Person:

Kerr, William

We analyze the spatial determinants of female entrepreneurship in India in the manufacturing and services sectors. We focus on the presence of incumbent female-owned businesses and their role in promoting higher subsequent female entrepreneurship relative to male entrepreneurship. We find evidence of agglomeration economies in both sectors, where higher female ownership among incumbent businesses within a district-industry predicts a greater share of subsequent entrepreneurs will be female. Moreover, higher female ownership of local businesses in related industries (e.g., those sharing similar labor needs, industries related via input-output markets) predict greater relative female entry rates even after controlling for the focal district-industry's conditions. The core patterns hold when using local industrial conditions in 1994 to instrument for incumbent conditions in 2000–2005. The results highlight that the traits of business owners in incumbent industrial structures influence the types of entrepreneurs supported.

We analyze the career trajectories of STEM workers and firm-level hiring of immigrants using the Longitudinal Employer-Household Dynamics (LEHD) database of the US Census Bureau. We find STEM career adjustments during periods of abnormally high immigration into the firm to be more difficult on several dimensions compared to non-STEM workers. Most notably, STEM workers do not acquire a new job as quickly as non-STEM workers; moreover, their earnings are reduced after the job transition occurs. This latter earnings effect is strongest for the first five years after the transition, abating somewhat by the tenth year.

This paper reviews recent academic work on the spatial concentration of entrepreneurship and innovation in the United States. We discuss rationales for the agglomeration of these activities and the economic consequences of clusters. We identify and discuss policies that are being pursued in the United States to encourage local entrepreneurship and innovation. While arguments exist for and against policy support of entrepreneurial clusters, our understanding of what works and how it works is quite limited. The best path forward involves extensive experimentation and careful evaluation.

We build a model of firm-level innovation, productivity growth and reallocation featuring endogenous entry and exit. A key feature is the selection between high- and low-type firms, which differ in terms of their innovative capacity. We estimate the parameters of the model using detailed US Census micro data on firm-level output, R&D and patenting. The model provides a good .t to the dynamics of firm entry and exit, output and R&D, and its implied elasticities are in the ballpark of a range of micro estimates. We find industrial policy subsidizing either the R&D or the continued operation of incumbents reduces growth and welfare. For example, a subsidy to incumbent R&D equivalent to 5% of GDP reduces welfare by about 1.5% because it deters entry of new high-type firms. On the contrary, substantial improvements (of the order of 5% improvement in welfare) are possible if the continued operation of incumbents is taxed while at the same time R&D by incumbents and new entrants is subsidized. This is because of a strong selection effect: R&D resources (skilled labor) are inefficiently used by low-type incumbent firms. Subsidies to incumbents encourage the survival and expansion of these firms at the expense of potential high-type entrants. We show that optimal policy encourages the exit of low-type firms and supports R&D by high-type incumbents and entry.

We identify the impact of local firm concentration on incumbent performance with a quasi-natural experiment. When Germany was divided after World War II, many firms in the machine tool industry fled the Soviet occupied zone to prevent expropriation. We show that the regional location decisions of these firms upon moving to western Germany were driven by non-economic factors and heuristics rather than existing industrial conditions. Relocating firms increased the likelihood of incumbent failure in destination regions, a pattern that differs sharply from new entrants. We further provide evidence that these effects are due to increased competition for local resources.

This paper studies the impact that immigrant innovators have on the global activities of U.S. firms by analyzing detailed data on patent applications and on the operations of the foreign affiliates of U.S. multinational firms. The results indicate that increases in the share of a firm’s innovation performed by inventors of a particular ethnicity are associated with increases in the share of that firm’s affiliate activity in their native countries. Ethnic innovators also appear to facilitate the disintegration of innovative activity across borders and to allow U.S. multinationals to form new affiliates abroad without the support of local joint venture partners. Thus, this paper points out that immigration can enhance the competitiveness of multinational firms.

We investigate the impact of the Golden Quadrilateral (GQ) highway project on the Indian organized manufacturing sector using enterprise data. The GQ project upgraded the quality and width of 5,846 km of roads in India. We use a difference-in-difference estimation strategy to compare non-nodal districts based upon their distance from the highway system. We find several positive effects for non-nodal districts located 0-10 km from GQ that are not present in districts 10-50 km away, most notably higher entry rates and increases in plant productivity. These results are not present for districts located on another major highway system, the North-South East-West corridor (NS-EW). Improvements for portions of the NS-EW system were planned to occur at the same time as GQ but were subsequently delayed. Additional tests show that the GQ project’s effect operates in part through a stronger sorting of land-intensive industries from nodal districts to non-nodal districts located on the GQ network. The GQ upgrades further helped spread economic activity to moderate-density districts and intermediate cities.

We quantify the link between the timing of state-level implementations of political reservations for women in India with the role of women in India's manufacturing sector. While overall employment of women in manufacturing does not increase after the reforms, we find significant evidence that more women-owned establishments were created in the unorganized/informal sector. These new establishments were concentrated in industries where women entrepreneurs have been traditionally active and the entry was mainly found among household-based establishments. We measure and discuss the extent to which this heightened entrepreneurship is due to channels like greater finance access or heightened inspiration for women entrepreneurs.

We model spatial clusters of similar firms. Our model highlights how agglomerative forces lead to localized, individual connections among firms, while interaction costs generate a defined distance over which attraction forces operate. Overlapping firm interactions yield agglomeration clusters that are much larger than the underlying agglomerative forces themselves. Empirically, we demonstrate that our model's assumptions are present in the structure of technology and labor flows within Silicon Valley and its surrounding areas. Our model further identifies how the lengths over which agglomerative forces operate influence the shapes and sizes of industrial clusters; we confirm these predictions using variations across patent technology clusters.

We develop a microeconomic model of endogenous growth where clean and dirty technologies compete in production and innovation, in the sense that research can be directed to either clean or dirty technologies. If dirty technologies are more advanced to start with, the potential transition to clean technology can be difficult both because clean research must climb several rungs to catch up with dirty technology and because this gap discourages research effort directed towards clean technologies. Carbon taxes and research subsidies may nonetheless encourage production and innovation in clean technologies, though the transition will typically be slow. We characterize certain general properties of the transition path from dirty to clean technology. We then estimate the model using a combination of regression analysis on the relationship between R&D and patents, and simulated method of moments using microdata on employment, production, R&D, firm growth, entry and exit from the US energy sector. The model's quantitative implications match a range of moments not targeted in the estimation quite well. We then characterize the optimal policy path implied by the model and our estimates. Optimal policy makes heavy use of research subsidies as well as carbon taxes. We use the model to evaluate the welfare consequences of a range of alternative policies.