Firm numbers first rise, then later fall, as an industry evolves. This nonmonotonicity is explained using a competitive model in which innovation opportunities fuel entry and relative failure to innovate prompts exit; equilibrium time paths for price and quantity also share features of the data. The model is estimated using data from the U.S. automobile tire industry, a particularly dramatic example of the nonmonotonicity in firm numbers.

Is the capital function distinct from the entrepreneurial function in modern economies? Or does a person have to be wealthy before he or she can start a business? Knight and Schumpeter held different views on the answer to this question. Our empirical findings side with Knight: Liquidity constraints bind, and a would-be entrepreneur must bear most of the risk inherent in his venture. The reasoning is roughly this: The data show that wealthier people are more inclined to become entrepreneurs. In principle, this could be so because the wealthy tend to make better entrepreneurs, but the data reject this explanation. Instead, the data point to liquidity constraints: capital is essential for starting a business, and liquidity constraints tend to exclude those with insufficient funds at their disposal.

This paper explores a one-agent Bayesian model of learning by doing and technological choice.To produce output, the agent can choose among various technologies.The beneficial effects of learning by doing are bounded on each technology, and so long-run growth in output can take place only if the agent repeatedly switches to better technologies.As the agent repeatedly uses a technology, he learns about its unknown parameters, and this accumulated expertise is a form of human capital.But when the agent switches technologies, part of this human capital is lost.It is this loss of human capital that may prevent the agent from moving up the quality ladder of technologies as quickly as he can, since the loss is greater the bigger is the technological leap.We analyze the global dynamics.We find that a human-capital-rich agent may find it optimal to avoid any switching of technologies, and therefore to experience no long-run growth.On the other hand, a human-capital-poor agent, who because of his lack of skill is not so attached to any particular technology, can find it optimal to switch technologies repeatedly, and therefore enjoy long-run growth in output.Thus the model can give rise to overtaking.

Matching models usually assume an exogenously given distribution of match productivity, and the act of changing jobs then has the worker taking a new, independent sample from this distribution. Using a "characteristics" approach to matching two heterogeneous populations, this article shows that assumptions concerning the normality and serial independence of match productivity (across successive matches) follow from some simple axioms. Moreover, the normality assumption receives support from an empirical test that uses data on the output of a large group of workers.

On news of a takeover, the sum of the stock market values of the firms involved often falls, and the value of the acquirer almost always does. Does this mean that takeovers do not raise the values of the firms involved? Not necessarily. We set up a model in which the equilibrium number of takeovers is constrained efficient. Yet upon news of a takeover, a target's price rises, the bidder's price falls, and most of the time the joint value of the target and acquirer also falls.

We develop a dynamic model with knowledge spillovers in production. The model contains two opposing forces. Imitation of other firms helps followers catch up with leaders, but the prospect of doing so makes followers want to free ride. The second force dominates and creates permanent inequality. We show that the greater are the average spillovers and the easier they are to obtain, the greater is the free-riding and inequality. More directed copying raises inequality by raising the free-riding advantages of hanging back. Using Compustat and patent-citation data we find that copying is highly undirected.

When a production process requires two extremely complementary inputs, conventional wisdom holds that a firm would always upgrade them simultaneously. We show, however, that if upgrading each input involves a fixed cost, the firm may upgrade them at different dates, “asynchronously.” This insight helps us understand why productivity rises with the age of a plant, why investment in structures is more spiked than equipment investment, and why plants have spare capacity. The bigger point of the paper is that complementarity does not necessarily imply comovement—not even for a single decision maker.

Technological progress comes in waves. The British Industrial Revolution ( 1760 – 1850) ushered in Cort’s puddling and rolling process for making iron, Crompton’s mule for spinning cotton, and the Watt steam engine. The Second Industrial Revolution ( 1890 – 1930) witnessed the rise of electricity, the internal-combustion engine, and the chemical industry. The birth of information technology (IT) may herald the start of a Third Industrial Revolution. A new technology or product is often developed by the single entrepreneur who initially finds it hard to get funds, develop the product, and find customers. But if the product is good, customers eventually line up, and investors flock in. Other firms then move in to make the product and may drive the innovator out or acquire him. Whether he reaches the initial public offering (IPO) stage or is acquired by a listed firm, though, it takes time for the innovator to add value to the stock market. Indeed, the innovation may, at first, reduce the market’s value because some firms, usually large or old, will cling to old technologies that have lost their momentum. Figure 1 plots the market value of U.S. equity relative to GDP. This paper argues that (a) the market declined in the late 1960’s because it felt that the old technologies either had lost their momentum or would give way to IT, and that (b) IT innovators boosted the stock market’s value only in the 1980’s. If the stock market provides a forecast of future events, then the recent dramatic upswing represents a rosy estimate about growth in future profits for the economy. This translates into a forecast of higher output and productivity growth, holding other things equal (such as capital’s share

We propose a theory of the market for venture capital that links the excess return to venture equity to the scarcity of venture capitalists (VCs). High returns make the VCs more selective and eager to terminate nonperforming ventures because they can move on to new ones. The scarcity of VCs enables them to internalize their social value, and the competitive equilibrium is socially optimal. Moreover, the bilaterally efficient contract is a simple equity contract. We estimate the model for the period 1989–2001 and compute the excess return to venture capital, which turns out to be 8.6 percent. Finally, we back out the return of solo entrepreneurs, which is increasing in their wealth and ranges between zero and 3.5 percent.

Why are contracts not fully indexed? In a setting in which fully indexed contracts are feasible, we find that when price‐level data are gathered with delay, these contracts are not renegotiation‐proof. The contracts that replace them entail a lower level of welfare for the parties to that contract. They also imply that real variables respond to nominal shocks.