We empirically assess the implications of the common ownership hypothesis from a historical perspective using the set of S&P 500 firms from 1980 to 2017. We show that the dramatic rise in common ownership in the time series is driven primarily by the rise of indexing and diversification and, in the cross section, by investor concentration, which the theory presumes to drive a wedge between cash flow rights and control. We also show that the theory predicts incentives for expropriation of undiversified shareholders via tunneling, even in the Berle and Means (1932) world of the widely held firm. (JEL D22, G32, G34, L21, L25)
Incomplete product availability is an important feature of many markets; ignoring changes in availability may bias demand estimates. We study a new dataset from a wireless inventory system installed on 54 vending machines to track product availability every four hours. The data allow us to account for product availability when estimating demand, and provides a valuable source of variation for identifying substitution patterns. We develop a procedure that allows for changes in product availability even when availability is only observed periodically. We find significant differences in demand estimates, with the corrected model predicting significantly larger impacts of stock-outs on profitability. * We thank
Differentiated products demand systems are a workhorse for understanding the price effects of mergers, the value of new goods, and the contribution of products to seller networks. Berry, Levinsohn, and Pakes (1995) provide a flexible random coefficients logit model which accounts for the endogeneity of prices. This article reviews and combines several recent advances related to the estimation of BLP-type problems and implements an extensible generic interface via the PyBLP package. Monte Carlo experiments and replications suggest different conclusions than the prior literature: multiple local optima appear to be rare in well-identified problems; good performance is possible even in small samples, particularly when "optimal instruments" are employed along with supply-side restrictions. If Python is installed on your computer, PyBLP can be installed with the following command: pip install pyblp.Up-to-date documentation for the package is available at https:// pyblp.readthedocs.io.
Incomplete product availability is an important feature of many markets; ignoring changes in availability may bias demand estimates. We study a new dataset from a wireless inventory system installed on 54 vending machines to track product availability every four hours. The data allow us to account for product availability when estimating demand, and provides a valuable source of variation for identifying substitution patterns. We develop a procedure that allows for changes in product availability even when availability is only observed periodically. We find significant differences in demand estimates, with the corrected model predicting significantly larger impacts of stock-outs on profitability. * We thank
Schmalz, and Glen Weyl. All remaining errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w25454.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
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