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Federal Reserve Bank of Boston
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What do cross-sectional growth regressions tell us about convergence?
Daniel G. Swaine

This paper tests the dynamic implications of beta-convergence with time-series data from 48 contiguous U.S. states. The motivation for this paper rests with the interpretation of results from cross-sectional growth regressions. These results show that poor regions experience faster per-capita income growth than rich regions. This is interpreted as evidence of convergence. However, convergence is a dynamic adjustment process with testable implications in time-series data, while the literature employs cross-sectional data to estimate this dynamic concept. A set of strong assumptions must be made to jump from this cross-sectional correlation to its interpretation as a speed of convergence. We find that the time-series properties of the data appear to be inconsistent with beta-convergence dynamics. Further, our analysis rejects the assumptions necessary to interpret the cross-sectional correlation as a speed of convergence. Therefore, our results call into questions the interpretation that has been placed on this important cross-sectional finding.

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Daniel G. Swaine, What do cross-sectional growth regressions tell us about convergence?, Federal Reserve Bank of Boston, Working Papers 98-4, 1998.
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Keywords: Economic development
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