بازگشت به سرمایه گذاری های شرکت در سرمایه انسانی

In this paper we estimate the rate of return to firm investments in human capital in the form of formal job training. We use a panel of large firms with detailed information on the duration of training, the direct costs of training, and several firm characteristics. Our estimates of the return to training are substantial (8.6%) for those providing training. Results suggest that formal job training is a good investment for these firms possibly yielding comparable returns to either investments in physical capital or investments in schooling.

Individuals invest in human capital over the whole life-cycle, and more than one half of life-time human capital is accumulated through post-school investments on the firm (Heckman et al., 1998). This happens either through learning by doing or through formal on-the-job training. In a modern economy, a firm cannot afford to neglect investments in the human capital of its workers. In spite of its importance, economists know surprisingly less about the incentives and returns to firms of investing in training compared with what they know about the individual's returns of investing in schooling1 Similarly, the study of firm investments in physical capital is much more developed than the study of firm investments in human capital, even though the latter may be at least as important as the former in modern economies. In this paper we estimate the internal rate of return of firm investments in human capital. We use a census of large manufacturing firms in Portugal, observed between 1995 and 1999, with detailed information on investments in training, its costs, and several firm characteristics.2 Most of the empirical work to date has focused on the return to training for workers using data on wages (e.g., Bartel, 1995, Arulampalam et al., 1997, Mincer, 1989 and Frazis and Lowenstein, 2005). Even though this exercise is very useful, it has important drawbacks (e.g., Pischke, 2005). For example, with imperfect labor markets wages do not fully reflect the marginal product of labor, and therefore the wage return to training tells us little about the effect of training on productivity. Moreover, the effect of training on wages depends on whether training is firm specific or general (e.g., Becker, 1962 and Leuven, 2005).3 More importantly, the literature estimating the effects of training on productivity has little or no mention of the costs of training (e.g. Bartel, 1991, Bartel, 1994, Bartel, 2000, Black and Lynch, 1998, Barrett and O'Connell, 2001, Dearden et al., 2006, Ballot et al., 2001 and Conti, 2005). This happens most probably due to lack of adequate data. As a result, and as emphasized by Mincer (1989) and Machin and Vignoles (2001), we cannot interpret the estimates in these papers as well defined rates of return. The data we use is unusually rich for this exercise since it contains information on the duration of training, direct costs of training to the firm as well as productivity data. This allow us to estimate both a production and a cost function and to obtain estimates of the marginal benefits and costs of training to the firm. In order to estimate the total marginal costs of training, we need information on the direct cost of training and on the foregone productivity cost of training. The first is observed in our data while the second is the marginal product of worker's time while training, which can be estimated. We do not distinguish whether the costs and benefits of training accrue mainly to workers or to the firm. Instead, we quantify the internal rate of return to training jointly for firms and workers.4 This implies that, to obtain estimates of the foregone opportunity cost of training we will not take into account whether firms or workers support the costs of training. The major challenge in this exercise are possible omitted variables and the endogenous choice of inputs in the production and cost functions. Given the panel structure of our data, we address these issues using the estimation methods proposed in Blundell and Bond (2000). In particular, we estimate the cost and production functions using a first difference instrumental variable approach, implemented with a system-GMM estimator. By computing first differences we control for firm unobservable and time invariant characteristics. By using lagged values of inputs to instrument current differences in inputs (together with lagged differences in inputs to instrument current levels) we account for any correlation between input choices and transitory productivity or cost shocks. Our instruments are valid as long as input decisions in period t − 1 are made without knowledge of the transitory shocks in the production and cost functions from period t + 1 onwards.5 Several interesting facts emerge from our empirical analysis. First, in line with the previous literature (e.g., Pischke, 2005, Bassanini et al., 2005, Frazis and Lowenstein, 2005, Ballot et al., 2001 and Conti, 2005) our estimates of the effects of training on productivity are high: an increase in training per employee of 10 h (hours) per year, leads to an increase in current productivity of 0.6%. Increases in future productivity are dampened by the rate of depreciation of human capital but are still substantial. This estimate is below other estimates of the benefits of training in the literature (e.g., Dearden et al., 2006 and Blundell et al., 1996). If the marginal productivity of labor were constant (linear technology), an increase in the amount of training per employee by 10 h would translate into foregone productivity costs of at most 0.5% of output (assuming all training occurred during working hours).6 Given this wedge between the benefits and the foregone output costs of training, ignoring the direct costs of training is likely to yield a rate of return to training that is absurdly high (unless the marginal product of labor function is convex, so that the marginal product exceeds the average product of labor). Second, we estimate that, on average, foregone productivity accounts for less than 25% of the total costs of training. This finding shows that the simple returns to schooling intuition is inadequate for studying the returns to training, since it assumes negligible direct costs of human capital accumulation. In particular, the coefficient on training in a production function (or in a wage equation) is unlikely to be a good estimate of the return to training. Moreover, without information on direct costs of training, estimates of the return to training will be too high since direct costs account for the majority of training costs (see also the calculations in Frazis and Lowenstein, 2005). Our estimates indicate that, while investments in human capital have on average zero returns for training for all the firms in the sample, the returns for firms providing training are quite high (8.6%). Such high returns suggest that on-the-job training is a good investment for firms that choose to undergo this investment, possibly yielding comparable returns to either investments in physical capital or investments in schooling.7 The paper proceeds as follows. Section 2 describes the data we use. In Section 3, we present our basic framework for estimating the production function and the cost function. In Section 4 we present our empirical estimates of the costs and benefits of training and compute the marginal internal rate of return for investments in training. Section 5 concludes.

In this paper we estimate the internal rate of return of firm investments in human capital. We use a census of large manufacturing firms in Portugal between 1995 and 1999 with unusually detailed information on investments in training, its costs, and several firm characteristics. Our parameter of interest is the return to training for employers and employees as a whole, irrespective of how these returns are shared between these two parties. We document the empirical importance of adequately accounting for the costs of training when computing the return to firm investments in human capital. In particular, unlike schooling, direct costs of training account for about 75% of the total costs of training (foregone productivity only accounts for 25%). Therefore, it is not possible to read the return to firm investments in human capital from the coefficient on training in a regression of productivity on training. Data on direct costs is essential for computing meaningful estimates of the internal rate of return to these investments. Our estimates of the internal rate of return to training vary across firms. While investments in human capital have on average negative returns for those firms which do not provide training, we estimate that the returns for firms providing training are substantial, our lower bound being of 6.7% and our preferred estimate being 8.6%. Such high returns suggest that company job training is a sound investment for firms that do train, possibly yielding comparable returns to either investments in physical capital or investments in schooling.