Management in production: from unobserved to observed
Are productivity estimates good proxies for unobserved management? And, does management affect production in a neutral and monotonic fashion as assumed by these proxies? We use Bloom and Van Reenen’s management data to show that two popular proxies, fixed effects and inefficiency scores, correlate with observed management practices. We find that the correlations are positive but weak. Also, management explains only a fraction of the proxies’ variances. The data rejects the assumptions of neutrality and monotonicity. Last, our results suggest that management has characteristics both of a technology and an input.
KeywordsManagement practice production functions stochastic frontier analysis semiparametric models
JEL classificationNos: D24 C14 M11
Economists often use productivity estimates as proxies for unobserved management, either to control for omitted variable bias or to measure management at the firm level. For instance, Nickell (1996) estimates the effect of competition on firm productivity and assumes that productivity estimates serve as proxies for management quality, among other things. As management is usually unobserved, the correlation between management and productivity estimates1 is assumed. Using the management data produced by Bloom and Van Reenen (2007) we are able to test this assumption empirically. We ask whether two popular productivity estimates, firm fixed effects and inefficiency scores, correlate with observed management practices. Also, we ask whether the assumptions underlying their use, neutrality and monotonicity, are valid.
[…] the technical efficiency of a firm or plant indicates the undisputed gain that can be achieved by simply ‘gingering up’ the management […].
Farrell’s model sets management apart from the conventional inputs of capital and labor in the sense that inefficiency indicates the potential for management to increase output for given conventional inputs, implying that management is a technology. Put differently, unlike conventional inputs, management alone cannot produce. Nuthall (2009, p. 413) expresses this as follows: “… the key factor in the management of land, labor and capital is the management ability applied.” On the other hand, Mundlak (1961)’s concern is about omitted variable bias when the management input is unobserved. His model uses firm fixed effects as proxies for management. The model requires panel data and the assumption that management is time-invariant. We empirically test whether the two proxies, inefficiency and fixed effects, correlate with observed management. Both proxies assume that management is a productivity shifter and that better management always increases productivity. Put differently, management affects productivity neutrally and the relation between management and productivity is monotonic. Together these assumptions suggest that management is a technology rather than an input.2 To empirically test these two assumptions we apply semiparametric technology models where the influence of management can be non-neutral as well as non-monotonic.
A semiparametric model is more general than the parametric linear model Bloom and Van Reenen (2007) use to correlate management with total factor productivity. Their model takes a log-linearized Cobb-Douglas form with management z added in an ad hoc fashion: Iny i = α0 + z′α + Inx′β. They find that management positively correlates with total factor productivity (TFP), α is statistically significant. The parametric linear model helps them validate their survey measure but our investigation of the goodness of productivity as a proxy for management requires a more flexible model. For instance, the use of TFP as a proxy for management requires that TFP increases in management everywhere and not just at the mean. Also, with the parametric linear model, whether management z is a technology depends on the interpretation of the estimated coefficient α. Inconsistent with the management as technology interpretation, management alone can produce positive output. The semiparametric models we apply (Robinson 1988; Li et al. 2002) make the technology explicitly management specific: f z (x), i.e., the production function parameters vary with management. Semiparametric models strike a balance between a standard parametric structure for the conventional inputs and a nonparametric structure for management. The nonparametric part makes the model fully flexible in regard to the effect of management on production and thereby accommodates our theoretical ignorance about how management affects the technology. As the model does not impose the assumptions of neutrality and monotonicity we can test them. Finally, the model allows us to express characteristics of the production function like returns to scale as functions of management.
We find that the correlation between fixed effects or inefficiency scores and management is positive but weak: the largest correlation coefficient is 0.35. Also, we find that management explains at most 5% of the variance in the proxies. Firms and policy makers use productivity or inefficiency estimates to assess potential performance improvements through better management. The scope for such improvements might be much lower than suggested by the face values of the proxies. Despite the effect of management on productivity not being strictly neutral, the neutral component gives a relationship between total factor productivity and management that is mostly increasing. Thus, productivity estimates are suitable for ranking firms on management practices but not for quantifying unobserved management practices.
This paper is organized as follows. Section 2 introduces the empirical models that we apply. Section 3 introduces the data. Section 4 gives the results and Section 5 concludes.
2 Empirical models
Identification of u is possible when we assume that the distribution of v is symmetric, e.g., normal, but the distribution of u is asymmetric, e.g., half-normal (Jondrow et al. 1982).
As we observe management we can ask whether estimates of fixed effects and inefficiency are good proxies for management by correlating them with management: we regress the productivity estimates on management using the OLS estimator. To better compare inefficiency and fixed effects we estimate inefficiency to be time-invariant and adjust for the intercept. Thus, α i from (2) is equivalent to α0−u i from (5) if u is time-invariant. We control for country and industry fixed effects by mean transforming the variables before estimation, i.e., measurement is in terms of deviations from country-industry means. For the fixed effects model in (2) and the stochastic frontier model in (5) the functional form is translog.
From the estimates of this model we can calculate the marginal effects of management on the mean, ∂u/∂z, and the variance ∂σ2/∂z of the inefficiency distribution. The intuition for the variance of u is that it captures production uncertainty, e.g. better management correlates with lower production uncertainty. Note that as we have a single z variable −∂u/∂z = ∂lny/∂z. Wang (2002) shows that the parametrization in (8) allows the effects of z to be non-monotonic, i.e., management can increase or decrease inefficiency. This is useful, because it allows us to test the monotonicity assumption implied by the proxies.4
In this semiparametric smooth coefficient (SPSC) model z affects productivity both neutrally and non-neutrally. The neutral effect is captured by α(z it ) and the non-neutral effects are captured via the inputs β(z it ). We think of these different effects as channels through which management operates. The PL model in (10) constrains the effects via the inputs to zero. Again, equations (10) and (11) are not linear in parameters since both α and β are nonparametric functions of z, which are not necessarily linear. The combination of being parametric in x and nonparametric in z makes the function semiparametric. Importantly, this difference captures the economic difference between the conventional inputs of capital and labor and the non-conventional management input.
Technically, these semiparametric models strike a balance between precision and robustness (Robinson 1988). Although fully parametric models are very precise they suffer from possible functional form mis-specification. Nonparametric models are robust but inefficient as they suffer from the curse of dimensionality problem. However, for SPSC models with a single z variable, like ours, the curse of dimensionality is not important (Li et al. 2002). Also, the parametric structure makes the semiparametric model less sensitive to outliers than the fully nonparametric model. Appendix A gives details on the estimation of the SPSC model, including outlier detection, and the construction of bootstrapped confidence intervals. For details on the estimation of the PL model see Robinson (1988). An important benefit of the semiparametric models is that they allow us to test the assumptions of neutrality and monotonicity for the influence of management. We test neutrality by testing the Null of the PL model, which restricts the management influence to be neutral, against the SPSC model, which allows for non-neutral effects. Li and Racine (2010) propose the test statistic we use. To test the assumption of monotonicity we visually analyze graphs that plot management-dependent total factor productivity estimates against management for different models. Finally, by testing the Null of a parametric linear model including management against the PL model, using the test by Hsiao et al. (2007) we can also assess whether the neutral effect of management is linear or not.
4.1 Do productivity estimates correlate with observed management?
The OLS coefficient estimates for management are 0.34 and −0.18 for fixed effects and inefficiency scores, respectively. For both regressions the coefficients are significant at a 1% level. To compare the magnitudes of the responses, a one point increase in the management index (which is roughly two standard deviations) implies an increase in productivity as measured by fixed effects of roughly 50% of a standard deviation but only 39% of a standard deviation for the inefficiency scores. A further test for the goodness of the proxies is that the residuals from the second stage regressions should not correlate with management. At standard levels of statistical significance, this is true for the fixed effects but not the inefficiency score residuals. However, there is no economically significant correlation between management and the residuals for either proxy.
Despite these positive correlations, management explains little of the variance in the proxies. The R-squares are 0.057 and 0.035 for the fixed effects and inefficiency models, respectively. Remember that our hypothesis is that the proxies are management, implying that the R-squares equal 1. These low R-squares are consistent with the fact that adding the management variable to a Cobb–Douglas specification of a conventional production function only increases R-squared from 0.665 to 0.672 (remember that our data is transformed around industry and country means).
When comparing fixed effects and inefficiency scores as proxies for management the former perform better but require panel data. Absolutely, fixed effects and inefficiency scores are both poor proxies for management as management explains only a small fraction of the proxies’ variation. This is no surprise as the proxies also capture many other unobserved factors of production.
4.2 Is the influence of management neutral and monotonic?
Above we investigated whether productivity estimates correlate with observed management. In this section, to test whether total factor productivity is always increasing in management and whether management affects productivity neutrally or non-neutrally, we apply production models where the total factor productivity estimates (or the entire technology) are functions of management.
Next, we use two semiparametric models where management shifts the technology not via the inefficiency term but directly via the coefficients. The semiparametric smooth coefficient (SPSC) model allows all coefficients to be nonparametric functions of management. The nested PL model allows only the intercept to be a fully flexible function of management. In both models the parametric part has a Cobb–Douglas form. Again, we control for country and industry fixed effects by mean transforming the variables before estimation, i.e., measurement is in terms of deviations from country-industry means.
Management is an important, but usually unobserved factor in production. Theoretically, management is important, because it is a key mechanism through which owners and regulators try to influence the performance of firms. Economists often use total factor productivity estimates as proxies for management. We take advantage of the management data produced by Bloom and Van Reenen (2007) to assess the empirical usefulness of productivity estimates as proxies for management. First, we correlate two popular proxies, fixed effects and inefficiency scores, with observed management. Second, we use flexible models of production to test whether management influences productivity as assumed by the proxies: neutrally and monotonically.
We find that the correlations between the proxies and management are positive but weak. Also, observed management practices explain only a small fraction, no more than 5%, of the variances of fixed effects and inefficiency scores. This result is no surprise as fixed effects and inefficiency scores are essentially residuals, which capture all factors that are time-invariant or follow an asymmetric distribution, respectively. Overall, fixed effects seem better management proxies than inefficiency scores but require panel data. Of course, our analysis assumes that Bloom and Van Reenen (2007)’s management measure is a good proxy itself. There might be dimensions of management quality omitted by their survey measure.
When measuring management by proxies the correlation is of interest but so are the assumptions of neutrality and monotonicity. Testing these assumptions requires models that do not impose them. To test neutrality, we contrast the results from two semiparametric models: one that allows non-neutral effects and another that imposes neutrality. We find that the effect of management is not strictly neutral. Besides total factor productivity, management shifts capital and labor elasticities, too. A formal test also rejects the neutral model in favor of the non-neutral model. We investigate monotonicity by plotting management-dependent total factor productivity estimates from different models. Across, these models TFP estimates are mostly increasing monotonically in management. Last, as management shifts TFP and correlates with the labor-capital ratio it seems that management has characteristics of a technology and an input.
Despite rejecting strict neutrality and also finding some evidence against monotonicity, we believe that overall our evidence supports the use of productivity estimates as proxies for management for some objectives. When the objective is to rank firms by their management abilities the use of the proxies is fine. However, the proxies do not quantify management practices well. For instance, many regulators base price caps on estimates of inefficiency (“benchmarking”). The scope for performance improvement via better management is probably only a fraction of the face value of inefficiency estimates.
A complication in testing the relation between management and productivity is that the latter is unobserved, too. Productivity is usually estimated from observed input and output quantities.
It is conceivable that management is also a conventional input, i.e., a choice variable. Bloom et al. (2012) refer to these alternatives as “management as a technology” and “management as a factor”, respectively. They test these alternatives and their empirical results support the theory that management is a technology. But they find that management has some features of a conventional input as well.
We used the Stata code made available by Hung-Jen Wang on his web site.
The data is available at worldmanagementsurvey.org.
The countries are Argentina, Australia, Brazil, Canada, Chile, China, France, Germany, United Kingdom, Greece, Italy, Japan, Poland, Portugal, Republic of Ireland, Sweden, and the United States.
Appendix A gives details on the bootstrap procedure. We use 100 replications.
We use a wild cluster bootstrap and 399 replications.
We use a wild cluster bootstrap and 399 replications. For the Null we use the beta coefficients from a OLS regression on the data transformed as suggested by Robinson (1988).
For details see Li and Racine (2010).
We would like to thank the participants of the 13th European Workshop on Efficiency and Productivity Analysis in Helsinki and the participants of the Eighth North American Productivity Workshop in Ottawa for their useful comments. We would like to thank Hal Fried and Loren Tauer for pointing out the potential of the data used here. We would also like to thank Boris Bravo-Ureta, Kai Sun, Daniel Henderson, Jaap Bos, Mark Sanders, and Mette Asmild for their comments. And we would like to thank two anonymous referees for their useful comments which led to an improved version of the paper. The usual disclaimer applies.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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