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Input-Output Analysis of Agriculture for Washington
Counties
Sean Ardussi, Phil HurvitzGeography 440, Spring 2005Prof. Bill Beyers
How would less agricultural dependence affect the economic base
of certain counties?
Original GoalsFor highly agricultural dependent counties (King, Kitsap, etc.), develop an alternative set of economic assumptions based on more localized agriculture.
Community based agricultureLess transportation expendituresFarmers Market Scenario
OverviewInput-output analysis for each county in Washington StateFocusing on agricultural industries
CropsLivestock
Data SourcesNAICS: employment, output, labor incomeBEA (does not match official WAIO model)USDA Census of AgricultureWA Employment Security Department
SIC → NAICSNAICS – North American Industry Classification System (1997 +)SIC – Standard Industrial Classification (1997 and prior)
NAICS BenefitsBusinesses that use similar production processes are grouped together
Expanded sectors to reflect changes in economy
Information sectorService sector
NAFTA compatibilityUSA, Canada, Mexico
NAICS DrawbacksLess than 50% of SIC codes can be directly linked to a NAICS counterpartConversion from SIC to NAICS is subject to error of judgment
MethodsWashington State Input-Output Model (official WA Office of Financial Management model)Implemented within an R statistical/programming language environment
RSoftware for handling statistical operationsGood for dealing with tabular dataHandles generic and matrix mathReads & writes standard filesProgramming interface allows batch jobs
Example of R code# run the conflation and add to the employment matrixfor (county in county.names) { # print (county) cty <- conflate.esd(county, 19) employment <- cbind(employment, cty)}colnames(employment) <- county.names
# sum across rows to get WA totals of employmentwa.employment <- rowSums(employment)wa.employment.sum <- sum(wa.employment)
# make location quotientsLQs <- NULLLQs.modified <- NULLfor (i in 1:ncol(employment)) { lqs.county <- NULL lqs.county.modified <- NULL county.sum <- sum(employment[,i]) for (j in 1:nrow(employment)) { lq.local.component <- employment[j, i] / county.sum lq.state.component <- wa.employment[j] /
wa.employment.sum lq <- lq.local.component / lq.state.component ifelse (lq < 1, lq.mod <- lq, lq.mod <- 1) lqs.county <- c(lqs.county, lq) lqs.county.modified <- c(lqs.county.modified, lq.mod) } LQs <- cbind(LQs, lqs.county) LQs.modified <- cbind(LQs.modified, lqs.county.modified)}
R output examples
ResultsComparison of metrics across counties
Location quotientsOutputEmploymentLabor income
Location Quotients: Livestock
LivestockHigh Counties
Adams – 11.82Pacific – 8.62Mason – 8.11Yakima – 6.86
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
harb
or
isla
nd
jeffe
rso
nki
ng
kits
ap
kitti
tas
klic
kita
tle
wis
linco
lnm
aso
no
kan
oga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
ansk
ag
itsk
am
ani
asn
oh
om
ish
spo
kan
est
eve
ns
thu
rsto
nw
ah
kia
kum
wa
lla_
wa
llaw
ha
tco
mw
hitm
an
yaki
ma
Livestock LQ with WA as benchmark, based on employment
LQ
05
10
15
20
Location Quotients: Livestock
LivestockLow Counties
King – 0.13Spokane – 0.16Pierce – 0.62Snohomish – 0.82
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
harb
or
isla
nd
jeffe
rso
nki
ng
kits
ap
kitti
tas
klic
kita
tle
wis
linco
lnm
aso
no
kan
oga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
ansk
ag
itsk
am
ani
asn
oh
om
ish
spo
kan
est
eve
ns
thu
rsto
nw
ah
kia
kum
wa
lla_
wa
llaw
ha
tco
mw
hitm
an
yaki
ma
Livestock LQ with WA as benchmark, based on employment
LQ
05
10
15
20
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Crops LQ with WA as benchmark, based on employment
LQ
05
10
15
20
Location Quotients: CropsLivestock
High CountiesOkanagon – 17.11Douglas – 15.8Klickitat – 12.24Grant – 10.82
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Crops LQ with WA as benchmark, based on employment
LQ
05
10
15
20
Location Quotients: CropsLivestock
Low CountiesKing – .03Kitsap – .064Spokane – .089Snohomish – .114
Crop Output
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Crop Outputo
utp
ut (
$m
illio
ns,
20
04
)
02
00
40
06
00
80
0
Crop Employment
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Crop Employmente
mp
loym
en
t (jo
bs)
05
00
01
00
00
15
00
02
00
00
Crop Labor Income
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Crop Labor Incomela
bo
r in
com
e (
$m
illio
ns,
20
04
)
01
00
20
03
00
40
0
Livestock Output
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Livestock Outputo
utp
ut (
$m
illio
ns,
20
04
)
05
01
00
15
02
00
25
03
00
35
0
Livestock Employment
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Livestock Employmente
mp
loym
en
t (jo
bs)
01
00
02
00
03
00
04
00
05
00
06
00
0
Livestock Labor Income
ad
am
sa
sotin
be
nto
nch
ela
ncl
alla
mcl
ark
colu
mb
iaco
wlit
zd
ou
gla
sfe
rry
fra
nkl
ing
arf
ield
gra
nt
gra
ys_
ha
rbo
ris
lan
dje
ffers
on
kin
gki
tsa
pki
ttita
skl
icki
tat
lew
islin
coln
ma
son
oka
no
ga
np
aci
ficp
en
d_
ore
ille
pie
rce
san
_ju
an
ska
git
ska
ma
nia
sno
ho
mis
hsp
oka
ne
ste
ven
sth
urs
ton
wa
hki
aku
mw
alla
_w
alla
wh
atc
om
wh
itma
nya
kim
a
Livestock Labor Incomela
bo
r in
com
e (
$m
illio
ns,
20
04
)
02
04
06
08
01
00
12
01
40
LimitationsNeeded to conflate data setsNeeded to impute dataExcel format not easy to translate to R
BenefitsR code can be altered and simply run again to generate output statistics & figuresReduces user error when programmed correctly
ConclusionsDifferent counties in the State vary widely with respect to agricultural economicsIncreased urbanization will have different effects on different locations in the State
