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Modern growth theory suggests that more than three-quarters of growth since 1950 reflects rising educational attainment and research intensity. As these transition dynamics fade, US economic growth is likely to slow at some point. However, the rise of China, India, and other emerging economies may allow another few decades of rapid growth in world researchers. Finally, and more speculatively, the shape of the idea production function introduces a fundamental uncertainty into the future of growth. For example, the possibility that artificial intelligence will allow machines to replace workers to some extent could lead to higher growth in the future.

Mathiness lets academic politics masquerade as science. Like mathematical theory, mathiness uses a mixture of words and symbols, but instead of making tight links, it leaves ample room for slippage between statements in the languages of words as opposed to symbols, and between statements with theoretical as opposed to empirical content. Because it is difficult to distinguish mathiness from mathematical theory, the market for lemons tells us that the market for mathematical theory might collapse, leaving only mathiness as entertainment that is worth little but cheap to produce.

Much of the analysis of economic growth has focused on the study of aggregate output. Here, we deviate from this tradition and look instead at the structure of output embodied in the network connecting countries to the products that they export. We characterize this network using four structural features: the negative relationship between the diversification of a country and the average ubiquity of its exports, and the non-normal distributions for product ubiquity, country diversification and product co-export. We model the structure of the network by assuming that products require a large number of non-tradable inputs, or capabilities, and that countries differ in the completeness of the set of capabilities they have. We solve the model assuming that the probability that a country has a capability and that a product requires a capability are constant and calibrate it to the data to find that it accounts well for all of the network features except for the heterogeneity in the distribution of country diversification. In the light of the model, this is evidence of a large heterogeneity in the distribution of capabilities across countries. Finally, we show that the model implies that the increase in diversification that is expected from the accumulation of a small number of capabilities is small for countries that have a few of them and large for those with many. This implies that the forces that help drive divergence in product diversity increase with the complexity of the global economy when capabilities travel poorly.

By operationalizing the notion of creative destruction, Schumpeterian growth theory generates distinctive predictions on important microeconomic aspects of the growth process (competition, firm dynamics, firm size distribution, cross-firm and cross-sector reallocation) which can be confronted using rich micro data. In this process the theory helps reconcile growth with industrial organization and development economics.

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.

In this paper we suggest that the spillover of knowledge may not occur automatically as typically assumed in models of endogenous growth. Rather, a mechanism is required to serve as a conduit for the spillover and commercialization of knowledge from the source creating it, to the firms actually commercializing the new ideas. In this paper, entrepreneurship is identified as one such mechanism facilitating the spillover of knowledge. Using a panel of entrepreneurship data from 18 countries, we provide empirical evidence that, in addition to measures of Research & Development and human capital, entrepreneurial activity also serves to promote economic growth.

This paper describes a growth model with the property that human capital accumulation can account for all observed growth. The model is shown to be consistent with evidence on individual productivities as measured by census earnings data. The central hypothesis is that we learn more when we interact with more productive people.

In recent years, there has been an increase in empirical and theoretical work that addresses the role of innovation as one of the main sources of firm growth. The purpose of this special issue is to strengthen the role played by innovation as a determinant of firm growth. Despite the emergence of a vast empirical literature on whether innovative firms grow more quickly in terms of sales and employees, a number of crucial questions and answers remain. While a large number of applied papers observe a positive link between innovation and firm growth, the complexity of R&D activities, together with the diversity of innovation strategies and the multiplicity of growth modes, requires a multidimensional approach to examine the contribution of innovations on firm growth. To shed light on the link between firm' growth and innovation sources, we organized a meeting of leading scholars of firm' growth and innovation. The papers of this special issue were presented at the workshop on 'Firm growth and innovation' held on 28 and 29 June, 2012, in Tarragona, Spain. The papers that compose this special issue deal in depth with innovation activity, firm growth and the interaction between firm growth and innovation.

This article investigates the strength of empirical evidence for various growth theories when there is model uncertainty with respect to the correct growth model. Using model averaging methods, we find little evidence that so-called fundamental growth theories play an important role in explaining aggregate growth. In contrast, we find strong evidence for macroeconomic policy effects and a role for unexplained regional heterogeneity, as well as some evidence of parameter heterogeneity in the aggregate production function. We conclude that the ability of cross-country growth regressions to adjudicate the relative importance of alternative growth theories is limited.

This paper consolidates two previously disconnected literatures. It integrates R&D-based innovations into a unified growth framework with micro-founded fertility and schooling behavior. The theory suggests a refined view on the human factor in productivity growth. It helps to explain the historical emergence of R&D-based growth and the subsequent emergence of mass education and the demographic transition. The model predicts that the erstwhile positive correlation between population growth and innovative activity turns negative during economic development. This \"population-productivity reversal\" explains why innovative modern economies are usually characterized by low or negative population growth. Because innovations in modern economies are based on the education of the workforce, the medium-run prospects for future economic growth—when fertility is going to be below replacement level in virtually all developed countries—are better than suggested by conventional R&D-based growth theories.