The Population-Economy Link

1. PE • Both the Economic Base Techniques and Input-Output Analysis analyze the economy without any explicit linkages to the population of an area. • Similarly, the Population Projection Techniques we discussed analyze local population trends without explicitly incorporating local economic characteristics and trends into these projections. • It would be desirable if we could develop a model that allows us to consider both economic and demographic factors simultaneously. It would be useful to develop methods for explicitly linking these two elements of change together. • With such a model we could develop projections that allow us to include assumptions about changes to both the local population and the local economy. • There are two broad types of models available to analysts: 1) Recursive models 2) Nonrecursive models The Population-Economy Link

2. Recursive Models PE • Recursive models offer one solution to this problem. These are models that are based on one way interactions; independent variables influence dependent variables • Typically in recursive models, analysts assume that economic factors influence population (more specifically, migration). Economy ===> Migration • These models are preferable because they: 1) Begin to model the population-economy relationship 2) A number of useful models have been developed 3) They are easier to develop than nonrecursive (two way) models • Smith et al discuss three types of recursive models: 1) Econometric models 2) Balancing Labor Supply and Demand 3) Population/Employment Ratios Let’s look at examples of #2 and #3.

3. PE The DEFM Model(Demographic and Economic Forecasting Model) Taken from Smith et al (2001), p. 199.

4. The BEA Pop/Emp Ratio Model PE Note: These are, in effect three separate submodels that together yield a population projection. Taken from Smith et al (2001), p. 203.

5. Nonrecursive Models PE • Nonrecursive models offer another solution to this problem. These models typically use multi-equation regression models to “simultaneously” solve for both population and economic characteristics. • Regional economists have developed very simple and/or very complex models that use these techniques to analyze regional economies. These equations include a wide variety of variables to model changes in consumption and production, employment, total income, imports and exports, and labor, among other variables. • The advantages of these methods are many: • Many variables can be included in the equation. In projecting local employment we can include other variables like Government expenditures, Availability of capital, and Regional attributes. • We can model a variety of public policy choices. By including other variables we can estimate the impacts of changes to national or local economic policy. • Time series data is widely available to build these models.

6. PE Linking Economic and Demographic Changes • It is generally understood that population changes and economic changes are related: --If a regional economy is strong, people are attracted to the area. --If an area is growing in population, employment will increase in that area. • We might develop a two-equation regression model to capture this idea: MIG = b1 + b2(EMP) + b3(WAGE) + residual EMP = y1 + y2(MIG) + y3(WAGE) + residual • Note that each of these equations contains the other equation’s dependent variable. • When modeled in this way, there is “reciprocal causality”: EMPLOYMENT <===> MIGRATION

7. PE Nonrecursive Example: REMI • REMI (Regional Economic Models, Inc.) is an integrated economic-demographic projection model widely used in both the public and private sector. • The REMI model “integrates demographic and economic processes by using a cohort-component model and a system of simultaneous equations… [it also] incorporates the impact of amenities and accounts for economic interdependencies by using input-output techniques.” (Smith et al, p. 211) • For more information and a downloadable demo go to:http://www.remi.com • An overview of the model is presented on the next slide.

8. Taken from Smith et al (2001), p. 212. PE The REMI Model