Sargent Reading Group Notes

Data The goal of this paper is to use firm-level data to determine the determinants of productivity and output growth. To do this, the authors use a nearly-balanced panel from 1987-1994 on 527 of the fortune 1000 firms to attempt to estimate the impact on investment in computers on output and productivity growth. This panel is a merging of compustat data with data from the Computer Intelligence InfoCorp, or CII. The CII surveys the IT managers at the largest firms in the US at least frequency, but up to monthly based on the value of computer equipment installed at the firm.

Model The model in this paper is a mashup of the DSGE framework of Cristiano, Eichenbaum, and Evans and the entrepreneur and financial friction component of Bernanke, Gertler, and Gilchrist. To this framework they add an additional feature they term a “risk shock”, which is stochastic volatility in the distribution of entrepreneur productivity. The model has too many pieces for me to describe in detail, so I will attempt to focus on the parts that are most important for understanding the key takeaway of the paper.

Intro This paper studies subset of intangible capital called organizational capital. The authors define organizational capital as intangible capital embodied in the firm’s key employees The authors maintain the hypothesis that returns from organizational capital are partly firm specific and partly due to key talent at the firm. Thus, shareholders must split revenues between themselves and key talent to entice them not to take their talents to another firm. This has a number of consequences:

Intro IN RECENT DECADES, INTANGIBLE capital has become an increasingly important factor of production. In many instances, such intangible capital is embodied in the firm’s key employees. We refer to this type of intangible capital as organization capital and develop a structural model to analyze its effect on asset prices. We argue that shareholders consider firms with high levels of organization capital to be riskier than firms with more physical capital, and provide empirical evidence supporting this claim.

McGratten starts by saying that firms invest almost as much in intangible capital as they do physical capital, but that intangible capital is not reported in GDP. This severely understates the actual change in output. She sees this as one way in which we can see a rise in labor and investment coincide with relatively little increase in measured total factor productivity. The goal of the paper is to build a multi-sector general equilibrium model that incorporates intangible capital and investment.

In this paper Hall seeks to quantify the price and stock of capital in the economy from 1960 to 2000 using data from the stock market. To do this he proposes the following simple model. Discrete time, infinite horizon Constant returns to scale production All produciton inputs except capital adjust frictionlessly Adjustment to capital stock can be fully adjusted in the long run (no long run rents) Factor markets are competitive In this setting firm profits is equal to capital times the product of capital, where the product of capital depends on non-capital production inputs.

This is a “facts” paper that documents some facts about the distribution of market power over time and then talks about some macroeconomic implications of the change in market power. To talk about some of these facts, they need to write down a very simple model. Facts Data They use firm-level Compustat data from 1950-2014. The data includes all publicly traded firms over that time horizon. They comment that while this clearly does not include all firms, it does capture most of subset of firms relative for their study (large firms are usually public, and they are the ones with significant market power).

This goal of this paper is to better understand how firm entry and exit interact with aggregate productivity growth. Empirical finding The authors main empirical finding is that the contribution to aggregate productivity growth from new firms is larger in periods of high productivity growth than in periods of low productivity growth. This finding is established using data on all manufacturing plants in Chile from 1995 to 2006 and in Korea for 1992-1997, 2001-2006, and 2009-2014.

The goal of this paper is to use the modeling concept of Global value chains to analyze the international impact of a lowering of trade costs. Model Most of the paper is about describing the model. It is too complicated to explain in full in just 5 minutes, so I will discuss some of the key features so we can understand the results. The model is static. There are N countries that are symmetric in technology, but will have different parameter values in the calibrated model.

The goal of this paper is to use the modeling concept of Global value chains to analyze the international impact of a lowering of trade costs. Model Most of the paper is about describing the model. It is too complicated to explain in full in just 5 minutes, so I will discuss some of the key features so we can understand the results. The model is static. There are N countries that are symmetric in technology, but will have different parameter values in the calibrated model.