Regionalism, Federalism, and Taxation: A Food and Farm Perspective. By

Patrick Canning and Marinos Tsigas. Food and Rural Economics Division,

Economic Research Service, U.S. Department of Agriculture. Technical Bulletin

No. 1882.

Abstract

This report documents an applied general equilibrium model of the United States.

The model features explicit treatment of Federal, State, and local taxes and is

segmented into 10 distinct subregions. These subregions engage in inter- and

intraregional trade, as well as international trade. Each region is distinguished by

its unique composition of industries, capital markets, and patterns of trade.

Regional data developed for calibrating the model are discussed and several tax

policy reform simulations demonstrate the modeling capabilities.

Keywords: Cost of capital, fiscal policy, marginal effective tax rate, regional

applied general equilibrium, regional household welfare, State and Federal

taxation and reform.

Acknowledgments

The authors received valuable review comments on drafts of this manuscript

from Roy Boyd, Ron Durst, Kenneth Hanson, Thomas Hertel, James Hrubovcak,

Howard Leathers, and James Monke. While most comments were incorporated,

remaining errors and omissions are solely the responsibility of the authors. The

authors acknowledge Michael LeBlanc and Patrick Sullivans encouragement to

pursue this research, and Mark Denbalys perseverance in encouraging the

projects timely completion. Sharon Lees editorial assistance is also gratefully

acknowledged.

Washington, DC 20036-5831 March 2000
Contents

Page

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

Overview of a U.S. Multi-Regional Applied

General Equilibrium Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

Structure of the AGE Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 

Welfare and Household Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

Industrial Demands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

Interregional and International Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

Macroeconomic Closure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

Primary Factor Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

Public Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

Accounting Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

The Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

Behavioral Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

The Intermediate Run . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

PC Implementation of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 

Disposition of Industries and Primary Factors of Production . . . . . . . . . . . . . 19 

Industry Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

Labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

Capital and Labor Disposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 

Taxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 

Comparisons of Federal Marginal Tax Rates on

Income From Capital and Labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 

Comparisons of Regional Marginal Tax Rates on

Income From Capital and Labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 

Simulated Effects of Comprehensive Tax Reform in the United States . . . . . 40 

Specification of Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

Overview of Tax Policy Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

Simulated Effects of Tax Reform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

ii Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA
Summary

In the United States, the tax system is multilayered. It includes a central Federal

tax system and State and other local tax systems. Reform of these systems has

always been part of the national economic policy debate. In this report, we

document a model developed to assess the economic effects of taxation in the

United States, and we simulate a tax reform to illustrate its multiple effects on

economic performance in different regions. While the model is economywide,

special attention is given to regional economies, food and farm industries, and

the food consumer.

For modeling purposes, we segmented the U.S. economy into 10 distinct

economic regions, and the model accounts for regional economic performance

and regional household well-being. Prominent features of the model include the

explicit treatment of local, State, and Federal taxes and the existence of several

subnational regions that engage in inter- and intraregional trade, as well as in

international trade. Each region is distinguished by its unique composition of

industries, disposition of capital factor markets, and patterns of trade. These

distinguishing characteristics also create a unique relationship in each region

with the U.S. tax system.

Some stylized facts about tax burdens on primary factors of production in our

1994 tax year simulations are noteworthy. Concerning effective marginal tax

burdens at the Federal level, agriculture is the most lightly taxed nonresidential

industry, while food manufacturing is among the most highly taxed industries.

Regionally, the Delta and Northeastern States realized the lowest effective

marginal Federal tax rates on farm capital, while the Appalachian and

Southeastern States have the highest effective marginal Federal taxes on food

manufacturing capital. Concerning effective marginal tax burdens among State

governments, agriculture is the most heavily taxed nonresidential industry, while

food manufacturing is near the bottom. The highest food manufacturing State

effective marginal rates are in the Northeast and Lake States, while the lowest

are in the Southeast and Appalachian States.

Tables and figures in the report present detailed information on the value of

different capital inputs used in production for each industry, recognizing the

possibility of 15 distinct types of production inputs. The 10 U.S. regions and a

region representing the rest of the world each engage in the production of seven

products (capital-intensive agriculture, other agriculture, capital-intensive food

manufacturing, other food manufacturing, capital-intensive other manufacturing,

other manufacturing, and other industry output). These products can be traced to

a far more detailed list of goods and services produced by industry and

consumed by private households. The model allocates production, consumption,

and tax burdens of the products to regional industries and households in

proportions consistent with the more detailed array of goods and services

actually produced and consumed. The 11 regions, 7 products, and 15 primary

factors of production lead to more than 1,000 distinct primary factor tax wedges.

With trade taxation also represented, as well as household taxation, a

comprehensive account of the multiple impacts from taxation is obtained.

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 iii
The complexity and diversity of industry, households, and the tax system lead to

many different consequences from taxation. With multiple tax policies and

multiple levels of government administering tax policy, many conflicting and

complementing effects of these taxes are realized to varying degrees across

industries, households, and regions. Current tax policy and several variations of

fundamental tax reform are considered and found to affect magnitudes and

distributions of several economic indexes. These effects varied across regional

households, inter- and intraregional industry aggregates, asset portfolios, terms

of regional and international trade, and relative consumer price and consumption

patterns. The report concludes with a comprehensive analysis and breakdown of

these effects, along with consideration of alternative tax reform scenarios

(unilateral Federal reform and harmonized Federal and State tax reform).

Regional economies, food and farm industries, and food consumers are featured

in these discussions. These simulations, while not intended to represent specific

reform proposal scenarios, do effectively demonstrate the extensive analytical

capabilities made available with this new modeling resource.

iv Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA
With no public policy having more profound effects

on markets than taxation, the Economic Research

Service has developed an adaptation of the

prevailing economywide modeling framework used

for examining tax policies. This adaptation makes

prominent, salient features of food and farm

industries and their markets, features generally

obscured in applied economywide tax models. A

characteristic of food markets is the geographic

fixity of primary factors in production, including

suitable farmland, regional climate conditions,

natural resource base, and proximity to primary

upstream industry. These geographic elements

motivate the modeling assumptions employed in the

model that we present. 

In the United States, the tax and social insurance

systems are multilayered. They include a central

Federal, State, and local tax and insurance trust

systems. Combined Federal and State taxes,

charges, and miscellaneous fees are estimated at

34.1 percent of personal income in 1994. In

addition, the Federal and State insurance trust

collections amounted to 11.4 percent of personal

income (fig. 1). The most extensive source of

revenues at the Federal level is the income tax, at 90

percent of total tax revenues and nearly 40 percent

of total revenue. The Federal insurance trust

(primarily social security and medicare) produces

one-third of all Federal revenues. Among State

governments, taxes account for more than 70 percent

of own-source general revenues (taxes plus

insurance trust). Income and wealth taxes are a

smaller share of State tax revenues, around 40

percent, while roughly half of State tax revenues are

raised through sales and excise taxes. Threequarters of local tax revenues come from annual

property taxation. There are no annual Federal 

property taxes, and under 10 percent of Federal

taxes are sales and excise taxes. At 12 percent, the

State insurance trust (mostly employee retirement)

is not as significant a revenue source as in the

Federal budget.

Reform of the tax system, to address inefficiencies it

creates, has always been part of the national

economic policy debate. By inefficiencies, we are

referring to the alterations in relative prices for

factors of production and consumption caused by a

tax system that creates unequal burdens on different

factors. The Federal tax system was reformed, in

various degrees, in 1986, 1993, and 1997.

Currently, there are proposals for fundamental 

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 1

Regionalism, Federalism, and Taxation

A Food and Farm Perspective

Patrick Canning

Marinos Tsigas

Introduction

Percent of personal income

Figure 1

Disposition of public revenues, 1994

Income tax Sales/excise tax Property/other tax

Federal State and local

S

t

a

t

e

o

c

a

l

Source: American Council on Intergovernmental Relations.

L

30

25

20

15

10

5

0

} {

Taxes and fees Insurance trust
change in the tax system. In this report, we

document a model develped to assess the economic

effects of taxation reform in the United States, and

we simulate a generic tax reform to illustrate its

multiple effects on economic performance in

different regions. While the model is economywide,

we give special attention to regional economies,

food and farm industries, and the food consumer.

There are significant regional dimensions to tax

policy initiatives, and region-specific policies are

likely to have significant effects in other regions.

Prominent features of our work include the explicit

treatment of local, State, and Federal taxes and the

existence of several subnational regions that engage

in inter- and intraregional trade, as well as in

international trade. We use a multiregional, applied

general equilibrium model of the U.S. economy.

Each region is distinguished by its unique

composition of industries, disposition of capital

factor markets, and patterns of both intra- and

interregional trade. These characteristics create a

unique relationship in each region with the tax

system.

We seek to represent the multiple inefficiencies in

each economic region in the tax system, stemming

from the differential treatment of primary factors of

production. We represent the distortionary effects on

industry capital, as well as the capital/labor

decisions, taking into account that industry employs 

a heterogeneous capital portfolio.1 Within each

region, misallocations of primary factors across

industry aggregates are also represented, and while

intersector mobility between corporate,

noncorporate, and residential capital is precluded,

the tax treatment of each sector is captured and will

affect resource allocation decisions. Similarly,

financial characteristics of each industry and across

industry aggregates are represented exogenously,

along with macroeconomic assumptions such as real

interest rates and inflation expectations. These

factors affect tax incidence in capital factor markets.

At the household level, the allocation of income

between present and future consumption is

represented, and the tax system affects this decision

through the differential tax treatment of consumption

and savings in each regional household, the latter

stemming from the tax treatment of investment

goods. While tax policy can affect labor supply

decisions, we hold fixed each regions supply of

labor, and thus ignore any such supply response.

2 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

1Because the model represents the intermediate-run

scenario, the industry-level decisions, such as capital

portfolios and labor/capital ratios do not translate to the

regional totals, since each labor and capital aggregate is

fixed within each region. The longrun results are

foreshadowed in the model by tracking the international

investment flows (see the Macroeconomic Closure

subsection).
Food and farm industries add 4.9 percent to the

value produced by the employment of capital in

domestic (nonresidential) industry. Yet in some

regions, such as the Appalachian (9.5 percent), Corn

Belt (6.7 percent), Lake States (8.0 percent), and

Northern Plains (10.5 percent), the productive

capital in food and farm industries contributes

significantly to the returns from regional investment

in business capital (fig. 2). Capital intensity,

measured as the value-added ratios of capital to

labor (U.S. Department of Commerce, Bureau of

Economic Analysis, 1994), is higher in farming (1.0)

and food manufacturing (1.4) than in other

manufacturing (0.6) and all other nonresidential

industry (0.8).

Since the work of Harberger, it has been widely

understood that taxation of capital income creates

significant distortionary effects. Studies since 1981

(Goulder and Thalmann; Fullterton and Henderson;

and Summers) found comparable burdens from the

non-neutral tax treatment of heterogeneous capital.

Other factors, such as inflation (Feldstein) and real

interest rates (Boadway, Bruce, and Mintz; and

Gravelle), have also been shown to have

distortionary effects on relative prices in factor

markets. Fiscal policy instruments significant in

farming and rural areas, including cost-share policy

for specific investments and the rural development

programs, which may include cost-share

arrangements and subsidized credit, have direct

consequences on factor-use decisions that relate to

the tax-inclusive cost of capital.

Evidence of real effects from taxation in agriculture

has been extensively documented in the applied

research literature (Carman). This evidence

indicates significant incentive effects from the

income tax system on investment (LeBlanc and

Hrubovcak; and Sisson), while general equilibrium

accounts of farm and food tax incidence (Boyd and

Newman; and Hertel and Tsigas) have found

substantial effects on agriculture and food prices.

The Tax Reform Act of 1986 generally created

substantial efficiency benefits from a leveling of tax

wedges on heterogeneous capital factor markets. 

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 3

Figure 2

USDA farm production regions

Background
4 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

However, in agriculture, this leveling reduced

investment incentives, particularly in the use of farm

machinery, primarily due to the repeal of the

investment tax credit (LeBlanc, Hrubovcak, Durst,

and Conway). Macroeconomic factors affecting the

tax system have produced significant structural

change in the factor portfolios of farmers (Canning

and Leathers).

Calls for fundamental reform, ranging from a flat

income tax to a national retail sales tax, have gained

important political allies. Along with goals such as

simplicity and fairness, many elements of these

proposals are intended to rectify inefficiencies of

taxation on industrial factor incomes. If effects of

these policy reforms are comparable to those found

in past reforms, it would be of considerable value to

trace out these effects among the many economic

entities that comprise the national economy, such as

those measured by geographic, demographic,

sectoral, and industrial disaggregation. Such an

effort would provide a richer economic context to an

is of targeted Federal program initiatives, and allow

for an assessment of the relative effects in the farm

and food economy from a fundamental reform of the

tax system.

Comprehensive reform of the Federal tax code does

not necessarily imply a harmonized reform of

regional fiscal policies. Economists have observed

(Nechyba) that a combination of logistic and

strategic advantage requires regional and subregional

governments to rely on different tax instruments to

finance localized budget demands. The data support

this finding. The importance of this is that economic

simulation of tax policy reform not incorporating

these salient features of fiscal federalism imposes a

de facto harmonization of Federal and regional tax

policy. Empirical work, however, indicates such

harmonization may be infeasible, or at the very least,

not a foregone conclusion. As data in this report

show, the difference between unilateral and

harmonized tax reform on tax incidence is not

trivial.
An applied general equilibrium (AGE) approach is

appropriate for this analysis because changes in tax

policy affect all industries of the economy at

different rates. Furthermore tax changes affect

disposable income and final demand. A

multiregional framework is appropriate because

State and Federal tax systems have very different

effects, and thus tax reform is expected to have

different consequences for different regions.

Because there are close economic links between

U.S. regions, it is appropriate to assess tax reform in

a national framework.

Our general equilibrium approach is based on

assumptions that are common in the literature

(perfect competition, constant returns to scale, and

full employment of resources). Also, our analysis is

of a comparative static nature with medium-term

economic adjustments. Our model is closely related

to static AGE models already available for the

analysis of international trade (for reviews, see

Shoven and Whalley, 1984 and 1992; Francois and

Shiells; and Hertel, Ianchovichina, and McDonald).

Each regional economy, including the rest-of-theworld (ROW) region, is specified with demand and

production structures. Subject to transportation

costs, each U.S. region engages in commodity trade

with other U.S. regions and the ROW region.

Commodity prices are determined by market 

clearing through intraregional, interregional, and

international trade. 

To formulate a theoretically consistent quantitative

model of those economic linkages, we are forced to

reduce the dimensions of the problem. In particular,

our regional and commodity specifications are

shown in figure 2 and table 1. There are 10

aggregate regions representing the U.S. economy

and the rest-of-the-world region representing all

foreign economies. In terms of industry, there are

seven aggregate industries (and commodities). Each

of the three broad industries (agriculture, food

processing, and manufacturing) includes a

distinction between high-capital-intensity and lowcapital-intensity industries. For example, grain

production is represented by our high-capital

agricultural industry. A seventh industry category

represents all other economic activity. In terms of

factor endowments, we specify 15 primary factors:

farmland (noncorporate business), labor, shelter, and

six types of capital (corporate and noncorporate).

Labor and shelter are allocated to the residential

sector.2 To capture important differences in taxation, 

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 5

Agriculture

Nongrain crops

Other livestock

Wool

Agriculture, high capital

Grains

Paddy rice and wheat

Food processing

Meat products

Milk products

Other food products

Processed rice

Food processing, high capital

Beverages and tobacco

Manufacturing

Fabricated metal products

Leather, etc.

Lumber

Machinery and equipment

Nonferrous metals

Nonmetallic minerals

Other manufacturing

Petroleum and coal

Primary ferrous metals

Pulp paper, etc.

Textiles

Transport equipment industries

Wearing apparel

Manufacturing, high capital

Chemicals, rubbers, plastics

Other industries

Coal

Construction

Fisheries

Forestry

Oil and gas

Other minerals

Water, gas, electricity

Trade and transportation

Ownership of dwellings

Other services (government)

Other services (private

Table 1Commodity specification

2This specification obscures that shares of income from

shelter are subject to corporate and noncorporate taxation.

We do factor in these tax provisions, but choose to

allocate only shelter to this sector.

Overview of a U.S. Multi-Regional Applied 

General Equilibrium Model
we specify three sectors: corporate business,

noncorporate business, and residential sectors.

These sectors are used to classify and allocate all

regional factor endowments for taxation.3

Some earlier analyses using multiregional AGE

models were conducted by Kimbel and Harisson;

Jones and Whalley, 1988, 1989, and 1990; Morgan,

Mutti, and Partridge; Kraybill, Johnson, and Orden;

and Buckley. Kimbell and Harisson developed a tax

model to explore the effects of Proposition 13, using

a California/rest-of-the-United States multi-industry

framework. Their model allowed for complete

mobility and substitution of some factor inputs, but

no changes in interregional commodity flow patterns

and without transportation costs.

Jones and Whalley (1988, 1989, and 1990)

developed a multiregional AGE model for Canada.

Their model has six Canadian regions and a rest-ofthe-world region, 13 industries, and a combination

of partially mobile and immobile primary factors.

A notable mobile factor is labor, which may change

its region of use and relocate its consumption to this

new location. Labor is assumed to be internationally

immobile, interindustry mobile, but interregionally

partially mobile. The goods produced in each region

were treated as qualitatively different from similar

commodities produced either in other regions or

abroad. Because of historical patterns of

interregional trade subsidies and tariffs in Canada,

the Jones/Whalley model did not include

interregional transportation costs explicitly.

Morgan, Mutti, and Partridge developed a six-region

general equilibrium model of the United States to

assess the potential longrun effects of State, local,

and Federal tax policies on output and the allocation

of factors across regions and industries. At the most

disaggregated level, the regionally differentiated

traded goods were treated as highly substitutable but

unique products. Transportation costs were ignored.

Capital was assumed to be perfectly mobile across

regions and industry, and it was reallocated until a

common aftertax return emerges. The availability of

labor in a region was assumed to be a function of

real wages offered in that region relative to real

wages available elsewhere.

Kraybill, Johnson, and Orden developed a tworegion AGE model of the United States with five

industry aggregates. Primary factor endowments

(capital) are fixed at the regional (industry) level, as

are regional government expenditures. Trade flows,

including interregional domestic trade, are

determined by relative prices and structural

rigidities. They found that certain industries,

including agriculture, bear a disproportionate burden

of adjustment to macroeconomic imbalances,

relative to other industries. The authors conclude

that national level industry analysis underestimates

such costs.

Buckley developed an interregional AGE model of

the United States with three regions and five

industries. The study differed from other

multiregional AGE models in the explicit

specification of intra- and interregional

transportation and wholesaling services for bilateral

trade in goods by industry. Buckley concluded that,

relative to other approaches, the AGEs explicit

specification provided a more focused description of

the spatial economic effects that result from changes

in economic conditions, such as transportation costs.

In this report, we present a multiregional AGE

model, and we perform simulations of fundamental

tax reform. The strengths of our approach are recent

estimates of relevant tax policy instruments at the

Federal and State/regional level and a theoretically

consistent general equilibrium framework that builds

on earlier works in this area. We focus on the

comparative static implications of taxation under the

assumption that the regional distribution of

productive resources does not change.

Improvements to this work may endogenize the level

of productive resources (through savings and

investment in a dynamic framework) or the regional

distribution of some productive resources (through

interregional migration).

Structure of the AGE Model

Each regional economy consists of several economic

agents. First, a super-household, which is a

combined public and private household (Hertel),

supplies all primary factor services in the region and

maximizes utility to determine commodity demands.

Utility for the super-household comes from three

general sources: private consumption financed by

6 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

3More specifically, these sectors are used for purposes

of determining marginal factor tax rates, without

implication of where the burden of this tax falls.
personal income, public consumption financed by

Federal and regional government transfers, and

regional savings. The concept of a super-household

is convenient because it allows us to measure

consistently the change in regional welfare. A

second agent class encompasses the cost-minimizing

industries that employ primary factor services and

use intermediate products to produce commodities,

each industry producing a single commodity. A

third agent is a regional government that collects

taxes from economic activity in the region. Finally,

the U.S. Federal Government collects taxes from

economic activity in all U.S. regions.

Regional income for the super-household consists of

returns to primary factors (personal income), net

regional taxes, and a transfer of funds from the U.S.

Federal Government. The regional household saves

part of its income and spends the rest to purchase

private and public goods. By assuming that regional

and Federal public goods are optimally supplied, we

can focus on inefficiencies created by taxation. 

Welfare and Household Behavior

Figure 3 outlines a two-level utility tree for

households in each region of the model. It is

assumed that preferences are separable, which

allows partitioning utility and commodities in

subgroups that can be described independently of 

quantities in other groups. Thus overall utility may

be expressed as a function of subutilities, which in

turn have more subgroupings within them. The

utility tree in figure 3 consists mostly of constant

elasticity of substitution (CES) functions.

At the top of the utility tree (fig. 3), the concept of a

super-household is implemented to model household

decisions regarding expenditures and savings and to

provide a theoretically consistent and comprehensive

measure of welfare. The regional welfare

implications of changes in exogenous variables, like

tax policies, will be exactly reflected by changes in

regional welfare. Regional welfare is derived from

four components: private household expenditures,

regional and Federal expenditures for public goods,

and savings. In particular, each super-household

maximizes utility subject to a regional income

constraint. In the U.S. regions of the model, there

are two types of public goods: regional/State and

Federal. In the ROW, there is only one type of

public good. It is assumed that the simulations we

perform do not change the allocation of regional

income across private and public goods, and

savings.4

4This assumption is implemented by applying a CobbDouglas function to describe substitutions between the

four components of welfare (in this case, the Allen partial

elasticity of substitution, σ, is equal to 1, and budget

shares are constant).

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 7

Regional welfare

 Private consumption Region r government Federal Government Savings

 expenditures expenditures

=1

CDE =1 =1

 Composite

commodity

 Composite

commodity

 Composite

commodity

 Composite

commodity 

 

Composite

commodity 

 Composite

commodity 

CDE: Constant difference in elasticities. : Partial elasticity of substitution 

in CES.

Figure 3

Modeling of household welfare and expenditures

1 N 1 N 1 N
Household demands are determined separately for

the two composite commodities. First, it is assumed

that substitutions between composite public good

commodities (Federal versus regional public goods)

can be described with Cobb-Douglas functions. This

simply implies that the relative expenditures

between the regional and Federal composite

commodities remain constant. Second, private

household (that is, consumer) demands for

composite commodities are based on the constant

differences of elasticities (CDE) expenditure

function.5 The CDE specification allows for more

flexibility in specifying varying degrees of

substitution between consumer goods purchases.

This specification is also less restrictive in how one

specifies correlations between household wealth and

private goods consumption patterns. For example,

holding the relative price of all consumption goods

constant, an increase in household wealth can lead to

different rates of increase in each composite private

good commodity, such as a less-than-proportionate

increase in food consumption, and a more-thanproportionate increase in nonfood manufactured

goods.

Industrial Demands

Producing industries demand two types of inputs:

primary factors and intermediate inputs. The model

treatment of substitution between inputs is outlined

in the production tree in figure 4. The primary

factor composite is a CES aggregate of land (where

appropriate), labor, and several capital types. The

CES, or constant elasticity of substitution, allows for

substitution between factors of production in

response to changes in relative factor prices. The

elasticity of substitution between primary factors,

σVA, is industry specific. There is no substitution

between the primary factor composite and

intermediate inputs (that is, a Leontief technology is

assumed).

Interregional and International Trade

The main feature of the model treatment of trade is

that intermediate (and final demand) users of

commodities are assumed to treat imports from

different sources as imperfect substitutes, that is, the

Armington assumption is applied (Armington, 1969a

and 1969b). Thus, demands reflect cost

minimization across within-region and out-of-region

sources of supply. One advantage of the Armington

specification is that it allows one to account for the

two empirical observations that, even at a very 

8 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Figure 4

Modeling of input substitutions in production

=0

Composite intermediate

 input 1

Industry j output in region r

Composite intermediate

 input N

Value added

>0

Land Labor K1 KN K1

corporate KN

corporate

 : Partial elasticity of substitution in constant elasticity of substitution 

technology.

VA

5The CDE expenditure function was developed by

Hanoch (1975 and 1978), discussed by Surry (1989 and

1993), and implemented in AGE models by Hertel et al.

(1991), and Hertel.
disaggregated commodity level, economies import

and export the same commodity and that most

commodities are produced in all economies. The

Armington assumption also allows for differing

degrees of substitution between foreign and

domestic goods across different commodities and for

changes in relative prices of imported goods.

In figure 5, the elasticity of substitution σL>0

determines the degree of substitution that occurs

between within-region and out-of-region composite

commodities. Within-region commodities are

produced in the region. Out-of-region composite

commodities are aggregates of imports from all

other U.S. regions. Out-of-region and within-region

varieties of the same commodity are aggregated to a

domestic composite commodity. The elasticity of

substitution σD>0 determines the degree of

substitution between the domestic composite variety

and its foreign counterpart. This yields composite

commodities for each commodity in the model. The

top two levels in figure 5 implement the Armington

assumption at the agent level. At the lowest level,

the elasticity of substitution σS determines the

degree of substitution across other regional sources

of supply for each commodity. Substitutions at this

level apply for the regional economy as a whole.

Elasticities σD, σL, and σS are commodity specific.

The values of the substitution elasticities in figure 5

are important for model results on regional effects of

policies. Elasticities of substitution between foreign

imports and the domestic composite commodity

(elasticity σD) largely determine the U.S. national

import price elasticities of demand for each

commodity. These elasticities determine the extent

to which policies cause changes in the composition

between foreign and U.S. domestic sources of

supply, and thus they influence the national terms of

trade. The values of elasticities of substitution

between within-region and out-of-region varieties

(elasticity σL) largely determine the extent to which

a regions terms of trade improve or deteriorate due

to a policy change. In addition to trade elasticities

of substitution, the extent of trade and trade patterns

also influences effects on terms of trade.

For each international transaction, there is a set of

ad valorem tax (or subsidy) rates and therefore a set

of world and regional prices. From an exporters

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 9

Figure 5

Modeling of the Armington assumption

>0

U.S. composite commodity J

Demand for composite commodity J

in U.S. region 1

Foreign commodity J

>0

Region 1

commodity J

 : Partial elasticity of substitution in constant elasticity of substitution technology.

L

D

Out-of-region

composite commodity J

>0

Commodity J imported

from U.S. region 2

S

Commodity J imported

from U.S. region R

At agent

level

At region

level
point of view, the market price of a commodity will

be different from its free on board (FOB) price when

the exporting region gives an export subsidy. When

the shipment of commodity reaches its region of

destination, its customs, insurance, and freight (CIF)

price may be different from its FOB price due to

transportation costs. From the importers point of

view, the CIF price of an imported commodity may

be different from its market price, in the importing

region, due to import tariffs levied by the importing

region.

There are two more aspects of the model that affect

the interregional and international linkages in the

model. First, a global industry demands services

from each regional transportation services industry

to provide a composite service used for transporting

commodities across regions (Hertel). In value terms,

each regions relative contribution to the global

transportation industry does not change due to the

simulation performed. It is also assumed that

transportation services are required in fixed

proportions with the quantity of a particular

commodity shipped along a particular route.

Macroeconomic Closure

As in most comparative static AGE models, we face

the problem of determining investment. In nonneoclassical closures, investment is fixed, and

another variable adjusts to obtain a solution to the

model. In this model, we apply a neoclassical

closure: there is no independent investment

relationship; investment simply accommodates any

change in savings. Instead of applying this closure

at the regional level, however, we apply this closure

at the global level, using the concept of a global

industry that intermediates between savings and

investment. In each region, aggregate investment is

represented by the output of a new capital goods

industry. The global savings-investment industry has

a portfolio of regional investments offered to

regional households to satisfy their demand for

savings. Globally, the sum of investment

expenditures cannot be greater than the sum of

household savings.

Regarding the regional composition of investment,

the model offers two alternative allocation

specifications. The first allocation specification

assumes that the regional composition of global

capital stock will not change due to the simulation

performed. The second specification assumes that

there is a negative relationship between the expected

regional rate of return on capital and the amount of

investment undertaken in a region. The global

savings-investment industry manipulates this

relationship until rates of return are equalized across

regions. In the simulations that we perform at the

end of this report, the second specification is applied

and we examine the sensitivity of selected results to

the investment allocation specification.

Primary Factor Mobility

Each region has a fixed endowment of land, labor,

and capital assets. Labor services and services

flowing from existing capital stocks are assumed to

be mobile between industries, but region specific.

This implies that all industries in a region face the

same market price for labor services and the same

market price for capital services. Regarding land,

our approach allows for changes in industrial

patterns of land use, but land rent differentials across

industries are sustained. This assumption is

implemented with a system of land supply functions

derived from a constant elasticity of transformation

function, with an elasticity of transformation σT<0.

Policies

A number of factors led to our choice of the 1994

tax policy and disposition of primary industrial

factor markets for our base year. This is a tax year

in which the significant reforms of the Revenue

Reconciliation Act of 1993 were in place, most

notably the changes in upper marginal tax brackets.

The base year also immediately follows the year that

the 1992 Economic Census and 1992 Census of

Agriculture were enumerated, providing us with an

extensive data resource based on surveys conducted

in the year just prior to our year of analysis.

To encompass the multiple inefficiencies of the U.S.

tax system, we employ linear tax instruments. This

approach allows us to reconcile the total budget

accounts for our tax base year of 1994, while

explicitly modeling the marginal incentive effects of

taxation. The relative size of the public sector

directly affects the regional measures of welfare, as

does the differential tax treatment of primary

production factors and industry output.

10 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA
Each regional household is endowed with all primary

factors of regional production (fig. 6). All

compensation that flows to these factors from

regional industry (Level III) are owned by the

household. All sources of taxation that fall on the

personal income tax base (including property and

output taxes) are explicitly modeled at their marginal

effective rates, so the personal income tax depicted

in Level V of figure 6 is a nondistorting intercept

term, which forces the overall income tax rate to

equal the actual 1994 rate on the relevant income tax

base. Mathematically, if the total income tax base, 

, is taxed as the sum of I linear factor

tax instruments and one ad valorem output tax

instrument, total income tax revenue (T) equals 

, where ai is the 

intercept (generally negative) of the linear factor tax,

bi is the slope (marginal effective tax rate) of the

linear factor tax, and c is the flat output tax rate

across all industry output.6 Notice that linear factor

taxes are progressive average taxes, which we

calibrate to actual average tax rates, but that also

reflect the marginal tax incidence on the factor

income. It is straightforward to show that total tax

revenue can be restated as, 

where is the weighted average marginal effective

tax rate on factor incomes. Relating this to Level V

in figure 6, the quotient within the squared brackets

of the above equation is the personal income tax rate

depicted in Level V, while the net of tax personal

income depicted across Level IV of figure 6 is equal

to in the above equation. As depicted in

figure 8, the bi are marginal effective factor taxes,

while tbi are the gross of tax factor incomes.

The private household uses all the net proceeds

(Level VI) to purchase consumption and investment

goods (Level VII). This consumption/savings

decision is determined by the relative prices of the

two activities, based on a Cobb-Douglas preference

specification. The decision to consume subjects the

household to an ad valorem consumption tax, while

the decision to save is indirectly subject to a tax on

the price of savings (Level VIII).7 The relative tax

rates on consumption and investment goods will

affect the household decision, and thus affect the

level of household welfare (Level VIII). The

consumption depicted in figure 6 is carried over to

figure 7. For exposition, assume the final good on

the top of figure 6 is the only good consumed by this

household, and arbitrarily place this household in

region 1 of a two-region U.S. economy. As depicted

in figure 4, this consumer good is a composite of

industrial output from domestic regions and the rest

of the world. Moving to the bottom center of figure

7, note that for any industry i in region 1, industrial

output is directed to four areas (downstream industry

within region 1, downstream industry in other

domestic regions, downstream international industry,

final good markets). All industry i output shipped to

international downstream industry may be taxed at

the border, and this is treated as a separate border tax

base. All domestically produced industrial output

used in consumer goods are subject to an industry

output tax, and all international industrial output

used in consumer goods purchased by U.S.

consumers is subject to a U.S. border tax. These

taxes are represented in the model as ad valorem

taxes and, except for border taxes, are assumed to be

transmitted backward to the factor owners. It is

assumed that no taxes are levied on shipments of

industrial output to domestic downstream industries.

Each regions tax burden on output includes Federal

and State excise taxes and other fees that are

proportional to output.

Industrial shipment proceeds must be allocated to

factors of production.8 This is depicted in figure 8.

Payments to upstream industry for intermediate

factors of production become part of the gross

proceeds of industry j. Gross payments to labor lead 

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 11

T

a cTB

TB b

TB b b tb

i

i

l

i

i ii

i

I

=

+

−

























− + =

=

∑

∑ 1

1 1

1

( )

x ( ) ( ),

6Due to the level of industry aggregation, output taxes

are viewed as nondistorting in this model, and so are

modeled as an ad valorem tax on all output.

7In the model, the price of savings is the numeraire.

The purchasing power of savings is the weighted average

price of investment goods, which are in turn affected by

factor and output taxes.

8Compensation of factors and from customers must be

determined simultaneously. The sequential description

here is arbitrary.

TB tb i

I = Σ =1 i

[ ] Σi

I

i ii =1 ( ) a b tb cTB + +

bi

TB bi ( ) 1−
12 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Figure 6

Sources of income and taxes from household capital and labor endowments

Regional private

household

Labor

endowment

Capital

endowment

Hired by

industry

Leased by

industry

Ad valorem

personal

income tax

Net regional

personal income

Savings Consumption

(see fig. 7)

Consumption

tax

Savings

tax

Level IV

Level III

Level II

Level I 

Level V

Level VII

Level VIII

Level VI

Net of tax

dispersals from 

capital

Net of tax

dispersals as

salary and wages
to a tax burden on industry i to cover Federal and

State taxes, both for labor income and to cover

Federal and State insurance trusts. This is

represented as an ad valorem tax in the model, with

a rate that reflects the weighted average marginal tax

burden on regional labor income.9 In other words, it

is one of the bi introduced at the beginning of this

Policies subsection.

While a single ad valorem tax on labor income is

sufficient for calibration of the base year model, it is

important to note that the labor factor tax has two

componentsincome taxes and insurance trust

taxes. These two tax instruments are computed

separately and are additive at the margin. The

purpose of this accounting procedure becomes more

evident when we carry out tax reform simulations,

since no serious tax reform scenarios propose

harmonizing the insurance trust tax with other tax

instruments. Our approach will be to leave the

insurance trust tax, which is a pure wage tax, in

place in all reform simulations not directly involving

social security and/or medicare reform.

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 13

9One dollar of wage income is distributed to regional

households in proportion to their existing wage income.

Based on 1993 Federal and State income and insurance

trust tax rules, new proceeds of these taxes from this

dollar of income is divided by 100 to arrive at an average

marginal wage tax rate.

Region 1

Figure 7

Sources of tax revenue in the industry/final-goods linkage

Region 2 Rest of

the world

Consumer good

(from fig. 6)

Industry 1 Industry i ... ... Industry 10

Shipped to final

goods sector

Shipped to downstream industry

Shipped to

other regions

Shipped to

rest of the world

Post production costs

Border

tax

Industry Industry

Border

tax

Output

tax

Output

tax
What is not paid to labor and intermediates is paid to

capital. In the model, there are 14 different

classifications of capital, and for each type of capital

in each region and for each of the 7 industry

aggregates, there is a unique marginal tax rate, or a

unique bi. The method and measurement of these

tax rates are discussed in the Taxation section and

further explained in the appendix. The fact that each

factor tax rate may be unique means the relative use

of factors will be different from a scenario of no tax

on factor incomes. Capital proceeds are allocated to

each type of capital consistent with the requirement

that net rates of return to the factor owner of each

type of capital are equal. After factor taxes are

deducted from industry proceeds, the net of factor

tax payments to labor and capital is paid to factor

owners, depicted in Level V of figure 6.

Public Expenditures

Government expenditures remain proportionally

fixed to personal income. Since the budget shares of

each government entity reflect household

preferences, and because public and private

consumption utilities are separable in the utility

function, the cost-minimizing expenditures for each

public good also remain proportionally fixed. In

revenue-neutral tax simulations, public savings will

vary inversely with the public good price index,

nationally and regionally. This result is obtained in

the model through both regional and U.S. Federal

Government transfers of tax proceeds in fixed

amounts to regional households.

Accounting Relationships

The model has a number of accounting relationships:

market clearing for traded commodities and primary

factor services, zero profit conditions, and income

constraints for households. It is these economywide

and global relationships that differentiate a partial

from a general equilibrium model. One of these

accounting relationships will be automatically

satisfied when all the other accounting relationships

are satisfied. This relationship is not included in the

model, and the corresponding price is the numeraire

14 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Industry i

 net of output

tax proceeds

Payments to

upstream

industry

Gross payment

to labor

Gross payment

to type 1

capital

Gross payment

to type c

capital

Gross payment

to type C

capital

Gross proceeds

to industry j

Income and

insurance trust

tax on wages

Tax on

type 1

capital

Tax on

type c

capital

Net of factor

tax payment

to labor

(fig. 6)

Net of factor

tax payment

to capital

(fig. 6)

Figure 8

Sources of tax revenue in industry factor markets

Tax on

type C

capital
in the model. In this model, the accounting

relationship that is automatically met is that the sum

of regional investment must equal the sum of

regional savings. Therefore, the numeraire is the

price of savings.

In the tax reform simulations that we perform in this

bulletin, we require that the amount of taxes

collected by the Federal and/or each regional

government does not change. These government

budget conditions are a part of the macroeconomic

closure of the model.

The Database

To implement the model outlined in this section,

initial equilibrium data and parameter values must

be specified. The next two subsections describe the

procedures applied to build a micro-consistent

regional data set for the United States and the ROW,

and the parameter values specified.

For each producing industry and household in the

model, there are three separate vectors with

expenditures, at agent prices: one for commodities

produced within the region, one for composite

commodities produced outside the region, and one

for foreign imports. For each producing industry,

there is also a vector with payments to primary

factors. Corresponding vectors have data for these

transactions evaluated at market prices. The sum of

all payments for intermediate inputs and primary

factors, at agent prices, gives the value of an

industrys costs. The sum of expenditures by

demanders, at market prices, shows total sales of

commodities produced within the region and total

exports. The sum of these two items gives total

sales for a commodity. Total costs of each regional

industry must equal total sales of the corresponding

commodity produced within the region.

The trade data record bilateral trade flows between

all regions and for all commodities. For each

bilateral trade flow, there are four measures. Two

measures are from the exporters perspective: one

evaluates exports at domestic market prices, and the

other evaluates exports at world prices (FOB prices).

The difference between these two measures is any

export tax or subsidy. The other two measures are

from the importers perspective: one evaluates

imports at world prices (CIF price), and the other

evaluates imports at domestic market prices. The

difference between the two measures is any import

tariff duties. The difference between CIF and FOB

values is due to transportation costs.

The 1987 input-output (IO) table of the U.S.

economy (U.S. Department of Commerce, Bureau of

Economic Analysis, 1994), the State-level

employment statistics (U.S. Department of

Commerce, Bureau of Economic Analysis, 1998b),

and the State capital accounts (see the appendix) are

the major building blocks in assembling regional

economic accounts. To derive producer accounts for

each U.S. State, statistics on employment by State

were used along with our State capital accounts, and

the assumption that for each industry/commodity,

average output per unit of value added is the same

across States. National private household

expenditures are prorated across States using

statistics on total personal income, by State (U.S.

Department of Commerce, Bureau of Economic

Analysis, 1999). U.S. national gross investment, by

industry, was prorated across States using the

computed State-level industrial output.

The IO table provides foreign import and export

statistics for the U.S. economy as a whole. Two

additional sources of information were used to

describe trade linkages between U.S. States and

those between U.S. States and the ROW. One of

them is the Commodity Flow Survey statistics on

interstate trade flows for commodities (U.S.

Department of Transportation, Bureau of

Transportation Statistics), and the other is the State

Merchandise Export statistics (U.S. Department of

Commerce, International Trade Administration).

Both of those data sets are published at a very

aggregated commodity specification. Therefore, it

was necessary to prorate the trade data across

commodities using regional production information.

For the commodities not covered by the trade

statistics, we assumed that regional trade patterns

were similar to those for total trade.

The commodity flow survey provided the data for

the value of shipments and ton-miles traveled by

commodity for State of origin and the value of total

shipments and ton-miles traveled by State of

destination for State of origin. From the latter data,

the composition of total exports by State of

destination for State of origin may be computed.

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 15
Assuming that this composition is the same for all

commodities, the data sources allow construction of

bilateral trade flow matrices by commodity.

Similarly, average ton-miles shipped for every trade

transaction (that is, by commodity, State of origin,

and State of destination) were computed. These data

along with information on U.S. industry

expenditures on transportation costs (from the IO

table) allow construction of transportation cost

information by commodity and trade route.

Percentage transportation costs for international

trade were obtained from the GTAP database

(Gehlhar et al.).

The State merchandise export statistics provide the

data for exports from the States to the ROW. For

commodity aggregates for which the State export

data do not have information, U.S. national exports

were prorated across States.

The protection data refer to trade between the States

and the ROW and are derived from the GTAP

database (Gehlhar et al.). Those protection data

include: (1) bilateral import tariffs derived from the

original country submissions to the GATT for the

Uruguay Round negotiations, (2) the Multi-Fiber

Arrangement, (3) antidumping duties levied by

Canada, the European Union, and the United States,

(4) export subsidies for agricultural commodities,

and (5) import nontariff barriers for agricultural and

food commodities. The database also has import

tariffs from the Uruguay Round agreement of the

GATT.

Behavioral Parameters

In addition to the domestic and interregional data,

the model requires specification of the behavioral

parameters discussed in the Welfare and Household

Behavior subsection. These parameters describe

for each region utility-maximizing opportunities

available to households, cost-minimizing

opportunities available to producers, and import

demands. Values for these parameters have been

adopted from the SALTER (Jomini et al.) and GTAP

(Huff et al.) modeling frameworks.

Demand systems for private households are based on

CDE expenditure function. Ideally, one should

obtain econometric estimates of the CDE

parameters. However, it is rather difficult to

estimate a CDE consumer demand system. Instead,

the CDE is calibrated to price and income elasticities

of demand from the econometric literature. This

requires implementation of a calibration procedure

outlined in Hertel et al. (1991) and Huff et al.

The first and second sections in table 2 show the

demand elasticities for the private household in the

model. As described in the Welfare and Household

Behavior subsection, the elasticities in table 2 are

based on the CDE functional form and are a function

of the underlying CDE parameters and budget shares

in the initial equilibrium. The substitution

elasticities in table 2 show the Allen partial

elasticities for the CES functions that describe

substitutions among primary factor services in value

added (see the Industrial Demands subsection) and

in trade (see the Interregional and International

Trade subsection).

The Intermediate Run

Policy simulations in the model assume a sufficient

passage of time for existing production capacity

within each economic region to be reallocated

among industries. Reallocation exploits differential

rates of return resulting from changes in parameter

values. While this interindustry mobility can occur

within each economic region, no such mobility

exists across regions or between asset types (for

example, transforming tractors into computers).

To motivate this intermediate-run scenario, we focus

on capital factor markets. We have representation of

four broad categories of capitaloffice machines,

heavy machinery, transportation equipment, and

industrial plantseach existing in the corporate and

noncorporate form.10 To maintain the production

capacity made possible by this capital, a

combination of regional public and private

infrastructure must be in place to accommodate and

service plant and machinery capacity and replace

worn-out capital.

If a new fiscal policy regime were put in place (or

some other change in economic conditions) that had

differential effects across industry, sectors, capital

types, and regions, we postulate a distinct ordering

16 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

10We will ignore land, utility plants, and residential

structures in this discussion.
of industrial response. Starting from the end,

assuming the new policy regime is perceived to be

permanent, public and private sectors in regions that

enjoy a distinct advantage in the new regime are

likely to take measures to attract to their region (or in

response to the attraction of their region) new

investment capital so as to have in place a greater

infrastructure for maintenance of higher capital (and

labor) capacity. In the model, this possibility is

addressed in our closure assumptions in the form of

an international investment arbiter that allocates

global savings regionally using one of two possible

arbitration rules. This flow of funds foreshadows

longrun effects of policy changes but has no real

effects on regional production capacity. This is 

consistent with our assumption of asset fixity at the

regional level, and a survey of industrial location

research literature substantiates this assumption. For

example, Blair and Premus conducted a literature

survey that summarizes the prevalent empirical

findings: [M]ost local growth is attributable to

differential growth rates of existing facilities.  The

complete shutdown of a plant in one area in order to

relocate to another area is rare (p. 74). Global

savings is directed to purchases of current specific

industrial output in regions proportional to the

planned future expansions (or retractionsfor

example, investment less than current period capital

consumption).

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 17

Table 2Model elasticities and parameters

Elasticities Agriculture Food Manufacturing Other Agriculture, Food, Manufacturing,

industry high capital high capital high capital

Compensated 

own-price 

elasticities:

Appalachian -0.09 -0.05 -0.76 -0.22 -0.01 -0.68 -0.88

Corn Belt -0.09 -0.06 -0.76 -0.20 -0.01 -0.70 -0.87

Delta States -0.07 -0.06 -0.72 -0.23 -0.01 -0.70 -0.86

Lake States -0.09 -0.05 -0.76 -0.20 -0.01 -0.70 -0.89

Mountain -0.08 -0.05 -0.77 -0.19 -0.02 -0.70 -0.88

Northeast -0.09 -0.04 -0.76 -0.20 -0.01 -0.70 -0.90

Northern Plains -0.08 -0.05 -0.76 -0.20 -0.02 -0.71 -0.88

Pacific -0.08 -0.04 -0.79 -0.17 -0.01 -0.69 -0.88

Southeast -0.09 -0.04 -0.75 -0.21 -0.01 -0.70 -0.88

Southern Plains -0.09 -0.06 -0.73 -0.22 -0.01 -0.70 -0.87

Rest of world -0.16 -0.17 -0.81 -0.19 -0.03 -0.51 -0.65

Income 

elasticities:

Appalachian 0.65 0.62 1.03 1.04 0.60 0.98 1.06

Corn Belt 0.69 0.67 1.02 1.03 0.65 0.98 1.06

Delta States 0.68 0.66 1.02 1.03 0.64 0.98 1.06

Lake States 0.62 0.69 1.03 1.04 0.56 0.97 1.07

Mountain 0.65 0.63 1.02 1.03 0.61 0.97 1.06

Northeast 0.54 0.52 1.03 1.04 0.48 0.96 1.07

Northern Plains 0.55 0.54 1.03 1.04 0.51 0.96 1.07

Pacific 0.63 0.61 1.02 1.03 0.59 0.97 1.06

Southeast 0.57 0.54 1.03 1.04 0.52 0.97 1.07

Southern Plains 0.66 0.64 1.03 1.03 0.62 0.98 1.06

Rest of world 0.48 0.33 1.05 1.10 0.16 0.90 1.17

Substitution 

elasticities:

S 10.33 8.80 12.68 9.21 8.80 12.40 7.60

L 5.10 4.40 6.12 3.96 4.40 6.20 3.80

D 2.53 2.20 3.13 1.99 2.20 3.10 1.90

VA 0.56 1.12 1.26 1.38 0.56 1.12 1.26
We assume that the passage of time required to get

investment capital up and running is similar to the

passage of time it takes to convert existing tractors

into computers, or vice versa. Without taking this

statement too literally, we simply mean that our

broadly defined capital aggregates are sluggishly

convertible. The same heavy machinery plant that

produces farm tractors also produces construction

cranes, and can transform tractor production to crane

production timelessly, but with transformation

limitations. On the other hand, current period shifts

in regional demand for computer chips must be met

using the regions office equipment capacity

infrastructure, not through transformed heavy

machinery capacity. Our partitioning of capital type

aggregates is intended to reflect distinct operation

infrastructure associated with each asset category.

Corporate, noncorporate, and residential capital

cannot be transformed across sectors. This

assumption is motivated by two factors. First, as

noted in Fullerton and Henderson, many decisions

related to incorporation and unincorporation reflect

risk preferences and size considerations, and such

factors are not explicitly represented in our model.

Also, there is a paucity of regional data on corporate

capital location, so it is difficult to make assertions

about their mobility within a region, although we

can allocate corporate capital regionally. As will be

evident when we carry out policy simulations, even

when national intersectoral shifts are small, regional

level changes can be significant, and we chose to

avoid the possibility of large regional intersectoral

shifts in factors of production in our interpretation of

the intermediate run. There is some theoretical

justification for this assumption, as is often pointed

out in the economic debates on the effect of capital

gains taxation reform (see Auten and Cordes). Many 

have theoretically and empirically challenged the

assertion that higher aftertax rates of return on

corporate capital necessarily lead to higher rates of

savings allocated to the corporate sector.

While each assumption employed in our

representation of the intermediate run is subject to

anecdotal counter examples, we believe that,

collectively, they are an accurate representation of

the intermediate response by economic agents to

changes in economic factors. Further, our closure

techniques provide a detailed foreshadowing of the

longrun response, as would be explicitly captured in

a dynamic policy simulation framework.

PC Implementation of the Model

To implement the model on a personal computer and

perform simulations, a simulation program has been

developed based on the GEMPACK suite of

software (Harrison and Pearson). GEMPACK is

designed to solve nonlinear economic models like

this one. In particular, the model has been

implemented in its linearized representation, and a

solution consists of percentage changes in relative

prices and quantities. GEMPACK obtains multistep

solutions: a shock is broken up into several smaller

pieces and, at each step, the linearized equations are

solved for these smaller shocks. After each step, the

data and price and income elasticities are

recalculated to take into account the changes from

the previous step. In general, the more steps a shock

is broken into, the more accurate the solution will

be. Pearson shows formally how a GEMPACK

solution based on the linearized representation of a

model can be as accurate as a solution of the

underlying nonlinear model (Harrison and Pearson,

appendix B; and Hertel, Horridge, and Pearson).

18 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA
Measurement of tax incidence from applied general

equilibrium tax analysis is usually obtained using

industry and factor market delineations with polar

factor input requirementscapital versus labor

intensive, durable versus short-lived capital,

business sector versus owner-occupied housing,

corporate versus non-corporate industry. We employ

this criteria with refinements designed to emphasize

the food and farm industries, and to use the most

advantageous interpretation of primary data sources.

Using procedures described in the following

subsection, we classify industry as follows: capitalintensive farms, other farms, capital-intensive food

manufacturing, other food manufacturers, capitalintensive nonfood manufacturing, other nonfood

manufacturing, and services, mines, and trade (all

other industry).

All value added produced by industry is either labor

or capital. Capital is heterogeneous, and our data

are based on 14 distinct aggregations from 54

classifications of capital types, as defined in BEA

detailed wealth accounts (U.S. Department of

Commerce, Bureau of Economic Analysis, 1998).

These 14 aggregates include both corporate and

noncorporate classifications. The purpose of this

distinction is to capture the tax differential due to the

corporate tax wedge. One industry, services, mines,

and trade, employs both corporate and noncorporate

capital, while all other industry aggregates employ

either all corporate or all noncorporate capital. The

six corporate-owned assets are computers/office

equipment, service and furnishing equipment, heavy

machinery, transportation equipment, industrial

plants, and utility plants. Noncorporate enterprise

also owns these six asset aggregates, as well as

farmland. Finally, residential shelter (which is a

hybrid of both corporate and noncorporate owned),

along with labor services, complete the list of 15

possible primary factor inputs available to industry.

Industry Classification

The national input-output accounts describe the

value added by capital and labor in nearly 500 U.S.

industry aggregates. All industry output is tradable

and must be produced in all regions. For this

condition to hold, aggregate groupings of industry

output in all regions must be developed. We,

therefore, map the industrial aggregates into these 35

industry categories (table 1), and determine the

capital and labor value added. Based on the ratio of

total capital value added per worker in each industry

and using a procedure for identifying exotic values

within a known distribution (Hoaglin, Iglewicz, and

Tukey), we determine that three special capitalintensive industries will be explicitly represented in

the numerical model: cash grain farms; tobacco and

beverages; and chemical, rubber, and plastic

manufacturing. All other industry is grouped as

follows: farms; food manufacturers; nonfood

manufacturers; and services, mines, and trade. Two

other capital-intensive industriesconstruction, and

owner-occupied shelterare grouped with the

services, mines, and trade industry.

Labor

Labor does have some special classifications in the

tax codefor example, employees over 65 years in

age, certain handicaps, marital status, and veterans

preferences. Most of these can be treated with

explicit accounts of labor dispositions in regional

markets. Industry bias in these characteristics has

not been convincingly demonstrated, so our

approach is to model the representative worker for

each regional industry, such that firm-level labor is

homogeneous, while industry and regional labor is

heterogeneous, strictly due to tax treatments.

Capital

Among the most complicated aspects of the Federal

tax code are the special provisions for the ownership

of business capital. Most of this complication

involves owner claims of capital depreciation over a

given tax yeara real expense to capital owners, but

difficult to quantify. While there are countless

different types and uses of business capital, and even

more rates of wear and tear on this capital, there are

six cost-recovery periods in the tax code (plus a few

special other classifications), one of which each

asset must follow for claims of cost recovery. A

number of economic studies have been conducted to

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 19

Disposition of Industries and 

Primary Factors of Production
20 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 3--Regional factor shares of industry value added, by capital type and labor

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Appalachian:

Corporate--

Computers/office equipment 0 0 0.672 3.083 1.262 2.809 0.586 0.105 0

Service and furnishing equipment 0 0 0.429 1.833 0.488 0.316 0.251 0.080 0

Heavy machinery 0 0 5.717 32.342 7.822 13.309 0.645 3.436 0

Transportation equipment 0 0 0.321 2.176 0.307 0.259 0.565 2.235 0

Industrial plants 0 0 2.714 12.469 3.988 5.416 2.083 1.350 0

Utility plants 0 0 0 0 0 0 0.731 0.021 0

Noncorporate--

Computers/office equipment 0.042 0.008 0 0 0 0 0.218 0.045 0

Service and furnishing equipment 0.001 0 0 0 0 0 0.123 0.034 0

Heavy machinery 2.710 0.433 0 0 0 0 0.227 1.457 0

Transportation equipment 0.989 0.113 0 0 0 0 0.286 0.949 0

Industrial plants 0.941 0.115 0 0 0 0 1.029 0.581 0

Utility plants 0 0 0 0 0 0 0.100 0.004 0

Farmland 4.477 1.074 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.869 0 9.121

Labor services 8.211 3.665 9.136 35.559 10.031 14.417 8.179 0 0

Corn Belt:

Corporate--

Computers/office equipment 0 0 1.683 0.646 1.961 2.322 1.024 0.148 0

Service and furnishing equipment 0 0 1.075 0.412 0.615 0.282 0.446 0.114 0

Heavy machinery 0 0 14.322 5.543 12.690 12.488 0.888 4.861 0

Transportation equipment 0 0 0.805 0.314 0.441 0.245 1.052 3.165 0

Industrial plants 0 0 6.779 2.661 5.942 6.588 3.699 1.769 0

Utility plants 0 0 0 0 0 0 1.218 0.025 0

Noncorporate--

Computers/office equipment 0.090 0.183 0 0 0 0 0.374 0.064 0

Service and furnishing equipment 0.003 0.006 0 0 0 0 0.215 0.049 0

Heavy machinery 5.665 9.684 0 0 0 0 0.317 2.062 0

Transportation equipment 1.675 2.561 0 0 0 0 0.495 1.343 0

Industrial plants 1.873 2.671 0 0 0 0 1.805 0.763 0

Utility plants 0 0 0 0 0 0 0.085 0.005 0

Farmland 10.042 24.325 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 1.411 0 14.703

Labor services 11.293 39.020 19.792 9.645 18.750 19.733 13.861 0 0

Delta States:

Corporate--

Computers/office equipment 0 0 0.326 0.114 0.424 1.092 0.215 0.034 0

Service and furnishing equipment 0 0 0.208 0.073 0.162 0.119 0.092 0.023 0

Heavy machinery 0 0 2.773 0.974 2.962 4.932 0.267 0.920 0

Transportation equipment 0 0 0.156 0.055 0.114 0.096 0.215 0.592 0

Industrial plants 0 0 1.672 0.533 1.384 1.253 0.766 0.308 0

Utility plants 0 0 0 0 0 0 0.610 0.207 0

Noncorporate--

Computers/office equipment 0.022 0.013 0 0 0 0 0.082 0.013 0

Service and furnishing equipment 0.001 0 0 0 0 0 0.044 0.009 0

Heavy machinery 1.269 0.585 0 0 0 0 0.077 0.384 0

Transportation equipment 0.397 0.200 0 0 0 0 0.098 0.249 0

Industrial plants 0.388 0.185 0 0 0 0 0.370 0.124 0

Utility plants 0 0 0 0 0 0 0.082 0.043 0

Farmland 2.723 1.734 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.314 0 2.908

Labor services 4.273 3.088 4.497 1.698 2.887 5.179 2.655 0 0

Continued--
Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 21

Table 3--Regional factor shares of industry value added, by capital type and labor--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Lake States:

Corporate--

Computers/office equipment 0 0 0.933 0.387 0.876 0.480 0.475 0.088 0

Service and furnishing equipment 0 0 0.596 0.247 0.299 0.065 0.222 0.068 0

Heavy machinery 0 0 7.938 3.292 5.973 3.046 0.402 2.931 0

Transportation equipment 0 0 0.446 0.185 0.227 0.060 0.475 1.909 0

Industrial plants 0 0 4.763 1.623 2.479 1.130 1.778 0.982 0

Utility plants 0 0 0 0 0 0 0.496 0.011 0

Noncorporate--

Computers/office equipment 0.047 0.033 0 0 0 0 0.189 0.038 0

Service and furnishing equipment 0.002 0.001 0 0 0 0 0.110 0.029 0

Heavy machinery 2.974 1.743 0 0 0 0 0.155 1.244 0

Transportation equipment 0.838 0.456 0 0 0 0 0.248 0.810 0

Industrial plants 1.031 0.466 0 0 0 0 0.886 0.421 0

Utility plants 0 0 0. 0. 0 0 0.041 0.002 0

Farmland 5.213 4.362 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.681 0 8.034

Labor services 7.926 9.844 10.097 4.898 11.266 8.092 6.867 0 0

Mountain:

Corporate--

Computers/office equipment 0 0 0.245 0.229 0.383 0.271 0.455 0.073 0

Service and furnishing equipment 0 0 0.157 0.146 0.107 0.029 0.182 0.053 0

Heavy machinery 0 0 2.086 1.949 1.917 1.204 0.496 2.186 0

Transportation equipment 0 0 0.117 0.110 0.079 0.023 0.419 1.419 0

Industrial plants 0 0 1.226 0.740 1.027 0.459 1.663 0.973 0

Utility plants 0 0 0 0 0 0 0.705 0.107 0

Noncorporate--

Computers/office equipment 0.041 0.044 0 0 0 0 0.167 0.031 0

Service and furnishing equipment 0.001 0.002 0 0 0 0 0.089 0.023 0

Heavy machinery 2.448 2.387 0 0 0 0 0.170 0.924 0

Transportation equipment 0.690 0.611 0 0 0 0 0.208 0.601 0

Industrial plants 0.803 0.581 0 0 0 0 0.828 0.420 0

Utility plants 0 0 0 0 0 0 0.092 0.022 0

Farmland 4.960 5.942 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.641 0 5.408

Labor services 7.088 6.516 3.799 5.765 3.807 1.132 5.621 0 0

Northeast:

Corporate--

Computers/office equipment 0 0 0.874 0.467 2.075 2.032 2.013 0.236 0

Service and furnishing equipment 0 0 0.558 0.297 0.654 0.233 0.847 0.181 0

Heavy machinery 0 0 7.440 4.012 10.484 9.969 1.352 7.755 0

Transportation equipment 0 0 0.418 0.228 0.422 0.195 1.600 5.051 0

Industrial plants 0 0 2.924 1.427 3.450 4.662 7.314 2.949 0

Utility plants 0 0 0 0 0 0 1.772 0.013 0

Noncorporate--

Computers/office equipment 0.032 0.005 0 0 0 0 0.742 0.102 0

Service and furnishing equipment 0.001 0 0 0 0 0 0.434 0.078 0

Heavy machinery 2.387 0.277 0 0 0 0 0.545 3.291 0

Transportation equipment 0.707 0.074 0 0 0 0 0.867 2.144 0

Industrial plants 0.891 0.077 0 0 0 0 3.728 1.272 0

Utility plants 0 0 0 0 0 0 0.138 0.003 0

Farmland 2.885 0.676 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 3.398 0 24.752

Labor services 8.052 2.319 15.640 11.938 20.581 21.416 26.660 0 0

Continued--
22 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 3--Regional factor shares of industry value added, by capital type and labor--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Northern Plains:

Corporate--

Computers/office equipment 0 0 0.357 0.032 0.369 0.151 0.148 0.027 0

Service and furnishing equipment 0 0 0.228 0.020 0.090 0.019 0.060 0.020 0

Heavy machinery 0 0 3.037 0.274 1.658 0.861 0.134 0.887 0

Transportation equipment 0 0 0.171 0.015 0.062 0.017 0.139 0.577 0

Industrial plants 0 0 1.442 0.077 1.249 0.559 0.497 0.283 0

Utility plants 0 0 0 0 0 0 0.239 0.025 0

Noncorporate--

Computers/office equipment 0.046 0.128 0 0 0 0 0.052 0.011 0

Service and furnishing equipment 0.002 0.004 0 0 0 0 0.029 0.009 0

Heavy machinery 2.554 6.843 0 0 0 0 0.052 0.376 0

Transportation equipment 0.696 1.764 0 0 0 0 0.066 0.245 0

Industrial plants 0.740 1.720 0 0 0 0 0.241 0.120 0

Utility plants 0 0 0. 0. 0 0 0.045 0.005 0

Farmland 5.911 17.137 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.214 0 1.980

Labor services 7.067 22.387 6.545 0.799 1.811 1.547 1.856 0 0

Pacific:

Corporate--

Computers/office equipment 0 0 0.735 0.465 1.102 0.568 1.202 0.143 0

Service and furnishing equipment 0 0 0.469 0.297 0.378 0.069 0.518 0.104 0

Heavy machinery 0 0 6.257 3.961 6.224 3.050 0.909 4.266 0

Transportation equipment 0 0 0.352 0.223 0.276 0.060 1.116 2.775 0

Industrial plants 0 0 3.023 1.689 1.760 0.551 4.330 1.899 0

Utility plants 0 0 0 0 0 0 1.122 0.134 0

Noncorporate--

Computers/office equipment 0.064 0.015 0 0 0 0 0.498 0.062 0

Service and furnishing equipment 0.002 0.001 0 0 0 0 0.262 0.045 0

Heavy machinery 3.785 0.812 0 0 0 0 0.436 1.807 0

Transportation equipment 1.101 0.221 0 0 0 0 0.618 1.177 0

Industrial plants 1.510 0.212 0 0 0 0 2.238 0.829 0

Utility plants 0 0 0 0 0 0 0.208 0.028 0

Farmland 7.393 2.059 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 2.548 0 14.899

Labor services 26.186 5.318 15.334 12.927 15.863 8.209 16.329 0 0

Southern Plains:

Corporate--

Computers/office equipment 0 0 0.494 0.359 0.723 1.282 0.783 0.087 0

Service and furnishing equipment 0 0 0.316 0.229 0.219 0.144 0.305 0.059 0

Heavy machinery 0 0 4.207 3.055 4.081 6.076 0.809 2.372 0

Transportation equipment 0 0 0.236 0.172 0.164 0.118 0.690 1.530 0

Industrial plants 0 0 2.060 1.237 1.287 1.333 2.920 1.001 0

Utility plants 0 0 0 0 0 0 2.065 0.438 0

Noncorporate--

Computers/office equipment 0.050 0.032 0 0 0 0 0.293 0.034 0

Service and furnishing equipment 0.002 0.001 0 0 0 0 0.148 0.024 0

Heavy machinery 3.194 1.672 0 0 0 0 0.266 0.994 0

Transportation equipment 0.960 0.446 0 0 0 0 0.312 0.644 0

Industrial plants 1.084 0.428 0 0 0 0 1.422 0.416 0

Utility plants 0 0 0 0 0 0 0.326 0.091 0

Farmland 5.435 4.251 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.975 0 7.339

Labor services 8.312 5.805 6.794 6.060 6.248 10.450 7.845 0 0

Continued--
Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 23

Table 3--Regional factor shares of industry value added, by capital type and labor--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Southeast:

Corporate--

Computers/office equipment 0 0 0.438 0.595 0.787 1.196 0.796 0.110 0

Service and furnishing equipment 0 0 0.279 0.363 0.350 0.138 0.330 0.082 0

Heavy machinery 0 0 3.724 5.822 5.805 5.890 0.650 3.373 0

Transportation equipment 0 0 0.209 0.372 0.212 0.115 0.684 2.197 0

Industrial plants 0 0 1.592 2.177 2.188 2.417 2.751 1.546 0

Utility plants 0 0 0 0 0 0 0.877 0.011 0

Noncorporate--

Computers/office equipment 0.029 0.003 0 0 0 0 0.294 0.049 0

Service and furnishing equipment 0.001 0 0 0 0 0 0.165 0.035 0

Heavy machinery 1.801 0.159 0 0 0 0 0.258 1.431 0

Transportation equipment 0.589 0.042 0 0 0 0 0.369 0.933 0

Industrial plants 0.645 0.045 0 0 0 0 1.396 0.678 0

Utility plants 0 0 0. 0. 0 0 0.093 0.002 0

Farmland 3.148 0.380 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 1.101 0 10.855

Labor services 11.594 2.037 8.367 10.712 8.756 9.823 10.126 0 0

United States:

Corporate--

Computers/office equipment 0 0 6.757 6.378 9.963 12.202 7.698 1.050 0

Service and furnishing equipment 0 0 4.314 3.918 3.364 1.414 3.253 0.784 0

Heavy machinery 0 0 57.503 61.223 59.617 60.826 6.552 32.985 0

Transportation equipment 0 0 3.232 3.848 2.303 1.189 6.954 21.449 0

Industrial plants 0 0 28.194 24.633 24.753 24.369 27.801 13.060 0

Utility plants 0 0 0 0 0 0 9.835 0.991 0

Noncorporate--

Computers/office equipment 0.463 0.463 0 0 0 0 2.911 0.449 0

Service and furnishing equipment 0.016 0.016 0 0 0 0 1.618 0.335 0

Heavy machinery 28.787 24.595 0 0 0 0 2.504 13.971 0

Transportation equipment 8.642 6.487 0 0 0 0 3.569 9.095 0

Industrial plants 9.905 6.499 0 0 0 0 13.943 5.624 0

Utility plants 0 0 0 0 0 0 1.210 0.207 0

Farmland 52.187 61.940 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 12.153 0 100.000

Labor services 100.000 100.000 100.000 100.000 100.000 100.000 100.000 0 0
measure economic rates of wear and tear for many

different forms of capital. For our purposes, we

have adopted the work of Hulten and Wykoff, with

adaptations reported in Jorgenson and Yun. This

work provides a geometric rate of capital decay for

the 50 different classifications of business capital

plus residential structures, as reported in the annual

detailed wealth accounts at the BEA. We use this

information in forming heterogeneous accounts of

business and residential capital. The appendix

describes the regional heterogeneous industry capital

accounts.

Capital and Labor Disposition

The disposition of an industrys primary factors of

production affects tax rates on that industry. A

number of factors are important, and in this section

we examine the regional and industrial dimensions

of factor-use dispositions. A longitudinal dissection

of factor-use disposition will show regional primary

factor (row) shares of national industry value added

by industry (column), where capital and labor factors

are distinguished. A latitudinal dissection will show

industry (column) shares of each regional factor of

production (row) total value added.

Table 3 presents the regional primary factor shares

of industry value added. When we exclude the

United States as a region, each column sums to a

value of 100 for heterogeneous capital and labor,

and each column entry represents a percentage of

U.S. total value added, by industry, for either capital

or labor. For example, 39 percent of labor services

in U.S. cash grain production was in the Corn Belt

States, while 25 percent of capital value added in the

owner-occupied shelter industry came from

Northeastern States.

At the bottom of table 3, we report the United States

as a region. Because labor and capital are treated

separately, all industries that use labor show values

of 100, indicating that 100 percent of U.S. labor by

industry is located in the U.S. region. For capital,

this U.S. region shows the disposition of capital, by

type, in each industry. The figures show that all

nonresidential industries using corporate capital are

most heavily leveraged in the use of heavy

machinery, except services, mines, and trade (the

largest industry). The only purely noncorporate

industry is cash grain and other farming. For these

industries, farmland is the most heavily leveraged

asset, particularly in cash grain farms. Noncash

grain farms are more likely to substitute plant and

heavy machinery for land in their operation.

We explicitly recognize the regional dispositions of

industry capital portfolios, but make no attempt to

endogenize the determinants of industry location.

Some stylized facts are noteworthy. The regional

disposition of farm industries is largely intuitive;

climate, weather, and resource characteristics limit

the range of commodity options within a given

geographic region. For example, cotton is a warmweather plant, so commercial cotton production is

limited to Southern States. Wheat thrives in cooler

climates, hence its more northerly concentration.

Citrus production, an uncharacteristically laborintensive industry, is almost exclusively in the warm

sunny climates of Florida and California.

Agriculture, in general, is a land-intensive industry,

so tends to be concentrated in areas with more

favorable land cost conditions (rural areas). Many

food manufactures have strong incentives to locate

near their primary upstream industry (farms) when

freshness or transportation costs of the preprocessed

product is a significant factor. Examples of other

industry-location determinants include strength of

regional labor unions, proximity to markets and

transportation, supply of skilled or unskilled labor,

regional demographic and taxation characteristics,

historical chance, agglomeration economies, and

dissemination of transportation barriers.11

Each of these factors, while not explicitly addressed,

may play a role in the calibration of the model, since

empirical accounts of relative factor prices, input

utilizations, and regional consumption patterns must

be reconciled with general equilibrium. This is done

through calibration of weighting parameters for both

supply and demand function specifications. So, for

example, a particular region that has a

disproportionate elderly population may bias their

consumption bundle, and our regional data will

reflect this. While we have no explicit account of

age demographics, the resulting model calibration

will produce a weighting parameter that favors a

consumption bundle likely to be more reflective of

the preferences of a more aged population. Another

way we address this issue is through our 

24 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

11A review of this literature is presented in Blair and

Premus, and a recent application is Ellison and Glaeser.
development of competing closure assumptions in

the model (see Macroeconomic Closure

subsection in the previous section). Depending on

which closure assumption we employ, our model

will reflect the public finance effects of regional

industry locations, due to a firms propensity to

target (or not) new capital investment based on

differential regional rates of return.

Regional and industrial variations in the disposition

of capital exist. For example, although computers

remain a small share of industrial capital portfolios,

the chemical, rubber, and plastic industry spends

approximately $1 in every $8 on such equipment,

while construction industries spend about $1 in

every $100. Farms are the only employers of land,

and cash grain farms are 20 percent more dependent

on land than other farms. Other farms substitute

plant and heavy machinery for land, relative to cash

grain farms. Another very important distinction of

the farm industry is that it does not use corporate

capital and competes only with service, mines, and

trade and construction industries for the use of

noncorporate capital. The most important farm

factor is land, and there is no competition for this

factor.

Within a given industry aggregate, the relative use

of primary factors is uniform across regions. If we

rank the cost shares from highest to lowest for each

of the primary factors in each of the nine

nonresidential industries, the U.S. average ranking is

nearly the same as all regional rankings, give or take

a ranking of 1. In fact, the only exception to this

result is for the cost shares of utility plants. In the

Delta and Southern Plains regions, utility plants

have unusually prominent cost shares among

primary factors in service, mines, and trade and

construction industry aggregates.

Changing the perspective of factor disposition to

industry shares of primary factor employment, table

4 presents column shares of row totals, such that

each row sums to 100. Due to the extremely broad

classification of the services, mines, and trade

industry, it is the number one employer of most

forms of capital in all regions. It is the number one

corporate employer of computers, service and

furnishing equipment, industrial plants, and utility

plants, in every region, and the number two

employer of corporate transportation equipment

(behind construction industries) in every region.

The only type of capital for which the service,

mines, and trade industry is neither the top nor

second largest employer is heavy corporate

machinery (except in the Mountain region, where it

is the second largest employer).

Either manufacturing or construction is the number

one employer of heavy corporate machinery in each

region, while construction is the top employer of

noncorporate heavy machinery in every region. The

ordinal ranking of farms and cash grain farms is

virtually the same in every region: the smallest two

employers of noncorporate capital and the only

employers of farmland. Cash grain farms are most

prominent in the Corn Belt and Northern Plains

regions. The only region where farms are a

significant employer of capital is in the Northern

Plains, where farms employ more than 12 percent of

noncorporate heavy machinery.

One industry that does show some regional variation

in ranking as an employer of capital is the food

industry. Food manufacturers are number three in

the use of labor services and most forms of

corporate capital in the Northern Plains. Food

manufacturers are also major employers of capital in

the Lake States, Corn Belt, and Delta States. Food,

beverages, and tobacco employ about 15 percent of

heavy corporate machinery in the Appalachian

region and over 10 percent in the Corn Belt, Lake

States, and Northern Plains. Food manufacturers are

also a large employer of corporate service and

furnishing equipment in the Northern Plains. The

per capita concentration of primary factor

employment for food manufacturing is depicted in

figure 9. The levels represent regional values

relative to the Northern Plains region, which has the

highest per capita production of manufactured food.

A concentration of such employment in the upper

Midwest is quite evident, indicating that a

disproportionate share of manufactured food is

produced in the Corn Belt, Lake States, and

Northern Plains.

Overall, nearly half of all capital and 70 percent of

the services from labor are employed in services,

mines, and shelter, and with construction, two-thirds

of all capital. Most of the remaining labor services

are used in manufacturing. Food and farm industries

are small employers of capital and labor services.

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 25
26 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 4--Regional industry shares of factor value added, by capital type and labor

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Appalachian:

Corporate--

Computers/office equipment 0 0 2.816 4.657 25.711 16.723 46.295 3.797 0

Service and furnishing equipment 0 0 4.599 7.081 25.439 4.807 50.636 7.437 0

Heavy machinery 0 0 4.924 10.040 32.740 16.280 10.461 25.554 0

Transportation equipment 0 0 0.976 2.383 4.541 1.118 32.331 58.650 0

Industrial plants 0 0 3.187 5.276 22.752 9.030 46.067 13.688 0

Utility plants 0 0 0 0 0 0 98.670 1.330 0

Noncorporate--

Computers/office equipment 0.138 0.006 0 0 0 0 91.193 8.664 0

Service and furnishing equipment 0.008 0 0 0 0 0 88.604 11.388 0

Heavy machinery 2.298 0.077 0 0 0 0 24.770 72.855 0

Transportation equipment 1.056 0.025 0 0 0 0 39.213 59.706 0

Industrial plants 0.561 0.014 0 0 0 0 78.971 20.453 0

Utility plants 0 0 0 0 0 0 97.985 2.015 0

Farmland 95.205 4.796 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 28.344 0 71.656

Labor services 0.373 0.016 1.998 1.689 26.407 5.144 64.373 0 0

Corn Belt:

Corporate--

Computers/office equipment 0 0 4.766 0.660 26.992 9.337 54.626 3.620 0

Service and furnishing equipment 0 0 7.682 1.061 21.377 2.864 60.010 7.007 0

Heavy machinery 0 0 9.275 1.294 39.935 11.484 10.834 27.179 0

Transportation equipment 0 0 1.593 0.224 4.236 0.689 39.191 54.067 0

Industrial plants 0 0 5.179 0.733 22.058 7.147 53.215 11.669 0

Utility plants 0 0 0 0 0 0 99.080 0.920 0

Noncorporate--

Computers/office equipment 0.172 0.074 0 0 0 0 92.526 7.229 0

Service and furnishing equipment 0.010 0.004 0 0 0 0 90.619 9.367 0

Heavy machinery 3.330 1.195 0 0 0 0 23.970 71.505 0

Transportation equipment 1.154 0.370 0 0 0 0 43.904 54.572 0

Industrial plants 0.670 0.201 0 0 0 0 83.041 16.089 0

Utility plants 0 0 0 0 0 0 97.288 2.712 0

Farmland 66.285 33.715 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 28.491 0 71.509

Labor services 0.300 0.097 2.532 0.268 28.871 4.119 63.814 0 0

Delta States:

Corporate--

Computers/office equipment 0 0 3.912 0.495 24.757 18.618 48.686 3.532 0

Service and furnishing equipment 0 0 6.679 0.845 25.256 5.439 55.490 6.291 0

Heavy machinery 0 0 7.395 0.936 38.388 18.676 13.421 21.184 0

Transportation equipment 0 0 1.555 0.197 5.537 1.355 40.326 51.030 0

Industrial plants 0 0 6.090 0.700 24.491 6.480 52.554 9.685 0

Utility plants 0 0 0 0 0 0 86.544 13.456 0

Noncorporate--

Computers/office equipment 0.196 0.023 0 0 0 0 92.963 6.818 0

Service and furnishing equipment 0.012 0.001 0 0 0 0 91.125 8.861 0

Heavy machinery 3.730 0.361 0 0 0 0 29.242 66.667 0

Transportation equipment 1.428 0.151 0 0 0 0 45.545 52.875 0

Industrial plants 0.700 0.070 0 0 0 0 85.990 13.240 0

Utility plants 0 0 0 0 0 0 80.626 19.374 0

Farmland 88.205 11.795 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 30.941 0 69.059

Labor services 0.614 0.041 3.111 0.255 24.038 5.845 66.096 0 0

Continued--
Table 4--Regional industry shares of factor value added, by capital type and labor--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Lake States:

Corporate--

Computers/office equipment 0 0 5.935 0.887 27.095 4.334 56.938 4.810 0

Service and furnishing equipment 0 0 8.510 1.272 20.791 1.317 59.750 8.360 0

Heavy machinery 0 0 10.534 1.574 38.521 5.741 10.047 33.582 0

Transportation equipment 0 0 1.644 0.246 4.063 0.316 32.980 60.750 0

Industrial plants 0 0 7.812 0.959 19.759 2.632 54.934 13.903 0

Utility plants 0 0 0 0 0 0 98.952 1.048 0

Non-corporate--

Computers/office equipment 0.176 0.026 0 0 0 0 91.485 8.313 0

Service and furnishing equipment 0.010 0.001 0 0 0 0 89.274 10.714 0

Heavy machinery 3.076 0.379 0 0 0 0 20.647 75.898 0

Transportation equipment 1.039 0.119 0 0 0 0 39.596 59.246 0

Industrial plants 0.736 0.070 0 0 0 0 81.446 17.747 0

Utility plants 0 0 0. 0. 0 0 97.425 2.575 0

Farmland 85.058 14.942 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 26.039 0 73.961

Labor services 0.402 0.047 2.470 0.260 33.161 3.229 60.431 0 0

Mountain:

Corporate--

Computers/office equipment 0 0 2.082 0.701 15.818 3.267 72.756 5.375 0

Service and furnishing equipment 0 0 3.359 1.131 11.169 0.893 73.605 9.844 0

Heavy machinery 0 0 4.963 1.671 22.163 4.066 22.240 44.898 0

Transportation equipment 0 0 0.566 0.191 1.843 0.160 38.080 59.160 0

Industrial plants 0 0 2.615 0.569 10.651 1.392 66.840 17.934 0

Utility plants 0 0 0 0 0 0 93.525 6.475 0

Non-corporate--

Computers/office equipment 0.177 0.040 0 0 0 0 91.865 7.919 0

Service and furnishing equipment 0.011 0.002 0 0 0 0 89.432 10.554 0

Heavy machinery 3.086 0.632 0 0 0 27.591 68.691 0

Transportation equipment 1.095 0.203 0 0 0 0 42.449 56.253 0

Industrial plants 0.607 0.092 0 0 0 0 80.568 18.732 0

Utility plants 0 0 0 0 0 0 90.015 9.985 0

Farmland 79.902 20.098 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 32.987 0 67.013

Labor services 0.574 0.049 1.481 0.488 17.857 0.720 78.832 0 0

Northeast:

Corporate--

Computers/office equipment 0 0 1.620 0.312 18.684 5.348 70.268 3.768 0

Service and furnishing equipment 0 0 2.574 0.494 14.649 1.527 73.552 7.204 0

Heavy machinery 0 0 4.471 0.869 30.616 8.507 15.298 40.239 0

Transportation equipment 0 0 0.546 0.107 2.677 0.361 39.348 56.961 0

Industrial plants 0 0 1.538 0.271 8.822 3.484 72.487 13.398 0

Utility plants 0 0 0 0 0 0 99.674 0.326 0

Non-corporate--

Computers/office equipment 0.031 0.001 0 0 0 0 94.058 5.910 0

Service and furnishing equipment 0.002 0 0 0 0 0 92.423 7.575 0

Heavy machinery 0.895 0.022 0 0 0 0 26.292 72.791 0

Transportation equipment 0.296 0.006 0 0 0 0 46.711 52.987 0

Industrial plants 0.160 0.003 0 0 0 0 86.335 13.502 0

Utility plants 0 0 0 0 0 0 99.128 0.872 0

Farmland 95.312 4.688 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 36.300 0 63.700

Labor services 0.132 0.004 1.239 0.205 19.629 2.769 76.021 0 0

Continued--

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 27
28 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 4--Regional industry shares of factor value added, by capital type and labor--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Northern Plains:

Corporate--

Computers/office equipment 0 0 6.622 0.215 33.252 3.968 51.658 4.286 0

Service and furnishing equipment 0 0 11.324 0.367 21.833 1.343 56.341 8.792 0

Heavy machinery 0 0 13.432 0.436 35.637 5.410 11.196 33.888 0

Transportation equipment 0 0 2.106 0.068 3.730 0.298 32.323 61.476 0

Industrial plants 0 0 7.163 0.137 30.135 3.940 46.484 12.140 0

Utility plants 0 0 0 0 0 0 95.455 4.545 0

Noncorporate--

Computers/office equipment 0.616 0.359 0 0 0 0 90.163 8.863 0

Service and furnishing equipment 0.037 0.021 0 0 0 0 87.928 12.014 0

Heavy machinery 7.769 4.371 0 0 0 0 20.398 67.462 0

Transportation equipment 2.894 1.541 0 0 0 0 35.506 60.059 0

Industrial plants 1.890 0.923 0 0 0 0 79.120 18.067 0

Utility plants 0 0 0. 0. 0 0 94.969 5.031 0

Farmland 62.158 37.842 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 30.954 0 69.046

Labor services 1.471 0.435 6.563 0.174 21.850 2.531 66.976 0 0

Pacific:

Corporate--

Computers/office equipment 0 0 2.377 0.542 17.310 2.609 73.169 3.993 0

Service and furnishing equipment 0 0 3.564 0.813 13.947 0.743 74.105 6.828 0

Heavy machinery 0 0 6.504 1.484 31.434 4.502 17.791 38.286 0

Transportation equipment 0 0 0.751 0.171 2.858 0.182 44.881 51.157 0

Industrial plants 0 0 2.726 0.549 7.710 0.706 73.522 14.787 0

Utility plants 0 0 0 0 0 0 94.823 5.177 0

Noncorporate--

Computers/office equipment 0.095 0.005 0 0 0 0 94.497 5.404 0

Service and furnishing equipment 0.006 0 0 0 0 0 92.700 7.293 0

Heavy machinery 2.272 0.102 0 0 0 0 33.655 63.971 0

Transportation equipment 0.733 0.031 0 0 0 0 52.993 46.243 0

Industrial plants 0.446 0.013 0 0 0 0 85.090 14.451 0

Utility plants 0 0 0 0 0 0 94.225 5.775 0

Farmland 94.475 5.525 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 41.519 0 58.481

Labor services 0.667 0.013 1.880 0.344 23.409 1.642 72.045 0 0

Southern Plains:

Corporate--

Computers/office equipment 0 0 2.303 0.602 16.372 8.482 68.747 3.493 0

Service and furnishing equipment 0 0 3.985 1.042 13.460 2.584 72.532 6.396 0

Heavy machinery 0 0 6.055 1.585 28.543 12.420 21.921 29.476 0

Transportation equipment 0 0 0.861 0.225 2.900 0.612 47.305 48.097 0

Industrial plants 0 0 2.772 0.600 8.419 2.548 74.024 11.637 0

Utility plants 0 0 0 0 0 0 91.130 8.870 0

Noncorporate--

Computers/office equipment 0.125 0.017 0 0 0 0 94.763 5.095 0

Service and furnishing equipment 0.008 0.001 0 0 0 0 93.003 6.987 0

Heavy machinery 3.314 0.364 0 0 0 0 35.496 60.826 0

Transportation equipment 1.211 0.118 0 0 0 0 50.718 47.953 0

Industrial plants 0.519 0.043 0 0 0 0 87.678 11.760 0

Utility plants 0 0 0 0 0 0 88.617 11.383 0

Farmland 85.891 14.109 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 35.547 0 64.453

Labor services 0.449 0.029 1.767 0.342 19.558 4.434 73.421 0 0

Continued--
Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 29

Table 4--Regional industry shares of factor value added, by capital type and labor--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Percent of industry value added

Southeast:

Corporate--

Computers/office equipment 0 0 1.979 0.970 17.284 7.679 67.785 4.303 0

Service and furnishing equipment 0 0 3.029 1.419 18.453 2.119 67.323 7.656 0

Heavy machinery 0 0 4.446 2.505 33.681 9.987 14.610 34.771 0

Transportation equipment 0 0 0.627 0.401 3.087 0.489 38.591 56.805 0

Industrial plants 0 0 1.950 0.961 13.031 4.206 63.498 16.354 0

Utility plants 0 0 0 0 0 0 99.450 0.550 0

Noncorporate--

Computers/office equipment 0.071 0.002 0 0 0 0 92.880 7.048 0

Service and furnishing equipment 0.004 0 0 0 0 0 91.007 8.989 0

Heavy machinery 1.507 0.028 0 0 0 0 27.827 70.638 0

Transportation equipment 0.571 0.009 0 0 0 0 46.082 53.338 0

Industrial plants 0.293 0.004 0 0 0 0 81.549 18.154 0

Utility plants 0 0 0. 0. 0 0 98.926 1.074 0

Farmland 97.525 2.475 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 29.634 0 70.366

Labor services 0.482 0.008 1.677 0.466 21.123 3.212 73.032 0 0

United States:

Capital services 0.368 0.077 2.511 0.905 12.203 3.566 47.295 21.683 11.393

Labor services 0.406 0.038 1.957 0.425 23.555 3.193 70.426 0 0

0

20

40

60

80

100

120

Northeast Southeast Mountain Pacific Southern

Plains

Delta Corn Belt Lake Northern

Plains

Percentage of Northern Plains per capita production

Appalachian

Figure 9

Regional per capita food manufacturing production

States States
Owners of capital have an economic incentive to

diversify their capital holdings. When capital held in

one form is earning a higher rate of return than

capital held in other forms, owners have incentive to

expand their holdings of the former through

diminished holdings of the latter and investments in

the former. This incentive will remain until rates of

return are equal across all capital holdings. We

denote this equal rate of return as the reservation rate

(r). The presence of taxes, however, means that the

owner must achieve a higher gross rate of return,

called a hurdle rate (h), to ensure that the net of tax

rate achieves its reservation value. The difference

between the hurdle rate and the reservation rate is

the tax wedge, and the marginal effective tax rate on

income from capital is measured as the ratio of this

wedge and the hurdle rate (tm).

Two important results concerning the measure of the

marginal effective tax rate are that, in general, it can

be significantly different from the statutory rate of

taxation, and the effective rate associated with

income from capital often varies significantly for

capital of different durabilities, statutory tax lives,

patterns of ownership, or geographic locations. The

marginal effective tax rate also adjusts with changes

in macroeconomic factors, such as revised

inflationary expectations and changing reservation

values. Since each of these factors is explicitly

represented in our modeling framework (some

exogenously specified), this section presents a

summary of tax rates on marginal income from

capital, based on the structure of aggregation

presented in table 2.

The fundamental principles of capital theory were

refined by Hall and Jorgenson to where information

on capital stock (price and quantity) can be directly

converted to measures of the flow of services from

capital in the process of production. The focal point

of this methodology is the user cost of capital, which

represents the market price of capital services.

When the user cost of capital is adapted to account

for the incentive effects created by a tax system,

which drives a wedge between buyer and seller price

of capital services (see King and Fullerton), it is

possible to measure the marginal effective tax rate

on income from capital. While this measure is data

intensive, it is also intuitive and transparent.

Readers interested in this methodology can refer to

Computing the Marginal Effective Tax Rate on

Income From Capital, in the appendix.

Comparisons of Federal Marginal Tax

Rates on Income From Capital and Labor

We computed the tax rates on the 15 primary factors

used in the model for each of the 7 industry

aggregates in each production region. Table 5

presents the results for the Federal factor taxes,

providing further breakdowns of other industry for

construction of owner-occupied shelter. The wellknown corporate wedge is quite evident in the table.

For example, the overall U.S. average tax on

corporate income from computers and office

equipment by the service, mining, and trade industry

is more than double that for noncorporate income

(0.14) from the same machinery. The overall tax on

capital by industry has a standard deviation of 38.3

percent of the mean tax on capital. The largest

standard deviation across industries by type of

capital is for both corporate and noncorporate plants

and for owned versus leased residential shelter. The

standard deviation of the tax in labor across

industries is 2.1 percent of the mean tax on labor

income. Food manufacturers face a 38-percent

marginal Federal tax rate on capital income, while

farms face around a 21-percent marginal tax on

capital. All other industries have tax rates between

these two extremes and are heavily skewed toward

the higher end. In services, mines, and trade and

farm industries, labor is taxed more than capital at

the margin when the insurance trust is factored in,

while all other industries face higher taxes on

capital.

This national disposition of factor taxes gives a

glimpse of potential resource allocation issues

associated with Federal fiscal policies. With labor

uniformly taxed across industry, any reform of tax

rules to a more neutral system should not lead to

major migration of labor across industry. At another

level, however, industries that have lower taxes on

labor will face higher rates after reform, since the

insurance trust tax would not be part of such reform.

This result would lead these industries to replace

some of their labor services with capital. The nearly

40 percent standard deviation of industry capital

30 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Taxation
Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 21

Table 5--Federal primary factor tax wedges, by industry, region, and factor type

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per dollar of value added

Appalachian:

Corporate--

Computers/office equipment 0 0 0.368 0.363 0.352 0.367 0.348 0.381 0

Service and furnishing equipment 0 0 0.368 0.373 0.374 0.366 0.384 0.368 0

Heavy machinery 0 0 0.377 0.380 0.379 0.360 0.357 0.372 0

Transportation equipment 0 0 0.435 0.439 0.434 0.437 0.426 0.428 0

Industrial plants 0 0 0.440 0.445 0.440 0.441 0.390 0.421 0

Utility plants 0 0 0 0 0 0 0.288 0.174 0

Noncorporate--

Computers/office equipment 0.152 0.151 0 0 0 0 0.178 0.197 0

Service and furnishing equipment 0.131 0.130 0 0 0 0 0.203 0.184 0

Heavy machinery 0.157 0.151 0 0 0 0 0.182 0.190 0

Transportation equipment 0.183 0.183 0 0 0 0 0.250 0.246 0

Industrial plants 0.085 0.084 0 0 0 0 0.206 0.233 0

Utility plants 0 0 0 0 0 0 0.118 -0.002 0

Farmland 0.268 0.268 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.221 0 0.039

Labor services1 0.344 0.341 0.339 0.335 0.338 0.340 0.338 0 0

Corn Belt:

Corporate--

Computers/office equipment 0 0 0.350 0.343 0.331 0.346 0.322 0.356 0

Service and furnishing equipment 0 0 0.352 0.345 0.349 0.346 0.359 0.344 0

Heavy machinery 0 0 0.360 0.353 0.360 0.346 0.332 0.348 0

Transportation equipment 0 0 0.420 0.415 0.415 0.419 0.398 0.406 0

Industrial plants 0 0 0.426 0.420 0.420 0.423 0.366 0.397 0

Utility plants 0 0 0 0 0 0 0.256 0.159 0

Noncorporate--

Computers/office equipment 0.150 0.147 0 0 0 0 0.144 0.164 0

Service and furnishing equipment 0.125 0.122 0 0 0 0 0.169 0.151 0

Heavy machinery 0.152 0.145 0 0 0 0 0.156 0.157 0

Transportation equipment 0.188 0.186 0 0 0 0 0.219 0.217 0

Industrial plants 0.067 0.064 0 0 0 0 0.173 0.199 0

Utility plants 0 0 0 0 0 0 0.067 -0.022 0

Farmland 0.267 0.268 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.218 0 0.041

Labor services1 0.342 0.344 0.333 0.334 0.335 0.336 0.337 0 0

Delta States:

Corporate--

Computers/office equipment 0 0 0.340 0.338 0.331 0.332 0.311 0.338 0

Service and furnishing equipment 0 0 0.341 0.339 0.350 0.332 0.348 0.331 0

Heavy machinery 0 0 0.349 0.347 0.352 0.324 0.315 0.336 0

Transportation equipment 0 0 0.411 0.410 0.413 0.409 0.383 0.395 0

Industrial plants 0 0 0.418 0.415 0.421 0.418 0.358 0.388 0

Utility plants 0 0 0 0 0 0 0.193 0.132 0

Noncorporate--

Computers/office equipment 0.078 0.090 0 0 0 0 0.128 0.146 0

Service and furnishing equipment 0.049 0.062 0 0 0 0 0.156 0.135 0

Heavy machinery 0.090 0.094 0 0 0 0 0.135 0.142 0

Transportation equipment 0.121 0.132 0 0 0 0 0.203 0.206 0

Industrial plants -0.019 -0.003 0 0 0 0 0.159 0.188 0

Utility plants 0 0 0 0 0 0 -0.040 -0.066 0

Farmland 0.272 0.271 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.218 0 0.033

Labor services1 0.356 0.356 0.344 0.346 0.344 0.346 0.346 0 0
32 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 5--Federal primary factor tax wedges, by industry, region, and factor type--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per dollar of value added

Lake States:

Corporate--

Computers/office equipment 0 0 0.358 0.360 0.345 0.360 0.336 0.369 0

Service and furnishing equipment 0 0 0.359 0.362 0.363 0.360 0.372 0.356 0

Heavy machinery 0 0 0.367 0.369 0.373 0.366 0.345 0.360 0

Transportation equipment 0 0 0.427 0.429 0.427 0.434 0.418 0.418 0

Industrial plants 0 0 0.432 0.434 0.433 0.433 0.378 0.410 0

Utility plants 0 0 0 0 0 0 0.278 0.164 0

Noncorporate--

Computers/office equipment 0.208 0.210 0 0 0 0 0.154 0.175 0

Service and furnishing equipment 0.188 0.189 0 0 0 0 0.179 0.161 0

Heavy machinery 0.210 0.208 0 0 0 0 0.164 0.168 0

Transportation equipment 0.237 0.241 0 0 0 0 0.230 0.227 0

Industrial plants 0.141 0.146 0 0 0 0 0.182 0.210 0

Utility plants 0 0 0. 0. 0 0 0.096 -0.030 0

Farmland 0.262 0.262 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.219 0 0.038

Labor services1 0.325 0.325 0.334 0.333 0.339 0.341 0.339 0 0

Mountain:

Corporate--

Computers/office equipment 0 0 0.353 0.353 0.336 0.357 0.325 0.359 0

Service and furnishing equipment 0 0 0.354 0.354 0.356 0.356 0.363 0.349 0

Heavy machinery 0 0 0.363 0.363 0.367 0.346 0.337 0.353 0

Transportation equipment 0 0 0.425 0.425 0.424 0.433 0.412 0.412 0

Industrial plants 0 0 0.430 0.431 0.431 0.431 0.373 0.403 0

Utility plants 0 0 0 0 0 0 0.224 0.144 0

Noncorporate--

Computers/office equipment 0.156 0.162 0 0 0 0 0.142 0.162 0

Service and furnishing equipment 0.125 0.133 0 0 0 0 0.168 0.151 0

Heavy machinery 0.165 0.164 0 0 0 0 0.151 0.157 0

Transportation equipment 0.202 0.205 0 0 0 0 0.221 0.218 0

Industrial plants 0.053 0.069 0 0 0 0 0.174 0.201 0

Utility plants 0 0 0 0 0 0 0.035 -0.053 0

Farmland 0.280 0.276 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.219 0 0.036

Labor services1 0.379 0.371 0.342 0.347 0.340 0.342 0.342 0 0

Northeast:

Corporate--

Computers/office equipment 0 0 0.341 0.345 0.325 0.344 0.318 0.351 0

Service and furnishing equipment 0 0 0.342 0.346 0.347 0.343 0.356 0.338 0

Heavy machinery 0 0 0.351 0.354 0.354 0.339 0.329 0.343 0

Transportation equipment 0 0 0.410 0.414 0.413 0.415 0.402 0.401 0

Industrial plants 0 0 0.413 0.417 0.413 0.417 0.365 0.392 0

Utility plants 0 0 0 0 0 0 0.246 0.157 0

Noncorporate--

Computers/office equipment 0.064 0.089 0 0 0 0 0.144 0.162 0

Service and furnishing equipment 0.035 0.062 0 0 0 0 0.169 0.149 0

Heavy machinery 0.068 0.091 0 0 0 0 0.156 0.155 0

Transportation equipment 0.105 0.129 0 0 0 0 0.219 0.215 0

Industrial plants -0.041 0 0 0 0 0 0.174 0.198 0

Utility plants 0 0 0 0 0 0 0.071 -0.025 0

Farmland 0.277 0.275 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.218 0 0.044

Labor services1 0.371 0.367 0.335 0.330 0.334 0.332 0.331 0 0
Table 5--Federal primary factor tax wedges, by industry, region, and factor type--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per dollar of value added

Northern Plains:

Corporate--

Computers/office equipment 0 0 0.346 0.351 0.333 0.356 0.322 0.360 0

Service and furnishing equipment 0 0 0.347 0.352 0.355 0.357 0.363 0.346 0

Heavy machinery 0 0 0.356 0.361 0.368 0.360 0.333 0.351 0

Transportation equipment 0 0 0.419 0.424 0.427 0.432 0.405 0.413 0

Industrial plants 0 0 0.418 0.422 0.433 0.437 0.370 0.403 0

Utility plants 0 0 0 0 0 0 0.212 0.143 0

Noncorporate--

Computers/office equipment 0.170 0.163 0 0 0 0 0.131 0.154 0

Service and furnishing equipment 0.149 0.142 0 0 0 0 0.160 0.139 0

Heavy machinery 0.173 0.164 0 0 0 0 0.134 0.146 0

Transportation equipment 0.202 0.196 0 0 0 0 0.212 0.209 0

Industrial plants 0.102 0.098 0 0 0 0 0.163 0.191 0

Utility plants 0 0 0. 0. 0 0 0.034 -0.058 0

Farmland 0.264 0.263 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.219 0 0.036

Labor services1 0.330 0.329 0.340 0.341 0.340 0.339 0.342 0 0

Pacific:

Corporate--

Computers/office equipment 0 0 0.345 0.342 0.327 0.339 0.317 0.353 0

Service and furnishing equipment 0 0 0.347 0.344 0.348 0.340 0.354 0.342 0

Heavy machinery 0 0 0.355 0.352 0.355 0.339 0.333 0.345 0

Transportation equipment 0 0 0.417 0.413 0.413 0.414 0.400 0.405 0

Industrial plants 0 0 0.425 0.420 0.423 0.418 0.362 0.393 0

Utility plants 0 0 0 0 0 0 0.220 0.160 0

Noncorporate--

Computers/office equipment 0.148 0.129 0 0 0 0 0.139 0.159 0

Service and furnishing equipment 0.116 0.097 0 0 0 0 0.165 0.148 0

Heavy machinery 0.157 0.133 0 0 0 0 0.150 0.153 0

Transportation equipment 0.198 0.179 0 0 0 0 0.217 0.216 0

Industrial plants 0.042 0.020 0 0 0 0 0.170 0.196 0

Utility plants 0 0 0 0 0 0 0.058 -0.030 0

Farmland 0.280 0.278 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.219 0 0.030

Labor services1 0.384 0.377 0.348 0.349 0.349 0.350 0.348 0 0

Southern Plains:

Corporate--

Computers/office equipment 0 0 0.345 0.345 0.331 0.342 0.314 0.349 0

Service and furnishing equipment 0 0 0.346 0.346 0.352 0.342 0.355 0.341 0

Heavy machinery 0 0 0.356 0.356 0.357 0.335 0.326 0.345 0

Transportation equipment 0 0 0.422 0.422 0.420 0.423 0.397 0.408 0

Industrial plants 0 0 0.427 0.427 0.428 0.425 0.373 0.399 0

Utility plants 0 0 0 0 0 0 0.174 0.131 0

Noncorporate--

Computers/office equipment 0.130 0.143 0 0 0 0 0.109 0.135 0

Service and furnishing equipment 0.104 0.119 0 0 0 0 0.141 0.123 0

Heavy machinery 0.137 0.144 0 0 0 0 0.123 0.130 0

Transportation equipment 0.168 0.176 0 0 0 0 0.199 0.200 0

Industrial plants 0.051 0.067 0 0 0 0 0.153 0.177 0

Utility plants 0 0 0 0 0 0 -0.060 -0.086 0

Farmland 0.277 0.271 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.220 0 0.019

Labor services1 0.371 0.368 0.363 0.361 0.361 0.364 0.362 0 0

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 33
34 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 5--Federal primary factor tax wedges, by industry, region, and factor type--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per $ of value added

Southeast:

Corporate--

Computers/office equipment 0 0 0.336 0.330 0.325 0.337 0.307 0.345 0

Service and furnishing equipment 0 0 0.337 0.338 0.346 0.337 0.349 0.333 0

Heavy machinery 0 0 0.346 0.347 0.350 0.333 0.318 0.337 0

Transportation equipment 0 0 0.410 0.411 0.411 0.414 0.396 0.398 0

Industrial plants 0 0 0.416 0.417 0.416 0.412 0.354 0.386 0

Utility plants 0 0 0 0 0 0 0.235 0.115 0

Noncorporate--

Computers/office equipment 0.098 0.131 0 0 0 0 0.125 0.146 0

Service and furnishing equipment 0.067 0.103 0 0 0 0 0.154 0.133 0

Heavy machinery 0.106 0.135 0 0 0 0 0.135 0.138 0

Transportation equipment 0.145 0.173 0 0 0 0 0.208 0.206 0

Industrial plants -0.013 0.038 0 0 0 0 0.155 0.184 0

Utility plants 0 0 0 0. 0 0 0.072 -0.099 0

Farmland 0.274 0.272 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.219 0 0.024

Labor services1 0.375 0.360 0.352 0.354 0.350 0.346 0.356 0 0

United States:

Corporate--

Computers/office equipment 0 0 0.349 0.353 0.333 0.349 0.321 0.356 0

Service and furnishing equipment 0 0 0.351 0.359 0.354 0.348 0.359 0.344 0

Heavy machinery 0 0 0.359 0.368 0.361 0.345 0.332 0.349 0

Transportation equipment 0 0 0.420 0.429 0.419 0.422 0.403 0.408 0

Industrial plants 0 0 0.426 0.434 0.425 0.425 0.367 0.398 0

Utility plants 0 0 0 0 0 0 0.227 0.139 0

Noncorporate--

Computers/office equipment 0.144 0.154 0 0 0 0 0.140 0.162 0

Service and furnishing equipment 0.117 0.130 0 0 0 0 0.167 0.150 0

Heavy machinery 0.148 0.154 0 0 0 0 0.151 0.156 0

Transportation equipment 0.181 0.191 0 0 0 0 0.219 0.218 0

Industrial plants 0.054 0.076 0 0 0 0 0.171 0.199 0

Utility plants 0 0 0 0 0 0 0.026 -0.065 0

Farmland 0.272 0.268 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.219 0 0.036

Labor services1 0.363 0.345 0.341 0.341 0.341 0.342 0.342 0 0

All sectors--

Capital services 0.206 0.222 0.379 0.385 0.375 0.366 0.288 0.316 0.036

Value added services 0.288 0.262 0.362 0.371 0.353 0.354 0.322 0.316 0.036

1Rate includes Federal insurance trust wage tax.
Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 35

taxes around the industry mean capital tax rate

indicates that the Federal capital tax drives

significant amounts of capital out of higher taxed

industry and into industry with less efficient uses for

this capital.

Regional variations largely mirror the national

figures, but some trends do stand out. For example,

noncorporate capital taxes in the Northeast show far

more variation than in tax rates across regions.

Every type of noncorporate capital except farmland

has a standard deviation of tax rates across industries

of between 30 and 209 percent of the (unweighted)

mean tax rate. The Northeast also has an unusually

high standard deviation on labor tax rates across

industry at 5 percent of mean. The Northeast,

Southeast, and Southern Plains regions show the

highest overall variation of tax rates across

industries, but no region is significantly different in

such variation from the national average. All

manufacturing, food and nonfood, is more heavily

taxed than other industries in every region. This is

due to the pure corporate nature of this industry.

Comparisons of Regional Marginal Tax

Rates on Income From Capital and Labor

Regional taxation of primary industrial factors is in

stark contrast to Federal taxation (table 6).

Agriculture is the most heavily taxed industry by

regional governments, with marginal tax incidence

on capital averaging 12 percent overall and marginal

tax incidence for labor averaging 9 percent overall.

Beverages and tobacco are the most lightly factortaxed industries by regional governments. Aside

from the reversal of favoritism, the overall level of

taxation on industry capital is surprisingly high. The

reason for these high marginal rates is that property

taxation, a major tax instrument of regional

governments, uses value as its tax base, while the

fact that it is administered annually means that this

tax effectively falls on annual capital income. With

value of capital stock several times the value of

annual capital rents, clearly a low property tax rate is

converted into a much higher capital income tax

rate. If we exclude leased residential structures from

the services, mines, and trade industry, we find that

no other industry faces an overall U.S. average

capital tax rate of more than 9.4 percent. Farms also

face a significant plant/equipment tax wedge, with

farm buildings taxed significantly less than farm

equipment. For other industry, no such wedge

exists.

Overall variation of taxation across industries is

slightly lower than at the Federal level. Other than

farms, there is a fairly uniform tax rate across

industries. Labor is taxed at a uniform rate and,

unlike at the Federal level, residential shelter is

heavily taxed by regional governments.

Conspicuously absent from regional tax policy is the

existence of a corporate tax wedge.

What is not evident from the national average

numbers is the fact that regional variation of factor

taxation is significant. In every region, either a farm

or food industry has the highest marginal tax rate on

capital income. While most regions tax farms at a

higher rate than they do other industries, the Delta

States are an exception. With very low overall tax

rates in the Delta, farms are taxed at a lower rate

than three of the other six industries. Cash grain

farms rank third in taxation in the Lake States and

Southeast. Otherwise, cash grain and other farms

rank 1 or 2 in taxation in all other regions. The

overall level of taxation in agriculture ranges from

4.6 percent in the Delta to 18.8 percent in

Northeastern States. In the Northeast, the marginal

tax rate on capital (0.188) is actually higher than the

Federal marginal rate (0.145). The Delta and

Mountain regions are the only two areas where the

marginal tax on labor is higher than on capital. In

each region, land and buildings are taxed at a lower

rate than other equipment.

In 6 of the 10 regions, food manufacturers are

ranked between 4 and 6 in marginal tax burden on

capital income. Notable exceptions include the

Delta States, where food manufacturers are taxed

more heavily than any other industry, and the Pacific

States, where they are the most lightly taxed

industry.
36 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 6--Regional primary factor tax wedges, by industry, region, and factor type

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per dollar of value added

Appalachian:

Corporate--

Computers/office equipment 0 0 0.052 0.058 0.051 0.054 0.051 0.055 0

Service and furnishing equipment 0 0 0.052 0.059 0.053 0.054 0.054 0.054 0

Heavy machinery 0 0 0.054 0.061 0.053 0.053 0.051 0.054 0

Transportation equipment 0 0 0.058 0.065 0.058 0.060 0.058 0.060 0

Industrial plants 0 0 0.052 0.058 0.054 0.056 0.051 0.055 0

Utility plants 0 0 0 0 0 0 0.036 0.005 0

Noncorporate--

Computers/office equipment 0.101 0.096 0 0 0 0 0.031 0.035 0

Service and furnishing equipment 0.102 0.097 0 0 0 0 0.033 0.033 0

Heavy machinery 0.104 0.097 0 0 0 0 0.029 0.034 0

Transportation equipment 0.101 0.095 0 0 0 0 0.039 0.041 0

Industrial plants -0.017 -0.016 0 0 0 0 0.028 0.033 0

Utility plants 0 0 0 0 0 0 0.012 -0.036 0

Farmland 0.060 0.060 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.080 0 0.078

Labor services1 0.099 0.097 0.088 0.101 0.090 0.085 0.090 0 0

Corn Belt:

Corporate--

Computers/office equipment 0 0 0.081 0.077 0.087 0.079 0.080 0.085 0

Service and furnishing equipment 0 0 0.081 0.076 0.087 0.080 0.083 0.083 0

Heavy machinery 0 0 0.082 0.077 0.089 0.081 0.082 0.084 0

Transportation equipment 0 0 0.083 0.079 0.091 0.084 0.084 0.086 0

Industrial plants 0 0 0.097 0.091 0.100 0.088 0.100 0.101 0

Utility plants 0 0 0 0 0 0 0.098 0.108 0

Noncorporate--

Computers/office equipment 0.171 0.179 0 0 0 0 0.074 0.075 0

Service and furnishing equipment 0.175 0.185 0 0 0 0 0.074 0.074 0

Heavy machinery 0.172 0.181 0 0 0 0 0.075 0.075 0

Transportation equipment 0.164 0.171 0 0 0 0 0.075 0.076 0

Industrial plants 0.069 0.081 0 0 0 0 0.112 0.108 0

Utility plants 0 0 0 0 0 0 0.114 0.137 0

Farmland 0.097 0.102 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.129 0 0.150

Labor services 0.097 0.091 0.091 0.095 0.091 0.090 0.089 0 0

Delta States:

Corporate--

Computers/office equipment 0 0 0.049 0.045 0.047 0.039 0.036 0.042 0

Service and furnishing equipment 0 0 0.048 0.045 0.049 0.039 0.044 0.042 0

Heavy machinery 0 0 0.051 0.048 0.050 0.038 0.036 0.043 0

Transportation equipment 0 0 0.059 0.056 0.057 0.052 0.049 0.053 0

Industrial plants 0 0 0.062 0.062 0.060 0.057 0.053 0.058 0

Utility plants 0 0 0 0 0 0 0.022 0.008 0

Noncorporate--

Computers/office equipment 0.053 0.055 0 0 0 0 -0.013 -0.006 0

Service and furnishing equipment 0.049 0.051 0 0 0 0 -0.006 -0.008 0

Heavy machinery 0.056 0.056 0 0 0 0 -0.014 -0.006 0

Transportation equipment 0.060 0.059 0 0 0 0 0.006 0.009 0

Industrial plants -0.049 -0.052 0 0 0 0 0.015 0.021 0

Utility plants 0 0 0 0 0 0 -0.021 -0.026 0

Farmland 0.053 0.056 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.058 0 0.041

Labor services 0.098 0.100 0.096 0.092 0.094 0.091 0.092 0 0

See footnote at end of table. Continued--
Table 6--Regional primary factor tax wedges, by industry, region, and factor type--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per dollar of value added

Lakes States:

Corporate--

Computers/office equipment 0 0 0.085 0.086 0.085 0.096 0.093 0.089 0

Service and furnishing equipment 0 0 0.084 0.085 0.087 0.095 0.089 0.089 0

Heavy machinery 0 0 0.085 0.086 0.088 0.095 0.093 0.089 0

Transportation equipment 0 0 0.084 0.084 0.085 0.087 0.086 0.086 0

Industrial plants 0 0 0.131 0.138 0.138 0.141 0.161 0.151 0

Utility plants 0 0 0 0 0 0 0.203 0.317 0

Noncorporate--

Computers/office equipment 0.196 0.173 0 0 0 0 0.122 0.115 0

Service and furnishing equipment 0.200 0.176 0 0 0 0 0.120 0.115 0

Heavy machinery 0.205 0.173 0 0 0 0 0.123 0.115 0

Transportation equipment 0.199 0.168 0 0 0 0 0.119 0.113 0

Industrial plants 0.171 0.115 0 0 0 0 0.245 0.233 0

Utility plants 0 0 0. 0. 0 0 0.272 0.459 0

Farmland 0.168 0.154 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.268 0 0.346

Labor services 0.105 0.108 0.105 0.104 0.099 0.098 0.101 0 0

Mountain:

Corporate--

Computers/office equipment 0 0 0.047 0.072 0.062 0.041 0.066 0.064 0

Service and furnishing equipment 0 0 0.047 0.071 0.060 0.042 0.064 0.063 0

Heavy machinery 0 0 0.048 0.072 0.060 0.043 0.058 0.063 0

Transportation equipment 0 0 0.049 0.068 0.060 0.042 0.064 0.064 0

Industrial plants 0 0 0.053 0.071 0.082 0.050 0.081 0.088 0

Utility plants 0 0 0 0 0 0 0.088 0.041 0

Noncorporate--

Computers/office equipment 0.138 0.133 0 0 0 0 0.071 0.067 0

Service and furnishing equipment 0.142 0.134 0 0 0 0 0.071 0.067 0

Heavy machinery 0.139 0.134 0 0 0 0 0.066 0.068 0

Transportation equipment 0.134 0.131 0 0 0 0 0.072 0.070 0

Industrial plants 0.029 -0.016 0 0 0 0 0.100 0.114 0

Utility plants 0 0 0 0 0 0 0.108 0.033 0

Farmland 0.085 0.087 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.135 0 0.150

Labor services 0.100 0.109 0.101 0.091 0.095 0.084 0.089 0 0

Northeast:

Corporate--

Computers/office equipment 0 0 0.124 0.116 0.122 0.129 0.123 0.115 0

Service and furnishing equipment 0 0 0.124 0.116 0.123 0.129 0.120 0.115 0

Heavy machinery 0 0 0.124 0.117 0.122 0.129 0.121 0.116 0

Transportation equipment 0 0 0.123 0.115 0.121 0.125 0.119 0.115 0

Industrial plants 0 0 0.127 0.119 0.127 0.138 0.122 0.114 0

Utility plants 0 0 0 0 0 0 0.124 0.131 0

Noncorporate--

Computers/office equipment 0.273 0.238 0 0 0 0 0.141 0.127 0

Service and furnishing equipment 0.283 0.246 0 0 0 0 0.139 0.127 0

Heavy machinery 0.275 0.238 0 0 0 0 0.140 0.128 0

Transportation equipment 0.259 0.227 0 0 0 0 0.135 0.125 0

Industrial plants 0.113 0.098 0 0 0 0 0.146 0.127 0

Utility plants 0 0 0 0 0 0 0.170 0.176 0

Farmland 0.121 0.114 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.177 0 0.192

Labor services 0.106 0.103 0.101 0.106 0.103 0.101 0.109 0 0

See footnote at end of table. Continued--

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 37
38 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 6--Regional primary factor tax wedges, by industry, region, and factor type--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per dollar of value added

Northern Plains:

Corporate--

Computers/office equipment 0 0 0.046 0.040 0.045 0.034 0.039 0.039 0

Service and furnishing equipment 0 0 0.046 0.040 0.043 0.033 0.041 0.039 0

Heavy machinery 0 0 0.046 0.040 0.042 0.034 0.038 0.040 0

Transportation equipment 0 0 0.048 0.041 0.042 0.036 0.042 0.039 0

Industrial plants 0 0 0.102 0.086 0.064 0.028 0.064 0.061 0

Utility plants 0 0 0 0 0 0 0.056 0.040 0

Noncorporate--

Computers/office equipment 0.144 0.147 0 0 0 0 0.052 0.054 0

Service and furnishing equipment 0.148 0.151 0 0 0 0 0.053 0.052 0

Heavy machinery 0.144 0.148 0 0 0 0 0.052 0.054 0

Transportation equipment 0.138 0.142 0 0 0 0 0.058 0.059 0

Industrial plants 0.082 0.070 0 0 0 0 0.103 0.099 0

Utility plants 0 0 0. 0. 0 0 0.120 0.042 0

Farmland 0.109 0.103 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.125 0 0.144

Labor services 0.091 0.088 0.094 0.095 0.099 0.104 0.093 0 0

Pacific:

Corporate--

Computers/office equipment 0 0 0.076 0.078 0.077 0.080 0.076 0.076 0

Service and furnishing equipment 0 0 0.075 0.078 0.078 0.079 0.078 0.075 0

Heavy machinery 0 0 0.077 0.079 0.078 0.080 0.076 0.076 0

Transportation equipment 0 0 0.076 0.080 0.079 0.082 0.079 0.077 0

Industrial plants 0 0 0.078 0.083 0.078 0.083 0.083 0.082 0

Utility plants 0 0 0 0 0 0 0.082 0.104 0

Noncorporate--

Computers/office equipment 0.181 0.214 0 0 0 0 0.074 0.076 0

Service and furnishing equipment 0.188 0.224 0 0 0 0 0.073 0.075 0

Heavy machinery 0.183 0.214 0 0 0 0 0.077 0.077 0

Transportation equipment 0.173 0.197 0 0 0 0 0.078 0.079 0

Industrial plants 0.037 0.078 0 0 0 0 0.085 0.086 0

Utility plants 0 0 0 0 0 0 0.103 0.144 0

Farmland 0.091 0.088 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.116 0 0.131

Labor services 0.106 0.091 0.097 0.101 0.098 0.102 0.100 0 0

Southern Plains:

Corporate--

Computers/office equipment 0 0 0.042 0.042 0.041 0.044 0.046 0.042 0

Service and furnishing equipment 0 0 0.042 0.041 0.040 0.043 0.041 0.043 0

Heavy machinery 0 0 0.041 0.041 0.040 0.045 0.044 0.043 0

Transportation equipment 0 0 0.035 0.035 0.035 0.035 0.038 0.037 0

Industrial plants 0 0 0.050 0.050 0.049 0.051 0.058 0.055 0

Utility plants 0 0 0 0 0 0 0.091 0.100 0

Noncorporate--

Computers/office equipment 0.143 0.119 0 0 0 0 0.078 0.076 0

Service and furnishing equipment 0.150 0.123 0 0 0 0 0.074 0.078 0

Heavy machinery 0.139 0.118 0 0 0 0 0.077 0.077 0

Transportation equipment 0.132 0.115 0 0 0 0 0.067 0.067 0

Industrial plants 0.080 0.058 0 0 0 0 0.107 0.106 0

Utility plants 0 0 0 0 0 0 0.154 0.163 0

Farmland 0.066 0.066 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.098 0 0.144

Labor services 0.055 0.058 0.048 0.050 0.050 0.045 0.048 0 0

See footnote at end of table. Continued--
Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 39

Table 6--Regional primary factor tax wedges, by industry, region, and factor type--Continued

Region/ Cash Beverages, Manu- Chemicals, Services, Construc- Owner-occupied

factor Farms grains Food tobacco facturing rubber, plastic mines, trade tion shelter

Taxes per dollar of value added

Southeast:

Corporate--

Computers/office equipment 0 0 0.108 0.113 0.094 0.078 0.123 0.120 0

Service and furnishing equipment 0 0 0.107 0.112 0.087 0.077 0.119 0.120 0

Heavy machinery 0 0 0.107 0.110 0.087 0.078 0.114 0.119 0

Transportation equipment 0 0 0.100 0.100 0.086 0.077 0.111 0.111 0

Industrial plants 0 0 0.064 0.065 0.059 0.057 0.081 0.084 0

Utility plants 0 0 0 0 0 0 0.070 0.035 0

Noncorporate--

Computers/office equipment 0.267 0.206 0 0 0 0 0.166 0.162 0

Service and furnishing equipment 0.280 0.213 0 0 0 0 0.160 0.163 0

Heavy machinery 0.266 0.206 0 0 0 0 0.157 0.160 0

Transportation equipment 0.238 0.196 0 0 0 0 0.144 0.145 0

Industrial plants 0.049 -0.005 0 0 0 0 0.087 0.091 0

Utility plants 0 0 0. 0. 0 0 0.075 0.018 0

Farmland 0.065 0.065 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.099 0 0.121

Labor services 0.065 0.093 0.080 0.076 0.082 0.088 0.069 0 0

United States:

Corporate--

Computers/office equipment 0 0 0.078 0.072 0.081 0.074 0.087 0.084 0

Service and furnishing equipment 0 0 0.078 0.072 0.081 0.074 0.087 0.084 0

Heavy machinery 0 0 0.079 0.072 0.081 0.075 0.081 0.085 0

Transportation equipment 0 0 0.078 0.073 0.082 0.076 0.084 0.084 0

Industrial plants 0 0 0.090 0.073 0.088 0.084 0.094 0.092 0

Utility plants 0 0 0 0 0 0 0.091 0.074 0

Noncorporate--

Computers/office equipment 0.167 0.158 0 0 0 0 0.098 0.092 0

Service and furnishing equipment 0.172 0.163 0 0 0 0 0.097 0.092 0

Heavy machinery 0.171 0.160 0 0 0 0 0.093 0.093 0

Transportation equipment 0.161 0.152 0 0 0 0 0.094 0.091 0

Industrial plants 0.064 0.064 0 0 0 0 0.113 0.107 0

Utility plants 0 0 0 0 0 0 0.114 0.101 0

Farmland 0.094 0.099 0 0 0 0 0 0 0

Residential--

Shelter 0 0 0 0 0 0 0.137 0 0.160

Labor services1 0.094 0.092 0.092 0.095 0.092 0.089 0.092 0 0

All sectors--

Capital services 0.119 0.116 0.082 0.072 0.083 0.077 0.094 0.088 0.160

Value added services 0.106 0.108 0.086 0.080 0.089 0.082 0.093 0.088 0.160

1Rate includes State insurance trust wage tax.
At this point, it is useful to perform a comprehensive

tax reform simulation and examine its consequences

for endogenous variables in our model. In this

simulation, factor tax rates are flattened. This

simulation should not be confused with any of the

flat tax proposals under consideration. The purpose

of this simulation is to consider the implications to

industrial composition and welfare of unequal factor

taxation and the interregional terms of trade effects

that occur in a robust, regionalized, open economy.

Specification of Simulation

Our simulation of comprehensive tax reform in the

United States is specified as follows. Both Federal

and State/regional insurance trust taxes (for example,

social security) on wages remain unchanged at their

1994 rates. Border taxes (import tariffs and export

subsidies on international trade) also remain

unchanged. All other tax instruments in the model

are eliminated and replaced with a flat (equal rates

on all primary factors) ad valorem tax on all primary

industry factors: land, depreciable assets, shelter, and

labor. The rate of this flat tax is endogenously

determined, so that the new tax regime is revenue

neutral.

The effects of this simulation are decomposed using

three different simulations. First, we simulate

Federal tax reform only, such that all State/regional

tax policies remain intact. Second, we simulate

State/regional tax reform, such that Federal tax

instruments are not changed. Third, we simulate

simultaneous Federal and State/regional revenueneutral tax reform.

Overview of Tax Policy Change

The effects of our simulation will be largely

determined by the magnitude of the tax rates that

will be flattened, and the relative level of each

affected primary factor, industry, and regional

economy within the national economy. In this

section, we provide an aggregate analysis of the

relevant Federal and State/regional tax rates to

prepare the reader for the simulation results

presented in the next section.

The flat rate of Federal factor taxes at the industry

level that generates the same amount of taxes for the

Federal Government is 21.99 percent (last row in

table 7). In our simulations, labor contributions to

the Federal insurance trust fund (for example, social

security) remain at 10.17 percent, in addition to the

21.99-percent income tax. Thus, in our simulation,

the overall rate (including labor trust fund

contributions) of primary factor taxation is 28

percent.

The flat rate of State/regional factor taxation at the

industry level that generates the same amount of

taxes for each State/regional government is given in

the last row in table 8. In our simulations, labor

contributions to State insurance trust funds remain at

3.99 percent, in addition to the tax rates in the last

row in table 8.

Table 7 shows that Federal tax rates increase for

most agricultural industries, which suggests that for

the United States as a whole, resources would leave

agriculture. However, the regional implications for

agricultural output may be different from the

national outcome. For example, tax rates for

agriculture in the Delta and Northeast regions

increase the most in the United States, while tax

rates for agriculture in the Lake States, Mountain,

and Pacific regions increase the least. We expect

agricultural industries in the Delta and Northeast

regions to decline. Agricultural industry in the Lake

States, Mountain, and Pacific regions are expected to

expand. Federal tax rates are cut for food processing

and manufacturing industries, with tax cuts in food

processing being larger than the tax cuts in

manufacturing (table 7). However, production costs

in food processing may not decline that much

because agricultural prices are expected to increase

(due to increased taxes on agriculture). For

example, food processing in the Appalachian region

will face the biggest tax cuts. Thus, we expect food

processing in the Appalachian region to expand

substantially. However, agriculture in the

Appalachian region (and elsewhere) will face

substantial tax increases, which will raise costs to

food processing. 

40 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Simulated Effects of Comprehensive

Tax Reform in the United States
Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 41

Table 7Overall Federal tax rates of primary factors, by industry and region

Tax policy/ North- Appala- South- Lake Corn Delta Northern Southern Moun- Pacific

industry east chian east States Belt States Plains Plains tain

Percent

Overall tax rates, including

social security:

Agriculture 20.42 23.83 24.11 25.89 23.67 20.23 24.35 22.49 25.25 24.86

Capital-intensive

agriculture 21.86 23.61 24.42 25.94 23.66 20.65 24.02 23.49 25.23 24.19

Processed foods 35.26 36.92 36.98 36.29 35.86 35.63 35.74 35.91 36.63 36.38

Capital-intensive

processed foods 35.82 38.42 37.07 37.20 36.27 36.24 36.40 36.14 37.06 36.40

Manufacturing 34.49 35.94 36.39 35.59 34.94 35.48 35.75 35.56 35.64 35.50

Capital-intensive 

manufacturing 34.81 36.18 35.63 36.17 35.44 34.32 36.39 35.02 35.64 34.98

All other industries 28.85 30.12 29.03 30.02 29.06 27.80 29.39 28.80 29.46 29.95

Industry-level average 30.38 32.00 30.45 31.67 30.91 29.24 30.50 29.70 30.49 30.34

Industry/household 26.08 32.54 26.92 29.32 29.52 26.39 31.20 25.52 26.57 24.86

Flat tax, 

industry level:

Including social 

security tax 28.12 28.12 28.12 28.12 28.12 28.12 28.12 28.12 28.12 28.12

Excluding social

security tax 21.99 21.99 21.99 21.99 21.99 21.99 21.99 21.99 21.99 21.99

Table 8Overall State/regional tax rates of primary factors, by industry and region

Tax policy/ North- Appala- South- Lake Corn Delta Northern Southern Moun- Pacific

industry east chian east States Belt States Plains Plains tain

Percent

Overall tax rates, including

labor insurance tax:

Agriculture 15.91 8.16 8.57 15.46 11.80 6.19 11.11 12.08 10.28 11.84

Capital-intensive 

agriculture 14.75 8.03 7.97 13.92 12.14 6.29 10.97 11.43 10.35 12.83

Processed foods 11.45 6.89 4.56 10.20 8.81 7.73 7.63 8.88 7.12 8.51

Capital-intensive

processed foods 11.43 6.79 4.46 10.12 8.42 6.19 6.06 9.27 7.67 8.56

Manufacturing 11.06 7.57 4.69 9.93 9.13 7.62 8.06 8.16 8.37 9.04

Capital-intensive

manufacturing 11.82 6.82 4.53 10.20 8.60 6.36 6.53 7.94 6.13 8.98

All other 

industries 12.37 7.10 6.33 13.44 9.53 6.13 8.21 8.97 8.82 9.51

Industry-level 

average 12.06 7.19 6.00 12.40 9.39 6.41 8.26 8.87 8.70 9.42

Flat tax, industry level:

Including labor 

insurance tax 13.52 11.69 10.67 13.39 11.05 10.58 11.41 10.65 11.77 12.19

Excluding labor

insurance tax 11.32 9.58 8.66 11.13 8.90 8.54 9.27 8.67 9.72 10.09
Table 8 shows that agriculture in most regions will

face a cut in State/regional taxes. Exceptions to this

are the agricultural industries in the Appalachian,

Southeast, Delta, and Mountain regions. In

particular, agriculture in the Delta and Appalachian

regions will face the largest increase in State/regional

taxes, which suggests that agriculture in those two

regions will decline. The tax cuts for agriculture in

the Northeast and Lake States are larger than in any

other region, suggesting that agriculture will expand

in those two regions.

Simulated Effects of Tax Reform

Table 9 shows selected simulation results from tax

reform at both the Federal and State/regional levels.

As expected, agricultural output declines the most in

the Appalachian and Delta regions, while agriculture

expands in the Pacific region. For the United States,

output for high-capital agriculture declines by 0.58

percent, while output for the other agricultural

industry expands by 0.40 percent. Food processing

expands in the Pacific region as well as in the Delta,

Southern Plains, and Mountain regions. For the

United States, high-capital food processing expands

by 1.12 percent, and the other food-processing

industry expands by 0.10 percent.

High-capital manufacturing expands by 0.22 percent

for the United States, while the other manufacturing

industry expands by 0.01 percent. Manufacturing

expands in the Northeast and Pacific regions, and

significantly declines in the Appalachian. The all

other industry declines by 0.05 percent for the

United States.

In our model, welfare effects arise due to allocative

efficiency gains from nondistortionary taxation,

terms-of-trade changes, and changes in the regional

contributions to Federal tax collections. The amount

of Federal taxes raised in each region changes

because the level of Federal tax rates changes. Thus,

under tax reform, some regions may contribute more

to the Federal budget, and others may contribute less

to the Federal budget.

Table 9 shows that the regions experiencing the

largest (in percentage terms) welfare effects from

42 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 9Tax reform at Federal and State/regional levels

Tax policy/ North- Appala- South- Lake Corn Delta Northern Southern Moun- Pacific U.S. ROW

industry east chian east States Belt States Plains Plains tain

Percent

Output change:

Agriculture -0.33 -2.59 0.33 1.18 0.14 -2.11 -0.59 0.22 0.80 3.73 0.40 -0.07

Capital-intensive

agriculture -1.92 -5.40 -1.01 -1.24 0.19 -3.61 -1.22 -0.95 0.60 2.55 -0.58 -0.09

Processed food 0.88 -2.12 0.06 -0.73 -0.46 0.50 -0.40 1.12 0.31 2.30 0.10 -0.01

Capital-intensive

processed food -0.33 3.89 0.21 -2.15 -3.42 1.54 -2.47 1.36 2.07 3.12 1.12 -0.06

Manufacturing 1.04 -3.38 -0.29 -1.13 -0.48 1.40 -1.99 1.31 0.93 2.11 0.01 0.06

Capital-intensive

manufacturing 2.82 -2.56 -0.45 0.96 -0.78 -0.49 -1.76 0.55 -2.19 2.10 0.22 0.10

All other industries -0.46 1.28 0.07 0.38 0.30 -0.20 0.55 -0.20 -0.15 -0.69 -0.05 -0.01

Factor price change:

Land 2.23 1.61 5.45 6.60 4.78 0.36 6.39 0.36 3.95 7.96 NA -0.43

Labor -6.78 2.40 -2.59 -5.48 -2.95 0.11 1.54 -5.69 -3.61 -4.69 NA -0.25

Capital 4.75 10.67 4.88 9.38 7.49 0.77 7.53 2.56 4.00 0.97 NA -0.25

Welfare effects:

Terms of trade

change -0.08 0.34 0.08 0.06 0.08 -0.01 0.17 -0.08 0.10 -0.21 NA -0.18

Income change -1.11 3.79 0.37 0.77 1.23 -0.18 2.66 -1.24 -0.18 -1.68 NA -0.25

Welfare change -0.93 3.19 0.24 0.64 0.97 -0.21 2.09 -0.94 -0.19 -1.35 NA -0.05

Billion dollars

Welfare index:

Equivalent variation -11.36 15.13 1.02 2.27 7.22 -0.27 2.15 -2.79 -0.41 -11.34 1.62 -1.07

NA = Not applicable.
flattening tax rates are the Appalachian, Northern

Plains, Southern Plains, and Pacific regions. In

particular, the Appalachian and Northern Plains

regions gain in welfare by 3.19 and 2.09 percent,

respectively. The Pacific and Southern Plains

regions, however, lose in welfare by 1.35 and 0.94

percent, respectively. In monetary terms, the

Appalachian region gains $15.1 billion, while the

Pacific and Northeast regions lose $11.3 billion

(1987 dollars) each.

The Appalachian region benefits the most from this

simulation because allocative efficiency gains are

augmented with a significant improvement in its

terms of trade (+0.34 percent) and a reduction in the

amount of Federal taxes. The Pacific and Northeast

regions lose in welfare because efficiency gains are

eroded by deterioration in terms of trade and

increased Federal taxes.

The second to last row in table 7 suggests which

regions contribute more (or less) to Federal taxes

under reform. The Appalachian regions

contribution to Federal taxes will decline, as its

overall factor tax rate declines from 32.54 to 28

percent. Regions that experience an increase in their

Federal taxes (Northeast, Southeast, Delta, Southern

Plains, Mountain, and Pacific) tend to lose in

welfare from tax reform. Among those regions,

only the Southeast gains in welfare ($1 billion)

because tax reform at the Federal level is not so

important for the Southeast.

Tables 10 and 11 decompose the results of flattening

tax rates into components. The results suggest

Federal tax reform drives the welfare implications of

the combined simulation. The only exception to this

result is in the Southeast, which gains $2.3 billion in

welfare from reform at the State/regional level but

loses $1 billion from reform at the Federal level.

The national welfare effect of flattening tax rates at

the Federal and State/regional level is equal to

$1,621 million (that is, the sum of regional welfare

effects in monetary terms in table 9). Reform at the

Federal level increases national welfare by $623

million (table 10), whereas reform at the State level

increases welfare by $1,036 million (table 11). This

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 43

Table 10Tax reform at Federal level

Tax policy/ North- Appal- South- Lake Corn Delta Northern Southern Moun- Pacific U.S. ROW

industry east achian east States Belt States Plains Plains tain

Percent

Output change:

Agriculture -3.06 -2.90 0.73 0.57 -0.70 -1.64 -0.98 0.26 1.50 1.66 -0.26 0.14

Capital-intensive

agriculture -2.49 -4.58 1.33 1.10 -1.22 -1.27 -0.80 1.14 2.13 0.99 -0.46 0.15

Processed food 1.25 -1.80 0.36 -0.95 -0.26 -0.23 -2.38 -0.34 0.19 2.15 -0.01 -0.01

Capital-intensive

processed food 1.10 -0.41 1.21 -1.31 -0.98 0.81 -2.58 1.15 0.81 2.20 0.55 -0.10

Manufacturing 1.37 -2.98 0.53 -0.92 -1.08 1.04 -2.30 1.37 0.60 2.53 0.14 -0.11

Capital-intensive

manufacturing 1.00 -2.03 -0.04 0.22 -0.16 -0.05 -0.43 0.97 0.58 0.78 0.06 -0.04

All other industries -0.48 1.24 -0.13 0.34 0.44 -0.14 0.75 -0.21 -0.14 -0.72 -0.06 0.02

Factor price change:

Land -1.49 0.80 4.29 3.58 2.40 1.59 3.42 4.03 5.34 3.94 NA 0.27

Labor -4.25 0.62 -1.41 -1.90 -1.37 -2.15 0.99 -2.75 -3.15 -4.18 NA -0.08

Capital 0.91 8.12 -0.19 4.39 4.32 -0.17 5.39 -1.40 1.05 -1.41 NA -0.08

Welfare effects:

Terms of trade

change -0.09 0.29 -0.02 0.07 0.09 -0.02 0.28 -0.12 0.03 -0.23 NA -0.04

Income change -0.92 3.48 -0.24 1.12 1.39 -0.49 2.65 -1.18 -0.36 -1.73 NA -0.08

Welfare change -0.82 2.93 -0.24 0.84 1.14 -0.44 2.16 -0.92 -0.32 -1.42 NA -0.02

Billion dollars

Welfare index:

Equivalent variation -10.05 13.87 -1.01 3.01 8.53 -0.57 2.23 -2.74 -0.68 -11.95 0.62 -0.34

NANot applicable.
is a striking result, since it shows that Federal reform

dominates State reforms regionally, but the overall

benefits of State reforms are larger. This can be

explained by a number of factors, which we will only

briefly consider.

Federal reform creates a leveling of factor taxes

nationally, but shifts overall burdens regionally.

Thus, some regions realize a higher tax burden, even

as their allocative efficiency of factor use is

improved. Since primary factors of production are

regionally immobile, the Federal reform creates a

new allocative inefficiency, that of geographic

allocation.12 No such result occurs in the State

reforms, since relative regional burdens remain fixed.

Table 9 shows that tax reform causes the overall rent

received by owners of capital assets (excluding land)

to increase in all U.S. regions. As a result, the rate

of return (that is, capital rent over price of new

capital goods) also increases in those regions.

Investment is sensitive to rates of return, and an

optimal allocation of investment is achieved when

investors in all regions experience the same

percentage increase in expected rates of return to

capital. Returns to land also increase in all U.S.

regions, but less than capital rents (except in the

Pacific region), while returns to labor decline in most

U.S. regions. These results reflect the fact that

flattening of factor taxes shifts the overall burden of

taxation toward income from labor services.

Tables 10 and 11 show that the agricultural

implications of Federal tax reform are different from

those of tax reform at the State/regional level. For

the Nation as a whole, Federal tax reform leads to a

reallocation of resources from agriculture to other

industries. Reform at the State/regional level leads

to a decline only in capital-intensive agriculture.

44 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 11Tax reform at State/regional level

Tax policy/ North- Appala- South- Lake Corn Delta Northern Southern Moun- Pacific U.S. ROW

industry east chian east States Belt States Plains Plains tain

Percent

Output change:

Agriculture 2.69 0.27 -0.44 0.59 0.87 -0.41 0.37 -0.05 -0.67 2.13 0.67 -0.20

Capital-intensive

agriculture 0.49 -0.97 -2.39 -2.39 1.38 -2.34 -0.47 -2.05 -1.48 1.48 -0.15 -0.24

Processed foods -0.34 -0.36 -0.36 0.17 -0.19 0.82 1.95 1.48 0.12 0.19 0.11 0

Capital-intensive

processed foods -1.38 3.52 -1.05 -0.92 -2.44 0.81 0.03 0.22 1.24 0.95 0.59 0.05

Manufacturing -0.29 -0.47 -0.92 -0.32 0.62 0.47 0.20 -0.03 0.30 -0.37 -0.14 0.17

Capital-intensive

manufacturing 1.85 -0.57 -0.47 0.70 -0.61 -0.37 -1.39 -0.38 -2.76 1.35 0.17 0.15

All other industries 0 0.07 0.22 0.08 -0.15 -0.09 -0.18 0 0 0.01 0.01 -0.03

Factor price change:

Land 3.58 0.69 1.02 2.85 2.30 -1.12 2.80 -3.54 -1.31 3.97 NA -0.69

Labor -2.64 1.78 -1.17 -3.63 -1.60 2.29 0.57 -3.04 -0.46 -0.54 NA -0.18

Capital 3.97 2.42 5.43 5.38 3.15 0.80 2.29 4.11 3.12 2.53 NA -0.18

Welfare effects:

Terms of trade

change 0 0.06 0.11 0 -0.01 0 -0.10 0.04 0.07 0.01 NA -0.14

Income change -0.24 0.39 0.68 -0.24 -0.18 0.19 0.12 -0.08 0.20 0 NA -0.18

Welfare change -0.14 0.34 0.53 -0.12 -0.19 0.13 0.02 -0.04 0.13 0.04 NA -0.04

Billion dollars

Welfare index:

Equivalent variation -1.72 1.62 2.23 -0.43 -1.41 0.17 0.02 -0.11 0.28 0.33 1.04 -0.77

NANot applicable.

12This helps explain how overall welfare effects of

Federal tax reform in the model are small, compared with

similar reform simulations examined in national models,

such as Fullerton and Henderson. A longrun dynamic

model would produce greater welfare effects.
Sensitivity Analysis

Tables 12 and 13 show sensitivity analysis regarding

our trade and allocation of investment specifications.

Implementation of the Armington assumptions may

artificially insulate U.S. regions from both the

national market and international markets. Table 12

shows changes in selected variables due to tax

reform under larger trade elasticities. In particular,

the elasticities σD and σL (see fig. 5) are assigned

four times the values in their base specification.

Larger values for those two sets of elasticities imply

that importers in all regions, including the ROW,

will be more sensitive to relative price changes

when they consider their sources of U.S.-produced

commodities. Thus, importers will tend to import

more from U.S. regions where relative commodity

prices decline due to tax reform. With higher trade

elasticities, we expect exports from some regions to

change considerably more due to tax reform. Total

regional imports, however, are not expected to

change more in this simulation because they are an

aggregate of imports from all regions. Furthermore,

more price-sensitive imports from U.S. regions will

result in U.S. commodity prices changing by more

similar amounts. The results in table 12 show that,

for some regions and commodities, output changes

are considerably different from those in the base

simulation in table 9. Changes in the terms of trade

are smaller in the simulation with larger trade

elasticities. The welfare implications of tax reform

under the higher trade elasticities illustrate the

significance of terms of trade in our welfare

measure. Regions that enjoy better (worse) terms of

trade in this simulation experience an increase

(decline) in their welfare. With higher trade

elasticities, tax reform leads to a decline in overall

welfare improvement for the United States; welfare

gains decline from $1,621 million (table 9) to

$1,467 million (table 11).

As we have discussed in the Macroeconomic

Closure section, the model offers two alternative

specifications about the regional composition of 

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 45

Table 12Tax reform at the Federal and State/regional level, sensitivity analysis of selected results, trade

elasticities at 4 x base values

Tax policy/ North- Appala- South- Lake Corn Delta Northern Southern Moun- Pacific U.S. ROW

industry east chian east States Belt States Plains Plains tain

Percent

Output change:

Agriculture -1.42 -2.58 0.59 2.55 0.37 -3.82 0.70 -0.73 1.16 4.48 0.61 -0.09

Capital-intensive

agriculture -2.31 -5.81 -1.76 -1.82 1.19 -5.47 -0.70 -2.58 0.79 3.11 -0.35 -0.11

Processed foods 0.46 -3.19 0.31 -2.08 -0.66 1.37 2.26 2.44 -0.90 2.88 0.04 -0.04

Capital-intensive

processed foods -6.55 14.85 -0.12 -7.43 -11.80 2.05 -6.65 0.63 5.96 3.66 2.28 -0.03

Manufacturing 0.17 -3.26 -0.07 -1.57 0.73 3.08 -1.14 0.76 2.10 1.45 0.04 0

Capital-intensive

manufacturing 6.94 -4.79 -1.35 5.22 -1.71 -4.17 -3.16 -2.35 -10.55 2.46 0.24 0.05

All other industries -0.37 1.02 0.06 0.43 0.05 -0.31 0.11 -0.04 -0.22 -0.57 -0.07 0.01

Factor price change:

Land 0.15 0.80 5.86 8.31 5.99 -3.68 8.17 -1.88 4.72 10.09 NA -0.43

Labor -6.67 1.74 -2.66 -5.61 -3.04 0.22 1.14 -5.48 -3.53 -4.44 NA -0.21

Capital 4.93 10.25 4.78 9.25 7.21 0.79 7.10 2.82 4.01 1.30 NA -0.21

Welfare effects:

Terms of trade

change -0.01 0.11 0.07 0.01 0.02 0.02 0.01 0.03 0.10 -0.04 NA -0.15

Income change -1.00 3.37 0.31 0.68 1.09 -0.15 2.36 -1.06 -0.14 -1.44 NA -0.21

Welfare change -0.90 3.07 0.24 0.61 0.93 -0.19 2.01 -0.88 -0.21 -1.30 NA -0.04

Billion dollars

Welfare index:

Equivalent variation -11.04 14.53 1.01 2.19 6.94 -0.24 2.07 -2.63 -0.44 -10.91 1.47 -0.89

NA = Not applicable.
investment activity. The specification that we

applied in our simulation assumes that there is a

negative relationship between the expected regional

rate of return on capital and the amount of

investment undertaken in a region. The model

manipulates this relationship until rates of return are

equalized across regions. In this section, we apply

an alternative investment allocation specification

where it is assumed that the regional composition of

global capital stock will not change due to the

simulation performed (that is, the regional allocation

of investment is not sensitive to changes in returns

to investment).

Table 13 shows that tax reform, under this

investment allocation, causes capital rents in the

Delta and Southern Plains regions to increase by

more than under the alternative investment

allocation specification (table 9). As a result, the

rate of return (that is, capital rent over price of new

capital goods) increases more in those regions.

When investment is sensitive to rates of return, more

(less) investment will flow into regions where the

relative expected rate of return increases (declines).

An equilibrium is reached when all regions

experience the same change in expected rates of

return. Thus, less investment is undertaken in those

two regions in this simulation than in the base

simulation. The regional welfare implications of tax

reform do not change considerably for U.S. regions

when the allocation of investment is changed. The

United States, however, experiences a welfare gain

of $586 million, which is smaller than the U.S.

welfare improvement in the base simulation. This

result stems from our alternative investment rule

which will not produce an increased share of

international investment flows to the United States,

even though tax reform produces more favorable

relative returns on U.S. investment goods.

46 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

Table 13Tax reform at Federal and State/regional level, sensitivity analysis of selected results, alternative

investment allocation 

Tax policy/ North- Appala- South- Lake Corn Delta Northern Southern Moun- Pacific U.S. ROW

industry east chian east States Belt States Plains Plains tain

Percent

Output change:

Agriculture -0.50- 2.57 0.22 0.93 -0.12 -2.56 -0.80 -0.11 0.58 3.33 0.15 -0.12

Capital-intensive

agriculture -0.58 -4.22 -0.43 -0.06 0.66 -2.67 -0.79 -0.34 1.00 3.95 0.10 -0.14

Processed foods 0.83 -1.94 -0.01 -0.71 -0.48 0.23 -0.41 0.86 0.14 1.86 0.02 -0.05

Capital-intensive

processed foods -0.39 3.61 0.13 -2.13 -3.36 1.31 -2.48 1.12 1.86 2.61 0.95 -0.12

Manufacturing 1.15 -2.99 -0.25 -0.92 -0.33 1.16 -1.83 1.21 0.93 1.92 0.10 -0.07

Capital-intensive

manufacturing 2.43 -2.52 -0.64 0.85 -0.93 -0.89 -1.79 -0.22 -2.45 1.60 -0.04 -0.03

All other industries -0.47 1.16 0.07 0.31 0.26 -0.11 0.51 -0.16 -0.14 -0.62 -0.06 0.03

Factor price change:

Land 2.31 1.88 5.22 7.00 4.92 -0.12 6.62 0.22 3.85 7.48 NA -0.40 

Labor -6.8 2.30 -2.62 -5.54 -3.00 0.11 1.48 -5.69 -3.62 -4.64 NA -0.09

Capital 4.71 10.53 4.84 9.29 7.42 0.78 7.46 2.57 3.99 1.04 NA -0.09 

Welfare effects:

Terms of trade 

change -0.01 0.29 0.07 0.03 0.05 -0.01 0.14 -0.09 0.08 -0.20 NA -0.04

Income change -1.14 3.69 0.34 0.71 1.17 -0.19 2.60 -1.25 -0.20 -1.64 NA -0.09

Welfare change -0.95 3.15 0.22 0.61 0.94 -0.23 2.06 -0.95 -0.22 -1.36 NA -0.01

Billion dollars

Welfare index:

Equivalent variation -11.58 14.93 0.94 2.18 7.00 -0.30 2.12 -2.84 -0.47 -11.41 0.59 -0.16

NANot applicable.
By dividing the U.S. economy into regions, we

notice that the public sector is multi-tiered, and the

structure of each tier, as well as the interaction

among the different levels of government, can have

profound effects on equity and efficiency of the

market economy. We have explicitly modeled the

size of the public sector and significant features of

what has been called the technological externality

elements of the tax system.

With this modeling framework, the analyst can

create up to 51 distinct economic regions in the U.S.

economy, with 10 regions examined in this report.

Each region has seven distinct industrial outputs and

consumer goods. Industries employ as many as nine

different primary factors of production, with several

factors in corporate and noncorporate legal forms.

Incorporation and unincorporation decisions are

exogenous, so corporate and noncorporate factors

remain fixed. Primary factors of production are

perfectly mobile within a region but cannot leave the

region. Industry output is tradable, both within the

region and with other domestic regions and abroad.

The price of output produced in a region reflects the

industry-level costs of production and taxes, while

the prices of industry output and consumer goods

from other domestic regions reflect the same factors,

plus transportation costs, which are determined

endogenously. Regional tax bases include factor

income, industry output, personal income, and

personal consumption. The level of Federal and

regional taxation is fixed at an exogenous level,

consistent with 1994 Federal budget figures. The

Federal tax bases are the same as regional tax bases,

plus a border tax program.

Some important stylized facts emerged regarding the

disposition of regional primary factors and regional

State and Federal tax incidence. Notably, the

smallness of food and farm industries nationwide

obscures the fact that, in several regions, value

added from capital in food and farm industries

expands the capital income of these regions by

between 7 and 11 percent. The primary competitor

for noncorporate factors of production with

agriculture is the services, mines, and trade and

construction industries, which use both corporate

and noncorporate capital. Food manufacturers face

these same competitors, plus other manufacturers.

Food and farm industries are the most capital

intensive, with capital/labor value-added ratios

ranging from 1.0 to 1.4, compared with 0.6 to 0.8 in

other industries. Farmland is by far the most

important factor of farm production, particularly for

cash grain farms, and farms face no outside industry

competition for their land. Food manufacturers are

most reliant on heavy machinery for production.

Food and farm industries are major employers of

heavy machinery in the Northern Plains, Lake

States, and Appalachian regions.

Two important findings on tax incidence emerged in

this report. Farms are a favored industry, in terms of

taxation at the Federal level. In every region, farms

have the most lightly taxed factors of all industries.

In every region except the Delta, farms are the most

highly taxed industry by regional governments, and

in the Northeast, regional marginal taxation of farm

factors exceeds Federal marginal taxation. Overall,

there are disparities in tax rates between labor and

capital, between types and legal forms of capital,

between industries, and between regions. Similar

disparities exist between consumption and savings

decisions, and across regional households on their

personal income. The surprisingly high incidence of

regional factor taxation indicates that effects of

reforming the tax system depend on whether it is a

harmonized reform of Federal and regional tax

systems, or if it is unilateral reform of one or the

other layer of government.

While tax reform analysis was not the subject of this

report, we did carry out a generic transformation of

the U.S. tax system, a flattening of factor taxes.

These results clearly indicate the multiple

dimensions of economic response to fiscal policy.

Notably, relative tax burden disparities, including

intersectoral, interregional, and intertemporal, have

profound implications on resource allocation and

distribution of welfare. Yet terms of trade, resource

scarcities, and international investment flows can

have conflicting effects on many economic

indicators. This substantiates the assertion that an

applied general equilibrium framework is ideally

suited for assessing fiscal policy effects on food

producers and consumers.

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 47

Conclusion
Like any research program, developments and

refinements to this research are ongoing. We are

focusing on the representation of labor services in

the model and refining our account of regional sales

taxes and regional and national excise taxes.

Regarding labor services, two improvements are

underway. First, efforts are underway to allocate

labor value added between skilled and unskilled

labor units. Also, an explicit account of the

noncorporate, proprietorship, hired labor force will

be made to refine our measurements of labor income

and wage tax rates by industry. Regarding sales and

excise taxes, our assumption that these taxes do not

discriminate against any consumer good or producer

good is clearly in error. Some regions exempt taxes

on goods such as retail sales of food and prescription 

drugs, and some regions target output taxes, such as

severance, tobacco, and alcohol. Such accounts,

however great or small, will be a part of future

editions to the model data set. As mentioned

previously, an extension to a dynamic modeling

framework would enhance our ability to account for

the effects of taxation on the interregional and

international flow of investment capital. Plans to

extend the model and data in this area are also

underway. An important part of this extension is the

ability to specify a nonunitary price-elastic demand

for investment goods (supply of savings). Other

extensions and refinements will be determined by

the types of applications for which the model is

employed.

48 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA
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4 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA
The disposition of industrial inputs and outputs that characterizes the general equilibrium we calibrate in our

model replicates the input/output data of the 1987 National Income and Product Accounts (NIPA) of the

United States. Our regionalized accounts are produced in a manner consistent with the structure of

technology we present in this report. Specifically, production functions are linear homogeneous, with a

Leontief aggregation of value-added and intermediate inputs. This Leontief structure is unique to each

input/output industry but is unvaried across regions. By developing State-level value-added cost share

accounts for each input/output industry, we can use these features of the model to allocate industrial output

to regions. This appendix describes the development of our State value-added accounts.

Agriculture

There are 32 4-digit SIC farm commodity classifications (SIC4), and 17 I/O farm industries in the 1987

NIPA accounts (io87). With a few of the 32 SIC commodities appearing in several I/O industries, there are

a total of 39 unique SIC4/io87 combinations. Our premise is that once farm production is classified into 39

commodities, the value-added cost shares do not vary by State.

First we obtain direct estimates for value-added payments to land in each of the 39 io87/SIC4 industries.

Each of the 39 industries is classified as crop or pasture land users. Using multiple sources,13 and checking

for consistency of sources, we obtained acres harvested for each industry and computed the product of

acreage and national weighted average rental rates (either dry cropland or pasture, since irrigation rents go

to capital), where the weights were State-level industry-specific cash receipts. This gives us national rental

land payments for each commodity. To get the same for capital, we must benchmark our capital estimates

to these land estimates.

Unpublished USDA data on 1993 State-level market values for land, tractors, autos, trucks, machinery, and

buildings were obtained, and this represents over 99 percent of farm capital (U.S. Department of Commerce,

Bureau of Economic Analysis, 1998a). These data gave us State-level cost shares, which we used to

compute the U.S. total for land value. We allocated this value to each of the 39 agricultural industries,

based on their share of national cash rents. We then allocated to each State, based on State shares of cash

receipts.

Each capital type is allocated a value for each commodity, based on the statewide cost share ratios. This

implies, for example, that for every commodity in a given State, the tractor-to-land cost share ratio is the

same. The result is State cost share estimates for each commodity broken into land and five types of capital.

We compute a national weighted average value for relative cost shares, where the weights are again Statelevel cash receipts. This way, those States where a particular crop is important, and thus has more influence

on statewide average cost share profiles, is given more weight in forming a national commodity-specific

capital cost share profile.

We now have national cost share estimates of the 39 agricultural commodities. For each of the 17 io87 farm

industries, the subset of 39 SIC4 belonging to that industry are allocated NIPA capital value added in

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 53

Appendix

Derivation of Regional Primary Factor Accounts

13Acres harvested are from SICs 111-139USDA, NASS (1995); SIC 161USDA, ERS (1998a); SICs

171-181USDA, ERS (1998b); and SICs 211-279USDOC, Census Bureau (1990). Cash rental rates

are from USDA, ERS (1997a), and unpublished USDA data. Cash receipts are from USDA, ERS (1997b).
proportion to cost shares, and these are allocated to States, based on cash receipts. A final refinement is that

national (U.S. Department of Commerce, Bureau of Economic Analysis, 1998a) accounts indicate that 0.9

percent of farm capital is nonagriculture specific (for example, computers and furniture). We assume that a

farm operation uses this capital in a fixed proportion to their total farm capital and prorates each commodity

cost share estimate accordingly. The result is 39 unique cost share profiles allocated to States in proportion

to cash receipts. National value added in each io87 industry conforms to NIPA control totals. Labor

payments are allocated from io87 industries to SIC4 in proportion to cash receipts, so capital to labor ratios

are the same for all SIC4 within an io87 industry.

Manufacturing

A high level of industrial detail is provided in the NIPA value-added accounts of the manufacturing industry.

Net plant value added and net equipment value added are allocated to each io87/SIC4 industry at the U.S.

level. From the 1992 Census of Manufacturing, we obtained gross value of plant and gross value of

equipment for each SIC4. When no SIC4 data were available, we obtained the value for the corresponding

three-digit SIC. We converted these values to net values, using 1992 net-to-gross value ratios for plant and

equipment for each of the corresponding two-digit SICs, based on U.S. Department of Comme rce, Bureau

of Economic Analysis (BEA) data. This allows the allocations of io87 industry capital value-added data into

plant and equipment value-added. Using State-level data from the county business patterns (CBP) wages

data set, we allocated labor, plant, and equipment value added to each State io87/SIC4 manufacturing

industry.

Based on special tabulations of unpublished Census of Manufacturing data at the Center for Economic

Studies (CES), we computed State-level data on the share of plant value and the share of equipment value

for each of the 20 major manufacturing industry groupings (2-digit SIC). Using this information, all Statelevel plant and equipment value for the io87/SIC4 industry are summed to their respective major industry

State totals, and the ratio of this value to the value of the applying CES share tabulations to NIPA valueadded measures by major industry groupings is used to adjust plant and equipment values accordingly.

To summarize, direct estimates of labor value added for each State io87/SIC4 industry is computed using the

CBP data set and NIPA accounts. Direct estimates of U.S. plant and equipment value added for each

io87/SIC4 industry are computed using published Census of Manufacturing data, BEA major industry group

net-to-gross value ratios, and the NIPA capital value-added accounts. These are allocated to States by

holding constant the national plant/equipment/labor cost share ratios, at the SIC4/io87 industry level.

Finally, these cost share ratios are changed at the State-level, using direct State plant and equipment value

data based on CES tabulations. These estimates are then prorated to ensure national io87 capital and labor

value-added totals are reconciled.

In a final step, equipment value added is allocated in proportion to the shares of each of 27 equipment types

reflected in the corresponding major industry grouping, as in the annual fixed reproducible tangible wealth

accounts published by the BEA (1998a) and similarly for each of 23 plant classifications.

Construction

Construction industry value added is exclusively from capital. The economic census of construction

provides SIC4 detail for most construction industries, including national estimates of gross plant and

equipment value and State estimates of gross value of capital. Combining this information with the NIPA

value-added accounts and BEA data on net-to-gross plant and equipment value ratios in the construction

industry, we first allocate value added to each national io2/SIC4 construction industry into plant and

equipment. We then allocate each national industry total to the States, based on net capital value shares for

each minor industry classification. Finally, BEA data are used to share out plant and equipment value into

54 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA
several different types of capital. While each States plant/equipment value-added ratios may be unique, the

allocation of each share into asset types is uniform across all States.

Mining

The Economic Census provides little detail beyond that given in the NIPA accounts, but there is national

level detail by SIC4 for total gross value of capital. This is used to allocate national capital value added in

mining io87 aggregates to each of the io87/SIC4 minor industries. Using the county business patterns data,

these data are allocated to States in proportion to each States shares of total salary and wages in each

industry. Capital is then allocated among the different asset types based on BEA data. The State-level

shares are common across all States.

Other Business Industries

All remaining industries are generally labor-intensive. Because the county business patterns data allow a

direct means of allocating io87 national data to States, we do so while holding constant the national

capital/labor value-added ratios for each io87/SIC4 industry. Finally, we allocate capital between the 50-

BEA type classifications, corresponding to the relevant major industry grouping of each io87/SIC4 industry.

Residential Housing

The tax treatment of owner-occupied versus rental dwellings is very different. Further, corporate housing is

subject to different tax laws. The imputed rental value of owner-occupied housing is explicitly represented

in the NIPA accounts, and a portion of the capital value added in real estate industries of the NIPA accounts

is from tenant-occupied residential housing. We allocated these national accounts to the States as follows.

The national stock value of owner-occupied shelter and tenant dwellings is reported, along with business

capital, in the BEA fixed reproducible wealth data series (1998a). Using data of the Annual Housing

Vacancy Survey (U.S. Department of Commerce, Bureau of the Census, 1998), and State-level personal

income data (BEA, 1999), national housing wealth is allocated to States. For each State, owner occupancy

rates are multiplied by total household units to derive total State household owner-occupied units. The

residual is total State tenant units. By summing the number of owner-occupied units over all States and

Washington, DC, and dividing by BEA national owner-occupied dwelling value, we derive a national

average value per owner-occupied dwelling. By doing the same for tenant dwellings and taking the ratio of

these values, we imputed the national average ratio for owner-occupied to tenant dwelling unit price and

denote this in the O/T ratio.

Next, we assumed annual residential housing budgets are a common share of personal income in all regions

and allocated total dwelling value to the States. Using the O/T ratio to convert owner-occupied units into

tenant equivalent units, we allocated State dwelling value to tenant dwellings proportional to tenant dwelling

share of total dwelling equivalent units. This value is allocated to the appropriate io87 industry when cost

share ratios are employed. The national BEA tenant dwelling account also is broken down into corporate

and noncorporate shares, and this ratio is also held constant across States (corporate housing shares are quite

small). Finally, that which is not tenant dwelling value belongs to the owner-occupied residential io87

industry. Each State is allocated imputed rental value added in proportion to dwelling values.

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 55
Computing the Marginal Effective Tax Rate 

on Income From Capital

We begin with a measure of market value for capital stock. With no loss in generality, we will define a unit

of capital, in any form, to be the amount supplied at a market price of one monetary unit, for example, $1.

Ownership of this single unit of capital affords the owner a predetermined tax allowance that represents a

real value to a firm or household. However, this value is realized over an extended period of time, which we

call the tax life of the asset. Tax allowances depend on a number of factors, including the financial profile

of the household or firm, the nature of the capital owned, and the sector in which the owner will be

operating this capital. All of these factors can be described in an allowance function of the form:

A = b1Az + b2t + b3, (1)

where b1 represents the percentage of new investment subject to a depreciation allowance schedule, Az is the

present discounted value of total depreciation allowances over the tax life of the asset (assuming full

investment is depreciable), b2 represents the percentage of investment that can be fully expensed in the year

of investment, t is the marginal statutory rate of income taxation the capital owner faces, and b3 represents

the percentage of investment subsidized by the government.14 As the thick legal volumes describing State

and Federal tax code indicate, the details required to compute equation 1 are too numerous to summarize

here.15 A numerical example will serve this purpose.

Once the value of special tax allowances is determined, this value can be deducted from the $1 purchase

price and the residual is the effective market price of ownerships:

C = 1 - A. (2)

The hurdle rate (h) that we must determine will equal the residual of the annual revenue from employing this

unit of capital (R), net of the real economic depreciation to the productive capacity of this capital over the

production year (d), or h = R-d. Note that the depreciation measure d is independent of the depreciation tax

allowance Az. Tax rules are such that the gross of tax income R is the tax base and the depreciation

allowance becomes a fixed income to the proprietor. That is, net of tax income is equal to (1-t)×R, instead

of (1-t)×h.

In the theory of capital, real investment is viewed as permanent. This implies that the owner will anticipate

a stream of income from this capital, which will grow annually at the rate of output price inflation, e , and

will incur annual depreciation costs, ed. If we denote as e the subjective discount rate that leads to

indifference between income next period and income now, then the present discounted value of this

permanent stream of capital income is valued at (see Silberberg):

, (3)

where w is the rate of property taxation. In equilibrium, an assets value (equation 3) is equal to its purchase

price (equation 2). This result allows us to derive an expression for the hurdle rate:

56 Regionalism, Federalism, and Taxation/TB-1882 Economic Research Service/USDA

ρ

π

[ ] [ ] V tR we d dt

th d w

d = −− − +− ∞

∫ = − +−

+ −

( ) ( ) ( )( ) 1

0

ρ π 1

ρ π

14This report presents statewide aggregates, so b1 is determined as the percentage of new investment in a State by

investors who have exceeded their maximum annual expensing limit and b2 equals 1-b1.

15A rigorous explanation of the computations described in this section is presented in Canning and Rhoades.
h = (1-t)-1 × [(1-A) × ( + d - )] + w - d. (4)

Based on financial profiles of the asset owner, we can determine the appropriate marginal tax rates, t and w.

With knowledge of the type of asset, there are economic estimates of the value for the annual rate of

depreciation, d. For the discount rate, the value assumed depends on the manner of financing. For debt, it

is the opportunity cost of borrowed funds or the interest rate for borrowing (I), net of tax deductions on

interest payments, = I * (1-t). For equity-financed investments (retained earnings if corporate ownership),

we use = I * (1-m)/(1-k), where m is the marginal tax rate on interest income of the asset owner and k is

the effective rate of taxation on capital gains. For corporations, this reflects the investors tradeoff between

receiving interest payments from issuing debt on a competitive alternative investment, with a net of tax

return equal to (1-m)*I, and increasing the value of corporate stock, subject to taxation at a defermentadjusted rate for capital gains, (1-z)* . For corporate-owned capital, another finance option is through

issuance of new shares of corporate stock, which requires a dividend yield equal to the opportunity cost of

savings, so we have = I.16 Since I and now have an exact relationship, we have reduced our unknowns

to three (h, r, and I).

From here, we choose to exogenously specify an equilibrium real rate of return to savings (0.035), and

adopt the Fischer equation, which states that nominal interest rates on savings equal the real rate plus the

expected annual rate of inflation, I = 0.035 + . Using Feldsteins rule of thumb, we set equal to the

average annual rate of general price inflation over the previous three observed values. We can now define

the value for the reservation rate of return:

r = (1-m) × (0.035+ ) - . (5)

All that remains is to determine the appropriate tax provisions to use in equation 1, plug the solution for A

into equation 4, and calculate the marginal effective tax rate on income from capital as:

tm = (h-r)/h. (6)

The value of tm measures the share of all marginal capital income paid to meet the tax liability. Calculating

Az is somewhat complicated. Most forms of farm capital are allowed 150 percent of the straight-line

depreciation of a declining balance, with a switch to straight-line depreciation at the most advantageous

period for the taxpayer (rental dwellings are required to be depreciated on a straight-line basis). A midyearpurchase assumption rule (midmonth for some buildings, and first or third quarter for other assets) allows

only partial depreciation in the year of purchase and the final year of depreciation. Optimal switch to

straight-line depreciation is at one-third of the assets tax life (rounded to the nearest half-year interval).

Total value of the depreciation allowance over the assets tax life is:

(7)

,

were L is the assets tax life. This is the discrete time version of equation 6.5 in King and Fullerton. 

Economic Research Service/USDA Regionalism, Federalism, and Taxation/TB-1882 57

ρ

ρ

ρ

ρ ρ

π π

π

A L L L LL z [] [ ] t

j

L

t

j

k

k j

j

= ++ − − + −

=

−

= = +

+

15 1 15 15 2 15 1 ∑ ∑ ∑ 2 2

0

30 15

0

0 5

0 5

1 5

. / * ( ) ( . / . / )* ( . )/ * ( )

. /. .

.

.

ρ ρ

+− − + [ ][ ] [ ] −

=

( . ) / * ( . ) / *( / ) * ( ) ∑ / .

/

L LL L L x L i

i L

L

0 75 15 3 2 1 3 05

3

ρ τ

ρ π

π

16For corporate capital, we adopt the traditional view that new investment is financed by equal parts of each

financing method. For farm capital, debt and equity shares are based on the State-level primary USDA data of these

ratios. Noncorporate, nonfarm households are assumed to finance in the same ratio as farm households.