A Components of Growth Analysis of a Small, Vibrant Metropolitan Area: Spokane Washington Case Study

Dr. Roger CoupalAgricultural and Applied EconomicsUniversity of Wyoming

TPI/N = E/N + P/N + T/N + IA/N

N = PopulationTPI = Total personal incomeE = EarningsP = Property incomeT = Transfer paymentsIA = Income adjustments

Components of Income approach:E/N = H/J + E/H + J/N

H = Hypothetical earningsJ = Number of Jobs (full and part-timeH/J = Industry mix componentE/H = Differential Earnings ComponentJ/N = job / population Ratio

Framework: Components of Income approachBased upon: Smith, G. (1996): Garnick, D.(1990)

High Tech Mfg bust

Energy Booms

Energy Bust

Spokane Earnings / Job, Spokane Hypothetical Earnings / Job, and US Earnings / Job• Tends to trend the nation

but at a lower level. • Do lower energy prices

mean higher growth? • Growth during the high

tech growth but flat during the energy boom.

• Energy bust: an uptick.

Job to population ratio as a percent of the US ratio

Job to population ratio, Spokane and the United States

• Generally increased except during the lead up to the current recession

• Spokane J/N tracked slightly higher since the mid-90’s

• As a pct of the national

Summary of Fit RSquare 0.58965RSquare Adj 0.568606Root Mean Square Error 0.013718Mean of Response 0.017351Observations (or Sum Wgts) 42

Parameter EstimatesTerm EstimateStd Errort Ratio

Prob>|t|Intercept 0.0025275 0.002962 0.85

0.3986IndMixGrth 0.031583 0.158376 0.20

0.8430IndEarnGrth 0.5352067 0.082951

6.45 <.0001*

Residual by Predicted Plot

Industry Mix Component of relative per capita income and compared with earnings per Job, Pct Change from the preceding year

Job ratio component of relative per capita income growth

Summary of Fit RSquare 0.591299RSquare Adj 0.57034Root Mean Square Error 0.013691Mean of Response 0.017351Observations (or Sum Wgts) 42

Parameter EstimatesTerm Estimate Std Error t Ratio Prob>|t|Intercept 0.01389190.002226 6.24 <.0001*DifEarnGrwth 0.38623310.146913 2.63 0.0122*J/NGrwth 0.76842180.103109 7.45 <.0001*

Summary of Fit RSquare 0.233941RSquare Adj 0.194656Root Mean Square Error 0.018744Mean of Response 0.017351Observations (or Sum Wgts) 42

Parameter EstimatesTerm EstimateStd Errort RatioProb>|t|Intercept 0.0110054 0.003577 3.08

0.0038*DifEarnGrwth 0.4417462 0.215804 2.05

0.0474*IndMixGrth 0.7060462 0.2087653.38 0.0016*

Differential Earnings Growth Component of Relative PCI, Spokane

Model: AR(1) Model Summary DF 41Sum of Squared Errors 15881057.7RSquare 0.91296883RSquare Adj 0.91084611MAPE 1.74134482MAE 580.072929

Stable : YesInvertible: Yes

Parameter EstimatesTerm Lag Estimate Std Error t Ratio

Prob>|t| Constant AR1 1 0.993 0.009

101.39 <.0001* 254.408251Intercept 0 34896.543 4695.124 7.43 <.0001*

Summary of Fit RSquare 0.675913RSquare Adj 0.640877Analysis of VarianceParameter EstimatesTerm Estimate Std Error t Ratio Prob>|t|Intercept 0.0061186 0.022885 0.27 0.7907IndMixGrth -0.114912 0.161848 -0.71 0.4822E/JGrwth 0.554309 0.144011 3.85 0.0005*J/NGrwth 0.6939885 0.097906 7.09 <.0001*

USJ/N 0.0075217 0.042908 0.18 0.8618

• We don’t know how other metro areas perform.

• What is perhaps more important is whether capital flows facilitate more startups.

• Firms within groups of like industries tend to cluster. What an economic development initiative would like is separate from what it can actually accomplish, through no fault of its own.

Conclusions and Discussion• Slower growth in earnings per job than the national setting• Continued divergence between national hypothetical and

local earnings• Less connection between the national and local conditions• More reliance on growth factors (J/N, etc.)• They type of national growth may or may not affect a

metro area

Jeffrey City, Wy: Boeing branch plant location? Vet School, Medical School? Anything, please!!

Methodological / Geographic considerations