The Cross Section Estimation

Published Date: 02 Nov 2017

In Table education has a significant impact on establishment productivity in equation when the total number of workers is used as a measure of labor inputs. The coefficient on average educational level for the establishment suggests that raising the average educational level of the establishment by 10 per cent (approximately one more year of school) would increase productivity by approximately 5 per cent. This is similar to findings reported in Black and Lynch. However, the effect of education disappears when workers are divided into production and non-production workers in equations and. In this sample, there is relatively little variation in education once you distinguish between production and non-production workers. Production workers typically have just a high school degree while non-production workers have some college. Employers appear to adjust the educational quality of their workforce by changing the mix of production and non-production workers rather than hiring more educated production workers. Since there has been relatively little net new hiring in manufacturing in recent years, this finding makes sense.

None of the training variables we tried to include in our regressions are statistically significant. In previous work (Black and Lynch (1996)), using a larger sample of establishments, we found that the proportion of time spent training workers off-the-job raised establishment productivity in the manufacturing sector. This means that our estimates of the impact of training are most likely underestimates of the true returns to training. But given our finding that the proportion of non-managerial workers using computers has a significant and positive relationship to establishment productivity, we conclude that human capital investments can have an important impact on labor productivity. While new entrants into the labor market are more and more likely to have computer skills, new ways will have to be found to help incumbent workers acquire these skills either through their employers or off-site at their local community colleges or training institutes.

Workplace practices have very interesting effects on labor productivity. In particular, we find that simply introducing high performance workplace practices is not enough to increase establishment productivity. The increased employee voice that is associated with these practices seems to be a necessary condition to making the practices effective. For example, although almost three-quarters of all of the establishments in our sample have some form of a Total Quality Management (TQM) system in place, TQM is not itself associated with higher productivity. Instead, the percentage of workers involved in regular decision-making meetings is positively related to labor productivity. On average, about 54 per cent of employees in our sample are involved in some sort of regular meeting to discuss workplace issues. Benchmarking and profit sharing for production workers, both considered high performance workplace practices, are also associated with higher establishment productivity, while higher employee turnover is associated with lower establishment productivity.

Given that workplace practices are related to establishment productivity, it is interesting to think about complementarities of these practices. We tried a wide range of interaction effects and found that most were not even remotely significant. However, equation in Table presents results when we interact unionization and TQM, unionization and profit sharing for non-managers, the percentage meeting in groups and profit sharing for non-managers, and the percentage meeting in groups and TQM. When these interactions are included, the own effect of unionization becomes significant and negative while the interactions of unionization and profit sharing for non-managers and unionization and TQM are significant and positive. This indicates that more traditional labor management relations, where employees have little voice in decision-making and pay is not linked to performance, is associated with lower establishment productivity. At the same time, more cooperative unionized labor management relations (where employees have a greater role in decision-making but also have part of their compensation linked to firm performance) are associated with higher labor productivity.

Other results of interest that are not reported in Table include the lack of significance of the percentage of employees who are women or minorities. In manufacturing, everything else held constant, we find that there seems to be little evidence of lower productivity associated with hiring a larger proportion of women or minorities. In addition, we find that newer establishments have significantly higher productivity, all else constant, than older establishments.

In Table, we take the regression coefficients from equation in Table and present some different prototype plants to see how various combinations of workplace practices are related to labor productivity. We construct a base case which is a non-union multi-establishment plant, has profit sharing for managers but no profit sharing for non-managers, no TQM, no benchmarking, 1 per cent of employees meeting regularly about work issues, 10 per cent of non-managerial workers using computers, 1 per cent of employees in self-managed teams, zero values for interaction terms and mean values for all remaining continuous variables. We then alter some of the characteristics of this base case to see how labor productivity changes. If we make the plant unionized with no employee involvement, productivity drops by 15 percentage points. If instead we increase the percentage of non-managerial workers using computers from 10 per cent to 50 per cent, labor productivity increases almost 5 percentage points. Introducing workplace practices associated with what have been called ‘high performance work systems’ has large and positive effects on productivity. If we change the percentage of non-managers using computers to 50 per cent, have 50 per cent of workers meeting to discuss workplace issues regularly, profit sharing for non-managers, 30 per cent of workers in self-managed teams, TQM, and benchmarking, labor productivity increases almost 11 percentage points. Finally, if we introduce all of these ‘high performance workplace practices’ and make the plant unionized, productivity increases by 20 percentage points above the base case. This table helps highlight the synergies of workplace practices. In particular, those unionized firms who have succeeded in moving to a more cooperative labor management relations system which gives employees more voice in decision-making but at the same time links their compensation with performance have higher labor productivity.

Panel Data Two Step Estimation Based on Within Estimator. In this section we discuss how the results in Table 1 alter when we incorporate panel data on establishment inputs and outputs into the estimation to attempt to control for unobserved time invariant characteristics of the establishment. Our first step is the estimation of a simple Cobb Douglas production function with establishment fixed effects using the panel data from the LRD that includes controls for capital, labor, materials, and industry by time dummies. We again test and accept the restriction of constant returns to scale so our dependent variable is sales per production worker.

The estimates from the first stage estimation using the within estimator are reported in Table. Again, capital is small although still significant and positive10. Since we had to construct a measure of the capital stock there is likely to be significant measurement error in our proxy for the capital stock. Using these first step estimates we then calculated the average residual for each establishment in the sample. The second column in Table 3 contains the second step results from regressing the average residual on various workplace practices and employee characteristics. Again we see that the proportion of non-managerial workers using computers has a significant and positive effect on having higher than average productivity over the period 1988S1993. However, production workers’ education is now positively related to those businesses that did better on average over this six year period. TQM is negatively associated to the average residual, while benchmarking is positively associated. Unionization itself has no significant effect on which businesses did better or worse on average, but the interaction of unionization and profit sharing for non-managers is associated with better than average performance. In addition, we also find that those employers who cite communication skills as a priority in recruitment also did better than average over the period of 1988S1993. These findings are consistent with the idea that increased employee voice is positively related to establishment productivity, and that new forms of labor-management relations are significantly related to better performing businesses.

Panel Data Two Step Estimation Based on GMM Estimator. While the fixed effects estimator corrects for the omitted variable bias associated with unobserved time invariant factors in the cross-section estimation, the fact that current values of capital, labor, and materials are simultaneously determined with output leads to an upward bias of the estimates. However, measurement error in the capital and materials variables may be biasing our first step estimates of the vector of coefficients a’ on capital, labor and materials. In order to attempt to correct for this endogeneity bias, we use generalized method of moments (GMM) techniques to instrument for capital, labor, and materials.

It is important to note that if the coefficients in the equation using the within estimator are tainted because of measurement error, we would expect to see larger and more significant coefficients in the GMM first differences estimation. This is in fact what we see for capital. If one calculates what our reported GMM estimates in Table 3 imply about the share of labor (production and non-production workers) in value added (output minus materials costs), we find that labor accounts for two thirds of value added and capital one third. This is consistent with what we see in national income and product accounts. Note that the Hansen-Sargan test of overidentifying restrictions does not suggest misspecification of the model. When we look at the second step estimates based the GMM estimation we see a generally similar pattern of results compared to the within estimator or even the cross section estimates presented in Table. The only major changes are that the percentage of non-managers using computers becomes insignificant, as does the measure of employee turnover, although the sign and magnitudes are not inconsistent with previous estimates.

While our two-step procedure in Table addresses the biases that may arise in estimating the vector of coefficients a’ on capital, labor and materials, it does not address biases that may arise in the second step when we estimate the vector of coefficients d’ associated with observed workplace practices and characteristics. These biases may be due to correlations between the second stage regressors and unobserved time invariant plant level characteristics or with the average of the idiosyncratic shocks (since the time period over which we average is relatively short). Although we believe that our vector d’ extracts a substantial part of the previously unobserved fixed effect and that most of the endogeneity issues are related to labor, capital and materials, these potential biases may be affecting our estimates of the impact of workplace practices on labor productivity.

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