What Drives the Productive Efficiency of a Firm? The Importance of Industry, Location, R&D, and Size

This paper investigates the factors that explain the level and dynamics of manufacturing firm productive efficiency. In our empirical analysis, we use a unique sample of about 39,000 firms in 256 industries from the German Cost Structure Census over the years 1992-2005. We estimate the efficiencies of the firms and relate them to firm-specific and environmental factors. We find that (1) about half the model's explanatory power is due to industry effects, (2) firm size accounts for another 20 percent, and (3) location of headquarters explains approximately 15 percent. Interestingly, most other firm characteristics, such as R&D intensity, outsourcing activities, or the number of owners, have extremely little explanatory power. Surprisingly, our findings suggest that higher R&D intensity is associated with being less efficient, though higher R&D spending increases a firm's efficiency over time

Estimating Managers' Utility-Maximizing Demand for Agency Goods

An empirical model of managers' demand for agency goods is derived and estimated using the Almost Ideal Demand System of Deaton and Muellbauer (AER 1980). As in Jensen and Meckling (JFE 1976), we derive managers' demand for agency goods by maximizing a managerial utility function where managers allocate the potential value of their firm's assets to the consumption of agency goods and the production of market value (which, given their ownership stake, determines their wealth). The utility function is defined over wealth and the value of agency goods and is conditioned on managers' holdings of stock options, the proportion of the firm owned by outside block-holders, and the firm's capital structure. We obtain the maximum potential value of firms' assets by fitting a stochastic frontier (upper envelope) to the market value of assets given the investment in those assets. The difference between the potential market value of a firm's investment in its assets and their actual market value (corrected for statistical noise) is used to gauge managers' consumption of agency goods. The demand function for agency goods (lost market value) is estimated using U. S. data on publicly traded bank-holding companies. Using the adding-up condition, the demand for asset value is derived from it and restated as the utility-maximizing Q ratio. We apply Slutsky's equation to decompose the effect of a variation in the proportion of the firm owned by managers into a substitution and a wealth effect, which parallel the alignment-of-interest effect and the entrenchment effect. By estimating financial performance in a choice-theoretic framework, the alignment and entrenchment effects of ownership can be identified econometrically. We find evidence that the strength of both effects increases with insider ownership, but ownership by outside block-holders mitigates the entrenchment effect

Cartels, Managerial Incentives, and Productive Efficiency in German Coal Mining, 1881-1913

In this paper, we evaluate the impact of cartelisation and managerial incentives on the productive efficiency of German coal mining corporations. We focus on coal mining in the Ruhr district, Germany's main mining area. We use stochastic frontier analysis and an unbalanced dynamic panel data set for up to 28 firms for the years 1881-1913 to measure productive efficiency. We show that coal was mined with decreasing returns to scale. Moreover, it turns out that cartelisation did not affect productive efficiency. Controlling for corporate governance variables shows that stronger managerial incentives were significantly correlated with productive efficiency, whereas the debt-equity ratio did not influence it

Accounting for Distress in Bank Mergers

The inability of most bank merger studies to control for hidden bailouts may lead to biased results. In this study, we employ a unique data set of approximately 1,000 mergers to analyze the determinants of bank mergers. We use data on the regulatory intervention history to distinguish between distressed and non-distressed mergers. We find that, among merging banks, distressed banks had the worst profiles and acquirers perform somewhat better than targets. However, both distressed and non-distressed mergers have worse CAMEL profiles than our control group. In fact, non-distressed mergers may be motivated by the desire to forestall serious future financial distress and prevent regulatory intervention