Businesses’ perception of government red tape and its impact on participation in bribery: evidence from the

ASEAN region

Undergraduate Thesis

Presented toDr Tereso Tullao

Dr. Winfred VillamilMs. Mitzie Conchada

De La Salle University

By:

Edgardo Manuel JopsonSooyeon LeeKristianie Te

March 15, 2014

Table of ContentsIntroduction4

Background of the Study 4Statement of the Problem 6Objectives 6Significance of the Study 7Scope and Limitations 7

Literature Review 9Literature Map 9Bribery and Red Tape 9Bureaucratic Inefficiencies and Market Failure 12Rationale in Participating in corrupt activities 14Effects of Corruption on firms 16Eliminating bribery and other forms of corrupt practices 19Research Gap 21

Conceptual Framework 22Operational Framework 28

Econometric Model 28Measures of Corruption 30Methodology 33

Empirical Results 38Descriptive Statistics 38Econometric Results 43

Conclusion and Recommendations 49References 56

Appendix A. Complete STATA Results 58Appendix B. Initial Regressions 65

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Abstract

Excessive regulation, commonly known as red tape, is one of the problems that businesses face which hinders firm performance. Literature suggests that its presence in the economy comes from both the demand and supply side; both the government and firms conspire to defraud the public and take advantage of the situation, and the gravity of it will depend on how inefficient the government is, since a higher opportunity cost of starting a business can provide an incentive for firms to pay the government to hasten their services. Using the Enterprise Surveys, the Doing Business indicators as well as the World Development Indicators databases from the World Bank, we employed a series of econometric tests to determine and understand the impact of the business environment and red tape on the incidence of bribery. The study suggests that the probability of firms participating in bribery is largely affected not only by the characteristics of firms, taken from a study of Herrera and Lijane (2007), but also by the firm’s perception of red tape as well as the ease in doing business in the country.

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1. Introduction

1.1 Background of the Study

Literature defines corruption as the manipulation of institutional

power for private benefit; a pervasive and universal phenomenon

affecting almost every culture to differing degrees (Everhart,

Martinez- Vazquez, & McNab, 2009). Whether corruption has a

positive or negative effect on economic activity is still contested in

the literature, since studies have shown that both sides can be

proven to be true. However, government inefficiencies and

incompetence are problems that economies face, since it does not

only raise operational cost, but also dispels possible opportunities

for development. This problem creates an incentive for businesses

to pay more for hastening the process, may it be in legitimate formal

services such as express payments or informal contracts such as

bribery.

While it is in the government’s interest to reduce its

inefficiencies by eliminating core lapses in its system (i.e.

corruption), it is important to note the origin of this problem. For

every demand, there is a supply; both the government and the firms

conspire to defraud the public and take advantage of the poor

monitoring system, since they are less likely to be caught and

sanctioned for these illegal acts (Vogl, 1998). In fact, in most cases,

4

these institutional hurdles, otherwise the red tape in bureaucracy,

provide an opportunity for public administrators to participate in

rent-seeking activities, who may offer businesses willing to pay the

option of side-stepping formal procedures (Blackburn, Bose, &

Capasso, 2008), cutting time spent and lowering opportunity cost.

This study contributes to the literature by scrutinizing the

characteristics of firms that decide to participate in illicit activities,

specifically bribery from the different ASEAN countries. Moreover,

our research used an updated data set taken from Enterprise

Surveys which is of the same nature as World Business Environment

survey, used by most of the previous studies on firm level

characteristics and performance (Asiedu and Freeman, 2009). The

study of Asiedu and Freeman discussed the effect of corruption,

specifically bribes, in the investment growth of firms, which makes

the study more up to date. The country variables will be taken from

the World Bank’s Doing Business indicators. By examining the

business environment of these firms in their respective countries,

we can infer whether or not it has something to do with the decision

of firms to bribe.

The hypothesis which this research uses as backbone is that

firms have a higher incentive to participate in bribery with the

government when it is more difficult to do business in the country.

5

This direct correlation between the two is due to the fact that the

firm can penetrate the bureaucratic inefficiencies of business

processes. This can be reduced by charging a higher price, which

the public administrators receive informally, reducing the firm’s

opportunity loss which may be higher than the cost of the bribe.

Our paper is structured as follows:

Section II reviews the literature; previous studies conducted on

bribery and the business environment.

Section III, we develop a conceptual framework on how to

address the research problem.

Section IV, we describe the operational framework and the data

used in the research.

Section V, we empirically investigate the significance of the

hypothesized result of bribery and red tape. In the last section, we

will conclude and provide policy recommendations.

1.2 Statement of the problem

Corruption is a problem that is prevalent in the ASEAN region.

Not only are countries perceived as corrupt but also actual accounts

of corrupt activities such as bribery can be seen from the Enterprise

Survey, which interviewed at least a thousand firms in 135

countries. We want to know whether or not this problem can be

6

lessened by reducing red tape in a country, which may decrease the

incentive of firms to participate in bribes since the government is

efficient in the first place. Now the question is this: does red tape

have a significant input on the likelihood of firms to bribe?

1.3 Objectives

This research intends to:

1. Identify the forms of red tape that increase the likelihood of

firms to participate in bribery;

2. Identify the characteristics of the firms that increase the

likelihood of firms to participate in bribery;

3. Theoretically explain the relationship between red tape and

the incidence of bribery;

4. Empirically test the said relationship between red tape and

the probability of the firms’ participation in bribery by

providing an econometric analysis;

5. Make sound recommendations from the regression analysis

generated from the econometric model.

1.4 Significance of the Study

This research attempts to provide an analysis on the impact of

bureaucratic inefficiencies on the firm’s decision to participate in

bribery. The study may be of aid to policy makers who are

interested in theoretical basis and empirical evidence, especially in

7

reducing corruption. Since it also has been of recent interest from

the studies of Acemoglu and Robinson (2012) to consider not only

the economic inclusivity but also the political and bureaucratic

inclusiveness of countries, this research would provide key insights

regarding the firm – government relationship and possibly a glimpse

on how they interact.

1.5 Scope and Limitations

In its very nature as a micro – analysis study of corruption, it

captures corruption in the form of bribery and bribery alone,

meaning to say that the usual measure of corruption used in the

literature (Alemu, 2013), which uses the Corruption Perceptions

Index by Transparency International will not be discussed. The

study is also limited to the data from the ASEAN region, specifically

from the countries Indonesia, Laos, Vietnam and the Philippines, for

data consistency purposes. Furthermore, this study is interested in

the probability of firms to participate in bribery which we use in

capturing corruption; meaning to say that this study does not deal

with the gravity or intensity of corruption, but rather the maximum

likelihood (incentive) to participate in it. The data used in this study

are from the Enterprise Surveys, the World Development Indicators,

as well as the Doing Business datasets from the World Bank.

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2. Literature Review

2.1. Literature Map

2.2. Bribery and Red Tape

Corruption

Corruption is a controversial topic to discuss especially in cases

where computations in some studies show that corruption turn out

to be a positive contribution to the economy. We go deeper by

analyzing the consequences when corruption is present - whether it

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is beneficial or detrimental to society (in terms of efficiency). By

referring to previous studies on the topic, we can address questions

that can hamper our investigation such as; can bureaucratic

inefficiencies really affect the decision of firms to bribe.

According to Transparency International’s Corruption

Perceptions Index in 2013, Laos has an index of 26, rank 154 out of

178 countries, which has the highest among the four ASEAN

countries in our study, and for Vietnam, Indonesia, and the

Philippines, the indices are as follows at 31, 32, and 36 respectively.

For the countries Laos and the Philippines, from 2012 to 2013, their

CPI experienced a significant improvement from 21 to 26 and 34 to

36 (Transparency International, 2013).

The problem of Laos in publicizing the information for individual

firm’s corruption, resulted into setting up a department in the

government; the State Inspection Authority. The State Inspection

Authority is part of the Prime Minister’s Office, which provides

analysis on national level of corruption in order to present evidence

for inspection (Global Security, 2013).

Most incidences of corruption in the Philippines are petty, since

all levels of state apparatuses encounter corruption in different

degrees. Corruption can be witnessed in action in events such as

elections, wherein officials running for public office would bribe

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individuals in order to get their votes. Furthermore, among the

Filipino household’s attitude toward corruption, they consider

political parties as the most corrupt institution in the country

(Bolongaita, 2010).

Indonesia has a similar anti- corruption regulatory board, the

KPK, with the Philippines’ ombudsman. Their similarity extends

even further: both of these countries are considered as low-middle

income country, political parties are spearheaded by strong political

families, the Corruption Perceptions Indices are close to each other;

but the inefficiency of the ombudsman of the Philippines does

compare to the successful KPK in Indonesia. With a short period of

time from its establishment on 2013, the KPK already captured and

passed all the cases they caught related to corruption, and all the

these cases won in court; the guilty put into jail (Bolongaita, 2010).

Though the KPK anti- corruption program is considered to be

successful in cleaning up the government, the Philippines still rates

cleaner then Indonesia according to the index in 2013.

In the study Overview of corruption and anti-corruption in

Vietnam from U4, Vietnam’s high level of corruption is one of its

major challenges it needs to overcome. From the Enterprise Survey,

more than 50% of Vietnamese participants were “expected to give

gifts to public officials for thing to be done” (World Bank, 2009). In

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addition, 59% of the firms interviewed, they believe “informal

payment is common among firms like their own” (USAID, 2010).

Vietnam Provincial Competitiveness Index 2010 also concludes that

41% of firms think that having a private converse with tax officials is

essential practice in doing a business. Interestingly, more than 20%

of interviewed household concede that they had paid a bribe

regarding tax revenue services in last three years (Transparency

International, 2010).

Regulation as Red Tape

Regulations, in whatever form they may be, impose a cost for the

firms, since we do not only consider the out-of-pocket cost, but also

the opportunity cost that the firm incurs. It will be up to the firm to

decide whether or not the excessive time generates a higher loss

than the cost of the bribe, whether or not participating in bribery is

an option, also considering key factors such as firm size, its

characteristics, its industry (which market it belongs), among others

(Herrera, Lijane, & Rodriguez, 2007). However, bribery is a practice

that is both illegal and unethical, since the act itself suggests the

manipulation of institutional power for private benefit- in this case,

the government agency may abuse its power in terms of issuing

and/or renewing business licenses by charging additional rates for

better services. In addition, the whole inefficient system that tries to

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create a market for better service is unnecessary in the first place;

meaning to say that the whole economy does not need to be

inefficient, where both the firms and the whole economy is better off

without such hindrances in starting a business. Hence, in this firm-

government agency relationship, it is possible for us to study

corruption happening at the micro level.

2.3. Bureaucratic inefficiencies and Market failure

Bureaucratic inefficiencies

In order to fundamentally understand why corruption is in the

general sense inefficient for the aggregate economy, Acemoglu and

Robinson (2012) have discussed that the political and economic

institution of a nation has to be inclusive in order for development to

be sustainable; and corruption being an extractive act that the

government practices, is detrimental to the development of the

country. There will be opportunities for the government to

participate in corruption given the following conditions:

1) Government intervention requires "bureaucrats" to gather

information and    implement policies.

2) At least some of the agents who enter bureaucracy are

corruptible, in the sense that they are willing to

misrepresent their information at the right price.

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3) There is some amount of heterogeneity among bureaucrats.

Market Failure

Misallocation of resources would cause market failure (Acemoglu

& Verdier, 2000), which causes the price and quantity

appropriations in the market to be suboptimal, making it difficult to

predict. From this paper, it clearly indicates that the advantage of

corruption is avoiding the excessively high cost that government

would spend on their intervention. Therefore, they get to decide on

the second-best choice which is accepting certain bribes.

2.4. Rationale in participating in corrupt activities

Providing higher resources for private investment

For the firm and market, corruption might provide higher

resources for private investment, and even open more public

services or reduce taxes which makes the public revenue stronger

(Reinikka & Svensson, 2003).      Bribery may allow the considered

‘better’ firms to bypass red tape and thus reward market

performance (Lui, 1985). Moreover, it can be used to reduce the

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amount of taxes or other fees collected by the government from

private parties. Such bribes may be proposed by the tax collector or

the taxpayer. In many countries the tax bill is negotiable (World

Bank, 1997).

Eliminating Transaction time

The fact that there are additional costs incurred in the presence

of unnecessarily time-consuming government procedures, it has

been the agenda of the private sector to make the government more

efficient since significant opportunity losses are incurred (Ciccone &

Papaioannou, 2007). Red tape is quite a problem especially for firms

whose sunk costs are high, since the quicker that they would be

able to receive their return on investment, the better. However,

regulations by themselves, with their primary function of keeping

private institutions in check, also work as a screening device for the

government to maintain the quality and intention of the businesses

around, since firms who are willing to go through the rigid

paperwork in starting out and maintaining their business are most

likely firms that are doing well, since the firm’s system may be

within the standards, or their market exists and thrives in the

economy, or that the demand is high since it is of importance to

society. This trail of thought is similar to the signaling model in

labor economics, wherein college is merely a screening device that

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firms use to sort out the productive individuals from the ones who

are not (Ehrenberg & Smith, 2012), however instead of people,

firms are observed.

Such behavior has been already tackled in the literature, and one

example would be the theoretical study conducted by Blackburn,

Bose and Capasso (2008), wherein they studied the effects of red

tape on corruption by considering a scenario in which a government

seeks to provide a public good or service (such as cutting red tape)

that requires some privately- manufactured input for its production.

Their analysis is was based on a simple model of public gains in

which asymmetric information between the government and the

private sector allow public administrators can appropriate the

latter’s profits from bribe payments to reduce (for the private

sector) the costly regulations that the government imposes. The

result of their analysis is that there is a critical threshold level or

red tape and rent-seeking wherein procurement is unaffected by

these frictions (the marginal benefit is not strong enough to

compensate the marginal cost), which is more prominent in

economies with lower levels of development, implying that poorer

countries are more able to absorb a greater amount of red tape

without compromising procurement objectives.

Short- term higher productivity

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In the aggregate economy, lesser corruption increases

productivity of outputs, then when all outputs are more productive,

it translates throughout the economy- in a study conducted by

McArthur and Teal, they have presented that for firms operating in

economies, where bribes are pervasive, are on average are only one

third as productive as their counterparts operating in bribe-free

economies (McArthur & Teal, 2002). However, many firms do not

practice based on this because it is aiming for long- term. And also,

this is not going to work if there are some firms participating in

bribery which will make the other clean firms to suffer to take

longer transaction time; thus leads to lower productivity eventually.

2.5. Effects of corruption on firms

Another example of the impact of corruption and businesses was

presented in a study by Javorcik and Wei (2009), which in their

minimalist model provided a framework on the relationship between

FDI and corruption, particularly its effects on the individual firms.

According to them, the key factor that has to be considered is that if

the corruption present in the country is sufficiently high, then no

foreign investment in any ownership form (may it be wholly owned

or a joint venture) will take place. However given the constant level

of technological sophistication, foreign investors may consider a

joint venture with the government as corruption increases and

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participate in illicit activities in order to establish their business in

the country.

In addition, foreign firms often look at corruption as a cost of

doing business and if this costs is too high or unpredictable, foreign

firms will stay away from it if there is no need for them to  be in that

country. Therefore, high levels of corruption may lead a country to

being marginalized in the international economy.

The evidence shown in private sector assessments say that

corruption causes higher costs of doing business, and small

entrepreneurs in many developing and transition economies may

bear a disproportionately large portion of these costs, this is why

that bribes can prevent firm (especially in small enterprise) from

growing (World Bank, 1997).

This was stressed out by Rose-Ackerman (1996), who noted an

example that “… a corrupt firm may pay to be included in a list of

qualified bidders, to have officials structure the bidding

specifications so that it is the only qualified buyer… once selected, it

may pay for the opportunity to charge inflated prices or to skimp on

quality”. With this, ceteris paribus, firms that are actually

benefitting from corruption may increase their activities by pumping

up investments. This phenomenon does occur in real life - one

example of a recent issue regarding bidding can be found in the

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Philippines, wherein a recent article of the Philippine Daily Inquirer

(Torres-Tupas, 2013) showed the Anti-Trapo Movement of the

Philippines (ATM) anti-corruption group joined the call for the

government to look into the questionable P3.8-billion contract for

license plates by the Department of Transportation and

Communications- Land Transportations Office (DOTC- LTO) and J.

Knieriem B.V. Goes (JKG), a Dutch-based firm.

Gaviria (2002) concludes in his study that corruption as a whole

substantially reduces firm competitiveness, and differs from one

country to another. Furthermore, bureaucracies in firms are more

likely to be subjected to paying bribes, suggesting that government

regulations are strategically used to maximize bribe collection,

although this result contradicts several theories that predict that

bribes can increase efficiency by allowing firms to avoid

exaggerated government regulations. Given these studies prior to

conducting the research at hand, it is quite clear that the theoretical

impact of corruption on firm-level investments is ambiguous;

literature suggests that it can be positive, negative or neutral, and

depends on which has more impact to the firm.

Asiedu and Freeman (2009) on the other hand suggests that

another important fact that has to be considered is the possible

determinant of the entry of firms in the economy, or the loss of

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potential investment, and insists that the overall effect of corruption

to investment is negative despite no empirical proof.

In general, the literature suggests that corruption does impose

significant direct and indirect costs to firms. Direct costs come in

the form of bribes or kickbacks and are felt by the company from

nominal costs. These monetary costs to public administrators can be

quite expensive but the problem of indirect costs can prove to be a

bigger problem that the firm faces, such as opportunity costs and

sunk costs from delayed transactions which could have been

circumvented had the firm paid the bribe (Herrera, Lijane, &

Rodriguez, 2007). Since we have no knowledge whether or not

specific firms believe that paying the bribes are worth it would

depend on the firm’s characteristics and the nature of the

corruption at hand, whether or not there is indeed an incentive for

the market to exist.

2.6 Eliminating Bribery and other forms of Corrupt practices

Huther and Shah (2000) have evaluated the effectiveness of anti-

corruption programs for different countries with different qualities

of governance. Based on the opportunistic behaviour of public

officials, they considered that under the conditions that 1) the

expected gains exceed the expected costs of participating in corrupt

activities; and 2) little weight is placed on the cost that corruption

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imposes on others; a self-interested individual will join and transact

in corrupt activities when:

E [ B ]=n × E [ G ]−prob [ P ] × [ P ]>0

Where

E [ B ] = Expected Benefitn =number of corrupt transactionsE [ G ] = Expected Gross Gain from the corrupt transaction prob [ P ] = probability of paying a penaltyP = penalty for corrupt activity

With this consideration, the factors that affect the decision of the

individual not to participate in corrupt activities are affected by:

1. Benefits that the individual would incur – if the benefits

are smaller, then individuals are less likely to participate in

corrupt activities. Examples of policies: Scaling down of

individual projects, requiring popular referenda for large

projects with votes on expenses and taxation, de-monopolizing

public services, promoting competition, increasing the funding

of public offices i.e. for business-related offices like the

Bureau of Internal Revenue, the Bureau of Customs, etc.

2. The number of transactions – by reducing the number of

transactions that create opportunities for graft and private

manipulation of public programs, we are able to reduce the

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expected benefit of corruption. This can be done by

streamlining bureaucracy, deregulation, improving service

standards and decentralizing government services.

3. Increasing the probability of paying penalties – by

increasing the chances those agencies will be caught

participating in such activities and by increasing the cost of

such sanctions, then the expected benefit of participating in

corrupt activities will decrease by virtue of increasing the

expected costs.

From their study, they have recommended that for countries with

weak governments and high levels of corruption, the most effective

programs in eliminating corruption are to establish rule of law, to

strengthen institution in participation and accountability. The

objective is to limit government interventions to focus on directing

the economy. For countries with medium levels of corruption and

fair qualities of government, they urge for decentralization and

economic policy reforms. Finally for economies with low levels of

corruption and high qualities of governance, explicit anti-corruption

institutions, as well as strengthening financial management and

raising public awareness will reduce the incentive for corruption to

proliferate in the economy (Huther & Shah, 2000).

2.6. Research Gap

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Price of Bribes

Our study extends the literature on corruption and bureaucratic

inefficiencies as it applies the methodology used by Herrera and

Lijane (2007) to the ASEAN region. Since the Enterprise Survey

contains 130,000 firms across 135 countries, our research will

provide a powerful inference as the result from the econometric

analysis will approach the true value of the estimate due to the Law

of Large Numbers (Gujarati & Porter, Basic Econometrics, 2011).

Aside from the inference being updated, this research is very much

applicable especially in the Southeast Asian area, due to the

displayed significant boom in the economies, which would definitely

affect businesses in the region.

3. Conceptual Framework

To explain the relationship of red tape and bribery incidence, we

discuss it via a simple microeconomic framework.

In every market, there will always be a demand and a supply side

in order for the market to work. In this case, we tackle the market of

cutting red tape, which can be captured by bribery- since firms

bribe public administrators in order to facilitate faster transactions.

In the demand side are the firms and the public administrators and

government officials “sell” their illicit services to the firms.

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Figure 1. The market for bribery

Bribery Incidence

The trade-off here is about two cost minimizing choices that firms

have. One is to operate with the current system of government and

choose to pay for better service, opportunity to charge higher prices

or to manipulate the system in their favour in the form of bribery.

Another is to operate legitimately and work with the system, and in

exchange use the resources supposedly for the bribe for other

purposes. Despite the gains that the firm can make regarding these

two options, it is still not certain whether or not one is better than

the other until we consider the characteristics of the firm, whether

or not the gains from illicit activity is more valuable for the firm

than other gains.

The relationship between bribery and red tape follows the same

logic as a demand for normal goods. As it becomes easier to do

business, the demand for bribes decreases; and conversely the more

difficult it is, the higher the demand. Since the reason of the firm in

participating in bribery is to hasten the process of doing business,

24

D

S

0

the relationship that the firm and the government have is similar to

a market situation wherein services – which in our case is the faster

government service provided – are sold by the government to firms

willing to pay a higher price, depending on the firm’s need to

minimize opportunity cost. Supply of bribes depends on the

presence of the market for illicit activity; when firms are willing to

pay for this service, then the supply will increase. Basic laws of

classical economics are not violated: as the price of bribes go up, so

does the supply of the bribes, and when bribes become too

expensive then the demand would go down. The question at hand is

this: can we empirically prove this relationship or not?

For this study we base our condition for bribery with the

threshold level analysis by Blackburn, Bose, and Capaso (2008). In

their analysis, they proposed that when the marginal benefit is

greater than or equal to the marginal cost, the firm will choose to

bribe; wherein the marginal benefit (MB) are the benefits that the

firm receive if they choose to cut the red tape (for the benefits here

mean the shorter processing time and lower pocket cost), and

marginal cost (MC) is derived from the out-of-pocket cost, the firm

pay for legitimated application with the government, and the cost of

time spend for formal process. Moreover, the firm characteristic

25

together with its country environment does differ both of their

marginal benefit and marginal cost.

With a simple mathematical proof of the corruption and

investments function, we provide the following statements:

Let q be the added productivity derived by businesses from their

additional advantage from their choice when they participate in

bribes or allocate their funds to something else that would maximize

gains. This choice is such that the firm can allocate its resources to

the usual inputs of production and corrupt services respectively.

q= f (x1 , x2)

Here, x1 , x2 represents gains from getting the business permit

(incurring cost of both actual and opportunity cost) and gains from

other productive inputs. Note that the function is Cobb-Douglas in

form, where we assume that firms prefer averages over extremes,

yet the firm can still opt to consume as such. Without prejudice, this

function is subject to the budget line of the firm’s aggregate income.

Considering that the market for bribes is not competitive

(because there is only one government in the economy), them the

firm allocates resources depending on the additional gains per bribe

and the marginal cost of every unit of bribery.

Hence, solving for the maximum profit function:

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(3.1)

(3.2)

(3.3)

MB=MC

In this kind of market setup, the firm will choose to participate in

bribery when their marginal cost incurred from purchasing bribes is

less than or equal to the marginal revenue gained from cutting red

tape- reducing opportunity cost.

Yet for the firm itself, the marginal benefit from illicit services

from bribes would still depend on the firm and country

characteristics, which would influence the decision making of the

firm whether or not they would participate or not. This depends on

the elasticity of the firm to changes in the combination of x1 and x2

obtained, whether or not the substitution or income effect

dominates.

if∂ x1

c

∂ P1>

∂ x1¿

∂ mx1

¿ , thensubstitution effect dominates

if∂ x1

c

∂ P1<

∂ x1¿

∂ mx1

¿ , thenincomeeffect dominates

The variable x1c is the compensated demand for x1 instead of the

previous uncompensated demand. This is done in order to capture

the net substitution effect. Note the following observations (Besanko

& Braetigam, 2011):

27

When firm spends more for illicit activity; case where firm

is worse off when ∂ x1c

∂ P1>

∂ x1¿

∂ mx1

¿ , or when substitution effect

dominates

When firm spends more for illicit activity; case where firm

is better off when ∂ x1c

∂ P1<

∂ x1¿

∂ mx1

¿ , or when income effect

dominates

When firm does not spend on illicit services; case where

firm is worse off when ∂ x1c

∂ P1>

∂ x1¿

∂ mx1

¿ , or when substitution

effect dominates

When firm does not spend on illicit services; case where

firm is better off when ∂ x1c

∂ P1<

∂ x1¿

∂ mx1

¿ , or when income effect

dominates

The income and substitution effects of every firm will depend on

their characteristics and the country’s condition, especially

regarding the ease of doing business, since firms make a decision to

participate in bribery or not according to their cost benefit analysis-

if they find that marginal cost is greater than the marginal benefit,

then firms will opt not to participate in bribery. Therefore for firms

to participate in bribery (else they do not):

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MBcuttingred tape ≥ MCcutting red tape

Where:

MBcuttingred tape=f (benefitsof cutting bureaucratic regulation , reducing thetime needed for applicati on)

MCcutting red tape=out of pocket cost+( probability of paying a penalty × pentalty cost )

In order to empirically test this said relationship, this research

employs a qualitative response model in order to verify the

sensitivity as well as the direction of the relationship between

bribery and the firm’s perception of its biggest obstacle, wherein if

it finds the government to be their problem, assuming that

businesses know full well how to run a business and assuming

efficiency in knowing the business system in the country, then the

firm can consider that the government imposes excessive

regulation. Of course, without discounting the fact that there other

variables concerning the probability that the firm will participate in

bribery, this methodology will be able to provide an intuitive

analysis for us to derive results from, which the theory suggests.

29

(3.4)

4. Operational Framework

4.1 Econometric Model

In order to empirically test the probability that the firm would

participate in bribery, we use a Qualitative Response Model given:

Pr ( Bribe )=α+β customregulationi+β businesslicensei+β taxregulationi+β servicesi+β otherindustry i+β mediumi+β large i+βdomestic i+ β foreigni+β govt i+βl ocaltradei+β indirectexport i+β diretexporti+β costbusinessi+β timebusinessi+β tradegdpi+εi

Legend

For the variables that represent the perception of red tape, there

are 3 vector (β customregulationi+β businesslicencingi+β taxreguationi¿ used

dummy variable to indicate if the firms’ find customs, tax

regulations and business licensing the biggest obstacle in business ;

Firmi represents a vector of dummy variables that indicate firm

characteristics such as firm sizes and industry of the firms; while

30

Other Country irepresents the vector of country variables, and ε i

denotes the error term.

Our a priori expectations of the variables are presented as

follows:

31

32

bribei Whether or not the firm has participated in bribery; a dummy variable which has a value of 1 when the firm has participated in any form of bribe with the government (whether indicated in terms of percentage of the contract value or the percentage of annual income)

Source: The Enterprise Survey (2009)

customregulation i

businesslicensei

taxregulationi

A vector of dummy variables representing the firms’ perception of red tape; if the firm finds customs and trade, business licensing and permits, or tax regulation their biggest obstacle. If 1, it is the biggest obstacle, and it show that the firm is costly to avoid red tape, in other word, this mean the firm will have MB>MC , therefore , these variables expected to have a positive effect on bribe.

Source: The Enterprise Survey (2009)

smalli

mediumi

l argei

manufacturing i

servicesi

otherindustry i

domestic i

foreigni

govti

l ocaltradei

indirectexport i

directexport i

costbusinessi

timebusinessi

tradegdpi

A vector of variables indicating the characteristics of the firm, the firm size and firm sector. For firm size, it is expected to have a negative effect on bribes for smaller firms, since small firms would find the out-of-pocket cost of the bribe too expensive. For large firms it is expected to have a positive effect on the probability of participating in bribery because large firms would be inelastic to such costs as these firms would value production and steady sales much more than the out-of-pocket cost. And for the industry of firm, the effect is ambiguous, however we believe that the sectors from other industries that deal with distribution are more likely to bribe for they have more business licenses and permits to process. As to ownership, we expect to have a positive effect on bribery if the firm has more domestic owners, and when government has a share in ownership.

Source: The Enterprise Surveys (2009)

Variables that measure the percentage of firm sales– either to domestic trade, or export which could be direct or indirect. Similar to percentage trade in GDP, this measures the micro effect of trade competitiveness. Expected to have a positive effect on bribery, as competition increases opportunity losses due to inefficient government systems and regulations.

Source: The Enterprise Surveys (2009)

Other country characteristics that are not essential but greatly influence bribe, such as the percentage of trade in GDP (Trade%GDP), as well as the difficulty (on the average) of firms to start a business in a specific country, such as the cost and time required to start a business. These variables are expected to have a positive effect on corruption. For the percentage share of trade in GDP, it is positive because the bigger the presence of business in GDP, the government would have a higher incentive to allow corruption to exist since they are responsible for the growth of the country, which would increase the probability of the firm to participate in bribery. For the cost and time to do business, it is positive as well because the more difficult it is to do business in a country, there is a higher incentive for firms to recover the costs of starting a business, which affects the decision of firms to bribe.Source: World Development Indicators and Doing Business data set (World Bank, 2009).

4.2 Measures of Corruption

Asiedu and Freeman (2009) suggest that the empirical literature

in corruption and investment can be categorized into three groups:

micro, semi-micro and macro studies.

Micro Studies employ firm-level data on both investment and

corruption. This reflects the firm’s perception of corruption that

prevails in the country that it operates in. Of course, making short

and long term decisions are important for the firms. However, there

are disadvantages in using this kind of analysis. One of them can be

endogeniety, and another is the probability of understatement.

Studies by Gaviria (2002) suggest that there is no significant

relationship between growth of investment and corruption; however

this does not discount the fact that there exists a relationship

between the two. In this research, we will be using data for this

from the Enterprise Surveys.

Semi-micro analysis employs firm-level data on investment and

country-level data on corruption. Pervasiveness of corruption within

a country is captured since it is combined with country-level data,

which may lower the standard errors. Data for specific firms will be

taken from the same- Enterprise Surveys, while data for corruption

is taken from World Bank’s World Development Report survey,

which measures internal corruption, as well as the Corruption

33

Perception’s Index taken from Transparency International.

However, by doing this, the value that is assumed for corruption will

be implied to be the same for all firms in that country, ceteris

paribus.

Macro studies are the most available in the literature. Macro-

level studies usually deal with aggregate effects of corruption to

investment. Studies regarding their relationship are generally

negative, wherein corruption deters overall investments. One

example of this study would be the study of Alemu (2013) that

concluded that the effect of corruption to FDI is negative in Asia.

This study will not be using a macro framework.

There are three classifications that will be used to measure

corruption: internal, external and hybrid (Asiedu & Freeman, 2009).

Our standard procedure is to use perceptions and experiences in

corrupt practices of respective countries to survey firms about their

respective viewpoints.

Internal measures of corruption come from firms that operate

within the country- which reflects firm’s perception of investment

risk; however, this is limited by the different policies of different

economic settings. Meaning, corruption for firms in country A may

differ from corruption for firms in country B, which would make the

data difficult to compare directly to other countries. Secondly, firm

34

size can be a problem; since firm size would need to be considered

regarding its need for expansion, or there is also the possibility that

the government may be biased into practicing illicit activities

towards as specific group of firms- especially a type that is in line

with their self-interest. Another possible disadvantage is that

internal data can be underreported by the firms.

External measure is another way of analyzing corruption.

External measures of corruption are taken by agencies outside the

country, and are provided by risk-rating agencies. A great

advantage of using this kind of information is that the data is

generally more consistent and has less probability of statistical

errors. However, this kind of data usually has a limited coverage

regarding types of questions and number of observations

themselves, without taking out the fact that there may be

inaccuracies due to the probable overstatement of risk for countries

that are known to be risky.

An alternative method of measuring corruption is by hybrid

measures, which combine different sources of data for corruption

and turn it into some form of an index. Even if this type of data can

remove the “dirt” of the internal and external data, we lose track of

isolating the causal variable which may lead to a vague inference.

Furthermore, statistically speaking indices tend to be noisier as

35

variables tend to move much more, which may lead to higher

probability of error.

This study uses a combination of internal and external measures

of data. Specifically, all firm-level data is internal, while all country-

level data are externally measured by the World Bank.

4.3 Methodology

We use a procedure that is patterned after the methodology of

Herrera and Lijane; based on their investigation whether or not

bribery has anything to do with the nature of the firms (2007). Our

interest brought us to study the ASEAN economies.

The primary source of data used by this study is the Enterprise

Surveys of World Bank. The Enterprise Surveys contain 130,000

firms in 135 countries. Our dependent variable is the probability of

firms to participate in bribes (as a dummy variable). We will be

using the information for the year 2009 and construct a cross

section data for the available firms in the Philippines, Indonesia, Lao

PDR and Vietnam. We are using data these four ASEAN countries

because they are the only countries with identical datasets from the

Enterprise Surveys, as the data from Malaysia, Thailand and the

other ASEAN countries with available data use of a different data

36

collecting methodology as well as of a different year. For

consistency purposes, we are only able to use these four countries.

By employing Qualitative Response Model regression techniques,

we find the probability of the firm’s participation in bribery,

influenced by the set of independent variables such as government

regulations as major obstacle, firm characteristics as well as other

control variables for the country.

Our independent variables are divided into three groups; 1)

firms’ perception in the difficulty of going through government

regulation (red tape), 2) firm characteristics and 3) country

variables. The reason behind doing this is for capturing the pre-

existing phenomenon related to investments in the literature; else

we take the risk of omitting important variables. To take the firm’s

perception in its biggest obstacle in business, we capture it with

customregulation, which has a value of 1 if the firm finds that

customs and trade regulations to be the biggest obstacle and 0

otherwise; taxregulation, with a value of 1 if firm’s biggest obstacle

is tax administration and regulation; businesslicense, with a value of

1 when the biggest obstacle for firms is business licences and

permits. If all values of the dummy variables are zero, then firms

that find other problems such as access to finance, courts, practices

of competitors in the informal sector, corruption, electricity,

37

inadequately educated workforce, access to land, tax rates, political

instability, labour regulations, and crime. For the firm size: small: 1

if yes 0 otherwise; medium: 1 if yes 0 otherwise; if both zero, firm is

large, wherein firm size is measured by the number of employees-

small if number of employees is less than 20; medium if greater than

or equal to 20 but less than 100; otherwise the firm is large1.

Service variable service equals 1 if it is in the service industry in

addition; otherindustry captures the value whether or not it may not

be in the manufacturing or in the services sector.

Companies with domestic shareholders are indicated by domestic

in percentage, which captures whether or not domestic ownership

has a negative or positive effect on corruption, companies’ with

foreign shareholders is observed using the variable foreign, which

show the percentage of the share that is foreign based, government

owned firms’ variable govt is also in percentage of the ownership. In

addition, we captured the firm’s sales, whether or not it originates

from the domestic or foreign market; and if foreign, either direct or

indirect2 using the variables localtrade, directexport as well as

indirectexport. This allows us to measure if the distribution of sales

1 Basis of firm size is taken from the enterprise survey’s measure of firm size. Furthermore, in the regression we drop the small variable because conventionally, we drop the largest choice from the dataset given above.2 By direct export, we refer to sales that the firm directly sells to the foreign distributor, while indirect export refers to exports that are coursed through an intermediate company first, then distributed to other distributors abroad.

38

has anything to do with the probability that the firm would

participate in bribery, since if the firm has export sales, then it’s

direct interaction with customs and trade regulations would be

captured, and domestic as well as indirect export sales would

capture the direct dealings with the Department of Trade and

Industry of the country. Furthermore, this variable also captures

firm level competitiveness in the domestic and foreign markets,

which makes them quite important in our model to avoid omitted

variable bias (Ciccone & Papaioannou, 2007).

To capture country characteristics, this study uses trade as a

share of GDP (openness to trade). We hypothesize that trade as a

share of GDP has a positive effect to the probability of firms’

participation in bribery. This information will be taken from the

World Development Indicators, which is updated regularly by the

World Bank, however to preserve the accuracy of our inferences, we

will limit the data set to the year 2009. Other country variables will

be indicators of the ease of doing business, specifically the average

cost to start a business and the time required to start a business,

which will be taken from the Doing Business dataset of World Bank,

which would vary across the different countries of the ASEAN

region.

39

In this study, we focus our interpretations with the results

generated from the logistic model, specifically the unconditional

multivariate logistic model, because we want to retain the ability of

our model to present the probability of firms to participate in

bribery in consideration of the variables that we did not indicate to

be captured by the constant. And since our sample size is more or

less a lot larger than the recommended sample size of 30 to

accommodate the Law of Large Numbers (Gujarati & Porter, 2011);

in effect, the logistic regression already approximates the normal

distribution that a probit regression would provide, without trying to

force the model to be normally distributed. In addition, our sample

regression for logistic and probit do not really delineate too far from

each other in terms of the values of their coefficients – which would

be an indicator for us that our dataset already approximates the

normal distribution.

40

5. Empirical Results

5.1 Descriptive statistics

Table 1.

Firms that participated in Bribery (Enterprise Survey, 2009)

Number of FirmsPercentage in

Sample

Firms that participated in bribery 878 23.46%Firms that did not participate in bribery 2865 76.54%

Total 3743 100%

Looking at the descriptive statistics, we see that only an average

of 23% of the 3743 respondents have responded that they have

participated in bribery – to be exact only 878. Based on our sample

less than half of the firms in the countries of the Philippines,

Vietnam, Lao PDR and Indonesia participate in bribery. Since there

is no criterion in the literature regarding the amount of tolerable

corruption, then we can only infer that at least less than half of the

sampled ASEAN firms participate.

Table 2.

Firms that consider government their biggest obstacle in the sample (Enterprise Survey, 2009)

Number of Firms Percentage in sample

customs and trade regulations 117 3%tax administration 72 2%business licenses and 97 3%

41

permitsOthers 3457 92%Total 3743 100%

From the descriptive statistics, 3.1% of the firms find that their

biggest obstacle to be customs and trade regulations, while only

1.9% answered tax administration and 2.59% find business licensing

and permits difficult. The other factors that firms find their biggest

obstacle are access to finance, courts, practices of competitors in

the informal sector, corruption, electricity, inadequately educated

workforce, access to land, tax rates, political instability, labor

regulations, and crime. The 3.1% is higher than 1.9% for the tax and

2.59% for the business permit, which mean that custom and trade

regulation is the biggest obstacle (red tape) for the entire 3743 –

firm sample.

Table 3.

Industrial Classification of Firms in the sample (Enterprise Survey, 2009)

Number of Firms

Percentage in Sample

Manufacturing Sector 2621 70%Services Sector 647 17%Other Industries 445 12%Not specified 30 1%Total 3743 100%

Table 4.

42

Firm Size (Enterprise Survey, 2009)Number of Firms Percentage in Sample

small 1643 44%medium 1214 32%large 886 24%Total 3743 100%

Table 5.

Firm Ownership (Enterprise Survey, 2009)Mean Min Max

domestic ownership 84.70% 0.0 100%foreign ownership 12.71% 0.0 100%govt ownership 0.76% 0.0 90%Not Specified 1.83%Total 100.00%

Table 6.

Sources of Sales (Enterprise Survey, 2009)Mean Min Max

domestic trade 83.41% 0.00 100.00indirect exports 4.83% 0.00 100.00direct exports 11.74% 0.00 100.00Not specified 0.02%Total 100.00%

For firm characteristics, 70% of the firms are from

manufacturing sector and 17.3% of it is from service industry; for

the firm size, 44% of the total observed firm is a small firm, and 32%

43

of the firms is medium firm, and the other 24% left is considered as

a large firm; for the ownership of the firm, on the average domestic

ownership in the ASEAN sample is 84%, however foreign and

government ownership is on the average 12.7% and .75%

respectively. To avoid omitted variable bias, observations that are

neither of the three (defined as others), are also included but are

captured when all three criteria are not met by the firm.

For the other country variables, we have used the percentage of

GDP from trade to capture the importance of business in a country

in terms of its economic presence. On the average, the ASEAN

sample given has a 77.32% of its share of GDP in trade. This is quite

a significant amount since it accounts for more than 50% of the

economic growth of the region.

Table 7.

Ease of Doing Business in Selected Countries (World Bank, 2009)

Country

Cost to start a business (% of income per

capita)

Time required to

start a business

(days)

Ease of doing business index 2012 (1=easiest to 185=most difficult)

Philippines 21.60 42 138Vietnam 13.30 39 99Lao PDR 9.69 93 163Indonesia 25 62 128United States 0.69 6 4United Kingdom 0.69 13 7Germany 4.69 18 20France 0.89 7 34Brazil 6.9 119 130China 4.9 38 91Japan 7.5 23 24

44

Korea, Rep. 14.69 14 8United Arab Emirates 6.4 15 26Australia 0.8 2 10

Cost to start a business is recorded as a percentage of the

economy’s income per capita. It contains all official fees and fees for

legal or professional services if such services are required by law.

Fees for acquiring and legalizing company books are included if

these transactions are required by law. Although value added tax

registration can be counted as a separate procedure, value added

tax is not part of the incorporation cost. The company law, the

commercial code and specific regulations and fee schedules are

used as sources for computing costs. If these fees are not available,

a government officer’s estimate is taken as an official source; else

estimates of incorporation lawyers are used. If it so happened that

incorporation lawyers provide different estimates, the median

reported value is applied. In all cases the cost excludes bribes (The

World Bank , 2014).

The minimum cost of starting a business in the sampled ASEAN

firms is 9.7% of the income per capita of the average entrepreneur

(in Lao PDR), and the maximum cost is at 25% (Indonesia).

Intuitively speaking, this may be due to the development of the

industry of the country (with the Philippines at 21.6% and Vietnam

45

at 13.3%). This suggests that in countries that are more developed

in terms of business, then the cost of doing business increases. This

may be in line with classical theories of economics wherein

competition exists as the number of firms increase, and it would

lead to growth (since we can infer that the more expensive it is to

start a business then entrepreneurs would have to invest more

resources in order to enter the market). However on the average

19.8% of income per capital is used in order to start a business in

the selected ASEAN region – close to 20% of the total income per

capita of an average entrepreneur. In comparison to countries in the

Western Hemisphere such as the United States, Germany, and

France, who have an average of only 1.75% of the per capita income

of individuals starting a business, for domestic entrepreneurs it is

more expensive to start a business in the ASEAN compared to some

developed Western countries.

The time required to start a business, on the average in the

ASEAN sample, is 53 days. The minimum in ASEAN is Vietnam,

wherein it takes at least 39 days to start a business. Since the time

needed to start a business is a crucial consideration that firms have,

since more time means higher opportunity cost, since the firm could

have used that time to compensate for the fixed costs that they have

incurred in starting the business.

46

Having described the data, we proceed on presenting the

econometric findings of the study. Moreover, further interpretation

of the results would be discussed later on.

5.2 Econometric Analysis

Employing QRM, we obtain results to empirically test the validity

of the relationship between bribery and bureaucratic inefficiencies,

and including firm and country characteristics that are of interest in

this study. Logistic and probit models are practically the same,

except the logistic distribution follows the odds ratio to infer the

maximum likelihood, while probit follows the standard normal

distribution (Gujarati, D., 2009).

The option with robust standard errors was used in order to get

rid of the presence of heteroscedasticity, which was found to be

present using the Breusch-Pagan test. However, the coefficients for

both the standard and robust options do not vary significantly.

From the empirical findings that we have obtained from the

econometric analysis, we have gathered a substantial amount of

information regarding the impact of bureaucratic inefficiencies and

firm level characteristics. The results show that there is a

substantive amount of statistical evidence to suggest that our

empirical model provides key insights regarding this relationship

47

(see Table 8).

First of all, let us look at how the probability of the firm

participating in bribery increases as our focus variables – customs

and trade regulations, tax administrations and business licenses and

permits – are present. Note that all increase the probability of the

firm participating in bribery by 12-14 percent (14.91%, 12.06%, and

13.66% respectively relative to firms that do not find the

government their biggest obstacle). Being statistically significant,

this fact is quite alarming especially for firms starting out. In effect,

bribery is in the form of a percentage of the cost of the firms to

bribe, and as the likelihood for firms to participate in bribery

increases, the probability that the firm will incur additional costs

will increase as well – which acts as a barrier for new firms to enter

the market (Blackburn, Bose, & Capasso, 2008). Although barriers

could also act as a screening process to “sift” the competitive firms

from the ones that cannot cope up with the competition, the system

itself creates an opportunity loss as the firms taken out from the

competition could possibly gotten better in the future.

Other interesting findings that we can get from the regression is

that firm characteristics in terms of size and sales have a positive

and statistically significant effect on the probability of the firm to

participate in bribery; .0402951 for medium firms relative to small

48

firms, .0439025 for large firms relative to small firms, .0022692 for

every increase in the percentage of domestic trade, and .0018469

for every increase in the percentage of direct exports3. In

consideration of the marginal effects in the manufacturing sector

and small firms, we get the total effect of the firm characteristics

stated in the computation below:

Services−|Manufacturing|=total effect of the Manufacturing Sector ¿bribery

.0346959−|−.0396438|=−0.0049479=negative effect for services sector firms

Other Industries−|Manufacturing|=total effect of other industries ¿bribery

.0532141−|−.0396438|=0.0135703=positive effect for firms∈other industries

Medium−|Small|=Total effect of Mediumfirms ¿bribery

.0402951−|−.0412412|=−0.0009461=negativeeffect for medium firms

Large−|Small|=Total effect of Large firms¿bribery

.0439025−|−.0412412|=0.0026613=positive effect for large firms

Taking the absolute value of the base allows us to get the actual

3 Interpreting dummy variables: The values of the dummies are in relation to the dropped dummy (Gujarati & Porter, 2011). Dropped dummy then becomes the basis of the whole sample – meaning to say that the constant contains the dropped dummy. Marginal Effects of the dropped variables are shown in Appendix A.

49

difference between the dropped dummy with respect to what was

not dropped. With the calculations above, we were able to

determine the total marginal effects of the significant firm variables

of the sample (for interpretation). It can be noted that the total

effect of services sector is negative, yet significant only at the ten

percent level of significance, which goes to show that the possibility

of the policy becoming erroneous. However for other industries,

which include IT, transportation, wholesale, other services, hotels

and restaurants, and construction4, the total effect is positive –

which can be inferred that these industries are more likely to

participate in bribery.

As for firm size, it can be noted that as the size increases, the

values of the marginal effects of the size becomes positive. Although

medium firms have a total negative effect, firms that are large have

a total added probability to participate in bribery. This clearly

supports the theories of Blackburn, Bose and Capasso (2008) that as

the marginal benefit of bribery (as larger firms have a larger

opportunity cost in production and sales), the probability that the

firm will decide to participate in bribery increases.

It can be noted that firm ownership does not have any

statistically significant effect; which neither affirms nor rejects our

4 Taken from the Enterprise Survey’s classification of other industries, wholesale is not part of the Services sector.

50

a-priori expectations. Intuitive to the results from customs, tax and

business licenses and permits, firm size and competition do have a

strong significant effect on the firm’s decision to bribe, due to the

costs that are integral to starting a business.

The average cost and time to put up a business as well as the

percentage of GDP from trade (a proxy for firm competitiveness) all

have a positive and statistically significant effect on the probability

of the firm to participate in bribery. This goes to show that

competition drives the firm to start business early in order to

recover from the costs of doing business, which then again, affects

competition.

The results generated from the regression goes to show how the

institution itself allows corrupt activities to continue, due to the

increase in the incentive for the firms to bribe. In effect, the system

allows this to occur and furthermore affects the overall chance for

micro, small and medium businesses to begin and integrate into the

market.

Therefore, the sample logistic regression function is stated as

follows:

Pr ( Bribe )=.22191582+.1491254 customregulation i+.1365819 businesslicensei+.1206133 taxregulationi+.0346959 services+.0532141 otherindustry+.0402951 mediumi+.0439025 large i−.0009772 domestici−.0001095 foreigni−.000991 govti+.0022692localtradei+.0019383 indirectexport i+.0018469 diretexporti+.0181569 costbusinessi+.0031569timebusinessi+.0041012 tradegdpi

51

Table 8: Model Results (marginal effects)5

VARIABLES LOGISTIC REGRESSION PROBIT REGRESSIONDependent Variable: bribe Dependent Variable: bribe

Customregulation .1491254*** .1491352***(.04767) (0.04678)

taxregulation .1206133** 0.1210105**(.05704) (0.05689)

businesslicense .1365819*** 0.1342439***(.05131) (0.05041)

services .0346959* 0.0322529(.02436) (0.02052)

otherindustry .0532141** 0.0539037**(0.126) (0.02399)

medium .0402951** 0.0422265**(.01772) (.01748)

large .0439025** .0461865 **(.02128) (.02097)

domestic -.0009772 -.0009184(.00088) (.00088)

foreign -.0001095 -.0001934(.00089) (.0009)

govt -.000991 -.0009165(.00302) (.00304)

localtrade .0022692*** .0023808 ***(.00078) (.0008)

indirectexport .0019383 .0020304(.00136) (.00141)

directexport .0018469 ** .0018801**(.00085) (.00088)

costbusiness .0181569 *** .0180678 ***(.00359) (.00353)

timebusiness .0031569 *** .0031575 ***(.00087) (.00086)

tradegdp .0041012 *** .004149 ***(.00055) (.00055)

Pr (bribe) ceteris paribus .22191582 .22388944

Observations 3,741 3,741

5 Tests for critical assumptions – Muticollinearity, Heteroscedasticity (fixed by using robust option), normality, omitted variable bias and specification test - were conducted to verify if the model is statistically sound (see Appendix A). Robust standard errors are in parentheses. Stars interpreted as *** p<0.01, ** p<0.05, * p<0.1

respectively

52

53

6. Conclusion and Recommendation

Corruption is a problem that we have to minimize, if not

eliminate altogether, in order to have inclusive political agenda

which results to a more certain economic growth. This has not been

pointed out more clearly than economists such as Acemoglu and

Robinson (2012) who justify the need of inclusive political systems

in order for a nation to work. Gaviria (2002) as well strongly

expressed in his study that corruption as a whole substantially

reduces firm competitiveness, and differs from one country to

another. This is partly due to the unaccounted for costs that firms

incur from the government’s inefficiencies, which in one form be the

excessive regulation that particular countries have and end up

slowing the process of starting out and continuing a business.

Hence, misallocation of resources would cause market failure

(Acemoglu & Verdier, 2000), since the price and quantity

appropriations in the market to be suboptimal, making it difficult to

predict and to account for prior to the transaction. This leads to

barriers to entry and market failure in the short run – which is

important in boosting the economic situation of any country.

This study extends the literature on corruption and bureaucratic

inefficiencies as it applies the methodology used by Herrera and

Lijane (2007) to ASEAN and three other economies. And it has

54

indeed by providing key insights that can prove useful for policy

makers in making sound economic decisions. The results suggest

that there is indeed a strong positive relationship between them.

For clarifying the importance to make governments more

efficient, a theoretical model was formulated to explain the nature

of bribery and its market structure. Knowing full well that the

assumption of a single government per country, we have isolated

the case into a cost-benefit analysis wherein the participation of

firms to corruption would depend on the firm and the country

characteristics, whether or not their choice in participating will

increase or decrease the likelihood of them even thinking about

participating. These firm characteristics are its size, industry type

and its market – which also affects its relationship with the

government. Country characteristics include the average cost and

time required to start a business, specifically the costs incurred with

government transactions such as permits and licenses to sell. In

addition, the countries’ trade competitiveness is also taken into

account in order to see whether or not competition is of the essence

for the firm to participate in bribery.

Take for example a large firm that depends on its export sales.

This firm may have a larger probability in participating in bribery,

as it has to directly sell its products abroad. Since it is highly

55

dependent on export sales, the firm would prefer to have its

products delivered abroad as soon as possible in order to sell

abroad before other competitors get to sell. Moreover, if its country

of origin is highly competitive, then in order to be better than its

competitors, it has to sell more than the others – since market share

is essential for the firm to remain in the competition.

Hence, we conclude that firms consider the option to participate

in bribery depending on their elasticity to it. As the firm acquires

more benefit from the option to bribe, then the probability that it

would participate increases. With our econometric model, which

appropriately determined what the effect of the firm’s perception of

red tape, its characteristics as well as business environment to the

probability that the firm would participate in bribery, we were able

to obtain some interesting results.

Using a Quantitative Response Model, the study suggests that

according to the ASEAN sample of firms from Vietnam, the

Philippines, Lao PDR and Indonesia, countries with more excessive

regulation and overall difficulty in starting a business are more

likely to participate in bribery, along with some interesting results

from the control variables. This empirical part of this study provides

us with substantial reason to believe that bureaucratic

inefficiencies, otherwise known as red tape, provides an incentive

56

for corrupt practices such as bribery to occur, since the opportunity

cost for doing business substantially increases. This strongly

supports the study of Blackburn, Bose and Capasso (2008) that

suggest that asymmetric information between the government and

the private sector lead to procurement contracts (in the form of

bribery) that allow businesses to increase profits through the

government’s appropriation of the bribe to cut red tape.

Furthermore, this study supports Rose-Ackerman’s (1996) argument

that corrupt firms have an incentive to pay the government in order

to restructure the system for their own benefit.

Furthermore, we have identified the firms that are more likely to

bribe. Based on our econometric analysis, the probability for the

firm to bribe increases if the firm:

Is not part of the manufacturing sector

Gets larger in size

Has larger domestic and direct export sales, but

insignificant on indirect exportation

In a country with, on the average, a larger cost to start a

business, as well as a longer time to start a business

In a country with a high percentage of GDP from trade

In order to reduce corruption, one problem that governments

have to put effort on is to improve their services to the private

57

sector, especially in the bureau of customs, the bureau of internal

revenue, and the department of trade and industry, since

inefficiencies in these offices, according to our findings, increase the

likelihood of these firms to participate in bribery. This supports the

theories presented by Blackburn, Bose and Capasso (2008). Another

way to reduce corruption is by reducing the cost and time in doing

business, since the lower the opportunity cost is to start a business,

the firm is less likely to participate in bribery, and since this study

has established that the illicit service that firms buy from the

government becomes more and more expensive (since demand for it

would decrease due to a better government system), it is highly

likely that corruption will decrease, since the market will greatly be

weakened.

There are concrete strategies that have been recommended by

Huther and Shah (2000) in their study that evaluates the impact of

anti-corruption activities. In their study, they have recommended

based on the policies’ relevance, efficacy, efficiency and

sustainability, that for countries with weak to fair quality of

governance, the most effective anti-corruption policies are economic

policy reform, strengthen institutions of participation and

accountability (media, judiciary, as well as citizen participation),

reducing public sector size as well as enforcing the rule of law.

58

These policies that were recommended to the World Bank are in line

with the results of this study, as the strengthening of accountability

would definitely affect the cost as well as the time required to

acquire a business, and enforcing the rule of law will definitely

increase the probability that sanctions to those who do participate

in corrupt practices such as bribery may have a lower incentive to

participate. By introducing policies in order to increase the marginal

cost of participating in bribery, then the probability of firms

participating in bribery will decrease; if there is no incentive for the

firm to participate in bribery in the first place, then why would the

firm do so (Huther & Shah, 2000)?

Such Anti-corruption policies have been implemented by some of

the more economically successful economies in the ASEAN region,

specifically Hong Kong and Singapore. For Hong Kong, the

Independent Commission Against Corruption (ICAC) has eradicated

almost all overt and syndicated type of corruption in the

government, and has provided an excellent business environment in

the economy. They have based their strategies on 1) a Three-

Pronged Strategy, which comprises of deterrence, prevention and

education; 2) Enforcement-Led, which the ICAC primarily focuses

their funding on the enforcement of law; and 3) Professional staff,

which promotes an environment of professionalism in implementing

59

the enforcement of law. This is all backed up with an effective legal

framework, a review mechanism, an equal emphasis on the public

and private sector, and a strong political will in eradicating

corruption (Man-wai, 2011). For Singapore, the Corrupt Practices

Investigation Bureau (CPIB) which was established in 1952 however

before the separation from the United Kingdom was handicapped by

its lack of resources. The Prevention of Corruption Act (POCA)

established a year after Singapore’s independence from the UK,

which strengthened the institutional power that the Bureau has.

One of the strategies that the government took recommended by the

Bureau was to increase the wages of ministers and civil servants,

which generally lowered the loss of talented public servants and

effectively reducing the incidence of bribery and corrupt practices

(Quah, 2007).

It is in the government’s interest for firms to be allowed to

efficiently do business in their respective economies, which will

provide employment and increase the overall economic growth of

the country. However due to the inefficiencies, it has become

common for firms to simply bribe the individuals, for a sum of

money, in order to avoid the sunk costs incurred from waiting for

business contracts and permits. In order to reduce corruption, it

could be best for the government to improve the system and strive

60

to create a seamless relationship between the government offices in

charge of regulating trade in the economy and the businesses which

provide productivity and employment in the country. By doing this it

is possible to reduce the incentive of firms to participate in such

activities in the first place, which is an unnecessary cost that the

firm makes in order to do business. Specifically, had there not been

a problem in terms of inefficiency in the government, the firm would

not have spent for bribery from the very beginning.

61

7. References

Acemoglu, D., & Robinson, J. (2012). Why Nations Fail. New York: Crown Publishing Group.

Acemoglu, D., & Verdier, T. (2000). The Choice between Market Failures and Corruption. The American Economic Review, 194-211.

Alemu, A. (2013). The effect of corruption on FDI inflows: Empirical evidence from Asian economies. Global Conference on Business and Financial Proceedings, 280-288.

Asiedu, E., & Freeman, J. (2009). The Effect of Corruption on Investment Growth: Evidence from Firms in Latin America, Sub-Saharan Africa, and Transition Countries. Review of Development Economics, 200-2014.

Batra, G., & Kaufmann, D. A. (2003). Investment Climate around the World: Voices of the firms from the World Business Environment Survey. Washington, D.C.: World Bank.

Besanko, D. A., & Braetigam, R. R. (2011). Microeconomics. New Jersey: John Wiley & Sons, Inc.

Blackburn, K., Bose, N., & Capasso, S. (2008). Living With Corruption: Threshold Effects of Red Tape and Rent Seeking. Department of Economic Studies Working paper 4_2008.

Bolongaita, E. P. (2010, August). An exception to the rule? Why Indonesia’s Anti-Corruption Commission succeeds where others don’t – a comparison with the Philippines’ Ombudsman. U4 Issue, 4-30.

Ciccone, A., & Papaioannou, E. (2007). Red tape and delayed entry. Journal of the European Economic Association, 444–458.

Ehrenberg, R., & Smith, R. (2012). Modern Labor Economics (11th ed.). Boston, MA: Pearson Education.

Enterprise Surveys. (n.d.). The Enterprise Surveys. Retrieved September 2013, from The Enterprise Surveys website: http://www.enterprisesurveys.org/

Everhart, S., Martinez- Vazquez, J., & McNab, R. M. (2009). Corruption, governance, investment and growth in emerging markets. Applied Economics, 1579–1594.

Gaviria, A. (2002). Assessing the Effects of Corruption and Crime on Firm Performance: Evidence from Latin America. Emerging Markets Review, 245-268.

Global Security. (2013, July). Laos - Corruption. Retrieved February 2014, from Global Security Web site: http://www.globalsecurity.org/military/world/laos/corruption.htm

GMA news. (2013, June 3). Moody's hints at 4th PHL investment grade rating this year. Retrieved from GMA News Online: http://www.gmanetwork.com/news/story/311234/economy/business/moody-s-hints-at-4th-phl-investment-grade-rating-this-year

Gujarati, D., & Porter, D. (2009). Basic Econometrics. New York: Mc-Graw Hill.Herrera, A., Lijane, L., & Rodriguez, P. (2007). Bribery and the Nature of Corruption.

MI: Michigan State University.

62

Huther, J., & Shah, A. (2000). Anti-Corruption Policies and Programs. Policy Research Working Paper; World Bank 2501.

Javorcik, B., & Wei, S.-J. (2009). Corruption and Cross-Border Investment: Firm Level Evidence. Journal of International Money and Finance, 605-624.

Johnson, S., Kaufmann, D., McMillan, J., & Woodruff, C. (2000). Why do firms hide? Bribes and unofficial activity after communism. Journal of Public Economics, 495-520.

Man-wai, T. K. (2011). Successful anti-corruption strategy & international good practices. The 14th UNAFEI UNCAC Training Programme , 113-122.

Quah, J. S. (2007). Combating Corruption Singapore-Style: Lessons from other Asian countries. Maryland Series in Contemporary Asian Studies, 189-271.

Rose-Ackerman, S. (1996). The Political Economy of Corruption: Causes and Consequences. Washington, D.C.: World Bank note 74.

The World Bank . (2014). Starting a Business Methodology. Retrieved March 5, 2014, from Doing Business: http://www.doingbusiness.org/Methodology/starting-a-business#cost

The World Bank. (1997). Helping Countries Combat Corruption. The World Bank.Torres-Tupas, T. (2013, July 8). Anti- corruption group joins call for probe into P3.8-B

license plate deal. The Philippine Daily Inquirer.Transparency International. (2013, December). Coruptions Perceptions Index.

Retrieved December 2013, from Transparency International Web site: http://www.transparency.org/research/cpi/overview

USAID. (2010). Vietnam Provincial Competitiveness Index 2010.Vogl, F. (1998, June). The Supply Side of Global Bribery. Finance and Development,

pp. 30-33.

63

Appendix A. STATA Results

Linear Probability Model

_cons -.6978877 .1449986 -4.81 0.000 -.9821721 -.4136034 tradegdp .0043356 .0005291 8.19 0.000 .0032982 .005373 timebusiness .0032358 .0008109 3.99 0.000 .0016459 .0048258 costbusiness .0180996 .0033337 5.43 0.000 .0115637 .0246356 directexport .0021419 .0009093 2.36 0.019 .0003591 .0039248 indirectexport .0021332 .0014443 1.48 0.140 -.0006986 .0049649 localtrade .0026632 .0008179 3.26 0.001 .0010596 .0042667 govt -.0009003 .002986 -0.30 0.763 -.0067547 .0049541 foreign -.0001434 .0008865 -0.16 0.871 -.0018814 .0015946 domestic -.0009773 .0008568 -1.14 0.254 -.002657 .0007025 large .0402275 .0192154 2.09 0.036 .0025537 .0779013 medium .0371391 .0162626 2.28 0.022 .0052546 .0690236 otherindustry .0497736 .0215795 2.31 0.021 .0074648 .0920825 services .0327209 .0191391 1.71 0.087 -.0048034 .0702451 businesslicense .1238949 .0426001 2.91 0.004 .0403732 .2074167 taxregulation .1213383 .0492829 2.46 0.014 .0247142 .2179624customregulation .146062 .0391001 3.74 0.000 .0694023 .2227217 bribe Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 671.405507 3740 .179520189 Root MSE = .41187 Adj R-squared = 0.0551 Residual 631.715424 3724 .169633572 R-squared = 0.0591 Model 39.6900829 16 2.48063018 Prob > F = 0.0000 F( 16, 3724) = 14.62 Source SS df MS Number of obs = 3741

VIF Test for Multicollinearity

Mean VIF 2.22 businessli~e 1.01 0.989302taxregulat~n 1.01 0.989070customregu~n 1.02 0.978971otherindus~y 1.08 0.929112indirectex~t 1.12 0.893584 govt 1.14 0.876681 services 1.16 0.865426 localtrade 1.23 0.813375 foreign 1.24 0.809698 domestic 1.24 0.806122directexport 1.25 0.798734 medium 1.28 0.782494 large 1.47 0.679453timebusiness 3.79 0.263766costbusiness 7.22 0.138432 tradegdp 9.19 0.108781 Variable VIF 1/VIF

Result: Tolerable Multicollinearity: below 10 mean variance inflation factor (Gujarati & Porter, 2011)

64

Breusch-Pagan Test for Heteroscedasticity

Prob > chi2 = 0.0000 chi2(1) = 140.22

Variables: fitted values of bribe Ho: Constant varianceBreusch-Pagan / Cook-Weisberg test for heteroskedasticity

Result: Unrestricted Heteroscedasticity(remedied by using robust option)

Ramsey RESET test for Omitted Variable Bias

Prob > F = 0.1374 F(3, 3721) = 1.84 Ho: model has no omitted variablesRamsey RESET test using powers of the fitted values of bribe

Result: No Omitted Variable Bias at the 90% confidence interval

Specification Test

_cons -.0614725 .0421612 -1.46 0.145 -.1441338 .0211888 _hatsq -.8869522 .5585464 -1.59 0.112 -1.982038 .2081332 _hat 1.51029 .3279022 4.61 0.000 .8674054 2.153175 bribe Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 671.405507 3740 .179520189 Root MSE = .41096 Adj R-squared = 0.0592 Residual 631.289559 3738 .168884312 R-squared = 0.0597 Model 40.1159471 2 20.0579736 Prob > F = 0.0000 F( 2, 3738) = 118.77 Source SS df MS Number of obs = 3741

Since hatsq is insignificant, then there is no specification error.

65

Normality Test: Reject null at the 95% confidence interval; wherein Ho accepts hypothesis that data is not normal.

Doornik-Hansen chi2(34) = 8.94e+05 Prob>chi2 = 0.0000

Test for multivariate normality

Result: Data is normal at the 95% confidence interval

Corrected Linear Probability Model with robust errors

_cons -.6978877 .1404389 -4.97 0.000 -.9732324 -.4225431 tradegdp .0043356 .0005301 8.18 0.000 .0032963 .0053749 timebusiness .0032358 .000776 4.17 0.000 .0017145 .0047572 costbusiness .0180996 .0031995 5.66 0.000 .0118267 .0243726 directexport .0021419 .0010417 2.06 0.040 .0000996 .0041842 indirectexport .0021332 .0016763 1.27 0.203 -.0011534 .0054197 localtrade .0026632 .0009544 2.79 0.005 .000792 .0045344 govt -.0009003 .0034028 -0.26 0.791 -.0075718 .0057712 foreign -.0001434 .0008563 -0.17 0.867 -.0018223 .0015354 domestic -.0009773 .0008742 -1.12 0.264 -.0026913 .0007368 large .0402275 .0199357 2.02 0.044 .0011416 .0793134 medium .0371391 .0163732 2.27 0.023 .0050377 .0692405 otherindustry .0497736 .0224938 2.21 0.027 .0056722 .093875 services .0327209 .0188133 1.74 0.082 -.0041645 .0696062 businesslicense .1238949 .0472234 2.62 0.009 .0313086 .2164813 taxregulation .1213383 .0562372 2.16 0.031 .0110796 .2315971customregulation .146062 .0450349 3.24 0.001 .0577666 .2343574 bribe Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = .41187 R-squared = 0.0591 Prob > F = 0.0000 F( 16, 3724) = 12.44Linear regression Number of obs = 3741

Since study is not concerned with forecasting, a low R squared is tolerable.

Robust Logit Regression with marginal effects

66

_cons -6.508943 .9077681 -7.17 0.000 -8.288136 -4.729751 tradegdp .0237518 .0032151 7.39 0.000 .0174503 .0300532 timebusiness .0182832 .0050739 3.60 0.000 .0083384 .0282279 costbusiness .1051543 .0209584 5.02 0.000 .0640767 .1462319 directexport .010696 .004912 2.18 0.029 .0010687 .0203233 indirectexport .0112254 .0078791 1.42 0.154 -.0042173 .0266681 localtrade .0131421 .0045251 2.90 0.004 .004273 .0220111 govt -.0057394 .0174668 -0.33 0.742 -.0399737 .028495 foreign -.0006341 .0051414 -0.12 0.902 -.0107111 .0094428 domestic -.0056595 .0051257 -1.10 0.270 -.0157056 .0043867 large .2455079 .1150874 2.13 0.033 .0199407 .471075 medium .228309 .0983073 2.32 0.020 .0356301 .4209878 otherindustry .2905119 .125966 2.31 0.021 .0436231 .5374007 services .1941258 .1123116 1.73 0.084 -.0260009 .4142525 businesslicense .6770606 .2257732 3.00 0.003 .2345533 1.119568 taxregulation .6052266 .25528 2.37 0.018 .1048869 1.105566customregulation .7328163 .2070755 3.54 0.000 .3269558 1.138677 bribe Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Log pseudolikelihood = -1932.8779 Pseudo R2 = 0.0512 Prob > chi2 = 0.0000 Wald chi2(16) = 206.37Logistic regression Number of obs = 3741

Iteration 4: log pseudolikelihood = -1932.8779 Iteration 3: log pseudolikelihood = -1932.8779 Iteration 2: log pseudolikelihood = -1932.8784 Iteration 1: log pseudolikelihood = -1935.7305 Iteration 0: log pseudolikelihood = -2037.2482

(*) dy/dx is for discrete change of dummy variable from 0 to 1 tradegdp .0041012 .00055 7.46 0.000 .003024 .005178 77.3054timebu~s .0031569 .00087 3.61 0.000 .001445 .004869 53.0543costbu~s .0181569 .00359 5.05 0.000 .011113 .025201 19.8207direct~t .0018469 .00085 2.18 0.029 .000186 .003508 3.73269indire~t .0019383 .00136 1.42 0.154 -.000728 .004605 2.18899localt~e .0022692 .00078 2.90 0.004 .000737 .003802 6.74713 govt -.000991 .00302 -0.33 0.742 -.006902 .00492 1.29725 foreign -.0001095 .00089 -0.12 0.902 -.00185 .001631 3.26544domestic -.0009772 .00088 -1.10 0.269 -.002711 .000756 5.66266 large* .0439025 .02128 2.06 0.039 .002191 .085614 .236835 medium* .0402951 .01772 2.27 0.023 .005561 .075029 .324245otheri~y* .0532141 .02436 2.18 0.029 .00547 .100959 .118952services* .0346959 .02071 1.67 0.094 -.005903 .075295 .172948busine~e* .1365819 .05131 2.66 0.008 .036015 .237149 .025929taxreg~n* .1206133 .05704 2.11 0.034 .008819 .232407 .019246custom~n* .1491254 .04767 3.13 0.002 .055702 .242548 .031275 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X = .22191582 y = Pr(bribe) (predict)Marginal effects after logit

67

Robust Probit Regression with marginal effects

_cons -3.807825 .5135304 -7.41 0.000 -4.814326 -2.801324 tradegdp .0138729 .0018373 7.55 0.000 .0102718 .0174741 timebusiness .0105577 .0028699 3.68 0.000 .0049328 .0161826 costbusiness .060413 .0118318 5.11 0.000 .0372232 .0836028 directexport .0062865 .0029418 2.14 0.033 .0005207 .0120523 indirectexport .006789 .004701 1.44 0.149 -.0024248 .0160029 localtrade .0079606 .002679 2.97 0.003 .0027098 .0132114 govt -.0030645 .0101647 -0.30 0.763 -.022987 .0168581 foreign -.0006468 .0029979 -0.22 0.829 -.0065225 .005229 domestic -.0030707 .0029579 -1.04 0.299 -.0088681 .0027266 large .1500569 .0662945 2.26 0.024 .020122 .2799918 medium .1386957 .0564425 2.46 0.014 .0280704 .249321 otherindustry .1719691 .0733073 2.35 0.019 .0282893 .3156488 services .1051498 .065333 1.61 0.108 -.0229006 .2332002 businesslicense .3977442 .1361872 2.92 0.003 .1308222 .6646663 taxregulation .3613138 .1553835 2.33 0.020 .0567678 .6658598customregulation .438185 .1248943 3.51 0.000 .1933966 .6829734 bribe Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Log pseudolikelihood = -1932.0139 Pseudo R2 = 0.0517 Prob > chi2 = 0.0000 Wald chi2(16) = 208.80Probit regression Number of obs = 3741

Iteration 3: log pseudolikelihood = -1932.0139 Iteration 2: log pseudolikelihood = -1932.0139 Iteration 1: log pseudolikelihood = -1932.2733 Iteration 0: log pseudolikelihood = -2037.2482

(*) dy/dx is for discrete change of dummy variable from 0 to 1 tradegdp .004149 .00055 7.57 0.000 .003075 .005223 77.3054timebu~s .0031575 .00086 3.68 0.000 .001477 .004838 53.0543costbu~s .0180678 .00353 5.12 0.000 .01115 .024986 19.8207direct~t .0018801 .00088 2.14 0.033 .000156 .003604 3.73269indire~t .0020304 .00141 1.44 0.149 -.000725 .004786 2.18899localt~e .0023808 .0008 2.97 0.003 .00081 .003952 6.74713 govt -.0009165 .00304 -0.30 0.763 -.006875 .005042 1.29725 foreign -.0001934 .0009 -0.22 0.829 -.001951 .001564 3.26544domestic -.0009184 .00088 -1.04 0.299 -.002652 .000815 5.66266 large* .0461865 .02097 2.20 0.028 .005092 .087281 .236835 medium* .0422265 .01748 2.42 0.016 .007968 .076485 .324245otheri~y* .0539037 .02399 2.25 0.025 .006891 .100916 .118952services* .0322529 .02052 1.57 0.116 -.007959 .072465 .172948busine~e* .1342439 .05041 2.66 0.008 .035444 .233043 .025929taxreg~n* .1210105 .05689 2.13 0.033 .0095 .232521 .019246custom~n* .1491352 .04678 3.19 0.001 .057444 .240827 .031275 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X = .22388944 y = Pr(bribe) (predict)Marginal effects after probit

68

Finding the Marginal Effects of the dropped variables

(*) dy/dx is for discrete change of dummy variable from 0 to 1 tradegdp .0041025 .00055 7.46 0.000 .003025 .00518 77.3054timebu~s .0031589 .00087 3.62 0.000 .001447 .004871 53.0543costbu~s .0181432 .00359 5.05 0.000 .011102 .025185 19.8207direct~t .0018346 .00083 2.20 0.028 .000199 .00347 3.73269indire~t .001927 .00136 1.42 0.156 -.000736 .00459 2.18899localt~e .002265 .00078 2.90 0.004 .000735 .003795 6.74713 govt -.000857 .00299 -0.29 0.774 -.00672 .005006 1.29725 foreign -.0000993 .00088 -0.11 0.910 -.001831 .001632 3.26544domestic -.0009566 .00088 -1.08 0.279 -.002689 .000776 5.66266 small* -.0412412 .01519 -2.72 0.007 -.071009 -.011474 .43892manufa~g* -.0396438 .01649 -2.40 0.016 -.071965 -.007323 .70008busine~e* .1365429 .05137 2.66 0.008 .035854 .237231 .025929taxreg~n* .1205833 .05687 2.12 0.034 .009116 .232051 .019246custom~n* .1489769 .04768 3.12 0.002 .05553 .242424 .031275 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X = .22197345 y = Pr(bribe) (predict)Marginal effects after logit

. mfx

.

_cons -6.045729 .8822213 -6.85 0.000 -7.774851 -4.316607 tradegdp .0237547 .0032173 7.38 0.000 .0174489 .0300605 timebusiness .018291 .0050744 3.60 0.000 .0083454 .0282365 costbusiness .1050556 .0209475 5.02 0.000 .0639993 .146112 directexport .0106229 .0048356 2.20 0.028 .0011453 .0201004 indirectexport .0111578 .007866 1.42 0.156 -.0042593 .0265748 localtrade .0131154 .0045157 2.90 0.004 .0042647 .0219661 govt -.0049625 .0173228 -0.29 0.775 -.0389147 .0289896 foreign -.0005752 .0051154 -0.11 0.910 -.0106012 .0094507 domestic -.0055389 .0051218 -1.08 0.280 -.0155775 .0044998 small -.2407871 .0893869 -2.69 0.007 -.4159822 -.065592 manufacturing -.2240087 .0911634 -2.46 0.014 -.4026857 -.0453316 businesslicense .6768039 .2260254 2.99 0.003 .2338022 1.119806 taxregulation .605013 .2545319 2.38 0.017 .1061396 1.103886customregulation .7320823 .2071384 3.53 0.000 .3260984 1.138066 bribe Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Log pseudolikelihood = -1933.3432 Pseudo R2 = 0.0510 Prob > chi2 = 0.0000 Wald chi2(14) = 205.20Logistic regression Number of obs = 3741

69

(*) dy/dx is for discrete change of dummy variable from 0 to 1 tradegdp .0041472 .00055 7.56 0.000 .003073 .005222 77.3054timebu~s .0031586 .00086 3.68 0.000 .001478 .004839 53.0543costbu~s .0180388 .00353 5.11 0.000 .01112 .024957 19.8207direct~t .0018698 .00087 2.16 0.031 .000173 .003567 3.73269indire~t .002024 .0014 1.44 0.150 -.000729 .004777 2.18899localt~e .0023794 .0008 2.97 0.003 .00081 .003949 6.74713 govt -.0007719 .00302 -0.26 0.798 -.00669 .005146 1.29725 foreign -.0001888 .00089 -0.21 0.832 -.001937 .00156 3.26544domestic -.0008942 .00088 -1.01 0.312 -.002626 .000838 5.66266 small* -.04374 .01507 -2.90 0.004 -.073282 -.014198 .43892manufa~g* -.0389548 .01643 -2.37 0.018 -.07116 -.00675 .70008busine~e* .134427 .05044 2.67 0.008 .03557 .233285 .025929taxreg~n* .1208975 .05679 2.13 0.033 .009596 .232199 .019246custom~n* .1491178 .04679 3.19 0.001 .057408 .240827 .031275 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X = .22396528 y = Pr(bribe) (predict)Marginal effects after probit

. mfx

_cons -3.532474 .4996524 -7.07 0.000 -4.511774 -2.553173 tradegdp .0138643 .0018388 7.54 0.000 .0102604 .0174683 timebusiness .0105592 .0028702 3.68 0.000 .0049338 .0161846 costbusiness .0603045 .0118315 5.10 0.000 .0371151 .0834939 directexport .0062508 .0028943 2.16 0.031 .000578 .0119236 indirectexport .0067662 .0046957 1.44 0.150 -.0024372 .0159697 localtrade .0079545 .0026757 2.97 0.003 .0027102 .0131987 govt -.0025803 .0100949 -0.26 0.798 -.022366 .0172053 foreign -.0006311 .0029825 -0.21 0.832 -.0064766 .0052145 domestic -.0029893 .0029553 -1.01 0.312 -.0087815 .0028029 small -.1472866 .0511304 -2.88 0.004 -.2475004 -.0470729 manufacturing -.1278068 .0529882 -2.41 0.016 -.2316617 -.0239518 businesslicense .3981869 .1362299 2.92 0.003 .1311812 .6651927 taxregulation .3609567 .1550994 2.33 0.020 .0569674 .664946customregulation .4380837 .1249082 3.51 0.000 .1932681 .6828993 bribe Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Log pseudolikelihood = -1932.5364 Pseudo R2 = 0.0514 Prob > chi2 = 0.0000 Wald chi2(14) = 207.43Probit regression Number of obs = 3741

Iteration 3: log pseudolikelihood = -1932.5364 Iteration 2: log pseudolikelihood = -1932.5364 Iteration 1: log pseudolikelihood = -1932.7892 Iteration 0: log pseudolikelihood = -2037.2482

70

Appendix B. Initial Regressions

The results of the first regression analysis, which considers only

the 3 vectors: redtape, firm, other country; of the sample firms show

that the marginal effects of the logistic regression for customs and

trade regulations is .1516958– which indicates that according to the

sample, if the firm’s biggest obstacle is the regulations of customs

and trade, then the firm is more likely to participate in bribery by

0.15. This is rather alarming, since this we may infer that the

difficulty derived from customs is an increased incentive for firms to

result to corrupt practices in order to bypass this problem. For tax

administration, which has a logistic marginal effect of 0.1358,

indicates that firms that find tax administration related problems

their biggest obstacle increases the likelihood of participating in

bribery by 0.136 – which also means that tax administration

problems invoke an incentive for firms to participate in bribery also

in order to bypass the system. A similar result can be found as well

from the business licences and permits, with its marginal effects at

0.1399, which indicates that firms are more likely to participate in

bribery by 0.14 if they find business licences and permits their

major obstacle in business.

For firm characteristics, two of the seven variables are

statistically significant. One of them is the manufacturing sector,

71

which at the 10% confidence interval the marginal effects indicates

that for a typical firm in the manufacturing sector will decrease its

likelihood to bribe by -.038. This actually is interesting, since the

results suggest that the manufacturing sector may less likely

participate in bribery. It could be due to the high sanction that is

imposed once the firm is caught, or that reputation may be at stake,

however since this inference only relies on the firm level data

available, from at least 3000 firms per country (besides Lao PDR),

there are firms that did not answer the question, so it is possible

that it may be a data error. Nevertheless, it is still highly likely that

bribery is not as probable in the manufacturing sector. Services, on

the other hand, does not show as statistically significant, since the

confidence interval has both a negative and positive result from the

regression- meaning to say that there are too many firms from the

services sector that either did or did not participate in bribery,

increasing the error term and making it quite difficult to provide a

direct inference regarding the relationship.

Table I . Marginal Effects (First Regression)

LOGIT PROBITVARIABLES bribe bribe

dum_cust 0.1516958*** 0.1507742***(0.04767) (0.04676)

dum_tax 0.135875 ** 0.1371953 **(0.05896) (0.05829)

dum_business 0.1399513*** 0.1370315 ***72

(0.05097) (0.05029)manufacturing -0.0380613* -0.040287 *

(0.02216) (0.02212)services -0.0133041 -0.0167355

(0.02499) (0.02511)small -0.06552 *** -0.0683048***

(0.0186) (0.01859)

medium -0.0168252 -0.0178054(0.01815) (0.01841)

domestic -0.000578 -0.0008553(0.00078) (0.00089)

foreign -0.0005124 0.0000548(0.00088) (0.0009)

govt -0.0010741 -0.000736(0.00307) (0.00304)

tradegdp 0.0042643 *** 0.0043595***(0.00055) (0.00055)

costtostartabusiness 0.0186293*** 0.018927***(0.00362) (0.00354)

timerequiredtostartabusiness 0.0031376*** 0.0032333***(0.00088) (0.00086)

Constant 0.22325986 0.2251367

Observations 3,742 3,742

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

A small firm, in our sample, is less likely to participate in bribery,

since according to our logistic regression the marginal effect is

-.06552. This unlikelihood for small firms to participate in bribery

73

may suggest that these firms find the price of the bribe too high and

would rather incur the time cost. This can explain one assumption of

our theoretical model, wherein for these firms, the marginal cost of

bribes is higher than the marginal benefit that they get from the

illicit service. Regarding medium firms, they are deemed

insignificant in this model, simply because the interval has both

positive and negative signs – which may suggest that there is no

direct relationship between the probability of bribing and the firm

being medium sized. Considering dummy variable trap, we did not

include large firms.

The ownership of the firm in this sample is insignificant. This

suggests that there is no direct correlation between the different

stakeholders of the firm and its participation in bribery.

The other country variable used in this study, trade as share of

GDP, has a positive and significant correlation to bribery. From the

regression we can infer that a percentage increase in trade’s share

in GDP, then firms in that country are 1.02 times more likely to

bribe (with a marginal effects value at .0042643). This may be

caused by an overall income effect: the more people doing business

in a country, the higher the probability that the firm will participate

in bribery (since there are more firms in the economy). In addition,

the cost and time to start a business also increases the likelihood of

74

participating in bribery 1.11 times and 1.01 times respectively (with

marginal effects at .0186293 and .0031376 respectively). This

indicates that the overall system of doing business’ efficiency, from

cutting out-of-pocket costs and time will increase bribery incidence

when the system is more inefficient.

In running an unrestricted regression with all the interaction

variables resulted to a dubious result wherein all variables (except

the country variables) in the regression came out insignificant. This

may be a result from problems arising from the fewness of

interaction variables (for example, government and tax interaction

had only two observations, government and business licences and

permits interaction with one observation, while services and

customs did not have at all). In order to circumvent this problem, we

isolated the interactions in separate regressions.

The second regression result includes a set of interaction

variables specifically with industry type with respect to an obstacle

in government, which allows a more specific result. Of the sample

firms, the regression shows that the marginal effects of the logistic

regression for customs and trade regulations are now .1058868. The

biggest obstacle of the sample firms is still the regulations of

customs and trade, and by computing the odds ratio, then we

discover the firm is 2.66 times more likely to participate in bribery,

75

the incentive for the firm to bribe is still significant. However tax

administration as well as business and licenses are insignificant.

For firms under the manufacturing sector interacting with custom

and trade regulation, its marginal effects to bribery is .1256095 in

the logistic regression. Computing for the odds ratio, the

manufacturing firm with custom and regulation as their biggest

obstacle is 0.379 times more likely to bribe, directing to the point

that there is also incentive for the manufacturing firm with custom

and trade regulation as their biggest obstacle to bribe.

For service industry firms, which have tax administration as it

biggest obstacle’s marginal effect to bribery is -.0867874 and

indicates that there is a decrease in bribery incidence in the

services sector which finds tax administration their biggest obstacle.

Now this may indicate that in this sector, firms may have several

reasons not to participate despite the difficulty from tax

administration, leading to the point that their marginal benefit from

participating in bribery may be less than their marginal cost.

Table II. Marginal Effects (Second Regression)

(1) (2)

LOGIT PROBITVARIABLES Bribe bribe

dy/dx dy/dx

dum_cust 0.1058868 ** 0.1062888 **(0.05796) (0.0583)

76

dum_tax 0.0791249 0.0820129(0.06476) (0.06637)

dum_business 0.0926916 0.0907252(0.06655) (0.06596)

manufacturing 0.0115868 0.0110239(0.03613) (0.03667)

services -0.0362612 -0.0397856*(0.02507) (0.02546)

small -0.0033056 -0.0047088(0.03916) (0.03925)

medium 0.0150176 0.0149585(0.04069) (0.04084)

domestic -0.0009343 -0.0008697(0.00089) (0.00089)

foreign 0.0000433 -0.0000209(0.00088) (0.00089)

govt -0.0005258 -0.000486(0.00294) (0.00304)

manu_custom 0.1256095 * 0.1325385*(0.0854) (0.08666)

manu_tax 0.0625526 0.06331(0.07254) (0.07282)

manu_business -0.0018187 -0.0012566(0.05766) (0.06027)

ser_tax -0.0867874** -0.0884357***(0.03786) (0.03894)

ser_business -0.038411 -0.0390928(0.04173) (0.04269)

tradegdp 0.0042771*** 0.0043308 ***(0.00055) (0.00055)

costtostartabusiness 0.0188994*** 0.0188201***(0.0036) (0.00354)

timerequiredtostartabusiness

0.0031609*** 0.0031551***

(0.00088) (0.00086)

Constant 0.22251635 0.22457714Observations 3,742 3,742

77

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

The third regression includes the interaction variables

between the different government obstacles with respect to

ownership. The result yielded no significant marginal effect from the

interaction variables and only 2 firm characteristic variables,

manufacturing and small, resulted with significant marginal effects

of -.0381451 and -.065972 respectively. These show that

manufacturing, as well as the small firm have an unlikelihood of

0.123 and 0.115 to bribe.

Table 5. Marginal Effects (Third Regression)

(1) (2)LOGIT PROBIT

VARIABLES bribe bribedy/dx dy/dx

dum_cust 0.0938707 0.1015138(0.13217) (0.13552)

dum_tax -0.0763199 -0.0748292(0.13971) (0.15298)

dum_business 0.317157 0.3115161(0. 24599) (0.22799)

manufacturing -0.0381451 * -0.0397452*(0.0222) (0.02214)

services -0.0130653 -0.0166938(0 .02502) (0.02513)

small -0.065972 *** -0.0684533***(0.01871) (0.01871)

medium -0.017808 -0.0180138(0.01821) (0.0185)

domestic -0.0009147 -0.0008508

78

(0.00091) (0.00091)

foreign 1.65e-06 -0.0000239(0.00098) (0.00097)

govt -0.0008502 -0.0008214(0.00299) (0.00308)

dom_cust 0.0324053 0.0295547(0.1058) (0.11062)

dom_tax 0.3013302 0.2868782(0.26187) (0.24647)

dom_business -0.1010121 -0.1093167(0.10689) (0.11227)

foreign_cust 0.0596054 0.0552147(0.10302) (0.10453)

foreign_tax 0.0391033 0.0349341(0.1684) (0.16735)

foreign_business -0.0948163 0.16735(0.09216) (0.09678)

govt_cust -0.1132065 -0.1272519(0.11487) (0.12754)

tradegdp 0.0042923*** 0.0043458***(0.00055) (0.00055)

costtostartabusiness 0.0188886*** 0.0188295***(0.00363) (0.00355)

timerequiredtostartabusiness 0.0032001*** 0.0032007***(0.00088) (0.00086)

Constant 0.22293235 0.22482512

Observations 3,741 3,741Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Running all the regressions, the best model would still be the one

used in Chapter 4, since there is no omitted variable bias, as well as

the intuitiveness of the variables indicated in the model.

79