Identifying industries for employment development using input-output modeling: the case of prison industry employment.
Scott, Charles E. ; Williams, Nancy A. ; Derrick, Frederick W. 等
I. Introduction
Economic development involves creating new jobs while retaining
existing jobs. A step in the process is the identification of potential
industries and the assessment of the degree of complementarity or
competition with existing industry. Input-output (I/O) tables have
traditionally been used to determine the total economic impact of
changes in final demand or specific industry output changes. This paper
presents a new method which utilizes I/O table output for choosing
industries to foster employment that minimizes the negative crowding out
impacts of (and maximizes the positive benefits from) new industry
development in a region. The literature to date has not tied the
input-output method to industry choice or to the potential for crowding
out.
Industry policy is made primarily at the State and Federal levels,
but the effects are felt primarily at the local level. Prison industry
is a particular example where public policy choices have employment
implications at a variety of levels of aggregation. To address these
varying implications, we investigate the choice of industries and the
potential for crowding out on local, state, and national levels. We find
that a more diverse economy and greater aggregation lead to less job
creation resulting from a given employment increase.
When a jurisdiction chooses an industry for a prison, they are
faced with constraints that may reduce the set of viable industries to
those that do not conflict with the economic growth of the jurisdiction.
Pryor (2005) describes prison industries as: labor-intensive production,
with low levels of training, low economies of scale, low R&D
spending, no complex input supplies and little face-to-face contact with
customers. Our analysis here recognizes these constraints but focuses on
the crowding out aspect. Choosing industries for which the prisoner will
have job prospects after release may lead to crowding out local labor
not only while the prisoners are working inside prison, but also when
they are released. Thus, the potential crowding out of private sector
employment continues to be a hotly contested issue that dates back to
the 1800's. (1) The literature has not empirically investigated
prison industry choice at the local, state, and national levels in
relation to the production of the region or the secondary effects
generated.
The level of aggregation is crucial because prison labor amounted
to less than 0.06% of the labor force in 1997 and produced less than 2
hours worth of the national GDP (Miller, Shelton, and Petersik 1998).
Pryor (2005) reports similar numbers for 2002 with prison labor
approximately 0.04% of the labor force in 2002. As one expects, Derrick,
Scott and Hutson (2004) find that prison labor in aggregate has minimal
impact on national employment and wages of non-skilled, non-prison
labor. In contrast, studies (Scott and Derrick 2006; Derrick, Scott and
Ahmadi 2006) find significant crowding out at the local level.
II. Input-Output Modeling
Input-output modeling allows the examination of financial
transactions between businesses and between businesses and final
consumers in a region. Industries purchase goods and services from input
producers in order to produce output for final consumption. Input
producers have purchased goods and services to produce their output.
These backward linkages are called indirect purchases (indirect effects)
and they continue until leakages (imports, wages, profits, savings,
etc.) stop the circular flow expansion. The wages earned at each level
(direct, indirect) in turn produce induced effects as they are spent
within the jurisdiction.
Based on the concepts of direct, indirect and induced effects, an
I/O model constructs three employment multipliers describing the short
run, industry-specific, localized impacts of increased economic activity
in a given sector for the jurisdiction. In IMPLAN, the model used in
this paper, (2) the first is the number of direct jobs that are
associated with $1.0 million output in the given sector (Direct). The
second employment multiplier quantifies the number of jobs created by
input purchases in the creation of this $1.0 million output (Indirect).
The third employment multiplier estimates the number of jobs created by
the purchases by the (direct and indirect) employees hired as they spend
their incomes (Induced). The term, "secondary", is typically
used to indicate both indirect and induced effects.
These multipliers presume that the economic activity associated
with the spending stays in the jurisdiction, but that is generally not
the case. IMPLAN's Regional Purchase Coefficient (RPC) is the
construct used to measure the percentage of the goods or services in a
given industry produced inside the jurisdiction (county, state, or
nation). For example, an RPC of 0.6 indicates that 60% of the value of
the goods/services from a given industry is produced in the jurisdiction
and 40% is imported from outside the jurisdiction. If the secondary
employment multiplier is 10, and the RPC of 0.6, 6 new secondary jobs
are created within the jurisdiction and additional jobs are created
outside the jurisdiction due to leakage. Since these additional jobs are
outside of the jurisdiction, the model does not provide an estimate of
the number. The RPC's for the input sectors are incorporated into
the multipliers, causing them to be lower to the extent that some of the
inputs are purchased from outside the region. (3)
The total employment impact of creating a single, new private
sector job in-region is calculated from the I/O multipliers by scaling
each multiplier by the direct effect multiplier:
Total Employment per job created
= [(Direct/Direct) + (Indirect/Direct) +(Induced/Direct)] (1)
Combining Indirect and Induced into the Secondary In-region
Multiplier (Secondary IM):
= [1 + Secondary IM] (2)
The total employment impact will overestimate the additional job
creation in the jurisdiction to the extent that the new jobs replace
currently employed personnel.
In Figure 1, we depict the implications of the creation of one
direct in-state job, including the potential for crowding out already
existing employment. The creation of a direct job is totally captured by
the jurisdiction. We continue with the previous assumption of an
in-state secondary jobs multiplier of 10. With an RPC of 0.6, 60% of any
job creation replaces the in-state jobs already shown as existing jobs
in the region from Figure 1. The only truly new job creation is related
to the (1-RPC)%, or 40% of the jobs that would have been associated with
imports. Forty percent of the "new" jobs created are inside
the jurisdiction, including the associated multiplier effects (see solid
oval line). The remaining 60% of the direct job and its resulting six
secondary jobs replace 6.6 jobs that were already in the jurisdiction
(see dotted oval line). Of the 11 jobs created, only 4.4 jobs are new to
the region. Thus,
[FIGURE 1 OMITTED]
Total Private Sector Employment per private sector job created net
of crowding out is:
= Total Employment Created - Total Employment Crowded Out
= [1 + Secondary IM] -[1 + Secondary IM]*RPC
= [1 + Secondary IM]*(1 - RPC) (3)
For example, in a sector with an RPC of 1, all direct jobs
associated with a given purchase would be from in-jurisdiction
businesses. Hence, the "new" job is likely to be replacing a
current employee and would lead to a zero net impact, as the positive
indirect and induced effects would be countered by the indirect and
induced effects lost with the loss of the replaced job. The new direct
job will have an overall non-negative impact since RPC [less than or
equal to] 1, but it may be small. In summary, both RPC and secondary
employment are critical in determining whether new employment has a net
positive employment effect.
Large industry RPC's imply small net employment impact,
holding secondary effects constant, because the new employment will
replace existing jobs. If the secondary employment effects are large, a
strong positive employment impact is possible given any particular RPC.
As long as the RPC is less than one, private sector employment increases
will lead to net increase in employment with the only question being how
much. These results should be viewed as short run effects since the I/O
method assumes fixed prices, an unrealistic assumption in the long run.
III. Method Application to Prison Industry Selection
1. Discussion
Prison industries are a part of a rehabilitation effort with the
specific mandate to:
"employ the greatest number of prisoners reasonably possible;
concentrate on labor intensive manufacturing; diversify industries so
that no private industry faces undue competition; diversify products so
sales are widely dispersed; minimize competition with private industry
and free labor; limit market share for any specific product; sell
products to federal and other government institutions; provide
opportunity for prisoners to learn skills useful in the free market;
sell products at no more than the current market prices; and operate in
a self-sustaining economic manner" (Schwalb 1994). (4)
Historically, prison labor has been concentrated in industries that
are labor intensive, low skill, and slow growth. With market access
limited by state and federal laws, economies of scale are restricted.
Products are often in the later stages of the product lifecycle where
profit margins are low, future expansion is limited, and new production
avenues must be continually investigated (Yae 1999; Greiser 1989). A
long term implication is that these products are concentrated in poorly
performing industries. (5) Pryor (2005) further notes that the
industries do not demand a high level of research and development, do
not use complex input structures, and require limited face to face
contact between workers and customers.
Prison industry employment benefits prisoners (6) but has the
potential to displace private sector employees. (7) In the U.S. today,
there are two ways that prison labor programs address this crowding out
potential. State use industries are run by the prison system and are
allowed to sell only to government and non-profit firms to limit the
crowding out potential--in this case using output market restrictions to
limit competition with the private sector. In this type of organizing of
prison industries, the prison system acts as the company: buying inputs,
employing prisoners to do the production and selling outputs, but not to
the private sector. The most common products are wood/furniture, metal,
paper/printing, vehicle-related, and garment/ textile (The Criminal
Justice Institute 2000; Chang and Thompkins 2002).
In the case of the employment of prisoners, the positive net
employment outcome found in equation (4) is not assured since the
initial prison job is not private employment. To foster prisoner
employment without significant crowding out, Congress passed the Justice
System Improvement Act of 1979 which authorized the Prison Industry
Enhancement (PIE) program. PIE allows the sale of goods and services to
the private sector as long as the employment of prisoners in the
industry in question does not compete with current private industry
(Mizrahi 1996). (8) PIE programs have proven to be quite diversified and
include electronic circuit boards, custom embroidery, kitchen cabinets,
baseball caps, pig farming and fishing ties (American Correctional
Association 1995). Mizrahi (1996) notes that the diversity of products
reduces the risk of one or a few industries posing unfair competition to
the private labor market. In addition, there are stringent conditions
relative to the private sector labor market that must be met before PIE
programs are authorized. These restrictions are essentially providing
proof of a low RPC and assuring that market wages are paid to the
inmates. There are restrictions on the interstate movement of these
goods, allowing the use of an assumption of no export from the relevant
jurisdiction in the analysis below. (9)
RPC's can be used to assess the extent of direct crowding out
of private labor that would occur were the state to create a new job in
that industry. However, the size and diversity of the jurisdiction
matter because they impact the RPC, and hence, the potential and
measured competition in the industry chosen. Using too small a
jurisdiction will not take into account implications for the broader
community and can lead to a "beggar thy neighbor" issue. (10)
Secondary job creation is a second major impact that can be
estimated with input-output method. If there is crowding out, but also
new job creation, the net effect can still be positive on the labor
market as the new jobs replace the crowded out jobs. The size of these
employment backward linkages for a given industry are affected by the
characteristics of the industry--labor intensity, vertical integration,
the complexity of the good or service, etc. The resulting
in-jurisdiction impact will vary significantly depending upon the
ability of the local economy to provide the goods demanded. Large,
diverse economies will allow significant in-jurisdiction spending.
Small, narrowly focused economies will be less able to capture any
secondary spending.
Selecting an industry with in-state backward linkages will create
jobs to counter any crowding out of private labor that occurs,
potentially leading to a net positive impact on the labor market.
Choosing a low RPC industry minimizes the in-jurisdiction displacement
at the expense of crowding out imports from the
"rest-of-the-world." Optimally, the industry choice should
include both regional importing and backward employment creation
linkages. This would limit the direct displacement impact and create new
jobs for those who were crowded out by prison employment.
In summary, potential crowding out would predict lower employment
creation for the more aggregated measures due to possibly high RPC.
Secondary employment creation would lead to higher employment creation
measures with greater aggregation. The net effect of creating a
"new" job is an empirical question with the answer being
industry specific and varying both across jurisdiction and with the
level of aggregation.
2. Method and Data
Prison labor requires adjustment to the employment effects in
Figure 1 because the entire direct job is located in the prison and
remains entirely in the jurisdiction, but outside the private sector.
Recall that in Figure 1, the net effect of a new private sector job
consisted of the new 0.4 direct job and the four new secondary jobs for
a total of 4.4 jobs. However, the net private sector employment created
per prison job falls to 3.4 jobs. The private sector does not gain the
0.4 portion of the new direct job, nor does it retain the remaining 0.6
job that is crowded out by an inmate. The derivation of the 3.4 net
private sector jobs created as a result of a prison job is a modified
version of (3):
Net private sector employment created per prison job
= Total Employment Created -Total Employment Crowded Out -Prison
job = [1 + Secondary IM]*(1 - RPC) - 1 (4)
The net impact will be positive in the private sector if the
secondary job creation overshadows the negative effects of crowding out.
The input-output method described in Section II is applied to
prison labor using four IMPLAN I/O models. A state-level model for
Maryland is used to illustrate the implications of the industry
selections that have already been made in this state. (11) An IMPLAN
model for the state of Ohio is then introduced for comparison purposes.
Ohio, a larger state with a more diverse economy, provides additional
insights into the interaction between the prison industries choice of
product/service and its impact on the local job market. To highlight the
importance of the size of the jurisdiction on the analysis, a Washington
County, Maryland, model is used in conjunction with the Maryland and
national models. The local community will likely have a less diverse
economy, affecting both the potential for direct crowding out and the
ability to benefit from the input purchase effects. A county is also
likely to be the smallest jurisdiction concerned about the income,
employment, and tax revenue implications.
3. Prison Industries Employment Impacts in Maryland and Ohio
Using (3) and (4), we compute the employment impacts for all of the
sectors in Maryland using prison labor (see Table 1). Taking the example
of the wood products sector, the RPC of 0.325 indicates that a new
prison job will crowd out 32.5% of an in-state job. The secondary
impacts reflect the fact that a new prison job will generate employment
for inputs and wages spent by employees in the direct and indirect
sectors. A secondary impact of 0.449 indicates that, netting out the new
prison job, 0.449 secondary jobs will become available as a result of
the prison job. Although 0.449 of a job is created, 0.325 of a private
sector job is replaced with prison labor. Hence, the net addition to
private sector employment due to one new prison job in the wood products
sector is 0.124, a positive impact overall.
With the exception of agricultural, forestry and fishery services,
all of the sectors with RPC's less than 0.325 show positive net
employment impacts. The net employment impact on the economy is the
difference between this adjusted private sector job creation and the
"first round" job lost (the prisoner replacing a potential
private sector employee). For high RPC sectors, the job creation must be
quite significant to constitute a net increase in employment. Over half
of the sectors using prison labor in Maryland show net negative impacts
on private sector employment as evidenced by the negative signs in the
net private sector job creation column. If the goal of prison employment
is to employ prisoners without crowding out private employment, Maryland
may wish to consider a different set of industries.
In Table 2, we compute the same employment impacts for the sectors
in the state of Ohio using prison labor. All of the Ohio sectors with
RPC's less than 0.519 show positive net employment impacts as
compared to a breakeven point of 0.325 in Maryland. Although the pattern
in Ohio is similar to that of Maryland, Ohio has more sectors employing
prison labor, and there are more jobs being created in a number of
sectors. At the same time there are fewer sectors in which all of the
output is produced in the state, leading to complete crowding out. These
differences are likely due to the larger and more diverse economy in
Ohio. The gross state product (GSP) in Ohio is almost twice that of
Maryland and the Manufacturing GSP is six times as large
(http://www.bea.gov/ bea/regional/gsp/).
The comparison of the RPC's across states is more difficult,
but they appear to have similar diversity. As with Maryland, Ohio has a
number of sectors that have an RPC above 0.5 with a resulting potential
net negative employment impact. Also like Maryland, a number of sectors
have very low RPC's, with a resulting high potential employment
gain. Thus, the potential for crowding out in Ohio and in Maryland
depends very much on the sector chosen for the prison industry.
Ohio appears to have chosen sectors with less crowding out and more
job creation. In general, this result may be due to Ohio having slightly
lower RPC's than Maryland, the increased ability of the Ohio
economy to take advantage of the created jobs, or the choice of
industries that create more jobs. For the sectors common to both states,
the average new secondary job creation for Maryland was 0.74 as opposed
to its overall average of 0.591. Comparable numbers for Ohio are 0.62
and 0.947. Thus, in the sectors in which they both participate, the job
creation is greater than the average for Maryland, but less than the
average in Ohio. The significant contrast between the states is driven
by the difference in the average RPC for these sectors. In Maryland, the
average RPC is 0.09, indicating that for these sectors, most of the
output used in the state is imported to the state. For Ohio, the RPC is
0.48, indicating that the state produces 48% of what it uses in these
sectors. This leads to 0.64 net job creation in Maryland in these
sectors per prisoner employed, with a corresponding figure of 0.14 jobs
in Ohio. The diversity of the Ohio economy leads to a less advantageous
employment outcome from prison industries by half a job per prisoner due
to more private sector jobs being replaced by prison labor.
[FIGURE 2 OMITTED]
Figure 2 provides a visual comparison of the computations for
Maryland and Ohio in Tables 1 and 2. The horizontal axis measures the
new jobs created by one prison industry job (New Secondary In-region
Employment column in Tables 1 and 2). The vertical axis is the
sector's RPC, an estimate of the direct crowding that is occurring.
The diagonal line from the origin to the point (1,1) serves as the set
of break-even points such that if the sector is charted above this line,
the prison industry jobs are crowding out more jobs than they are
creating. Below, or to the right of, the line indicates that the job
creation more than makes up for the crowding out as the net increase in
jobs exceeds the expected value of direct jobs lost.
The correlations between the RPC's and net employment impacts
for Maryland and Ohio are -0.907 and -0.926, respectively. The strength
of these correlations provides support for the rule of thumb that
RPC's less than the breakeven will lead to positive employment
creation. The correlations also provide support for the common
no-compete (i.e., low RPC) criterion for authorizing prison industries
under PIE.
4. Jurisdictional Comparisons and their Impact
Moving to a comparison of local, state and national jurisdictions
using the I/O methods outlined in Section II, our first finding is that
the RPC's do not increase monotonically with the size of the
jurisdiction. Nor do the net job creation figures decrease monotonically
with the size of the jurisdiction. Table 3 provides a comparison of
several sectors in Maryland, some of which have a presence in Washington
County, the location of the Hagerstown prison. Taking the meat packing
sector as an example, the RPC at the local level of 0.234 is higher than
the RPC at the state level of 0.064. This is surprising and may be due
to the local (rural) jurisdiction being more specialized in this
industry than the larger jurisdiction. As expected, the RPC's at
the national level tend to be significantly larger than those at the
state and local levels. With its low local RPC, meat packing has a
relatively high new indirect and induced employment impact of 2.571 jobs
per prison job.
The fact that the average employment creation for Hagerstown prison
industries is almost one more than replacement indicates that the
majority of the prison industry output is in sectors with significant
input purchases and low RPC's. On average, 40% of a job is
predicted to be crowded out by one prisoner being employed while about
1.4 new jobs are created in the private sector to replace the loss.
These are positive signals relative to the private sector employment
impact of the Hagerstown prison industries employment.
In comparison to Washington County, the lower average Maryland RPC
of 0.158 and the lower average Maryland job creation figure of 1.215 are
somewhat surprising. One possible explanation is that a larger
percentage of the output consumed within the county is being produced
within the county compared to the state average. This outcome would be
the case if the industry were a net exporter to the state.
In comparison to the county and state level results, the national
level results in greater crowding out and less job creation. On average
nationwide in these sectors, 0.3 new jobs are created at the expense of
0.9 jobs for a net loss of almost 0.6 jobs to the nation as a result of
one prison industries job at the Hagerstown prison. The significant
employment drop is due almost entirely to sectors such as the meat
packing in which the majority of the Hagerstown output occurs. In meat
packing, the national RPC of 0.98 is significantly higher than the
Washington County RPC of 0.23, causing many of the jobs created by the
prison industries purchases to already exist in the larger jurisdiction.
The overall result is that using the state as the decision making
jurisdiction underestimates the crowding out potential locally and
overestimates the net job creation potential at the national level. The
national level findings agree with Pryor (2005) and Derrick, Scott, and
Hutson (2004) which found that, nationally, prison industry has limited
effect at the national level even if it were expanded. The differing
impact at county and state level is also consistent with Derrick, Scott,
and Ahmadi (2006) and Scott and Derrick (2006).
IV. Conclusion
Prison industry employment is a very controversial activity. Does
it crowd out private sector jobs? Does it create new jobs through the
inputs purchased in the process of prison industry activity? How should
the state choose industries, given that they want to have prison
industry employment? This paper has addressed the first two positive
questions and developed a model for addressing the third normative
question with input-output analysis. Utilizing I/O employment
multipliers, it is shown that prison industry employment does crowd out
private sector jobs, but it also creates new jobs at the local, state
and national levels.
It is clear from our analysis that industries exist where new
employment creates limited crowding out and net job creation in the
private sector. At the national or state level, the implications are
likely to be minimal relative to the more local implications of
introducing a new industry. Thus, the expected extent of crowding out
will depend on the size of the jurisdiction being modeled. Analysis at a
narrowly defined local jurisdiction, however, will not take into account
implications for the broader community and can lead to a "beggar
thy neighbor" issue. A more diverse economy and/or greater
aggregation lead to less job creation. The finding is that greater
aggregation leads to an increased probability that the "new"
job is not new, but a replacement. At the same time, greater aggregation
leads to the expectation of a more diverse economy, which would increase
the secondary job effect potential. Thus, the net effect of creating a
"new" job is an empirical question.
A major concern in economic development is creating new jobs while
retaining (not crowding out) existing jobs. Our method is broadly
applicable to public policy employment issues, such as the Economic
Development Administration's (EDA) stated goal of generating jobs,
retaining existing jobs, and stimulating industrial and commercial
growth in economically distressed regions of the United States (Economic
Development Administration 2009). Our contribution to this literature is
the unique utilization of I/O table output for choosing industries that
foster employment by minimizing negative crowding out impacts and
maximizing positive benefits from new industry employment in a region.
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Notes
(1.) The issue is not new as artisans and businesses argued in the
1800s that prisoners were taking "the means of livelihood from
local communities." (Walker 1988)
(2.) IMPLAN, IMpacts for PLANing, a product of Minnesota IMPLAN
Group, Inc., generates regional input-output models by converting the
United States Benchmark Study of input-output accounts to a regional or
local model and closely follows the BEA accounting conventions.
(3.) Indirect and induced employment effects do not include job
creation outside of the region.
(4.) In many cases, the state's choice of industry dates back
to the 1800s. In the North, the contract system was the predominant
model for providing inmate labor to the private sector. In particular,
inmate labor was used in the piece-price system in which a fixed price
was paid for each item completed. In the South, the lease system was
more common. Most of the work was performed outside of the prison - in
mines, or on railroads or agricultural projects. Prison labor waned in
the late 19th and early 20th Centuries as organized labor gained
prominence. Prison labor resurged in the 1970s when laws were passed to
protect laborers from exploitation and provide rehabilitative.
(5.) Given the constraints on prison industries, there is the
potential that changes will come slowly to prison industries, and if so,
the static nature of the I/O model is appropriate. The fact that prison
industries focus on poorly performing sectors may be a signal that they
are doing a relatively good job in choosing industries that do not
compete with the private sector.
(6.) Prison laborers may develop a work mentality including time
accounting, productivity, and economic reward, which improve employment
opportunities; increased future earnings; improved behavior in prison
and lower recidivism. (Derrick, Scott and Hutson 2004).
(7.) A direct comparison of prison and private jobs is not
appropriate due to differences in productivity. The value of marginal
product for prisoners is approximately one-fourth that of private labor
(Pryor 2005; Deloitte and Touche 1991) due lack of job skills, low
socialization skills, high labor/capital ratio, and high turnover rates
(Ripley 2002). In addition, production time is lost due to security.
(8.) PIE programs require that the prevailing wage be paid to
prisoners to assure that potential employers do not choose prisoners
over private sector employees on the basis of lower wages. A normal
eight hour shift contains approximately four and a half hours of
production. The remaining time is lost to shift changes by guards and
supervisors, counts of prisoners as they move between prison sections,
tool distribution in the morning and at lunch, and tool collection
before lunch and at the end of the day.
(9.) With limited PIE participation in both Maryland and Ohio, the
data analysis is framed exclusively in terms of "state use"
and not PIE, although the analysis is applicable to PIE.
(10.) This implication is especially possible for PIE proposals
when a major employment or population center is divided across two
states. Since states are the unit of measure for state prison systems
and some PIE proposals, these impact assessments may miss prisoners
replacing private labor that is in a nearby state.
(11.) The quantitative assessment of the net impact of the specific
Washington County prison and the SUI in Maryland as a whole is included
in Derrick, Scott, and Ahmadi (2006).
Charles E. Scott, Nancy A. Williams,* and Frederick W. Derrick
* Department of Economics, Loyola University Maryland, Baltimore,
MD 21210. Phone: 410-617-2825, Fax: 410-617-2118, Email:
nwilliams@loyola.edu
TABLE 1.
Net Private Sector Job Creation in Maryland Sectors in Prison
Industries (rank ordered by Regional Purchase Coefficient (RPC))
Net Private
Sector
RPC ** New Secondary Job Creation
or Replaced In-Region per 1 prison
Maryland Sector Employment) Employment *** job ****
Textile Goods, N.E.C * 0.014 1.485 1.472
Fruits 0.031 0.445 0.414
Meat Packing Plants 0.064 1.913 1.849
Metal Household 0.08 0.69 0.61
Furniture
Metal Office 0.126 1.169 1.043
Furniture
Apparel Made From 0.164 0.633 0.469
Purchased Materials
Agricultural, 0.191 0.114 -0.077
Forestry, Fishery
Services
Upholstered Household 0.21 0.465 0.255
Furniture
Furniture and 0.212 0.501 0.289
Fixtures, N.E.C
Wood Products, N.E.C 0.325 0.449 0.124
Switchgear and 0.391 0.379 -0.012
Switchboard Apparatus
Commercial Printing 0.481 0.431 -0.05
Wood Partitions and 0.658 0.259 -0.399
Fixtures
Wood Office Furniture 0.714 0.22 -0.494
Other Business 0.8 0.179 -0.621
Services
Computer and Data 0.8 0.174 -0.626
Processing Services
Mattresses and 0.82 0.145 -0.675
Bedsprings
Sanitary Services and 0.889 0.174 -0.715
Steam Supply
Watch, Clock, Jewelry 0.9 0.038 -0.862
and Furniture Repair
Other State/Local 0.906 0.208 -0.697
Government Enterprises
Motor Freight 0.952 0.055 -0.898
Transport and
Warehousing
Maintenance and Repair 1 0 -l
Other Facilities
New Industrial and 1 0 -1
Commercial Buildings
Weighted Mean 0.46 0.591 0.131
* N.E.C.--Not elsewhere classified.
** Source IMPLAN;
*** (Secondary IM)(1--RPC)
**** (Secondary IM)(1-RPC)-RPC
RPC = % of output currently produced within the jurisdiction;
Secondary IM = (Indirect/Direct) + (Induced/Direct);
Indirect = # of jobs created by input demand per prison job
created;
Induced = # of jobs created by income generated and spent
per prison job created.
TABLE 2.
Net Private Sector Job Creation in Ohio Sectors in Prison
Industries (rank ordered by Regional Purchase Coefficient (RPC))
New Net Private
RPC * (or Secondary Sector Job
Replaced In-Region Creation per 1
Ohio Sector Employment) Employment ** prison job ***
Misc. fabricated metal 0.014 1.556 1.542
product manufacturing
Broad-woven fabric 0.037 1.055 1.018
mills
Buttons, pins, and all 0.039 1.061 1.023
other misc.
manufacturing
Footwear manufacturing 0.041 1.216 1.175
Cut and sew apparel 0.086 0.833 0.748
manufacturing
Broom, brush, and mop 0.105 1.175 1.07
manufacturing
Soft drink and ice 0.146 2.361 2.214
manufacturing
Other leather product 0.159 0.721 0.562
manufacturing
Ophthalmic goods 0.227 0.796 0.569
manufacturing
Prepress services 0.23 0.779 0.549
Institutional 0.25 1.051 0.801
furniture
manufacturing
Paperboard container 0.262 1.081 0.819
manufacturing
Data processing 0.294 0.984 0.69
services
Sign manufacturing 0.303 0.883 0.58
Dental equipment and 0.383 1.024 0.641
supplies manufacturing
Periodical publishers 0.412 0.988 0.577
Wood office furniture 0.519 0.576 0.057
manufacturing
Commercial printing 0.559 0.5 -0.059
Office furniture, 0.579 0.566 -0.013
except wood,
manufacturing
Miscellaneous wood 0.611 0.445 -0.166
product manufacturing
Waste management and 0.672 0.565 -0.107
remediation services
Management of 0.688 0.498 -0.19
companies and
enterprises
Business support 0.688 0.244 -0.444
services
Commercial machinery 0.688 0.344 -0.344
repair and maintenance
Facilities support 0.688 0.27 -0.418
services
Showcases, partitions, 0.715 0.287 -0.427
shelving, and lockers
Electronic equipment 0.768 0.275 -0.493
repair and maintenance
Auto repair and 0.783 0.364 -0.419
maintenance, except
car
Mattress 0.85 0.228 -0.622
manufacturing
Commercial and 0.877 0.14 -0.736
institutional
buildings
Warehousing and 0.887 0.089 -0.798
storage
Weighted Mean 0.346 0.947 -0.601
* Source IMPLANT
** = (Secondary IM)(1--RPC):
*** = [(Secondary IM)(1--RPC)]--RPC
RPC = % of output currently produced within the jurisdiction;
Indirect = # of jobs created by input demand per prison job
created;
Induced = # of jobs created by income generated and spent per
prison job created.
TABLE 3.
Comparison of RPC's and Employment Effects for Maryland Sectors
in Prison Industries in Washington County, MD *
RPC
Washington
Maryland Sector County MD State National
Metal Office Furniture 0 0.126 0.909
Switchgear and Switchboard 0 0.391 0.892
Apparatus
Meat Packing Plants 0.234 0.064 0.976
Wood Partitions and 0.411 0.658 0.844
Fixtures
Upholstered Household 0.854 0.21 0.816
Furniture
Motor Freight Transport 1 0.952 1
and Warehousing
New Industrial and 1 1 1
Commercial Buildings
Weighted Average For WC 0.386 0.158 0.908
industries
New Secondary In-Region
Employment **
Washington
Maryland Sector County MD State National
Metal Office Furniture 1.337 1.169 0.308
Switchgear and Switchboard 0.621 0.379 0.322
Apparatus
Meat Packing Plants 2.571 1.913 0.191
Wood Partitions and 0.329 0.259 0.459
Fixtures
Upholstered Household 0.075 0.465 0.396
Furniture
Motor Freight Transport 0 0.055 0
and Warehousing
New Industrial and 0 1 0
Commercial Buildings
Weighted Average For WC 1.415 1.215 0.286
industries
* Source IMPLAN; RPC = % of output currently produced within
the jurisdiction;
*** Secondary IM = (Indirect/Direct) + (Induced/Direct);
*** Indirect = # of jobs created by input demand per prison
job created;
*** Induced = # of jobs created by income generated and spent
per prison job created.
** = (Secondary IM)(1--RPC)
