This paper views the standard production function in macroeconomics as a reduced form and derives its properties from microfoundations.The shape of this production function is governed by the distribution of ideas.If that distribution is Pareto, then two results obtain: the global production function is Cobb-Douglas, and technical change in the long run is labor-augmenting.Kortum (1997) showed that Pareto distributions are necessary if search-based idea models are to exhibit steady-state growth.Here we show that this same assumption delivers the additional results about the shape of the production function and the direction of technical change.

Global digital transformation contributes to the realization of national interests, the improvement of financial management, and the development of financial technologies in the corporate sector of the economy. In the current extremely difficult conditions of a full-scale war, as part of the renewal of the strategy of the financial sector, the Ministry of Finance of Ukraine, the National Bank of Ukraine, together with IMF experts, are gradually developing the principles of assessing the quality of assets and conducting stress tests for business entities and financial institutions.

Rising educational attainment and research intensity in recent decades suggest that the U.S. economy is far from its steady state. This paper develops a model reconciling these facts with the stability of U.S. growth rates. In the model, long-run growth arises from the worldwide discovery of ideas, which depends on population growth. Nevertheless, constant growth can temporarily proceed at a faster rate, provided research intensity and educational attainment rise steadily over time. Growth accounting reveals that these factors explain 80 percent of recent U.S. growth, with less than 20 percent coming from world population growth.

The property that ideas are nonrivalrous leads to a tight link between idea-based growth models and increasing returns to scale. In particular, changes in the size of an economy’s population generally affect either the long-run growth rate or the long-run level of income in such models. This paper provides a partial review of the expanding literature on idea-based models and scale effects. It presents simple versions of various recent idea-based growth models and analyzes their implications for the relationship between scale and growth.

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.

Data is nonrival: a person’s location history, medical records, and driving data can be used by many firms simultaneously. Nonrivalry leads to increasing returns. As a result, there may be social gains to data being used broadly across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard their data, leading to the inefficient use of nonrival data. Giving data property rights to consumers can generate allocations that are close to optimal. Consumers balance their concerns for privacy against the economic gains that come from selling data broadly.