€¦ · Web viewTotal Word Count is 8546 words. Across developed countries, a "race to the bottom" has driven down corporate taxes since the 1990s. Governments can improve their

NORMATIVE CORPORATE INCOME TAX

DYNAMIC RENT, CORPORATE POLITICAL EXPENDITURE, AND NORMATIVE

CORPORATE INCOME TAX

Mihoko SHIMAMOTO

Abstract

Across developed countries, a "race to the bottom" has driven down corporate taxes

since the 1990s. Governments can improve their finances by collectively implementing

normative tax rates. The normative corporate tax rate is the sum of monopoly and

transfer rents, which firms take from other economic agents and which do not include

Schumpeter rents and the corporate tax rate. We calculate the U.S. rents of 234 S&P 500

companies using the Cobb-Douglas function and dynamic profit optimal conditions.

Examining the relationship between rent and lobbying using a Granger test, we find that

rents both cause and are caused by lobbying.

JEL codes: H25 D72 P16 H26 D63

I. INTRODUCTION

Production and industrial organization theory have developed various indicators to

evaluate corporate activities. For example, production efficiency and total factor

This work was supported by JSPS KAKENHI Grant Number16K00685. Faculty of Social Sciences, Hosei University, Aihara-cho, Machida-shi, Tokyo [email protected] Total Word Count is 8546 words.

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productivity measure the efficiency and the productivity of a company's production

activities, and the degree of monopoly and monopsony are indicators for measuring

their price formation in the markets. Corporate activities have been evaluated on a

practical level using these indicators.

I.A. Corporate Political Expenditure and Rent

However, should current studies of corporate activities focus only on their market

activity? In recent years, corporate political (i.e., nonmarket) activities have increased as

have studies of this behavior. Drutman (2015) pointed that the average lobbying

expenditure of the 1066 companies in the U.S. that have been listed in the S&P 500 has

more than doubled between 1981 and 2004. The passage of the Lobbying Disclosure

Act (LDA) in 1995 has made data on total lobbying spending by corporations more

readily available; this spending increased from $1.13 billion in 1998 to $2.09 billion in

2010. According to de Figueiredo and Richter (2014), the expenditures of corporations

and trade associations (i.e., business lobbying groups) comprise the vast majority of

total lobbying expenditures. Wrona and Sinzig (2018) refer to various literatures and

state that the more regulated industries (e.g., industries such as the steel, oil,

telecommunications, and pharmaceutical industries) tend to engage in political activity

more frequently. Political behavior by corporations is not limited to the United States.

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Gorostidi-Martinez and Zhao (2017) find that corporations’ attempts to shape

government policy favorably are commonly employed across many countries. For

example, firms in Sweden, Japan, and Germany formally participate in the public policy

process; and in the U.S., Canada, and Mexico, companies informally compete with a

variety of other interest groups to affect public policy.

In addition, it has been argued that political activity by corporations generates high

returns. For example, Alexander et al. (2009) examined the costs and returns of

lobbying in support of the American Jobs Creation Act. The law allowed U.S.

corporations with multinational operations a one-time opportunity to deduct 85 percent

of dividends received during a single year from a foreign subsidiary, such that only 15

percent of this repatriated income would be taxed. As a result, lobbying activity had a

22,000 percent rate of return. In another example, Etzioni (2018) noted that Whirlpool

spent $1.8 million over the course of two years on lobbyists and secured a renewal of an

energy tax credit for manufacturing high-efficiency appliances, which increased its

profits by $120 million, a return of approximately 6700% on the initial lobbying

expenses.

These relationships have also been tested using quantitative analysis. Kerr et al.

(2014) analyzed the relationship between lobbying and return using panel data from

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3,260 firms headquartered in the U.S. Among relatively large firms, the average firm

that engaged in lobbying sold roughly four times more than firms that did not lobby;

also, firms that did lobby were only slightly more likely to engage in research and

development than those that did not.

Most of this corporate unproductive rent is classified as transfer rent or monopolistic

rent in the public choice literature. These resources should have been distributed to

other economic agents but were diverted to the firm as a result of barriers to entry or

political actions, which reduces economic efficiency. Moreover, as lobbying expenditure

is regarded as unproductive itself, Bhagwati (1982) has labeled it Directly Unproductive

Profit-seeking (DUP) activity.

I.B. Taxation for Rents

For this reason, governments should prohibit these unproductive and unfair activities.

However, this is difficult to accomplish because corporate political expenditures profit

the politicians who have the authority to legislate political rules. Alternatively, they

could charge corporations for the unproductive rent. This would be also difficult

because the government would fear that companies would move overseas, reducing the

number of jobs and the corporate tax base. In fact, the average corporate tax rate in

OECD member countries has dropped considerably since the 1990s, indicating that a

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“race to the bottom” is occurring.

However, reducing tax rates has not necessarily been effective in encouraging

companies to keep their profits in their home countries. Cobham and Jansky (2017) and

Clausing (2016) provide evidence that profit-shifting has grown significantly even as

effective tax rates have fallen sharply.

In recent years, increasing tax evasion has eroded the financial base for public

services across countries. Alstadsæter et al. (2018) cite data collected by the Bank of

International Settlements that shows that wealth equivalent to about 10 percent of world

GDP is held in tax havens globally. While Scandinavian countries hold wealth

equivalent to only a few percent of GDP offshore, this figure rises to about 15% in

Continental Europe and to as much as 60% in Russia, Gulf countries, and a number of

Latin American countries. Crivelli et al. (2016) and Cobham and Jansky (2018)

estimated the global tax loss as a result to be between $650 billion and $5 trillion

annually.

In recent years, international organizations have finally started taking action. Zhu

(2016) reported that the “G20 + OECD” regime has taken the initiative since 2012 to

fight against tax evasion. The basic role of the G20 is to set the agenda and provide the

political consensus guiding and steering the whole governance process, while the OECD

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provides technical support and facilitates the implementation of the consensus.

Weinzierl (2018) argued that achieving substantial reform of the international tax

system would be made easier if policy-makers could point to an additional,

complementary, normative logic.

Although measures to prevent tax evasion have been studied internationally as above,

they have not stopped international competition for legal corporate tax cuts. The

solution for this issue is to calculate a normative corporate tax rate for each country,

especially for large corporations. All countries should then work together to impose

their prescriptive tax rates on corporations.

I.C. Normative Corporate Tax

Orthodox taxation theory in public economics has developed various arguments

against imposing corporate taxes. Stiglitz and Rosengard (2015: 713) define corporate

income tax as follows: “In a perfectly competitive economy, there would presumably be

no pure profits, so the tax is just a tax on the return to capital.” They also point out the

following essential problem: “Corporate earnings that are transferred to individuals in

the form of dividends are taxed twice – once in the form of the corporate tax and again

by way of the individual income tax” (729). Generally, neoclassical taxation theory

assumes an efficient state under perfect competition is assumed before taxation in order

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to identify the distortions caused by taxes.

However, with global companies taking over economic activity, it is necessary to

assume an oligopolistic market structure in the product market and the factor market in

the first place. If rents are created as a result, they should be attributed to other

economic entities and should be taxed and redistributed appropriately. It is often argued

that even in an oligopolistic market, competitive prices are formed in a contestable

market; however, this proposition should be verified empirically to determine whether

rent is being generated. In addition to the oligopoly in the product market and the factor

market, companies should be considered to have a monopsony for corporate taxes. In

principle, corporations conduct business activities utilizing human resources that have

been locally educated and trained, public safety resources, and various infrastructure in

the country where they are located. Corporate tax should be considered as payment for

public services, which is one of the production factors of a company. However, the

threat of capital flight may cause governments to receive too little in corporate taxes as

payments for public services. Based on these considerations, this paper defines the

normative corporate tax rate as the corporate tax rate that includes monopolistic and

transfer rents in the product, factor, and public service markets.

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I.D. The Issues Taken up in This Article

This paper will examine the following three issues. First, we construct a method to

divide the profits of companies into proper rewards obtained through market

competition and rents obtained through political activities and monopolistic means.

Second, we calculate rents for the past 33 years for 234 companies from the S&P 500

that have lobbying data in LDA reports1and estimate a normative corporate tax rate for

large US companies. Third, we analyze the causal relationships between rent and the

amount of lobbying reported in the LDA report and R&D investment.

II. DEVELOPMENT OF INDICATORS FOR MEASURING COMPANY

PERFORMANCE AND DATA LIMITATIONS

We assess corporate activities using the process discussed above. In order to develop

indicators that can be used widely in economic analysis and administrative procedures,

it must be easy to obtain data. We discuss this issue in this section.

II.A. Production Efficiency

First, we select a purely technical indicator of the efficiency of producers. A number

of prior studies address the measurement of this concept. Farrell (1957) indicated that

production efficiency could be measured as the distance between an observation and an

1The US Congress passed the Lobbying Disclosure Act in 1995, which requires registration and disclosure of certain lobbying activities. The LDA database is available on the website of US Senate: https://www.senate.gov/legislative/Public_Disclosure/LDA_reports.htm.

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estimated ideal referred to as an efficient frontier.2 Subsequent empirical studies of

productive efficiency have been divided into two streams: parametric and nonparametric

approaches. In parametric approaches, the production frontier is estimated using a

functional form such as a Cobb-Douglas function (see Aigner and Chu 1968). The use

of stochastic methods, such as in the survey of Bauer (1990) and panel data analysis by

Schmidt and Sickles (1984), has improved the accuracy of the estimations. The

nonparametric approach might be said be to have progressed more. Varian (1984)

consolidated the theoretical background by using revealed preference theory to show the

relationship between actual data and the production frontier behind it. Charnes, Cooper,

and Rhodes (1978) reduced the maximization of a ratio of weighted outputs to weighted

inputs (an index of a production efficiency) to a linear programming form. This

watershed discovery, which has come to be called Data Envelop Analysis (DEA), has

become the workhorse of efficiency studies today, and it is widely used for practical

analysis in the field of operational research as well as economics.

Data on the quantity of inputs and output are required to calculate this indicator.

However, in existing empirical analyses, only one industry is targeted, and the region

examined limits the quantity of data that can be obtained, as can be confirmed from the

2 Førsund, Lovell, and Schmidt (1980) provide an instructive review and outline of the initial research in this area.

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above empirical analyses and literature review. Although output and labor input data are

relatively available, data for other production factors are extremely difficult to obtain.

With regard to DEA, some analyses use accounting information on revenue and

expenses of as proxy variables for output and the input of production factors. However,

if the markets are not competitive, such an approach would not be theoretically

supported.

II.B. The Degree of Monopoly or Monopsony

The indicator that we imagine first as measuring the market performance of

corporations would be profit as defined in economics or accounting. However, markets

do not always function properly. Indicators that measure whether or not corporations are

operating under market competition are also necessary. The Lerner index (Lerner 1934)

captures the degree of monopoly in a supplier's market generally by dividing the

difference between the price of the product and its marginal cost by the price. In order to

obtain this value, calculation of the price elasticity of demand is necessary. A similar

index can be used to measure monopsony, and the calculation of the price elasticity of

supply is necessary to in order to obtain it. The empirical studies of Appelbaum (1982),

Schroeter (1988), and Azzam and Pagoulatos (1990) utilize standard models to obtain

these parameters by solving an equation system. Shimamoto (2018) reviews these

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studies in detail.

There are, however, limitations on the availability of data to calculate these

parameters. The estimation requires quantity and price data for both products and

production inputs; although most empirical studies employ cost or profit functions at the

firm level or even the industry level, it is difficult to obtain data for labor and energy

inputs, which are used in many different industries and production processes.

II.C. Rent

The indicators of corporate activity in orthodox economics have been related to

corporate production activities and market economic activities as described above. But

public choice has focused on the profits of corporate nonmarket behavior, that is,

political action. Rent is defined as additional income that is higher than that earned from

economic efficiency. The actions of people who lobby governments and bureaucrats to

achieve their advantageous business environment and secure rents are called rent-

seeking.

Tullock (1967) argued that both the dead weight loss caused by monopolies and tariffs

and the rents accruing to firms from these measures are social costs. Rents obtained

through monopoly price formation due to entry barriers are called monopoly rents, and

rents generated by tariffs, taxation, or subsidies set by government policies are called

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transfer rents. Krueger (1974) argued that rent-seeking activities are often competitive,

and rewards from rent-seeking activities can be expended on non-productive activities

for further rent acquisition.

However, since the 1990s, public choice theory seems to have shifted its focus to

game-theoretic clarification of political phenomena rather than exploring methods of

rent quantification. In order to analyze this relationship empirically, it is important to

measure rent with a standardized method that can be compared with other analyzes.

Nevertheless, as Shimamoto (2018) noted, rents have only been measured at the macro

and industrial levels using methods that are unsystematic, and a comprehensive and

versatile method for measuring rents has not been established. One of the objectives of

this paper is to establish a standard method of rent measurement using only value data

in financial statements.

III. CONCEPT OF DYNAMIC RENT

As noted above, the profits of a company can be divided into normal profits

generated by production efficiency and rents. Because monopoly rents or transfer rents

are surplus taken away from other economic entities and are social costs, according to

Tullock’s (1967) logic, they should be taxed and an original distribution should be

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restored.

Shimamoto (2018) explained in detail how to calculate dynamic monopoly rents for

each corporation using their accounting data and analyzed them for Japanese

companies. This is a method of calculating the time series of excess profits on the

assumption that companies are under monopolistic price formation. If, under the market

price given by the shadow price (i.e., the marginal profit at the optimal production

volume of the monopoly model = marginal cost at the same volume) in the monopoly

price formation model rather than the actual selling price (monopoly price), the

producer maximizes the dynamic profit, as in a competitive market, the actual time

series of input-output would be the result of long-term profit maximization in this model

in theory. We calculated dynamic monopoly rents as the difference between the actual

price and the shadow price multiplied by production amount.

In this paper, we consider this method to calculate both the monopoly rent and transfer

rent of a company, with special attention to companies’ bargaining power over public

services. For this purpose, we propose the following behavioral hypothesis. The

government provides various public services to companies and receives corporate

income tax from the companies in exchange. Multinational companies can move their

bases to foreign countries at any time, putting pressure on the government to reduce

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corporate taxes. This can be regarded as analogous to the markets for public services are

under monopsony. Governments are forced to discount the price of public services for

companies, so they have to reduce the amount of the other public services (such as

social security and education) provided to other actors in the country. Therefore, the part

of the corporate tax that the corporation has deducted can be considered as monopsony

rents in the markets for public services or as rents transferred from other members of

society. Any subsidy received by the company is included in the calculation of product

market rents because they are accounted for in revenue but are transfer rents in nature.

IV. MODEL

We apply the mechanism discussed in the previous section to the model constructed

in Shimamoto (2018) to calculate the company's rent.

IV.A. Short-term Equilibrium Conditions

We assume monopolistic and monopsonistic product markets. Producers maximize

short-term profits and also have a long-term equilibrium.

Assume a producer uses a general Cobb–Douglas production technology to produce

one product using four production factors. This function is denoted by

y=α5 v1α 1 v2

α 2 v3α3 K❑

α 4 (1)

where y is the output quantity, v1, v2 and v3 are the quantities of variable inputs, and K is

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the quantity of the fixed input, namely capital. In the short-term equilibrium, K is a

given value, and α 1+α2+α 3<1, which ensures that the marginal cost function is convex.

As the producer simultaneously faces both a monopolistic product market and

monopsonistic factor markets, the short-run profit maximization problem is given by

max πm=p ( y ) ∙ y−w1 ( v1 ) ∙ v1−w2 (v2 ) ∙ v2−w3 (v3 ) ∙ v3−rK

s.t. (1) (2)

The optimal conditions come from differentiating the Lagrange equation by the

variable factors v1 , v2 ,¿v3. These are given as

{ p' ( y ) ∙ y+ p ( y ) }∙ f v i

' ={wi' (v i ) ∙ v i+w i(v i)}, i =1, 2, 3. (3)

{ p' ( y ) ∙ y+ p ( y ) } can be expressed as (1+γ)∙p(y), where now γis assumed to have a

constant inverse demand elasticity and −1<γ ≤0. pm(y) can be defined by

pm(y)= { p' ( y ) ∙ y+ p ( y ) }=(1+γ)∙p(y) (4)

{wi' (v i ) ∙ v i+w i(v i)} can be expressed as (1+σ i¿ ∙wi, where now σ i is assumed to have a

constant inverse factor supply elasticity and σ i≥ 0. In the same way, wℑ(v i) can be

defined by

wℑ(v i) = {wi' (v i ) ∙ v i+w i(v i)}=(1+σ i)∙ wi(v i), i =1, 2, 3 (5)

The short-run optimization conditions in the monopoly and monopsony markets can be

expressed by arranging eqs. (1), (3), and (4) as follows.

y=α5 ∙( w1 m1−α2−α 3 ∙ w2m

α 2 ∙w 3mα 3

α 5 ∙ α 1❑1−α2−α 3 ∙α 2α2 ∙ α 3α 3∙ pm Kα 4 )

α 1α 1+α2+α3−1 ∙( w1m

α 1 ∙w2m1−α1−α 3 ∙w3 m

α3

α 5 ∙ α 1❑1 ∙ α 21−α 1−α3 ∙ α 3α 3∙ pm Kα 4 )

α 2α 1+α2+α3−1 ∙( w1 m

α 1 ∙ w2mα2 ∙ w3m

1−α 1−α 2

α 5 ∙ α 1❑1 ∙ α 2α 2 ∙ α 31−α1−α 2∙ pm Kα 4 )

α 3α 1+α 2+α3−1 ∙Kα 4

(6)

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vi=α 5∙( wℑ1−αj−αk ∙ w jm

αj ∙ wkmαk

α 5 ∙ αi❑1−αj−αk ∙ αjαj ∙ αkαk ∙ pm Kα 4 )

1α1+α 2+α 3−1, i, j, k=1,2,3 (i≠ j ≠ k ¿ (7)

It is important to note that these equations are not normal supply and factor demand

functions: pm(y) and wℑ ( v i ) are endogenous variables and differ from the exogenous

prices of a product and factors in the case of competitive markets. This interdependence

makes it difficult to find an optimal point and to formulate an empirical model.

To facilitate these calculations, we can utilize the relation between imperfect

competition models and perfect competition models. Now y t∗¿¿ indicates the short-term

optimum production level, and pt∗¿¿ is the equilibrium price in this imperfect

competition model, as described in Figure Ⅰ. A superscript t indicates a value in the t

period.

[Figure Ⅰ here]

Under the same production technology and the same given value of K t, pt∗¿(1+γ t)¿ is

defined as a given market price with constant pt∗¿¿and γt in a perfect competition. Then,

they bring the same production level y t∗¿¿as the short-term competitive equilibrium

value. γt is the degree of monopoly in this imperfect competition model at the optimal

point.

We can consider the production factor markets in the same manner. In this model, the

producer is a monopsonist in the factor markets. v1t∗¿ ,v2

t∗¿ ,v3t∗¿¿ ¿¿are the short-term optimal

factor quantities, and w1t∗¿ ,w2

t∗¿,w3t ∗¿¿¿¿ are the equilibrium prices in this monopsonic

equilibrium, as described in Figure Ⅱ. Under the same production technology and

the same given value of K t, w1t∗¿ (1+σ1

t ) , w2t∗¿ ( 1+σ2

t ) ,w3

t∗¿ (1+ σ3t ) ¿

¿ ¿ are defined as the market prices with

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constant w1t∗¿ ,w2

t∗¿,w3

t ∗¿ ,σ1t ,σ2

t ,∧σ3t ¿¿ ¿, which bring the short- term equilibrium factor quantities

v1t∗¿ ,v2

t∗¿ ,v3t∗¿¿ ¿¿in a perfect competition. Then, we can get mark-up rates of σ 1

t , σ2t , σ3

t , which

satisfy the condition where w1t∗¿ v1

t∗¿, w2

t∗¿ v 2t∗¿, w3

t∗¿ v3t∗¿¿¿

¿¿¿ ¿are the costs for each realized by profit

maximization in the imperfect competition in the t period.

[Figure Ⅱ here]

Therefore in consideration of the long-term equilibrium conditions, the supply

equation (6) and factor demand equation (7) can be regarded as the supply function and

factor demand function, respectively, under the given Kt, pt∗¿(1+γ t)∧w it∗¿ (1+σi

t ) ¿¿, which satisfy

the short-term equilibrium conditions in the perfect competitive model.

IV.B. Long-term Equilibrium Conditions

Long-term equilibrium conditions are derived by maximizing the time-series total of

the discounted present value of profits defined by the short-term pseudo-competitive

equilibrium model minus capital costs. By deriving these long-term pseudo-competitive

profit maximization conditions, we can find the optimal γt , σ1t , σ2

t ,∧σ3t that make the

time series data of Kt, pt∗¿ y t∗¿ ,w1

t∗¿ v1

t∗¿ ,w2

t∗¿ v2t∗¿ ,¿w3

t∗¿ v 3t∗¿ ¿¿

¿¿¿¿¿ ¿ for each of the four periods the optimal dynamic

solution. These past data can be regarded as results that have satisfied both the short-

term and long-term equilibrium conditions. Then we can determine the rent ratios,

γt∧σ it, which are the proportions of rent in the product price and factor prices,

respectively.

Now let us formulate the long-term pseudo-competitive profit maximization

conditions. It is maximized for discrete time periods from 1 to T. The pseudo-

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competitive profit function in period t is defined as follows.

π t=p t∗¿ ¿¿)∙ y t ¿

−w1t∗¿ (1+σ1

t ) ∙ v1t ¿ ¿

−w2t∗¿ (1+σ2

t ) ∙ v2t ¿ ¿

−w3t∗¿ (1+σ3

t ) ∙ v3t ¿ ¿

−Qt ∙ I t (K t−1 , K t , δ t)❑

(8)

wherey t ( ∙ )∧v it ( ∙ )are defined by Equations 6 and 7, and γt , σ 1

t , σ2t ,¿σ3

t are assumed to

change over time. Investment in period t, I t ( K t−1 , K t , δt ) is defined as follows.

I t(K t−1 , K t , δ t)❑= K t−(1−δ t)∙ K t−1 (9)

where δ t is the depreciation rate in period t, and Qt is the exogenous unit price of

investment in period t.

The long-term equilibrium condition arises from maximizing the sum of the

discounted present value of π t from period 1 to T based onK t, as follows.

maxK t

Π=π1+∑t=2

T

∏s=2

t 1(1+rs )

π t (10)

Thus, the necessary condition for optimization, is given as follows,

∂ Π∂ K t =∏

s=2

t 1(1+r s )

∙¿¿

−w3

t∗¿ (1+σ3t ) ∙ ∂v3

t

∂ K t −Qt ∙∂ I t

∂ K t ¿+∏

s=2

t+1 1(1+rs )

[−Qt+1∙ ∂ I t+1

∂ K t ]=0 (11)

By modifying the partial differentiation using logarithmic differentiation,3 this condition

3 See Shimamoto (2018) for the detailed derivation process. Generally, the following equation holds for partial differentiation.

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can be finally arranged into the following simple equation,

(1+γt )∙ p t yt

K t −(1+σ1t ) ∙ w1

t v1t

K t −(1+σ2t ) ∙ w2

t v2t

K t −(1+σ3t ) ∙ w3

t v3t

K t

−α 1+α2+α 3−1−α 4

∙[Qt− 1(1+r t+1 )

∙ Qt+ 1∙ (1−δ t+1 )]=0

(12)4

V. APPLICATION TO RENT CALCULATION FROM CORPORATE ACCOUNTING

DATA

As mentioned in Section 3, the profit of a company can be separated into the profit

gained by market competition and rent. In following sections, we will calculate the rent

(monopolistic rent + transfer rent) by applying the model in the previous section using

corporate financial data.

Each variable of the model in Section 4 is operationalized using the following financial

data. The output value (py) is the total sales, and the four production factors

∂ B∂ A

= ∂ B

∂ ln B ∙ ∂ ln B

∂ ln A ∙ ∂ ln A

∂ A

Referring to the short-term optimizing conditions, eqs. (6) and (7):∂ ln y∂ ln K

=−α1 α 4

α1+α 2+α3−1+

−α 2α 4

α 1+α2+α 3−1+

−α 3 α 4

α1+α 2+α3−1+α 4=

−α 4

α1+α2+α 3−1

∂ ln v i

∂ ln K=

−α4

α1+α 2+α3−1 , i = 1, 2, 3

Using ∂ ln x /∂ x=1/ x, ∂ y∂ K =

−α 4

α1+α2+α 3−1 ∙ y

K

∂ v i

∂ K =

−α 4

α1+α2+α 3−1 ∙

v i

K, i = 1, 2, 3

4 In this empirical study, the data are collected from the financial statements of each company. The depreciation amount for a year is included as an item among the costs. Therefore, we setδ t+1 as zero in order to avoid double counting.

19


w1t v1

t ,w2t v2

t , w3t v3

t , Kare operating expenses, non-operating expenses plus extraordinary

losses, corporate taxes, and total assets.

Among the four parameters γt , σ1t , σ2

t ,∧σ3t of the model, one should be removed when

R(= α1+α2+α 3−1

−α 4) is regarded as fourth variable. At this time, σ 2 will be set to 0

because of the nature of the production factor; γ represents the degree of monopoly in

the product market; and σ 1 represents the degree of monopsony for production factors

(labor, raw materials, etc.). As described in Section 3, σ 3 represents the degree of

monopsony for public services, assuming that taxes are discounted due to monopsony in

markets for public services.

When we regard the variable R relating to scale as the fourth parameter,γ , σ1 , σ3 and

R can be obtained by quadratic programming using data for at least four years, as

calculated in Shimamoto (2018). However, when the parameters are determined in this

way, the time series values of R fluctuate drastically from year to year, which makes the

sequential values of rent unstable. Therefore, we define a scale variable, S, as follows:

S=α1 +α 2+α3+α 4 (13)

The calculation is performed by setting S to 1 (i.e., R=1) for the time being. Sensitivity

analysis will be performed with S = 1.2, which is the upper bound of induced returns to

scale in the U.S. in recent years according to Boussemart et al. (2018).

VI. CAPITAL REWARD AND RENT DISTRIBUTION

In the case of constant returns to scale (i.e., S=1), total rent is regarded as −γt pt yt+

σ 1t w1

t v1t +σ2

t w2t v2

t +σ3t w3

t v3t . However, if the yield shows increasing returns to scale, it is

20


necessary to devise profit and rent distribution. In order to explain this point, we must

define the distribution of capital reward and rent specifically.

Transforming Equation 12,

p t yt

K t −w1

t v1t

K t −w2

t v2t

K t −w3

t v3t

K t

¿ R ∙[Qt− 1(1+r t+1 )

∙Qt+1∙ (1−δt+1 )]+−γt pt y t

K t +σ1

t w1t v1

t

K t +σ3

t w3t v3

t

K t (14)

In the accounting data, the capital depletion δ is recorded as an item in w 1t v1

t as

depreciation expense, so δ = 0. Also, regardingI t as the investment amount, both Qt and

Qt+1 are equal to 1. Taking these things into account, we multiply both sides of Equation

14 by K.

pt y t−w1t v1

t −w2t v2

t −w3t v3

t

¿ R ∙ rt+1

1+r t+1 ∙ K+¿+σ 1t w1

t v1t +σ2

t w2t v2

t +σ3t w3

t v3t ¿ (15)

Since rt+1 is the interest rate, it can be generally regarded as (discounted present value

of) the marginal efficiency of capital. When R = 1, R ∙ r t+1

1+rt+1 ∙ K means a competitive

and normative capital reward. Thus, this equation can be interpreted as,

Net Income = Capital Reward + Rent (16)

In other words, in the case of constant returns to scale, rewards above the marginal

efficiency of capital are rents.

What about the case of increasing returns on scale? When S>1 ,R<1 and R

converges to 0 as S increases. Regarding R ∙ r t+1

1+rt+1 ∙ K as capital reward according to

Equation 15 in this case, as the scale harvest increases, the proportion of capital rewards

21


among net income decreases and the proportion of rents increases.

[Figure Ⅲ here]

However as shown in Figure Ⅲ, as the yield increases, MC decreases (from MC1 to

MC2), and the ratio of rent to revenue or profit increases, covering the grid area rather

than just the grey area. Therefore, in the case of increasing returns to scale, the

distribution of net income needs to be revised. It may be appropriate to consider the

capital reward under the constant return to scale multiplied by the scale factor S as the

capital share. In this case, the capital reward and the rent distribution can be expressed

as the first and second terms on the right side of the following equation.

Net Income =¿ S ∙ r t+1

1+rt+1 ∙ K

+{(−γ t p t y t+σ 1t w1

t v1t +σ2

t w2t v2

t +σ3t w3

t v3t )++R ∙ rt+1

1+rt+1 ∙ K−S ∙ rt+1

1+rt+1 ∙ K } (17)

In this case, all of the scale effects are attributed to the capital, so this distribution

method can be regarded as the upper limit of the capital reward.

Some people may be dissatisfied with the absence of the Schumpeter rent gained by

operations that exceed those of other companies, including R&D investments. They

would suggest counting Schumpeter rent separately. In this study, as described in the

next chapter, the rent could not be divided into γ and σ i segments. But, if it could be

split, the Schumpeter rent would be considered part of γ segment.

VII. DATA AND CALCULATION

The system of equations was solved using MATLAB software.5 Using matrix

5 MATLAB ver. R2016a. These equations systems were solved using the “lsqlin” command in MATLAB, which is a solution for quadratic programming (i.e., constrained linear least-squares problems).

22


expressions, simultaneous equations that solve for γ, σ 1, ¿σ 3 in Equation 12 can be

specified and then estimated with three years of financial data as follows:

[ R Qt+V 2t

RQ t+1+V 2t+1

RQ t+2+V 1t+2]=[ Y t −V 1

t −V 3t

Y t+1 −V 1t+1 −V 3

t+1

Y t+2 −V 1t+2 −V 3

t+2] [ (1+γt )(1+σ1

t )(1+σ3

t ) ] (18)

where Qt ≡Qt− 1

1+rt+1 ∙ Qt+ 1, Y t ≡ pt yt

K t , V it ≡

wit v i

t

K t . γt, and σ it (i=1,3) are the mark-up

rates for output and inputs in periods t to t+2. If the coefficient matrix has an inverse

matrix, equation 19 holds.

[ ( 1+γ t )(1+σ 1

t )(1+σ 3

t ) ]=[ R Qt+ V 2t

RQ t+1+V 2t+1

RQ t+2+V 1t+2][ Y t −V 1

t −V 3t

Y t+1 −V 1t+1 −V 3

t+1

Y t+2 −V 1t+2 −V 3

t+2]−1

(19)

By calculation of these equations, we obtain γ, σ 1, and σ 3. When the actual

calculations were made, we found a considerable difference between the value on the

left side and the value on the right side of Equation 18. Therefore, the parameters were

modified by an algorithm for absorbing the difference between the values on both sides

in the order of γ , σ1 ,¿ σ3. Therefore, the value of the resulting total rent (the second term

of Equation 15) is appropriate, but the respective values of the parameters γ , σ1 ,¿ σ3 are

considerably distorted.

Using financial data covering 36 years, it is possible to set t from the first year to the

34th year. For each t, the solutions ofγ, σ 1 and σ 3 are given; however, the equation for

period t contains the discount rate of the t+1 period, rt+1, and so the maximum length of

the time series of solutions is 33.

We collected the following data. The accounting data of 234 corporations registered

23


in the US S&P 500 in 2018 were downloaded from Mergent online; these were the

corporations for which there was lobbying data. The lobbying data was taken from LDA

Reports available on the US Senate website. These companies were classified into 28

industries according to their Standard Industrial Classification (SIC) codes.6

Data on discount rates are U.S. interest rates for each year, as found in the

International Financial Statistics of IMF. The unit price of investment (Q) was set as one

in every year.

VIII. RESULTS

VIII.A. Characteristics of Rent in Industrial Sectors and in Time Series

First, we compare industries based on the ratio of rents to corporate net income. Table I

shows that the time series average rent rate for each industry in cases of scale variable S

was set at 1 and 1.2. Comparing the cases, the rent rate is slightly lower when assuming

economies of scale (i.e., S=1.2). The relative rankings among industries show that the

top industries are almost the same, although their ranks changed slightly.7

The industries with the highest rent rates are Business Services, Measuring Analyzing

and Controlling Instruments, Chemicals, Health Services Products, and Petroleum

Refining.

This is almost consistent with the results of previous studies introduced at the

6 While the numbering followed SIC codes wherever possible, several sectors that had few firms were consolidated. For this reason, there are numbers up to 33, but there are only 28 industries.7 For the results in Tables I and , Equation 18 was solved for the case of S = 1, and the rent Ⅱcalculated from the value of the parameter after error correction was used. However, in the case of S = 1.2, it is necessary to adjust the capital reward by the scale factor, so the rent is the value obtained by subtracting the capital reward from net profit. As a result, it seems that there is a slight difference in the ranking between industries when S = 1 and when S = 1.2.

24


beginning. Since the size of the rent for one company is not known from the rent rate,

the average rent amount for a single corporation in the case of S = 1 is also shown in

Table I. The largest rent amounts are for Petroleum Refining, Communications, Mining,

and Chemicals sectors.

[Table I here]

How would the corporate tax rate change if the rent calculated in this study were

added to the corporate tax? Table shows a comparison between the ratio of corporateⅡ

income tax to profit before tax and the ratio of corporate income tax plus rent to profit

before tax. From this result, even considering scale economy (i.e., S=1.2), it can be seen

that the corporate tax rate including rent is around 20% higher than the current corporate

tax rate.

[Table here]Ⅱ

Figure shows the time series of rent and corporate income taxes derived from theⅣ

accounting data. We find in the last 30 years that the corporate tax rate has fallen

gradually while the rent rate has risen monotonically. A rough analysis of this trend

shows that the rent rate rose at roughly the same pace that the corporate tax rate fell

until around 2000. Since then, the rent rate has increased more rapidly than the tax rate

has declined.

[Figure here]Ⅳ

25


In the next step, we estimate the normative tax rate. While one possibility would be to

consider the normative tax rate to be the sum of the tax rate and the rent rate, this

overstates the optimal rate as Schumpeter rents would not be deducted. Schumpeter rent

should be considered as a part of the γ segment of rent. However, as explained in

Section XII, the rent could not be extracted in this analysis. We attempt to estimate the

normative tax rate under the assumption that Schumpeter rent results from R&D

expenditures. As shown in Figure , the ratio of R&D expenditure to total costs,Ⅴ

including taxes, increased from 2% to 6.8% between 1982 and 2014. Assuming that the

entire rent rate in 1982, that is, 3.0% of profit before tax in S=1.2, resulted from R&D

expenditures, we could set the 2014 Schumpeter rent rate at 3.4 times the rent rate, or

10.2%. In 2013, the Schumpeter rent rate was 3.3 times the rent rate, or 9.9%. The sum

of tax rate and rent rate is 56.7% in 2013 and 77.4% in 2014. Therefore, the normative

tax rate would be 46.8% in 2013 and 67.2% in 2014. Even if Schumpeter rent were

deducted in a similar manner from annual rents, the normative tax rate in recent years

would be at least around 40%.

[Figure here]Ⅴ

VIII.B. Relationship of Rent with Political Action by Panel Data Analysis

26


Do the political actions of corporations generate high returns? To assess this, panel

data analysis was performed using rent as the explained variable and lobbying amount

as the explanatory variable. Since rent should be heavily influenced by the size of the

company, we added annual sales to the explanatory variables.

First, we estimated a regression without lagging lobbying, and we used a Hausman test

to determine whether fixed or random effects models were appropriate. Then, the

coefficients of determination were compared for models of the lobbying data with

various time lags. When moving the lag of the lobbying data forward and backward,

peak values of the coefficient of determination appeared in each direction. The models

in Table use the lag structures that generated the two peak values. Based on theⅢ

results, when S = 1, a $1 increase in lobbying expenditure would increase the rent by

about $52; and at S = 1.2, it would increase the rent by about $43. However, we note

that this result is based on a correlation and not a causal relationship between lobbying

and rent, an issue we address below.

[Table here]Ⅲ

Another concern is that each of these variables is likely to have an autoregressive

process. Therefore, we also tried Population-Averaged estimation (PA) for setting the

autoregressive process for the error term of each panel. However, it is difficult to find

27


significant results, and only the successful case is included in Table .Ⅲ

A more difficult econometric problem occurs when both of the independent variable

and the dependent variable are unit root processes. If this is the case, observed

relationships between the variables may be spurious. Therefore, a unit root test was

performed on rent and lobbying data. In recent years, unit root tests for panel datasets

has been developed; we used a method developed by Harris and Tzavalis (1999) (HT).8

Since missing values are not allowed, the tests were conducted using a balanced dataset

of 65 companies during the years from 1999 to 2014. For both variables, the null

hypothesis was rejected in the test. However, since the null hypothesis states that all

panels contain unit root in HT, this result still allows the possibility that some of the

panels have unit root processes.

Next, using this balanced data, the causal relationship between the rent and the

lobbying expenditure described above was tested. Although Granger causality testing

(Granger 1969) has long been used for single time series, methods for panel data have

developed more recently. We performed a panel Granger test developed by Dumitrescu

and Hurlin (2012).9 The lag period was set to one and we used a bootstrap procedure. As

a result, both of the null hypotheses that rent was not the cause of lobbying expenditure

8 The xtunitroot package in Stata version 15 was used for the test.9 It was calculated using a library called xtgcause of stata ver15. See Lopez and Weber (2017) for a specific explanation.

28


and that lobbying expenditure was not the cause of rent were rejected. That is, it was

concluded that rent was the cause of lobbying expenditure for at least one panel and that

lobbying expenditure was the cause of rent for at least one panel.

From the above results, it can be said that while some companies have obtained high

rents by lobbying, other companies have invested in lobbying because they have

obtained high rents in the past.

VIII.C. Which Generates Rents: R&D or Lobbying?

It may be argued that rent is the outcome of investment in corporate R&D rather than

the outcome of looting from other economic agents. We tried to analyze this issue with

panel data for 73 companies whose accounting data include R&D expenditures. We

considered adding R&D expenditures to the explanatory variables, but unfortunately the

correlation coefficient between R&D and lobbying was 0.7214, creating a

multicollinearity problem. We performed separate panel data analyses using lobbying

expenditure and sales and then R&D expenditure and sales as explanatory variables.

Table shows the results. The coefficients of determination for the two models areⅣ

very similar, and both models have almost the same explanatory power. However, in

this context it is not possible to separate the relationships between these two variables

29


and rent.

[Table here]Ⅳ

IX. CONCLUSIONS AND REMARKS

As mentioned in Section I.D., this article makes three contributions. The conclusions

and the remarks for each are as follows.

The first contribution was the construction of a method to separate corporate profits

as appropriate compensation from market competition from the rents derived from

political expenditure, monopoly, and monopsony. Applying the method of Shimamoto

(2018), we solved the pseudo-competitive dynamic profit maximization model and

found the optimal conditions when the markets of products, production factors, and

public services were all monopolistic. The results showed that profits (net income after

tax in accounting) can be divided into capital compensation and rents (Equation 15). In

the case of increasing rate of return, adjustments were made with the scale factor.

The second contribution was the estimation of the normative corporate tax rate for large

US companies using the S&P 500 accounting data. Orthodox taxation theory has

basically assumed competitive markets and considered corporate tax as a tax on capital

rewards. Therefore, sometimes it has been argued that corporate tax is a double taxation.

However, this paper considered corporate tax to be a payment for public services and

30


calculated the corporate tax rate including the monopsony rent associated with public

service. Figure shows the time series of this rent.Ⅳ However, Schumpeter rent was

deducted assuming that all such rents in 1982 came from R&D and taking into account

the recent increase in R&D expenditure as a percentage of total costs. As a result, the

normative tax rate in recent years has been at least around 40%.

If accurate estimates ofγ, σ 1 and σ 3 could be made, a clearer conclusion on this point

would have been obtained. This is because Schumpeter rent occurs in the product

market; it is, a part of γ. However, after calculating γ, σ 1 and σ 3 by the quadratic

programming method, considerable errors were identified by checking Equation 17.

Recalculation was performed using an algorithm that absorbs the error sequentially into

γ, σ 1 and σ 3. Therefore, we were able to estimate total rent, but an effective result could

not be shown this time for each value of γ, σ 1 and σ 3. Finding or developing software to

calculate more accurate parameters is a task for future research.

If γ, σ 1 and σ 3 can be estimated more accurately, it will be possible to calculate the

normative corporate tax rate for each country from corporations’ accounting data. By

building international cooperation based on these results, each country will be able to

achieve an appropriate corporate tax rate. Also, if the OECD+G20 regime takes more

actions on tax havens, it will be possible for each government to achieve greater

31


financial soundness.

The third contribution was the identification of a causal relationship between rent and

lobbying, which was assessed with a panel Granger test on rent and lobbying data

obtained from LDA reports. It was concluded that rent was the cause of lobbying

expenditure for at least one panel and that lobbying was the cause of rent for at least one

panel.

By including panel data for R&D expenditure, we tried to test the proposition that rent

was due to R&D rather than corporate political spending. However, the correlation

between lobbying expenditure and R&D expenditure was high and no clear results were

obtained.

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37

Figure Ⅰ Imperfect Competition and Perfect Competition in the Products Market


38

Figure Ⅱ Imperfect Competition and Perfect Competition in the Factor Market

Marginal Factor Cost

w it (v i

t ) vit +w i

t(v it)

Marginal Revenue Products

pt∗¿(1+γ t)f vi' ¿

0

Factor Supply Curve

w it(v i

t)

vivit*

wit*

wit*(1+σit)

wi


Table . Average Rate of Rent Occupying NetIncome in Each Industry from 1982-2014Ⅰ

S=1 S=1.2

39

pMC1

MC2

q0

D

MR

Figure Ⅲ Increasing Returns to Scale and Capital Reward

rent2

rent1


Industry SamplesRent

RateRanking rent (US$)

Rent

RateRanking

2 Mining 15 0.261 1,144,003,230 0.210

3 Construction 4 0.232 112,732,040 0.172

4 Food, Tabacco 6 0.296 480,769,673 0.246

5 Textile and Apparel 4 0.303 112,477,315 0.235

6 Wood,Furniture and Paper 5 0.242 195,833,288 0.186

7 Printing and Publishing 1 0.216 140,561,234 0.159

8 Chemicals and Allied Products 17 0.411 4 1,087,798,371 0.363 3

9Petroleum Refining and Related

Industries2 0.394 6 1,623,837,565 0.362 4

1

0

Rubber and Miscellaneous Plastics

Products2 0.369 10 324,167,006 0.298

1

2Stone, Clay Glass and Concrete Products 1 0.247 571,116,385 0.208

1

3Primary Metal Industries 1 0.284 246,663,068 0.231

1

4

Fabricated Metal Products, Except

Machinery and Transportation

Equipment

2 0.247 133,539,785 0.184

1

5

Industrial and Commercial Machinery

and Computer Equipment8 0.396 5 305,172,979 0.343 6

1

6

Electronic and Other Electrical

Equipment and Components, Except

Computer Equipment

10 0.313 359,995,846 0.264

1

7Transportation Equipment 6 0.252 536,212,084 0.204

1

8

Measuring Analyzing and Controlling

Instruments; Photographic, Medical and

Optical Goods;

15 0.423 2 318,938,582 0.363 2

1

9Miscellaneous Manufacturing Industries 1 0.394 7 192,234,039 0.325 9

2

0Transportation and Postal Service 8 0.178 478,307,429 0.140

2 Electric, Gas and Sanitary Services 22 0.089 104,824,650 0.051

40


1

2

2Communications 4 0.286 1,146,942,982 0.239

2

3Wholesales Trade 6 0.385 9 237,060,851 0.327 8

2

4Retail Trade 18 0.392 8 536,566,790 0.329 7

2

5

Finance, Security and Commodity

Brokers, and Holding and Other

Investment Offices

22 0.077 51,612,337 0.038

2

6Insurance 17 0.048 110,421,389 0.032

2

8Real Estate 6 0.299 145,190,501 0.231

2

9Health Services 5 0.421 3 394,481,552 0.358 5

3

0Business Service 25 0.470 1 636,786,584 0.415 1

3

3Public Administration 1 0.365 66,261,437 0.309 10

Note: 1) The average for each industry is the average of the average from 1982 to 2014 for each

sample.

2) Rent Rate = Rent / Net Income

41


Table Corporate Income Tax Including RentⅡ

S=1 S=1.2

Industry W3/TI(a) (W3+rent)/TI (W3+rent)/TI(b) (b)-(a)

2 0.382 0.571 0.534 0.152

3 0.294 0.502 0.459 0.166

4 0.321 0.545 0.505 0.184

5 0.282 0.508 0.460 0.178

6 0.356 0.599 0.555 0.199

7 0.067 0.402 0.274 0.207

8 0.189 0.520 0.484 0.295

9 0.391 0.669 0.647 0.256

10 0.419 0.670 0.624 0.204

12 0.261 0.580 0.542 0.281

13 0.331 0.521 0.485 0.153

14 0.313 0.502 0.457 0.144

15 0.182 0.520 0.476 0.294

16 0.230 0.446 0.417 0.188

17 0.301 0.491 0.456 0.155

18 0.297 0.638 0.592 0.295

19 0.267 0.564 0.516 0.249

20 0.308 0.424 0.399 0.092

21 0.352 0.419 0.390 0.038

22 0.415 0.625 0.593 0.178

23 0.304 0.480 0.462 0.158

24 0.359 0.610 0.570 0.211

25 0.272 0.292 0.300 0.028

26 0.265 0.310 0.297 0.033

28 0.186 0.439 0.384 0.197

29 0.386 0.655 0.616 0.230

30 0.289 0.650 0.610 0.321

33 0.278 0.509 0.473 0.196

Average 0.296 0.524 0.485 0.189

42


Note: W3=Income Tax TI=Profit before Tax

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

S=1, Rent Rate

S=1.2,Rent Rate

Income Tax / Profit before Tax

S=1, (Income Tax+Rent) / Profit be-

fore TaxS=1.2, (Income Tax+Rent) / Profit be-

fore Tax

%

43

Figure Ⅳ Time Series of Rent and Income Tax


1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

012345678

%

44

Figure Ⅴ R&D Expenditure Occupying Total Costs


Table Rent and lobbying of corporationsⅢn=234 Independent variables

Scale

parameterType

lag or

lead

operator

lobby py R2 method

S=1 l1 b) 52.21511 0.043241 0.4644 re

(0.002) a) (0.000)

f1 34.60893 0.043641 0.4692 re

(0.029) (0.000)

log-

logl2 0.08999 0.965632 0.4595 re

(0.002) (0.000)

f3 0.060189 1.048933 0.4865 re

(0.029) (0.000)

f3 0.122773 0.928

(0.005) (0.000) pa ar(4)

(d

S=1.2 l1 42.79533 0.041408 0.4445 re

(0.009) (0.000)

f3 29.26516 0.042817 0.4451 re

(0.049) (0.000)

log-

logl2 0.089021 0.940016 0.4766 re

(0.003) (0.000)

f6 0.128032 1.014605 0.5195 re

(0.000) (0.000)

45


Note a) () is p value

Note b) fn is lead operator of n periods, ln is lag operator of n periods.

Note c) fe: fixed-effects estimator, re: random-effects estimator, pa:

population-averaged estimator

Note d) ar(4) is a fourth order autoregressive process.

46


Table Comparison of lobbying and R&D as explanatory variablesⅣ

n=73dependent

variable

lag or

lead

operator

Independent variables

S=1 rd py R2metho

d

rent l2 b)0.507037

7

0.087868

1

0.739

4fe c)

(0.000) a) (0.000)

rent f20.451807

5

0.087436

8

0.741

6fe

(0.000) (0.000)

lobby py R2metho

d

rent75.86993

00.075207

0.744

8re

(0.013) (0.000)

rent f1 101.68440.075180

6

0.751

3re

(0.001) (0.000)

S=1.2 rd py R2metho

d

rent l2 0.4755820.083766

8

0.720

6fe

(0.000) (0.000)

rent f20.419551

2

0.083239

7

0.722

7fe

(0.001) (0.000)

lobby py R2metho

d

rent 64.141240.071694

4

0.731

9re

(0.034) (0.000)

rent f 89.576070.071685

5

0.737

9re

47


(0.002) (0.000)

Note a) () is p value

Note b) fn is lead operator of n periods, ln is lag operator of n periods.

Note c) fe: fixed-effects estimator, re: random-effects estimator,

48

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