GEOG 3404Economic Geography

Dr. Zachary KlaasDepartment of Geography and Environmental

StudiesCarleton University

LECTURE 7:Economic Base, Shift-Share and Input-

Output Analysis

Reading in the Dicken text For this week, I'd like you to have a look at Chapter 8 in

the Dicken text. This chapter is somewhat obliquely related to today's topic, which covers methods in

economic geographic analysis. The chapter is about relationships between transnational corporations (TNCs)

and states. When you read through the chapter, what I want you to focus upon is the extent to which TNCs (industries) and

states (regions) each have their own negotiating powers. Each of the methods we're considering in today's lecture paints a certain picture of relationships between industrial

sectors (as one unit of analysis) and regions (either localised or more broad/national in scope). Might these

methods have something to say about the kind of interrelationships that exist between TNCs and states

that Dicken describes in Chapter 8?

Economic Base Analysis In our last class, we discussed the concept of a

location quotient. Location quotients model the extent to which employment in a given economic sector (as a

proxy variable representing productivity within that sector) is concentrated in a particular location.

The location quotient is the measure used in a larger method of economic analysis known as economic base

analysis (or EBA). EBA is principally used to identify basic industries, or

industries of relative concentration. If a particular location has a high concentration of employment in a given industry with respect to some larger reference

region (e.g., the nation), then this is taken to represent production for export to that larger reference region.

Implicit in the approach taken by EBA is that specialisation in a particular field of employment (that is, specialisation in a particular industrial sector) brings an

infusion of money into a location from the larger reference region. In other words, consumers from the

larger reference region are buying the products (goods or services) made within the local area, and the money

spent by these consumers is thus brought into the local economy.

The money that is brought in, in turn, is either spent by consumers again within the local area, or paid out to some other location (that is, through the purchase by those consumers of some other location's exported

product). When the money is re-spent within the local area, however, this is referred to as the multiplier effect, as the value from this outside money has been realised more than once in the local economy through this re-

spending.

For the purposes of our lecture today, we will concentrate on this aspect of the location quotients /

economic base analysis approach to the understanding of local economic realities. The use of this particular method is premised on a certain understanding of

the benefits of production for export, and on a certain understanding that producing for export

leads to the creation of a multiplier effect. To put this in the simplest terms: people who use EBA as an approach are interested in promoting production for

export as a means of promoting an infusion of money from outside the local area, which, it is assumed, will

lead to a certain amount of spending and re-spending within the local area, boosting the local

economy not just once, but several times over as the cycles of re-spending continue.

These are by no means unproblematic assumptions. In last week's lecture we discussed certain problems with the notion that “production for export” is a worthwhile

strategy. To begin with, there is a certain dependency on external consumers fostered by this approach – if those external consumers stop buying, there can be dramatic effects on a local economy, especially if the

local economy overconcentrates in one specific industry, or does not reinvest some of its profits in developing its nonbasic industries (industries that

principally serve the local population). Also, it is possible that competition from other locations exporting products in a particular industry may cause the local area to be

“crowded out” from the market.

Beyond these factors we already discussed, however, are problems with the assumption that this multiplier

effect (the re-spending of money brought in from outside the local area) will even occur.

Local areas may be dependent upon imports from outside areas, and where this is the case, money will not be re-spent within the local area, but leaked back out to

other locations. Indeed, implicit in the whole EBA approach is the idea that specific areas concentrate or specialise in certain

industries, so money brought into the local area due to its particular specialisation are quite likely to be leaked back

out to pay for goods or services that are the particular specialisation of some other location.

Shift Share Analysis

The purpose of shift-share analysis is to gauge to what extent any changes in the regional "economic picture" are

due to decisions actually being made at the regional level.

Measures of sectoral economic performance are broken down into three "effects", one representing the performance of the nation (or some other reference

region) taken on the whole, one representing the performance of the industry (economic sector) taken on the whole, and one representing the performance of the

specific local region under study.

If the regional economy is doing well simply because the nation as a whole is doing well, this is reflected in the

national growth effect measure. If the regional economy is doing well simply because a

certain industry is doing well (i.e., both nationally and regionally), this is reflected in the industry mix effect

measure. If the local/regional economy is doing well for reasons

independent of these other two effects, then the region is performing better than we would assume simply because

it is part of the nation or because it concentrates in certain industries. This performance is reflected in the

regional share effect measure.

Shift-share calculations: The “normal share” of employment concept

The national growth effect is calculated merely by multiplying the region's employment in a particular

economic sector at the beginning of the study period by the percentage change in national employment for all

sectors during the study period. This shows the share of employment the region should have if the region's employment changes exactly as the

nation's employment changed. In other words, this is the region's normal share of

employment in a particular economic sector. If a region's growth is entirely due to a larger trend of growth in the nation (or reference region), then we will see no additional growth effects beyond what is part of

this “normal share”.

Shift-share calculations: Adjusting the “normal share” with reference to specific

industry strength

The industry mix effect is calculated by multiplying the region's employment in a particular economic sector at

the beginning of the study period by a quantity reflecting the difference between the percentage change in

employment in that sector nationally, during the study period, and the percentage change in employment in all

sectors nationally, during the study period.

This shows the degree to which the normal share of employment in a particular economic sector for a region is raised or lowered by virtue of that particular sector's

strength. In other words, this is an adjustment to the normal

share based on the strengths of particular industries. If a region's growth is not explained by its “normal share” of employment with respect to the nation (or

reference region) but is explained by growth of employment within a specific industry, then we will see growth beyond the “normal share”, but not beyond an

industry-based adjustment to the “normal share”.

Shift-share calculations: Adjusting the “normal share” with reference to specific

regional strength

The regional share effect is calculated by multiplying the region's employment in a particular economic sector

at the beginning of the study period by a quantity reflecting the difference between the percentage change

in employment in a sector regionally, during the study period, and the percentage change in employment in a

sector nationally, during the study period.

This shows the degree to which the normal share of employment, even after being adjusted to reflect the strength of the particular economic sector, is raised

or lowered, presumably due to regional economic decisions or local geographic factors.

In other words, whatever is not explained by the region's normal share of employment in a particular

economic sector, or by the adjustment we make to reflect the particular strength of that sector, is part of this

measure.

Advantages of the Shift-Share approach The key thing to understand about Shift-Share: it's a method that can be used by economic geographers to

understand to what extent regional growth can be "explained away" by national growth or industry growth.

In many cases, national growth and industry growth do not explain a regional growth phenomenon. In these cases, local areas are doing a good job of keeping

money in the local economy and not leaking it back out through excessive dependence on other areas.

Though Shift-Share does not necessarily shed light on what, about specific local/regional decision-making or

policies, actually seems to be contributing to local/regional growth, it does a good job of establishing to what extent local choices are the cause of that growth.

Disadvantages of the Shift-Share approach

A debate exists currently about whether some elements may be misclassified by the method. This is a debate about model assumptions, and as such, should not be

unfamiliar to us. An operational model's assumptions is always important for understanding its results.

For starters, what's a "nation"? Nations are generally defined by political, not economic means, and though

there may be a particular coherence to a "national" economy, certainly in an age of globalisation, we are not

permitted to blithely assume that the economies of nations are closed systems. Yet, Shift-Share essentially assumes that the national economy is a closed system

and that it is a proper region to which to refer as the yardstick of what is economically "normal" and is the

basis of the "normal share".

We do not necessarily avoid this problem by selecting another "reference region" besides a nation-state to use

as our yardstick of what is "normal" - why select the region we select? This needs to be defended.

Ultimately, the proper reference region might be the entire planet. However, planet-wide economic data is

hard to come by for obvious reasons (nations have different statistical agencies, for starters.)

The selection of the proper "reference region" colours everything shift-share produces as output. Choose an

unrepresentative reference region and regional economic policy may end up looking unduly good or unduly bad by

comparison.

Also, we basically have two effects (national and industry) and a third effect that comprises everything that

does not fall into the last two. But why do we only measure two effects? Is it possible that there might be other employment effects which the method does not measure, which "explain away" some of the things we otherwise explain as a regional share effect? We are essentially dependent on a notion of decomposition of total effect here which assumes only certain elements

need to be part of the decomposition.

Input-Output Analysis

The purpose of Input-Output Analysis (I-O) is to gauge how the multiplier effect works operationally in terms

of exchanges between sectors in a given economic arrangement. Where the exchanges between sectors

modeled are sectors in a regional economy, then input-output analysis can help us understand the regional

multiplier effect. The principal tool used by I-O is an input-output

matrix. On one side of an input-output matrix are the input sectors, or sectors from which exchanges flow.

On the other side are output sectors, or sectors toward which exchanges flow.

For example, if a farmer buys a tractor, then an exchange is being made between the agriculture sector and the manufacturing sector. Agriculture is in this case the input sector, as it is the farmer who is parting with his cash. This money is being "input" into the manufacturing sector. It is the manufacturing sector in this case which is the output sector, as it is the tractor company which is

releasing its product in exchange for the cash. This product is the "output" back to the agriculture sector.

Sometimes, the input sector can be the same sector as the output sector. For example, suppose our farmer buys

some seed for his farm. The seed company is in the agriculture sector, as is the farmer. Thus, the agriculture sector is both the input sector (the farmer pays cash) and

the output sector (the seed company provides seed).

We can represent the proportion of exchange between any input sector-output sector combination by means of an input coefficient. This coefficient represents what

percentage of total inputs into an output sector is constituted by inputs from the specific input sector.

This is useful information in itself, as we can get a picture of just how important for any given industry

exchanges from other industries happen to be. We can also use this information, however, to set up a series of equations which can calculate the total output for each of the output sectors after all of the exchanges

modeled in the input-output matrix have taken place.

Input-output analysis essentially follows the path of a dollar from the hands of an initial buyer, through the hands of all the "middleman" industries, and on to

the industries where products are produced for final demand. We follow, in other words, how money inputs

are cycled through the various sectors in the entire economy, and how each of these sectors are affected by

the transfer. Using the raw figures for final demand outputs as a

baseline, we can use the input coefficients to determine the value of outputs for each of the output sectors, after

the inputs have finished cycling through the economy. In other words, it gives a measure of the multiplier effect

of money spent in this industry.

There is one calculation difficulty with the formula shown above: equations for each of the output sectors must be solved simultaneously, as the answer for one of the output sector equations depends for its answer on

information contained in other of the output sector equations.

In order to solve for output sector equations, we must use a technique called linear programming, which is

basically another term for simultaneous equation solving. In linear programming, constraints in one equation are used in order to solve for other equations, and then the

constraints in the solved equations may be used in return to solve for the initial equation.

An example: suppose we have the following two equations:

Manufacturing = (.1 * Service) + Final Demand for Manufacturing Service = (.3 * Manufacturing) + Final Demand for Services If we want to solve for Manufacturing, we need to know what the value of Service is. But if we want to solve for

Service, we need to know what the value of Manufacturing is. The only way we can deal with this

problem is to substitute information from one equation into another.

Plugging in the Manufacturing information to the Service equation, we arrive at the following Service equation:

Service = (.3 * ( (.1 * Service) + Final Demand for Manufacturing) ) + Final Demand for Services

Since the Final Demand figures are raw numbers, we can now solve for Service. Having done that, we can return to the Manufacturing equation and solve for it.

Interregional Input-Output Analysis: A special case of I-O

In order to use I-O as a method similar to Shift-Share with regard to its ability to point to the wisdom (or lack thereof) of local/regional decision-making, a particular form of I-O called Interregional Input-Output Analysis

would have to be used. This is a form of I-O which not only tracks exchanges

between input sectors and output sectors, but also between input regions (locations from which exchanges

flow) and output regions (locations to which those exchanges flow.

Advantages and Disadvantages of Input-Output Analysis

The principal advantage of Input-Output Analysis is its specification of every point in a production chain. I-O

tracks exchanges between input and output sectors through each stage in the production of a product, from the raw materials stage through to the provision of the product for final demand. All this information gives one

tremendous insight into the actual functioning of the economy.

A principal disadvantage, however, is the time-consuming nature of data collection for this method, as well as the calculation difficulty involved with the

linear programming technique.