Economic Growth and Productivity
Research Guide

The Solow growth model, presented in "A Contribution to the Theory of Economic Growth" by Robert M. Solow (1956), describes long-run economic growth through capital accumulation, population growth, and exogenous technological progress. It predicts convergence to a steady-state growth path where output per worker grows at the rate of technological progress. The model analyzes interest and wage rates alongside possible growth patterns.

Endogenous growth theory, as in "Increasing Returns and Long-Run Growth" by Paul Romer (1986), treats knowledge as an input with increasing marginal productivity, generating sustained growth via endogenous technological change. Unlike exogenous models like Solow (1956), it features a competitive equilibrium without diminishing returns to capital. This framework explains long-run growth without relying on external technological shocks.

Data Envelopment Analysis (DEA) estimates technical and scale inefficiencies, as developed in "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis" by Rajiv D. Banker, A. Charnes, and W. W. Cooper (1984). DEA uses mathematical programming to evaluate decision-making units relative to an efficiency frontier. It reverses traditional planning roles by assessing performance from observed data.

Mankiw, Romer, and Weil (1992) in "A Contribution to the Empirics of Economic Growth" demonstrate that an augmented Solow model including human capital accumulation fits cross-country income data well. Physical and human capital explain most international variation in living standards. The model provides an excellent description of empirical patterns.

Human capital accumulation augments the Solow model to match international data, per Mankiw, Romer, and Weil (1992). Their analysis shows human capital alongside physical capital accounts for cross-country differences in output per capita. This extension improves the model's explanatory power over the basic framework.

Panel unit root tests like those in "Unit root tests in panel data: asymptotic and finite-sample properties" by Andrew Levin, Chien-Fu Lin, and Chia-Shang James Chu (2002) assess stationarity in cross-country growth data. They handle asymptotic and finite-sample properties for reliable inference. Such tests address cross-section dependence, as extended by Pesaran (2007).