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The cost in lost productivity of new regulation in the U.S. chemical industry
Menahem Prywes, OECD prod;u&ivity growth in U.S. manufacturing in the 1970s might have been hijgher had the capital invest;ed to meet regulations imposed since the late 1960s been inst~d invested in dine&y productive calpital. Policy debate in the United States ofltencex&m on whether the costs of regulation have been too high, and hence, on whether to relax or maintain such new regulation. ‘This study seeks to inform that debate by a&hating, for one industry, the costs af investing capital to meet new regulations in terms of lost labor productivity. The chemical industry is a g& subject for a cs~e study of capital and manufacturing productivity in the mid ;97Os. It is a very heavy user of eneqy as both a fuel and a raw mate&l. The chemical industry is vulnerable, as are many manufacturing industries, to a substantial increase in the cost of operating its capital when energy’s price rises. The chemical industry is also heavily burdened by regulation. Investments to meet environmental regulations rose as high as 14.1 percent of current dollar gross investment in 1972. Investments to meet occupational safety and health requlations comprised an additional 2.6 percent of investment in that year. This heavy capital expense of meeting regulations is in itself a reason ibr focusing on the chemical industry. Productivity in the chemical industry might be particularly hurt because of the large proportion of gross investment devoted to meeting new regulations. Studies such as Denison’s (1 Y79) estimate the cost in lost r. oductivity of new regulations for the entire business sector. However, in evaluating the success of new reguWons, policy makers rnKf be concerned that a few industries might suffer an unusual and severe setback of their productivity.
I fhank Professor Adams for his direction of the Universiry of Pennsylvania’s Industrial Policy Pmject, of which this research is a part. I thank Professm Lawrzrace Klein and Dat id Crae;ford, and Drs. Corn&s Los and A. Steven Enghmder , for their comments and criticism, and the Labor ~ep~~~~~t for its SWsport of this research. T thank Mr. R&m-t Stodbrd for editor-ml assistance. I aicme an. respm~sible kx all mxxs. Ati ~pimms expressed here are my own and not those of bhi: OECD.
412
Menahem Prywes
pOfiqf makers may want to reverse the decline & productivity brought &@u% by new regulation by adjusting the tax system to encouralge capital investment for all h~y&ss. Cuts in the corporate income tax rate, acc&erated depreciation, or an increased investment tax credit could all tend to increase investment for the entire bu&ess sector. Such measures could increase investment in the chemical industry boti by lowering & cost of q$tal and-by ~ncrcas@g“he qu@&~.of-&em-i&s demanded. Alternatively, poiticy makers could off&rs-a r&f tim the dxpenge of capital equipment used to meet new regulation by ~&33hBtIn~ tI!@depie&tion or increasing the investmem tax credit on such .eqtipment. I?olicy makers may want to consider allowing only the chemical industry to &n&t jFfomsome of these tax cuts if the efFects of new regulations on chemical industry productivity are unusustly severe. This sttidy could also be useful to policy m&ers because i& methodology probably disentangles much of the effect of the energy crisis on labor productivity f?om the effect of new re@ation. This is important because a setback of productivrty caused by an energy crisis calls for an entirely different policy csponse than a setback caused by new regulations. A setback imposed by an energy crisis calls for research into energy cost reducing production processes and more general efl’orts to ease pressures in the world energy market, rather than investment promoting tax cuts. Th!s article is divided into five sections: The fast section explains the approach taken to measuring the cost in lost productivity of capital investments used to meet nepti reclation; the followiq section briefly describes the data set; the third section develops a measure oF the capital stock used to meet new regulation; the fourth section briefly describes the production function; and the final section presenis the results of estim&on.
CAPITAL INVESTED TO MEET LABOR PRODUCTIVITY
NEW
REGULtATTON
AND
T5.s st:;dy’s estimates of the cost in lost labor productivity of new regulation dept nd on the methou of m&asuring the connection between capital invested to meef new regulation and labor productivity. This connection exists because extra
output co&i be pnxiuced if the funds invested in capital to meet new regulation wer; invested in Erectly productive capital, holding a.0 other inputs constant. IGo:-eovcr, extra output, for fixed labor, mew higher labor produ Aivity. Ths sI&y estira;ttes the cost in terms of labor productiv&y of the capital invested lo me& new regulation as the difference between actual labor productivity and wh;!\t labor productivity would be if the capital invested to meet new replation were irrxlested in directly productive capital. nxis study’ focx~as on the two main types of new rec*l* ion imposed since t t e letm i 96&: snvironmentaf and occupational health and safety regulation. These new-regulati~xxs follow from legislation such as the Water Quality Act of 1965, the V4ater Po,ffu+n Ace Amendments of 1972, the Air Quality Act of 1967, the Ckan kr ~~~~~~~n~~ of f972, and the W~~i~s-Stc~ger ~ccu~~~~o~a~ Sakty rtrr;dHealth act of 1970.
THE COST OF NEW REGULATION
413
Firms would most likely direct some capital expenditure towards environmet=tal md occupatiorkal health and safc:ty even without regulation. Therefore, 02s study focuses on new regulation introduced after 1966, for it is regulatory capital created after the introduction of new regulation, not the level of regulatory capital that existed all along, that may restr$n FroducGvity growth. T&s aiiplr&%h para2Iek the approach in the classic study by De&son (I 978, 19%) 3n mmy-ways, It EMluws the same opportunity cost app&oach in measuring the cost of n&w regulation on output and pmductivity. It focuses, like Den&on, on incrementi! costs of environmental and occupational health and safety regulation int~o~aced a& 1966. However, this study differs significantly f?om Denison’s in its method of meastiug what output firms would produce if funds devoted to capital to meet new regulation (regulatory capital) were ingested in directly productive capital. De&on uses an accounting method that values output at factor cost. Tlzrefore, the lost output is the current period depreciation of regulatory capit plus what the return on the tinds invested in regulatory capital would be ifthey were invested in directly productive capital. We cticulates this return as the product of the stock of regulatory capital times the ratio of earnings net of depreciation to the capital stock in the business sector. An estimated production function makes an alternative to Denison’s approach possible. The production function relates the value of the capital stock with and without new regulation to values of ottput, :Forfixed l&or and other inputs. After compu.ting these alternative values of output, alternative values of labor productiv3y are computed by dividing through by fixed labor. In order to carry out this calculation it is necessary to develop a measure of the regulatory capital stock (Krrg). Measured capital in the CSP dzta set (K, ) consists of both directly productive (K) and regulatory capital. With an estimate of&,, it is possible to estimate K by subtracting Krzg from Km (K = K’ - J&J. Thus, the three main elements necessw to estimate the ef%ct of capital requirements to meet new regulations are the data, K&, and the production function. These are developed in the next three Eections. These sections may be skipped by the reader who is uninterested in methodology.
THE DATA All the empirical! analysis relies on the very disaggregated Census-SFJ-Penn (CSP) data set, which was desiped for analyzing p&uction. A chemical industry production function is estimated from the CSP data set. This is the most important analytical tool in all the analyses of productivity growth. The CSP data set covers all 450 four-digit standard industrial classificatiorl (SIC) industries within U.S. manufacturing. It consists of obsc:wations on rra.i gross output (.X), the capital stock (K), labor (L), energy (E). Ger intermediate materials CM), their price indices (which arc, respectively, P, PK. _PL,_PEqand PIFf). and other variables. _XIs real shi~~mtnts adjusted for changes in inventories 50 thar it measures production. K is the real capital stock, constructed using the perpetu4 inventory method. E is a measure of energy used for power and heat, &F I,; EII
414
Menahem Pqjwes
intermediate r‘raterials, including t)oth raw materials and parts. M includes petroleum used as raw material rather than as a f&l. The CSY data set includes observatioas on all 28 four-digit SIC industries within the U.S. chemical industry for the years 197 1-75, a period spanning the initial Gnergy crisis andthe I recession that folkiwed.’
rmmmgy
TBie proldution fk3ntii0nis estimate4lfraana pooled time series ofcr0ss sections. Therefm, 8~1bbseferationon UIQWand the inputsfar each four-digitSIC industry in each year is treated as ditl observation on %e overall industry pr&utiion processTfais prooeduuueassumes that all fourdigit SK industries use the sampt prod&on process. Althoughlass than ideal, this assumes far less than da studies that are con&ted ti the aggregatebusiness sec&r or manufaaring level. Such studies inrplicitlyassume that ;allindustries within the relevant aggregateuse the same ~roductbn pr0cess.
ESTltMATTON OF THE REGULA.TORY CAPITAL STOCK An estim~~ c2ftbep&t?&devoted to meetingnew regulation(K&) is developed by the perpetualinvsntorymethod. Since only capital invested to meet regulation intr&uced &.er II966 is considered,K& is defined as zero in 1966. Afierwti, K, is computed by adding 1972 dolk capital expendbres for environmental and occupational health and safety purposes and depreciating the previous ye&s value of K& Ktg is the sum o? pollution abatement capital @PA) and occupational safety F?nd health capital {KOSH), which are estimated separately. This calculation requires data on current dollar cap:ital expenditures on pollution abatement (CPA) and occupational safety and health (COSH), their deflators, and the depreciation rates of these sorts of capital goods. The Census publishes four-digit SIC data on CPA, but only from 1973 on. Estimation of KFA requires CPA data for 1967-76. McGraw-Hill data on CPA for the aggregate chemical industry form a basis for extending the Census data buck to 7967. The Census &a on CPA consist of separate series on air, water, and solid ~raste pollution abatement. The M&raw-Hill data consist of series on only air and water pollution abatement. An estimate of aggregate chemical industry CPA on solid waste fur 1967-71 is calculated by multiplying the sum of the McGrawHill air and water CPA series in those years by the (aggregate chemical industry) ratio of x&d waste to air and water C?A in the 1974 Census data. f-fu~ver, the .t967-7 1 air, water, ar:d solid waste c*P/‘~data still exist only at tie zggrre;gate chemical iudustry 12~1. Estimates of the fourdigit SIC industry CPA series are computed by muftiplying the 1967-7 f aggregate:; by the 1974 distibution of CPA over four-digit SIC industrie ‘n the Ceusus data. (1974 instead of’ 19?3 dsta are wed bexxmse the WV i n.+ was pm.)
415
THE COST OF NEW REGULATION
The CPA data are put on a 197 2 dollar basis using Bureau of Economic Analysis (EEA) price deflators. Air, water, and solid waste CPA are divided by separate deflators and then summed to produce 1S ‘712dollar CPA (CPA72). The MA nnofficid& estimated the mean rife of_n&luticmabatement capital equipment at 25 years. The depreciation rate is then J 125. This completes the development of data necessary to calculate KPA by the perpetual inventory method. KPA is c alculated %om.the Followingequation with a base of zero in 1966.
KPA*,i = KPA,4
1425)
f- lr,PA72+
which can be: rewritten as
KPAbi= vk_,
CPA72,,0
96,
;
l
96k,
where t = 1967 , . . . ,1976, i is an index of four-digit SIC industries, and k = 1976-v. KOSH measures the extra capital needed to meet new occupational safety and health regulation. Therefore, KOSH begins in 197 1 when the major relevant new legislation-the Williams-Steiger Act-became ef Fe&-4,;. McGraw-Hill publishes aggregate chemical indu,jl:rydata on COSH from 1972 on. The 1971 COSH is extrapolated by assuming that the 197 1 to 1972 growth rate qf COSH was the came as the 1972 to 1973 growth rate. No four-digit SIC data on COSH are available, :jo the aggregate McGraw-Hill data are disaggregated to the four-digit SIC leve: by multiplying by the 1973 distribution of reti output over four-digit SIC industries. Unfortunately, this procedure eliminates any statistically meaningful variation of KOSH in the fourdigit SIC MOSS section part of the pooled cross !,ection-time series oata set. However, since KOSH is small compared to KPA, this probably does not much affect the meaningfulness of cross-section variatior in J&. There are no deflators available for occupatioxtl safety and healtir capital goods, SL COSH is put on 1972 dollar terms (to fornil COSH72) by dividing by the BEA’s investment price deflator for manufacturing. Following Denison (19791, a 1/lO depreciation rate is used. This ccmpletes the development of data necessaty to calculate KOSH by the perpetual inventory method. KOSH is calculated from the following equation with a base of zero in 1970: KO SH,,i = KOSH,-,
(1 -
l/10)
t- COSH72,Vi,
I
KOSH,,j
=
I) =1971
COSH7.~!,,~0.9”,
J&s is then the sum of KOSH and KPA. The fm;iRestimates of_..re, com:xise a _I d the 23 fDuI’fiislltime series of cross sections. An estimate ef&&,, exi c*9 fm each
Menahem Prywes
4%
‘The particxlar fimctionai form of the gem3ral production knatior~ F &~MBI here is *the nested co&ant ekticity of substitution (CEL3) pro&&on fun&ion. The nested CES furxtion emphasizes the role of enem in restridirtg capital use. Xt at= c;f&xxxvefy models the relationAip between cuts in energy use and the prorhxtivity of i&xx and the other fa42tors. Therefore, the ne8ted-aS fkmH&n k appropriate for modeling *he 1971-76 data period when energy &backs may havl: re&kted capital use anti produ;ity. An&her a&&age of the nested CES fun&ion is that it is rel&vely simpie to e&&e. Furthermore, its parameters cani be used to con&ruct usefix! estimates of the &sticities of sub&.itution within the chemical indum,. The nested CES production Function wm developed by Satxl ( 1967) foUowing clo!seiy related work Qn consumption by Brown and Heien (1972). The par5cuk.r form of the nested CES &irnated here origIate;S with Sheinen ( 1988). The mathematkaf form of the nested CES function is2
a.-
13 the intennedia+~ A input prudwed by K znd E working together. XKEL is the fi;E Sx&easflediateinput ors-Wed by A’& and L:,. The ps are the “substl ution”
les in this
pmuctiun function
cim
st of pded
cross section-+ime series
417
T3!IE COST OF MEW RHXJLATION
parameters, which determine the engineering (gross) elasticities of substitution, ti-& -&?@&& &B&&$&s &k&Mk?s Gf &X&&!On? The 08 are the 43btfs;itiss of
s~~~~ betimen ix&Us io 101~XSI&&S level CES leioIlctionholding the iIWW&M tipat8 ~mdWed by that lm31 CES Gm4Sioncom%t,4 The 8s 3-r:the shwe c*lr6elREici&s. that determine the relative weights of the factors within a ~w&x& level CES fun&m. ,D(r) is i;tt&l f&tur produfivity change term. 11.is I-Iicks neutral for XKE ancl M. ti productivity change time trend term has ti e f mm
The subscripted& are dummy variables for the years 1972 through 1976. They are defined so that their coefficients, the subscripted dt;, have the interpretation of percentage annual changes in the total factor prodmti vity.5
El!WIRICAL RESULTS AND CONCLUSIWW To recapitulate, this experiment compares labor prc ductivity at the fitted V&K. of output using the actual inputs iqK, L, E, M)IL
14)
to labor productivity at the fitted value of output using what capital would be if there had been no new regulation after 1966 and the other actual inpubs:
Table 1 shows the results of this experiment. 3T’he economic elasticities of substitution depend on ihe eng’necring elasticities and 1nput shares. The Allen elasticities of substitution (AES) show the interaction of engineerring elasticities ,?mdcost shares.
PjXj
AESKE = TV+ pK,Kj
PjXj
~
-k pE,Bj
WO--~l~ +
PKJxj
+ ‘EjEj
+ pI.jLj
0+-Q!
Metzahem Prywes
THE COST OF NEW ,REGUEAT% ON
4h9
Tabde 1 lists the percentage point dirference between expressions (4) and (5) for each of the four-digit SIC industries within the chemical industry, and for the aggrq5ettct: chemical industry, for each of the years 1971-76. As expected, there is a loss of labo r prod-@iv& because labor has less capital to work with when there is new regulation. The 1~~s of productivity tends to grow from 197 1 to 1976 because the capital used to meet new regulation increases over time as fiis make further investments in regulatory capital. However, the loss in labor productivity can fall over time if E or M-which increase labor’s productivity-rise enough. The estimated loss of labor productivity caused by capital invested to me& new regulation for the overall chemical industry rose from 1.O percent in 1’971 to 2.3 percent in 1976. This is not a shockiqly kge loss, especially for such a heavily environmentally regulated industry. Yet, it is large enough for Folicy makers to take notice. Thr:y can combat this :shortf’all in labor productivity by relaxing regulaticln or with traditional policies that increase the quantity of capital.
REFERENCES Brown, Murray, a;?d Heien, Dale (1972) The S-Branch Utility Tree: A Generalization of the Linear Expenditure System. Econometica 40:737-747. Census Bureau ( 1974-77) Pollution Abatement Costs and Expenditures. Current Industrial Reports. Washington, D.C.: U.S. Crovernment Printing Office. De&son, Edward (1979). Accounting for Slower Economic Growth: The United States in the 197&b, Washington, D.C *: The Brookings Institution. (1978). E*ffects of Selected Changes in the Institutional and H:uman Envircnment -Upon Output Per Unit of Input. Ahwey of Current Business !E1:21-44 (January). McGraw-Hill ( 1975) Department of Economics ( 1975) Historical Polizltion Control ExpePrditures and Related Data. New York: McGraw-Hill. hdimeo. (I 972-77) Annual McGraw-Hill survey of Investment in Employee SqfL’ty and ~HeaZth . New York: McGraw-Hill.
Prywes, Menahem (1981) Three Essays on the Econometrics of Production, Productivity. and Capacity LKlization. Ph.D. di:?sertation, University of Pennsylvania. (1983). A Nested CES Approach to Capital-Energy Substitution. Federal Rescwe Bank of New York Discussion Paper No. 8313: New York, October. Rutledge, Gary, and O’Connor, Betsy I<1981) Plant ynd Equipment Expenditures ‘3~ Business for F~rllution Abatement, II973.-8C and Planned 198 1. Surly ojf Cur~nr Business 5i:19 -25 (June).
I fhank Professor Adams for his direction of the Universiry of Pennsylvania’s Industrial Policy Pmject, of which this research is a part. I thank Professm Lawrzrace Klein and Dat id Crae;ford, and Drs. Corn&s Los and A. Steven Enghmder , for their comments and criticism, and the Labor ~ep~~~~~t for its SWsport of this research. T thank Mr. R&m-t Stodbrd for editor-ml assistance. I aicme an. respm~sible kx all mxxs. Ati ~pimms expressed here are my own and not those of bhi: OECD.
412
Menahem Prywes
pOfiqf makers may want to reverse the decline & productivity brought &@u% by new regulation by adjusting the tax system to encouralge capital investment for all h~y&ss. Cuts in the corporate income tax rate, acc&erated depreciation, or an increased investment tax credit could all tend to increase investment for the entire bu&ess sector. Such measures could increase investment in the chemical industry boti by lowering & cost of q$tal and-by ~ncrcas@g“he qu@&~.of-&em-i&s demanded. Alternatively, poiticy makers could off&rs-a r&f tim the dxpenge of capital equipment used to meet new regulation by ~&33hBtIn~ tI!@depie&tion or increasing the investmem tax credit on such .eqtipment. I?olicy makers may want to consider allowing only the chemical industry to &n&t jFfomsome of these tax cuts if the efFects of new regulations on chemical industry productivity are unusustly severe. This sttidy could also be useful to policy m&ers because i& methodology probably disentangles much of the effect of the energy crisis on labor productivity f?om the effect of new re@ation. This is important because a setback of productivrty caused by an energy crisis calls for an entirely different policy csponse than a setback caused by new regulations. A setback imposed by an energy crisis calls for research into energy cost reducing production processes and more general efl’orts to ease pressures in the world energy market, rather than investment promoting tax cuts. Th!s article is divided into five sections: The fast section explains the approach taken to measuring the cost in lost productivity of capital investments used to meet nepti reclation; the followiq section briefly describes the data set; the third section develops a measure oF the capital stock used to meet new regulation; the fourth section briefly describes the production function; and the final section presenis the results of estim&on.
CAPITAL INVESTED TO MEET LABOR PRODUCTIVITY
NEW
REGULtATTON
AND
T5.s st:;dy’s estimates of the cost in lost labor productivity of new regulation dept nd on the methou of m&asuring the connection between capital invested to meef new regulation and labor productivity. This connection exists because extra
output co&i be pnxiuced if the funds invested in capital to meet new regulation wer; invested in Erectly productive capital, holding a.0 other inputs constant. IGo:-eovcr, extra output, for fixed labor, mew higher labor produ Aivity. Ths sI&y estira;ttes the cost in terms of labor productiv&y of the capital invested lo me& new regulation as the difference between actual labor productivity and wh;!\t labor productivity would be if the capital invested to meet new replation were irrxlested in directly productive capital. nxis study’ focx~as on the two main types of new rec*l* ion imposed since t t e letm i 96&: snvironmentaf and occupational health and safety regulation. These new-regulati~xxs follow from legislation such as the Water Quality Act of 1965, the V4ater Po,ffu+n Ace Amendments of 1972, the Air Quality Act of 1967, the Ckan kr ~~~~~~~n~~ of f972, and the W~~i~s-Stc~ger ~ccu~~~~o~a~ Sakty rtrr;dHealth act of 1970.
THE COST OF NEW REGULATION
413
Firms would most likely direct some capital expenditure towards environmet=tal md occupatiorkal health and safc:ty even without regulation. Therefore, 02s study focuses on new regulation introduced after 1966, for it is regulatory capital created after the introduction of new regulation, not the level of regulatory capital that existed all along, that may restr$n FroducGvity growth. T&s aiiplr&%h para2Iek the approach in the classic study by De&son (I 978, 19%) 3n mmy-ways, It EMluws the same opportunity cost app&oach in measuring the cost of n&w regulation on output and pmductivity. It focuses, like Den&on, on incrementi! costs of environmental and occupational health and safety regulation int~o~aced a& 1966. However, this study differs significantly f?om Denison’s in its method of meastiug what output firms would produce if funds devoted to capital to meet new regulation (regulatory capital) were ingested in directly productive capital. De&on uses an accounting method that values output at factor cost. Tlzrefore, the lost output is the current period depreciation of regulatory capit plus what the return on the tinds invested in regulatory capital would be ifthey were invested in directly productive capital. We cticulates this return as the product of the stock of regulatory capital times the ratio of earnings net of depreciation to the capital stock in the business sector. An estimated production function makes an alternative to Denison’s approach possible. The production function relates the value of the capital stock with and without new regulation to values of ottput, :Forfixed l&or and other inputs. After compu.ting these alternative values of output, alternative values of labor productiv3y are computed by dividing through by fixed labor. In order to carry out this calculation it is necessary to develop a measure of the regulatory capital stock (Krrg). Measured capital in the CSP dzta set (K, ) consists of both directly productive (K) and regulatory capital. With an estimate of&,, it is possible to estimate K by subtracting Krzg from Km (K = K’ - J&J. Thus, the three main elements necessw to estimate the ef%ct of capital requirements to meet new regulations are the data, K&, and the production function. These are developed in the next three Eections. These sections may be skipped by the reader who is uninterested in methodology.
THE DATA All the empirical! analysis relies on the very disaggregated Census-SFJ-Penn (CSP) data set, which was desiped for analyzing p&uction. A chemical industry production function is estimated from the CSP data set. This is the most important analytical tool in all the analyses of productivity growth. The CSP data set covers all 450 four-digit standard industrial classificatiorl (SIC) industries within U.S. manufacturing. It consists of obsc:wations on rra.i gross output (.X), the capital stock (K), labor (L), energy (E). Ger intermediate materials CM), their price indices (which arc, respectively, P, PK. _PL,_PEqand PIFf). and other variables. _XIs real shi~~mtnts adjusted for changes in inventories 50 thar it measures production. K is the real capital stock, constructed using the perpetu4 inventory method. E is a measure of energy used for power and heat, &F I,; EII
414
Menahem Pqjwes
intermediate r‘raterials, including t)oth raw materials and parts. M includes petroleum used as raw material rather than as a f&l. The CSY data set includes observatioas on all 28 four-digit SIC industries within the U.S. chemical industry for the years 197 1-75, a period spanning the initial Gnergy crisis andthe I recession that folkiwed.’
rmmmgy
TBie proldution fk3ntii0nis estimate4lfraana pooled time series ofcr0ss sections. Therefm, 8~1bbseferationon UIQWand the inputsfar each four-digitSIC industry in each year is treated as ditl observation on %e overall industry pr&utiion processTfais prooeduuueassumes that all fourdigit SK industries use the sampt prod&on process. Althoughlass than ideal, this assumes far less than da studies that are con&ted ti the aggregatebusiness sec&r or manufaaring level. Such studies inrplicitlyassume that ;allindustries within the relevant aggregateuse the same ~roductbn pr0cess.
ESTltMATTON OF THE REGULA.TORY CAPITAL STOCK An estim~~ c2ftbep&t?&devoted to meetingnew regulation(K&) is developed by the perpetualinvsntorymethod. Since only capital invested to meet regulation intr&uced &.er II966 is considered,K& is defined as zero in 1966. Afierwti, K, is computed by adding 1972 dolk capital expendbres for environmental and occupational health and safety purposes and depreciating the previous ye&s value of K& Ktg is the sum o? pollution abatement capital @PA) and occupational safety F?nd health capital {KOSH), which are estimated separately. This calculation requires data on current dollar cap:ital expenditures on pollution abatement (CPA) and occupational safety and health (COSH), their deflators, and the depreciation rates of these sorts of capital goods. The Census publishes four-digit SIC data on CPA, but only from 1973 on. Estimation of KFA requires CPA data for 1967-76. McGraw-Hill data on CPA for the aggregate chemical industry form a basis for extending the Census data buck to 7967. The Census &a on CPA consist of separate series on air, water, and solid ~raste pollution abatement. The M&raw-Hill data consist of series on only air and water pollution abatement. An estimate of aggregate chemical industry CPA on solid waste fur 1967-71 is calculated by multiplying the sum of the McGrawHill air and water CPA series in those years by the (aggregate chemical industry) ratio of x&d waste to air and water C?A in the 1974 Census data. f-fu~ver, the .t967-7 1 air, water, ar:d solid waste c*P/‘~data still exist only at tie zggrre;gate chemical iudustry 12~1. Estimates of the fourdigit SIC industry CPA series are computed by muftiplying the 1967-7 f aggregate:; by the 1974 distibution of CPA over four-digit SIC industrie ‘n the Ceusus data. (1974 instead of’ 19?3 dsta are wed bexxmse the WV i n.+ was pm.)
415
THE COST OF NEW REGULATION
The CPA data are put on a 197 2 dollar basis using Bureau of Economic Analysis (EEA) price deflators. Air, water, and solid waste CPA are divided by separate deflators and then summed to produce 1S ‘712dollar CPA (CPA72). The MA nnofficid& estimated the mean rife of_n&luticmabatement capital equipment at 25 years. The depreciation rate is then J 125. This completes the development of data necessary to calculate KPA by the perpetual inventory method. KPA is c alculated %om.the Followingequation with a base of zero in 1966.
KPA*,i = KPA,4
1425)
f- lr,PA72+
which can be: rewritten as
KPAbi= vk_,
CPA72,,0
96,
;
l
96k,
where t = 1967 , . . . ,1976, i is an index of four-digit SIC industries, and k = 1976-v. KOSH measures the extra capital needed to meet new occupational safety and health regulation. Therefore, KOSH begins in 197 1 when the major relevant new legislation-the Williams-Steiger Act-became ef Fe&-4,;. McGraw-Hill publishes aggregate chemical indu,jl:rydata on COSH from 1972 on. The 1971 COSH is extrapolated by assuming that the 197 1 to 1972 growth rate qf COSH was the came as the 1972 to 1973 growth rate. No four-digit SIC data on COSH are available, :jo the aggregate McGraw-Hill data are disaggregated to the four-digit SIC leve: by multiplying by the 1973 distribution of reti output over four-digit SIC industries. Unfortunately, this procedure eliminates any statistically meaningful variation of KOSH in the fourdigit SIC MOSS section part of the pooled cross !,ection-time series oata set. However, since KOSH is small compared to KPA, this probably does not much affect the meaningfulness of cross-section variatior in J&. There are no deflators available for occupatioxtl safety and healtir capital goods, SL COSH is put on 1972 dollar terms (to fornil COSH72) by dividing by the BEA’s investment price deflator for manufacturing. Following Denison (19791, a 1/lO depreciation rate is used. This ccmpletes the development of data necessaty to calculate KOSH by the perpetual inventory method. KOSH is calculated from the following equation with a base of zero in 1970: KO SH,,i = KOSH,-,
(1 -
l/10)
t- COSH72,Vi,
I
KOSH,,j
=
I) =1971
COSH7.~!,,~0.9”,
J&s is then the sum of KOSH and KPA. The fm;iRestimates of_..re, com:xise a _I d the 23 fDuI’fiislltime series of cross sections. An estimate ef&&,, exi c*9 fm each
Menahem Prywes
4%
‘The particxlar fimctionai form of the gem3ral production knatior~ F &~MBI here is *the nested co&ant ekticity of substitution (CEL3) pro&&on fun&ion. The nested CES furxtion emphasizes the role of enem in restridirtg capital use. Xt at= c;f&xxxvefy models the relationAip between cuts in energy use and the prorhxtivity of i&xx and the other fa42tors. Therefore, the ne8ted-aS fkmH&n k appropriate for modeling *he 1971-76 data period when energy &backs may havl: re&kted capital use anti produ;ity. An&her a&&age of the nested CES fun&ion is that it is rel&vely simpie to e&&e. Furthermore, its parameters cani be used to con&ruct usefix! estimates of the &sticities of sub&.itution within the chemical indum,. The nested CES production Function wm developed by Satxl ( 1967) foUowing clo!seiy related work Qn consumption by Brown and Heien (1972). The par5cuk.r form of the nested CES &irnated here origIate;S with Sheinen ( 1988). The mathematkaf form of the nested CES function is2
a.-
13 the intennedia+~ A input prudwed by K znd E working together. XKEL is the fi;E Sx&easflediateinput ors-Wed by A’& and L:,. The ps are the “substl ution”
les in this
pmuctiun function
cim
st of pded
cross section-+ime series
417
T3!IE COST OF MEW RHXJLATION
parameters, which determine the engineering (gross) elasticities of substitution, ti-& -&?@&& &B&&$&s &k&Mk?s Gf &X&&!On? The 08 are the 43btfs;itiss of
s~~~~ betimen ix&Us io 101~XSI&&S level CES leioIlctionholding the iIWW&M tipat8 ~mdWed by that lm31 CES Gm4Sioncom%t,4 The 8s 3-r:the shwe c*lr6elREici&s. that determine the relative weights of the factors within a ~w&x& level CES fun&m. ,D(r) is i;tt&l f&tur produfivity change term. 11.is I-Iicks neutral for XKE ancl M. ti productivity change time trend term has ti e f mm
The subscripted& are dummy variables for the years 1972 through 1976. They are defined so that their coefficients, the subscripted dt;, have the interpretation of percentage annual changes in the total factor prodmti vity.5
El!WIRICAL RESULTS AND CONCLUSIWW To recapitulate, this experiment compares labor prc ductivity at the fitted V&K. of output using the actual inputs iqK, L, E, M)IL
14)
to labor productivity at the fitted value of output using what capital would be if there had been no new regulation after 1966 and the other actual inpubs:
Table 1 shows the results of this experiment. 3T’he economic elasticities of substitution depend on ihe eng’necring elasticities and 1nput shares. The Allen elasticities of substitution (AES) show the interaction of engineerring elasticities ,?mdcost shares.
PjXj
AESKE = TV+ pK,Kj
PjXj
~
-k pE,Bj
WO--~l~ +
PKJxj
+ ‘EjEj
+ pI.jLj
0+-Q!
Metzahem Prywes
THE COST OF NEW ,REGUEAT% ON
4h9
Tabde 1 lists the percentage point dirference between expressions (4) and (5) for each of the four-digit SIC industries within the chemical industry, and for the aggrq5ettct: chemical industry, for each of the years 1971-76. As expected, there is a loss of labo r prod-@iv& because labor has less capital to work with when there is new regulation. The 1~~s of productivity tends to grow from 197 1 to 1976 because the capital used to meet new regulation increases over time as fiis make further investments in regulatory capital. However, the loss in labor productivity can fall over time if E or M-which increase labor’s productivity-rise enough. The estimated loss of labor productivity caused by capital invested to me& new regulation for the overall chemical industry rose from 1.O percent in 1’971 to 2.3 percent in 1976. This is not a shockiqly kge loss, especially for such a heavily environmentally regulated industry. Yet, it is large enough for Folicy makers to take notice. Thr:y can combat this :shortf’all in labor productivity by relaxing regulaticln or with traditional policies that increase the quantity of capital.
REFERENCES Brown, Murray, a;?d Heien, Dale (1972) The S-Branch Utility Tree: A Generalization of the Linear Expenditure System. Econometica 40:737-747. Census Bureau ( 1974-77) Pollution Abatement Costs and Expenditures. Current Industrial Reports. Washington, D.C.: U.S. Crovernment Printing Office. De&son, Edward (1979). Accounting for Slower Economic Growth: The United States in the 197&b, Washington, D.C *: The Brookings Institution. (1978). E*ffects of Selected Changes in the Institutional and H:uman Envircnment -Upon Output Per Unit of Input. Ahwey of Current Business !E1:21-44 (January). McGraw-Hill ( 1975) Department of Economics ( 1975) Historical Polizltion Control ExpePrditures and Related Data. New York: McGraw-Hill. hdimeo. (I 972-77) Annual McGraw-Hill survey of Investment in Employee SqfL’ty and ~HeaZth . New York: McGraw-Hill.
Prywes, Menahem (1981) Three Essays on the Econometrics of Production, Productivity. and Capacity LKlization. Ph.D. di:?sertation, University of Pennsylvania. (1983). A Nested CES Approach to Capital-Energy Substitution. Federal Rescwe Bank of New York Discussion Paper No. 8313: New York, October. Rutledge, Gary, and O’Connor, Betsy I<1981) Plant ynd Equipment Expenditures ‘3~ Business for F~rllution Abatement, II973.-8C and Planned 198 1. Surly ojf Cur~nr Business 5i:19 -25 (June).