This paper analyses the effect of strategic patenting on firm and competitor performance, productivity, innovative output and market concentration. Using a novel definition of strategic patenting, this paper finds a positive effect of strategic patenting on market concentration. The results for the patentee show a positive effect of profit growth, and positive but significantly smaller contribution of strategic patenting to total factor productivity compared to novel technological patents. In contrast, peers suffer from a decrease in total factor productivity, innovative output and both profit and sales growth following strategic patenting by the focal firm. These findings suggest a conflict between patent policies designed to promote innovation while still providing incentives for the firms to capture market share and defend monopolistic positions.
U.S. universities have experienced a shift in research funding away from federal and towards private industry sources. This paper evaluates whether the source of funding – federal or private industry – is relevant for commercialization of research outputs. Babina, He, Howell, Perlman, and Staudt link person-level grant data from 22 universities to patent and career outcomes (including IRS W-2 records). To identify a causal effect, the researchers exploit individual-level variation in exposure to narrow federal R&D programs stemming from pre-existing field specialization. They instrument for the researcher's funding sources with aggregate supply shocks to federal funding within these narrow fields. The results show that a higher share of federal funding reduces patenting and the chances of joining an incumbent firm, while increasing the chances of high-tech entrepreneurship and of remaining employed in academia. A decline in the federal share of funding is offset by an increase in the private share of funding, which has opposite effects. Babina, He, Howell, Perlman, and Staudt conclude that the incentives of private funders to appropriate research outputs have important implications for the trajectory of university researcher careers and intellectual property.
Robinson and Viceisza study how media exposure to entrepreneurship affects startup activity by connecting Nielsen ratings for the ABC show Shark Tank to a variety of entrepreneurship outcomes. To deal with the endogeneity of the show's popularity, the researchers instrument TV ratings using NBA basketball games that aired in some markets, but not others, during the same time as the show. Viewership increases soft measures of entrepreneurship, such as seeking advice from an SBA training center, but has no impact on larger-stakes outcomes, such as new business formation or patenting. Consistent with a role-model effect, Robinson and Viceisza find that more women show up for advice-seeking sessions at SBA centers when greater fractions of women contestants appear on the show. Also, more people seek advice when a larger percentage of contestants are successful in receiving funding. The findings indicate that media exposure can nudge would-be entrepreneurs to take steps towards launching a business.
Evidence from Multinationals
This paper analyses the effect of a firm's organizational capacity on reported profitability of multinational enterprises (MNEs). Better organizational practices improve productivity and, in principle, increase potential taxable profits of firms. However, higher adoption of these practices may also enable more efficient allocation of profits across tax jurisdictions. Bilicka and Scur present new evidence that MNE subsidiaries with better practices, located in high-tax countries report significantly lower profits and have higher incidence of bunching around zero returns on assets. This is in contrast with the positive relationship with firm performance in these subsidiaries. The researchers show these results are driven by patterns consistent with profit shifting behavior. Using an event study design, Bilicka and Scur find that firms with better practices are more responsive to corporate tax rate changes. Their results suggest organizational capacity, especially monitoring- related practices, enable firms to engage in shifting profits away from their high-tax subsidiaries.
How do children affect scientific output, promotions, and gender inequality in science? Kim and Moser investigate this question by analyzing 82,094 biographies - matched with patents and publications - in 1956, at the height of the baby boom. Examining life cycle patterns of productivity, the researchers find that mothers' productivity peaks in their early 40s, long after other scientists have started to decline. Event studies of marriage show that mothers become more productive 15 years after marriage, when children are in their teens. Differences in the timing of productivity have important implications for tenure. Just 27% of mothers who are academics achieve tenure, compared with 48% of fathers and 46% of other women. Examining selection, Kim and Moser find that women are half as likely to survive in science, but more likely to hold a PhD, and much less likely to marry and have children compared with men. Output data show that others who survive in science are extremely positively selected. Employment data indicate that a generation of women was lost to American science during the baby boom.
This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance "exploitation" (selecting from groups with proven track records) with "exploration" (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on "supervised learning" approaches, are designed solely for exploitation. Instead, Li, Raymond, and Bergman build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, the researchers show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. In an extension, Li, Raymond, and Bergman show that exploration-based algorithms are also able to learn more effectively about simulated changes in applicant hiring potential over time. Together, their results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.