By Peter Berck, Christopher
Costello, Sandra Hoffmann and
Louise Fortmann
1

 "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
2

Economic Trends in Major California Timber Counties
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
1984 1985 1986 1987 1988 year
1989 1990 1991 1992 1993
Timber Harvest
Timber Employment
AFDC-UP Cases



Endangered Species and Running Out
– Spotted Owl, Marbled Murrelet, Many
Salmonids
– Just plain ran out of big trees to cut
“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”
4

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

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



()
D y t
P y t-1
+
=
 f
D t
+
G
1
D y t-1
+ e t
,
+ ··· + G k-1
D y t-k+1 number of coint vectors is rank of
P
+
 is also number of long run relations
 some variables can be excluded from long run relations
 some variables don’t adjust to LR rel.
8

 Monthly from 1984-1993
 County
– timber employment
– non timber employment
– AFDC UP caseload
 State
– timber employment
– non timber employment
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
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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Lag Length and Rank of the Cointegrating Space
Amador
Del Norte
Humboldt
Lassen
Mendocino
Plumas
Shasta
Lag Length Rank
1
1
2
1
2
3
3
Siskiou
Tehama
4
4
Trinity 3
Tuolumne
Source: Computed. See Text
3
2
1
1
2
2
3
3
1
1
1
1
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County
County
Amador
Del Norte
Humboldt
Lassen
Mendocino
Plumas
Shasta
Siskiou
Tehama
Trinity
Tuolumne
p Values for Tests of
Null Hypothesis:
County Timber
Employment Is
Excluded from
County AFDC-UP
Caseload Is
Excluded from
County Timber
Employment
Is Proportional
A Job Is Long-Run County Long-Run County to Total County a Job Relationships Relationships Employment
0.67
0.42
0.12
0.00
0.01
0.00
0.02
0.00
0.02
0.31
0.04
0.98
0.96
0.05
0.00
0.04
0.00
0.01
0.00
0.02
0.74
0.13
0.00
0.01
0.28
0.00
0.01
0.03
0.00
0.72
0.08
0.00
0.01
0.04
0.00
0.18
0.00
0.00
0.04
0.09
0.36
0.28
0.94
0.00

 Exclusion of Timber or Poverty
– Not Poverty: Humboldt, Siskyou
– Not Timber: Trinity, Del Norte, Amador,
Tuolumne.
 Poverty Weakly exogenous
– Plumas, Mendocino
 Increase Timber, INCREASE poverty
– Tehama
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 Rank 3: Stabile povery unless state level variables change
– Shasta
 Only in Lassen of the 11 counties may timber employ reduce AFDC-UP in LR
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
“Job is a Job”
 In four of 11 counties timber jobs shift cointegrating space same as any other job. Poverty same.
 100 new timber jobs = 78 jobs 2 years later. Mult is less than 1!
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 100 new timber jobs = 3 less cases of
AFDC-UP or
 1% timber jobs increase = 14/100% poverty decrease
 Non timber employment does fractionally better
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
Cutting more trees won’t do anything for poverty in the LR and very little in the
SR.

Employment Cutting more trees doesn’t have base like multipliers.
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