Heterogeneous Convergence

Andrew T. Young; Matthew J. Higgins; Daniel Levy

doi:10.1016/j.econlet.2013.04.017

Heterogeneous Convergence

Andrew T. Young, Matthew J. Higgins, Daniel Levy

Department of Economics

Bar-Ilan University

Research output: Contribution to journal › Article › peer-review

Abstract

We use US county-level data to estimate convergence rates for 22 individual states. We find significant heterogeneity. E.g., the California estimate is 19.9% and the New York estimate is 3.3%. Convergence rates are essentially uncorrelated with income levels.

Original language	English
Pages (from-to)	238-241
Number of pages	4
Journal	Economics Letters
Volume	120
Issue number	2
DOIs	https://doi.org/10.1016/j.econlet.2013.04.017
State	Published - Aug 2013

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

Keywords

Conditional convergence
Economic growth
Heterogeneity
US county level data

All Science Journal Classification (ASJC) codes

Finance
Economics and Econometrics

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10.1016/j.econlet.2013.04.017

Cite this

@article{37771ba053734385a1c461bf6a5862b2,

title = "Heterogeneous Convergence",

abstract = "We use US county-level data to estimate convergence rates for 22 individual states. We find significant heterogeneity. E.g., the California estimate is 19.9% and the New York estimate is 3.3%. Convergence rates are essentially uncorrelated with income levels.",

keywords = "Conditional convergence, Economic growth, Heterogeneity, US county level data",

author = "Young, {Andrew T.} and Higgins, {Matthew J.} and Daniel Levy",

note = "Funding Information: We are grateful to the referee for constructive comments that helped us improve the manuscript. We thank Steven Durlauf, Jordan Rappaport and Jerry Thursby for helpful comments and suggestions and Paul Evans for answering our questions. We are grateful to Jordan Rappaport for kindly sharing with us data and computer codes. Matthew Higgins acknowledges financial assistance from a National Science Foundation IGERT Fellowship and The Imlay Professorship. Daniel Levy gratefully acknowledges financial support from Adar Foundation of the Economics Department at Bar-Ilan University. All errors are our own.",

year = "2013",

month = aug,

doi = "10.1016/j.econlet.2013.04.017",

language = "الإنجليزيّة",

volume = "120",

pages = "238--241",

journal = "Economics Letters",

issn = "0165-1765",

publisher = "Elsevier B.V.",

number = "2",

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TY - JOUR

T1 - Heterogeneous Convergence

AU - Young, Andrew T.

AU - Higgins, Matthew J.

AU - Levy, Daniel

N1 - Funding Information: We are grateful to the referee for constructive comments that helped us improve the manuscript. We thank Steven Durlauf, Jordan Rappaport and Jerry Thursby for helpful comments and suggestions and Paul Evans for answering our questions. We are grateful to Jordan Rappaport for kindly sharing with us data and computer codes. Matthew Higgins acknowledges financial assistance from a National Science Foundation IGERT Fellowship and The Imlay Professorship. Daniel Levy gratefully acknowledges financial support from Adar Foundation of the Economics Department at Bar-Ilan University. All errors are our own.

PY - 2013/8

Y1 - 2013/8

N2 - We use US county-level data to estimate convergence rates for 22 individual states. We find significant heterogeneity. E.g., the California estimate is 19.9% and the New York estimate is 3.3%. Convergence rates are essentially uncorrelated with income levels.

AB - We use US county-level data to estimate convergence rates for 22 individual states. We find significant heterogeneity. E.g., the California estimate is 19.9% and the New York estimate is 3.3%. Convergence rates are essentially uncorrelated with income levels.

KW - Conditional convergence

KW - Economic growth

KW - Heterogeneity

KW - US county level data

UR - http://www.scopus.com/inward/record.url?scp=84878164000&partnerID=8YFLogxK

U2 - 10.1016/j.econlet.2013.04.017

DO - 10.1016/j.econlet.2013.04.017

M3 - مقالة

SN - 0165-1765

VL - 120

SP - 238

EP - 241

JO - Economics Letters

JF - Economics Letters

IS - 2

ER -

Heterogeneous Convergence

Abstract

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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