Prison labor: the local effects of Ohio Prison Industries.

Abstract

Prison labor has both positive and negative effects. Keeping prisoners active, training and socializing them to be productive citizens after prison, and helping to pay for their incarceration are some of the positives. Potential crowding out of free labor and industry is the major

potential negative. This paper addresses the quantitative financial and employment consequences on local free labor and industry of prison industries in Ohio using an input-output model for Ohio. Based on the analysis, prison industries employment in Ohio has negligible to positive employment consequences for the Ohio economy and partially offsets the incarceration cost of the inmates who participate--providing between 5 and 10 percent of the incarceration cost in net cash flow and induced tax revenues. (JEL H70)

Introduction

The cost of incarcerating criminals in the United States has been escalating over the past two decades leading to attempts to determine ways of covering the cost. Correctional expenditures nationally increased 9.95 percent annually between 1982 and 2002. From 1985 to 1995, the prison population grew 7.3 percent annually, and then grew with a 3.6 percent annual growth rate to a total of two million prisoners in state and federal prisons in 2002 [Harrison, 2003]. Each of these rates far exceeds the population growth rate of 1 percent from 1970 to 2000 by a considerable margin.

One proposal for alleviating the incarceration cost burden is prisoner labor. Prisoner labor is inmate employees producing goods and services for sale and use outside of the prison. (1) Prison labor provides positive cash flow from the economic activities, helping to fund the prisons. It also provides productive activity and training for the prisoners. However, prison labor impacts the local community surrounding the prison. While the rhetoric in favor and opposed to prison labor is heated, there is limited quantitative evidence of these impacts and costs.

Scott and Derrick [2004], using a labor supply and demand model, provided the first empirical estimates of crowding out. They conclude that employment of 100 prison laborers crowds out between 13 and 20 unskilled private sector jobs nationally. Although the aggregate number of jobs lost is low, the jobs crowded out are thought to be localized near the prisons and focused in the industries in which the prisoners participate. Thus, the empirical question remains as to the extent of crowding out in the local labor market. Countering these negative effects are economic expansion effects resulting from prison industries hiring free labor as managers and purchasing inputs from the private economy, as well as consumer spending of their income by prisoners. Spending on private sector labor, goods and services leads to subsequent spending by the private firms and their employees generating additional private jobs, income and tax revenue.

This paper initially enumerates a number of these costs and benefits and then quantifies the local impact that happens through purchasing of inputs and displacement of local labor for the Ohio Prison Industries. It is the first to provide empirical estimates of the net local economic effects, incorporating crowding out and secondary effects, at the state level. An input-output model for Ohio was used to estimate the local impacts of prison labor for the state of Ohio.

Costs and Benefits of Prison Labor

Supporters of prison labor focus on improved prisoner behavior, (2) lower recidivism, and increased future employment and earnings resulting in higher tax revenue and lower costs for social programs. Active prisoners are likely to be cooperative within the prison itself. This is enhanced since prisoners view prison industry work as desirable, leading to their conforming to the guidelines necessary to qualify for this work. These requirements can be pursuing a GED if they have not graduated from high school or avoiding 'infractions' (e.g., fights or severe rules violations). The behavior of both the active participants and the applicant queue is affected, extending the impact on behavior.

Prison labor also provides skills and socialization which may affect prisoner behavior after they are released. Successful work post-release decreases the probability of returning to a life of crime and being reincarcerated (recidivating), affecting the future size of the prison population. This effect has been studied by Scott and Derrick [2005] in the case of Ohio Prison Industries (OPI). The overall conclusion is that effective job and educational programs can create significant cost savings for state prison systems with fewer inmates returning to prison at all and some returning later than previously expected.

Positive effects also include the net positive cash flow from prison industry activities (offsetting taxpayer cost of incarceration) and the expanded economic activity related to the purchase of inputs. Reynolds [1996] projected that the employment of 25 percent of all prisoners would decrease taxpayer costs by approximately 10 percent, 2.4 billion dollars. In addition, proponents argue that some prison labor is actually repatriating unskilled and semi-skilled jobs currently being exported internationally.

Proponents argue that the negative effects of prison labor are overestimated and are outweighed by the positive effects. Low prison wages must be interpreted in the context of prisoner productivity due to lack of job skills, a generally low level of literacy, low socialization skills, high turnover rates, inefficient shipping, and time lost due to security. Furthermore, market access is severely limited by state and federal laws, restricting opportunities for economies of scale. 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; Grieser, 1989]. Prison industries choose products to produce for their high labor intensity (3) due to low wages and the expected value of the socialization impacts on prisoners.

Local labor is not displaced if the goods and services produced would have been produced out-of-state, or internationally. Furthermore, to the extent that industry selection minimizes competition with the local community, this generates secondary (input) purchases to the local community without significant crowding out of local labor.

Adversaries to prison labor cite unfair competition noting that prison industries are exempt from the Fair Labor Standards Act that protects workers and restricts employer behavior. Prison workers can be hired, fired, or reassigned at will, have no right to organize or strike, and have no means of filing a grievance or voicing any kind of complaint. Inmate workers have 'sub-minimum wages, no required health benefits, no unions, no vacation time, no absenteeism, and no overtime [Flanagan et al., 1988].' (4) In addition, opponents argue that mandatory sourcing requirements in prison industry programs restrain competitive bidding by providing prison goods a monopoly. (5)

Modeling of the Prison Labor Impact

The circular flow of income model provides the theoretical basis for the methodology in this paper. Asset ownership is housed in the household sector from which all consumption occurs and to which all income flows. All economic activity occurs in the business sector. Consumption funds flow from the household sector to the business sector, with goods and services being received in return. Businesses purchase inputs in the form of assets and labor from the household sector providing the income to support the households' consumption spending.

The government sector collects taxes from the households and purchases goods and services from the business sector to serve the citizens. The capital market describes the saving behavior of households and the investment activities in businesses. This completes the basic, simple model of aggregate activity in the economy.

When prison industries are incorporated, they confound the traditional circular flow model, causing adjustment in the current flow pattern, and adding other potential flows as allowed by the enabling legislation. The complexity of the various economic flows may indicate why the local effects of prison labor have not been estimated and why we ultimately use an input-output model for the estimation.

The most obvious adjustments to the traditional circular flow are the money flows to the prison industry sector. The first is the investments in equipment used in the prison industries. These lead to some crowding out of investments in private businesses as these funds are diverted from projects other than prisons. A second impact is that some of the goods and services necessary to run the prisons can now be provided by the prison industries. Some of these goods and services have traditionally been provided through the labor of the inmates, so prison industries are more an extension than a new phenomenon. A third added flow is government purchases from prison industries of goods and services previously purchased from non-prison suppliers. This entails some crowding out as these purchases decrease the spending in the non-prison business sector.

These flows of funds to the prison industry sector lead to two flows out. The first is purchases by prison industries of inputs from the business sector. These consist of materials and support services. The second flow is the income that the prisoners and prison industry staff receive. Prisoner income is distributed to room and board (back to the prisons themselves) and restitution funds (to victims), with the balance being added to the income flow for the economy. Prison staff income is private sector income, and as such, restores some of the crowded out private sector income.

Purchases from prison industries are different than private sector purchases in a couple of ways. The first is the obvious fact that these goods generate sales for prison industries rather than for private sector firms. This, inherently, can lead to some displacement (crowding out) of private sector economic activity--in particular of private labor. This is one of the concerns about prison industry activities, and most states have restricted prison industries activities in some way to minimize this crowding out.

A second direct difference is the difference in income generated at the initial stage of the economic activity. Prison industries employ prisoners (at low salaries), production managers who may double as security personnel, and administrative staff to market and organize the overall prison industries effort. The biggest difference this makes from private sector production of these same goods and services is that the income generated will be different, (6) leading to a different level of down-stream (secondary) purchases. This difference in income generated needs to be adjusted for in the estimation of the effects of purchases by the direct employees.

There are no other significant differences with respect to the down-stream effects between prison industry production and private production. Prison industries, like private sector firms, must purchase inputs, leading to circular flow expansion effects. Thus, traditional methods of measuring down-stream purchase implications of economic activity (adjusted for the prior two differences) can be used for evaluating the impact of prison industry activity on the economy.

Prison industry production can be either completely new to the area or replace other activity that would have been there in its place, or some combination. If the first, combining the (actual) direct purchases, employment and income with the (model-estimated) secondary purchases employment, income and tax revenue generated gives an estimate of the total impact of the prison industries activity on the economy. An input-output model is ideally suited for constructing this estimate.

Input-output models are based upon a matrix measuring the business-to-business purchases which result from final purchases (defined as spending by the ultimate user of the good or service) in a given sector. One dimension of this matrix lists all categories of goods and services; the other dimension lists the production sectors from whom these businesses purchase their inputs (most industries in the model are at the 3-4 digit SIC code detail level). Most sectors appear in both lists as they both purchase from and sell to businesses. The estimation effort consists of taking given purchases and tracing (accumulating) the purchases (labor, goods and services) resulting from these final purchases.

In the case of prison industries, the purchases of intermediate goods and services should (theoretically) be the same as those for private sector production of this same output. Adjustments need to be made for the differing income generated at the initial production stage in prison industries. This will lead to different induced spending (that resulting from consumer spending of the earned income) in the prison industry situation. Comparing the actual income of prisoners and prison industry staff with the model-predicted income related to the sale of the goods the prison industries are producing provides the adjustment ratio for tailoring the private-sector-based input-output model to the prison industries context.

The second difference referenced above is adjusted for by determining the extent to which the prison industry output is new to the state, as opposed to being replacement of private sector business activity. Three alternative assumptions are presented. The first is the assumption of 'no crowding out'--that the prison industry production is all new to the state, crowding out NO private sector activity. This would lead to the largest expected impact, as all of the purchases are assumed to be new purchases.

The second, '100 percent crowding out' assumption is diametrically opposite. In this case ALL of the prison industry activity is assumed to be replacing private sector activity. The goods and services would have been produced by private firms, were the prison industries not there. In this case, the impact of prison industries from the first assumption must be reduced by the impact that would have occurred from the private sector production of these same goods and services.

The third, more realistic, assumption is that of 'in-state crowding' out. Some of the prison industry activity is replacement and some is new. The critical question here is how much would have been produced in the private sector, and how much is new activity. Given that this is an effort to estimate the impact on a given state, the relevant question is how much would have been produced in-state and how much would have been imported from another state or from abroad.

The input-output model provides an estimate of the percentage of goods that would constitute new production. The Regional Purchase Coefficient measures the percentage of goods and service in a given sector that are purchased from in-area vendors (in-state, in-county, etc.). Under the assumption that the state-wide importing percentage in a given sector would be the same in the absence of prison industry activity, we can use this in-state percentage multiplied times the estimate of the private sector impact as an estimate of the crowding out that is occurring. Netting this reduced estimate out of the no crowding out estimate provides the most realistic estimate of the prison industry impact.

The impacts of Ohio's OPI production are estimated using the IMPLAN input-output model for Ohio. (7) IMPLAN 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 accounting convention used by the Bureau of Economic Analysis. The model allows examination of financial transactions between businesses and between businesses and final consumers in a region. Industries purchase goods and services from other producers in order to produce output for final consumption. These input producers, in turn, purchase goods and services to produce their output. These indirect purchases (indirect effects) continue until leakages from the region (imports, wages, profits, etc.) stop the circular flow expansion. The indirect effects and the effects of increased household spending (induced effects) are measured by a set of multipliers for production, income and employment.

IMPLAN uses regional economic accounting to construct state and local level multipliers describing the short run, industry-specific, localized impacts of increased economic activity in a given sector. Regional Purchase Coefficients (RPCs) are available in the model to adjust for purchases made from out-of-area vendors. The results are industry-specific because IMPLAN measures the ripple effects of given output or employment spending changes by industry on all other industries (and the sector itself). This methodology assumes fixed prices, an unrealistic assumption in the long run, so the results should be treated as short run effects.

Ohio Prison Industries Effect on the Local Economy

Ohio Prison Industries generated $31.6 million revenue in FY 2004, and paid out $10 million in employee wages and $1.5 million in prisoner wages. Table 1 summarizes the sectoral breakdown of sales, employment, and income. Revenues were fairly narrowly disbursed with almost 80 percent of sales accounted for by the top seven categories of goods. Income and employment are more broadly dispersed as the same seven sectors only accounted for 40 percent of employee income, 60 percent of inmate income, 40 percent of employees, and 64 percent of inmate workers. The major factor in these differentials is the headquarters staff not being allocated to the various sectors, but the inter-product differences are also less.

Under the No Crowding Out assumption, the direct effects of OPI production are simply the Output, Income, and Employment of the prison industries. Estimating the indirect effects (purchases of goods and services as inputs and the accumulated secondary purchase effects) entails introducing the output of OPI into the input-output model. It provides estimates of the expected accumulated output, income, employment, and taxes generated by the production of these goods and services.

In order to estimate the induced effects (induced by purchases from the income generated by the production), the ratio of the actual direct (prisoner and employee) income generated to the predicted private sector income associated with this production was used to adjust the estimated induced effects. These induced effects are combined with the model-estimated indirect effects to provide total secondary impacts.

These no crowding out results are presented in Table 2 Part A. The $31.6 million of prison industry output generates an additional $29 million of secondary output, almost as much as the direct output. In addition to employing 2,031 prisoners, OPI generated 537 non-prisoner jobs and $22.5 million of income in Fiscal 2004, about two-thirds of the initial output value.

Under the 100 percent crowding out assumption, one must first estimate the impact of private sector production of this same output. These estimates are included in Table 2 Part B. The direct output is the same, as it is assumed in this scenario that the output in the absence of prison labor would have been the same. Employee income, employment, and secondary effects are all estimated using the input output model. Comparing Parts A and B shows that private sector production would have generated more jobs but less income than does prison industry production. This is likely due to the greater supervision and headquarters presence in prison industry at higher pay, and the replacement of lower paid production personnel by (considerably more) prisoners.

The last column of Part B shows the estimate of the net impact under the 100 percent crowding out assumption. As prison labor critics argue, prison labor does lead to a net loss of jobs under this assumption, but the loss in private sector jobs is quite small-less than one non-prisoner job lost per 100 prisoner jobs created. And there is a net increase in non-prisoner income. This is an unexpected outcome, highlighting the fact that jobs created by prison industry production would be different than those created by private sector production. Using the employment and income figures in the third columns in parts A and B, prison industry generates 3 percent fewer private sector jobs but raises average income of the non-prisoners employed by 15 percent.

Before moving on, it should be noted that there will be redistribution hidden in this netting out process. The direct jobs involved in private production of these goods would be gone but would be replaced by jobs in the input sectors and in the prisons. On average, the prison jobs are higher paying, and there are more secondary jobs (and slightly higher paying ones) generated by prison labor, so the net effect on labor is relatively small.

The impact of prison labor under the more realistic assumption of in-state crowding out is summarized in Part C of Table 2. The first three columns show the impact of the private sector production that would have occurred had the prison labor not been there. This is comparable to the first three columns of Part B. The difference is that for each sector, the direct output in that sector was multiplied by the regional purchase coefficient (RPC) to determine the actual in-state production that would have occurred in the absence of prison labor. This reduced figure is then introduced into the input-output model to determine the secondary output, income and employment losses the prison industry crowding out would cause. These losses are netted out of the Part A prison industry gross impact numbers to arrive at the numbers in the last column of Part C.

Under this more realistic assumption, the conclusion about prison industry's impact on Ohio is that it added almost $39 million to output, $15 million to private sector income, and 322 private sector jobs. All of these additions were in addition to employing 2,031 prisoners at an income of $1.5 million. Using the third column results from Parts A and C, the average salary of the jobs created by prison industry production were on average higher paid jobs than those crowded out--almost 17 percent higher. Therefore, although the Part C estimates indicate a positive impact on the state in all categories, prison industry does cause a change in the nature of the private sector employment. This issue needs to be looked at more closely before drawing an unambiguous positive conclusion about the impact of prison labor on the state's economy.

One implication of these results, especially the comparison of Parts B and C is that the state has done a good job in choosing the industries for prison labor. Over two-thirds of the output would have been imported to the state, were OPI not producing these goods. Thus, the negative crowding effects are minimal, leaving the positive employment and spending effects to assist in financing the prison system.

One last issue addressed in Table 2 is the revenue implications of prison industry production. These are summarized at the bottom for the three alternative assumptions. In 2004, OPI remitted $2.5 million in net revenue to the state from its operations. This helps to pay for the cost of the prison system. It is net of variable costs, as it does not include any payment for the capital--primarily land and buildings--which are used by the prison industry establishments.

However, this is does not include the income tax, sales tax, and property tax revenues generated by the economic activity. These are summarized in the first row of the bottom panel. These figures are based upon the income generated by the prison industry activities. The net result is an estimate of from $2.8 million to $4.9 million added to the state and local government revenues because of the existence of prison industries in Ohio, with a more realistic figure being $4.1 million. In the context of escalating cost of prisons, this is a welcome offset of these costs--a 13 percent return on sales for the state.

Ohio Prison Industry Effect per Prisoner Employed

In order to get a better indication of the true cost benefit of prison employment, the summary information from Table 2 has been restated in Table 3 as values per prisoner employed. Assuming all prison industry production is new, $30,000 revenue is generated per prisoner employed creating $11,000 of private income and $734 in prisoner income. The net private sector employment impact is to increase private employment by one private sector job per four prisoners employed, assuming no crowding out. All of this is in the context of increasing funds to the state by $2,434 per prisoner, which are available to help defray the cost of the incarceration itself. This is a net cash flow to the government of approximately 10 percent of the cost of incarcerating this prisoner.

If the output would have been produced in the private sector in the absence of OPI, the net effect of OPI is a loss of $452 in revenue and $1,199 additional private sector income per prisoner employed, and a negligible loss of jobs. The net revenue effect for the state is $1,377 per prisoner.

Assuming that OPI only crowds out in-state activity, the net effect of OPI is $19,057 additional revenue, $7,303 in additional private sector income and $734 in prisoner income per prisoner employed. One additional private sector job is created for every six prisoners employed. In addition, OPI generates a total positive cash flow for the state of $2,028 per prisoner employed.

Conclusions

The prison labor debate is usually anecdotal due to the absence of quantitative evidence. This paper adds substance to the debate by providing state-level economic estimates of the effect of prison labor. Using input output analysis adjusted to account for the unique nature of prison labor, the conclusion is that, in Ohio, the net impact of prison labor is positive.

Ohio Prison Industries production created 537 private jobs and $22.6 million in private labor income in Fiscal 2004. If this is all new output for the state, OPI unambiguously has a positive economic value. Assuming the pessimistic assumption that none of this production is new to the state requires one to net out the equivalent production as if it would have been produced in the private sector. In this case, prison production crowded out 15 jobs but actually increased labor income by $2.4 million. This is one private job lost for every 135 prison jobs created, hardly a cause for concern. In addition, the jobs created have a higher average salary than the jobs crowded out. This is because prison labor replaces production but entails more supervisory personnel.

These two estimates are the 'most optimistic' and 'most pessimistic' views of prison labor advocated by the proponents and opponents of prison labor, respectively. They provide bounds for the crowding out debate. Under a more realistic assumption that prison production crowds out only in-state production, prison labor created 322 jobs and $14.8 million in income in Ohio in FY 2004. This is counter to the argument of opponents of prison labor.

The debate over prison labor will continue because some jobs are lost while others are gained. In addition, prison industry employment may affect recidivism, the costs for incarceration of active prisoners, and the safety of prisons. The American Federation of State, County and Municipal Employees (AFSCME) and the American Federation of Labor and Congress of Industrial Organizations (AFL-CIO) are supportive of prison labor with the caveat that 'prison labor never should be used to compete with free labor' [Korpi, 1998]. The policy issue remains that of balancing social benefits with the private costs resulting from potential crowding out. To the extent that states carefully select industries for prison production which do not directly compete with the local economy, the crowding out will be minimal, the lost jobs may be recouped by the secondary effects, and OPI will provide a positive net economic impact.

Footnotes

(1) Prisoner labor is to be distinguished from prisoners performing prison institutional jobs, such as laundry or kitchen work. There is no discourse over prisoners working in these internal 'housekeeping' jobs.

(2) See, for example, Flanagan, et al.[1988] and Schwalb [1994].

(3) Furniture, textiles and apparel, electronic components, and metal products are common examples.

(4) Federal prisoners have a five step industrial pay scale from $0.23 to $1.15 per hour [Marshall, 1999, note 5]. Flanagan et al. [1988] report that the average pay of working inmates in New York during 1981-1982 averaged $0.26 per hour with a range from $.09 to $0.75 per hour. Earnings averaged $12.00 per week. In 2000, inmates in Maryland were paid between $1.10 to 2.60 per day depending upon skill level plus incentive pay for attendance and the number of quality products produced. The average wage per day for State Use Industries (SUI) employees was $4.46 per day [Maryland Division of Corrections, 2001].

(5) Mandatory sourcing requires the federal government to purchase certain products if they can meet the needs of the contract at a competitive rate from Federal Prison Industries. Supporters of mandatory sourcing argue that it makes up for the economic disadvantage faced by firms from the low quality of prison labor and of security costs.

(6) This may be lower due to significantly lower wages for prisoners or higher due to increased managerial supervision necessary in a prison industry context.

(7) IMPLAN is a menu-driven computerized input-output model that was originally developed in 1979 by the USDA Forest Service in cooperation with the Federal Emergency Management Agency and the USDA Bureau of Land Management. In 1993, Minnesota IMPLAN Group, Inc. was formed to privatize the development of IMPLAN data and software.

References

Flanagan, T. J.; Thornberry, T. P.; Maquire, K.; McGarrell, E. "The Effect of Prison Industry Employment on Offender Behavior: Final Report of the Prison Industry Research Project," Albany, New York: The Hindelang Criminal Justice Research Center, State University of New York at Albany, January 29, 1988.

Grieser, R. C. "Do Correctional Industries Adversely Impact the Private Sector," Correctional Industries, 1989, pp. 18-24.

Harrison, P. M. "Prisoners in 2002," available at http://www.ojp.usdoj.gov/bjs/prisons.htm, 2003.

IMPLAN, http://www.economicanalysis.com/about.html.

Korpi, K. "Re: Prison Industry Enhancement Certification Guideline [OJP(BJA)-1150]," Letter from the Director of Department of Research and Collective Bargaining Services, AFSCME, to Bureau of Justice Assistance, Office of Justice Programs, September 8, 1998.

Marshall, R. "Preliminary Opinion on Inmate Labor Participation," National Symposium on the Economics of Inmate Labor Force Participation, George Washington University, Washington, District of Columbia, May 21, 1999.

Maryland Division of Corrections. State Use Industries Annual Report FY 2001. September 1, 2001.

Reynolds, M. "Factories Behind Bars," NCPA Policy Report no. 206, 1996.

Schwalb, S. "The State of Correction," Proceedings of the American Correctional Association Conference, American Correctional Association, 1994.

Scott, C. E.; Derrick, F. W. "Prison Labor Effects on the Unskilled Labor Market," American Economist, 48 (2), Fall 2004, pp. 74-81.

__. "Prison Labor and Education Program Effects on Recidivism." Unpublished Working paper, 2005.

Yae, M. "An Analysis of Correctional Industries Programs," Corrections Today, 61 (6), 1999, pp. 94-97.

CHARLES E. SCOTT* AND FREDERICK W. DERRICK*

* Loyola College in Maryland--U.S.A. Presented at the 59th International Atlantic Economic Conference, London, March 9-13, 2005.

TABLE 1 Ohio Prison Industries Sales and Employment, FY 2004

                 Gross                   Inmate
Good or Service  Output      Employee    Income     Number of  Number of
Category         ($)         Income ($)  ($)        Employees  Inmates

Furniture         8,119,518   1,147,486    292,994   24          422
  assembly
Signs and         4,595,177     385,741    115,523    8          198
  painting
Metal working     3,944,677     786,182    126,167   12          184
Beverage          2,773,716     300,016     16,869    7           22
  processing
Sewing and        2,365,984     465,341    166,886    9          256
  garment
Janitorial        1,544,371     283,691     32,894    2           55
Auto repair       1,397,280     676,642    126,306   11          162
Distribution      1,039,869     345,638      9,712   14           14
Shoes               885,645     208,440     21,045    5           45
Printing            869,480     271,137     36,793    5           73
Construction        832,952     590,326    121,476    8           53
Mattress            722,472      53,032     25,376    2           41
Asbestos            620,902     470,666     89,140    4           31
  removal
Deflashing          552,122     195,573     68,981    4          104
Medical             500,641     195,864     38,087    1           49
  services
Computer            432,264     171,641    100,605    4          156
  graphics and
  entry
Box production      117,706     161,630     23,003    3           38
Computer            100,078      43,211     41,680    3           71
  recycle and
  repair
Packaging            88,169      56,287     26,091    1           33
Storage              84,044      51,436      6,822    2           17
Mulch                52,292      56,623      4,071    1            7
Bagging                   0      23,495          0    2            0
Total from       31,639,359   6,940,098  1,490,521  132        2,031
  operations
Admin., fiscal                3,094,693              54
  and sales
Total            31,639,359  10,034,791  1,490,521  186        2,031

TABLE 2 Impact of Prison Ohio Industries Employment, 2004

Part A: Economic Impact, Ohio Prison Industries (No Crowding Out)

                   Direct       Secondary    Total
Impact category    impact       impact       impact

  Gross output     $31,639,359  $29,137,633  $60,776,992
  Employee income  $10,034,791  $12,513,431  $22,548,222
  Prisoner income   $1,490,521           $0   $1,490,521
  Employment               186          351          537
  Prisoner               2,031            0        2,031
    employment
  State personal      $463,738     $488,024     $951,762
    income tax
  Property tax        $234,313     $312,836     $547,149
  Sales and           $354,416     $538,078     $892,494
    excise tax
  Net cash flow     $2,552,281
    of OPI

Part B: Economic Impact, Private Production

                                                          Net impact
                   Direct       Secondary    Total        100%
  Impact category  impact       impact       impact       crowding

  Gross output     $31,639,359  $30,056,190  $61,695,549    -$918,557
  Employee income   $9,069,062  $11,043,351  $20,112,412   $2,435,810
  Prisoner income           $0           $0           $0   $1,490,521
  Employment               244          308          552          -15
  Prisoner                   0            0            0        2,031
    employment
  State personal      $366,964     $430,720     $797,685     $154,078
    income tax
  Property tax        $220,651     $276,078     $496,730      $50,420
  Sales and           $378,072     $474,837     $852,909      $39,584
    excise tax

Part C: Economic Impact, In-state Private

                                                          Net impact
                   Direct       Secondary    Total        in-state
  Impact category  impact       impact       impact       crowding

  Gross output     $10,939,100  $11,133,306  $22,072,407  $38,704,585
  Employee income   $3,582,674   $4,134,046   $7,716,720  $14,831,502
  Prisoner income           $0           $0           $0   $1,490,521
  Employment                98          117          215          322
  Prisoner                   0            0            0        2,031
    employment
  State personal      $141,855     $161,245     $303,100     $648,662
    income tax
  Property tax         $88,731     $103,348     $192,079     $355,070
  Sales and           $152,062     $177,748     $329,811     $562,683
    excise tax

                                100%         In-state
                   No crowding  crowding     crowding

Total tax effect    $2,391,405     $244,082   $1,566,415
Net cash flow       $2,552,281   $2,552,281   $2,552,281
Revenue effect      $4,943,686   $2,796,363   $4,118,696
  on Ohio Gov.

TABLE 3 Impact of OPI Per Prisoner Employed

                                                        In-state
Impact Category             No Crowding  100% Crowding  Crowding

Gross output                $29,925       -$452         $19,057
Employee income             $11,102      $1,199          $7,303
Prisoner income                $734        $734            $734
Employment                        0.264      -0.007           0.159
Prisoner employment               1           1               1
State personal income tax      $469         $76            $319
Property tax                   $269         $25            $175
Sales and excise tax           $439         $19            $277
Total tax effect             $1,177        $120            $771
Net cash flow of OPI         $1,257      $1,257          $1,257
Revenue effect on Ohio Gov   $2,434      $1,377          $2,028