Energy & Environment 

This paper develops a new empirical and theoretical approach to studying network effects in the diffusion, or internal firm adoption, of new technology. The empirical model devises a two step method which first categorizes geographies based on a spatial panel or network model. Then, it compares firm behavior across these categories when there is an exogenous change in information flow that affects all areas. The case study in the American oil & gas fracking revolution documents novel results that firm adoption of new technology is more sensitive to an increase in usable knowledge in highly networked areas. I then propose a theoretical framework for understanding diffusion as a dynamic transition process which can explain these empirical trends. I take a classic model of lumpy firm investments and incorporate a technology adjustment dimension. The model parameters are estimated using SMM. The structural model supports the reduced form result that productivity increases due to shared knowledge drives investment in new technology. The model sheds light on how increased competition as adoption rates increase interacts with the productivity gains. I show that adoption decisions are distinct from innovation decisions. Both models of firm behavior are important to the aggregate technology process at different times in the transition process. 

 Household & Real Estate

Integrated Intermediation & Fintech Market Power

with Greg Buchak & Adam Jørring

We document that in the US residential mortgage market, the share of integrated intermediaries acting as both originator and servicer has declined dramatically. Exploiting a regulatory change, we show that borrowers with integrated servicers are more likely to refinance, and conditional on refinance, are more likely to be recaptured by their own servicer. Recaptured borrowers pay lower fees relative to other refinancers. This trend is partially offset by a rise in integrated fintech originator-servicers, who recapture at higher frequency but at worse terms. We build and calibrate a dynamic structural model to interpret these facts and quantify their impact on equilibrium outcomes. Our model suggests that integreated intermediaries enjoy a marginal cost advantage when refinancing recaptured borrowers, and fully disintegrating them would reduce refinancing frequencies and increase fees. Fintechs use technology to reacquire customers and reduce borrower inertia against refinancing. This endogenously creates market power, which fintechs exploit through higher fees. Despite worse terms ex-post, fintechs increase consumer welfare ex-ante by increasing refinancing frequencies. Taken together, our results highlight the importance of intermediaries’ scope in consumer financial outcomes and highlight a novel, quantitatively important application of fintech: customer acquisition.

Do Intermediary Constraints Matter? Evidence from Household Finance

(available upon request)

Using servicers in the mortgage backed security market, I show that constraints placed on intermediaries through a specific channel, mortgage advances, has a statistically significant impact on loan outcomes even after controlling for borrower factors like credit score. Using a loan level dataset, advance behavior is shown to affect foreclosure timelines, a previously unexplored area. The probability of a mortgage resolving through borrower payoff rather than foreclosure, as well as the loss amount on liquidated mortgages is also shown to be affected by differences in the servicer's advance propensity. 

The Image shows the the ratio of advances conditional on rolling over into 90 dpd and distress between bank and non-bank servicers. Starting in 2010, the probability that a bank chooses to advance conditional on a homeowner entering distress diverges between the bank types. 

 Innovation & Growth

Aggregate Technological Change: The joint effect of firm heterogeneity & knowledge spillovers

In the presence of knowledge spillovers, the distribution of firm types becomes an important input in the process of aggregate technology improvement. Firms are heterogeneous in where they choose to invest as well as how much they are able to invest. Using recentered influence function regressions, I provide empirical evidence for these differences in the American oil & gas industry. I show the effect of changing the distribution of firms on each part of the technology distribution and compare that effect across regions with different knowledge sharing propensity. The higher quantiles of the technology distribution is shown to be positively affected by distributions with higher average firm skills while the lower quantiles are negatively impacted. On the other hand, when the average firm size is higher, the median part of the technology distribution is positively impacted while both the quantiles on either end are either negative or neutrally impacted by the distribution of firm size. 

 Other Work

Robust Regimes: the direct impact of model uncertainty

Hansen-Sargent models of robustness consider agents who are concerned with model misspecification. Detection error probabilities are sometimes used to train the amount of uncertainty with which agents should be worried. I propose a method to empirically estimate the impact of model misspecification on decisions. For a set of models with distorted transition matrices relative to the estimated (approximating) model, I show that the set of models which are statistically difficult to distinguish is not monotonic in the size of the distortions applied. For any level of detection error tolerated, forecasts which incorporate all models in that set outperform the approximating model regardless of the distortion size. Finally, the larger the forecast differences between the approximating and distorted models, the larger te impact on the probability that firms execute on long term real options. By contrast, short term decisions do not show as strong of an effect when the set of indistinguishable models imply smaller forecast differences. The method directly incorporates not just potential model misspecification but the  forecast implications between models.