As Romer and Weitzman emphasized in the 1990s, new ideas are often combinations of existing ideas, an insight absent from recent models. In Kortum's research around the same time, ideas are draws from a probability distribution, and Pareto distributions play a crucial role. Why are combinations missing, and do we really need such strong distributional assumptions to get exponential growth? This paper demonstrates that combinatorially growing draws from standard thin-tailed distributions lead to exponential growth; Pareto is not required. More generally, it presents a theorem linking the max extreme value to the number of draws and the shape of the upper tail for probability distributions.

This paper considers top income taxation when (i) new ideas drive economic growth, (ii) the reward for successful innovation is a top income, and (iii) innovation cannot be perfectly targeted by a research subsidy—think about the business methods of Walmart, the creation of Uber, or the “idea” of Amazon. These conditions lead to a new force affecting the optimal top tax rate: by slowing the creation of new ideas that drive aggregate GDP, top income taxation reduces everyone’s income, not just income at the top. This force sharply constrains both revenue-maximizing and welfare-maximizing top tax rates.

Some technologies save lives—new vaccines, new surgical techniques, safer highways. Others threaten lives—pollution, nuclear accidents, global warming, and the rapid global transmission of disease. How is growth theory altered when technologies involve life and death instead of just higher consumption? This paper shows that taking life into account has first-order consequences. Under standard preferences, the value of life may rise faster than consumption, leading society to value safety over consumption growth. As a result, the optimal rate of consumption growth may be substantially lower than what is feasible, in some cases falling all the way to zero.

This paper argues that the 'scale effects' prediction of many recent R&D-based models of growth is inconsistent with the time-series evidence from industrialized economies. A modified version of the Romer model that is consistent with this evidence is proposed, but the extended model alters a key implication usually found in endogenous growth theory. Although growth in the extended model is generated endogenously through R&D, the long-run growth rate depends only on parameters that are usually taken to be exogenous, including the rate of population growth.

This paper argues that the "scale effects" prediction of many recent R & D-based models of growth is inconsistent with the time-series evidence from industrialized economies. A modified version of the Romer model that is consistent with this evidence is proposed, but the extended model alters a key implication usually found in endogenous growth theory. Although growth in the extended model is generated endogenously through R & D, the long-run growth rate depends only on parameters that are usually taken to be exogenous, including the rate of population growth.

Top income inequality rose sharply in the United States over the last 40 years but increased only slightly in France and Japan. Why? We explore a model in which heterogeneous entrepreneurs, broadly interpreted, exert effort to generate exponential growth in their incomes, which tends to raise inequality. Creative destruction by outside innovators restrains this expansion and induces top incomes to obey a Pareto distribution. Economic forces that affect these two mechanisms—including information technology, taxes, and policies related to innovation blocking—may explain the varied patterns of top income inequality that we see in the data.