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The impacts of education spending and finance reform on manufacturing property shadow values: a cost function approach

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Abstract

The effects of court-ordered education finance reform on property values and residential choice have received increasing attention in recent years (Fischel 2001). However, little attention has been focused on the effects of education finance reform on manufacturing sector property values within an optimizing framework. This is pursued here by modeling education expenditures and education finance reform as “free” variables to manufacturing firms in a cost function model together with input demand equations. This framework is applied to panel data on manufacturers’ capital (building and structures) stocks for the 48 continental US for 1982–1996 to estimate implicit (shadow) values to the manufacturing sector of education spending and school finance reform. On average, school finance reform lowers the implicit value of manufacturing firms’ stock of buildings and structures capital, while greater education spending lowers manufacturing variable costs.

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Notes

  1. Many states have attempted to accomplish this redistribution through a system of foundation grants (Evans et al. 1997; Hoxby 2001). Foundation grants essentially attempt to ensure that each district within a state receives at least some minimum amount of education funding. This is accomplished by the states setting a minimum tax rate, and then the state makes up the difference between the minimum education funding and the amount that a district can pay with this tax rate. According to Hoxby (2001), the total amount that a state spends through foundation grants is often equal to the total taxes paid into the system by all districts, implying that the difference between the minimum funding level per district and the amount a particular district pays into the system based on the foundation tax rate could be negative.

  2. According to Evans et al. (1997), between 1971 and 1996, AL, AZ, AR, CA, CT, KS, KY, MA, MT, NJ, TN, TX, WA, WV, WI, and WY have each experienced court ordered reform. These reforms differed across states in their timing and implementation. Since some states did not experience court-ordered education finance reform during this period, below we represent court-ordered school finance reform with a dummy variable.

  3. Dee (2000) examines how school finance reform affects property values, although not within the context of an optimizing framework.

  4. The use of state level education spending data here follows the approach of Blankenau and Skidmore (2004). Ideally, use of district level data in the present study would have been preferred, which would have allowed for heterogeneity within states. Obtaining the corresponding manufacturing production data at this level of disaggregation would not have been feasible, however, and thus the focus on state level data is more appropriate for a study of the manufacturing sector.

  5. The inclusion of public education expenditures as an external factor in the cost function is motivated by the Morrison and Siegel (1997) results, which show that external knowledge factors augment growth. A key difference between the present analysis and Morrison and Siegel (1997) is that they constructed a human capital stock, whereas we are using education expenditures.

  6. Including both court ordered and legislative reform in this dummy had negligible impacts on the sign and significance of the elasticity estimates.

  7. Over 50% of the funds for financing local education are provided by state governments (Hyman 2002). In addition to this reason, the analysis here is conducted at the state level opposed to the school district level, due to the availability of the production data at this level of aggregation, as mentioned above.

  8. See Cohen and Paul (2004) for a description of the output, materials, production labor, and non-production labor price, and quantity data. The rest of the variables are explained in the data appendix of this paper.

  9. Higher E might be expected to affect manufacturing costs through a more highly educated workforce. School finance reform (D) might impact firms through changes in property taxes. An anonymous referee pointed out that this analysis is conducted at the state level while in many states local property taxes typically are used to finance fiscal equalization, so caution should be used in interpreting the D elasticities.

  10. An anonymous referee suggested that it may not be possible to identify the separate impacts of D and E by including the interaction term δ ED . To address this comment, the parameter δ ED was set equal to 0, and the system was re-estimated. The results were that the estimated elasticities all had the same signs and significance, except for ε D,t , which is marginally insignificant when allowing δ ED to be non-zero but subsequently becomes significant. Thus, due to the similarity of results in both cases, the elasticities presented here allow for δ ED to be nonzero.

  11. As noted by Cohen and Paul (2004), this is consistent with a linear demand curve.

  12. The state fixed effect (F i ) parameter estimates are omitted from Table 2 for ease of presentation.

  13. Following the convention of Morrison (1985), we define the shadow value as a positive number for ease of interpretation.

  14. Since D is a dummy variable, ɛ S,D is defined as (∂SHVS/∂D)· (1/SHVS)

  15. Since D is a dummy variable, εLP,D is defined as ∂L P/∂D· [1/L P].

  16. A simpler specification was tried where E was omitted, and the signs and significance for most of the elasticities of interest were unchanged, except ε S,K became insignificant.

  17. These estimates are the mean fitted elasticity estimates for individual states over the sample period, and the regional elasticities are means of the state means. Since some regions have differing numbers of states that experience reform, as well as differing dates when reform is implemented in these states, it is not completely surprising that there is some regional variation.

  18. The regional breakdowns are: Pacific (Washington, Oregon, California), Mountain (Arizona, Colorado, Idaho, Montana, New Mexico, Nevada, Utah, Wyoming), West N. Central (Minnesota, Iowa, Missouri, North Dakota, South Dakota, Nebraska, Kansas), East N. Central (Illinois, Indiana, Michigan, Ohio, Wisconsin), New England (Connecticut, Massachusetts, Maine, New Hampshire, Rhode Island, Vermont), Mid Atlantic (New York, New Jersey, Pennsylvania), South Atlantic (Delaware, Maryland, Virginia, West Virginia, North Carolina, South Carolina, Georgia, Florida), East S. Central (Kentucky, Tennessee, Alabama, Mississippi), and West S. Central (Arkansas, Louisiana, Oklahoma, Texas). See below for further discussion of the regional elasticities.

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Authors and Affiliations

  1. Department of Economics Barney School of Business, University of Hartford, 200 Bloomfield Ave., West Hartford, CT, 06117, USA

    Jeffrey P. Cohen

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Correspondence to Jeffrey P. Cohen.

Data Appendix

Data Appendix

1.1 Equipment and structures capital stocks

The perpetual inventory method was applied to ASM data on annual state level capital expenditures separately for the equipment category and the structures category, to generate, respectively, capital stocks for machinery and equipment (K) and buildings and structures (S). The initial buildings and structures capital stock and machinery and equipment capital stock values were taken from the “Beginning of Year” capital stock data in 1982 Economic Census of Manufactures, Geographic Area Series. Depreciation rates and investment deflators for each type of capital are from the Bureau of Labor Statistics, Office of Productivity, and Technology.

1.2 Education expenditures

State level spending data on education from the annual Census Bureau publication Government Finances (1982 through 1996) were deflated by state level GSP deflators to obtain education expenditures (E).

1.3 School finance reform dummy (D)

Takes the value of 1 for observations in which there had been court ordered reform in the concurrent or earlier years in the particular state, and 0 otherwise. A summary of states having experienced court ordered reform, and the date reform in the state was first encountered, includes: AL, 1993; AR, 1983; AZ, 1994; CA, 1971; CT, 1977; KS, 1976; KY, 1989; MA, 1993; MT, 1989; NJ, 1973; TN, 1993; TX, 1989; WA, 1978; WV, 1979; WI, 1976; WY, 1980. School finance reform data are from Table 1 in Evans et al. (1997).

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Cohen, J.P. The impacts of education spending and finance reform on manufacturing property shadow values: a cost function approach. J Prod Anal 26, 181–190 (2006). https://doi.org/10.1007/s11123-006-0012-8

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