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E-MailElena Simintzi is Assistant Professor of Finance at University of British Columbia Sauder School of Business. This post is based on a recent paper by Professor Simintzi; Paige Ouimet, Associate Professor of Finance at the Finance Department at the University of North Carolina at Chapel Hill; and Wenting Ma.
A substantial rise in wage inequality in the United States and other developed countries has garnered significant attention in the media and among policy circles. Economists have argued that rising inequality is a consequence of technology adoption. Technology may be skill-biased enhancing the productivity of high-skill labor (Katz and Autor, 1999) or routine-biased enabling firms to automate routine tasks replacing middle-skill workers (Autor, Levy, and Murnane, 2003; Acemoglu and Autor, 2011; Autor and Dorn, 2013). But what drives technology adoption? In recent research, we argue firm reorganization, in the form of M&As, acts as a catalyst for the adoption of both skill-biased and routine-biased technology. Considering the large scale of M&A activity, with over 4 $trillion in activity in 2015 alone, it is plausible to expect M&A activity may have economically important effects on increased income inequality and other changes in labor demand.
To understand why the speed of technology adoption depends on the organizational structure within an industry, let us consider the example of automatic teller machines (ATMs). As ATMs began being deployed by banks, this reduced the need for employees to perform the same tasks of taking deposits and dispensing cash. The adoption of this new technology did not lead to dramatic changes in gross banking employment but did change the types of skills needed (Bessen 2015). There was a decrease in the relative demand for junior bank tellers, a middle-skilled occupation substitutable for the new technology, as compared to employment in other occupations within the industry. This new technology also improved banks’ profitability, leading to an increase in the number of branches, thereby increasing relative demand for the higher- and lower-skilled occupations at the bank. Interestingly, ATMs were not uniformly adopted. From a customer’s perspective, the value of an ATM increased, the more ATMs at a given bank, thereby benefiting larger banks relatively more (Saloner and Shepard, 1995).
In the ATMs example, the size of the bank was critical for investing in the new technology suggesting one explanation why M&A may alter the speed and nature of how and when firms integrate new technology. M&As increase the scale of firms and can reduce the fixed costs of investing in new technologies. Other explanations may also be in effect. As argued by the M&A literature, M&As often target underperforming firms leading to ex-post efficiency gains (Maksimovic and Phillips, 2001). A higher productivity acquirer may transplant best practices, including how to effectively integrate computers and automation to the target. Alternatively, M&As may resolve financial constraints at the target firm (Erel, Jang, and Weisbach, 2015). This may induce automation if financially constrained targets were unable to finance the initial fixed costs necessary to invest in new technologies. All three explanations (increase in scale; increase in efficiency; and lower financial constraints) suggest that M&As can reduce frictions such as adjustment costs, thereby lowering the opportunity cost of investing in new technologies, and make investment in such technologies more profitable.
All three mechanisms predict a pattern where investments in automation increase post-M&A, leading to a lower demand for routine tasks, greater demand for high-skilled workers, higher average wages and greater overall wage inequality. We test these hypotheses, with data from Thomson’s SDC on M&A activity, starting in 1980. We measure M&A intensity as the count of deals in an industry-decade, normalized by the count of total deals in the decade. Data on occupational employment is collected from the Integrated Public Use Microdata Service (IPUMS). Using the 5% extract from Census years 1980, 1990, 2000 and the American Community Survey (ACS) for 2010, we identify the fraction of employment in a given occupation and the share of employees with college education within each industry as well as industry wage distributions. To identify the routine-task content of each occupation, we replicate the approach in Autor and Dorn (2013) and construct time-varying shares of routine intensity using an employment-weighted mean to aggregate at the industry-level.
Our results suggest that M&As spur technology adoption with important implications on industry wage inequality, and they seem economically important:
- Consistent with routine-biased technological change, an increase in M&A intensity by 10% is associated with a 33% reduction in routine share intensity within a given industry.
- Consistent with skill-biased technological change, an increase in M&A activity by 10% is associated with an increase in employees with college education by 10 percentage points within a given industry.
- Consistent with technology adoption, an increase in M&A activity by 10% is associated with a 25% increase in mean hourly wages within a given industry.
- Consistent with technology adoption, an increase in M&A activity by 10% increases wage disparity by 22% within a given industry.
- Consistent with technology adoption, high M&A intensity is followed by investment in equipment, while there is no simultaneous change in investment in buildings and structures.
Our findings also support the three non-mutually exclusive mechanisms outlined above. We show larger effects for our baseline findings in cases where: i) the industry experiences larger changes in scale, ii) the acquirer’s industry follows best practices, iii) target firms are more likely to be financially constrained at the time of deals’ announcements. Our results are robust to the concern that technology shocks may be responsible both for M&A activity and the subsequent technology adoption and suggest that M&As are a necessary and important driver of technology adoption, job polarization, and the overall wage inequality.
The full paper is available for download here.
