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1 Poverty and Employment in Timber Dependent Counties By Peter Berck, Christopher Costello, Sandra Hoffmann and Louise Fortmann.

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Presentation on theme: "1 Poverty and Employment in Timber Dependent Counties By Peter Berck, Christopher Costello, Sandra Hoffmann and Louise Fortmann."— Presentation transcript:

1 1 Poverty and Employment in Timber Dependent Counties By Peter Berck, Christopher Costello, Sandra Hoffmann and Louise Fortmann

2 2 The ESA n "The loss is evident in the lines at the soup kitchens. And the loss is evident in the homes where unemployed workers, anxious, depressed, sunk in despair, lash out at their loved ones or find solace in alcohol or drugs.” –Archbishop Thomas Murphy

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4 4 Decrease in Cutting Timber n Endangered Species and Running Out –Spotted Owl, Marbled Murrelet, Many Salmonids –Just plain ran out of big trees to cut n “And then came the spotted owl, and almost overnight the hauling jobs dried up and we had our electricity turned off and finally we received a foreclosure notice on this farm”

5 5 Does Cutting Trees n Reduce Poverty in Timber dependent counties? n Increase Employment –by more than one job for each new timber job? –by one job or less?

6 6 Two Modeling Philosophies n CGE/IO/SAM multiplier models –capture all relevant economics –assumptions on difficult to measure parameters can drive results labor mobility (no real migration data) openness to trade (interstate trade unmeasured) relation of product to labor input (product unmeasured)

7 7 The Error Correction VAR n Nearly no economics imposed on model n Uses available real data n Can’t explain why n but Can measure impact multipliers n and be used to find Long Run Relationships

8 8 Form of Cointegrating Equation ()  y t =  +    y t-1 + ··· +  k-1  y t-k+1 +  y t-1 +  D t +  t, number of coint vectors is rank of  n is also number of long run relations n some variables can be excluded from long run relations n some variables don’t adjust to LR rel.

9 9 The Data n Monthly from n County –timber employment –non timber employment –AFDC UP caseload n State –timber employment –non timber employment

10 10 Model for each County n Johansen’s MLE of the possibly cointegrated VAR using the 3 county and 2 state variables. –lag length –rank of cointegrating space –coefficient estimates, incl. coint vectors –Exclusion and Weak Exogeneity tests –Calculate SR impact multipliers

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12 Results

13 13 Timber and Poverty: LR n Exclusion of Timber or Poverty –Not Poverty: Humboldt, Siskyou –Not Timber: Trinity, Del Norte, Amador, Tuolumne. n Poverty Weakly exogenous –Plumas, Mendocino n Increase Timber, INCREASE poverty –Tehama

14 14 Poverty Conclusion n Rank 3: Stabile povery unless state level variables change –Shasta n Only in Lassen of the 11 counties may timber employ reduce AFDC-UP in LR

15 15 Timber Jobs Special? n “Job is a Job” n In four of 11 counties timber jobs shift cointegrating space same as any other job. Poverty same. n 100 new timber jobs = 78 jobs 2 years later. Mult is less than 1!

16 16 SR timber Multipliers n 100 new timber jobs = 3 less cases of AFDC-UP or n 1% timber jobs increase = 14/100% poverty decrease n Non timber employment does fractionally better

17 17 Conclusion n Cutting more trees won’t do anything for poverty in the LR and very little in the SR. n Employment Cutting more trees doesn’t have base like multipliers.


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