for helpful comments. Ki Beom Binh, Yong Hyuk Choi, Jiyoon Lee and Andre De Souza provided outstanding research assistance. All errors are our own. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

In this paper we study the determinants of business groups' ownership structure using a unique dataset of Korean chaebols, and a set of new metrics of group ownership structure. We find that chaebols grow vertically (that is, pyramidally) as the family uses well-established group firms ("central firms") to set up and acquire firms that have low profitability and high capital requirements. Chaebols grow horizontally (that is, using direct family ownership) when the family acquires firms that are highly profitable and require less capital. We also provide direct evidence that the low profitability of firms owned through pyramids is partly due to a selection effect: the profitability of new group firms in the year before they are added to the group predicts whether they are added to pyramids or controlled directly by the family. The relationships between pyramids, profitability, and capital intensity that we uncover do not appear to be due to the separation between ownership and control induced by pyramids. Finally, we find that the selection of low-profitability firms into pyramids causes the group's central firms to trade at a discount relative to other public group firms. Taken together, these results suggest that controlling families optimally design the ownership structure of the group in a manner that is consistent with theory.

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. Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.

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