Endogenous growth models have reignited interest in institutional and path dependencies in the economic growth process. One reason for the interest in endogenous growth models is that they may explain why countries consistently grow at different rates. In this vein, it has been recently proposed that greater economic inequality reduces future economic growth. An important paper in this literature is by Torsten Persson and Guido Tabellini (1994), who will be referred to as PT. PT's model shows why it is reasonable to expect a negative relationship between inequality and future economic growth. Moreover, their empirical evidence is consistent with their contention. If PT's findings are robust to other data sets, there would be important policy implications. For example, they imply that policy makers should not only be concerned with the distributional implications of government policies for political and social reasons, but also because income distribution has long-run effects on economic growth. This indicates that greater U.S. income inequality since the early 1970's may have resulted in lower subsequent economic growth. However, PT's results are somewhat fragile to various specifications, suggesting that they should be replicated with different data sets and over different time periods (PT p. 617). With these implications in mind, this study employs data from a panel of U.S. states to further explore the relationship between economic growth and income inequality. In what follows, Section I summarizes PT's study and discusses how this comment extends their findings. The empirical implementation and results in Section II directly examine the link between overall income inequality and growth. Section III expands the analysis to alternative measures of income distribution and government policy. One emphasis in Section III is the distinction between how the overall income distribution (especially at the tails) influences economic growth from how the relative well-being of the median voter affects economic growth. Section IV provides some concluding discussion.

We measure the economic impact of climate on land prices. Using cross-sectional data on climate, farmland prices, and other economic and geophysical data for almost 3,000 counties in the United States, we find that higher temperatures in all seasons except autumn reduce average farm values, while more precipitation outside of autumn increases farm values. Applying the model to a global-warming scenario shows a significantly lower estimated impact of global warming on U.S. agriculture than the traditional production-function approach and, in one case, suggests that, even without CO_2 fertilization, global warming may have economic benefits for agriculture.

Standard specifications of the production function assume that an increase in labor given the stock of physical capital will reduce the capital-labor ratio. Since the stock of physical capital is quasi-fixed, the elasticity of output with respect to labor should be less than 1 as long as there are constant returns to scale. However, empirical studies of productivity typically find short-run increasing returns to labor. But the effective stock of capital should not be regarded as fixed if, when labor increases, it goes onto a previously inoperative shift. Labor that works the late shift will have at least as much capital as labor working days. Hence, for increases in labor that are accompanied by increases in the workweek of capital, there is no presumption of diminishing marginal product of labor. This paper describes briefly a data set that provides a direct measure of the workweek of capital and then investigates its role in cyclical productivity. It finds that much of the apparent cyclicality of total factor productivity is accounted for by variation in the workweek of capital.

The influence of academic science on industrial R&D seems to have increased in recent years compared with the pre-World War II period. This paper outlines an approach to tracing this influence using a panel of 14 R&D performing industries from 1961-1986. The results indicate an elasticity between real R&D and indicators of stocks of academic science of about 0.6. This elasticity is significant controlling for industry effects. However, the elasticity declines from its level during the 1961-1973 subperiod, when it was 2.2, to 0.5 during the 1974-1986 subperiod. Reasons for the decline include exogenous and endogenous exhaustion of invention potential, and declining incentives to do R&D stemming from a weakening of intellectual property rights. The growth of R&D since the mid-1980s suggests a restoration of R&D incentives in still more recent times.

Futures prices were well above spot prices for most commodities during most of the Great Depression; evidently the spectacular declines in agricultural prices caught many people by surprise. Based on the historical correlations between commodity prices and consumer prices, commodity markets anticipated stable consumer prices during the first year of the Great Depression. The dramatic drop in nominal Treasury bill yields, thus, should be read as a drop in ex ante real rates. Later in the Great Depression, markets anticipated deflation, but not as severe as actually occurred.