In this paper I try to move away from the Extreme Bounds method of identifying robust empirical relations in the economic growth literature. Instead of analyzing the extreme bounds of the estimates of the coefficient of a particular variable, I analyze the entire distribution. My claim in this paper is that, if we do this, the picture emerging from the empirical growth literature is not the pessimistic Nothing is Robust that we get with the extreme bound analysis. Instead, we find that a substantial number of variables can be found to be strongly related to growth.

GDP per capita and household survey means present conflicting pictures of the rate of economic development in emerging countries. One of the areas in which the national accounts–household surveys debate is key is the measurement of developing world poverty. We propose a data-driven method to assess the relative quality of GDP per capita and survey means by comparing them to the evolution of satellite-recorded nighttime lights. Our main assumption, which is robust to a variety of specification checks, is that the measurement error in nighttime lights is unrelated to the measurement errors in either national accounts or survey means. We obtain estimates of weights on national accounts and survey means in an optimal proxy for true income; these weights are very large for national accounts and very modest for survey means. We conclusively reject the null hypothesis that the optimal weight on surveys is greater than the optimal weight on national accounts, and we generally fail to reject the null hypothesis that the optimal weight on surveys is zero. Additionally, we provide evidence that national accounts are good indicators of desirable outcomes for the poor (such as longer life expectancy, better education and access to safe water), and we show that surveys appear to perform worse in developing countries that are richer and that are growing faster. Therefore, we interpret our results as providing support for estimates of world poverty that are based on national accounts.

A key economic issue is whether poor countries or regions tend to grow faster than rich ones: are there automatic forces that lead to convergence over time in the levels of per capita income and product? We use the neoclassical growth model as a framework to study convergence across the 48 contiguous U.S. states. We exploit data on personal income since 1840 and on gross state product since 1963. The U.S. states provide clear evidence of convergence, but the findings can be reconciled quantitatively with the neoclassical model only if diminishing returns to capital set in very slowly. The results for per capita gross domestic product from a broad sample of countries are similar if we hold constant a set of variables that proxy for differences in steady-state characteristics.

This paper examines the robustness of explanatory variables in cross-country economic growth regressions. It introduces and employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates by averaging OLS coefficients across models. The weights given to individual regressions have a Bayesian justification similar to the Schwarz model selection criterion. Of 67 explanatory variables we find 18 to be significantly and robustly partially correlated with long-term growth and another three variables to be marginally related. The strongest evidence is for the relative price of investment, primary school enrollment, and the initial level of real GDP per capita.

The recent literature on endogenous economic growth allows for effects of fiscal policy on long-term growth. If the social rate of return on investment exceeds the private return, then tax policies that encourage investment can raise the growth rate and levels of utility. An excess of the social return over the private return can reflect learning-by-doing with spillover effects, the financing of government consumption purchases with an income tax, and monopoly pricing of new types of capital goods. Tax incentives for investment are not called for if the private rate of return on investment equals the social return. This situation applies in growth models if the accumulation of a broad concept of capital does not entail diminishing returns, or if technological progress appears as an expanding variety of consumer products. In growth models that incorporate public services, the optimal tax policy hinges on the characteristics of the services. If the public services are publicly-provided private goods, which are rival and excludable, or publicly-provided public goods, which are non-rival and non-excludable, then lump-sum taxation is superior to income taxation. Many types of public goods are subject to congestion, and are therefore rival but to some extent non-excludable. In these cases, income taxation works approximately as a user fee and can therefore be superior to lump-sum taxation. In particular, the incentives for investment and growth are too high if taxes are lump sum. We argue that the congestion model applies to a wide array of public expenditures, including transportation facilities, public utilities, courts, and possibly national defence and police.

The neoclassical growth model accords with empirical evidence on convergence if capital is viewed broadly to include human investments, so that diminishing returns to capital set in slowly, and if differences in government policies or other variables create substantial differences in steady-state positions. However, open-economy versions of the theory predict higher rates of convergence than those observed empirically. We show that the open-economy model conforms with the evidence if an economy can borrow to finance only a portion of its capital, for example, if human capital must be financed by domestic savings.

We argue that the relevant monetary decision for the majority of U.S. households is not the fraction of assets to be held in interest‐bearing form, but whether to hold any such assets at all (we call this “the decision to adopt” the financial technology). We show that the key variable governing the adoption decision is the product of the interest rate times the total amount of assets. This implies that the interest elasticity of household money demand at low interest rates can be estimated from the variation in asset holdings in a cross section of households rather than historical interest rate variations. We do so with the 1989 Survey of Consumer Finances. We find that (a) the elasticity of money demand is very small when the interest rate is small, (b) the probability that a household holds any amount of interest‐bearing assets is positively related to the level of financial assets, and (c) the cost of adopting financial technologies is negatively related to participation in a pension program. At interest rates of 5 percent, roughly one‐half of the elasticity can be attributed to the Allais‐Baumol‐Tobin or intensive margin and half to the new adopters or extensive margin. The intensive margin is less important at lower interest rates and more important at higher interest rates. Finally, we argue that ignoring extensive margins may lead to an empirically important overestimation of the cost of inflation at low interest rates.

Longevity Complementarities under Competing Risks by William H. Dow, Tomas J. Philipson and Xavier Sala-i-Martin. Published in volume 89, issue 5, pages 1358-1371 of American Economic Review, December 1999