[Research]

Energy & Environment

Knowledge and experience learned from investing in physical, tangible capital is sometimes non-excludable between firms. I explore how this simple externality affects technological growth. Using the recent fracking revolution in American oil & gas, I develop a two-stage empirical procedure to 1) provide evidence that this shared intangible exists and 2) show that firms value this knowledge externality when making investment decisions. I use a spatial panel model that is a natural network structure for localized knowledge diffusion to identify cross-sectional differences in intangible capital value across counties. Then, I use global oil prices as a plausibly exogenous source of variation in investment levels. I find that one extra investment made by other firms in strong knowledge network areas is associated with a 13% increase in monthly productivity. I show that this effect is particularly important for growing technologies; tests using older methods of production do not have the same impact. In a heterogeneous firm model, I formalize how this mechanism drives growth cycles by effecting technology improvement and widespread adoption jointly. As more firms learn by doing, the technology improves for everyone. As the technology improves, more firms invest. Technology transition is the result of firms optimally re-weighting their capital portfolios towards newer technology. Because tangible capital investment is the primary mechanism for technology growth, the distribution of firms in the economy becomes an important state variable which determines the rate of technology adoption and the phase-out of old technology.

The plot shows the coefficient from regressions of productibvity on instrumented investment activity by other nearby firms. As you move up the quartiles of estimated knowledge networks along the x-axis, the relationship between productivity and ex-firm investment increases significantly.

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.

Innovation & Growth

Knowledge Spillovers & Technology Adoption: A heterogeneous firm model

I build a heterogeneous firm model of technology adoption. Firms have the choice to produce a single final good using two different types of capital. Firms jointly solve an investment problem as well as stopping time problem which determines their technology specialization rate. The key spillover mechanism is captured in the function governing the productivity of new type capital which includes both the firm's own experience and the aggregate industry-wide experience as an input. I study the comparative statistics for economies with different knowledge sharing and learning propensities. I show that the spillover mechanism alone can generate the gradual adoption patterns often observed empirically. At the same time, learnng by doing within firms does not result in the same transition patterns.


(coming soon, preliminary draft available upon request)
The plot shows transition dynamics for an economy that starts with very few firms using the new type technology. The different lines show comparative statics of economies with dfferent shared knowledge and own learning propensities.

Household & Real Estate

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.

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 the 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.

nchau0@chicagobooth.edu