Econometric Analysis of Regional Systems. Explorations in Model Building and Policy Analysis

215 Pages·1977·8.087 MB·English

by  Norman J. Glickman and Edwin S. Mills (Auth.)

STUDIES IN URBAN ECONOMICS
Under The Editorship of
Edwin S. Mills
Princeton University
Norman J. Glickman. ECONOMETRIC ANALYSIS OF REGIONAL SYS-
TEMS: Explorations in Model Building and Policy Analysis
In Preparation
J. Vernon Henderson. ECONOMIC THEORY AND THE CITIES
ECONOMETRIC ANALYSIS
OF REGIONAL SYSTEMS
Explorations in !Model Building
and Policy Analysis
Norman J. Glickman
Department of City and Regional Planning
University of Pennsylvania
Philadelphia, Pennsylvania
ACADEMIC PRESS New York San Francisco London
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COPYRIGHT © 1977, BY ACADEMIC PRESS, INC.
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Library of Congress Cataloging in Publication Data
Glickman, Norman J
Econometric analysis of regional systems.
(Studies in urban economics series)
Based on the author's thesis, University of
Pennsylvania, 1969.
Bibliography: p.
1. Philadelphia metropolitan area—Economic con-
ditions - Mathematical models. 2. Regional econom-
ics - Mathematical models - Case studies. I. Title.
II. Series.
HC108.P5G55 1976 339'.09748'll 76-9155
ISBN 0-12-286550-2
PRINTED IN THE UNITED STATES OF AMERICA
FOREWORD
Some doctoral dissertations become once-and-for-all studies that
gather dust in library files, never to be used again by the author or
less-committed users. Others become the fountainhead of lasting in-
vestigation. The latter fate is more applicable than the former in the
case of Norman Glickman's thesis and subsequent line of research in
building and using regional econometric models.
There are at least two dimensions to be considered. One is general
modeling and expanding the size of accepted regional models,
whether they be for census regions, states, or other geographical areas.
The other is municipal model building. Norman Glickman's thesis
was among the first to build a model of a city area, the subject being
Philadelphia. A good part of the present volume deals with a descrip-
tion and equation/variable listing of the Philadelphia model. That
model is now sharper, larger, and better adapted to the latest com-
puter software. The discipline of regular use of the Philadelphia model
has been instrumental in taking the model from its pioneering thesis
vii
viii Foreword
state to its smooth working "live" state in which there is continual
updating, improvement of data detail or accuracy, extensive forecast
error evaluation, analysis of time response characteristics, and improve-
ment of specification.
The first part of the book deals largely with broad questions of
regional modeling. Some interesting references in what is a fairly new
scholarly area show how widespread the effort is when one goes into
it carefully.
The modeling of a metropolitan area is, however, more demanding
and challenging. It is necessary to be more creative, imaginative, and
hardworking to overcome the greater data deficiencies. Also, some
interesting problems of model specification arise in metropolitan areas
such as that of Philadelphia that cross state boundaries and encompass
more than one municipal government. Now that deep-seated urban
problems are surfacing, especially in local finances, econometric
models will have to rise to the occasion in order to deal with the issues
through forecasting and the techniques of policy simulation.
In the case of national model building, complete data information
systems have been developed. These consist of data banks; software
packages for parameter estimation, model simulation, and testing; and
instructive presentation of results. The Philadelphia model has acquired
all these trappings of the age of automation from the systems used
for the Wharton models of the U.S. economy. The complete transfer
of this technology for use in regional modeling, while straightforward,
is laborious to implement. Dr. Glickman is to be congratulated for
showing how this can be done in operating with the Philadelphia
model.
His original thesis work lives on in the embodiment of this model,
but it will not remain in an "as is" static state. This model is used
repeatedly, and it will be improved to be a better forecasting instru-
ment and better all-round tool of analysis. The general perception
of regional model building as exposited in the first part of this volume
will be of great assistance in keeping the Philadelphia model alive and
prospering—after Philadelphia, then New York, Chicago, Boston, and
the other great metropolitan centers of the United States.
November 2, 1976 LAWRENCE R. KLEIN
PREFACE
In today's complex and volatile economic climate, decision makers
in both the public and private sectors are growing ever more de-
pendent on economists for a sense of what the future holds in store.
Businessmen and public officials alike need answers, on a regular
basis, to such questions as
"What is the likely level of demand for my firm's product?"
"How much will my town be able to raise in taxes next year?"
"Should we build a new elementary school in our community?"
"How high will prices and unemployment be in the future?"
Economists have approached such questions in several ways, rang-
ing from ad hoc "seat of the pants" judgments to the construction of
systematic economic "models."
In recent years, sophisticated models, employing time series and
cross-sectional data, have proved invaluable for forecasting on a
national scale. Input-output models (pioneered by Leontief) and
ix
χ Preface
econometric models (such as those constructed earlier by Tinbergen,
Klein, and others ) have been effectively employed to forecast national
growth trends and to analyze structural problems in the U.S. and
other economies.
But it is only in the last ten years that economists have begun to
grapple with the problem of making economic predictions for urban
and regional economies, and it is to this more narrowly focused field
that this book addresses itself.
Like their national counterparts, regional economic models have
progressed from ad hoc methods to more formal models. The principal
types, economic base, input-output, and econometric, are analyzed
in Chapter II.
Of these techniques, the econometric have proved the most effective
for national forecasting. The bulk of this monograph explores the
application of these techniques to specific regional problems.
In Chapter III, I summarize a large-scale econometric model for the
Philadelphia region. The model, based on time series data, makes fore-
casts for output, employment, wages, prices, income, government
activity, and other economic aggregates. In Chapter IV, the usefulness
of the model is examined. Tests of its accuracy in forecasting the
business cycle are presented along with a set of policy-related simula-
tion experiments. These latter include impact analyses of the 1973 oil
"crisis," revenue sharing proposals, local tax policy, "no growth policy,"
and an examination of metropolitan decentralization. Thus, the useful-
ness of regional econometric models for policy analysis is demonstrated.
In all, this volume brings together my efforts since 1967 in the area
of macroeconometric forecasting for metropolitan regions. The work
began with my doctoral dissertation at the University of Pennsylvania
which was completed in 1969. Further work was initiated in 1971 and,
in 1973, the Philadelphia Region Econometric Project was launched
under the auspices of the Economics Research Unit of the University of
Pennsylvania. The Project brought together economists from the
business and the government communities in an effort to make regular
and accurate forecasts for the Philadelphia Standard Metropolitan
Statistical Area. Funds generated from the Project allowed for the
construction of the model outlined in Chapter III and for the policy
simulations in Chapter IV.
Hopefully, this work will provide a modest starting point for scholars,
private economists, and government officials who want to build and use
regional econometric models for forecasting and policy analysis.
ACKNOWLEDGMENTS
Many people were very helpful in the work which resulted in this
monograph. Kenneth Ballard served valiently with programming and
other computational help for nearly four years. Without him, the
Project would not have been as successful as it has been.
Professor F. Gerard Adams, Director of the Economics Research
Unit, gave me the opportunity to develop this research and has often
been a careful and perceptive critic of my work. His collaboration on
two related research efforts provided important learning processes
for me.
Carl Weinberg helped with the firm submodel in Chapter IV and
careful typing was carried out by Mary Lou Artime, Ana Gajski, Jackie
Harmon, and Susan Long. Sherri Reisner, Carol Romm, and Nancy Post
aided in proofreading the manuscript and in collecting data.
Finally, I would like to thank Professor Lawrence R. Klein who
taught me econometrics and has guided my research since what now
appears to be the beginning of time. His counsel and intellectual
simulation were both "necessary and sufficient" conditions for the
production of this book. He has my deepest respect and admiration.
This book is dedicated to my parents whose support and love have
made my life full and productive.
xi
I
INTRODUCTION
1.1 PURPOSE OF THIS STUDY
The problem of forecasting regional economic activity has become
an important component of regional research. Local policy makers, for
planning purposes, need accurate predictions of levels of such key
economic aggregates as employment, income, and output. In order
to forecast such regional variables, analysts have constructed several
types of "models," theoretical forms which are said to correctly reflect
the workings of the economy. Parameters are estimated and applied
to these models in the forecasting process.
The most frequently used macroeconomic forecasting techniques
for regions have been economic base and input-output models. The
characteristics and associated weaknesses of these formulations are
explored in Sections 2.2 and 2.3. Since the deficiencies in both ap-
proaches are significant, other analytic forms have been devised. No-
table among these have been econometric models: macroeconomic re-
1
2 I Introduction
gression models using time series data. Econometric models have
been used extensively for analysis of national economies and have
compiled credible forecasting records.
However, econometric models have rarely been used in forecasting
subnational economies, principally due to lack of data. Quality data
for states, cities, and other small areas have not been collected on a
regular basis for longer than fifteen to twenty years. As a result, the
"raw material," i.e., the data, for regression analysis often does not
exist in sufficient quantity. Furthermore, there has been a deficiency
of human resources devoted to regional econometric research; regional
planners are rarely trained to employ sophisticated econometric tech-
niques and econometricians are not often interested in regional
problems.
Econometric models have been constructed for several states in the
United States as well as for regions in foreign countries. Few models
have been constructed for regions smaller than states for this country.
Yet the latter, consisting of cities, counties, and Standard Metropoli-
tan Statistical Areas (SMSAs), are the areal units most often studied
by planners and regional economists. Clearly, therefore, a deficiency
exists in econometric model building. The major thrust of this volume
is toward the correction of this deficiency. In Chapter III, an econo-
metric model of the Philadelphia SMSA is presented which has
evolved from the first model known for so small a region. While not
as sophisticated as some of its national counterparts (due to data
constraints) it is quite an accurate predictor (as shown in Chapter
IV) of local activity. Since its structure is general (see Hall and Licari
[1974]), it must be seen as a feasibility study for other small area
models.
1.2 THE PHILADELPHIA REGION
The region under analysis in this volume coincides with the Phila-
delphia SMSA. It consists of five counties in Pennsylvania (Bucks,
Chester, Delaware, Philadelphia, and Montgomery) and three in
New Jersey (Burlington, Camden, and Gloucester). As measured by
either income or population, it is the fourth largest SMSA in the
United States.
The SMSA is a logical definition for regional analysis. It is a nodal