not on the number of workers it discharges or lays off. Because a firm's UI tax in that state is less related to its dismissal and layoff behavior than in other states, we would expect more dismissals and layoffs to occur in Washington. A dummy variable was also created for states in which a firm's UI tax is not raised if it dismisses a worker for misconduct, as firms in these states would be more likely to dismiss workers. Finally, a dummy variable was created for rural areas since labor markets are generally thinner in these areas, making it more difficult for workers to find a new job at a given level of the unemployment rate. We...