Propagation and amplification of local productivity spillovers

Xavier Giroud, Simone Lenzu, Quinn Maingi, Holger Mueller 01 December 2021



Local governments spend billions of dollars every year on subsidies for companies to locate within their jurisdictions (Story 2012, Bartik 2020). From an efficiency standpoint, a common rationale for such place-based incentives is the existence of ‘Marshallian agglomeration economies’ – spillovers to the local economy in the form of input sharing, labour market pooling, and knowledge externalities – that raise the productivity of local workers and businesses (Moretti 2010, Kline and Moretti 2014, Neumark and Simpson 2015, Gaubert 2018a, 2018b). While the success of each individual place-based policy ultimately depends on how the policy affects the equilibrium balance of agglomeration and congestion forces (Redding and Esteban 2016), it is widely believed that productivity gains are highly localised and confined to the region in which the policy is implemented. In a recent paper (Giroud et al. 2021), we challenge this view, providing both reduced-form and model-based evidence showing that local productivity spillovers may propagate throughout the economy through the plant-level networks of multi-region firms.

Local versus global productivity spillovers

To identify local productivity spillovers, our empirical analysis builds on the natural experiments in Greenstone et al (2010), who study the effects of large plant openings (‘million dollar plants’, or MDPs) on the productivity of incumbent plants. In this setting, plants in ‘runner-up’ counties, which narrowly lost the competition to attract an MDP, provide a counterfactual for plants in the ‘winner’ county, where the plant was ultimately located. 

Using confidential plant-level data from the US Census Bureau, we show that the MDP openings raise the productivity of incumbent plants by 4%. This local productivity spillover is strong within a 50-mile radius around the plant, weaker within a 100-mile radius, and insignificant beyond. Hence, and consistent with a large empirical literature, the productivity spillovers between (plants of) different firms decay rapidly with geographical distance.

A different picture emerges when one examines how productivity spillovers spread spatially within firm boundaries. We consider large multi-plant, multi-region firms which are exposed to the MDP openings (and thus to the local productivity spillover), by having a plant in the winner county. Comparing plants in the same county, industry, and year that belong to firms which either own plants in the winner county (‘treated plants’) or in the runner-up counties (control), our research finds that in response to the MDP openings, treated plants outside the winner county experience productivity gains of 1.8%, along with employment gains of 1.6%. Unlike the local productivity spillover, which takes place across plants of different firms, this global productivity spillover, which takes place across different plants of the same firm, does not decay with geographical distance. Indeed, the estimated global spillover effect remains virtually unchanged if we exclude all plants within a 500-mile radius, or within the same state or census division as the MDP in question. 

In a recent survey article, Rosenthal and Strange (2020) write:

“Implicit in the idea that spatial concentration increases productivity is another idea: the degree of proximity matters. Agglomeration economies must decay with distance. How close, then, do firms and workers need to be to each other to benefit from agglomeration economies? Or more colloquially, how close is close?”

We provide a nuanced answer to this question. On the one hand, firms must have a nearby plant to benefit from local knowledge spillovers, as the local agglomeration economy is strongly significant only within a 50-mile radius around the MDP . On the other hand, not all of a firm’s plants need to be located nearby. In fact, it may suffice if only one of the firm’s plants is near the MDP. Once the knowledge spills over to that plant, it can be passed on to other plants inside the firm’s boundaries, increasing the productivity of distant plants hundreds of miles away.

Knowledge sharing across regions within firm boundaries

What explains the global productivity spillover? The evidence presented in our latest paper is consistent with knowledge sharing between plants of the same firm across different regions. While the local productivity spillover may be due to either labour market pooling or knowledge externalities, it is unlikely that a thicker labour market in the winner county would affect the productivity of treated plants hundreds of miles away. Knowledge, on the other hand, can be used in local and distant plants alike. Indeed, once it spills over to the firm’s local plant, it can be freely shared with other plants inside the firm’s boundaries (Markusen 1984). Consistent with this notion, the global productivity spillover does not decay with geographical distance. Further, and consistent with knowledge sharing, the global productivity spillover is much stronger if the distant plant and either the MDP or the firm’s plant in the winner county are in the same industry or in knowledge-based industries characterised by mutual R&D flows or patent citations.

Does knowledge sharing between plants reduce regional disparities?

While the reduced-form evidence points to sizeable spillover effects on distant regions, the importance of the significance of plant-level networks for regional and aggregate outcomes requires a meaningful quantitative framework. Since all plants in the economy are affected through general equilibrium effects (labour flows, goods trade), non-treated plants in distant counties provide an imperfect counterfactual to quantify the significance of knowledge sharing for the amplification of local productivity shocks. For example, as (manufacturing) goods are tradable, changes in goods prices affect real wages across all plants in the economy, including non-treated plants. 

To quantify the significance of plant-level networks for the propagation and amplification of local productivity shocks, we develop and estimate a quantitative spatial model with goods trade, labour mobility, plant-level networks, and a rich geography – where plants of the same firm, across regions, are linked through shared knowledge. We estimate the model via indirect inference by exploiting the close link between structural parameters and reduced-form regression coefficients, and then use the structure of the model to undertake counterfactual analyses. 

We first assess the effects of an increase in within-firm, across-location knowledge sharing on the distribution of economic activity. Intuitively, an increase in knowledge sharing between plants of multi-region (‘multi-county’, or MC) firms makes these plants more productive. Given a reduction in marginal costs, the plants increase labour demand, pushing up nominal wages (but not enough to offset the reduction in marginal costs). As a result, goods prices decline, which pushes up real wages. At the macroeconomic level, this influences the spatial distribution of economic activity. Surprisingly, knowledge sharing between plants widens economic disparities across regions. In relative terms, highly populous regions with high real GDP become richer, while rural regions with low real GDP become poorer (Figure 1, panel A).

Figure 1 Increase in within-firm, across-region knowledge sharing and location of MC plants

Notes: Panel A shows changes in average real wages and employment at the county level from an increase in knowledge sharing. Counties are sorted into percentiles based on their population in 1987 Census data. Panel B shows the distribution of MC plants across counties. Counties are sorted into deciles based on their population in 1987 Census data. The gray bars depict the share of MC plants, relative to all MC plants in the economy, associated with a given decile in the 1987 CMF. The blue bars depict the corresponding shares in the model. 

The fundamental economic force driving this result is the sorting across locations by multi-county plants. In equilibrium, counties with more multi-county plants experience relatively large average real wage increases, as well as labour inflows, while counties with fewer multi-county plants experience smaller average real wage increases and thus labour outflows. In our model economy, as well as in the data, 60% of all multi-county plants are in the 10% most populous counties (Figure 1, panel B). Hence, the productivity gains from an increase in knowledge sharing between plants are disproportionately concentrated in the richest, most populous counties.

In our second counterfactual, we examine the welfare implications of place-based policies in environments with and without knowledge sharing between plants. In each scenario, we simulate local productivity shocks – such as those arising from place-based policies – and measure the resulting changes in worker-level utility. While, on average, the welfare gains are much higher with knowledge sharing (64%), they are unevenly distributed. In particular, workers in other regions working for plants that are connected (through plant-level networks) to the region with the productivity shock experience utility gains that are five times larger than workers at non-connected plants. At the regional level, the welfare gains are disproportionately concentrated in highly populous regions, as those tend to have the most connected plants. Hence, knowledge sharing through plant-level networks leads to a highly uneven distribution of the welfare gains from local productivity shocks. These results complement the ones in Gaubert (2018a, 2018b), proving an additional explanation for why place-based policies may increase spatial disparities across regions.

Figure 2 Propagation and amplification of local productivity shocks 

Notes: This figure shows changes in average worker-level utility at the county level from a 4% increase in productivity in one state. All 50 states are independently shocked; the figure shows averages across all 50 experiments. Counties are sorted into percentiles based on their population in 1987 Census data. Panel A considers an economy without knowledge sharing between plants. Panel B considers an economy with knowledge sharing between plants. 


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Topics:  Industrial organisation Productivity and Innovation

Tags:  regional economics, agglomeration, spillovers, firms, innovation, productivity

Roger F. Murray Associate Professor of Finance, Columbia Business School

Assistant Professor of Finance, New York University, Stern School of Business

Nomura Professor of Finance at the Leonard N. Stern School of Business, New York University


CEPR Policy Research