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Working Papers

[ 1 ] Accounting Labor in the Virtuous Circle of Economic Development

(with Yi Chen and Paul Madsen) [SSRN LINK]

R&R at the Journal of Accounting Research

Abstract

How and how much does accounting contribute to aggregate economic welfare? While these questions are clearly important, they have not been thoroughly addressed by archival studies because accounting is ubiquitous, making it difficult to identify research settings with informative comparisons, and because accounting institutions operate in conjunction with many other mutually complementary and coevolved economic institutions, making it difficult to causally attribute economic outcomes to any particular institution. In this study, we use the setting of the American industrial revolution from 1850 to 1930 to provide new empirical evidence on the welfare effects of accounting, specifically of specialist accounting labor. Our setting is attractive because it was a period when specialist accounting labor went from extremely scarce to common while, at the same time, the economy transformed from agrarian to a far more productive industrial economy. We find evidence of a virtuous circle at the county level in which specialist accounting labor was associated with economic development and economic development was associated with growth in the supply of specialist accountants. We do not argue that accounting labor is a mono-cause of economic growth. Rather, our evidence suggests it was part of a cluster of complementary growth-promoting institutions including large industrial firms, managerial hierarchies, education systems, and research investment. The local share of accounting labor is a strong predictor of economic development relative to other institutions. Our evidence suggests that specialist accountants, especially cost accountants working inside of industrial firms, were essential enablers of economic development.

[ 2 ] Peer Effects of Supplier Finance Program Disclosures: Evidence from ASU 2022-04

(with Lisa Hinson and Jaemin Kwon) [SSRN LINK]

Abstract

We examine the impact of Supplier Finance Program (SFP) disclosures on peer firms’ short-term liquidity. ASU 2022-04 (Liabilities—Supplier Finance Programs) requires buyers engaging in SFPs to disclose the amount and the balance sheet classification of outstanding obligations, expanding debt-like information and the salience of financial leverage. In response to the heightened salience of liquidity following ASU 2022-04, we find that peer firms without SFP disclosures increase their short-term liquidity positions. The increase is concentrated in cash holdings, with no offsetting change in current liabilities and no evidence that peers draw down credit lines. Increased short-term liquidity through visible, unencumbered cash suggests that peer firms seek to avoid risk contagion associated with SFP disclosures. Further analyses continue to support this avoidance channel. First, an event study around focal firms’ first SFP disclosures shows that peer firms, but not the disclosers themselves, experience negative abnormal returns, and the effect is confined to close product-market peers. This negative price spillover gives peers an incentive to distance themselves from SFP-disclosing firms. Second, peers’ liquidity response is concentrated among peers of disclosers with low liquidity, consistent with signaling a different type. Finally, the liquidity response is particularly pronounced among firms that (1) issue additional shares, (2) are financially constrained, and (3) have greater information asymmetry with shareholders, consistent with the notion that peer firms adjust liquidity to mitigate shareholder concerns. Collectively, our findings suggest that mandatory SFP disclosures generate spillover effects and that peer firms’ liquidity responses are consistent with an avoidance channel. 

[ 3 ] Disclosures and SEC Enforcement in the Financial Advisory Industry (draft available upon request)

(with Gabriel Pündrich and Brian Miller)

[ 4 ] When do non-audit services impair audit quality? Evidence from machine learning (draft available upon request)

(with Beverly Larson)

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