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The University of Tennessee Institute of Agriculture

Agri-Industry Modeling and Analysis Group



 

 


Input-output analysis and multipliers

Input-Output Analysis

Input-output analysis, also known as the inter-industry analysis, is the name given to an analytical work conducted by Wassily Leontief in the late 1930's. The fundamental purpose of the input-output framework is to analyze the interdependence of industries in an economy through market based transactions. Input-output analysis can provide important and timely information on the interrelationships in a regional economy and the impacts of changes on that economy.

To identify the interrelationships in a regional economy, IMPLAN (Impact Analysis for Planning) software and databases are used. IMPLAN employs a regional social accounting system and can be used to generate a set of balanced economic/social accounts and multipliers. The social accounting system is an extension of input-output analysis. Input-output analysis has been expanded beyond market-based transaction accounting to include non-market financial flows by using a social accounting matrix or SAM framework. The model describes the transfer of money between industries and institutions and contains both market-based and non-market financial flows, such as inter-institutional transfers.

The model uses regional purchase coefficients generated by econometric equations that predict local purchases based on a region's characteristics. Output from the model includes descriptive measures of the economy including total industry output, employment, and value-added for over 500 industries in the Tennessee economy. Total industry output is defined as the value of production by industry per year. Employment represents total wage and salary employees, as well as self-employed jobs in a region, for both full-time and part-time workers. Total value added is defined as all income to worker paid by employers; self-employed income; interests, rents, royalties, dividends, and profit payments; and excise and sales taxes paid by individuals to businesses. The model also can be used for predictive purposes by providing estimates of multipliers.

Multipliers measure the response of the economy to a change in demand or production. Multiplier analysis generally focuses on the effects of exogenous changes on: a) output of the sectors in the economy, b) income earned by households because of the new outputs, and c) employment (in physical terms) that is expected to be generated because of the new outputs.

The notion of multipliers rests upon the difference between the initial effect of an exogenous change (final demand) and the total effects of a change. Direct effects measure the response for a given industry given a change in final demand for that same industry. Indirect effects represent the response by all local industries from a change in final demand for a specific industry. Induced effects represent the response by all local industries caused by increased (decreased) expenditures of new household income and inter-institutional transfers generated (lost) from the direct and indirect effects of the change in final demand for a specific industry. Total effects is the sum of direct, indirect, and induced effects.

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Multipliers

The notion of multipliers rests upon the difference between the initial effect of an exogenous change (final demand) and the total effects of a change. Direct effects measure the response for a given industry given a change in final demand for that same industry. Indirect effects represent the response by all local industries from a change in final demand for a specific industry. Induced effects represent the response by all local industries caused by increased (decreased) expenditures of new household income and inter-institutional transfers generated (lost) from the direct and indirect effects of the change in final demand for a specific industry. Total effects is the sum of direct, indirect, and induced effects.

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Multipliers-Computation and Use

When total sales of a particular industry sector is expected to change, increase or decrease, three types of impacts economy wide are measured: Direct, Indirect and Induced effects.

  • Direct effects are the immediate effects associated with the change in the final demand for a particular industry. For example, an increase in the final demand for cotton of $10 million will cause the farm sector to produce $10 million worth of cotton.
  • The indirect effects are the secondary effects or production changes in backward-linked industries caused when inputs needs change due to the impact of directly affected industry. Thus, following our last example, $10 million worth of cotton will require for the fertilizer industry to produce an additional $1 million worth in fertilizer, $0.2 million in seeds, $0.5 million in pesticides, etc.
  • The induced effects represent the response by all local industries caused by increased expenditures of new household income and inter-institutional transfers generated from the direct and indirect effects of the change in final demand for a specific industry.
  • From the direct, indirect, and induced effects, Type I and Type SAM multipliers are built for total industry output, employment, income, and value added.

    Type I and Type SAM multipliers are calculated as follows:
    Type I = (Direct Effects + Indirect Effects)/Direct Effects
    Type SAM = (Direct Effects + Indirect Effects + Induced Effects)/Direct Effects

Type SAM multipliers take into account the expenditures resulting from increased incomes of households as well as inter-institutional transfers resulting from the economic activity. Therefore, Type SAM multipliers assume that as final demand changes, incomes increase along with inter-institutional transfers. As these people and institutions increase expenditures, this leads to increase demand from local industries.

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Output Multipliers

For IMPLAN sector ten, cotton, the Type I output multiplier is 1.4733. In other words, for each dollar of output produced by the cotton industry, 0.4733 dollars is generated by other industries that supply the cotton sector. If the Type SAM output multiplier for the cotton sector is 1.8430; then 0.8430 dollar of indirect and induced output is generated in other local industries. The induced output is estimated as Type SAM - Type I (1.8430-1.4733) = 0.3397 dollars for each dollar of output produced by the cotton sector.

If the expected final demand change is $100 million, the total effects from this final demand change for total industry output (TIO) is calculated as:

Final Demand Change x Type SAM TIO Multiplier

$100 x 1.8430 = $184.30 million in TIO

A change in $100 million of final demand in the cotton sector will generate an estimated $184.30 million of total industry output.

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Value Added Multipliers

The IMPLAN Type SAM value added multiplier for cotton is 3.7357. This means that 2.7357 dollars of indirect and induced value added are generated in local industries. Value added includes employee compensation, proprietary income, other proprietor income, and indirect business taxes.

Again, if the expected final demand change is $100 million, the total effects from this final demand change for value added is calculated as:

Final Demand Change x Type SAM Value-Added Multiplier

$100 million x 2.7357 = $273.57 million in value added

When using output and value added multipliers and the estimated total effects (direct, indirect and induced) for a study region is desired, information needed is the change in final demand.

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Income Multipliers

The income multiplier is used when the initial change in sector income is known and the total effects (direct, indirect, and induced) for the study area is desired.

If the change in income is $85 million, the total change in income for the study area is calculated as follows:

Change in Income x Type SAM Income Multiplier

$85 million income x 4.4617 = $379.24 million in income for the study region.

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Employment multipliers

Employment multipliers are used when final demand changes are not available. However, data exists on changes in sector employment level. Assume the expected change in employment for cotton is 3,000 jobs and the total effects in number of jobs created for the study area is as fallows:

Change in Employment x Type SAM Employment Multiplier

3,000 jobs x 2.2314 = 6,694 jobs

The increase of 3,000 jobs for the cotton sector will generate a total of 6,694 jobs for the study area.

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