Diego Comin

Associate Professor of Business Administration (Leave of Absence)

Diego Comin is an Associate Professor of Business Administration at HBS since 2007. He received his B.A. in Economics in 1995 from the University Pompeu Fabra, Barcelona, Spain and his PhD in Economics from Harvard University in 2000. Between 2000 and 2007, Comin has been Assistant Professor of Economics at New York University. He is also Research Fellow at the Center for Economic policy Research and Faculty Research Fellow in the National Bureau of Economic Research’s Economic Fluctuations and Growth Program. Comin is a fellow for the Institute of New Economic Thinking (INET) and his work has been supported by the Gates foundation, the National Science Foundation, the C.V. Star Foundation, and the Zentrum für Europäische Wirtschaftsforschung (ZEW). Comin has also advised the government of Malaysia on its development strategies and consulted for the World Bank, IMF, Federal Reserve Bank of New York, Citibank, Danish Science Ministry, and the Economic and Social Research Institute (ESRI) of the government of Japan. 

Comin works on macroeconomics broadly understood. Part of his research consists of studying the process of technological change and technology diffusion both across countries and over time. A second avenue of Comin’s work studies the sources and propagation mechanisms of fluctuations at high and medium term frequencies. A third line of research pursued by Comin has explored the evolution of firm dynamics and their implications for the evolution of the US economy. His work has been published mainly in academic journals, including the American Economic Review, the American Economic Journal, the Journal of Monetary Economics, the Review of Economics and Statistics and the Journal of Economic Growth

Comin teaches his elective course (Drivers of Competitiveness) in the MBA program and also a module in the executive program (Program for Leadership Development). For four years, Comin has taught in the MBA first year required course: Business, Government and the International Economy (BGIE). He has also designed and led immersion programs in Peru and Malaysia.

  1. Technology Adoption

    How large are cross-country differences in technology adoption? How important are they to explain the large observed cross-country differences in per capita income? What factors accelerate of slowdown the adoption of technology? What factors affect the shape of the diffusion of technology?

    To answer these questions I have put together several historical data sets on technology adoption and develop new models that allow me to map the micro data to macro aggregates such as labor productivity and total factor productivity (TFP). In several papers I have documented that cross-country differences in technology adoption are even larger than cross-country differences in per-capita income. They are also very persistent. Technology adoption history as far back as 1500 AD can account for a significant fraction of current development. Similarly, current technology adoption differences may account for at least 25% of cross-country variation in current per-capita income. Many factors affect the speed of technology adoption. Two that I have studied are lobbies and capital markets. Both of them seem to have an important effect especially in rich countries. In Developing countries, it seems that private savings are important to attract foreign investors with familiarity in frontier technology.

  2. Output and asset price fluctuations

    What are the sources of business cycles? How are these shocks propagated in the economy? Why are their effects so persistent? How can we explain asset price fluctuations? How are shocks transmitted internationally?To study these questions, I have developed a series of macro models where the technology available for production is endogenous. In booms, when aggregate demand is higher, agents find more profitable to invest in improving their technologies. As a result, the business cycle affects the rate at which technologies improve the technology. Modeling this mechanism yields many interesting consequences. First, it provides a theory for medium term fluctuations in TFP. Second, since temporary macro shocks lead to persistent deviations in the level of technology relative to trend, these type of models generate lots of endogenous persistence. This feature of the model is important, for example, to explain why Japan experienced a lost decade during the 1990s despite the facts that the shocks that hit the Japanese economy at the beginning of the decade were not nearly as persistent.

    Standard macro models have a difficult time in explaining asset price volatility. In those models, the stock market is given by the value of installed capital which does not fluctuate much. Once we endogenize the level of technology, the stock market is also affected by fluctuations in the value of current and future producers of the goods that embody the technology. Since profits are very pro-cyclical, and their present discounted value is very volatile, the stock market generated by my models can replicate the statistical properties of the stock market in the data. In addition, volatility and persistence of dividends are consistent with the data. In particular, the model does not rely on highly volatile counter-cyclical risk premia to explain asset prices. Rather, the macro model generates a small persistent component in dividends. We find evidence of the presence of such persistent component in the data.

    The endogenous diffusion of technology is important also to explain the co-movement between developed and developing countries. In particular, since technologies diffuse from rich to poor countries, cyclical variation in the speed of technology diffusion will affect the medium term fluctuations in developing countries. This mechanism can explain, for example, why high frequency fluctuations in the US lead medium term fluctuations in Mexico.

  3. Firm and aggregate volatility

    US publicly traded companies have become more volatile over the postwar period. This trend has been the result of increased competition in product markets through deregulation, through more intensive innovation activity, and through easier access to capital markets. Since the wages of publicly traded companies are a function of the firm’s performance, the higher volatility faced by firms has affected the volatility of wages. Further, since around 1980, firms have responded to the more volatile environment by making compensations more dependent on the firm performance. As a result, wage volatility has increased very significantly for workers in publicly traded companies.

    This trend at the firm level is in sharp contrast with the decline in the volatility of most macro aggregates such as GDP, investment, consumption and hours worked known as the great moderation. In several papers I show that these trends can be reconciled if we recognized the secular trend towards faster technology diffusion. When technologies diffuse faster, there is more turnover in market leadership. Further, there is less amplification of shocks through endogenous technology adoption because the stock of technologies waiting to be adopted is smaller. As a result, aggregate volatility should decline. Interestingly, this theory is also consistent with the last of any moderation in the volatlity of stock returns.