Abstract
Using aggregate data from 31 Organization for Economic Co-operation and Development (OECD) countries covering periods from 1982 to 2017, this study examines the notion that the level of product complexity is a good determinant of economic growth in the long run. We use the impulse-response function (IRF) computed from the consistent generalized method of moment panel vector autoregressive (GMM pVAR) model to estimate the response of the real output growth to a change in the economic complexity index. The IRF shows that the economic complexity index has a significant impact on economic growth; a 1 standard deviation shock to the economic complexity index at time 0 contributes around 2.34 percentage points to the average rate of growth of output within the first period. The point estimates are positive and significant up to the third period. The cumulative IRF shows that the aggregate impact on economic growth is about 4.4% in the long run. Compared to some widely used innovation proxies such as the gross expenditure on research and development and secondary school enrollment, the economic complexity index performs relatively better in our model in determining economic growth in the long run.
Highlights
The view that economic complexity is the main determinant of economic growth in the long run is undoubtedly the highlight of the debate started by the pioneers of economic development theories in the 1940s
The consensus is that when economies move from high dependency on agriculture and extractive industrial products to technologically advanced manufacturing and services—a process often referred to as “structural transformation”—they tend to witness an acceleration of economic development in the long-term
The “innovation-based” growth models of Romer (1990) and Aghion and Howitt (1992, 1998a, 1998b)—the main architects of the modern growth theory— suggest that long-run economic growth derives from innovations that come in the form of product complexity and variety, process, and organizational innovation
Summary
The view that economic complexity is the main determinant of economic growth in the long run is undoubtedly the highlight of the debate started by the pioneers of economic development theories in the 1940s. The paper uses the recently developed economic complexity index (ECI) by Hidalgo and Hausmann (2009) in a dynamic panel setting to estimate economic growth in the long run. It applies an efficient and consistent econometric technique that is robust to endogeneity bias that often arise from possible omission of relevant variables in the model, variables measurement errors, or simultaneous causality. The use of exogenous control variables and instruments allow for the capturing of the quality of political and economic institutions available while the panel transformations allow for the control of unobservable country and period effects. Discussion of some of these additional tests, such as the test for stationarity of the variables, cross-sectional independence, and slope homogeneity are relegated to the Appendix to save space
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