San José State University
Department of Economics

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Thayer Watkins
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Local Economic Multipliers

Local Income Multipliers

The economic base model is commonly used for estimating the short-term impacts of a particular project on employment. It has also been used occasionally for projections, although there is a great deal of controversy among economists over the assumptions it makes and the methods it uses to determine the components of the model. Also, it has one limitation which is particularly important for local governments to understand: it is most appropriate for estimating the change in employment in an economic region. Using this method, it is much more difficult to estimate what share of the indirect and induced employment a particular city or county will receive. This is not so great a problem in single-county economic regions, but in the Los Angeles or San Francisco areas, employees of a new project may commute substantial distances, thus spreading the effects of employment over a number of cities and counties.

The economic base model divides employment into two major categories: 1) export or basic industries which produce and sell goods that bring in new income from outside the area; and, 2) service or residentiary industries which produce and sell goods that simply circulate existing income in the area. The export industries in a region make up the economic base.

Hereafter basic employment may sometimes be denoted as BE and employment serving the local market may be denoted as LMSE. Total employment TE is just the sum of BE and LMSE.

Employment in the export sector creates a multiplier effect by providing additional employment from the new income it brings in. The service industries develop largely to serve the local needs of the existing population. Thus, the economic base model can be used for projecting employment if it is assumed that a ratio of service to export employment can be measured and that the ratio will remain sufficiently stable so that forecasts of export employment can be used to determine future total employment. The total number of jobs resulting from one export job is the employment multiplier.

Example: Forecasting Employment
Using an Employment Multiplier

 Actual
Employment
2010
Predicted
Employment
2015
Basic employment200,000 300,000
Local market
serving employment
400,000600,000
Ratio of LMSE
to BE
22
Ratio of TE
to BE
33
Total employment600,000900,000

The model is most commonly used, however, as a rule-of-thumb to estimate the effect of specific projects or policies on employment. For example, if a new industrial plant proposes to locate in a region, the impact on employment may be estimated for: (1) a direct impact from the jobs provided by the business itself; (2) an indirect impact if the business buys production materials and services locally; and (3) an induced impact or multiplier effect from the flow of wages spent by new employees, which may provide new jobs in other businesses and in turn the spending of those wages--and on and on.

Example: Forecasting Employment
Using an Induced Impact Multiplier

 Employment
Increas in permanent jobs
(provided by a particular industry)
780
Indirect impact
on other basic employment
(from industries supplying the above industry)
125
Total basic
employment
905
....Value
Induced impact multiplier 1.7
Total employment increase1,539

Assuming that the multiplier also works in the reverse, this process may in turn be used to estimate the total unemployment caused by an industry leaving a city or cutting back employment.

Multipliers take information on employment and income for one part of the economy and use it to estimates employment and income in several parts of the economy or all of it. In trying to apply income and employment multipliers, the analyst needs to keep in mind one cardinal fact: the components of the calculations require information that is just not available and the final result is all too often based upon a host of assumptions. Therefore, though convenient to use, multipliers often rely upon poor data and unverified assumptions. When faced with the need to use a multiplier, the best practice is to trust local experience, testing it against as many examples as can be found of multipliers used in comparable jurisdictions. Next, it is always wise to consult with experts who work in this field, for they can provide sound advice and help the novice (and even the experienced) to avoid pitfalls.

Finally, the analyst must choose the components of the multiplier carefully. For example, if the project includes manufacturing and warehousing activities in a new industrial park, applying an export-derived multiplier to employment (e.g. one that includes all base industries) would count the warehouse twice because it moves goods between industries.

Many economists have written about income and employment multipliers. The following examples give empirical values of the multipliers for various regions. Frequently, these studies have computed multipliers for a variety of sectors, but only a respresentative example appears in the tables. The references cited in the tables include some of the more important articles available in the economic journals. Unfortunately, many of the papers which estimate regional multipliers are difficult to acquire because they are the working papers of economic research organizations.

Derivation of the Multipliers

In order to explain the employment multiplier, it is necessary to understand the concept of the income multiplier. The income multiplier measures the change in local income that results from a change in local production stimulated by outside, independent demand:

Let Y denote local income, which is made up of the direct, indirect and induced effect. Indirect effects are the effects on other local industries of a one particular industry. Induced effects come through the spending of local households.

ky = [ΔY)]/[ΔDD]

where the symbol Δ stands for change in a quantity and DD stands for direct demand.

The use of k as a symbol for the income multiplier may be puzzling to noneconomists. The k probably stands for the British economist John Maynard Keynes; y is often used to symbolize income because i represents other variables.

Example: Changes in Regional Income in Santa Clara County

Suppose the local income multiplier is 1.5.

If the demand for electronics products increases by $32,000,000, then:

Increase in regional income = (1.5) x ($32,000,000)
= $48,000,000

The value of k depends upon the increased demand generated per unit increase in local production. This ratio will be denoted as m. For example m might be 1/3 as used in the calculation below. This ratio measures the extent to which demand feeds back into the local economy. The value of m can be used to derive the value of ky

ky = 1/(1-m)

Thus if m=1/3 then ky = 1/(2/3) = 3/2 = 1.5.

Example: Computation of the Income Multiplier

For the U.S. economy, m is approximately 0.6. Therefore, the income multiplier for the U.S. is:

ky = 1/(1-0.6) = 1/(0.4) = 2.5

The increase in demand for local production for each increase in local production, m=(Δdemand for local production)/(Δlocal production), can be represented as the product of four factors:

f1 = (Δlocal incomes)
(Δlocal production)
f2 = (Δspending of residents)
(Δlocal income)
f3 = (Δlocal spending of residents)
(Δspending of residents)
f4 = (Δdemand for local production)
(Δlocal spending of residents)

Then

m = f1×f2×f3×f4

The following combines an example drawn from information about San Francisco with an explanation of the ratio m. The first factor measures the change in income of local households that results from a change in local production:

(Δlocal household income)/(Δlocal production

Of the employees working in San Francisco, 60.2% live in the city according to the Journey to Work Survey of the U.S. Census. There is no readily available source of statistics on the proportion of property income in San Francisco that goes to residents. The owernship of business property by national corporations would tend to take property income out of San Francisco.

Purchasing power which gets diverted to residents of other regions or to state and federal government does not necessarily induce any spending in the local economy. Payments to local government, however, generally lead to spending in the local economy.

Nationwide, about two-thirds (.67) of any additional production becomes disposable income for households. The share of local income going to households in San Francisco is not very different from the national average - that is, 0.67. About one-fourth (.25) of additional income goes for taxes, while corporations retain one-twelf th (.083) as earnings. Although they are rough, the national ratios give an idea of relative magnitude.

The second factor determining m is the marginal propensity to spend:

(Δspending of residents)/(Δlocal income)

Nationally, this figure stands at 90%. The ratio for San Francisco residents also is probably not much different.

The third factor determining m is the share of spending done in the local economy:

(Δlocal spending of residents)/(Δspending of residents)

This may involve a sum of terms because of the way income is divided between residents and nonresidents and their spending habits.

For purposes of this example, the distribution of property income between residents and nonresidents is the same as the distrubiton of jobs: 60% to residents and 40% to nonresidents. If 60% of the income from San Francisco's economy goes to San Francisco residents who do 95% of their buying in San Francisco and 40% of the income goes to non-residents who do 30% of their spending in San Francisco, the increase in spending in San Francisco would be:

(0.6)(0.95) + (0.4)(0.3) = 0.69

The final factor determining m is the change in demand for the local economy's products compared to the change in the demand for goods and services in the economy:

(Δdemand for local production)/(Δlocal spending of residents)

Consumers want various goods and services. The extent to which the local economy satisfies their wants depends on the diversity of the local economy. The precise ratio that is involved is the value added to the sales in the local economy. Value added may be created by producing goods locally or by wholesaling or retailing imported goods.

The share of local sales which is locally produced is difficult to establish. It may differ significantly for goods and services. Locally purchased services have to be locally produced. Locally purchased goods are, of course, not necessarily produced locally. According to the 1964 Census of Manufacturers, 42% of the value of sales in San Francisco derived from value added by the local manufacturing sector.

The final factor determining m is a weighted average of 100% local production figure for services and 42% local production figure for goods. The weights represent the share of services and goods in local spending. These shares are not available for San Francisco, but in 1972 for the U.S. as a whole, 38% of consumer purchases went for services. Using this share, the final factor is:

(.38)(1.00) + (.62)(.42) = 0.64

The value of m for San Francisco based upon the four factors previously computed is:

m = (0.67) × (0.9) × (0.69) × (0.64) = 0.27
and hence the income multiplier is:
ky = 1/(1.0-0.27) = 1/(0.74) = 1.36

The values of m and ky for the U.S. economy, where consumers spend virtually all of their money for U.S. goods and services, are:

m (.67) × (.9) × (1.00) × (1.00) = 0.6
and thus
ky = 1/(1-0.6) = 1/(0.4) = 2.5

The difference between the multipliers for the U.S. and San Francisco economies is due solely to the dispersion of income out of the San Francisco economy and the lower share of local purchases accounted for by local production.

The Employment Multiplier

Economic studies often make use of the concept of the employment multiplier kE.

This quantity is defined as:

kE = (ΔTotal Jobs (Direct, Indirect & Induced))/(ΔJobs Directly Generated)

The employment multipler, kE may be derived from the income multipler, ky , through the following relationship:

kE = 1 + (ky-1)(aI/aD)(WD/WI)

where

The following tables give a few income and employment multipliers. Some of these come from studies that presented a variety of multipliers. These studies are listed at the end of the table.

Example: Computation of Employment Multiplier from income Multiplier

Suppose a region has an income multiplier, ky , of 1.6, the ratio of the wage shares of industries serving the local market to the industry with directly generated employment (aI/aD) is 1.00 and the ratio of wage rates for direct jobs to induced jobs (WD/WI) is equal to 1.3.

The employment multiplier for this region will be:

kE = 1 + (.6)(1.0)(1.3) = 1.78

Therefore, total employment from 100 new direct employment jobs would be 178, including induced employment of 78.

Estimates of Multipliers for Various Regional Economies

Regional Income Multipliers
RegionPeriodIncome
Multiplier
Study
Utah19471.81Input-OutputDirect and Indirect DemandMoore and
Peterson1
Detroit1946-19701.41EconometricPrivate ExportMattila2
St. Louis19552.16Input-OutputDirect DemandHirsch3
Los Angeles1959-19702.43EconometricPrivate ExportHall and Licari4
Washington State19633.07Input-OutputPrivate ExportBourqe5
Washington State19632.75EconometricPrivate ExportGarnick6
Nebraska19632.52Input-OutputRoesler7
Nebraska19632.01EconometricGarnick6
Boulder, Colorado19631.2Input-OutputSpace Program
Demand
Miernyk8
    Sources:
  • 1 Frederick Moore and James W. Peterson, "Regional Analysis: An Interindustry Model of Utah," Review of Economics and Statistics, 37 1955), 369-383.
  • 2 John M. Mattila, "A Metropolitan Income Determination Model and the Estimation of Metro politan Income Multipliers," Journal of Regional Science 13 (1973), 1-16.
  • 3 Werner Z. Kirsch, "Interindustry Relations of a Metropolitan Area," Review of Economics and Statistics , 41 (1959), 360-369.
  • 4 Owen P. Hall and Joseph A. Licari, "Building Small Region Econometric Models: Extension of Glickman's Structure to Los Angeles," Journal of Regional Science, 14 (1974), 337-353.
  • 5 P. 3. Bourque, Income Multipliers for the Washington Economy (Seattle: University of Washington, Center for-Urban and Regional Research, 1969).
  • 6 Daniel H. Garnick, "Differential Regional Multiplier Models," Journal of Regional Science 10 (1970), 35-47.
  • 7 T. Roesler, F. Lamphear and M. Beveridge, The Economic Impact of Irrigated Agriculture on the Economy of Nebraska (Lincoln: University of Nebraska, Bureau of Business Research, 1969).
  • 8 William H. Miernyk, E.R. Bonner, J.H. Chapman and K. Shellharnmer, Impact of the Space Program on a Local Economy (Morgantown- West Virginia University, West Virginia University Foundation, 1967.

Regional Employment Multipliers
RegionPeriodIncome
Multiplier
Study
New York-Philadelphia19472.14Input-OutputSteel EmploymentIsard and Kuenne1
Hawaii1947-19551.28EconometricExport-OrientedSasaki2
Philadelphia1949-19663.67EconometricExport-OrientedGlickman3
Portsmouth, N.H.1955-19641.80EconometricPrivate ExportWeiss and Gooding4
California19602.76Input-OutputPrivate ExportHansen and Tiebout5
Los Angeles-
Long Beach
19602.13Input-OutputPrivate ExportHansen and Tiebout5
San Francisco-Oakland19602.06Input-OutputPrivate ExportHansen and Tiebout5
Washington State19662.749SpecialBasic ExportGarnick6
    Sources:
  • 1 Walter Isard and Robert E. Kuenne, "The Impact of Steel Upon the Greater New York-Philadelphia industrial Region," The Review of Economic Statistics, 35 (1953), 289-301.
  • 2 Kyohei Sasaki, "Military Expenditures and the Employment Multiplier In Hawaii," The Review of Economics and Statistics, 45 (1963), 289-304.
  • 3 Norman J. Glickman, "An Econometric Forecasting Model for the Philadelphia Region," Journal of Regional Science vol. 11 (1971)9 15-32.
  • 4 Steven J. Weiss and Edwin Gooding, "Estimation of Differential Employment Multipliers in a Small Regional Economy," Land Economics 44 (1968), 235-244.
  • 5 W. Lee Hansen and Charles M. Tiebout, "An Intersectoral Flows Analysis of the California Economy," The Review of Economics and Statisticsv 45 (1963)9 409-419.
  • 6 Daniel H. Garnick, "Differential Regional Multiplier Models," Journal of Regional Science, 10 (1970), 35-47.

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