The effects of space and scale on unconditional beta convergence: test results from the United States, 1970-2004

Beta convergence theorizes that regions with low levels of income will grow faster than regions with higher incomes as investment is moved to regions where returns can be maximized. The formal test for beta convergence is an OLS regression with income change as the dependent variable and initial inc...

Full description

Saved in:
Bibliographic Details
Published in:GeoJournal 2013-10, Vol.78 (5), p.803-815
Main Authors: James, Ryan D., Campbell, Harrison S.
Format: Article
Language:English
Subjects:
Aggregation
America
Bgi / Prodig
Conflict
Convergence
Cost control
Econometrics
Economic geography
Economic history
Economic modeling
Economic models
Economic regions
Economic statistics
Economic theory
Environmental Management
Foreign investment
Geographic regions
Geography
Human Geography
Income
Investment
Investments
Local economy
Low income
Modeling
Per capita
Productivity
Regional analysis
Regional differences
Regions
Regression analysis
Scale modeling
Social Sciences
Spatial models
Studies
U.S.A
United States of America
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Beta convergence theorizes that regions with low levels of income will grow faster than regions with higher incomes as investment is moved to regions where returns can be maximized. The formal test for beta convergence is an OLS regression with income change as the dependent variable and initial income as the predictor. Beta convergence is present when the coefficient on initial income is negative. Empirical evidence of convergence is mixed, however. We contend that omission of spatial effects, and the confounding problem of geographic scale, help explain inconsistent empirical findings. We, therefore, examine the role of spatial effects on the standard convergence model at three spatial scales—states, Economic Areas, and counties—in the United States from 1970 to 2004. Our results indicate failure to account for spatial and scalar effects can lead to conflicting results. Convergence evidence is strongest at smaller levels of spatial aggregation, yet model fit is better at larger levels. In addition, accounting for spatial effects improves model diagnostic performance but weakens convergence evidence.
ISSN:0343-2521
1572-9893
DOI:10.1007/s10708-012-9467-5