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ABSTRACT
Outsourcing is an important strategic decision of high-tech firms. However, while the research has extensively studied the implications of outsourcing to high-tech clients, its impact on high-tech vendors remains underexplored. This study empirically estimates the impact of government outsourcing contracts on high-tech vendors. Employing the earnings-return analyses framework, we find that, for high-tech vendors engaged in government outsourcing contracts, the stock market places a higher value on each unit of unexpected earnings compared to other firms. Additionally, this impact becomes stronger for contracts with longer terms, for contracts outsourced by the U.S. government or by countries with better political and economical stability. We obtain causal evidence through difference-in-differences (DID) analyses of high-tech firms’ initiations of government contracts. Mechanism analyses uncover two primary drivers behind this impact: increased persistence of future earnings and improved alignment between accrual earnings and cash flows. Overall, our research indicates that when valuing high-tech firms, the stock market incorporates information from supply-chain networks, especially that related to government customers. Our results underscore the importance of obtaining government outsourcing contracts for high-tech firms’ managers. Becoming a vendor to the government helps a high-tech firm reduce the uncertainty faced by its outside investors, who in turn value the high-tech firm’s earnings to a greater extent.
Supplementary Information
Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2024.2340823
Disclosure statement
No potential conflicts of interest are reported by the authors(s).
Notes
1. The issue is important to high-tech vendors. For example, in April 2016, SpaceX received an award of $82.7 million contract from the U.S. Air Force to send a satellite into space. Prior to receiving this award, Elon Musk, CEO of SpaceX, fought hard for the right the bid for the contract as it would provide a new revenue stream for SpaceX and beat Boeing-Lockheed’s long-time dominance in the field of military launches. Furthermore, for our empirical sample of 8,174 high-tech firm-years, 15.9 percent of them have government contracts.
2. Under accrual accounting, a firm’s reported revenue recognizes accruals defined as value increases that are not cash flows [Citation54]. The most common revenue accruals are receivables (e.g., sales on credit). This accrual component is part of a firm’s earnings, but may not result in cash flows, should a customer default on its obligation. Generally Accepted Accounting Principles (GAAP) in the United States, along with accounting standards for most countries around the world, mandate accrual accounting for firms when preparing financial statements. By contrast, cash accounting records revenue on the condition that cash is received.
3. Although some previous literature has explored the impact of government contracts on firm valuations, our work differs from them by (1) examining the earnings-return association, and (2) by focusing on high-tech firms. For example, Esqueda et al. [Citation27] find that government contractors have lower valuations (proxied by Tobin’s Q) than non-contractors. They further show that, although contractors do enjoy lower costs of capital, they suffer from lower future growth. Notably, our study differs from Esqueda et al. [Citation27] in that we examine shareholders’ reactions to earnings announcement. This empirical framework allows us to address the issue that, for the same unit of current earnings, whether contractors and non-contractors are perceived differently by shareholders. Glegg et al. [Citation30] find that having the government as a client shapes the supplier firm’s accounting choices. More specifically, due to regulatory scrutiny, the supplier firm is more likely to use real earnings management (e.g., abnormal expenditures, abnormal production costs) than using accrual earnings management.
4. Li [Citation42] finds that when earnings information suggests lower future cash flows or greater risks, the earnings response coefficient is lower. Wei and Zhang [Citation66] show that when investors have lower trust for a company’s earnings number, the earnings response coefficient is lower.
5. Schilit and Perler [Citation59] describe the typical financial shenanigans of U.S. corporations. The authors note various methods employed by these firms to report misleading accounting earnings under accrual accounting, such as: (1) recording revenue too soon (e.g., Computer Associates), (2) recording bogus revenue (e.g., AIG), and (3) boosting income using one-time or unsustainable activities (IBM).
6. Beneish and Harvey [Citation7] find that the earnings-return relation is approximately linear for small changes but is “S” shaped globally; such nonlinearity is largely caused by measurement errors. A ranking procedure can significantly reduce the impact of measurement errors in the continuous variable. Livnat and Mendenhall [Citation43] further note that “To address the existence of outliers and nonlinearities in the earnings surprise-return relation, most drift studies classify firms into 10 portfolios based on SUE (standardized unexpected earnings)” (p. 186).
7. The cumulative daily return measure RET (-1, 1) poses fewer statistical problems compared with the compounded buy-and-hold return BHRET (-1, 1) [Citation28]. However, the buy-and-hold return has its advantage in that it is the return experienced by an investor. Fama [Citation28] shows that the two measures often draw different inferences in empirical studies and we employ both to seek robustness in our results.
8. Hayn [Citation35] contends that investors can choose to liquidate a firm when expecting continued losses in the future, thus lowering the persistence of firm losses and investors’ responses to current-period loss, i.e., lower ERCs.
9. Our firm-level empirical approach yields the interesting issue of potential non-linearity in the effect of having multiple contracts within a firm-year. In untabulated analyses, we examine whether a vendor firm with an existing government outsourcing contract experiences additional ERC increase when it obtains additional contracts. Employing a difference-in-differences specification, we find no evidence of incremental effect, suggesting non-linearity in the association between government outsourcing contracts and ERCs.
10. Rather than interacting the continuous measure of contract length with Rank(ES), we use the decomposition approach. The interaction approach requires the interacting variable to be available for all sample firms, including treated and control firms. However, the control group in our sample does not have government contracts.
11. In the analyses of contract length and contract initiation, we construct our measures based on the contract with the earliest initiation date when a firm has multiple contracts outsourced by the government on the earnings announcement date.
12. For heterogeneity analyses in , we also perform robustness analyses by controlling for the two new indicators rather than GOV. For example, we use GOV_Long and GOV_Short to replace GOV. We find consistent results from Panel A through Panel E. Therefore, our inferences are not sensitive to this empirical choice. We thank an anonymous reviewer for pointing out this econometric issue.
13. An empirical concern relates to the potential serial correlation in the error terms. Such correlations, if existing and being unaccounted for, would result in inflated t-statistics and false significance. In our main analyses, we follow the standard econometric approach to deal with potential serial correlations by clustering standard errors [Citation55]. Furthermore, we construct a subsample wherein the issue of serial correlation is least of a concern. Specifically, we retain observations with only one observation for both the pre-treatment and the post-treatment period. Re-estimating our DID analyses (EquationEq. 4(4)
(4) ) using this subsample, we find consistent results – positive and significant coefficients on the interaction term Rank(ES)*Treat*Post. We thank an anonymous referee for pointing out this econometric issue.
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Notes on contributors
Yi Dong
Yi Dong ([email protected]) is an Associate Professor in Institute of Accounting and Finance, Shanghai University of Finance and Economics, China. Dr. Dong received her PhD in Finance from Nanyang Technological University in Singapore. Her research focuses on information intermediaries, capital markets, and technological advancement. Her work has appeared in leading journals in accounting and finance, such as Journal of Financial and Quantitative Analysis and Review of Accounting Studies.
Nan Hu
Nan Hu ([email protected]) is an Associate Professor of Information Systems at School of Computing and Information Systems, Singapore Management University. He received his PhD in Management, with a major in MIS, from University of Texas at Dallas. His interdisciplinary research focuses on studying the value implications of unstructured data by combining traditional econometrics approach with deep technologies (e.g., Artificial Intelligence, Natural Language Processing, Machine Learning, and Deep Learning). Dr Hu’s major research has been published in journals such as MIS Quarterly, Journal of Management Information Systems, Production and Operations Management, Decision Support Systems, and many others.
Yonghua Ji
Yonghua Ji ([email protected]; corresponding author) is a Professor of Information Systems at School of Business, University of Alberta, Canada. He received his PhD in Management, with a major in MIS, from University of Texas at Dallas. His research interests include economics of information systems, optimal software development methodologies, and social networks. He has published in such journals such as INFORMS Journal on Computing, Information Systems Research and Production and Operations Management. He is an editorial board member at Production and Operations Management and Information Technology and Management.
Chenkai Ni
Chenkai Ni ([email protected]) is a Professor in School of Management, Fudan University, Shanghai, China. He received his PhD in Accounting from National University of Singapore. Dr. Ni’s research focuses on capital markets, informational efficiency, and government fiscal policies. His work has appeared in leading journals in accounting and finance, such as The Accounting Review, Journal of Financial and Quantitative Analysis, and Review of Accounting Studies. He is an editorial board member at Accounting and Business Research and Asia-Pacific Journal of Accounting and Economics.
Jing Xie
Jing Xie ([email protected]) is an Associate professor of finance in Department of Finance and Business Economics at the University of Macau. He obtained his PhD in Finance from National University of Singapore. Dr. Xie’ research interests include financial innovation and corporate finance. His research has appeared in top-tier academic journals, including Journal of Financial Economics, The Accounting Review, and Journal of Financial Intermediation.