The Industrial Sectors

In this SAM we have chosen to follow the North American Industry Classification System (NAICS) method of describing industrial sectors.  The NAICS system is a six digit classification.  The codes are arranged so that major classifications share the same first digit, for instance, agriculture, hunting and forestry all have 1 as the first digit.  Crop production sectors all have the same first three digits, 111, while animal products are 112.  Our industrial sectoring basically follows the first three digits of the NAICS codes, with some exceptions which we will discuss below.  By following the NAICS codes, we are able to match different types of data, like employment data and input output data with a fair degree of accuracy.  In the future when the NAICS system is completely adopted, without variation, by the major government statistical entities perfect matching will be possible.

By using the 3-digit NAICS codes as the basis for our sectoring we achieved a very fine (for a SAM) differentiation among sectors.

For a complete display of the names of the sectors, their descriptions and their concordance with the NAICS system, click here.

 

Major California Industries

The first criterion considered when establishing adequacy of the industrial sectoring is the importance of the industry in terms of its employment and revenue.  Tables 1 and 2 display these figures.  The largest net output sectors in the state are real estate, retail, computer related manufacturing, business services, banking, transportation, machinery, electronic parts manufacturing, agricultural manufacturing and agriculture.  (The correspondence between the sector names and their descriptions is to be found in the appendix table.) 

Table 1  Ten Largest Industries According to Value of Total Payment From Other Sectors  (in billion$)

Sector

output (in billion)

Ratio to All Industry Output

RETAIL

125.99

6.68%

FIREAL

117.921

6.25%

FIBNKS

74.514

3.95%

CMPMFG

61.346

3.25%

AGRIC

55.693

2.95%

INFOPC

52.288

2.77%

MEDAMB

51.275

2.72%

CMPRTS

51.145

2.71%

WHLDUR

51.043

2.70%

CONOTH

48.734

2.58%

Total output in all industry

1887.199

 

 

The ranking of major industries according to the number of employees presented in the table below reproduces results similar to those in the previous table; service and trade industries are the dominant employment sectors for the State.  The largest sector’s is 7% of the State’s output. 

 

Table 2  Wage and Salary Workers by Major Industry, 2003

 

Sector name

Employment

Employment (in million)

Ratio to All Industry Emply

RETAIL

1584938

1.584938

12.74%

 PERSRV

619458

0.619458

4.98%

 CONOTH

519176

0.519176

4.17%

 MEDAMB

498262

0.498262

4.00%

 ACCRST

458189

0.458189

3.68%

 ACCFST

435385

0.435385

3.50%

 ADMTMP

428136

0.428136

3.44%

 TRANSP

419580

0.41958

3.37%

 BUSSRV

406096

0.406096

3.26%

 AGRIC

374047

0.374047

3.01%

Total emply in all industry

12441952

12.441952

 

 

 

 

 

Source: JOBS is CA employment by sector, found by averaging employment levels in EDD data over Q2 2002 through Q1 2003

 

The retail sector has the largest employment in the state with 13% of the employees.

In terms of sectoralization, this scheme has isolated most of the potential targets for tax policy, particularly in the manufacturing sectors.  In 1994, the major payers of taxes are given by table 3.

 

Table 3 Firms Reporting Net Income Subject to State Taxation of $1 Billion or More, 1992 (in thousand $)

Industry

Net income subject to state taxation

Investment and Insurance Companies

5,320,185

Wholesale Trade

4,126,940

Banks and Savings and Loans

3,388,928

Retail Trade

3,341,524

Electric Machinery and Equipment

2,636,639

Communications

2,537,911

Electric, Gas, and Utilities

2,406,728

Business Services

2,091,496

Petroleum, Coal, and Rubber Products

1,761,465

Beverages

1,608,991

Real Estate

1,538,680

Chemicals and Allied Products

1,512,653

Construction

1,088,195

Source:  Information reported in California Statistical Abstract, DOF, 1994.

Note:  The treatment of taxation sectors remains the same in this SAM as in the 1998 SAM.

 

All of these sectors are well isolated in our sectoring scheme.

The third criterion considered when establishing the industrial sectoring is the distributive impact of government taxation and spending.  In order to trace effectively the impacts of government spending and taxation on the distribution and incidence of production, income, spending and savings in the economy, it is important to establish an industrial sectoring that can be used to map the effects of government policy.  The sectoring in SAM distinguishes those industries that clearly stand to benefit from increased government spending from those industries that may incur negative repercussions from such spending. 

As a first cut at differentiating the impacts of government policy, it is important to distinguish major taxpayers by size and by type of tax as was done in the previous section.  Not only do the major taxpayers represent the primary source of funding for government spending but they also represent important variables in any industrial-development strategy.  Targeted tax cuts or even general tax cuts to industry are primary tools in industrial-development incentive policy.  The industrial sectoring must explicitly include the major taxpayers in order to trace the impact of such policies. 

The industrial beneficiaries of government spending on infrastructure or education are difficult to isolate.  Both theory and empirical observation suggest that the benefits of infrastructure and education are diffused throughout the economy.  The direct beneficiaries of industrial-development spending are likely to be more narrowly delineated.  A primary focus of many industrial-development strategies has been creating employment in wage-premium, high-export industries.  Wage-premium jobs have a high salary to education ratio, and the earning effects of local employment are greater for new jobs in wage-premium industries.  More jobs at higher wages provide the biggest “payoff” for employment-creation projects.  Export industries are targeted because out-of-state earnings can have large economy wide impacts. 

California’s largest wage-premium export industries (in 1998) were Aerospace, Motion Pictures, Engineering and Management Consulting, and Computer Software and Systems Development.  Even if these industries are not specifically targeted by development incentives, they are important industries to track with the model.  The economy wide impact resulting from changes in these industries should be large because they are large employers paying high salaries, making large export earnings.

Again, these sectors are well isolated in our sectoring scheme.

 

The sectoralization scheme mostly differs from a straight 3-digit NAICS set up by the breakout of energy or pollution important sectors, for instance cement, natural gas distribution and power generation.  These sectors were selected for special treatment by ranking the 4 and lower digit NAICS sectors, so far as data would allow, by energy usage and preserving those with high energy usage as sectors by themselves.  Table 4 shows the energy purchases by the major energy producing sectors in our final sectoralization.

 

Table 4.

Industry Total Purchases of Goods from Energy Industries (all $bn)

 

 

 

 

 

 

 

 

 

 

 

OILREF

21.46928

 

PLASTC

0.340307

 

TEXLTH

0.173429

 

PROOTH

0.08295

DSTGAS

6.111024

 

ACCHOT

0.331747

 

FDPROC

0.172258

 

PROCOM

0.081157

DISTEL

4.290048

 

CHMDRG

0.324989

 

FINOTH

0.155621

 

CONCRT

0.063585

FIREAL

3.413969

 

PRINT

0.314367

 

ADMBLD

0.144514

 

PROLEG

0.06302

TRANSP

2.517194

 

INFOPC

0.314333

 

WOOD

0.1398

 

CMPCMM

0.055395

AGRIC

2.334113

 

WHLNON

0.310282

 

CONUTL

0.130648

 

CMPMED

0.05102

OILGAS

2.193922

 

PRIMTL

0.277611

 

SCAOTH

0.12783

 

DSTOTH

0.050288

CONOTH

1.125058

 

MEDAMB

0.263604

 

EDUC

0.123511

 

ACCSPC

0.048594

OTHPRI

0.744965

 

FINSEC

0.245905

 

APPREL

0.122085

 

VEHPRT

0.047999

CHMBAS

0.627995

 

CONNON

0.223896

 

ELCTRC

0.120694

 

INFCOM

0.042115

BUSSRV

0.596703

 

FIBNKS

0.223319

 

INDGAS

0.119477

 

PROADV

0.038759

PERSRV

0.572432

 

MACHIN

0.222316

 

FDMFG

0.119465

 

PROCNS

0.036452

RETAIL

0.56207

 

CMPMFG

0.217954

 

CEMENT

0.112848

 

ADMSEC

0.036121

WHLDUR

0.540194

 

INFOTL

0.215443

 

PAPER

0.106925

 

PROACC

0.031906

CMPRTS

0.518327

 

BEVTOB

0.209393

 

WHLAGN

0.106565

 

AUTOMF

0.024037

FDOTH

0.472232

 

CHMSPS

0.208917

 

PROARC

0.101806

 

PRODES

0.022482

ACCRST

0.469289

 

RECAMS

0.203625

 

GLASS

0.099425

 

ACCBRS

0.020381

CONSTR

0.456687

 

VEHAER

0.192586

 

MSCMFG

0.097282

 

VEHOTH

0.02032

ADMOTH

0.424884

 

CMPINS

0.19117

 

LABDNT

0.096067

 

VEHBDY

0.015226

CONRES

0.379514

 

MEDSA

0.18653

 

RECENT

0.095344

 

VEHSHP

0.012382

ACCFST

0.364136

 

PRORES

0.185788

 

INFOTH

0.092498

 

VEHMFG

0.007515

MTLFAB

0.354626

 

MEDNRS

0.179013

 

PLPMLL

0.091634

 

FINSUR

0.007218

MEDHSP

0.347479

 

CHMOTH

0.174594

 

FURN

0.090092

 

ADMTMP

0.005305

 

In order to evaluate pollution control, including greenhouse gas control proposals, the automotive sector has been preserved in less than three digit aggregation.  Automobile and light truck manufacturing is kept distinct from heavy trucks; and body manufacturing and parts manufacturing are distinct industries.

 

Producing the Energy Consistent SAM for Industries

The fundamental method for producting the SAM rows and columns for the industries was to use the BEA data for 1997 to create an Input Ouput table for the US.  The columns of the US table for each industry were then scaled by the ratio of wages paid in the the industry in California in calendar year 2003 the the ratio in the US in 1997 to produce estimates of the expenditures and revenues of California industry for 2003. 

The industry flows estimated from the BEA data are not entirely consistent with the energy flows in CALEB, which are based on energy data.  The reasons for the differences include: (1) the fundamentally different sources for the data:  data on firms versus data on energy flows and (2) the construction of California from national data described above.  In order to make the SAM consistent with CALEB, the CALEB data for energy use were assigned to the SAM sectors, to derive the use of each energy type by each sector in energy units (trillion Btu).  Then, these data were combined with 2003 price information to correct the major SAM energy transactions for the actual Btus that changed hands at the actual prices.  For example, the purchase of wholesale natural gas by the gas distribution sector (i.e. the payments made by DSTGAS to OILGAS) reflect actual Btus at the average 2003 utility gas procurement price.  Further, smaller purchases by end use sectors were also adjusted for the changes in energy prices from 1997 to 2003, which were generally larger than wage inflation.