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The pace and scale of China's economic transformation have no historical precedent. In 1978, China was one of the poorest countries in the world. The real per capita GDP in China was only one-fortieth of the U.S. level and one-tenth the Brazilian level. Since then, China's real per capita GDP has grown at an average rate exceeding 8 percent per year. As a result, China's real per capita GDP is now almost one-fifth the U.S. level and at the same level as Brazil. This rapid and sustained improvement in average living standard has occurred in a country with more than 20 percent of the world's population so that China is now the second-largest economy in the world. I will begin by discussing briefly China's historical growth performance from 1800 to 1950. I then present growth accounting results for the period from 1952 to 1978 and the period since 1978, decomposing the sources of growth into capital deepening, labor deepening, and productivity growth. But the main focus of this paper will be to examine the sources of growth since 1978, the year when China started economic reform. Perhaps surprisingly, given China's well-documented sky-high rates of saving and investment, I will argue that China's rapid growth over the last three decades has been driven by productivity growth rather than by capital investment. I also examine the contributions of sector-level productivity growth, and of resource reallocation across sectors and across firms within a sector, to aggregate productivity growth. Overall, gradual and persistent institutional change and policy reforms that have reduced distortions and improved economic incentives are the main reasons for the productivity growth.

Large cities produce more output per capita than small cities. This higher productivity may occur because more talented individuals sort into large cities, because large cities select more productive entrepreneurs and firms, or because of agglomeration economies. We develop a model of systems of cities that combines all three elements and suggests interesting complementarities between them. The model can replicate stylized facts about sorting, agglomeration, and selection in cities. It also generates Zipf’s law for cities under empirically plausible parameter values. Finally, it provides a useful framework within which to reinterpret extant empirical evidence.

We use a newly assembled sample of 1,528 regions from 83 countries to compare the speed of per capita income convergence within and across countries. Regional growth is shaped by similar factors as national growth, such as geography and human capital. Regional convergence rate is about 2 % per year, comparable to that between countries. Regional convergence is faster in richer countries, and countries with better capital markets. A calibration of a neoclassical growth model suggests that significant barriers to factor mobility within countries are needed to account for the evidence.

Does culture have a causal effect on economic development? The data on European regions suggest that it does. Culture is measured by indicators of individual values and beliefs, such as trust and respect for others, and confidence in individual self determination. To isolate the exogenous variation in culture, we rely on two historical variables used as instruments: the literacy rate at the end of the 19th century, and the political institutions in place over the past several centuries. The political and social history of Europe provides a rich source of variation in these two variables at a regional level. The exogenous component of culture due to history is strongly correlated with current regional economic development, after controlling for contemporaneous education, urbanization rates around 1850, and national effects.

Inequality affects economic performance through many mechanisms, both beneficial and harmful. Moreover, some of these mechanisms tend to set in fast while others are rather slow. The present paper (i) introduces a simple theoretical model to study how changes in inequality affect economic growth over different time horizons; (ii) empirically investigates the inequality—growth relationship, thereby relying on specifications derived from the theory. Our empirical findings are in line with the theoretical predictions: Higher inequality helps economic performance in the short term but reduces the growth rate of GDP per capita farther in the future. The long-run (or total) effect of higher inequality tends to be negative.

The linkage between economic activity and geography is obvious: Populations cluster mainly on coasts and rarely on ice sheets. Past studies of the relationships between economic activity and geography have been hampered by limited spatial data on economic activity. The present study introduces data on global economic activity, the G-Econ database, which measures economic activity for all large countries, measured at a 1° latitude by 1° longitude scale. The methodologies for the study are described. Three applications of the data are investigated. First, the puzzling \"climateoutput reversal\" is detected, whereby the relationship between temperature and output is negative when measured on a per capita basis and strongly positive on a per area basis. Second, the database allows better resolution of the impact of geographic attributes on African poverty, finding geography is an important source of income differences relative to high-income regions. Finally, we use the G-Econ data to provide estimates of the economic impact of greenhouse warming, with larger estimates of warming damages than past studies.

Large-n studies of conflict have produced a large number of statistically significant results but little accurate guidance in terms of anticipating the onset of conflict. The authors argue that too much attention has been paid to finding statistically significant relationships, while too little attention has been paid to finding variables that improve our ability to predict civil wars. The result can be a distorted view of what matters most to the onset of conflict. Although these models may not be intended to be predictive models, prescriptions based on these models are generally based on statistical significance, and the predictive attributes of the underlying models are generally ignored. These predictions should not be ignored, but rather need to be heuristically evaluated because they may shed light on the veracity of the models. In this study, the authors conduct a side-by-side comparison of the statistical significance and predictive power of the different variables used in two of the most influential models of civil war. The results provide a clear demonstration of how potentially misleading the traditional focus on statistical significance can be. Until out-of-sample heuristics — especially including predictions — are part of the normal evaluative tools in conflict research, we are unlikely to make sufficient theoretical progress beyond broad statements that point to GDP per capita and population as the major causal factors accounting for civil war onset.

This paper shows that foreign aid has a significant positive average effect on real per capita gross domestic product (GDP) growth if, and only if, the quantitatively large negative reverse causal effect of per capita GDP growth on foreign aid is adjusted for in the growth regression. Instrumental variables estimates show that a 1 percentage point increase in GDP per capita growth decreased foreign aid by over 4%. Adjusting for this quantitatively large, negative reverse causal effect of economic growth on foreign aid shows that a 1% increase in foreign aid increased real per capita GDP growth by around 0.1 percentage points.

We investigate the consequences of population aging for long-run economic growth perspectives. Our framework incorporates endogenous growth models and semi-endogenous growth models as special cases. We show that (1) increases in longevity have a positive impact on per capita output growth, (2) decreases in fertility have a negative impact on per capita output growth, (3) the positive longevity effect dominates the negative fertility effect in case of the endogenous growth framework, and (4) population aging fosters long-run growth in the endogenous growth framework, while its effect depends on the relative change between fertility and mortality in the semi-endogenous growth framework.

We assess quantitatively the effect of exogenous reductions in fertility on output per capita. Our simulation model allows for effects that run through schooling, the size and age structure of the population, capital accumulation, parental time input into childrearing, and crowding of fixed natural resources. The model is parameterized using a combination of microeconomic estimates and standard components of quantitative macroeconomic theory. We apply the model to examine the effect of a change in fertility from the UN medium-variant to the UN low-variant projection in Nigeria. For a base case set of parameters, we find that such a change would raise output per capita by 5.6 percent at a horizon of 20 years and by 11.9 percent at a horizon of 50 years. We conclude with a discussion of the quantitative significance of these results.