developing a judicial perceptions index as a measure of institutional quality and estimating the...

Developing a Judicial Perceptions Index as a

Measure of Institutional Quality and

Estimating The Effects of Judicial Quality on

Firm Growth in the Philippines

A thesis submitted to the

University of the Philippines School of Economics

In partial fulfillment of the requirements for

Economics 199

Josemaria Gabriel V. Agregado

Jose Maria L. Marella

Acknowledgments

We wish to acknowledge our esteemed thesis adviser, no less than the Dean of

the UP School of Economics, Dr. Ramon Clarete, for his continued guidance in

helping us write this paper. Because of him, we felt as if we were not only writing

a paper, but also actually fighting for ideas and promoting scholarship. We also

give credit to the UPSE College Secretary, Dr. Toby Monsod, for her invaluable

advice on this paper’s econometric rigors. We also wish to thank The Asia

Foundation for allowing us to utilize their dataset, so that out of raw information

we were able to produce knowledge. We also credit Dr. Ronald Mendoza and

Ms. Ivyrose Baysic of the Asian Institute of Management Policy Center for

allowing us to use their dataset and for letting us expand their work on firm-level

growth. The expertise of statistician Ms. Clemence Cruz was indispensable in

helping us create our biggest contribution in this paper, that of creating an index

as a measure of institutions. We also wish to thank our mothers for their

continuing love and support in completing this paper.

Finally, we are grateful for the gift of friendship, without which the conditions for a

productive synthesis of this paper would not be possible.

April 1, 2014

ii

Abstract

This paper is a contribution to the nascent discourse on institutional economics in

the Philippines. Its objective is twofold: first, it proposes a quantitative measure of

the judicial institution; and second, it estimates the effect of judicial quality on firm

growth. This study applies Principal Components Analysis (PCA) to a dataset of

from a perception survey of 1,072 judges from various courts across 13 regions,

developing a Judicial Perceptions Index (JPI), capturing perceptions of corruption

in Regional and Municipal Trial Courts. The JPI is then used as a proxy to

estimate the effects of judicial quality on firm performance. Using OLS and 2SLS,

the study’s findings are that judicial quality does not have any direct effect on firm

growth but indirectly reduces the magnitude of the positive contribution of

informal payments or “grease money” on firm sales growth. The need to bribe to

raise firm growth is dampened by a higher quality judicial system.

Keywords: Institutional Economics; Judiciary; Firm-level Growth; Principal

Components Analysis

iii

Waiver

Relevant portions of this work may be quoted and used for research and other

scholarly purposes, provided the proper citation is made.

Josemaria Gabriel V. Agregado Jose Maria L. Marella

iv

Honor Statement

We attest that this undergraduate thesis we have submitted is our own. We have

not cheated, plagiarized, or received unauthorized assistance in the completion

of this paper.

We understand that the University of the Philippines may impose commensurate

sanctions and penalties for instances of academic dishonesty committed in the

completion of this paper.

Josemaria Gabriel V. Agregado Jose Maria L. Marella

v

Table of Contents

CONTENTS Page

Chapter I: Introduction........................................................................................1

Chapter II: Review of Related Literature............................................................4

2.1 Theory of Institutions and the Judiciary.................................................4

2.2 The Judiciary and Firm Growth...........................................................10

Chapter III: Data and Methodology...................................................................14

3.1 Developing a Judicial Perceptions Index.............................................14

3.2 Estimating the effects of the JPI on firm performance.........................21

3.3 Empirical Model...................................................................................23

3.3.1 OLS.......................................................................................25

3.3.2 2SLS......................................................................................26

Chapter IV: Results............................................................................................27

Chapter V: Conclusions, Recommendations and Policy Implications.........34

Bibliography………………..................................................................................37

Appendix.............................................................................................................43

vi

List of Figures and Tables

Page

Figure 1. Theoretical Framework 10

Table 1. Breakdown of Respondents from TAF & SWS dataset 17

Table 2a. Descriptive Statistics per Judicial Indicator 18

Table 2b. Descriptive Statistics of Indicators (Banded) 18

Table 3. Factor Loadings and Weights per Judicial Indicator 20

Table 4. JPI across Regions 20

Table 5. Descriptive Statistics of Variables 23

Table 6. OLS 30

Table 7. Estimates of Model Coefficients Using Two Stage Least Squares Regression w/o Index as IV 31

Table 8a. Estimates of Model Coefficients Using Two Stage Least Squares Regression with Index as IV (First stage) 32

Table 8b. Estimates of Model Coefficients Using Two Stage Least Squares Regression with Index as IV (Second stage) 33

vii

Developing a Judicial Perceptions Index as a

Measure of Institutional Quality and

Estimating The Effects of Judicial Quality on

Firm Growth in the Philippines

I. Introduction

Quite recently there has been a lot of growing interest in the study of the

New Institutional Economics as earlier hypothesized by North (1991). He argues

that the role institutions play in a society is very essential. He discusses how

institutions act as the “rules of the game” for society to play in, or more formally

that institutions are the limitations made by humans to shape certain human

behavior.

Precisely how do institutions affect growth? Frances (2004) finds that in

New Zealand, institutions influence the resources that firms use. Good

institutions enable firms to maximize human capital, improve labor productivity,

and in turn promote innovations. Furthermore, property rights may be

constrained by inefficiently run institutions.

1

One such institution that may affect economic performance is the judiciary.

If the presence of a judiciary, which represents the just application of the rule of

law, allows for lower contracting costs, or even allows more flexible laws for

organizational structure, then market conditions may be more competitive, further

encouraging investments.

This study seeks to measure the impact of judicial quality on firm

performance. It builds on a firm-level growth model by Mendoza, Lim, and Lopez

(2013) which employed a wide range of variables that cover firms’ sales levels,

amount of educated employees, taxes, trade, informal payments as a proxy for

corruption, and even the presence of political dynasties. The Mendoza model,

however, did not include any measure of judicial quality however, thereby

stopping short of identifying possible policy handles to contain corruption.

The major contribution of this study is the addition of a Judicial

Perceptions Index (JPI) to the Mendoza model, developed using Principal

Components Analysis on a dataset from a perception survey of 1,072 judges

across the regions and courts of the Philippines1. Respondents were asked how

they perceived the quality of the judicial institution; perception of judicial quality

gives a glimpse of how efficiently an institution is being run which in turn

determines the type of incentives people face. Judicial quality is important for a

sound business environment for it indicates the certainty and consistency of the

1 Funded by The Asia Foundation, conducted by Social Weather Stations July 2005-February 2006

2

legal environment in its protection of property rights and enforcement of

contracts, among others. Larger scores are expected to foster confidence in the

system and incentivize businesses to invest.

This paper proceeds by first reviewing the literature on the links between

transaction costs, the judiciary and the economy, particularly firm performance –

both theory and empirical evidence. A discussion of our methodology follows,

explaining how exactly we develop our JPI and how this variable is integrated

into the firm-level growth model as developed by Mendoza et al (2013). It then

explains the study’s results and discusses possible policy implications.

3

II. Review of Related Literature

2.1 Theory of Institutions and the Judiciary

The idea of the importance of institutions in economics is owed mostly to

the works of Coase (1937; 1960), North (1990) and Acemoglu, Johnson and

Robinson (2000). It was Coase (1937) who first elucidated the idea that

institutions can inflict transaction costs on entrepreneurs, which greatly

contributes as a factor to the size of the firm and its functions. He expounds even

further in his work “The Problem of Social Cost” (Coase 1960) where he

theorizes a world without any form of transaction costs and how this would result

in a highly efficient market without having to take into consideration the initial

allocation of wealth or assets.

North (1991) argues that institutions play a crucial if not primordial role in

the development of societies. Other than the “rules of the games” that institutions

instill in a society, the author mentions how institutions shape the economic

incentives leading a nation to growth, stagnation or decline. The author even

mentions the importance of having to enforce certain third party enforcement

political or judicial institutions so that economic players can trust the system they

participate in.

4

Acemoglu, Johnson and Robinson (2000) explain the impact of the

institutions created by European colonialists in the earlier colonies. They

expounded on the two different types of institutions earlier established in the

Americas, Australia, New Zealand and in different parts of Africa. The “inclusive”

type enforced the rule of law, property rights and promoted investment. In the

other extreme, the other type established “extractive” institutions meant to exploit

the local population and carry the riches to the home country. They attempt to

explain how modern day institutions, as proxied by the “Risk of Expropriation”

index by the Political Risk Services, have a significant effect on per capita

income, and how these institutional effects can be traced to the challenges in

living conditions faced by early colonialists. Their study suggests that the

differences in institutions explain three-quarters of the difference in per capita

income across former colony countries.

Thus, institutions arise as pragmatic social constructs that seek to mitigate

transaction costs inherent in any economy. Institutions, being predicated on the

multitude and intricateness of human inter-relationships, strive to set a rule or

some standards in order to govern human behavior in an organized manner. The

judiciary is one such institution that emerged in order to organize a manner

through which private parties can fairly and effectively contract with one another.

Norman Marsh, a law reformer and academic, put it succinctly:

5

The inevitability of human error, especially when human interest (which includes the exercise of power as an end in itself) comes into conflict with the claims of others, requires that a judiciary should interpret the law, and the assumptions, which underlie it…(Marsh, 1959, pg. 279)

This paper takes the discourse on institutions and applies it to the

Philippine context with particular focus on how the judiciary has been effective in

lowering transaction costs for private business firms. It focuses particularly on

how the judiciary acts as a policing and enforcing agent when it comes to

transaction costs involving corruption.

According to CleanGovBiz, an initiative of the Organisation for Economic

Co-operation and Development, corruption is the “abuse of public or private

office for personal gain”2. It creates a distortion in incentives as the gainers and

losers in a transaction are ambiguous; outcomes may tilt in favor of those who

have capacity to pay bribes.

Why would firms engage in corruption in the first place? Collins,

Uhlenbruck, and Rodriguez (2008) examined the social and institutional factors

behind corruption; they found that agents engage in corruption because the gains

to distorting the distribution of government-mediated resources outweigh the

costs of engaging in corruption. In a sense, corruption then is both an outcome

and a contributor of the high cost of transacting in the country.

2 “The Rationale for Fighting Corruption” CleanGovBiz, http://www.oecd.org/cleangovbiz/49693613.pdf (April 6, 2014)

6

With good quality judicial institutions in place, the courts can also provide

an avenue through which legal liability may be imposed. As such, certain types of

harm that private parties inflict on others are punishable by law (Coase 1960). In

other words, relying on the courts allows a method to create disincentives for

illegal behavior. As an example, Sumida (2002) highlights the importance of the

judiciary in combating bribery and corruption. The judiciary, as a public institution,

functions as an arbiter of disputes; it should provide the expeditious resolution of

cases and controversies; and the business community is replete with cases of

such nature.

As an example, in American Hospital Supplies/Philippines Inc.

(AHS/Philippines, Inc.) vs Court of Appeals and Alfonso Bayani, respondent

Bayani filed a complaint for damages in the Regional Trial Court of Cebu City.

AHS/Philippines, Inc. was allegedly bribing various government hospital officials

in the form of “commissions”, “entertainment expenses”, and “representation

expenses”. Alfonso Bayani was unjustly dismissed for abstaining from the

misdeeds. The Cebu RTC awarded actual and compensatory damages to

Bayani.

In another case, Sia Bon Suan, a stockholder and manager of the cockpit

in Cagayan Gold City Coliseum, indirectly bribed Pacianito Paraguya, a BIR

Agent assigned in the BIR District Office. The latter purportedly “demanded,

solicit[ed], and receive[d]” the sum of 300 php, passed off as a “gift”. This was

7

filed at the Circuit Criminal Court on Cagayan de Oro. However, this case was

not tried as the Supreme Court ruled that Circuit Criminal Courts have no

jurisdiction to try such cases.

Furthermore, Goel and Nelson (2008) find that countries using the English

Common Law System, as a proxy for Judicial Independence, tend to have lower

corruption. They explain this due to the fact that both bribe-takers and bribe-

givers are very much aware of the costs of corrupt acts under a legal system that

is well defined, efficient and consistent.

While institutions seek to mitigate transaction costs, they themselves are

subject to inefficiencies. Measures of the judicial institution’s efficiency in

dispensing important services in economic transactions is multifaceted as it

involves consistency of court decisions, delays, cumbersome rules of court,

corruption, and over-clogging of court dockets, among others. De Dios, Sereno,

and Capuno (2007) discuss that (i) simple resource constraints that tax the

judiciary’s capacity to dispose of the case backlog; (ii) lack of sufficient expertise

or competence on the part of judges or lawyers to process cases in timely

fashion; (iii) judicial corruption; (iv) political pressure, which complicates the

resolution of cases; and (v) poor design of the law, which also renders cases

complex are among the reasons why the judiciary may make business

transactions more costly. In the Philippines, the judiciary itself may also be

8

subject to inefficiencies posed by corruption; this creates distortions in its delivery

of services, which are then passed on to the private transacting parties.

Judges and courts engage in corruption for varied economic reasons.

Langseth (2001) articulates that corruption in the judiciary is a confluence of

many factors: low remuneration, far reaching discretionary powers, weak

monitoring, and lack of transparency, among others. Wang (2013) discusses that

judges in China engage in corruption simply because it is a viable alternative.

Insufficient funding of courts creates incentives for judges to inconsistently and

unjustly apply the rule of law. Wang uses data on public perception on corruption

from Transparency International, World Bank, and Political Risk Services to test

this. The key independent variable is court funding, data on which was taken

from their Ministry of Finance. The paper indicates a strong negative relationship

between court funding and judicial corruption.

The American Bar Association’s Judicial Reform Index for the Philippines,

released March 2006, finds that resource constraints are factors that complicate

the inefficiency and corruption problems in the judiciary. Particularly in the lower

courts, inadequate allocation of funds leads to deficiencies in maintenance,

equipment, and supplies. Judges and clerks are forced to rely on local

governments, “friends”, or their own pockets, in order to make up for this.

9

The susceptibility of the judiciary to certain weaknesses therefore calls for

initiatives in institutional strengthening and reform. It would be worthwhile to

undertake costs in improving the judiciary since the services it dispenses are vital

to the health of the economy. The theoretical framework that this study employs

is as follows:

Figure 1. Theoretical Framework

2.2 The Judiciary and Firm Growth

Existing international studies show that the judiciary affects economic

performance, particularly firm level growth. A cross-country study done by

Sherwood (2002) shows that poor judicial quality has a great toll on economic

performance. The author surveyed 278 Brazilian firms, 700 Peruvian firms, 200

Argentinian firms, 100 Canadian firms, 320 Filipino firms, 500 Spanish firms and

Efficiency of Economic Outcomes

Transaction Costs Uncertainty Bureaucratic Red Tape Corruption

Private Contracting

Party # 2

Private Contracting

Party # 1

Judicial System Rule of Law Enforcement of contracts Property rights Recourse for private wrongs

10

602 Portuguese firms of different economic sectors primarily service and

manufacturing based, asking questions regarding firms’ perceptions on their

respective judicial systems. The study’s findings are that if the judicial system

were to function well, investment was expected to increase by about 13.7% in

Brazil, 9.4% in Peru, 28% in Argentina, 6-11% in the Philippines, and 9.9% in

Portugal. No data were obtained for Canada and Spain. The author also

observes that in countries where judicial institutions are of poor quality, then

transactions would take place more within personal networks such as between

families, church-groups, communities, neighborhoods etc.

Dove (2014) shows that Judicial Independence through the method of

selecting both state and Supreme Court justices and intermediate appellate

judges has great effects on entrepreneurship in the United States. Chemin

(2007) discusses how in India a newly enacted judicial reform back in 2002 was

implemented to modify the efficiency of case procedures and how this led to

fewer breaches of contracts, encouraged investment, and increased the access

to finance for firms. Garcia and Mora (2013) of the Banco de España also found

how the efficacy of Spanish courts substantially affected the size of the

companies at the provincial level, giving an explanation as to why Spanish firms

are smaller in international terms. Moreover, Chakraborty (2013) also discovers

that judicial quality plays a very significant role in India through higher firm

performance, most especially through exports, total trade and total sales.

11

De Dios, Sereno, and Capuno (2007) discuss the avenues through which

the judicial institution affects business decisions in the Philippines. There are two

of these: uncertainty and high costs. The former aspect is concerned with the

credibility and validity of contracts. A good legal environment provides for a good

business environment since it protects property rights, the enforcement of

contracts, and consistency of economic policies. High costs pertain to monetary

costs and nonmonetary costs. The first involves fees paid to the courts, lawyers’

fees, documentation, etc. And the nonmonetary costs stem mainly from delays in

deciding cases. They find that the quality of the judiciary translated to economic

losses worth P7 billion to P13 billion in 1999. And at least one-fourth to half of a

percentage point of annual GDP growth is foregone due its inefficiencies.

Sicat (2007) made a comprehensive analysis on the legal and

constitutional disputes in the Philippine economy where the author discusses the

institutional costs of the judiciary. According to the study, court cases can inflict

certain costs on business operations similar to the transaction costs discussed

earlier in this section. Litigation is a major feature of the Philippine judicial system

and at the same time can be largely abused by the system as a weapon of

harassment. The study finds that such court cases “impose nuisance costs on

business projects” and such transaction costs can “lengthen the time for

implementing the project”. From 1998 to 2005, the number of new cases,

backlogged cases and the amount of caseload per court in the lower courts has

decreased, though still substantially high. He goes on to mention that the major

12

cause of backlogged cases is the scarce resource of judges with only 2 judges

per 100,000 Filipinos, as compared to 5 for England and 12 for Germany.

As regards the judiciary’s role in correcting the distortions created by

corruption, there is evidence that firms use bribery in order to facilitate business

transactions. Collins, Uhlenbruck, and Rodriguez (2008), in a survey of 341

private executives in India find that executives who have personal ties with those

in government exploit these connections in order to gain favor or get ahead. As

such, these forms of illegal activity may be a strategy for firm competitiveness.

Mendoza, Lim, and Lopez (2013) find that, in specific contexts, bribery

serves as an input that facilitates commerce; it “greases” the wheels of

commerce as firms resort to bribes in order to do away with the bureaucratic red

tape and inefficiencies in day-to-day transactions. This paper aims to build on

Mendoza’s as it attempts to provide a mechanism through which bribes, as

facilitating payments, are affected by the judicial environment.

The literature leads to a clamor for the institutional strengthening of the

judiciary since, according to Roberts (2012), the Philippines’ weak judicial system

undermines its ranking in Economic Freedom. The author asserts that the

country’s judicial institutions lack political independence, are unable to protect

property rights, and lack law enforcement and transparency. Corrupt courts

erode the sanctity of contracts, which clouds the investment climate.

13

III. Data and Methodology

The objective of this paper is to explore the impact of the judiciary on firm

performance. Methodologically, we do this in two parts. First, (1) we develop a

measure of institutional quality for the Philippine courts per region, aptly called

the Judicial Perceptions Index (JPI). Second, we estimate the effects of the JPI

on firm performance via econometric techniques, building on the model by

Mendoza et al.

3.1 Developing a Judicial Perceptions Index

As regards developing the JPI, the study utilizes a perception survey3

dataset of 1,072 judges sponsored by The Asia Foundation (TAF) and conducted

by the Social Weather Station (SWS). Perception surveys allow: 1) a

measurement of the intangible; 2) the communication of private information; 3)

expert opinion vs. public opinion; and 4) rebalancing information asymmetries as

not all types of information are collected and published as data (Herbert 2013).

Moreover, information from a perception survey may be used to construct

perception indices, which have been used as a proxy measure of quality. For

3 At prima facie, the collection of data may be subject to bias and conflict of interest since the ones critiquing the judiciary are themselves members of the institution. However, TAF noted that the judge-respondents welcomed this survey as it provided them with an opportunity to give vent to the legal system. They voiced out their concerns as individuals operating within a bigger system. Compared to public perception surveys, which focus on the confidence of the users of the judiciary, this TAF survey relied on the expertise of the respondents being the ones who know how to characterize the institution from within.

14

instance, Transparency International, the international watchdog agency that

annually releases the Corruption Perception Index (CPI), claims that perception

of corruption is a good proxy for measuring institutional quality since corruption is

not a factor easily and empirically measured. Although this may give it a hint of

subjectivity, perceptions of corruption may, nevertheless, give a good glimpse on

institutional quality because these relate to the trust that rational agents vest into

an institution.

Perceptions surveys to measure institutional quality are becoming widely

used. The Indonesian Legal Roundtable has released what it calls the 2012 Law

Perceptions Index. Other than the CPI, Transparency International has also

released what it calls the Global Corruption Barometer 2013. Respondents were

1,000 Mexicans and among the information they disclosed related to bribery to

the judiciary. Cyprus has also released its 3rd annual Corruption Perceptions

Survey in 2013, which deals with political accountability, institutional setting, law

enforcement, and judiciary.

In The Asia Foundation – Social Weather Stations (TAF-SWS) survey,

judges were asked to answer a comprehensive questionnaire in order to evaluate

the judicial institution. The questions pertained to various aspects of the courts

and the legal system as a whole – questions about pre-judicature training,

corruption in the courts, court relations with media, caseload at present, to name

a few. Since responses in the TAF/SWS were mainly of a qualitative nature,

15

responses were transformed into a mathematically tractable form using Principal

Components Analysis (Jolliffe 1986), or “PCA” for short. PCA provides an

empirical methodology (rather than criteria from theory) for assigning weights to

important factors, which is then used to develop an index. Basically, PCA

compresses the variables into components by taking the variance of these and

redistributing them orthogonally. Krishnan (2010) employed this method in order

to construct an area-based socioeconomic index based on variables derived from

national census data. Cahill and Sanchez (2001), on the other hand, construct an

economic and social development index (ESDI) which they contrast to the

Human Development Index (HDI). Their ESDI employs PCA in assigning weights

to factors derived from a 36-variable state-level data for Latin America and the

US. They claim that using their ESDI allows one to examine the structure of the

development process and compare country characteristics inter-temporally.

A breakdown of the courts and the regions where the TAF-SWS

respondent judges belong to is provided in Table 1 below4.

Table 1. Breakdown of Respondents from TAF-SWS Dataset4 The data is disaggregated into 13 regions. CAR, ARMM, and CARAGA were not included in the study. Regions IV-A and IV-B are merged. Courts are disaggregated into the Regional Trial Court (RTC), Municipal Trial Court (MTC), Municipal Trial Courts in Cities (MTCC), Municipal Circuit Trial Court (MCTC), Metropolitan Trial Court (METC), and Shari’a Courts (SCC).

16

By Court Across Philippine RegionsSource: TAF-SWS Survey

Court

Region RTC MTC MTCC MCTC METC SCC Total

I 45 25 7 20 0 0 97

II 24 5 3 11 0 0 43

III 58 29 14 9 0 0 110

IV-A & IV-B 51 43 13 15 0 0 122

V 43 17 11 12 0 0 83

VI 48 14 19 28 0 0 109

VII 31 11 17 21 0 0 80

VIII 25 23 2 19 0 0 69

IX 13 1 7 4 0 0 25

X 30 0 8 27 0 0 65

XI 22 16 12 0 0 0 50

XII 12 1 4 7 0 12 46

NCR 133 0 0 0 40 0 173

Total 535 185 117 173 40 22 1072

From the PCA runs, four indicators were identified as the most relevant5.

The first two have to do with corruption with the Regional Trial Courts (RTC) and

Metropolitan Trial Courts (MTC) as a whole6. The other two depict the

respondents’ perceptions of corrupt personnel within the RTC and in the MTC. It

is believed that if firms have any dealings with the judicial system, it is in these

courts that they have the most direct contact. Summary statistics of these

indicators are presented in Tables 2a and 2b below.

Table 2a. Descriptive Statistics per Judicial Indicator

5 Other indicators initially tested had to do with consistency of court decisions, court-media relations, and pre-judicature training of judges but these did not come up relevant.6 These items are general in definition since judges would have their respective information sets regarding different facets that could characterize corruption in the court.

17

Indicator Response (for all) Mean Std. Dev.

Corruption in RTC 1 – Very Many

2 – Many

3 – Some

4 – A Few

5 – Very Few/None

6 – No Answer

7 – Don’t Know

9 – Refused

4.553172 2.217612

Corruption in MTC 4.841418 2.303945

Corrupt Personnel

in RTC

4.441231 2.201836

Corrupt Personnel

in MTC

4.627799 2.207383

Source: TAF-SWS Survey

Table 2b. Descriptive Statistics per Judicial Indicator (Banded)

Indicator Mean Std. Dev.

Corruption in RTC .5755 .2681

Corruption in MTC .5798 .2543

Corrupt Personnel in RTC .5621 .2670

Corrupt Personnel in MTC .5864 .2668

Source: Author’s Calculations

In Table 2b, Likert scales of the responses were treated with an ordinal

nature; thus the responses could be banded7, transformed into having values

between 0 and 1; 0 pertaining to the least quality of the judiciary (or most corrupt)

and 1 being characteristic of the best quality (no corruption at all).

In developing the JPI, this paper puts forth five different permutations of

the same set of characteristics in judiciary. Specifically,

7 For a discussion on banding, see Appendix.

18

Index 1 – consists of all four indicators; corruption in the RTC as a

whole and the MTC as a whole as well as corrupt personnel in both

the RTC and the MTC

Index 2 – corruption in the RTC as a whole and the MTC as a

whole

Index 3 – corruption in the RTC as a whole and corrupt personnel

in the RTC

Index 4 – corruption in the MTC as a whole and corrupt personnel

in the MTC

Index 5 – corrupt personnel in the RTC and corrupt personnel in the

MTC

Depending on which combination is used, their respective factor loadings

were added and used as a denominator. The particular factor loading is then

divided by this denominator in order to generate a weight. The mean value per

region of a given indicator is multiplied to its respective weight in order to

generate the overall score for the Index for that particular region. The JPI in other

words is the same for the firms that operate within a given region.

Tables 3 and 4 below present the factor loadings and weight per judicial

indicator, and the mean values per indicator across regions after applying the

factor loadings.

19

Table 3. Factor Loadings and Weights per Judicial Indicator


Index

(1) (2) (3) (4) (5)

Indicator Factor

Loading

Weight Factor

Loading

Weight Factor

Loading

Weight Factor

Loading

Weight Factor

Loading

Corruption in

RTC

0.4882 0.2442 0.4882 0.5024 0.4882 0.4872 - - -

Corruption in

MTC

0.4835 0.2418 0.4835 0.4976 - - 0.4835 0.4849 -

Corrupt

Personnel in

RTC

0.5139 0.2571 - - 0.5139 0.5128 - - 0.5139

Corrupt

Personnel in

MTC

0.5136 0.2569 - - - - 0.5136 0.5151 0.5136

Total 1.9992 1 0.9717 1 1.0021 1 0.9971 1 1.0275

Table 4. JPI across Regions

Index

Region (1) (2) (3) (4) (5)

I 0.596921 0.57732 0.584721 0.609181 0.615457

II 0.548302 0.567419 0.529398 0.567301 0.530223

III 0.529875 0.535512 0.534219 0.525509 0.524545

IV-A & IV-B 0.507127 0.5008 0.499243 0.515051 0.513111

V 0.620587 0.61561 0.602348 0.638918 0.625294

VI 0.614832 0.62567 0.606022 0.623687 0.604583

VII 0.59721 0.606159 0.582372 0.612123 0.588747

VIII 0.626673 0.604313 0.611966 0.641455 0.64782

IX 0.624054 0.624116 0.627487 0.620604 0.623995

X 0.586089 0.586027 0.561578 0.610723 0.586147

20

XI 0.639024 0.63799 0.640103 0.63794 0.640001

XII 0.622754 0.626129 0.621405 0.62411 0.619562

NCR 0.531325 0.549167 0.536505 0.526118 0.514452


3.2 Estimating the effects of the JPI on firm performance

Apart from the JPI, this paper uses firm-level data from the AIM Philippine

Cities Competitiveness Ranking Project (PCCRP), a project in partnership with

the Asian Development Bank and the National Competitiveness Council. Data

was gathered from 34 cities, 16 regions, and 2,040 SMEs spanning 13 different

industries nationwide.8 In order to properly align city-level data used in this

survey with the JPI created in this paper, the cities in the same region were

aggregated together so as to represent regional data.

Explanatory variables from this data set that are hypothesized to influence

firm performance are: (i) Bribery or the % revenue allotted for informal payments

in 2009 (ii) BIR and LGU Taxes in 2009 (iii) total firm sales in 2009) (iv) College

Graduate Concentration and (v) Sales in the base year of 2009.9 Also available

in the dataset, we include variables such as a proxy for infrastructure reported in

number of power outages per month, and dummy variables for firm size. The

8 Mendoza et al (2013) account the credibility of this data set on: (i) professionalism of the partner agencies involved such as the ADB and the NCC (ii) the dataset provides hard objective information such as total sales of the year, percent of revenue submitted as taxes, and firm size just to name a few and (iii) questions on corruption were asked towards the end of the survey employing indirect approaches to elicit true perspectives on the nature of corruption.9 Other variables used by Mendoza et al (2013), such as a trade, a concentration of political dynasty and employment growth were not retained for this study due to insignificant results yielded, and the lack of available data as only certain sections of the AIM dataset were available for research.

21

expected effects of these variables on firm performance (or firm sales’ growth)

are as follows:

Bribery: (+/-) Following Mendoza et al. (2013), a positive sign is expected

if it serves as a means to avoid frictional costs in weak institutions, and

negative if these serve as unnecessary costs to the firm

Total Taxes (BIR & LGU): (-) Taxes are seen as costs to the firm thus

contributing to lower or negative sales growth

College Graduates: (+) Having a better pool of employees who are more

educated suggest that a firm is more competitive

Sales 2009: (+/-) The amount of sales of the firm during this base year

affects the growth trajectory of the firm. On one end, faster growing firms

may be exploited by bureaucrats in extracting bribes; but, on the other

hand, higher sales in 2009 may also contribute firms’ consistent

performance in the long run.

Size of firm: (+/-) Larger firms may have economies of scale to be more

competitive, contributing to growth. However if larger firms are near their

optimal size, they may also have less room for growth and therefore be

experiencing slower growth rates.

Power outages: (-) Power outages proxy for inefficiency in infrastructure

and utilities of the region, thus inflicting costs to the firm.

As regards the JPI, if better judicial quality directly improves the business

environment, then we expect a positive sign on the JPI variable. If not, we

expect a negative sign. However, if the effect of a better judiciary is to reduce the

22

incidence of “bad behavior’, thereby reducing the need to ‘grease the system’,

then JPI would not have a direct effect on firm growth but would work primarily

through its impact on bribery, reducing the need to grease the system.

The following table provides basic information on these said factors, used

in explaining causes of growth:

Table 5. Descriptive Statistics of Variables

Variable Obs Mean Std. Dev.

2012 Total Sales 923 6118029 4.31e+07

2009 Total Sales 923 6480837 8.59e+07

% of revenue reported as BIR tax 2009 923 9.772979 8.156503

% of revenue reported as local tax 2009 923 5.843933 6.00778

% of employees with 4 year degree 923 22.89068 33.72511

Number of power interruptions per month 923 9.600217 22.45211

Bribery (Informal Payments as % of revenues) 923 1.04935 4.834931

Micro firms (dummy) 923 .5189599 .4999113

Small firms (dummy) 923 .2437703 .4295885

Medium firms (dummy) 923 .2372698 .4256396

Source: Mendoza et al (2013), Grease or Sand in the Wheels of Commerce? Firm Level Evidence on Corruption and SMEs

23

3.3 Empirical Model

Though the TAF dataset was collected between 2005 and 2006, this may

still be applied to AIM’s 2009 data since it is assumed that institutions persist

through time (Acemoglu & Robinson, 2012). Mendoza et al (2013) runs two

models. The first is an OLS, where the direct effects of bribery on firm

performance, proxied by firm sales’ growth, is estimated. The second is an IV,

where bribery is instrumented after observing that there may be endogeneity.

Firms that perform well may do so precisely because they pay bribes in order to

get ahead. However, various governmental agencies may selectively approach

better performing firms or firms with higher “willingness-to-pay” bribery, so to

speak. There may also be measurement errors due to self-reported data for

bribery. Mendoza et al reconstructs the IV method of Fisman and Svensson

(2007) wherein the approach is to group industry-location averages of corruption

with respect to the informal payments variable used.

Following Mendoza et al (2013), we likewise run two models. The

Ordinary Least Squares (OLS) estimation tests whether there is a direct effect of

judicial quality on firm performance as well as the magnitude and direction of this

effect. The second is Two Stage Least Squares (2SLS) where judicial quality is

added to the instrumentation of bribery. A better quality judiciary provides a

disincentive for the solicitation of bribes. Reducing its incidence, as discussed in

the literature. This accounts for possible indirect effects of the JPI on firm growth.

24

3.3.1 OLS

The OLS regression model is as follows:

GROWTHi = β0 + β1 Indexi + β2 Briberyi + β3 TotTaxi + β4 CollegeGradi + Β5 SmallDUMi + β6 MediumDUMi + β7 Poweri + β8 Sales2009i + i

Firm sales growth is measured as [log(2012 Salesi) - log(2009 Salesi)]/2.

Index for Judicial Quality is as described in the previous section. Bribery is

informal payments indicated by each firm as a percentage of firm revenues; it is

considered as something of an “input” in order for them to get their businesses

going. Taxes paid to the local Bureau of Internal Revenue (BIR) and those paid

to the Local Government Unit (LGU) are measured also measured as a

percentage of firm revenues. CollegeGrad pertains to the percentage of

employees with four or more years of college education. The Small and Medium

variables are dummies that pertain to the firm size, of which the base category is

Micro. Firm size is defined in terms of employment size; Micro firms consist of 1-

9 employees, Small firms 10-99 employees, and Medium firms 100-199

employees. Power is the number of times that the firm experiences a power

interruption during a month. Finally, the natural logarithm of Sales in 2009 is

25

included in the equation since the initial performance of firms affects future

growth.

Computed magnitudes for our dependent variable are to be read in

proportions; meaning the coefficients have to be multiplied by 100 in order for the

interpretation of unit changes to be in percentage points.

3.3.2 2SLS

For the two stage least squares (2SLS), equation (1) is the first stage

regression, which estimates bribery with industry location averages10 and the

new JPI variable as instruments. Thus, equation (2) is the second stage linear

regression where bribery is taken into account as an endogenous variable.

(1) Briberyi = α0 + α1 Indexi + α2 IndLocAvei + α3 TotTaxi + α4 CollegeGradi + α5 SmallDUMi + α6 MediumDUMi + α7 Poweri + α8 Sales2009i + i

(2) GROWTHi = β1 Briberyi + β2 TotTaxi + β3 CollegeGradi + β4 SmallDUMi + β5 MediumDUMi + β6 Poweri 10 In order to compute for industry location averages for bribery, firms were classified according to region and industry classification as per the 1994 Philippine Standard Industrial Classification (1994 PSIC)

26

+ β7 Sales2009i + i

Variables are defined as they were in the OLS model.

27

IV. Results

The results indicate that there is no statistically significant direct effect of

judicial quality on firm sales growth but that there is a significant indirect effect

operating through an impact on bribery.

1. Table 6 presents the results of the OLS without and with the indices. JPI

has no statistically significant influence on firm sales growth and has the wrong

expected sign. The signs and coefficients of the other explanatory variables are

consistent with results of Mendoza et al. Specifically,

A one-percentage point increase in bribery increases firm growth by less

than 1 percentage point – a little more than 0.8 percentage points in all

five models, to be exact.

The effect of total taxes paid is also statistically insignificant.

Increasing the number of employees with at least four years of college

education by one-percentage point increases firm growth by 0.08

percentage points.

The number of power interruptions per month has a statistically

insignificant effect on firm growth.

The approximate difference in sales growth between micro and small firms

is 5 percentage points. Between medium and micro firms, it is 11

percentage points.

28

The natural logarithm of sales in 2009 is negative and significant. As

expected, firms that have bigger initial sales in 2009 experience have a

flatter growth trajectory. To be exact, a 1 percentage point increase in the

ln of sales in 2009 hurts growth by a little over 5 percentage points.

2. Tables 7a and 7b present the results of the First Stage and 2SLS

respectively, without JPI as an instrument for bribery. Tables 8a and 8b present

the First Stage and 2SLS respectively with JPI as an instrument. In Table 8a, the

effect of judicial quality on bribery is highlighted: JPI exerts a significant and

negative effect on bribery. This paper believes that the JPI is a significant

instrument for bribery because capturing the quality of a judiciary and its ability to

enforce justice without corruption in a region can affect both firms and other

government institutions by lessening the occurrence of bribery. Underscoring the

role of the judiciary as an enforcer of justice, a 0.01 increase in the quality of the

judiciary decreases the proportion of revenue allotted to bribery by between 6

and 7.3 percentage points. This suggests that the quality of the courts may not

have a significant direct effect on the growth of firms. Rather, quality of the

judiciary affects firms indirectly by lessening the incidence of bribery. In a sense,

adding the JPI as an instrument enriches the discussion by providing the context

within which firms operate.

29

JPI improves the instrumentation of bribery. Compared with the IV results

without the JPI as an instrument, the magnitude of the coefficient of bribery on

firm growth is reduced by a little less than 2 percentage points. This suggests

that a better judiciary dampens the contribution of (and therefore the need

for) bribery on firm growth. Also consistent with Mendoza et al, the magnitude

of the effect of bribery on firm growth has increased drastically versus the OLS

results, from less than 1 percentage point to between 6.7-7.8 percentage points.

The effect of the other variables is as follows: total taxes on firm growth

are still statistically insignificant. Increasing the number of employees with at

least four years of college education by 1 percentage point increases firm growth

by 0.1 percentage points. Number of power interruptions per month remained

statistically insignificant while the dummy variable for small firms became

insignificant. The dummy variable for medium firms is still significant indicating

that these firms have higher growth than micro-sized firms by 12 to 13

percentage points. Natural logarithm of sales in 2009 still remained significant

and negative indicating that a 1 percentage increase in sales in 2009 increases

firm growth by 7 to 8 percentage points.

30

31

Table 6. Coefficient Estimates of the Model Using Ordinary Least Squares Regression

Dependent Variable: Growth

(I)(II) (III) (IV) (V) (VI)

Explanatory Variables:

Informal Payments

(Bribery)

0.0084371**

*

0.0081644**

*

0.0082306**

* 0.008268***

0.0081588**

*

0.0081464**

*

Total Taxes 0.0010981 0.0010978 0.001115 0.0011757 0.0010522 0.0010763

College Grad 0.0008414** 0.0008433** 0.0008372** 0.0008387** 0.0008467** 0.0008479**

Power Outages -0.0000517 0.0001397 0.0001385 0.0001365 0.0001177 0.0001393

Small Firms (dummy) 0.0565553** 0.0561808** 0.0556882** 0.0555807** 0.0565575** 0.0564631**

Medium Firms (dummy)

0.1167907**

*

0.1151726**

*

0.1146573**

*

0.1147227**

*

0.1157381**

*

0.1156231**

*

Index1 -0.3502494

Index2 -0.347683

Index3 -0.3034681

Index4 -0.3315301

Index5 -0.3030185

Constant

0.6938278**

*

0.8906899**

*

0.8864442**

*

0.8577387**

*

0.8849324**

*

0.8653182**

*

Observations 808 808 808 808 808 808

R-Squared 0.0768 0.0797 0.0795 0.0787 0.0796 0.0798

Robust standard errors in parentheses

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

Source: Authors' computation

Table 7. Estimates of Model Coefficients Using Two Stage Least Squares Regression w/o Index as IV

First Stage Least Squares w/o Index as IV

2SLS w/o Index as IV

Dependent Variable: Informal Payments (Bribery)

Dependent Variable: Growth

IndLocAve 0.2091314**

Informal Payments (Bribery) 0.0904298*

Total Taxes 0.0491218*** -0.0030282

College Grad -0.0043291 0.0011908**

Power Outages -0.0052351 0.0005955

Small Firms (dummy) -0.0069578 0.0632772

Medium Firms (dummy) -0.17997 0.1347324*

lnSales09 0.4225062*** -0.0895402***

Constant -5.680794*** 1.178363***

Table 8a. Estimates of Model Coefficients Using Two Stage Least Squares Regression with Index as IV (First stage)

32

33

Dependent Variable: Informal Payments (I) (II) (III) (IV) (V)Total Taxes 0.490309*** 0.0493506*** 0.0506555*** 0.0480518*** 0.0485566***

College Grad -0.0042531 -0.0043792 -0.0043646 -0.0041744 -0.004144

Power Outages -0.0016245 -0.002168 -0.0014939 -0.0020189 -0.0014965

Small Firms (dummy) -0.0269708 -0.0288831 -0.0328259 -0.0208728 -0.0233058

Medium Firms (dummy) -0.219178 -0.2198633 -0.2253133 -0.209004 -0.2120079

lnSales09 0.4329607*** 0.4351871*** 0.4343064*** 0.4309426*** 0.4293454***

IndLocAve 0.173924* 0.1890577* 0.1928164* 0.1688309* 0.1677658*

Index1 -7.341819**

Index2 -6.021255**

Index3 -6.322182*

Index4 -7.202861**

Index5 -6.684281*

Constant -1.558948 -2.345835 -2.263043 -1.537173 -1.905232

Table 8b. Estimates of Model Coefficients Using Two Stage Least Squares Regression with Index as IV (Second stage)

34

Dependent Variable: Growth (I) (II) (III) (IV) (V)

Informal Payments (Bribery) 0.0698191* 0.0784249** 0.0765291* 0.0679933* 0.0671389*

Total Taxes -0.001991 -0.002424 -0.0023286 -0.0018991 -0.0018561

College Grad 0.001103** 0.0011397** 0.0011316** 0.0010952** 0.0010916**

Power Outages 0.0004328 0.0005007 0.0004858 0.0004184 0.0004116

Small Firms (dummy) 0.0615875 0.062293 0.0621376 0.0614378 0.0613678

Medium Firms (dummy) 0.1302224** 0.1321055** 0.1316907** 0.1298228** 0.1296359**

lnSales09 -0.0801086*** -0.0840467*** -0.0831791*** -0.0792731*** -0.0788822***

Constant 1.056564*** 1.10742*** 1.096217*** 1.045775*** 1.040726***

Observations 808 808 808 808 808

V. Conclusions, Recommendations & Policy Implications

This paper highlights an important facet of economic performance, that is:

institutions matter. Literature that focuses on the role of institutions in economic

outcomes, in the Philippine context at least, has not yet been exhaustive. As

such, this paper hopes to have enriched the growing discourse in this topic, or to

have contributed to its nascent development. Developing a measure of

institutional quality requires the synthesis of some indicators that encapsulate

facets of an institution.

The JPI developed here captures the characteristics in terms of

perceptions of corruption at the regional and municipal trial courts (RTC, MTC)

and the personnel in each of these courts on a regional scale. The JPI thus

served to describe the environment where firms operate. Firms that were located

in a given region were given the same values for the JPI. Perceptions surveys

may suffer from certain limitations (bias, incomplete information, lack of

responses, etc.), therefore future studies may rely on hard data to better capture

institutional quality – examples of such include the length of time before a case is

decided; number of corruption cases filed against a court, found in the Supreme

Court or Sandiganbayan; or number of times a hearing is reset. These give a

glimpse on the transaction costs entailed in utilizing the judicial system.

35

OLS estimation techniques indicated that quality of the judicial

environment, as indicated by the JPI, has no direct effect on firm growth. Rather,

as shown by 2SLS wherein the JPI was employed as an instrument for bribery,

its effects are felt indirectly by reducing the intensity and incidence of bribery that

facilitates firm growth. Furthermore, the contribution of bribery to firm growth is

dampened. A 0.01 unit increase in the JPI translates to a 6 to 7 percentage point

decrease in the incidence of bribery as indicated in the IV estimation’s first stage

regressions. The second stage regressions without the JPI as an instrument

indicate a 9-percentage point increase in firm growth for every 1 percentage point

increase in bribery as a fraction of revenues. Adding the JPI as an instrument,

the coefficient of bribery was reduced in magnitude by between 1 and 2

percentage points.

The IV estimation results are in line with the theoretical framework that this

study has employed. Corruption poses a transaction cost in the market by

distorting economic outcomes. The presence of a judiciary helps reduce this

transaction cost thereby making economic outcomes more efficient.

This study has built on the work of Mendoza et al (2013) by providing a

mechanism whereby the presence of corruption as a “greasing payment” can be

corrected. We have underscored the capacity of the judiciary as an institution

that can correct the distortions caused by corruption in business transactions. By

36

promoting the rule of law and securing the sanctity of property rights, it can help

businesses attain more efficient outcomes.

The results of our study clamor for strong policy implications especially in

institutional strengthening. It may be worthwhile to undertake costs in improving

the delivery of judicial services, as this would greatly benefit the performance of

private business firms and in turn the macro-economy.

37

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Appendix – Banding Survey Responses

As a first step, some values in the responses were recoded as such:

1 – Very Many

2 – Many

3 – Some

4 – No Answer/Don’t Know/Refused

5 – A Few

6 – Very Few/None

Rather neutral sounding answers were grouped together and recoded to have a

value of 4, somewhere in between the spectrum of responses. After which, the

Likert scales were banded using the following formula in order to have a unit of

measure between the values 0 and 1:

X – Banded = [Xi – min(X)] / [max(X) – min(X)]The progression of values from 1 to 6 indicates an improvement in perceptions of

corruption and this was transposed to values from 0 to 1.

44