Introduction
For every cause there is an effect and for every action
there is an equal and opposite reaction. You may recall these concepts
from your high school physics class and how they were used to illustrate
the rules of motion. If the thought of your high school physics class
frightens you, you can relax. This article is not about Newton's third law
of motion, but rather another science: economics. And these concepts,
which were originally conceived under an apple tree in merry old England,
are surprisingly but equally at home in the world of economics and aptly
describe the nature and dynamism of an economic impact analysis.
An economy is fluid. It ebbs and flows in a constant
struggle for equilibrium. Imagine a marble dropped in a bowl. It will
continue to roll around the inside of the bowl until it comes to rest. At
this point it has reached its "stationary state" (or "steady state" if all
relevant variables grow at an identical rate). It will remain stable until
it encounters another stimulus. The magnitude of the stimulus will
determine the path the marble takes and the amount of time it will spend
rolling around in search of its "stationary" or "steady" state.
An economic impact is the path the marble takes around
the inside of the bowl, and is measured by its velocity and the span of
time it takes to reach equilibrium. An economic impact analysis is an
attempt to quantify the overall effects (economic impacts) that various
actions and events have on an economy. In other words, it is an attempt,
through the use of a quantifiable, systematic, and scientific methodology,
to understand what has happened to the marble when it reaches its
"stationary" or "steady" state.
What follows is a brief discussion of the process of
conducting an economic impact analysis, the role of economic analysis in
economic development and the creation of public policy, the different
types of economic impact studies and tools used to prepare them, and the
limitations of economic impact analysis.
The Role of Economic Impact Analysis
The primary goal of economic development policy must be
to build stronger and better communities through sustained economic
growth. Sound public policy begins with a firm understanding of the
challenges and opportunities that exist within the geo-political
environment. Within that context, governments also have a fiduciary
responsibility to their taxpayers to invest their tax dollars in an
efficient and responsible manner, while also maximizing economic and
social benefit.
It is important to realize that a principal reason for
doing many economic and community development projects is to achieve
public policy objectives other than job creation and retention, such as,
brownfield remediation and redevelopment, urban revitalization,
infrastructure improvements, job training, cultural/quality of life
improvements, promoting economic diversity, and maintaining and expanding
the state and local tax base. While job creation and retention is
certainly one of the more important goals of a government's economic
development efforts, it is not the only goal. The other socio-economic
benefits derived from economic and community development investments must
not be overlooked. And to ensure that public funds are appropriately
directed, government has at its disposal numerous tools in which to gain
insight into the needs of its citizenry and to construct and test public
policy alternatives.
One such tool is the Economic Impact Analysis (EIA),
which is utilized to determine the economic development need of a project,
its return on investment and, ultimately justify public funding. These
studies are an assessment of the likely impacts of proposed actions and/or
possible events or the economic activity associated with past or current
actions on the economy. Such studies are used in the assessment of
numerous types of projects such as business expansion, business retention,
industrial or commercial park development, transportation (highways, rail,
airports, ports), downtown revitalization, or the impact of state and/or
local tax policies, environmental remediation, and community development
projects.
Based on an EIA, governments can develop a fiscal
impact study, which determines the cost/benefit ratio of an action or
activity. A "fiscal impact" is an effect on government finances resulting
from or related to economic policies or activities. Fiscal impacts, while
related to economic impacts, are not the same and the differences between
the two should be noted. A fiscal impact study can assist decision makers
in making informed decisions on the highest and best use of public
funds.
Many modeling methodologies exist to assist in the
preparation of an economic impact assessment and range from simplistic,
accounting-based, pencil-driven cost benefit formulations to complex
equation-intensive computerized econometric models. These tools can be
used in conjunction with one another or independently. Some of the more
notable tools are as follows:
Input-Output Modeling-IMPLAN
Input-output modeling begins with an input-output table
which basically shows inter-industry relationships. The table is a matrix
of rows and columns, each labeled with the name of different industries.
The "cells" within the table contain the amount of output from some other
industry that is used to produce final goods in the "row industry."
The "cells" of the table represent "row-industry"
demand, or input for "column-industry" output. The origin of such models
is generally attributed to the writing of Francois Quesnay in 1758. In the
twentieth century, Wassily Leontief would develop the concept of
"multipliers" from input-output (I-O) tables in work for which he received
a Nobel Prize in 1973.
Building on such an analysis system is the "Impact
Analysis for Planning" model known as IMPLAN. One of its primary
advantages is that it offers the user very great industry detail and a
capability to examine how a "shock" in one industry ripples through all
other industries. One major disadvantage, however, is that it does not
depict change over time. As a "static," or unchanging measure of
inter-industry relationships at an existing point in time, such a model is
less suitable for forecasting or for predicting longer-term trends.
Since in I-O models the inter-industry relationships
are defined for a given geographic region, such as the U.S. or a given
state, I-O tables and multipliers are state-specific. The Connecticut I-O
tables and multipliers used in a typical statewide impact analysis are
available through the United States Department of Commerce's Bureau of
Economic Analysis (BEA). Currently, the BEA offers what are known as
Regional Input-Output Modeling System or RIMS-II multipliers for both
major industry aggregations and detailed industries of which the larger
groups are composed.
RIMS-II Multipliers
In general, a "multiplier" relates the change in
output, earnings, or employment in any one industry to its total effect on
all other industries, or it may show the change that results in earnings
or employment in all other industries from a given dollar amount of change
in spending in any row-industry. Multipliers are used to measure the
"ripple effects" of spending that results in other rounds of spending,
earning, and employment generated by an initial change in investment,
earnings, or employment. RIMS II provides five types of multipliers:
final-demand multipliers for output, for earnings, and for employment, and
direct-effect multipliers for earnings and for employment.
The 1997 BEA RIMS-II documentation for the Connecticut
multipliers shows, for example, that the direct-effect earnings multiplier
for the insurance industry is 2.6342. This means that there would be an
additional $1.6342 in earnings in all industries for each $1.00 change in
payroll in the insurance industry. (Such multipliers are generally around
the magnitude of 2.0.) Note that the total effect is the initial change in
new payroll multiplied by 2.6342, but the total includes a "direct" and an
"indirect" effect. That is, the total effect includes the change in
insurance payroll as well as the earnings indirectly "generated" because
new insurance employees are spending some of their earnings in the region,
which means another round of "indirect" earnings by the recipients of
their new "income." The "rounds" of spending continue - an "induced
effect," and so forth. The ripples expand.
Multiple Regression
In the real world, many variables are changing
simultaneously. It is often of interest to examine the influence of a
single variable, holding other things constant. In economic modeling, this
is approximated by a methodology that introduces numerous "independent"
variables and estimates their effect on a single "dependent variable." The
process is known as "multiple regression." It is perhaps the most widely
used technique in the quantitative economic field of econometrics. In this
methodology, parameters are estimated which measure the degree
("statistical significance") or nature (positive or negative) of
association of the independent variables and the dependent variable. For
example, consumer spending or "demand" could be the dependent variable for
which price and income could be used as "explanatory" or "independent"
variables. Demand is then said to be a function of both price and income.
Price would likely have a negative or inverse correlation and income a
positive association, meaning price and quantity demanded would move in
opposite directions, but income and quantity demanded would move in the
same direction.
REMI Model
Expanding on the multiple regression technique and
estimating numerous equations, one could build an entire model to explain
the workings of a given regional or national economy. An internationally
known example of such a model is the Regional Economic Model, Inc. (REMI)
model. As a recent user guide explains: "Founded in 1980, REMI constructs
models [for specific geographic regions] that reveal the economic and
demographic effects that policy initiatives or external events may cause
on a local economy." Moreover, "A major feature of REMI is that it is a
dynamic model which forecasts how changes in the economy and adjustments
to those changes will occur on a year-by-year basis. The model is
sensitive to a very wide range of policy and project alternatives and to
interactions between the regional and national economies."
The REMI model is structured to rely on a solid
grounding in economic theory. A "control" forecast is the basis for
comparison with the "simulation" forecast. Differences between the two
constitute the "economic impact" of a given project or development. One of
the greatest challenges of the model is choosing from among thousands of
policy variables. Employment, sales, changes in investment in plant or
equipment, for example, are among the input variables that can be
modified. The dynamic nature of the model also makes it unique. As input
variables are modified, one can examine their impact on other results
variables such as personal income (the aggregate of new income for the
whole state or county), gross state product (a measure of final output for
state or county), total employment (after taking into account multiplier
effects), and the tax revenues (plus or minus) after the model takes into
account induced state and local spending. Population, for example, is one
of the dynamic variables. Users are sometimes surprised to find that
population expands in a rapidly growing economy. This may in turn induce
changes in local government spending as towns meet new demand for schools,
fire, police, and other municipal services.
The REMI model forecast horizon is currently 2035.
Typically a 20-year or 10-year analysis is done. Because the dollar values
may come many years from the present, the future dollar values are usually
"discounted," or adjusted for their present value. The choice of a
discount rate is usually made consistent with the "opportunity cost" of
money, that is the rate at which money available now could earn a return
if it were otherwise invested.
One of the most important "results variables" is gross
state product (GSP), a measure of the dollar value of all final output
produced in Connecticut in a given year as a result of the employment or
investment. A strong positive change in GSP is a typical indicator of a
successful project, because GSP is a very comprehensive measure of impact.
Other key variables are growth in total personal income and total state
and local tax revenues.
Gravity Model
In a few cases, proposed projects may be examined with
the application of a "gravity model." A new entrant into a sales
territory, for example, may "steal" sales from existing merchants. Density
of population and distance from the project location are factors that
influence the probability of sales. A widely accepted version holds that
migration between two cities is proportional to the product of the two
cities' populations and inversely proportional to the intervening
distance. Unlike the other "models" discussed so far, a gravity model
uniquely incorporates spatial considerations in location decisions. In
transportation modeling or travel demand forecasting these can have major
consequences.
Other Models
Still other models can be employed to conduct "what if"
scenarios. Sometimes a policy-maker may raise the question of the source
of past trends. To what extent is some policy variable changing as a
result of a shift in composition and to what degree is it changing as a
result of market share? Such "shift/share" analysis may be employed to
measure the nature of an industry trend for example. Suppose a state has
exceptionally large employment in a slow growth industry. To some extent,
overall employment may "suffer," but as the composition of overall
employment reduces this share and employment "shifts" to other sectors,
the overall employment may be compensated. Shift/share analysis may be
conducted to examine the interplay between intensity of employment and its
source of change.
Measuring Economic Impacts
Economic impacts are most routinely measured in these
terms: Business Output/Sales Volume, Gross State Product/Added Value,
Wealth, Personal Income, and/or Jobs (employment).
Employment is the measure most often highlighted, not
because it is the most accurate or informative, but because it is the most
tangible or understandable. A job is something the average person can
relate to. The other measures, listed above, are more abstract and their
importance can often be overlooked. Business Output is the broadest
measure of economic activity. It is the gross dollar value of final goods
and services produced. Gain in total state output represents the full
income effect - the contribution to final goods and services as a result
of both government (public investments) and private spending (wages,
capital expenditures, profits generated within an economy). Wealth is the
economic value captured within property or other tangible and intangible
assets. New Personal Income: This is the collective gain in the aggregate
of all income received in total by state residents as a result of the
initial spending. The amount is based on multiplier effects and summation
of income from all sources including income that may accrue to state
residents from out of state sources. It includes proprietor's income,
income from rent, wages and salaries, and other sources. This is pre-tax
income. (Disposable income is income after taxes). Employment reflects
changes in the level of labor within an economy.
None of these measures is absolute or perfect. They
each have their shortcomings or limitations. Employment often does not
reflect the quality of the jobs created or retained and cannot easily be
equated to the public costs associated with their creation or retention.
Business output does not distinguish between high and low value added
activities. Increases in property values (wealth) may indicate a
redistribution of wealth rather than a net increase of wealth within an
economy. Workers that reside outside of a specific economic area (the
study area) will dilute the impact of personal income growth and must be
accounted for. It is because of the limitations of each of these measures
that an economic impact analysis should seek to include as many of them as
possible and consider them in aggregate.
Garbage In Garbage Out: The Importance of Accurate
Data and Assumptions
It has been said (and correctly so) that there is no
substitute for good data (or for that matter, accurate assumptions). The
sophistication of one's model matters not, if the inputs are incomplete or
erroneous and/or based on incomplete or flawed assumptions. The most
important component of any economic impact analysis is the collection and
verification of data, the formulation of assumptions and the selection of
appropriate measures.
Pitfalls and Limitations
As mentioned previously, economic impact analyses are
not without their limitations. They are, after all, only estimations based
on, hopefully, the best available data. As valuable as they are, economic
impact analyses can be misleading if they are not appropriately
constructed and executed. Problems that can occur include confusing the
gross effect of a project with its net impact and using these
interchangeably. Also, applying measures inappropriately or combining
different measures of the same economic change will lead to overstating
the economic effects of an activity as will blurring or confusing
different time-frames, such as the immediate and long-term effects of a
project. Ignoring the effect of market forces on inputs (such as labor and
fixed capital) and confusing the capacity of a facility or full occupancy
of a residential or commercial building with actual or historic activity
levels can also distort the results of the analysis.
Conclusion
Economic impact analysis is an important and valuable
tool available to decision makers in government. If implemented and
interpreted correctly, it can be extremely powerful and provide incredible
insight into the benefits and costs of public decisions. Economic impact
analysis, however, is only one of many sources of information on which
policy makers and the investors of public funds should rely upon in the
creation of public policy and the investment of public funds. The results
of any economic impact analysis should be balanced against other important
considerations, such as the fiscal impacts on state and local revenues,
quality of life issues and other socio-economic benefits/impacts,
environmental impact, local zoning laws and traffic patterns, and
consistency or compatibility with state and local development strategies
and policies.
On April 10, the CT Technology Council, the State’s
largest technology industry association, released a study titled
“Connecticut Information Technology: Powering the Connecticut
Economy.”
The report details the significance of “essential” and
“related” Software/IT jobs to the Connecticut economy by showing the vast
ripple effects they exert throughout the economy. Ten percent of workers
are engaged in a Software/IT-related job— producing or using Information
Technology—representing approximately 175,000 jobs out of 1.7 million jobs
statewide.
For each of Connecticut’s “essential” Software/IT jobs
(those that directly produce computer hardware, software or
networks—approximately 66,000 jobs in 2001), another 2.33 jobs were
created in the Connecticut economy. And each IT-related job generated an
additional $195,562 in personal income for Connecticut residents and more
than $23,400 in new State revenue through multiplier effects.
The growth of jobs in the Software/IT cluster over time
translates into increases in disposable income, productivity, and GSP
(Gross State Product) and decreases in selling prices, labor and capital
costs.
The CT Technology Council commissioned the study as
part of its on-going mission to promote the growth and awareness of
Connecticut’s vital Software/IT Cluster—an organization established to
increase the competitiveness of software and information technology
companies through investments, innovation, and collaboration. The
University of Connecticut’s Connecticut Center for Economic Analysis
conducted the research.
Commissioner James F. Abromaitis of the Connecticut
Department of Economic and Community Development announced that
Connecticut communities authorized 600 new housing units in March 2003, a
21.3 percent decrease compared to March of 2002 when 762 units were
authorized.
The Department further indicated that the 600 units
permitted in March 2003 represent a 32.2 percent increase from the 454
units permitted in February 2003. The year-to-date permits are down 15.7
percent, from 1,996 through March 2002, to 1,683 through March 2003.
The Stamford Labor Market Area (LMA) is the only LMA to
show an increase in permits through the first three months of 2003.
Southington led all Connecticut communities with 30 new units, followed by
Trumbull with 18 and Avon and Berlin both with 16 units. From a county
perspective, Fairfield County had the smallest year-to-date loss of 4.1
percent.
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