Single Sales-Factor Corporate Income

Tax Apportionment: Evaluating the

Impact in Wisconsin

Prepared for

The Wisconsin Department of Revenue

By

Jamie Bernthal

Dana Gavrila

Katie Schumacher

Shane Spencer

Katherine Sydor

Workshop in Public Affairs

May 2012

©2012 Board of Regents of the University of Wisconsin System

All rights reserved.

For additional copies:

Publications Office

La Follette School of Public Affairs

1225 Observatory Drive, Madison, WI 53706

www.lafollette.wisc.edu/publications/workshops.html

publications@lafollette.wisc.edu

The Robert M. La Follette School of Public Affairs is a teaching

and research department of the University of Wisconsin–Madison.

The school takes no stand on policy issues; opinions expressed in these pages

reflect the views of the authors

iii

Table of Contents

List of Tables and Figures....................................................................................... v

Foreword ............................................................................................................... vii

Acknowledgments.................................................................................................. ix

Executive Summary ............................................................................................... xi

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

Background: Unitary Businesses ............................................................................ 2

Background: Apportionment Formulae .................................................................. 4

Policy Considerations for Single Sales-Factor Apportionment .............................. 7

Literature Review – State Apportionment Formulae and Effects on Employment 8

Goolsbee and Maydew, 2000 .......................................................................... 9

Goolsbee, Maydew, and Schadewald (Goolsbee et al.), 2000 ...................... 10

Lightner, 1999 ............................................................................................... 10

Edmiston, 2002 .............................................................................................. 11

Edmiston and Arze del Granado, 2006 .......................................................... 12

Swenson, 2011 ............................................................................................... 12

State Evaluations ................................................................................................... 13

Replicating and Extending the Goolsbee and Maydew Model (2000a) ............... 15

Data Collection ..................................................................................................... 16

Apportionment Formula Data Sources ................................................................. 16

Data Comparison ........................................................................................... 20

Replication of 1978 to 1994 Goolsbee and Maydew Model ......................... 21

Extension of Goolsbee and Maydew Model through 2010 ........................... 22

Alternative Model Specification: Total Private Employment ....................... 23

Alternative Model Specification: Manufacturing Payroll ............................. 24

Evaluation of Wisconsin’s Single Sales-Factor Apportionment .......................... 25

Variables for Future Analysis ............................................................................... 26

Combined Reporting...................................................................................... 26

Throwback Rule ............................................................................................ 27

Personal Income Tax ..................................................................................... 29

Energy Costs .................................................................................................. 29

Industrial Composition .................................................................................. 29

Summary ............................................................................................................... 30

iv

Works Cited .......................................................................................................... 31

Appendix A: Example of How Apportionment Formulae Work .......................... 34

Appendix B: Background on the Taxation of Business Profits ............................ 36

Appendix C: Comparison of Combined and Separate Reporting ......................... 39

Appendix D: Catalogue of Apportionment Law Changes .................................... 41

Appendix E: State Apportionment Formula Changes Since 2000 ........................ 46

Appendix F: States Considering Apportionment Formula Changes..................... 49

Appendix G: Revenue Effects in the Literature .................................................... 51

Appendix H: Summary of Fiscal Note Estimates ................................................. 55

Appendix I: Sources for Macroeconomic Indicators ............................................ 56

v

List of Tables and Figures

Table 1: Corporate Profits Subject to the Wisconsin Corporate Income Tax ......... 5

Table 2: Comparison of Means and Standard Deviations .................................... 20

Table 3: Replication of Goolsbee and Maydew’s Model, 1978 to 1994 .............. 21

Table 4: Extension of Goolsbee and Maydew’s Model, 1978 to 2010 ................. 22

Table 5: Alternative Model with Private Employment

as the Dependent Variable, 1978 to 2010 ............................................................. 23

Table 6: Alternative Model with Manufacturing Payroll

as the Dependent Variable, 1978 to 2010 ............................................................. 24

Figure 1: Predicted Manufacturing Jobs in Wisconsin

with and without Single Sales-Factor Apportionment, 2006 to 2010 ................... 26

Table D1: Catalogue of Apportionment Law Changes......................................... 41

Table E1: Apportionment Formulae of States in 2000 ......................................... 46

Table E2: States That Made Apportionment Formula Changes

from 2000 to 2012 ................................................................................................. 47

Table E3: Apportionment Formulae for States in 2012 ........................................ 48

Table F1: States Considering Apportionment Formula Changes ......................... 49

vi

vii

Foreword

This report is the result of collaboration between the Robert M. La Follette

School of Public Affairs at the University of Wisconsin–Madison and the

Wisconsin Department of Revenue (DOR). Our objective is to provide graduate

students at La Follette the opportunity to improve their policy analysis skills while

contributing to the capacity of the state government to provide public services to

the residents of Wisconsin.

The La Follette School offers a two-year graduate program leading to a master’s

degree in public affairs. Students study policy analysis and public management,

and they can choose to pursue a concentration in a policy focus area. They spend

the first year and a half of the program taking courses in which they develop the

expertise needed to analyze public policies.

The authors of this report are all in their last semester of their degree program

and are enrolled in Public Affairs 869 Workshop in Public Affairs. Although

acquiring a set of policy analysis skills is important, there is no substitute for

doing policy analysis as a means of learning policy analysis. Public Affairs 869

gives graduate students that opportunity.

This year the students in the workshop were divided into six teams, three

under my supervision and three supervised by my La Follette School colleague

Professor Karen Holden. The topic for this report was proposed by John

Koskinen, Chief Economist and Administrator of the Department of Revenue’s

Division of Research and Policy. The five authors of this report were given the

assignment of replicating and updating a published study of the impact of state

corporate income tax apportionment formulae on the growth of manufacturing

employment.

In the year 2000, Austan Goolsbee and Edward Maydew, at the time both

professors at the University of Chicago, published a paper entitled “Coveting

Thy Neighbor’s Manufacturing: The Dilemma of State Income Apportionment.”

The paper focused on the formula states use to apportion income of multi-state

corporations for the purpose of levying a state corporate income tax. Based on

data from all states with a corporate income tax, the authors concluded that the

formula weight placed on payrolls had a substantial impact on the growth of

manufacturing employment within each state. Because the results of this paper

appear to have influenced Wisconsin to change its apportionment formula, the

DOR is interested in replicating the original study, which relied on data for the

years 1978 through 1994, and in redoing the study based on data that extends

through 2010.

This report would not have been possible without the support and encouragement

of John Koskinen and Michael Oakleaf at the Department of Revenue. The

authors also benefited from the advice and assistance generously offered by

viii

Professor David Merriman, an economist at the University of Illinois at Chicago,

who shares an interest in this topic.

The report also benefited greatly from the support of the staff of the La Follette

School. Cindy Manthe and Marjorie Matthews contributed logistic support, and

Karen Faster, the La Follette Publications Director, provided editorial assistance

and managed production of the final bound document.

By involving La Follette students in the tough issues confronting the government

in Wisconsin, I hope they not only have learned a great deal about doing policy

analysis but have gained an appreciation of the complexities and challenges

facing state government in Wisconsin and elsewhere. I also hope that this report

will contribute to an ongoing discussion of tax policy within the state of

Wisconsin.

Andrew Reschovsky

May 2012

Madison, Wisconsin

ix

Acknowledgments

We would like to thank the individuals who provided assistance, guidance, and

support throughout the course of our research and analysis. We thank the

Wisconsin Department of Revenue, specifically John Koskinen and Mike Oakleaf

for their thoughtful feedback throughout the project. We would also like to thank

Michael Mazerov of the Center on Budget and Policy Priorities and Professor

David Merriman at the University of Illinois, Chicago, for generously providing

advice and sharing information and data with us. Finally, we thank the faculty and

staff at the Robert M. La Follette School of Public Affairs, especially Professor

Andrew Reschovsky for his thoughtful and enthusiastic support.

x

xi

Executive Summary

In recent years, a number of states, including Wisconsin, changed their corporate

income tax laws to apportion corporate income based on a corporation’s sales

(single sales-factor) in that state rather than basing the tax on an average of the

corporation’s sales, property, and payroll. A study by two University of Chicago

professors, Austan Goolsbee and Edward Maydew (2000a), played an important

role in influencing the decision to enact single sales-factor apportionment. Their

study provided empirical evidence showing that moving to a single sales-factor

formula would encourage manufacturing and nonmanufacturing employment

growth.

This report replicates and extends Goolsbee and Maydew’s model through 2010.

Based on information provided in studies by Goolsbee and Maydew (1998,

2000a), we compiled a data set that allowed us to re-estimate Goolsbee and

Maydew’s models. Their models, which were estimated using data from the years

1978 through 1994, attempt to explain the growth in manufacturing employment

in states that levied a corporate income tax as a function of the apportionment

formula weight on payroll and a set of controls for other policies and economic

trends that might affect employment.

Our major findings are:

Contrary to the results reported by Goolsbee and Maydew (2000a), we

find that there is no statistically significant relationship between the

apportionment formula weight on sales (measured implicitly by the

payroll burden) and manufacturing employment for the years 1978 to

1994. We found the magnitude of the effect of payroll burden to be

approximately 15 times smaller than that reported by Goolsbee and

Maydew.

When we estimate the Goolsbee and Maydew model using data from 1978

through 2010, we find a small, but statistically significant, relationship

between the apportionment weight on sales and manufacturing

employment. The magnitude of the effect we found was approximately

four times smaller than the magnitude of the effect found by Goolsbee and

Maydew (2000a).

Using the extended Goolsbee and Maydew model, we calculated that

Wisconsin’s adoption of single sales-factor apportionment in 2006 led

to the creation of an additional 7,533 manufacturing jobs by 2010. This

job growth resulted in a level of manufacturing employment that was

1.7 percent higher than it would have been without the policy change.

xii

No conclusions about the employment impacts of single sales-factor

apportionment should be made without exploring the role of several

additional variables that might influence job creation. These variables

include the presence of combined reporting, throwback rules, personal

income taxation, energy costs, and industrial composition.

1

Introduction

In 2003, the Wisconsin State Legislature passed Wisconsin Act 37, modifying the

state corporate income tax as part of a job creation package. The changes adjusted

Wisconsin’s corporate income tax apportionment formula, which affects how

much corporations operating in more than one state pay in income tax in each

state. Without apportionment, a corporation operating in all 50 states would have

50 state tax liabilities, each calculated on 100 percent of its profits. To prevent the

same profits from being taxed multiple times, each state uses an apportionment

formula to divide corporate profits among all of the states in which the

corporation conducts business

Forty of the 46 states with a corporate income tax had an apportionment formula

in 1978 that consisted of an equally weighted three-factor formula. Identical

weights were given for a corporation’s share of property, payroll, and sales in that

state.1 For example, if a generic corporation had 30 percent of its property in State

A, 20 percent of its payroll in State A, and 10 percent of its sales in State A, the

corporation would have 20 percent of its profits (the average of 30, 20, and 10)

taxed by State A. For a detailed explanation of how apportionment formulae are

calculated, see Appendix A.

Wisconsin Act 37 mandated that, starting in 2005, Wisconsin begin phasing

out payroll and property factors from its apportionment formula. By 2008,

the apportionment formula would include only sales. Thus, in the example

corporation referenced above, if Wisconsin were State A, only 10 percent of

the corporation’s sales would be subject to the Wisconsin corporate income tax.

Because this corporation had a smaller percentage of sales in Wisconsin

compared to its percentage of property and payroll, under the new apportionment

scheme, it has fewer profits subject to Wisconsin’s corporate income tax.

A study published in 2000 (2000a) by Austan Goolsbee and Edward Maydew, at

the time both professors at the University of Chicago, may have influenced the

enactment of single sales-factor apportionment in Wisconsin by providing the

intellectual foundation and empirical evidence that increasing the weight on sales

would spur employment in the state. The study by Goolsbee and Maydew was

frequently cited in states that subsequently changed their apportionment formulae

1 The exceptions were Florida (double-weighted sales formula); Iowa (single sales-factor);

Massachusetts (double-weighted sales formula); Nevada (no corporate income tax); New York

(double-weighted sales formula); South Dakota (no corporate income tax); Texas (single sales-

factor); Washington (no corporate income tax); Wisconsin (double weighted sales formula); and

Wyoming (no corporate income tax). We classified Texas as a single sales-factor state based on

the data we found even though some would classify Texas as having no corporate income tax

since it uses a franchise tax. Texas was not included in our replication or extension of the

Goolsbee and Maydew model due to its use of the franchise tax.

2

by increasing the weight on sales. In 2000, Goolsbee and Maydew also worked

with Michael Schadewald, a professor of accounting at the University of

Wisconsin-Milwaukee, to tailor their original study to Wisconsin. The goal of

their study was to estimate the potential effects on employment growth in

Wisconsin of adopting single sales-factor apportionment (Goolsbee, Maydew, and

Schadewald, 2000). In June 2001, the Wisconsin Legislative Fiscal Bureau

referenced this Wisconsin-specific study in its report summarizing the Governor’s

budget request which included the first official proposal to enact single sales-

factor apportionment in the state (Shanovich, 2001).2

The primary goal of our report is to replicate Goolsbee and Maydew’s 2000 study

(published in the Journal of Public Economics) and then to update it using data

through 2010 (2000a). The goal of both Goolsbee and Maydew’s study and our

study is to estimate the relationship between the weight placed on sales in state

corporate tax apportionment formulae and the growth of manufacturing

employment in each state. Goolsbee and Maydew’s study influenced many states

to raise their apportionment formula weight on sales, and thereby lower their

weight on payrolls and property. Despite that impact, very few studies have

attempted to analyze the effect on job creation that may result from a switch to

single sales-factor apportionment. We replicate Goolsbee and Maydew’s original

study and extend it by adding more recent data. Our replication of their study,

however, produced different results. While their study found that raising the

weight on sales resulted in a statistically and economically significant increase in

job creation, our replication of their study using data for the same years found no

statistically significant relationship between the sales tax weight and

manufacturing employment. Before discussing the detailed results of our

statistical analysis, we provide a brief history of apportionment formulae and

background on the arguments in favor of single sales-factor apportionment.

Background: Unitary Businesses

Groups with multiple corporate entities that have centralized operations and

management are called unitary businesses. Corporations operating in multiple

states may be subject to taxation in each of those states. In the earlier part of the

20th

century, states used separate accounting to tax corporations on profits earned

in each particular state. Additionally, the share of an integrated firm’s profit

attributable to a particular state can depend on malleable and arbitrary accounting

conventions. So, as corporations began to expand operations into many states, a

formula method became necessary to inhibit strategic accounting designed to

minimize tax burdens.

2 Single sales-factor apportionment was not adopted as part of the 2001 budget request. Two years

later, the proposal was accepted by the legislature and passed into law.

3

Presently, according to Bloomberg Bureau of National Affairs, Inc. (BNA),

profits of a multistate group are apportionable if the group is a unitary business.

Several courts have established tests to determine unity. The U.S. Supreme Court

in the ASARCO and Woolworth cases determined a unitary business by three

criteria: (1) functional integration; (2) centralization of management; and (3)

economies of scale.3 According to BNA, other courts rely on the “three unities”

test established by the California Supreme Court in the Butler Bros. v. McColgan

case: “(1) unity of ownership; (2) unity of operation as evidenced by central

purchasing, advertising, accounting, and management divisions; and (3) unity of

use in its centralized executive force and general system of operation.” Lastly, the

contribution–dependency test established in Edison California Stores, Inc. v.

McColgan looks at whether one segment of the business contributes to, or

depends upon, another segment.

Each state determines which factors it uses to apportion a unitary corporate

group’s taxable income to determine the corporate group’s taxable income in that

particular state. Because each state’s percentage of the sales, property, and payroll

can vary depending on the factor weighting that each state utilizes, taxable

income allocation may also vary from state to state. In 1977, the U.S. Supreme

Court developed a four-prong test for state taxation of interstate commerce in the

Complete Auto Transit, Inc. v. Brady case. The interstate commerce clause is an

enumerated power in the U.S. Constitution and allows the Federal government to

ensure that commerce across state lines is not unfairly impeded by a specific

state’s laws which would give that state an unfair advantage. According to BNA,

a state tax will be sustained against a commerce clause challenge if it: (1) is

applied to an activity with a substantial nexus with the taxing state; (2) is fairly

apportioned; (3) does not discriminate against interstate commerce; and (4) is

fairly related to the services provided by the state (Complete Auto Transit, Inc. v.

Brady, 1977). While the apportionment provision is directly enumerated in the

second prong, it is also implied in the third and fourth prongs. The state taxing

system cannot unfairly tax an out-of-state taxpayer by discriminately taxing the

out-of-state taxpayer differently than an in-state taxpayer.

3 Bloomberg Bureau of National Affairs, Inc. is a source of tax and accounting research, news,

practice tools, and guidance for tax attorneys, CPAs, corporate tax managers, estate planners, and

financial accountants. Bloomberg BNA’s Tax Management Portfolios are written by leading

expert practitioners and cover a broad range of tax topics. The authors consulted the portfolios for

technical discussions on the tax concepts discussed throughout the paper, including business

profits and apportionment methodologies.

4

Background: Apportionment Formulae

Depending on whether a state uses separate, combined, or consolidated reporting,

the apportioned taxable income may differ. There are four types of apportionment

formulae. The first is an equally weighted formula: property, payroll, and sales all

have equal weight in the formula (33.33 percent). The second is a double-

weighted sales formula. Sales are weighted 50 percent and property and payroll

are each weighted 25 percent. The third type of formula involves giving the sales

factor more than 50 percent weight, but less than 100 percent. Thus, one state may

weight sales 60 percent and property and payroll 20 percent each while another

state may weight sales 80 percent and property and payroll 10 percent each.

In states that use one of these three-factor formulae, businesses calculate a

weighted average fraction of their sales, property, and payroll located in a given

state compared to their total nationwide sales, property, and payroll. Total taxable

income is multiplied by this weighted average fraction to calculate the amount of

income apportioned to that state, which is then taxed at the applicable corporate

income tax rate for that state (Lightner, 1999).

In a single sales-factor formula, sales are given a weight of 100 percent and

payroll and property in that state is ignored for the purposes of determining how

much of the corporation’s profit is attributable to that state. In 1978, 40 of the

46 states that taxed corporate income used a three-factor equal weighted formula.4

Fifteen states were using a single sales-factor apportionment formula by 2011.5

Table 1 illustrates how apportionment formula weights determine the amount of

corporate profits that are subject to the state corporate income tax.

4 We considered Texas as a single sales-factor state based on the data we found. Texas, however,

was not included in our replication or extension of the Goolsbee and Maydew model.

5 The following 15 states use single sales-factor formulae for 2012: California, Colorado, Georgia,

Illinois, Indiana, Iowa, Louisiana, Maine, Michigan, Nebraska, New York, Oregon, South

Carolina, Texas, and Wisconsin.

5

Table 1: Corporate Profits Subject to the Wisconsin Corporate Income Tax

Step 1: Determine sales, property, and payroll ratios for corporation

Sales Ratio (Sr) Property Ratio (Pr) Payroll Ratio (Wr)

Wisconsin Sales

Total Nationwide Sales

Wisconsin Property Total Nationwide Property

Wisconsin Payroll Total Nationwide Payroll

Step 2: Determine weight for each factor

Factor Weights Sales (Sw) Property (Pw) Payroll (Ww)

Weight Under Equally Weighted Three Factor Formula

1/3 1/3 1/3

Weight Under Double- Weighted Sales Factor Formula

1/2 1/4 1/4

Moving Toward Single Sales-Factor Formula

>1/2 and <1 >0 and <1/4 >0 and <1/4

Weight Under Single Sales-Factor Formula (**Wisconsin’s current formula**)

1 0 0

Step 3: Determine Corporate Profits Subject to Tax in Wisconsin

{(Sr * Sw) + (Pr * Pw) + (Wr * Ww)} * Nationwide Profits of Corporation Source: Authors

While the U.S. Supreme Court has approved many different apportionment

methods, it has declined to mandate one method be used in all states. The Court

recognized that even though the lack of uniformity in apportionment formulae

between states creates a risk of double taxation on interstate commerce, Congress,

and not the Court, needs to decide whether there should be uniform rules

(Moorman Mfg. Co. v. Bair, 1978). Congress has investigated the issue. Although

no legislation has been enacted, states have attempted to structure uniform

apportionment formulae on their own.

Before an individual state can apportion the total business income of a unitary

business to determine the portion of that businesses’ income that is allocated to

that individual state, the state must first determine the corporation’s total business

income. An individual state may not arbitrarily tax a corporation; it may only tax

6

the corporation if that corporation has sufficient legal contact (“nexus”) with the

state.

Depending on the state, a unitary business may determine its business income

using either a separate return, a combined report, or a consolidated return method.

Under the separate return method, each corporation in the affiliated group of

corporations determines its business income separately, on a stand-alone basis,

without regard for the other affiliated corporations. Under the combined method,

the business income of all of the corporations in the affiliated group of

corporations is combined and that income is then apportioned to a particular state

that has nexus with the affiliated group. The consolidated method is not uniform

across states. It is similar to the combined method in that it includes the income of

more than one of the corporations in the affiliated group, but states utilize

different methods to determine the consolidated business income that will then be

apportioned to that state. Once a state’s taxable income is determined using one of

these methods, it is then apportioned using that state’s apportionment formula.

Refer to Appendices B and C for a discussion and comparison, respectively, of

these different methods of calculating a corporation’s taxable profits.

After World War II most states apportioned corporate taxable income using an

equally weighted three-factor formula. In 1971 Florida became the first state to

double weight the sales factor with additional states following in the 1970s,

1980s, and 1990s. For detailed information on each state’s apportionment formula

starting in 1978, see Appendix D. By 1994, in an effort to improve their state

business climate, 17 states passed legislation to weight sales more than payroll or

property in their apportionment formulae, and a few states, such as Iowa, had

already adopted single sales-factor weighting (Lightner, 1999; Mazerov, 2005

(revised)). Since the mid-1990s, additional states have eliminated payroll and

property in apportionment weighting, moving to single sales-factor

apportionment. Between 2000 and 2011, 11 states moved to single sales-factor

apportionment. For detailed information regarding the timing of apportionment

formula changes from 2000 to 2012, see Appendix E.6

In 2012, 35 of the 46 states with a corporate income tax utilize a formula weight

of greater than 33 percent for sales with 15 of those states using a single sales-

factor apportionment formula. California, Kansas, Minnesota, New Mexico,

North Carolina, and Vermont are currently considering bills that would modify

each state’s apportionment formula. For more information about these bills, see

Appendix F.

6 Goolsbee and Maydew’s data only covered apportionment formula from 1978 to 1994. We

include changes from 2000 to 2012 because Goolsbee and Maydew’s results would not have

influenced state tax policy until 2000 when their paper was published.

7

Policy Considerations for Single Sales-Factor

Apportionment

The use of payroll, property, and sales factors in the apportionment of corporate

income implicitly taxes each of these factors. For example, if a corporation

increases its payroll in Wisconsin, it will increase the ratio of Wisconsin payroll

to total payroll. If the payroll ratio were one of the three factors in apportioning

corporate profits in Wisconsin, a higher ratio for Wisconsin would result in more

of the corporation’s profits subject to Wisconsin’s corporate income tax. If payroll

were not a factor in the apportionment formula, Wisconsin corporations would be

able to hire additional personnel without increasing their corporate income tax

liability. Therefore, decreasing or eliminating the role of payroll or property

factors when calculating state corporate income tax liability effectively makes

hiring personnel or owning property in that state less expensive.

With any adjustment of a state’s apportionment formula, some companies would

pay more corporate income taxes and some would pay less, depending on the mix

of payroll, property, and sales in that state. Any changes made by businesses in

response to a change in the apportionment formula, such as increasing payroll,

could further affect tax revenues in a given state. For example, Goolsbee and

Maydew hypothesized that increased personal income tax revenue from jobs

created by the policy would partially offset the state’s revenue losses due to the

elimination of property and payroll from apportionment formulae.7

Many government bodies, advocacy groups, and individual economists have

attempted to value or predict both the employment and revenue impacts of

adopting single sales-factor apportionment. Estimating static changes in net

revenue following the implementation of lower weighting on payroll and property

is relatively simple because the immediate effects on businesses based on their

share of payroll, property, and sales in the state can be estimated using

information reported on corporate tax returns. It is more difficult to estimate the

behavioral responses of corporations to tax changes. It is far from clear whether

the adoption of single sales-factor sales apportionment would lead to the

corporate behavioral responses necessary for job creation.

Within any given state, the level and growth rate of employment are influenced by

a number of factors. Regional economic shifts, globalization (related to

7 Using average income data for manufacturing and nonmanufacturing workers from 1995,

Goolsbee and Maydew estimated that increased employment would compensate corporate tax

revenue losses in Wisconsin by increasing personal income tax revenues roughly $51 million per

year in the long term (Goolsbee et al., 2000). This compares to a 2003 Wisconsin Department of

Revenue fiscal estimate for Wisconsin Act 37 that single sales-factor apportionment, once fully

phased in, would result in reduced corporate income tax revenues of $45 million per year

(Walgren, 2003).

8

manufacturing), changes in public services (such as education), regulatory

changes, and any apportionment formula changes occurring in neighboring states

may have an impact on employment growth.

An important factor considered by the Wisconsin Legislative Fiscal Bureau in a

2001 report was the status of apportionment laws in neighboring states:

“On a more specific level, Wisconsin’s current three-factor [double-

weighted sales] formula creates a disincentive for businesses that require

large investments in tangible property and payroll to locate in the

state, when compared with the surrounding states. Iowa and Illinois use a

single sales apportionment factor and Michigan is phasing in such a

formula. Minnesota attributes a 70 percent weight to the sales factor. All

of these apportionment formulas place a relatively lower income tax

burden on property and payroll than Wisconsin’s. Because of these

impacts, converting to a single sales-factor is viewed as a means of

generating economic growth” (Shanovich, 2001).

Goolsbee and Maydew argue in their 2000 paper (2000a) that if single sales-factor

apportionment were enacted by all of the states with a corporate income tax, the

net employment impact of single sales-factor apportionment would be zero. The

employment boosting effects of a switch to single sales-factor apportionment

decrease as more states adopt the policy. Moreover, changes to any state’s

corporate income tax apportionment formula since the Goolsbee and Maydew

study did not happen in isolation. It is, therefore, challenging to measure the

effects on employment.

Literature Review – State Apportionment Formulae and

Effects on Employment

We reviewed existing literature on the employment effects of changes in state

corporate income tax apportionment formulae. The majority of the literature in

this area of tax policy is focused on revenue effects of formula changes and

evaluating apportionment as one of a number of other “business friendly” tax

variables. Few studies exclusively estimate potential employment effects and even

fewer focus on the impact after the apportionment change has been implemented.

Therefore, our literature review serves both to inform our analysis and

demonstrates the need for additional analysis of employment effects due to

apportionment formula changes.

Overall, the literature indicates that greater sales factor weighting results in

employment growth with the caveat that employment gains in a given state could

be depressed by sales factor weighting in other states. If all of the states adopted

the same apportionment formula, gains would not be expected in any of the states

(Goolsbee and Maydew, 2000a; Mazerov, 2005 (revised)). Given this caveat, the

9

literature associates employment growth with the introduction of greater sales

factor apportionment (with the exception of Lightner, 1999).

As noted in the literature, increasing the sales factor weight has different

implications for different businesses. Double-weighting the sales factor or using

single sales-factor apportionment tends to lower the tax burden of businesses that

sell to a national market, but concentrate their payroll and property in a smaller

geographic area. On the other hand, companies that sell in the state, but with a

low payroll or property presence, face a higher tax burden from double-weighted

or single sales-factor apportionment (Lightner, 1999; Edmiston, 2002).

Nevertheless, given static tax rates, increasing the sales factor weight can make

allocating more resources to property and payroll in a particular state attractive for

businesses.

Finally, although we concentrate on apportionment’s effects on employment,

several of the papers included here have also noted large revenue effects resulting

from the implementation of double or single sales-factor apportionment. We

review this as well as other literature on revenue effects in Appendix G. In

Appendix H, we summarize the fiscal note revenue estimates that accompanied

the legislative proposals to decrease the weight on sales in state corporate tax

apportionment formulae.

Goolsbee and Maydew, 2000

In Goolsbee and Maydew’s (2000a) influential study, they use panel data from

1978 to 1994 to examine the impact of sales apportionment on employment

growth (see Appendix G for their findings regarding government revenues). A

three-factor apportionment formula implicitly produces a tax on payroll equal to

the state’s corporate tax rate multiplied by the weight placed upon payroll. The

tax, which they refer to as “payroll burden,” decreases if a higher weight is

applied to the sales factor. The principal Goolsbee and Maydew regression uses

the log of manufacturing employment as the dependent variable and includes a

variable for payroll burden and the average of all states’ payroll burdens in the

same year (weighted by average manufacturing employment). In addition to

controls for state fixed effects and time trends using dummy variables, it includes

control variables for state personal income growth, the national unemployment

rate, and the log of national employment interacted with state dummy variables to

control for growth in the work force.

Goolsbee and Maydew find that implementing a double-weighted sales factor

raises manufacturing employment by 1.1 percent and nonmanufacturing

employment by 0.7 percent for the average state, with larger effects in the long

term (2.8 percent for manufacturing employment over three years) (Goolsbee and

10

Maydew, 2000a).8 Goolsbee and Maydew provided one important caveat in their

paper (2000a): any employment gains in one state would be followed by

corresponding employment declines in other states that have not implemented

double-weighting. They note that the effects would be expected to decline as the

number of states with heavier weighting grows.

Goolsbee, Maydew, and Schadewald (Goolsbee et al.), 2000

Goolsbee and Maydew worked with Michael Schadewald of the University of

Wisconsin -Milwaukee to make specific estimates of the potential effects of

single sales-factor apportionment for Wisconsin (Goolsbee et al., 2000). The

authors apply the model described above in Goolsbee and Maydew for the years

1978 to 1995, applying it to the implementation of single-sales, rather than

double-weighted, apportionment. They find that switching from a double-

weighted to a single sales-factor formula would result in 2.4 and 1.9 percent

growth in manufacturing and nonmanufacturing employment, respectively, for a

state with the mean corporate tax rate. For Wisconsin, rather than the mean state,

growth attributable to the switch to single sales-factor apportionment would be

2.9 percent (18,000 jobs) and 2.4 percent (49,000 jobs) for manufacturing and

nonmanufacturing employment, respectively, based on Wisconsin’s employment

base in 1995.

Lightner, 1999

Lightner (1999) analyzes employment growth across states for the years 1994 to

1995. Her ordinary least squares regression includes state corporate tax rates as

well as dummy variables for whether the state placed an equal weight on sales,

payroll, or property in its apportionment formula (or rather placed a higher weight

on sales or applied no corporate income tax). These tax variables are the focus of

her analysis and discussion. She also controls for the presence of a throwback

rule, the ratio of government expenditure growth to personal income growth, the

change in worker’s compensation payments from 1993 to 1994, the average

hourly manufacturing wage, the percent of unionized workers, and natural gas

costs for industrial users.

Lightner finds that low corporate income tax rates were associated with

employment growth for the years 1994 to 1995 (Lightner, 1999). Apportionment

weights were insignificant. Lightner’s and Goolsbee and Maydew’s conflicting

results, however, could be due to the large difference in the time periods studied

8 Goolsbee and Maydew (2000a) calculate an implicit tax rate on the payroll apportionment factor

by multiplying the state’s weight on that factor by its highest corporate income tax rate, which can

then be used to predict change in employment based on their analysis. Lightner (1999) uses a

similar approach.

11

(Klassen, 1999).9 Like Goolsbee and Maydew, Lightner notes that the impact of

increased sales factor weighting can be expected to decrease as the number of

states that have implemented such a change grows. Lightner also finds that low

levels of unionization, lower wages, and slower growth in worker’s compensation

costs were associated with higher employment growth; growth in worker’s

compensation were only marginally significant. Higher growth in state and local

government expenditures per capita relative to growth in personal income was

associated with lower employment growth. She finds that, at a marginal level of

significance, higher natural gas prices were associated with higher employment,

but notes that other studies have found mixed results for the influence of natural

gas costs (1999). Finally, Lightner finds that the presence of a throwback rule in a

state was not influential (1999).10

Edmiston, 2002

Edmiston (2002) uses an eight-region applied general equilibrium model that

incorporates corporate tax rates, region size, and industrial composition (including

eight industries, such as mining, manufacturing, and agriculture). The model was

calibrated to data from the 1992 U.S. economy and assumes that each region

starts with an apportionment formula with equal weights for sales, payroll, and

property. Consumers and labor are assumed to be homogenous across regions,

and each industry is represented by a single corporation that operates in all of the

regions. A “short-run” version of the model assumes that labor is immobile, while

a “long-run” version assumes that both labor and capital are mobile across

industries and regions. Edmiston uses a sensitivity analysis to examine the effects

of differing assumptions about the mobility of labor and capital.

Using this model, Edmiston found a significant positive relationship between

greater sales factor weights and employment growth in the long-run. He found

negligible short-term effects (Edmiston, 2002). This relationship only holds when

one region adopts single sales-factor apportionment while the other regions do

not. For example, the Great Lakes region would experience a 1.18 percent

increase in employment over the long-term when acting alone; if all of the regions

adopted single sales-factor weighting, the increase would drop to 0.3 percent. If

all of the regions adopted single sales-factor weighting, some regions would see

drops, rather than growth, because regional employment gains depend partly on

the corporate tax rate, region size, and initial industrial composition of the region.

These findings for the long-term are roughly consistent with Goolsbee and

9 Klassen (1999) reviewed a 1999 working paper for the Goolsbee and Maydew study.

10 States with throwback rules require that in-state businesses that sell into other states without

establishing nexus in those states “throw back” that income for tax purposes; therefore, not having

a throwback rule is expected to be more attractive to businesses seeking to minimize their tax

burden (Mazerov, 2005 (revised)).

12

Maydew’s (2000a) analysis of an independent move to double-weighting of the

sales factor. Edmiston’s (2002) model, however, would only predict short-term

(one year) employment growth similar to Goolsbee and Maydew’s (2000a)

finding of 0.7 percent if it placed few or no constraints on businesses changing

their production locations.

Edmiston and Arze del Granado, 2006

Edmiston and Arze del Granado analyze firm-level data for multistate

corporations before and after the implementation of a double-weighted sales

factor in Georgia in 1995 (Edmiston and Arze del Granado, 2006). Using panel

data from 1992 to 2002 from the State of Georgia tax returns for multistate firms,

they identify whether changes in the values of payroll, sales, and property

reported by multistate firms were associated with the switch to a double-weighted

sales factor. With a log-linear model, their outcomes of interest (in three

equations) are the values of payroll, sales, and property reported by each firm

during a given year. Their main explanatory variables are tax differentials

calculated for each firm which reflect the difference between Georgia’s corporate

income tax before and after the implementation of a double-weighted sales factor

relative to average taxation in other states (weighted by distance and size of the

economy). They also control for national and state economic trends and firm

characteristics by including variables for the national dollar value of sales,

payroll, and property for each year; each firm’s profit margin on each factor; each

firm’s relative presence in the state; each firm’s ratio of property to payroll; and

the portion of Georgia’s gross state product that came from manufacturing

revenues each year.

Using this model, Edmiston and Arze del Granado (2006) find that the adoption

of a double-weighted sales factor in 1995 led to a 6.5 percent decrease in the

amount of sales reported by multistate corporations and 2.0 and 2.1 percent

increases in payroll and property, respectively. For the average multistate firm in

Georgia, payroll increased by $37,110 as a result of the change, for a total of $600

million. The authors do not state whether this increase in payroll comes from

increased wages or from increased employment. The authors argue that multistate

corporations would probably respond in similar ways in other states; although,

responses in any given state would be sensitive to weighting regimes in other

states at the time of policy changes. Edmiston and Arze del Granado’s (2006)

findings are consistent with Goolsbee and Maydew’s (2000a), suggesting that

increased sales factor weights should increase payroll in the state.

Swenson, 2011

Swenson compares the rate of employment growth for employers in Georgia,

Louisiana, New York, Oregon, and Wisconsin during two periods, 2002 to 2005

and 2006 to 2008, with 2006 roughly corresponding to when each of these states

implemented single sales-factor apportionment (Swenson, 2011). Swenson uses

13

the National Establishment Time-Series (NETS) Database, developed from Dun

and Bradstreet data used for commercial use. The NETS database includes data

from establishments at a particular location. Of the approximately 602,000

establishment locations available for Wisconsin, only 299,000 had data complete

enough to use for this study. Using this data, Swenson observes that employment

declined in Wisconsin by 12.2 percent during 2006 to 2008, but grew by 4.3

percent at firms with locations in multiple states (those firms affected by single

sales-factor apportionment), suggesting that single sales-factor apportionment

may have encouraged job retention.11

Swenson also estimates a statistical relationship between the adoption of single

sales-factor apportionment and employment growth. He finds that employment

growth was significant and positive for locally based firms with multi-state

locations, as compared to firms based out of state, for which growth was negative.

This finding implies that single sales-factor apportionment benefited firms

headquartered in-state more than other firms. He uses industry dummy variables

in his analysis, but does not specify how industries are defined.

Mazerov and Tannenwald question the legitimacy of an earlier analysis that

Swenson conducted for a business coalition in California in 2010 (Mazerov and

Tannenwald, 2010). Several of their criticisms also apply to the updated paper.

Swenson uses proprietary employment data that differ markedly from U.S.

Bureau of Labor Statistics (BLS) data.12

He also compares data before and after

2006 even though not all states implemented single sales-factor apportionment in

2006. For example, Oregon began before 2006, while Georgia and Wisconsin did

not reach full implementation until 2008. Finally, the Swenson study lacks a

control group of states during the same time period that did not change their

apportionment formula.

State Evaluations

Those who argue in favor of single sales-factor apportionment often point to the

Goolsbee and Maydew study, claiming the apportionment change would create

jobs, particularly in the manufacturing sector.13

This increase in employment

could potentially increase state individual income tax revenue, partially offsetting

11 Employment declines overall, but growth for multistate firms held for the other states in the

analysis as well as for an aggregate analysis.

12 For example, while Swenson reports that overall employment declined by 12.2 percent in

Wisconsin from 2006 to 2008, BLS data reflect growth of 1.4 percent for the same time period.

13 The Associated Industries of Massachusetts commissioned a report by Ernst & Young LLP

which advocated for maintaining single sales-factor apportionment in Massachusetts in 2003. In

Wisconsin, the Wisconsin Manufacturers and Commerce organization commissioned a report

supporting single factor apportionment.

14

the loss in corporate tax revenue from zeroing out the weights on payroll and

property in the apportionment formula. As part of the legislative process, many

states produced fiscal note estimates of what apportionment formula changes

would cost in tax revenue. Most of these estimates did not attempt to calculate a

corresponding increase in personal income tax revenues that might result from an

increase in manufacturing jobs. A summary of these fiscal notes can be found in

Appendix H. The majority of the state fiscal note estimates predicted a net loss in

revenue — the corporate tax revenue gained through a greater weight on sales

would not offset the revenue lost from excluding the property and payroll factors

from the apportionment formula.

In addition to state fiscal estimates, a number of advocacy groups, business

associations, or state budget and tax think tanks produced reports arguing for and

against single factor apportionment that included estimates of potential revenue

gains or losses and estimates for job creation (see footnote 7). Supporters of single

sales-factor apportionment invoked the Goolsbee and Maydew study which

showed an increase in manufacturing jobs for states with a double-weighted sales

factor in their apportionment formulae. In fact, Goolsbee and Maydew produced

this study for several states, including New York (Goolsbee and Maydew, 2000b)

and Wisconsin (Goolsbee et al., 2000).

Michael Mazerov of the Center on Budget and Policy Priorities, a budget and tax

think tank based in Washington, D.C., conducted a broad analysis and critique of

single sales-factor apportionment in 2001 and revised his analysis in 2005. He

documented changes in manufacturing employment across states. For the five

states in which single sales-factor apportionment was in effect from 1995 through

2004, one state experienced manufacturing job losses greater than the median and

three experienced smaller declines in manufacturing jobs than the median state,

consistent with the idea that single sales-factor apportionment may support

manufacturing employment. But, five of the eight states in which single sales-

factor apportionment was in effect between 2001 and 2004 suffered

manufacturing job losses worse than the median. While this analysis controls for

fewer factors than the analyses described above, it provides an illustration of how

manufacturing employment has fared in states that have adopted single sales-

factor apportionment.

The California Legislative Analyst’s Office issued a report in May of 2010

evaluating the apportionment changes passed in California the prior year which

allowed companies to select double-weighted or single sales-factor apportionment

for corporate income tax purposes (California Legislative Analyst’s Office, 2010).

The California Department of Finance’s Dynamic Revenue Analysis Model

(DRAM), which provides rough estimates of the effects of legislation having an

impact of at least $10 million, estimated that about one job would be created for

each $17,400 (in 2001 dollars) of initial state revenue loss. The $17,400 per new

job estimate compares with work summarized by Goolsbee and Maydew (2000a)

that reports “dollars-per-job” cost of business incentives ranging from $4,500 to

15

$60,000, but averaging about $10,000. These figures corresponded to a long-term

gain of about 40,000 jobs, given the California Franchise Tax Board’s estimates

of the initial revenue losses of allowing companies to elect single sales-factor

apportionment.

In 2003, the Associated Industries of Massachusetts Foundation, Inc., a pro-

manufacturing lobbying group, commissioned a study by Ernst and Young, a

global accounting firm, on the impact of single sales-factor apportionment for

Massachusetts manufacturers. Massachusetts is a distinct case because it

implemented single sales-factor apportionment in 1996, when only a few states

had changed their formulae. Ernst and Young first estimate the increase in

corporate income taxes if the apportionment formula were returned to a double-

weighted sales factor. Second, using a model, they simulate the impact of these

higher taxes on economic activity. Third, they calculate the net effect of the

apportionment change on tax revenue (Ernst and Young LLP, 2003).

At the time the report was written in 2003, Ernst and Young estimated that if

Massachusetts reverted to a double-weighted, three-factor formula, 6,200 jobs

would be lost (Ernst and Young LLP, 2003). They also estimated that for every

dollar of reduced corporate tax revenue $7 would be gained in net personal

income in the state. This evaluation predicted a loss of jobs if the formula reverted

to multi-factor apportionment, but it did not give an estimate of the jobs created as

a result of the change to single sales-factor apportionment.

While other studies exist, they are state-specific and none of them have had the

impact of Goolsbee and Maydew’s study. Because of the study’s influence on

states’ policies, we believe a replication and extension of their study is warranted.

Replicating and Extending the Goolsbee and Maydew Model

(2000a)

In their 2000 study, Goolsbee and Maydew evaluated the impact of state tax

changes on both manufacturing and non-manufacturing employment. They

estimated a multivariate regression model with log of manufacturing employment

as the key dependent variable and payroll burden (defined below) as the policy

variable of interest.

In our study, we undertake two tasks: to replicate the Goolsbee and Maydew data

set from 1978 to 1994 and to extend their model to 2010. We discuss our data

gathering process for the replication and extension of their model. We included

information on data sources for the variables in Goolsbee and Maydew, compare

our 1978-1994 data to their data by reporting descriptive statistics for each data

set, and we address the difference in observations between our 1978 to 1994 data

set and Goolsbee and Maydew’s data set.

16

The next two sections detail our results in replicating the 1978 to 1994 Goolsbee

and Maydew model and then extending that model to 2010. In our replication, the

coefficient on payroll burden is not significant and is 15 times smaller than

Goolsbee and Maydew’s. For the extension, the coefficient on payroll burden

becomes significant, but is four times smaller than the payroll burden’s coefficient

for Goolsbee and Maydew’s 1978-1994 model. Our replication and extension

suggests that single sales-factor apportionment leads to a smaller increase in

employment than the Goolsbee and Maydew model predicted.

We have not found a wholly satisfactory explanation for why our results differ so

greatly from those of Goolsbee and Maydew since we tried to replicate their data

and methodology exactly. While we obtained a greater number of observations,

we consider it unlikely that this difference was sufficient to cause such a dramatic

change in the magnitude and significance of the regression coefficients. The

results of Goolsbee and Maydew (2000a) appear to be sensitive to slight

differences in data specification.

The final two sections address alternative specifications of the model. Goolsbee

and Maydew (2000a) presented a model estimating the effect on total

employment of increasing sales weight in a state’s apportionment formula. In

their study, they found that total employment was less affected than

manufacturing employment by changes in a state’s apportionment formula. When

we replicated this model using our extended 2010 data set, we also found that

private employment was less affected by changes in apportionment. We created

another alternative model using manufacturing payroll as a dependent variable to

examine whether wages were affected along with employment. We found that

manufacturing payroll was not significantly affected by increasing sales weight in

a particular state. But, adoption of higher sales weights by other states decreased

manufacturing payroll in that state.

Data Collection

Although we contacted Austan Goolsbee and Edward Maydew, we were not able

to get the original data set used in their regression. Based on the information

described in their articles (1998, 2000a), and using additional data, we believe we

have collected the necessary data to replicate the regression. The complete data

set, including all of the variables, is available from the authors upon request.

Apportionment Formula Data Sources

The variable of interest in Goolsbee and Maydew’s model, state payroll burden,

is determined by multiplying a state’s top corporate tax rate and a state’s

apportionment formula payroll weight. To construct the mean state payroll burden

and the state payroll burden variables needed for our analysis, we documented the

apportionment formula applicable in each state with a corporate income tax for

17

each year from 1978 to 2010. We provide a detailed description of the sources

we used to compile the data set to provide transparency and to encourage further

research in this area. We also gathered apportionment formula data for 2011 and

2012 that was not used in the model, but was used in other sections to provide the

most current information on state apportionment formulae.

Like Goolsbee and Maydew, we drew on a number of sources to compile a full set

of apportionment formulae data.14

Our complete data set on state apportionment

formulae was compiled from the following data sets, each identified by the years

they cover.

1978 to 1998

We relied, in part, on information contained in a study, Competitive, Political,

and Economic Factors Influencing State Tax Policy Changes, by Thomas Omer

and Marjorie Shelley (2004). The authors examined apportionment changes in

states from 1978 to 1998. In Table 2 of their paper, they observed the year the

apportionment formula changed for states that had an equally weighted three

factor formula in 1978. The authors noted that no state made more than one

change in its apportionment formula during the 1978 to 1998 period and all of the

apportionment formula changes resulted in an increased weight on sales (Omer

and Shelley, 2004). If there was no change listed in Table 2, we knew that the

state still had a three factor formula through 1998. If there was a change, we knew

that the state no longer had a three factor formula that year and used our other

data sets to determine the new apportionment formula of the state and verify the

year the change occurred. We spoke with Professor Omer, who informed us that

the apportionment formula data for this paper was gathered from state revenue

departments and state apportionment statutes and by examining state legislative

changes in past editions of The Book of the States.

1985 to 1992 and 1994 to 2007

Professor Don Bruce at the University of Tennessee provided us with data on

apportionment formulae for these years. These data sets were compiled by several

of his graduate students.15

The 1985 to 1992 data were verified by comparing

them with information gathered from the Omer and Shelley paper (2004). The

14 In their study, Goolsbee and Maydew (2000a) stated that they collected their apportionment

formula data from the Commerce Clearing House’s State Tax Handbooks, various state tax codes,

from the Advisory Commission on Intergovernmental Relations’ Significant Features of Fiscal

Federalism, and from discussions with several states’ Department of Revenue.

15 We would like to acknowledge and thank graduate students Zhou Yang and Rebekah McCarty

at the University of Tennessee for their work in compiling these data sets.

18

source of the 1994 to 2007 data is the Commerce Clearing House (CCH) State

Tax Handbooks.16

2000 to 2011

We examined the apportionment formula statutes in each state. To determine the

apportionment formula for each state from 2000 to 2011, we searched the taxation

portion of state statutes for a corporate income tax. The apportionment formula

was almost always contained within the corporate income tax section or at least in

close proximity to that section. Once we found the state’s statutory section on

apportionment formula, we searched the bill history of that section to determine

how long the current statute had been effective. If the effective date was later than

2000, we analyzed previous acts affecting the statutory section to see what the

apportionment formula was prior to the change.17

2012

These data are from the Federation of Tax Administrators (FTA). The FTA

compiled this data from state sources.

State Payroll Burden: Goolsbee and Maydew (2000a) measured the effects of

changes in apportionment by evaluating the weight assigned to payroll in each

state’s apportionment formula. They hypothesized that states with lower taxes on

payroll would experience gains in employment. The authors calculated payroll

burden by multiplying the state’s top corporate income tax rate by the weight on

payroll in that state’s apportionment formula.18

Goolsbee and Maydew measured changes in the payroll burden, which is the

weight given to payroll in the apportionment formula (e.g., 0.33 for the traditional

even-weighted formula, 0.25 for a double-weighted sales formula, 0 for a single

16 When the 1978 to 1998 data set conflicted with the 1985 to 1992 data set over the precise year

when an apportionment change occurred, the authors relied on the 1985 to 1992 data set because

the 1978 to 1998 data set did not specify whether the “Change Year” included the last year of the

old apportionment formula or represented the first year of the new apportionment formula. When

the 1978 to 1998 data set conflicted with the 1985 to 1992 data set and 1994 to 2007 data set, the

authors relied on the latter two data sets because the 1994 to 2007 data relied on a single source,

the CCH State Tax Handbooks.

17 When the 1994 to 2007 dataset conflicted with the 2000 to 2011 data set on the year when an

apportionment change occurred, the authors relied on the 2000 to 2011 data set because the

specific statutes and effective dates were examined by the authors.

18 Bruce et al. (2007) argue that using the top rate for states with progressive taxation is legitimate,

because the progression generally begins at such a low level of taxable income that accounting for

the lower rates would change results insignificantly.

19

sales-factor apportionment formula) multiplied by the top state corporate tax rate.

We constructed this variable identically. For example:

Alabama Burden 1978 = Alabama Payroll Weight 1978 * Alabama Corporate Tax

1978

To calculate each state’s payroll weight, we used four different data sets

documenting states’ apportionment formulae from 1978 to 2010.

In addition to the apportionment formulae data, we also gathered data on each

state’s top corporate tax rate in order to reconstruct the state payroll burden

variable. To determine the top state corporate tax rate from 1978 to 2010, we

relied on multiple data sources. Researchers at the University of Tennessee

compiled data on state corporate tax top rates for the years 1994 to 2006. We

collected the data for the years 1978 to 1993 from Significant Features of Fiscal

Federalism, published by the Advisory Commission on Intergovernmental

Relations (multiple editions), and for the years 2007 to 2010 from our own

research of states’ corporate income tax statutes.

Mean State Payroll Burden : The authors also included a variable to describe

the impact of payroll burdens in other states when one state changes its

apportionment formula to a single sales-factor formula. This variable is calculated

as the weighted average of the state payroll burden for all states in each year.

States are weighted by their share of national manufacturing employment. Since

we already collected data on the payroll burden and on macroeconomic indicators

(including manufacturing employment), no additional data was needed for this

variable.

Goolsbee and Maydew measured the effects of a payroll burden decrease on

neighboring states by calculating an average payroll burden for all states in each

year, weighted by the importance of the manufacturing sector in that state. We did

the same. An example of the construction of this variable is:

Average payroll burden 1978 = ((Alabama burden 1978 * Alabama share of

national manufacturing employment 1978) + (Alaska burden 1978 * Alaska share

of national manufacturing employment 1978) + ... + (Wyoming burden 1978 *

Wyoming share of national manufacturing employment 1978)) / 50

Macroeconomic Indicators : Goolsbee and Maydew included basic

macroeconomic indicators in their model in order to control for fluctuations in

manufacturing employment due to broad economic trends. The national

unemployment rate is used as an indicator of basic economic health and personal

income growth rate by state and is used to control for differences in state

economies. To control for individual characteristics of states, they included state

fixed effects and time trends. To control for population growth (which should

increase the number of employees), they interacted the log of national

20

employment with the state fixed effects variables. For information on the sources

used to create the macroeconomic indicators in our data set, see Appendix I.

Data Comparison

To verify our data set to the data used by Goolsbee and Maydew, we compare the

means and standard deviations of our data with the means and standard deviations

of the same variables reported by Goolsbee and Maydew (2000a, 1998).19

The

results of these comparisons are reported in Table 2. The data have generally

comparable mean values.

Table 2: Comparison of Means and Standard Deviations

Source: Authors, using data described in the data collection section and Goolsbee and Maydew (1998 and 2000a). Means are reported, with standard deviations in parentheses.

One significant difference between our model and Goolsbee and Maydew’s model

is that we have 27 more observations. We were able to find data for all states that

had a corporate tax (46) and for each of the 17 years between 1978 and 1994. We

also excluded Michigan for the six years after 1988, following Goolsbee and

Maydew.20

The District of Columbia was not included. Four states (Nevada,

South Dakota, Washington, and Wyoming) did not have corporate taxes during

the study period and were, therefore, excluded. Texas was not included because it

uses a franchise tax that is not comparable to other states. We obtained 759

19 Goolsbee and Maydew’s 1998 study was a working paper for the National Bureau of Economic

Research. Goolsbee and Maydew’s 2000 study used the same data (2000a). Each of the papers

reported different sets of variables; Table 2 synthesizes information from both reports.

20 Goolsbee and Maydew (2000a) indicate that Michigan’s “single business tax” after 1988

functions more like a value-added tax than a corporate tax and should be excluded.

Variables Goolsbee and Maydew’s results

Authors’ results

Ln(manufacturing employment)

12.432 (1.095) 12.296 (1.203)

Payroll weight 0.313 (0.047) 0.302 (0.063)

State corporate tax rate

0.073 (0.022) 0.0753 (0.021)

National unemployment rate

0.069 (0.012) 0.069 (0.012)

State personal income growth

0.017 (0.022) 0.016 (0.029)

Ln(Total employment) 14.135 (0.95) 14.073 (0.97)

Number of observations

732 759

21

observations (45 states times 17 years, minus 6 years of Michigan data). It is

unclear to us why Goolsbee and Maydew obtain only 732 observations.

Replication of 1978 to 1994 Goolsbee and Maydew Model

To replicate the Goolsbee and Maydew study, we ran a basic panel regression

using data between 1978 and 1994, following the methodology used in the

regression presented in column (1) Table 2 of Goolsbee and Maydew (2000a).

In Table 3 we report our regression results next to those of Goolsbee and Maydew

(2000a). Estimates for coefficients are different than those found by Goolsbee and

Maydew, but the signs for all of the estimates are the same.

Table 3: Replication of Goolsbee and Maydew’s Model, 1978 to 1994

Variables Goolsbee and Maydew’s results

Authors’ results

Payroll burden -1.920* (0.876) -0.126 (0.152)

Mean payroll burden (weighted)

6.252* (2.726) 3.782 (2.902)

State personal income growth

0.380*** (0.089) 0.493*** (0.093)

National unemployment rate

-2.092*** (0.362) -2.319*** (0.598)

National employment x state dummies

Yes Yes

State fixed effects Yes Yes

State time trends Yes Yes

Adjusted R2 0.99 0.99

Number of observations 732 759 Source: Authors, using data described in the data collection section and Goolsbee and Maydew (2000a). The dependent variable for both regressions is the natural log of state manufacturing employment. Standard deviations are in parentheses. * significant at the 5% level, and *** significant at the 0.1% level.

As in Goolsbee and Maydew’s regression, a lower state payroll burden is found

to have a positive effect on manufacturing employment. Unlike Goolsbee and

Maydew, however, we do not find this effect to be statistically significant. Also,

our estimated coefficient on payroll burden (-0.126) is one-fifteenth the size of

Goolsbee and Mayhew’s payroll burden coefficient (-1.920). Our model predicts a

0.09 percent increase in manufacturing jobs when the sales factor changes from

regular weight to double weight, while Goolsbee and Maydew found a 1.1 percent

increase. We calculated these results by multiplying the mean state corporate

income tax rate of 7.58 percent by the payroll burden coefficient (-0.126), and

then multiplied that figure by the change in the payroll burden [(25% minus 33%)

or (0% minus 33%)]. Not only is our 0.09 percent finding statistically

insignificant, it is also less than a tenth of the size of Goolsbee and Maydew’s

22

1.1 percent employment effect. We also do not find a statistically significant

effect of the mean payroll burden, which measures the extent to which

employment in a given state is reduced by an increase in the weight on sales in

other states’ apportionment formulae. The coefficient on this variable is only half

of the size of that found by Goolsbee and Maydew, while the coefficient we find

on payroll burden is 15 times smaller.

Both the state personal income growth and the national unemployment variables

are significant at the 1 percent level. Our model predicts a 1 percentage point

increase in the national unemployment would decrease manufacturing jobs by

2.3 percent, and a 1 percentage point increase in personal income for a state

would increase manufacturing jobs by 0.5 percent. These results are generally

comparable to those of Goolsbee and Maydew.

Extension of Goolsbee and Maydew Model through 2010

We extend the model by running the same regression with data through 2010.

Results are presented in Table 4.

Table 4: Extension of Goolsbee and Maydew’s Model, 1978 to 2010

Variables Coefficients

Payroll burden -0.465* (0.212)

Mean payroll burden (weighted) -1.868** (0.625)

State personal income growth 0.362*** (0.056)

National unemployment rate 0.621* (0.307)

National employment x state dummies Yes

State fixed effects Yes

State time trends Yes

Adjusted R2 0.99

Number of observations 1,463 Source: Authors, using data described in the data collection section. The dependent variable is the natural log of state manufacturing employment. Standard errors are in parentheses. * significant at the 5% level, ** significant at the 1% level, and *** significant at the 0.1% level.

The estimates for coefficients on the macroeconomic control variables have the

same signs as the previous regression, but the variable of interest, state payroll

burden, is statistically significant. The magnitude of the coefficient, however, is

about one-fourth the size of the payroll burden coefficient estimated by Goolsbee

and Maydew. They predicted a 1.1 percent increase in manufacturing jobs when

the sales factor changes from the regular weight (0.33) to double weight (0.5).

By contrast, our model predicts a 0.29 percent increase in manufacturing jobs when

the sales factor changes from regular weight to double weight and a 1.22 percent

increase in manufacturing jobs when single sales-factor apportionment is adopted.

We calculated these results by multiplying the mean state corporate income tax rate

23

of 7.57 percent by the payroll burden coefficient (0.465), and then multiplied that

value by the change in the payroll burden [(25% minus 33%) or (0% minus 33%)].

Goolsbee and Maydew found a positive coefficient for mean state payroll burden,

which implied that an employment increase experienced in one state would result in

employment declines for other states. In contrast, our 1978 to 2010 regression finds

a negative and significant coefficient, indicating that states would benefit when

their neighbors move toward sales-based apportionment. This counterintuitive

result, however, was not found in the alternative specifications of our model.

The unemployment variable is not significant in this model (11 percent

significance level), but its coefficient indicates that as the national unemployment

rate increases by 1 percentage point, manufacturing jobs decrease by 0.62 percent.

State personal income growth is significant at the 1 percent level, and the

coefficient indicates that a one percentage point increase in the income growth

rate would cause a 0.36 percent increase in manufacturing jobs.

Alternative Model Specification: Total Private Employment

Previous studies indicate that single sales-factor apportionment should provide

greater benefits to the manufacturing sector, which generally has more out-of-

state sales, than in the private sector as a whole. Goolsbee and Maydew (1998)

tested total private employment as an alternative dependent variable: if workers

shift from manufacturing to non-manufacturing jobs, the coefficient on payroll

burden would be zero. To test the policy’s effect on the overall economy, we ran

an identical model using total private employment as the dependent variable

instead of total manufacturing employment. Results are presented in Table 5.

Table 5: Alternative Model with Private Employment as the Dependent Variable, 1978 to 2010

Variables Coefficients

Payroll burden -0.332*** (0.082)

Mean payroll burden (weighted) 0.610 (0.321)

State personal income growth 0.049 (0.029)

National unemployment rate 0.289 (0.180)

National employment x state dummies

Yes

State fixed effects Yes

State time trends Yes

Adjusted R2 0.99

Number of observations 1,463 Source: Authors, using data described in the data collection section. The dependent variable is the natural log of state private employment. Standard errors are in parentheses. *** significant at the 0.1% level.

24

The effect of lowering the payroll burden is reduced compared to the original

specification with manufacturing employment as the dependent variable. A

0.20 percent increase in private employment occurs when the sales factor

changes from regular weight to double weight and a 0.83 percent increase in

private employment occurs when single sales-factor apportionment is adopted

(as compared to 0.28 and 1.16 percent in the original specification). We

conclude that non-manufacturing jobs appear to be less sensitive to changes in

the payroll burden than manufacturing jobs, consistent with previous studies.

Mean state payroll burden has a positive coefficient, unlike in the previous

model where it was negative; however, it is not statistically significant. This

insignificant result indicates that employment in other states is unaffected

when another state adopts a greater sales factor apportionment.

Alternative Model Specification: Manufacturing Payroll

Another concern addressed by Goolsbee and Maydew (1998) is that single sales-

factor apportionment might cause a shift toward lower-paying jobs. To search

for evidence of this effect, we ran an identical model using total manufacturing

payroll (the sum of wages paid to employees) as the dependent variable instead

of manufacturing employment. This model simultaneously measures the effects

of sales tax apportionment on manufacturing jobs and on manufacturing wages.

Results are presented in Table 6.

Table 6: Alternative Model with Manufacturing Payroll as the Dependent Variable, 1978 to 2010

Variables Coefficients

Payroll burden -0.304 (0.219)

Mean payroll burden (weighted) 8.335*** (0.766)

State personal income growth 0.684*** (0.071)

National unemployment rate 5.600*** (0.395)

National employment x state dummies

Yes

State fixed effects Yes

State time trends Yes

Adjusted R2 0.99

Number of observations 1,463 Source: Authors, using data described in the data collection section. The dependent variable is the natural log of total state manufacturing payroll, in dollars. Standard errors are in parentheses. *** significant at the 0.1% level.

In this model, the payroll burden coefficient indicates that sales tax apportionment

does not have a significant effect on manufacturing payroll. The mean payroll

burden coefficient, however, is significant and very large – about one and a half

times larger than any of the mean payroll burden coefficients found by Goolsbee

25

and Maydew (1998; 2000a). When states enact single sales factor apportionment,

they may be substantially lowering the wages and salaries of neighboring states.

Evaluation of Wisconsin’s Single Sales-Factor

Apportionment

Wisconsin began phasing in single sales-factor apportionment in 2006, and it was

fully implemented by 2008. Using a model with five time-lagged variables for the

effects of previous years’ payroll burdens on this year’s employment, we

estimated the total difference in employment attributable to this policy. Our model

predicts 440,470 manufacturing jobs in Wisconsin in 2010 given the phase-in of

single sales-factor apportionment, and 432,937 given a counterfactual case where

the apportionment formula remains as it was in 2005. The marginal change in

manufacturing jobs that can be attributed to the policy is 7,533, which represents

1.7 percent of Wisconsin’s actual manufacturing employment of 451,930. We

created a similar model with total private employment as the dependent variable

and estimated an increase of 26,901 jobs due to the phase-in of single sales-factor

apportionment, an increase of 0.9 percent.21

The graph in Figure 1 shows the number of manufacturing jobs predicted under

these two scenarios, from the beginning of the sales factor apportionment phase-in

in 2006 to the full implementation in 2008 and beyond. The difference

attributable to the policy increases over time, as the policy takes effect, but

remains a small fraction of total employment throughout the time period.

Macroeconomic fluctuations, such as the 2008 recession, account for much more

of the difference.

Goolsbee et al. (2000) also modeled the effects of a switch to single-sales factor

apportionment in Wisconsin using data for the years 1978 to 1995. They predicted

2.9 percent growth for manufacturing jobs in Wisconsin. Our 1.7 percent estimate

is only a little more than a half the size of Goolsbee et al.’s (2000) estimate for

growth in manufacturing jobs in Wisconsin. Goolsbee et al. (2000) also estimated

2.4 percent growth for nonmanufacturing jobs in Wisconsin; our 0.9 percent

estimate for growth in total private jobs (manufacturing and nonmanufacturing) is

markedly different from this estimate. Including data through 2010 appears to

have lowered the predicted impact of single sales-factor apportionment in

Wisconsin.

21 This increase includes both manufacturing and non-manufacturing jobs in the private sector.

26

Figure 1: Predicted Manufacturing Jobs in Wisconsin with and without Single Sales-Factor Apportionment, 2006 to 2010

Source: Authors, using data described in the data collection section

Variables for Future Analysis

Goolsbee and Maydew’s (2000a) study did not control for several variables that

other authors have utilized in similar studies. We attempted to replicate Goolsbee

and Maydew’s (2000a) study in order to answer questions about whether the

implementation of single sales-factor apportionment had a positive effect on

employment in Wisconsin. We also estimated the same equation used by

Goolsbee and Maydew with data that extended from 1978 to 2010. The results

provide a preliminary updated analysis of the impact of changes in states’

apportionment formulae on manufacturing employment. Further work on this

topic should refine our analysis by utilizing additional explanatory variables,

some of which have been used by the scholars whose research has been reviewed

earlier in this report. We discuss additional variables that might be considered in

future analysis.

Combined Reporting

According to BNA, in a state with combined reporting, the members of a unitary

corporate group that have nexus in a particular state determine their taxable

income in that state by apportioning the group’s combined business income to the

state on the basis of combined apportionment factors.22

The total state taxable

22 BNA provides an example using California. First, the combined business income of the unitary

group is determined. Next, the combined business income is apportioned to California based on its

420,000

440,000

460,000

480,000

500,000

520,000

540,000

2005 2006 2007 2008 2009 2010

Tota

l Man

ufa

ctu

rin

g Jo

bs

Predicted, with single sales-factor apportionment

Predicted, without single sales-factor apportionment

27

income of the nexus members of a unitary group under the combined report

methodology can differ from their total state taxable income under the separate

return method. An example in Appendix C illustrates this difference.

Bruce, Deskins, and Fox (2007) contend that a state’s combined reporting

requirement could reduce economic activity by driving away firms where the

requirement would increase their corporate income tax base or disallow certain

tax planning devices. Results from their study show that combined reporting led

to an increase in firms’ corporate income tax base. The relationship between

combined reporting and the size of a firm’s taxable income was strengthened

when combined reporting was interacted with the presence of a throwback rule (to

be described below). The interaction between these two variables was related to a

decrease in both the state gross product and the state corporate income tax base in

states that require both combined reporting and throwback rules. Their findings

show that the corporate income tax base increases with a combined reporting

requirement and no throwback rule, but decreases in states with both, suggesting

that the throwback rule may offset some of the gain in the corporate income tax

base that states achieve with the combined reporting requirement.

Beginning in 2009, Wisconsin imposed both a combined reporting requirement

and the throwback rule.23

Based on Bruce et al.’s (2007) findings, we would

expect the combination of both requirements to offset each other and that there

would be no significant effect on the corporate income tax base.

Throwback Rule

According to BNA, the throwback rule applies to sales that are made to a

customer located in a state in which the seller does not have nexus, i.e., no

employees or property. Under the throwback rule, the sales are “thrown back” to

the state of origin and included in the numerator of that state’s apportionment

factor. According to BNA, when sales are thrown back in a combined reporting

state, a problem arises in attributing those sales to the unitary group as a whole or

to one member of the affiliated group. If the group member making the sale is not

taxable in the destination state, but one or more other members of the group are

taxable there, should the sale be allocated there or thrown back to the state from

which the goods were shipped? The answer to this question is not clear according

apportionment rules. Finally, any nonbusiness income that is specifically allocated to California is

added to the apportioned amount of unitary business income and this total becomes the tax base

for California’s income and franchise tax.

23 Sales included in the apportionment factor are sales of tangible personal property. Wisconsin

considers these sales to be in-state sales if the property is delivered or shipped to a purchaser

within Wisconsin. If sales to out-of-state purchasers are not taxable in the destination state,

Wisconsin will throw these back and include 50 percent of such sales in Wisconsin’s in-state sales

factor numerator as if they were sold to a Wisconsin purchaser (Shanovich, 2009).

28

to the ambiguous guidance in the Uniform Division of Income for Tax Purposes

Act (UDITPA) § 16(b),24

which says that the sale is to be thrown back if “the

taxpayer is not taxable in the state of the purchaser.” The UDITPA, however, does

not provide guidance as to whether a “taxpayer” in a combined reporting state

refers to each corporation in the combined group or to the combined group as a

unit.

In a combined group, the total income for all of the affiliated entities is combined

to create the income base to be apportioned by only those states with nexus. If the

“taxpayer” is the total combined group, then all of the sales would be thrown back

and subject to tax. But, if the “taxpayer” is the individual corporation that sale

originated from, it will not be thrown back and subject to tax.

Sales are typically apportioned to the destination state; however, if the destination

state does not have nexus with the corporation, then that sale is thrown back to the

origin state (which does have nexus with the corporation) so that sale is then

included in the numerator of the sales factor in the origin state. This throwback

sale increases the origin state’s taxable income because the origin state now has a

higher sales factor. Therefore, states adopting single sales-factor apportionment

that do not have a throwback rule are expected to be relatively more attractive

than states that have a throwback rule. As noted by Mazerov (2005 (revised)),

lack of the throwback rule allows states to minimize their tax burden because

taxes based on sales made into states in which a business does not have nexus

may be avoided. In addition, Edmiston (2002) notes that in states with throwback

rules, the tax on profits that is partially assigned based on the in-state to total sales

ratio is a cost of production in that state for any products that are sold out of state

where a business does not have nexus.

Similarly, Gupta and Hofmann (2003) find a modest negative association between

state corporate tax rates interacted with the property factor apportionment weight

in each state and capital investment, with a stronger negative association for states

that impose combined reporting or the throwback rule. This finding supports the

notion that companies subject to combined reporting or the throwback rule are

less able to use tax-planning (“paper”) techniques to reduce their tax burden and

are thus more inclined to respond to differences in apportionment weights with

changes in capital investment (Gupta and Hofmann, 2003).

Lightner’s (1999) study, however, found the throwback rule variable had no

association with employment in the two years of her study.

24 Drafted in 1957, UDITPA provides a uniform apportionment formula standard for states to

follow to divide a corporation’s income between states. UDITPA endorses the equally weighted

three-factor formula.

29

Personal Income Tax

Wasylenko’s literature review (1997) reports that two studies (Wasylenko and

McGuire, 1985; Goss and Phillips, 1994) found states with higher personal

income taxes have lower employment growth, but that Carlton (1983), among

other studies, have not found statistically significant effects for personal income

taxes. Thus, the effects of personal income tax rates across states are unclear, but

possibly significant.

Energy Costs

Wasylenko (1997) reports that studies focused on firm location or employment

growth have found energy costs insignificant. But in a more recent paper, Bruce

et al. (2007) found that energy prices had a statistically significant effect on the

gross state product. If energy prices decreased, the gross state product would

increase. Their results are to be expected since reduced business expenses would

allow for increased productivity. Gupta and Hofmann’s study (2003) found

similar results. In contrast, Lightner (1999) found that an energy (natural gas cost)

variable was positively related to employment. Thus as energy prices increased,

employment also increased. This effect, however, was not statistically significant

in all of her regressions. In our model, fixed effects account for energy costs to

the extent that energy costs differ consistently across states over time; however,

energy price variables in Bruce et al. (2007) and Gupta and Hofmann (2003) were

significant even with the use of fixed effects. Evidence on the economic effects of

energy costs is therefore mixed, and the fixed effects in our model may not be

sufficient to account for energy cost variance.

Industrial Composition

Edmiston (2002) reports that capital-intensive industries, such as manufacturing,

experience greater employment growth in response to greater sales-factor

apportionment. Goolsbee and Maydew (2000a), and our analysis, show that

manufacturing employment is more sensitive to apportionment formula changes

than nonmanufacturing employment. On the other hand, Edmiston (2002) notes

that employment growth associated with a switch to greater sales-factor weighting

may be lower than expected for states in which production is relatively fixed in

location (e.g., mining activities). Because the prevalence of industries varies

across states, controlling for the industrial composition of state economies could

account for these possible effects. The fixed effects in our model should account

for industrial composition to the extent that industrial composition differs

consistently across states over time.

30

Summary

Our study analyzes the effect of single sales-factor corporate tax apportionment

on job creation in Wisconsin. Previously, most states used a three-factor

apportionment system, giving equal weight to property, payroll, and sales.

Goolsbee and Maydew, however, found that moving to a single sales-factor

apportionment formula would reduce the overall tax burden on payroll through

the elimination of the payroll factor, spurring job creation. Even though 11 states

switched to a single sales-factor apportionment system between 2000 and 2012, to

date there are few studies that have analyzed the effect on employment as a result

of this shift in state tax policy.

We replicate the Goolsbee and Maydew (2000a) regression analysis which, using

data from 1978 to 1994, predicted an increase in a state’s employment from

switching to a single sales-factor for corporate tax apportionment. When we

replicate the Goolsbee and Maydew analysis, we do not find a statistically

significant relationship between payroll tax burden and the level of manufacturing

employment. In fact, our estimated coefficient on the payroll burden, in addition

to be statistically insignificant, is less than one-fifteenth the size of the payroll

burden coefficient estimated by Goolsbee and Maydew in their oft-cited 2000

Journal of Public Economics study. Our results call into question the validity of

the policy prescriptions in favor of single sales-factor apportionment that were

based on the empirical evidence provided by the Goolsbee and Maydew study.

We also estimated the Goolsbee and Maydew model using data from 1978

through 2010. Our model shows that increasing the weight on sales in the

apportionment formula, and the consequently lower weight on payroll, results in a

statistically positive effect on manufacturing employment. Our estimated

coefficient, however, is quite small – less than one-quarter of the magnitude of

Goolsbee and Maydew’s coefficient on payroll burden.

We utilize our extended regression analysis to estimate the impact on

manufacturing employment of Wisconsin’s adoption of single sales-factor

apportionment. We conclude that adopting single sales-factor apportionment

increased manufacturing employment by 1.7 percent and total private

employment by 0.9 percent. Our estimate is substantially lower than that of

Goolsbee et al. (2000), which predicted 2.9 percent growth in manufacturing

employment and 2.4 percent growth in nonmanufacturing employment. The jobs

potentially created by this policy should be weighed against losses in corporate

income tax revenue for the State of Wisconsin due to the policy.

A complete assessment of the impacts of adopting single sales-factor

apportionment requires future research, including re-estimation of the updated

Goolsbee and Maydew model with additional variables, including those suggested

in the previous section.

31

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34

Appendix A: Example of How Apportionment Formulae

Work

To determine how state apportionment formulae work, it is helpful to begin with a

hypothetical example of Corporation A. This example will illustrate how giving

different weights to the sales factor (33.33%, 50%, 100%) relative to the property

and payroll factors affect how much profit is subject to a state’s corporate tax.

Corporation A: Property, Payroll, and Sales in Wisconsin

Corporation A: Profits of $100,000

Wisconsin Total for All 50 States

Property $500,000 $500,000

Payroll $1,000,000 $1,000,000

Sales $200,000 $1,000,000

As shown above, Corporation A locates all of its payroll and property in

Wisconsin but only has some of its sales in Wisconsin. It is necessary to use the

apportionment formula to determine how much of Corporation A’s $100,000 in

profits will be subject to tax, assuming no deductions or credits.

Corporation A: Profits Subject To Wisconsin’s Tax under Different Apportionment Formulae

Equally Weighted* Sales Double- Weighted**

Sales Only***

Property 1 (500,000/500,000)

1 1*0

Payroll 1 (1,000,000/1,000,000)

1 1*0

Sales 0.2 (200,000/1,000,000)

0.2*2=0.4 0.2*1

Total 2.2 (1+1+0.2)

2.4 0.2

Divided by 3 (equally weighted)

4 1

Profit Subject to Tax Rates

$73,333 ((2.2/3)*100,000)

$60,000 $20,000

*Payroll, property, and sales are each given an equal one-third weight. **Payroll and property are each weighted 25 percent, and sales is weighted 50 percent. ***Payroll and property do not receive any weight, and sales is weighted 100 percent.

35

Corporation A would prefer sales be weighted 100 percent because all of its

property and payroll is in Wisconsin (thus 100 percent of its profits would be

subject to tax if only property or payroll were weighted). Corporation A may be

less well off if it had 50 percent of its sales in New York and New York weighted

sales at 100 percent in its apportionment formula.

Corporation A’s Property, Payroll, and Sales in New York

Corporation A-$100,000 Profits

New York Total for All 50 States

Property $0 $500,000

Payroll $0 $1,000,000

Sales $500,000 $1,000,000

In the state of New York, Corporation A would prefer that property, payroll, and

sales be equally weighted.

Corporation A: Profits Subject To New York’s Tax Under Different Apportionment Formulae

Equally Weighted Sales Double- Weighted

Sales Only

Property 0 0 0

Payroll 0 0 0

Sales 0.5 0.5*2=1 0.5

Total 0.5 1 0.5

Divided by 3 4 1

Profit Subject to Tax Rates

$16,667 $25,000 $50,000

36

Appendix B: Background on the Taxation of Business

Profits

In order to understand how apportionment formulae fit in the context of state

corporate income taxation, we provide a brief background on the taxation of

business profits in multiple states. Depending on the state’s tax laws, a

corporation with subsidiaries and operations in multiple states may determine its

taxable income for each state using the separate return, combined report, or

consolidated return methods. Once a state’s taxable income is determined using

one of these methods, it is then apportioned using that state’s apportionment

formula.

State Methods for Taxing Multistate Corporate Groups

Each corporation in an affiliated group of corporations that has a sufficient legal

contact (“nexus”) with a particular taxing state may be subject to corporate

income tax in that state. “Nexus” describes sufficient contact for tax purposes

between the state and the taxpayer; if the taxpayer is a non-resident of the state,

the state may only tax the taxpayer to the extent of the taxpayer’s in-state

activities. Maintaining an office in the state and maintaining fixed property in the

state are two examples of activities that most states recognize as sufficient contact

to create nexus with the state and subject the non-resident taxpayer to taxation

based on their activities within the state.

Depending on the particular state’s convention, a unitary business may determine

its state taxable income using the separate return, combined report, or

consolidated return methods. Appendix C provides an example of separate versus

combined reporting.

Separate

According to BNA, under the separate return method, each corporation in an

affiliated group that has nexus with a particular taxing state will first determine its

total separate business income by considering its stand-alone operations, separate

from the rest of the affiliated companies (for all jurisdictions, not just the

particular taxing state jurisdiction). Then, the corporation’s separate business

income is apportioned to the state using that state’s apportionment formula.

Combined

The purpose of a combined report is to geographically source the income of a

unitary business, according to BNA. On a combined report, the group’s total

combined income, regardless of nexus, is apportioned using the combined

apportionment factors of only the members that have nexus with the taxing state.

According to BNA, combined reporting poses a unique problem: even though the

apportionment factors for the corporations in the affiliated group that do not have

37

nexus with the taxing state are not included in the combined apportionment factor,

the state will include income from all of the entities in the combined group, even

though the state does not have jurisdiction to tax the entities which do not have

nexus with the state. Therefore, a state may end up taxing the group on income

that was not earned through contact with that state. See Appendix C for a generic

example of combined reporting.

Consolidated

The term “combined report” is used to refer to the combined method of

computing taxable income on a unitary group basis. By comparison, according to

BNA, the term “consolidated return” is used to refer either to a single state return

that reflects the separately computed state taxable incomes of related corporations

or to a state consolidated return that calculates the group’s apportionable business

income based on the federal consolidated return regulations.

Many states allow a unitary business to file a single, consolidated return;

however, some states require the consolidated return. According to BNA, the term

“consolidated return” does not have a consistent definition between the states and

can vary widely. In all consolidated returns, however, related corporations jointly

file a single return.

According to BNA, some states that do not allow or require combined reporting

do allow unitary businesses to file a consolidated return on which the state tax

liability of each member is separately determined under the separate return

method and then added together. Consolidated returns of this type are permitted,

for example, in Connecticut and South Carolina. Other states allow consolidated

returns that follow the federal consolidated return and use the group’s

consolidated taxable income and combined in-state apportionment factor.

The statutory provisions for including corporations in state consolidated returns

generally depend on the type of consolidated return used. When each corporation’s

state taxable income is separately determined under the separate return method and

then reported on the single consolidated return, only members of the group having

nexus with the taxing state are included. Additionally, the group must own greater

than 50 percent of an entity to include it on the consolidated return. According to

BNA, in states that follow the federal consolidated return, federal consolidated

return requirements may apply either to members of the federal consolidated group

that have specified ties to the state, or to all members of the group whether or not

they have nexus.

Effective January 1, 2009, Wisconsin became a combined reporting state under

2009 Wisconsin Act 2 for all corporations that are not exempt by statute and that

engage in a unitary business with at least one other corporation. As illustrated in

Appendix C, combined reporting could produce an increase in the Wisconsin

taxable income of a unitary group of corporations if the non-nexus companies are

38

relatively more profitable than the companies that have nexus with Wisconsin

(Pomp, 2004). This result could occur because all of the group’s combined

income is taxable, even though income is only apportioned using the entities with

nexus combined apportionment factors.

39

Appendix C: Comparison of Combined and Separate

Reporting

The following example, as provided by BNA, illustrates the differences in taxable

income dependent on whether separate reporting or combined reporting is used.

Separate Return Method

Three unitary corporations have the following income and apportionment factors

with respect to State A.

Corporation Income Apportionment Factor

P $200 30/100

S1 $300 10/500

S2 $100 0/400

Using the separate return method, corporations P and S1 would apportion their

separate incomes to State A as follows. S2 has no nexus in State A and, therefore,

no taxable income in State A.

P: $200 x 30/100 = $60

S1: $300 x 10/500 = $ 6

Combined Reporting Method

P and S1 will apportion the group’s combined income ($600) using the group’s

total in-state numerator as follows:

P and S1: $600 x 40/1,000 = $24

When using the separate return method, $66 was reported. When using combined

reporting, however, income from the profitable company with a nexus is diluted

by the less profitable, non-nexus company. Even so, combined reporting can

result in more income being apportioned to a state if the non-nexus companies are

relatively more profitable.

Whether the combined report method by group (shown above) or by company-by-

company (shown below) is applied, the result is the same:

P: $600 x 30/1000 = $18

S1: $600 x 10/1000 = $ 6

40

In the first example, the total taxable income that would be apportioned to State A

on a consolidated return filed by P and S1 (prepared using the separate return

method) is $66. In the second example which uses the combined report method,

only $24 is apportioned to State A. The combined report method would result in

substantially less income being apportioned to State A in the second example

because the income from the in-state profitable operations of the nexus

corporations was diluted by combining it with the large, out-of-state factors of the

less profitable operations of S2, which did not have nexus with State A. By

contrast, combined reporting will result in more income being apportioned to a

state in which the unitary group’s non-nexus members are relatively more

profitable (per dollar of apportionment factor) than the nexus members.

41

Appendix D: Catalogue of Apportionment Law Changes

Table D1 catalogues the apportionment law changes in the 50 states since 1978,

the first year used in the Goolsbee and Maydew (2000a) study. Citations for the

state statute are included, along with the information regarding the payroll weight

in the apportionment formula.

Table D1: Catalogue of Apportionment Law Changes

State Statutory Citation for

Apportionment Formula Year(s) Weight on

Payroll

Alabama ALA. CODE § 40-27-1 1978-2010 33.33%

2011-2012 25%

Alaska ALASKA STAT. § 43.19.010 1978-2012 33.33%

Arizona25 ARIZ. REV. STAT. ANN. §

43-1139

1978-1990 33.33%

1991-2006 25%

2007 20%

2008 15%

2009-2012 10%

Arkansas ARK. CODE ANN. § 26-51-

709 1978-1994 33.33%

1995-2012 25%

California CAL. REV. & TAX. CODE §§25128 and 25128.5

1978-1992 33.33%

1993-2010 25%

2011-201226 0%

Colorado COLO. REV. STAT. § 39-22-

303.5 1978-2008 33.33%

2009-2012 0%

Connecticut27 CONN. GEN. STAT. § 12-

218 1978-1982 33.33%

1983-2012 25%

Delaware DEL. CODE ANN. tit. 30, §

1903 1978-2012 33.33%

Florida FLA. STAT. § 220.15 1978-2012 25%

25 Starting in 2007, Arizona permitted an election between a double-weighted sales factor and a

higher weighted sales factor. When assigning weights for our data set, we assumed that businesses

will always choose a higher weighted sales factor if given the option.

26 In 2011 and 2012, California permits an election between a single sales factor and a double-

weighted sales factor. When assigning the weight for California, we assumed businesses will elect

the single sales-factor.

27 There is a single sales-factor option in Connecticut, but it only applies to specific sectors of

manufacturing. Thus, for our data set, we assumed Connecticut has double-weighted sales.

42

State Statutory Citation for

Apportionment Formula Year(s) Weight on

Payroll

Georgia GA. CODE ANN. § 48-7-31

1978-1994 33.33%

1995-2005 25%

2006 10%

2007 5%

2008-2012 0%

Hawaii HAW. REV. STAT. § 235-29 1978-2012 33.33%

Idaho IDAHO CODE ANN. § 63-

3027 1978-1992 33.33%

1993-2012 25%

Illinois 35 ILL. COMP. STAT. 5/304

1978-1986 33.33%

1987-1997 25%

1998 16.67%

1999 8.33%

2000-2012 0%

Indiana IND. CODE § 6-3-2-2

1978-1995 33.33%

1996-2006 25%

2007 20%

2008 15%

2009 10%

2010 5%

2011-2012 0%

Iowa IOWA CODE § 422.33 1978-2012 0%

Kansas KAN. STAT. ANN. § 79-

3279 1978-2012 33.33%

Kentucky KY. REV. STAT. ANN. §

141.120 1978-1985 33.33%

1986-2012 25%

Louisiana LA. REV. STAT. ANN. §

47:287.95

1978-1996 33.33%

1997-2005 25%

2006-2012 0%

Maine ME. REV. STAT. ANN. tit.

36, § 5211

1978-1990 33.33%

1991-2006 25%

2007-2012 0%

Maryland MD. CODE ANN. TAX-GEN.

§ 10-402 1978-1991 33.33%

1992-2012 25%

Massachusetts MASS. GEN. LAWS ch. 63, §

38 1978-2012 25%

43

State Statutory Citation for

Apportionment Formula Year(s) Weight on

Payroll

Michigan MICH. COMP. LAWS §

208.1301

1978-1990 33.33%

1991-1996 25%

1997-1998 10%

1999-2005 5%

2006-2007 3.75%

2008-2012 0%

Minnesota MINN. STAT. § 290.191

1978-1986 33.33%

1987-1999 15%

2000-2006 12.5%

2007 11%

2008 9.5%

2009 8%

2010 6.5%

2011 5%

2012 3.5%

Mississippi MISS. CODE ANN. § 27-7-

2328 1978-2012 33.33%

Missouri MO. REV. STAT. § 32.200 1978-2012 33.33%

Montana MONT. CODE ANN. § 15-

31-305 1978-2012 33.33%

Nebraska29 NEB. REV. STAT. § 77-

2734.16

1978-1987 33.33%

1988 26.67%

1989 20%

1990 13.33%

1991 6.67%

1992-2012 0%

Nevada None 1978-2012 None

New Hampshire

N.H. REV. STAT. ANN. § 77-A:3

1978-1991 33.33%

1992-2012 25%

New Jersey N.J. STAT. ANN. § 54:10A-6

1978-1999 33.33%

2000-2011 25%

2012 15%

New Mexico N.M. STAT. § 7-4-10 1978-2012 33.33%

28 The details are in Income Tax Regulations, Title 35, Part III, Mississippi Administrative Code,

http://www.dor.ms.gov/info/rules/Part_III_effective_20090701.pdf, p.74.

29 Review of the statute shows that the change was more gradual than the 1985 to 1992 data set

shown.

44

State Statutory Citation for

Apportionment Formula Year(s) Weight on

Payroll

New York N.Y. TAX LAW § 210

1978-2005 25%

2006 20%

2007-2012 0%

North Carolina N.C. GEN. STAT. § 105-

130.4 1978-1988 33.33%

1989-2012 25%

North Dakota N.D. CENT. CODE § 57-

38.1-09 1978-2012 33.33%

Ohio OHIO REV. CODE ANN. §

5733.05

1978-1984 33.33%

1985-1998 25%

1999-2012 20%

Oklahoma OKLA. STAT. tit. 68, § 2358 1978-2012 33.33%

Oregon OR. REV. STAT. § 314.650

1978-1990 33.33%

1991-2003 25%

2004-2005 10%

2006-2012 0%

Pennsylvania 72 PA. CONS. STAT. § 7401

1978-1994 33.33%

1995-1999 25%

2000-2006 20%

2007-2008 15%

2009 8.5%

2010-2012 5%

Rhode Island R.I. GEN. LAWS § 44-11-14 1978-2012 33.33%

South Carolina S.C. Code Ann. § 12-6-2252

1978-1993 33.33%

1994-2006 25%

2007-2012 0%

South Dakota None 1978-2012 None

Tennessee TENN. CODE ANN. § 67-4-

2012 1978-1996 33.33%

1997-2012 25%

Texas TEX. TAX CODE ANN. §

171.106 1978-2012 0%

Utah UTAH CODE ANN. § 59-7-

311

1978-2005 33.33%

2006-2010 25%

2011 16.67%

2012 8.33%

Vermont VT. STAT. ANN. tit. 32, §

5833 1978-2005 33.33%

2006-2012 25%

Virginia VA. CODE ANN. § 58.1-408 1978-1999 33.33%

2000-2012 25%

Washington None 1978-2012 None

45

State Statutory Citation for

Apportionment Formula Year(s) Weight on

Payroll

West Virginia W. Va. Code § 11-24-7 1978-1985 33.33%

1986-2012 25%

Wisconsin Wis. Stat. § 71.25

1978-2005 25%

2006 20%

2007 10%

2008-2012 0%

Wyoming None 1978-2012 None

46

Appendix E: State Apportionment Formula Changes Since

2000

In 2000, Wisconsin placed a 50 percent weight on the sales factor in the state

corporate income tax apportionment formula with a 25 percent weight on the

property factor and a 25 percent weight on the payroll factor. In 2000, almost half

of all states placed double weight on the sales factor. The apportionment formulae

each state used in 2000 is shown in Table E1. The vast majority of states either

had a double-weighted sales factor or an equally weighted, three-factor formula.

Table E1: Apportionment Formulae of States in 2000

No Corporate

Income Tax

Equally Weighted Formula

Double-Weight on Sales

More than 50% Weight on Sales

But Less Than 100%

Single Sales-Factor

4 states 15 states 23 states 4 states 4 states Nevada South Dakota Washington Wyoming

Alabama Alaska Colorado Delaware Hawaii Kansas Mississippi Missouri Montana New Mexico North Dakota Oklahoma Rhode Island Utah Vermont

Arizona Arkansas California Connecticut Florida Georgia Idaho Indiana Kentucky Louisiana Maine Maryland Massachusetts New Hampshire New Jersey New York North Carolina Oregon South Carolina Tennessee Virginia West Virginia Wisconsin

Minnesota (75%) Michigan (90%) Ohio (60%) Pennsylvania (60%)

Illinois Iowa Nebraska Texas30

Source: Authors, using data described in the Data Collection section

30 We classified Texas as a single sales-factor state based on the data we found even though some

would classify Texas as having no corporate income tax since it uses a franchise tax. Texas was

not included in our replication or extension of the Goolsbee and Maydew model due to its use of

the franchise tax.

47

From 2000 to 2011, many states made changes in their apportionment formulae

(Table E2). All of the changes resulted in an increase in the weight given to sales.

Many states moved to a single sales-factor formula.

Table E2: States That Made Apportionment Formula Changes from 2000 to 2012

Year Equally Weighted Formula

Double- Weight on

Sales

More than 50% Weight on Sales

But Less Than 100%

Single Sales-Factor

2000 Alabama Colorado Utah Vermont

Arizona California Georgia Indiana Louisiana Maine New Jersey New York Oregon South Carolina Wisconsin

Michigan (90%) Minnesota (75%) Pennsylvania (60%)

2004 Oregon (80%) 2006 Utah

Vermont Georgia (80%) Michigan (92.5%) New York (60%) Wisconsin (60%)

Louisiana Oregon

2007 Arizona (60%) Georgia (90%) Indiana (60%) Minnesota (78%) Pennsylvania (70%) Wisconsin (80%)

Maine New York South Carolina

2008 Arizona (70%) Indiana (70%) Minnesota (81%)

Georgia Michigan Wisconsin

2009 Arizona (80%) Indiana (80%) Minnesota (84%) Pennsylvania (83%)

Colorado

2010 Indiana (90%) Minnesota (87%) Pennsylvania (90%)

2011 Alabama Minnesota (90%) Utah (66.67%)

California Indiana

2012 Minnesota (93%) New Jersey (70%) Utah (83.33%)

Source: Authors, using data described in the Data Collection section

48

In 2012, 15 states had single sales-factor apportionment. The majority of states

with a corporate income tax weighted sales at least 50 percent in their state

corporate income tax apportionment formulae (Table E3) .

Table E3: Apportionment Formulae for States in 2012

No Corporate

Income Tax

Equally Weighted Formula

Double-Weight on

Sales

More Than 50% Weight on

Sales But Less Than 100%

Single Sales-Factor

4 states 11 states 14 states 6 states 15 states

Nevada South Dakota Washington Wyoming

Alaska Delaware Hawaii Kansas Mississippi Missouri Montana New Mexico North Dakota Oklahoma Rhode Island

Alabama Arkansas Connecticut Florida Idaho Kentucky Maryland Massachusetts New Hampshire North Carolina Tennessee Vermont Virginia West Virginia

Arizona (80%) Ohio (60%) Minnesota (93%) New Jersey (70%) Pennsylvania (90%) Utah (83.33%)

California Colorado Georgia Illinois Indiana Iowa Louisiana Maine Michigan Nebraska New York Oregon South Carolina Texas Wisconsin

Source: Authors, using data described in the Data Collection section

49

Appendix F: States Considering Apportionment Formula

Changes

The states that are considering changes to their corporate tax apportionment

formulae in 2012 are listed in Table F1, along with the bill numbers and the

proposed change. To find this information, we used State Net through Lexis Nexis

and searched the Current Session database for each state’s apportionment formula

statute. The statutes are provided in Appendix D.

Table F1: States Considering Apportionment Formula Changes

State Bill Proposed Change

California 2011 Bill Text CA A.B. 1500

This bill would, for taxable years beginning or after January 1, 2012, require a taxpayer, except as provided, to apportion their income in accordance with a single sales-factor.

2011 Bill Text CA S.B. 116

This bill would, for taxable years beginning or after January 1, 2012, require a taxpayer, except as provided, to apportion their income in accordance with a single sales-factor.

Kansas 2011 Bill Text KS H.B. 2157

This bill would, for taxable years beginning after December 31, 2012, at the election of a taxpayer who relocates such taxpayer’s business activities and at least 10 or more full-time equivalent employees to a facility, office or other operation in Kansas, permit that taxpayer to multiplying its business income by the sales factor.

Minnesota 2011 Bill Text MN H.B. 202

This bill would accelerate adoption of single sales apportionment.

New Mexico 2012 Bill Text NM S.B. 42

This bill would allow a taxpayer whose principal business activity is manufacturing to elect to have business income apportioned to this state using a single sales-factor if the taxpayer has invested in New Mexico a certain amount of dollars for capital equipment and facility construction or renovation.

50

State Bill Proposed Change

North Carolina 2011 Bill Text NC S.B. 752

This bill would require income of corporations other than public utilities, excluded corporations, and qualified capital intensive corporations to be apportioned to North Carolina by an equally weighted three-factor formula rather than a double-weighted sales formula.

Vermont 2011 Bill Text VT H.B. 619

This bill would increase the weight of sales in the apportionment formula, ultimately leading to a single sales-factor formula that takes effect on July 1, 2014.

2011 Bill Text VT S.B. 196

This bill moves the corporate income tax apportionment formula to a single sales-factor with no throwback rule.

Source: StateNet Current Session database from Lexis Nexis.

51

Appendix G: Revenue Effects in the Literature

Several of the papers on the employment effects of changes in apportionment

formula weighting described in the literature review also explore possible revenue

effects associated with such changes. At a minimum, papers discuss possible

changes in state corporate income tax revenues and state personal income tax

revenues. Edmiston and Arze del Granado (2006) also estimate sales tax revenue

changes in Georgia, but none of the papers estimate possible business property tax

revenue changes. Conclusions on whether revenue effects are positive or negative

vary and depend partially upon the sources of revenue included; however, it is

clear that the magnitude of revenue effects may be large. For example, Edmiston

and Arze del Granado (2006) use Georgia tax returns for multistate companies

and determined that lost corporate income tax revenues and sales tax revenues

overwhelm gains in personal income tax revenue gains, while the Great Lakes

region in Edmiston’s (2002) model is expected to see gains in both personal and

corporate income tax revenues in the long term even if all regions were to act

simultaneously (Edmiston and Arze del Granado, 2006; Edmiston, 2002). Several

studies that focused on state revenue (rather than employment outcomes) have

also found large revenue effects associated with changes in apportionment

formula weighting. For example, Gramlich et al. (2009) used state-level panel

data for the years 1982 to 2002 to determine that double-weighting (rather than

equally weighting) the sales factor is associated with a decrease in state corporate

income tax revenues of about 16 percent for the mean state. Their results do not

eliminate the possibility that longer term business responses to the policy may

increase state tax revenues (Gramlich et al., 2009).

Dubin (2010) examines changes in corporate income tax bases, as measured by

the capacity of state and local governments to raise revenue from the corporate

income tax, resulting from increased sales factor weighting for the years 2001 to

2008.31

Dubin finds that corporate income tax bases increased in most states

following an increase in the sales factor weight, and decreased in only a few,

including Wisconsin. This result is contrary to what would be expected from a

policy intended to act as an economic stimulus, but may signal that effects may

vary from state to state based on the existing characteristics of state economies

(Dubin, 2010).

Similarly, Pinto (2007), using an analytical framework, finds that regional (state)

governments would experience both losses and gains in corporate tax revenue,

rather than exclusively losses, even if all of the regional governments were to

adopt the strategy consistent with attracting capital, i.e., single sales-factor

apportionment (Pinto, 2007). While the adoption of a greater sales factor weight is

31 This measure is published by the Federal Reserve Bank of Boston and was first developed by

the former U.S. Advisory Commission on Intergovernmental Relations (Dubin, 2010).

52

considered an economic development incentive, no consensus exists thus far for

the precise magnitude or direction of revenue effects that any given state could

expect, especially in the longer term.

Goolsbee and Maydew, 2000a

Goolsbee and Maydew (2000a) use panel data from 1978 to 1994 to examine the

economic impact of state apportionment formulae, focusing on impacts on

employment growth. Additionally, they comment on possible revenue effects.

They take the estimated decreases in corporate income tax revenues forecasted by

the Pennsylvania and New Jersey Departments of Revenue resulting from the

implementation of a double-weighted factor ($41 million and $33 million,

respectively). Taking their finding that short-term manufacturing employment

would be expected to increase by 1.1 percent, they then calculate the “cost per

job” as $6,000 per job in New Jersey and $2,000 per job in Pennsylvania – or

“cost competitive” compared to other government jobs programs. They also note

that increased revenue resulting from personal income tax revenues associated

with increased employment could, to some degree, offset corporate income tax

revenue decreases.

Goolsbee, Maydew, and Schadewald (Goolsbee et al.), 2000

Goolsbee et al. (2000) adapt the Goolsbee and Maydew (2000a) model to make

estimates specifically for the implementation of single sales-factor apportionment

in Wisconsin. Based on their findings that single sales-factor apportionment

would increase Wisconsin manufacturing and nonmanufacturing jobs by 18,000

and 49,000 jobs, respectively, and data indicating that average manufacturing jobs

paid (in 1995) $31,700 and $17,500, respectively, they calculate that personal

income tax revenues in Wisconsin would increase by roughly $51 million per year

in response to the implementation of single sales-factor apportionment. They

acknowledge the possibility of a “positive dynamic effect” on revenues such as

sales and property tax, but do not measure such effects. The authors compare

estimate to a Wisconsin Department of Revenue’s estimate that the

implementation of single sales-factor apportionment would decrease corporate

income tax revenues by $58 million per year (Goolsbee et al., 2000). In 2003, the

Wisconsin Department of Revenue’s fiscal estimate for Wisconsin Act 37 for the

effect of the full implementation of single sales-factor apportionment was $45

million per year (Walgren, 2003).

Edmiston, 2002

Edmiston’s (2002) eight-region applied general equilibrium model includes

changes in corporate income tax revenue and personal income tax revenue for

each region associated with each region’s move to single sales-factor

apportionment. Edmiston also models results for the simultaneous adoption of

single sales-factor apportionment by all of the regions. The distinction between

53

acting independently or simultaneously is important because it illustrates the

importance of apportionment factors elsewhere relative to a given region in

determining the revenue effects of changing apportionment weighting. This

would also apply at the state level.

Edmiston’s (2002) model accounts both for the immediate corporate income tax

revenue impacts from changing factor weights on businesses and for corporate

income tax revenue impacts associated with businesses’ expected reallocation of

payroll, property, and sales to maximize profitability in response to apportionment

weighting changes. Acting independently, corporate income tax revenues decline

by 0.8 percent in the Great Lakes region when accounting for immediate revenue

impacts alone, but increase in total by 0.5 percent when the model accounts for

the expected business response to the apportionment change. Total changes for

other regions range from –10.9 percent for the Southwest to 6.6 percent for New

England. If all regions were to act simultaneously, corporate income tax revenue

changes would range from –11.7 percent in the Southwest to 5.8 percent for New

England, with a negative 0.3 percent change for the Great Lakes.

Edmiston’s (2002) model also predicts personal income tax revenue changes

based on changes in employment and impacts on each industry’s unincorporated

sector in each region expected to result from the implementation of single sales-

factor weighting. In Edmiston’s long-run model of regions acting independently,

only one region (the Southwest) experiences net revenue losses once personal

income tax revenue changes are taken into consideration. Personal income tax

revenues for the Great Lakes region are expected to increase by 1.18 percent

under this model (or by $213.5 million). Combined with the 0.5 percent increase

in corporate income tax revenue described above ($21.3 million), the Great Lakes

region would gain $237.3 million in corporate and personal income tax revenue in

the long term when independently implementing single sales-factor

apportionment. If all of the regions acted simultaneously, the Great Lakes region

would gain $53.9 in personal income tax revenue (0.3 percent increase) and lose

$11.7 million in corporate income tax revenue, for a combined gain of $31.8

million (6 percent). Under this scenario, half of the eight regions would

experience revenue losses.

Edmiston and Arze del Granado, 2006

As described in the literature review, Edmiston and Arze del Granado analyze

firm-level data for multistate corporations before and after the implementation

of a double-weighted sales factor in Georgia in 1995 using panel data from 1992

to 2002 from State of Georgia tax returns for multistate firms (Edmiston and

Arze del Granado, 2006). They find that the adoption of a double-weighted

sales factor in 1995 led to a 6.5 percent decrease in the amount of sales reported

by multistate corporations and 2.0 and 2.1 percent increases in payroll and

property, respectively. This corresponds to increases in payroll and property

54

of $600 million and $3.1 billion, respectively, and a decrease in sales

(gross receipts) of $10.4 billion.

The authors use these changes to estimate some revenue effects of the

implementation of a double-weighted sales factor in Georgia. Applying Georgia’s

personal income tax rate of 2.36 percent to estimated personal income (payroll)

gains, personal income tax revenues increase by $14.4 million for the year 1995.

Assuming that half of increased personal income was spent on sales-taxable

items, sales tax revenues would increase by $500,000. Assuming that 10 percent

of the reduction in sales (gross receipts) was taxable, lost sales tax revenues could

have amounted to $73.1 million. For 1995 to 2002, the authors calculate directly

from their returns data that reductions in corporate income tax revenue ranged

from $11.5 million (in 2002) to $52.2 million (in 1996). These values represent

the difference between the taxes that would have been collected under an equally

weighted formula versus the actual double-weighted sales formula. Taken

together, the revenue effects described here amount to a decline of $58.2 million

per year minus an additional $11.5 million to $52.2 million in decreased corporate

income tax revenues.

Property tax increases from increased property in the state are not calculated.

In addition, the authors are not able to control for additional effects including

declines in payroll or property for firms that are not multistate. They conclude that

the revenue effects on personal income tax, property tax, and sales tax could be

substantial compared to corporate income tax revenue changes resulting from

double-weighting the sales factor, but cannot make a conclusion about the overall

effects.

55

Appendix H: Summary of Fiscal Note Estimates

Many states require a fiscal note estimate of potential revenue effects and the

impact on the budget when a legislative change is considered. These fiscal note

estimates vary from state to state. When estimating state revenue impacts from a

change in the apportionment formula, some states produced detailed predictions,

while other states did not attempt to estimate the impact at all.

Most of the fiscal note estimates we found simply calculated the immediate effect

on the state’s corporate tax revenue by first calculating the lost revenue from

zeroing out the property and payroll weights in the apportionment formula and

then by estimating the increased corporate sales tax revenue due to the greater

sales weight in the apportionment formula. For example, the Pennsylvania

Department of Revenue estimated that the move to single sales-factor

apportionment would result in a revenue loss of $64 million for tax year 2000

(Hassell, 2004).

Most state fiscal note estimates we examined predicted a net loss in revenue; the

corporate sales tax revenue gained through single sales-factor apportionment

would not offset the revenue lost from excluding the property and payroll factors

from the apportionment formula. We did not find any official state estimates that

attempted to calculate the potential increase in personal income tax revenues that

might result from an increase in manufacturing jobs.

Additionally, several states included provisions in the legislation authorizing the

apportionment change requiring a future report on the revenue impact. In

Maryland, the Comptroller is required to provide this information to the Governor

on March 1 of every year. Thus far, all of the reports in Maryland have indicated a

net revenue loss under the new apportionment rules (Roose, 2012). The

calculations by the Comptroller of Maryland, however, do not include the

potential increase in individual income tax revenue resulting from the job creation

economists theorized would follow from the apportionment formula change.

The move to single sales-factor apportionment in Wisconsin was not approved

in that budget bill, but was approved two years later. In 2003, the Wisconsin

Department of Revenue produced a fiscal estimate of a phased-in move to single

sales-factor apportionment. In addition, the long-range fiscal estimate of the

impact of this change was as follows: a loss of $5 million in 2006, a loss of

$19 million in 2007, a loss of $36 million in 2008, and a loss of $45 million in

2009 when the move to single sales-factor would be complete (Walgren, 2003).

56

Appendix I: Sources for Macroeconomic Indicators

We gathered data on macroeconomic variables from a variety of sources. For state

personal income growth, state employment rates in total private and

manufacturing, and employee compensation in manufacturing, we used the

Bureau of Economic Analysis data tool at

http://www.bea.gov/regional/index.htm, under State Annual Personal Income &

Employment. The series used were SA1-3 from 1978 to 2010 for personal income

growth, SA25 from 1978 to 1989 and SA25N from 1990 to 2010 for private

employment and private manufacturing employment, and SA06 from 1978 to

1989 and SA06N from 1990-2010 for manufacturing payroll. We adjusted state

personal income growth for inflation using the Bureau of Labor Statistics

Consumer Price Index For All Urban Consumers (CUUR0000SA0), found at

http://www.bls.gov/news.release/cpi.t01.htm. Unemployment rates were taken

from the Bureau of Labor Statistics’ Current Population Survey, found at

http://www.bls.gov/cps/cpsaat01.pdf (column: Unemployed, percent of labor

force). Federal corporate tax top rates were taken from the Tax Policy Center of

the Urban Institute and Brooking Institution, table Historical Top Bracket and

Rate, found at

http://www.taxpolicycenter.org/taxfacts/displayafact.cfm?Docid=65&Topic2id=7

0.