THE IMPACT OF BUREAUCRACY ON FIRM PERFORMANCE. THEORY AND EVIDENCE FROM INDIA AND IRELAND

THE IMPACT OF BUREAUCRACY ON FIRM PERFORMANCE. THEORY AND EVIDENCE FROM INDIA AND IRELAND

University of Groningen Faculty of Economics and Business

Trude Faber

1365215

t.s.faber@student.rug.nl

July 15

, 2010

Supervisor Dr. Gjalt de Jong

ABSTRACT

Due to the increasing complexity of the economy and society, there is a need for thorough regulation. However, red tape and bureaucracy are one of the greatest obstacles to business operations. We present a theoretical model for assessing the effects of administrative burdens on firm performance. We specifically study the effects on total sales of smaller Irish and Indian firms in the manufacturing sector in 2004. Administrative burdens are measured by the amount of negotiation time, the number of safety inspections and the use of external expertise in compliance with dealing with permits and licensing. In contrast to our hypotheses, we find that administrative burdens and firm performance are positively related. Apparently, bureaucracy not only has negative effects as hypothesized but also positive spillover effects to firm performance.

Key words: Administrative burdens, firm performance, negotiation time, safety

inspections, outside expertise.

TABLE OF CONTENTS

1. INTRODUCTION 4

2. LITERATURE REVIEW AND THEORETICAL MODEL 6

2.1 Administrative burdens: definitions and measurement 6

2.2 Hypotheses 11

2.3 Theoretical model 12

3. DATA AND METHODS 13

3.1 Data sources 13

3.2 Sample and Measures 14

3.3 Econometric Methods 16

4. EMPIRICAL RESULTS 18

4.1 Descriptive statistics 18

4.2 Regression results 19

5. CONCLUSIONS 21

REFERENCES

APPENDICES

1. INTRODUCTION

The adverse impact of regulations on small businesses has been registered by different organizations like the Small Business Service and the Better Regulation Commission (BRC). Regulation imposed by governments is one of the major obstacles to business success. It imposes costs that hinder employment, innovation, investment, and growth and can weaken economic performance (Nicoletti and Scarpetta, 2003). Advocates argue that regulation is necessary in order to achieve environmental, economic and social targets and in order to protect employees, employers, consumers, investors and the environment (World Bank, 2006).

Nevertheless, reducing the costs that are associated with administrative burdens are important policy targets (Cabinet Office, 2005).

The purpose of this study is to provide an understanding of the relationship between administrative burdens and firm performance of small businesses in the manufacturing sector. Besides that, we will test this in two different settings, an emerging and an advanced economy. We will look in particular at India and Ireland.

In the United Kingdom, including Ireland, most executives expect the business costs of regulation to rise within the next years, according to a global survey by the Economist Intelligence Unit

. Due to the increasing complexity of the economy and society, there is a need for thorough regulation. In India, the same need is apparent, only due to different reasons. Endless red tape and obstructive bureaucrats with overlapping responsibilities are one of the greatest obstacles to business operations there

. Besides that, India still deals with great amounts of corruptions within the governmental system. The comparison between these two countries facilitates therefore optimal research on the influence of administrative burdens on firm performance, which can in the future contribute to better facilitate business creation and operations.

We will focus in particular on small business in the manufacturing sector, since small business are more severely affected by administrative burdens than larger economies, e.g. because small companies are less competent in dealing with complex regulations and are less able to spread costs (Baldwin, 2004). The manufacturing

1


Accountancy
Ireland
October
2006
Volume
37
No.
5
Red
Tape
is
biggest
business
threat.


2


The
Economist
Intelligence
Unit
Limited
2009.
Business
India
Intelligence
July
22nd
2009


Business
India
Intelligence
Fortnightly
report
on
business
developments
in
India.



sector covers a wide range of firms and provides therefore a solid base for this research.

The aim of this study is to understand the relationship between administrative burden and firm performance of small manufacturing businesses. The main research question therefore is:

“What is the impact of administrative burden on firm performance?

In order to support the main research question the following specific research questions are formulated:

“What are administrative burdens and how can we measure these?”

“What is the relationship between administrative burdens and firm performance?”

“Does this causal relationship exist in reality?”

The rest of the paper is structured as follows. Section 2 will explore the definitions of

administrative burdens, the different methods of measurements there are for

measuring administrative burdens and the relation between administrative burdens

and firm performance. Consequently, we formulate the hypotheses and frame them in

a theoretical model. Section 3 examines the data and methods we will use to measure

the theoretical model. Section 4 presents the empirical results. Finally, section 5

presents the conclusions. A detailed list of source materials may be found in the

bibliography.

2. LITERATURE REVIEW AND THEORETICAL MODEL

This study investigates how administrative burdens influence firm performance and thereby the focus is in particular on small sized Indian and Irish firms in the manufacturing sector. Thus, instead of investigating the actions of governments on avoiding administrative burdens, we analyze the effects of certain bureaucratic behaviour on firm performance within companies. From now on, no distinction is made between the terms ‘red tape’ and ‘administrative burdens’ and they are used interchangeably. After specifying several definitions for administrative burdens, we will discuss different methods for measuring administrative burdens.

Then, we will elaborate on the relationship between administrative burdens and firm performance. Consequently, we will formulate hypotheses and build a theoretical model that we will use to measure the relation between administrative burdens and firm performance.

2.1 Administrative burdens: definitions and measurement

There are numerous studies on how to define red tape. In table 1 the different studies

on defining red tape are summarized. Buchanan accomplished the earliest research on

administrative burdens in 1975. He does not give an explicit definition of

administrative burdens, however he states that public managers face greater

restrictions from bureaucracy than private managers do (Pandey and Scott, 2002). In

1984 Rosenfeld focuses on a different area where red tape exists, namely on

federalism and intergovernmental programs. Baldwin (1990) follows and identifies

two types of administrative burdens, namely formal and informal red tape. Formal red

tape refers to constraints to an organization’s freedoms as a result of laws, rules,

regulations and procedures. Informal red tape is defined as constraints to an

organization’s freedoms caused by the influence, not formal sanctions, of key bodies

in the political system, with key bodies being media, public opinion, political parties,

interest groups and public officials (Pandey and Scott, 2002). Baldwin (1990) defines

red tape more clearly than his predecessors did. He defines red tape as: ‘constraints

employees face in carrying out their day-to-day activities, not the constraints

members of the public face’. The distinct difference with Buchanan (1975) and

Rosenfeld (1984) is that he directly refers to employees. Bozeman, Reed and Scott

(1992) focus on the negative effects of rules and procedures in defining red tape. For

example, they take the time in weeks it takes managers to perform tasks into account.

The problem with this is that administrative delay is an indirect measure of red tape.

Administrative delay cannot explain all delays.

Table 1. Definitions of administrative burdens.

Year Author Definition

1975 Buchanan No explicit definition, but public managers face greater restrictions from bureaucracy than private managers do.

1984 Rosenfeld Red tape exists in federal and intergovernmental programs.

1990 Baldwin Administrative burdens: constraints employees face in carrying out their day-to-day activities, not the constraints members of the public face.

He defines two types of administrative burdens:

-Formal red tape: constraints to an organization’s freedoms as a result of laws, rules, regulations and procedures.

-Informal red tape: constraints to an organization’s freedoms as a result of the influence of key bodies in the political system.

1992 Bozeman, Reed and Scott Administrative burdens: the negative effects of rules and procedures in defining red tape, in other words administrative delay.

1995 Rainey, Pandey and Bozeman

Administrative burdens: rules, regulations and procedures that remain in force and entail a compliance burden for the organization, but have no efficacy for the rules’ functional object.

1997 Pandey and

Brettschneider

Attempt to separate administrative delays from other delays. The results, however, make no distinction between the delays.

2000 Pandey and Kingsley Administrative burdens: constraints imposed by

rules and procedures.

A novel definition of administrative burdens is brought by Rainey, Pandey and Bozeman (1995). They identify administrative burdens as ‘rules, regulations, and procedures that remain in force and entail a compliance burden for the organization but have no efficacy for the rules’ functional object’. The correspondence between theoretical and operational definitions is somewhat inconclusive. Pandey and Brettschneider (1997) try to separate red tape-based delay from other delays.

However, the result of the measurement comes basically to the same result as the measure of administrative delay. Pandey and Kingsley (2000) bring a new element in the definition of red tape that makes the operationalization of the theoretical definition better. The theoretical definition can be put as ‘constraints imposed by rules and procedures’ (Pandey and Scott, 2002). This definition is distinct for two reasons: it avoids the necessity of a detailed case study of every rule for determining organizational/social significance of the rule’s functional object and, rather than leaving determination of organizational/social significance as an open matter, it provides a clear guideline. Simplified, when managers perceive formalization as difficult and unfavourable to other organizational purposes, red tape exists.

Measures of administrative burdens

Different proxies have been developed in order to measure administrative burdens.

Hahn and Hird (1991) describe five general approaches to estimate the burdens of regulation. Econometric studies use product and cost functions and measure output of a market. A drawback is that this kind of measuring requires a considerable amount of data in order to perform significant statistical calculations. Expenditure evaluations base their estimations on surveys of businesses. A problem may be respondent biases.

A respondent may, for example, choose to fill out the questionnaire in a way that the answer may induce policy makers to changes policies that benefit the respondent.

Engineering approaches assess the cost of installing devices immediately to obey

regulation. A drawback is that when costs change consumer preferences beneficial to

the firm, it is not correct to attribute these costs to regulations. Productivity studies

measure the difference between changes in productivity due to regulation and the

changes that would have attended if the regulation would not have existed. General

equilibrium models estimate how perfectly competitive markets respond to a new

policy. Although it takes a large amount of observations, it may provide an improved

representation of regulatory effects. Other approaches that estimate the burdens of

regulations (Chittenden et al., 2002) are actuarial techniques that use statistical analyses of historical data on the costs that can come from environmental liabilities (Hahn & Hird, 1991; Hazilla and Kopp, 1990; Robinson, 1995; Ruteledge and Vogan, 1993). Professional judgement uses judgement of experts to measure administrative burdens. Decision analysis techniques are used in structuring professional judgement.

These techniques are probability distributions or the level of confidence for instance.

Valuation approaches entail a range of legal and economic techniques in order to assess compensation.

Another widely employed method is the cost-benefit analysis (Nijsen, Hudson and Müller, 2009). It investigates the trade-offs in terms of the cost and benefits of a policy. It has been criticized because of the difficulty of measuring costs and benefits with respect to regulations. In the 1990s, the development of a method to measure administrative burdens led to the creation of MISTRAL (EIM, 1997). It is based upon a theoretical framework that estimates the costs resulting from regulation and legislation, which is presented in figure 1. The framework identifies the administrative procedures that businesses must undertake (Chittenden et al., 2002).

Box one entails all administrative procedures that enterprises must undertake. Box two captures the routine procedures that are essential to firms for doing business. Box three seizes the compulsory administrative procedures resulting from regulation and legislation that firms face. Box 4 and 5 are deducted from box 3 and capture the procedures that firms would or would not perform even though no legislation exists.

These administrative burdens can then be assessed with a top-down approach that measures costs at the level of the complete organization, or with a bottom-up approach that estimates the costs of each element of compliance (Chittenden et al., 2002).

About a decade later, further research led to the development of the Standard

Cost Model (SCM), developed in The Netherlands (SCM Network, 2006). It is a

method for determining administrative burdens for businesses imposed by regulation

and for estimating administrative costs imposed by central governments. With this

method, one can measure a single law, selected areas of legislation or all legislation in

a country. Besides this, the SCM is also used to measure simplification proposals as

well as administrative consequences of new legislative proposals. According to the

International SCM Network, the key strength of the model is that it uses a high degree

of detail in the measurement of administrative costs, going down to the level of

Figure 1. MISTRAL (Source: EIM, 1997).

individual activities. The basic SCM formula, presented in formula 1, consists of the cost per administrative activity, calculated by multiplying price with time with quantity (SCM Network, 2006):

(1) Administrative Cost = Price x Quantity = (tariff x time) x (population x frequency)

where,

- Tariff is the wage costs (plus overheads) for activities done internally or the cost of external service providers

- Time is the amount of time required to complete the activity

- Population is the number of businesses or not-for-profit organizations affected - Frequency is the number of times that an activity must be completed each year

Another frequently used measure of red tape, developed by King et al. (2004), is the

‘anchoring vignettes technique’ that estimates the validity of key informant perception-based measures. Anchoring vignettes allow for comparison of perceptions

(1) Administrative Procedures of

Enterprises 


(2) Routine Business Administration


(3) Compulsory Administrative Procedures Resulting

from Legislation 


(4) Administrative Procedures Firms would perform if no

Legislation existed 


(5) Administrative procedures Firms would not perform if

no Legislation existed

with reality and for identifying the level of differential item functioning (DIF) on survey questions that measure administrative burdens. DIF happens when respondents understand the same survey questions in different ways. This is an obstacle in cross cultural survey research on abstract ideas like fairness, efficacy or justice, because people with different social communities encounter these ideas differently. Although there exists evidence for DIF, there are not yet indisputable results (Pandey and Marlowe, 2009).

Administrative burdens and firm performance

The fact that there exists a relation between administrative burdens and firm performance can be ascribed to the importance of regulatory reform on the agenda of politicians. With the current financial crisis, even more interest in improving rules and legislation has emerged so that economic adjustment can be supported (World Bank, 2010). The complexity and dynamism of the economy causes some regulation to lose its purpose. If circumstances in the economy change, certain rules may no longer be effective and new regulation may be needed. Consequently, the amount of regulation increases. The time and costs that are involved may be a drawback for business and firm performance. Administrative burdens can affect firm performance in a sense that when regulations are no longer effective, developed weakly or practiced improperly they can hamper innovation, create barriers to entry or limit investments. Other examples that exhibit the role of administrative burdens in firm performance are the regulatory costs that are involved with filling out forms, asking for permits and licenses, reporting notification requirements for the government, and preparing for inspections (OECD, 2006). Research has shown that administrative burdens are especially oppressing to smaller firms and that it precludes the start of new business (Baldwin, 2004).

2.2 Hypotheses

In accordance with the previous, we expect that there is a negative relationship

between administrative burdens and firm performance. Administrative burden is a

multidimensional concept. We study therefore three key components that each relate

to the human factor in bureaucracy as built by the Enterprise Analysis Unit of the

World Bank:

− Negotiation time (NT) is the time spent by senior management in dealing with permits and licenses.

− Safety inspections (SI) is the time spent on safety inspections and regulations.

− Outside expertise (OE) is the use of outside expertise in order to deal with permits and licenses. If a firm is not capable itself in dealing with permits and licenses, due to lack of expertise or time, it may hire an external expert.

Taking our main proposition into account, we formulate the following hypotheses:

H

: Negotiation time is negatively related to firm performance.

H

: The number of safety inspections is negatively related to firm performance.

H

: The use of outside expertise is negatively related to firm performance.

2.3 Theoretical Model

The theoretical model of this study is presented in formula 2:

(2) FP = C + β

NT + β

SI + β

OE + β

MS+ β

SF + ε

We can summarize this as follows:

FP = firm performance NT = negotiation time SI = safety inspections OE = external expertise MS = the firm sector SM = the firm size

Many variables are likely to have an effect on firm performance. In our model we

need to control for variables that may explain firm performance as well. We use firm

size and firm sector, because they are both likely to influence firm performance

(Graybeal Lobingier, 1997). By including the two control variables in our model we

minimize performance and other side effects.

3. DATA AND METHODS

3.1 Data sources

Evidence on administrative burdens can be classified into four broad types (Chittenden et al., 2002). Types 1 are papers that use results from other studies.

Usually this data are collected over a number of years. Types 2 are reports with data from government programs and obtain this information at the level of the economy or for a particular agency. Types 3 include research that collects primary data form businesses about the cost of legislation and regulation activities. Types 4 are evidence that exists of primary data from businesses about the impact of a particular area of legislation (Chittenden et al., 2002). For this research, we use type 3 evidence. The data we will use were collected from The Enterprise Surveys

. The Enterprise Survey is a firm-level survey of a representative sample of an economy’s private sector. The surveys collect information about the business environment, how it is perceived by individual firms, how it changes over time, and about the various constraints to firm performance and growth. These surveys have been collected since 2002 by different units of the World Bank. One of the key aspects of the Enterprise Survey is guaranteeing that the information given is treated confidentially in order to ensure the greatest degree of participation, integrity and quality of the data. Top managers and business owners answer the surveys. The service and manufacturing sector are of primary interest. State owned firms are not included in the sample. The sampling methodology for Enterprise Surveys is a “stratified random sampling with replacement”. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary.

In a stratified random sample, all population units are grouped within homogeneous

groups and simple random samples are selected within each group. This method

allows computing estimates for each of the strata with a specified level of precision

while population estimates can also be estimated by properly weighting individual

observations. The sampling weights take care of the varying probabilities of selection

across different strata. The strata for Enterprise Surveys are firm size, business sector,

and geographic region within a country. Firm size levels are 5-19 (small), 20-99

(medium), and 100+ employees (large-sized firms). In most economies the majority

of firms are small and medium-sized. Enterprise Surveys may thus oversample large

firms in proportion to their presence. Geographic regions within a country are selected based on which cities or regions collectively contain the majority of economic activity.

3.2 Sample and measures

We will use two subsamples of the Enterprise Survey Databases, one of an emerging economy and one of an advanced economy. The samples that we use to analyze the effect of administrative burdens on firm performance are conducted in Ireland and in India in 2004. The data allow us to verify potential differences between India and Ireland. The databases provide data for both countries in 2004 (the surveys were presented in 2005).

The raw data lists 501 potential Irish respondents and 2279 potential Indian respondents. The number of surveys conducted per country is related to the size of the economies, India being a larger economy than Ireland. The Sampling Note provides the rationale for these sample sizes. The firm level data consist of information on, e.g., governance and ownership structure, sales and supplies, innovation and learning, finance, infrastructure and land. The data we use apply to sales, regulation and a set of firm characteristics. As a result, cross-sectional data from Indian and Irish firms were collected for the variables under consideration in this study. Table 2 presents the constructs and measures of this study. We use total sales to measure firm performance since this best indicates the financial health of a firm (Bantel and Osborn, 1995)

4


For Ireland a survey performed by the European Bank of Reconstruction and Development, in cooperation with the World Bank is used. For India a Firm Analysis and Competitiveness Survey is used which the Confederation of Indian Industry and the World Bank Group undertake.

www.enterprisesurveys.org


Table 2. Constructs and measures.

Construct Measure Scale

Dependent variable

Firm performance (FP)

Total sales 2004 Euros (log)

Independent variables

1 Negotiation time (NT)

The time it costs senior management to deal with permits and licensing.

Percentage

2 Safety

inspections (SI)

The number of times a firm was inspected by or required to meet with officials for safety regulations.

Ratio

3 Outside

expertise (OE)

The use of external expertise in order to deal with regulation, licensing and permits

1 = Yes 0 = No (Dummy)

Control variables

1 Firm sector (MS)

The sector the firm is active in

1 = manufacturing 0 = other

(Dummy) 2 Firm size (SF) The number of employees 1 = 2 – 49 employees

0 = other (Dummy)

3.3 Econometric methods

So far, we have determined the variables, theoretical model and the sample. Now, we will need a method to test the model and estimate the parameters coefficients. We will use cross-sectional ordinary least square regression (OLS). The OLS model is able to give unbiased results if the Gaus-Markov theorem holds. This states that the estimators need to be best linear unbiased estimators (BLUE). There are four assumptions that need to be met in order for the estimators to be BLUE, i.e., there is no autocorrelation, no correlation between the included explanatory variables, x

, and the error term, ε

, no heteroskedasticity, and the normality of the residuals holds.

Since we use cross-sectional data, only the assumptions of no multicollinearity and no heteroskedasticity are relevant.

Multicollinearity

In order to test for multicollinearity we first correlate the independent variables with each other. We find that there is little correlation between the independent variables, because all values are smaller than 0.80. We also calculate the variation inflation factor values (VIF) and tolerances (1/VIF). The results can be found in appendix 1. A VIF-value higher than 8 would indicate that there is a problem with multicollinearity.

Tolerances should be larger than 0.4, which indicates that more than 40% of the variance of a particular independent variable is not explained by the other independent variables. We find that the assumption of no multicollinearity holds for the data of both India and Ireland.

Heteroskedasticity

When heteroskedasticity is present, observations with large error variances get more

weight in the regression than observations with small error variances. This can lead to

biased estimates of the variances of each of the estimated parameters (Pindyck and

Rubinfeld 1991). We create a scatter plot for fitted values and residuals - which can

be found in appendix 2 – and perform the White test, because the White test is

sensitive to the BLUE assumptions such as the assumptions of normality. In the

scatter plot we cannot detect a clear pattern of the observations. We do find however

that most data are centred on the line where y = 0 in the first graph. In the second

graph, a small downward slope can be detected as a pattern, but we need to perform

the White test to detect its relevance. The results of the White test can be found in

appendix 2. If the p-value is very small, we can reject the null-hypothesis that the

variance of the error term is homogeneous. The White test for India (78.92) rejects the

null-hypothesis of no heteroskedasticity. The White test for Ireland (9.30) is unable to

reject the null-hypothesis of no heteroskedasticity with a p-value close to 1. We find

higher values of skewness and kurtosis for the Indian dataset, which indicates

asymmetric data. In order to correct the data for heteroskedasticity, we use robust

standard errors.

4. EMPIRICAL RESULTS

4.1 Descriptive statistics

The subsamples contain 1928 observations in India and 160 observations in Ireland. The original datasets contained 2279 observations for India and 501 for Ireland. This decline in observations is explained due to the fact that there are missing observations. Besides missing observations, there are a few outliers. The scatter plot that can be found in appendix 2 endorses this. We estimated the robust standard errors in order to solve minor problems of heteroskedasticity our data have. In table 3, the descriptive statistics that are extorted from the new subsample are presented. We standardize the variables in order to be able to interpret the variables better.

Table 3. Descriptive statistics.

Variable Observations Mean Std. Dev. Min Max India

Sales (FP) 1928 4412.9141 26974.39668 0 471,844.6 Log (Sales) 1928 5.1527 2.25108 -5.625278 13.06441

NT 1928 12.7191 13.51644 0 100

SI 1928 .65 1.347 0 31

OE 1928 .28 .447 0 1

MS 1928 .73 .446 0 1

SF 1928 .72 .451 0 1

Ireland

Sales (FP) 160 41747.54 183986.349 70 1800000 Log (Sales) 160 8.2450 1.99565 4.25 14.40

NT 160 4.2694 9.12104 0 75

SI 160 1.75 1.664 1 12

OE 160 .29 .457 0 1

MS 160 .38 .487 0 1

SF 160 .60 .491 0 1

4.2 Regression results

The main hypotheses predict that there exists a negative relationship between the three dimensions of administrative burdens and firm performance. Hypothesis 1 predicts that the amount of negotiation time is negatively related to firm performance.

Hypothesis 2 predicts that there is a negative relationship between the number of safety inspections and firm performance. Hypothesis 3 predicts there is a negative relationship between the use of outside expertise and firm performance. The regression results are presented in table 4.

The R-square measures the proportion of variation in the dependent variable that is explained by the variations in the independent variables. Consistently, 26.06%

of the variation in total sales is explained by the variation in the independent variables in the model for Indian data. The adjusted R-square is a modification of the R-square that adjusts for the number of explanatory terms in the model and shows that 25.87%

of the variance is explained. The test statistic is the F-value of 135.51. Using an α of 0.001 we have that F

equals 4.11, which means that the model is significant at the 99.9% confidence level. For the variables of the dataset of Ireland, 50.4% is the variation in the dependent variable that is explained by the variations in the independent variables. The adjusted R square is 48.78%. This shows that these results better fits a straight line than the results of India. The F value is 31.29. Using an α of 0.001 we have that F

equals 4.29, which implies that the model is significant at the 99.9% confidence level. Ergo, the model fit in both countries is acceptable.

Table 4 shows that the parameter estimates for negotiation time are positive and significant (p = 0.003, p < .05 and p = 0.001, p < 0.1). Hypothesis 1 is rejected.

The parameter estimates for safety inspections are positive and significant (p = 0.129, p < 0.01). Hypothesis 2 is rejected. The parameter estimates for outside expertise are positive and significant (p = 0.795, p < 0.01 and p = 0.430, p < 0.05). Hypothesis 3 is rejected.

Table 4. Regression results.

Firm performance (log sales) India

Firm performance (log sales) Ireland

Constant 6.418565*

(.1567985)

69.141386 ***

(.3217655) Control variables

Industry .0287464*

(.1079027)

.1149794*

(.2425054)

Firm size -2.268238***

(.1145875)

-2.491855 ***

(.2627341) Independent variables

Negotiation time .0027202**

(.0030894)

0.0013669*

(.0103569)

Safety inspections .129155***

(.0427128)

.2415254***

(.0722666)

Outside expertise .7948169***

(.1019845)

.430409**

(.2755556)

R

0.2606 0.5040

Adj. R

0.2587 0.4878

F 135.51***

(119.28)

31.29***

(30.37) White heteroskedastic consistent standard errors in parentheses, with

* = p < 0.1

** = p < 0.05

*** = p < 0.01

To summarize, based on the empirical results we cannot conclude that there is a

negative relationship between administrative burdens and firm performance. The

results show opposite effects for all of the three dimensions in India and Ireland. We

are also unable to make a distinction between both economies, because the regression

results in terms of signs and significance levels are similar for the same independent

variables in both countries.

5. CONCLUSIONS

The aim of this study is to provide an understanding of the impact of administrative burdens on firm performance. We therefore studied the manufacturing sectors in India and Ireland. We will present a brief overview of the findings in this study and in so doing provide an answer to the research questions.

The first research question asked is “What are administrative burdens and how can we measure these?”. In the literature review, we provide an extensive overview of the definitions and methods that are used to measure administrative burdens. We define administrative burdens as ‘rules, regulations, and procedures that remain in force and entail a compliance burden for the organization but have no efficacy for the rules’ functional object’ (Rainey, Pandey and Bozeman 1995). We measure administrative burdens by means of perceptions of the local actors. We focus on a specific subset of administrative burdens and study negotiation time, safety inspections and the use of external expertise in order to deal with permits and contracts.

The second research question asked is “What is the relationship between administrative burdens and firm performance?”. The increasing amount of regulation is a drawback for business and involves different kinds of costs. Not only the costs that come from the increasing production of regulation and rules, but also time delays and costs that are involved with filling out forms, asking for permits and licenses, reporting notification requirements, and preparing for inspections are regulatory costs (OECD 2006). We therefore hypothesize that there is a negative relationship between administrative burdens and firm performance in general as well as for the three sub dimensions.

Finally, the third research question we will answer in order to propose an answer to the main research question: “Does this causal relationship between administrative burdens and firm performance and exist in reality?” To answer this question we used information from 1928 firms in India and 160 firms in Ireland. With few exceptions, the results show for both India and Ireland a positive relationship between administrative burdens and firm performance. This contradicts our hypothesis but offers helpful insights for future studies.

Our findings are general because they apply to two completely different

institutional systems. A potential explanation for the positive relationship between

administrative burdens and firm performance is the following. Our theoretical foundations focus only on the cost sides of bureaucracy. There might be positive effects as well. Rules may allow firms to learn about inefficiencies in their own organization. The interaction with civil servants allows firms to learn about the requirements of the institutional system and better align their organization. These positive effects apparently outweigh the negative effects as hypothesized. Our study has limitations that offer opportunities for further research. We used cross-sectional data, whereas longitudinal data may offer more understanding of the relationship between administrative burdens and firm performance overtime. We studied two countries, whereas future research could focus on different countries or more countries. Finally, we used three particular measures in order to measure administrative burdens. Future research could use other measures.

With the above limitations acknowledged we are confident that this study

offered a valuable contribution to the understanding of administrative burdens and

firm performance.

REFERENCES

Accountancy Ireland (2006) Red Tape is biggest business threat. 37: 5.

Baldwin, J.N. (1990) Perceptions of public versus private sector personnel and informal red tape: their impact on motivation. American Review of Public Administration, 20:7-28.

Bantel, K.A. and Osborn, R.N. (1995) The influence of performance, environment and size on the identifiably of firm strategy. British Journal of Management, 6: 235- 248.

Baldwin, R. (2004) Better Regulation: Is it better for business? Federation of Small Businesses, http://www.fsb.org.uk/documentstore/filedetails.asp?id=49

Better Regulation Task Force (2005) Less is more reducing burdens, improving outcomes. Report.

Bloomberg, Cooper and Schindler (2008) Business Research Methods. McGraw Hill Education: Berkshire.

Bozeman, B., Reed, P. And Scott, P. (1992) Red tape and task delays in public and private organizations. Administration and society, 24: 290-322.

Buchanan, J.M. (1975) A Contractarian Paradigm for Applying Economic Theory.

American Economic Review, American Economic Association, 65: 225-30.

Cabinet Office (2005) A Bill for Better Regulation: Consultation Document, Cabinet Office, London.

Chittenden, F. Kauser, S. and Poutziouris, P. (2002) Regulatory Burdens of

Small Business: A Literature Review, Report.

Graybeal Lobingier, P. (1997) Compensation choice – the effect on firm performance: an interindustry look at performance plans and restricted stock.

Virginia Polytechnic Institute and State University

Hahn, R.W. and Hird, J. (1991) The Costs and Benefits of Regulation: Review and Synthesis. Yale Journal on Regulation 8: 233–78.

Hahn, R.W. and Stavins, R.N., (1990) Incentive-Based Environmental Regulation:

A New Era From An Old Idea? Papers 183d, Harvard - J.F. Kennedy School of Government.

Hazilla, M. and Kopp, R.J. (1990) The Social Cost of Environmental Quality Regu- lations: A General Equilibrium Analysis. Journal of Political Economy 98(4): 853–

73.

International Standard Cost Model Manual, Measuring and reducing administrative burdens for businesses. SCM Network 2006

King, G., Murray, C. J. L., Salomon, J. A., and Tandon, A. (2004) Enhancing the validity and cross-cultural comparability of measurement in survey research.

American Political Science Review, 44: 341–355.

Kingston University (2005) Regulation and small firm performance and growth: A review of the literature Small Business Research centre.

Kitching J. (2006) A burden on business? Reviewing the evidence base on regulation and small-business performance. Environment and Planning C: Government and Policy, 24: 799-841.

Nicoletti, G. and Scarpetta, N. (2003) Regulation, Productivity and Growth: OECD Evidence. World Bank Policy Research Working Paper 2944, OECD, Paris.

Nijsen, A., Hudson, J. and Müller, C. (2009) Business Regulation and Public Policy:

The Costs and Benefits of Compliance. New York, p. 45.

Organization for Economic Cooperation and Development (2006) Cutting Red Tape:

National strategies for administrative simplification.

Pandey S. and Brettschneider, S. (1997) The impact of red tape’s administrative delay on public organizations. Interest in new information technologies. Journal of Public Administration Research and Theory, 7:113-130.

Pandey, S. and Kingsley, G. (2000) Examining red tape in public and private organizations: Alternative explanations from a social psychological model. Journal of Public Administration Research and Theory, 10: 779-799.

Pandey, S.K. and Scott, P.G. (2002) Red Tape: A review of concepts and measures.

Journal of Public Administration Research and Theory, 4:553-580.

Pandey, S.K. and Marlowe, J. (2009) Taking stock of survey-based measures of bureaucratic red tape: mere perceptions or more than meets the eye? Paper presented at the 10th Public Management Research Conference, Colombus, October.

Pindyck, R.S. and Rubinfeld, D.L. (1991) Econometric Models & Economic Forecasts. McGraw-Hill.

Rainey, H., Pandey, S. and Bozeman, B. (1995) Research note: Public and private managers’ perceptions of red tape. Public Administration Review, 55: 567-574.

Rosenfeld, R.A. (1984) An expansion and application of Kaufman’s model of red tape: the case of community development block grants. The Western Political Quarterly, 37:603-620.

The Economist Intelligence Unit Limited (2009) Business India Intelligence

Fortnightly report on business developments in India. Business India Intelligence

July 22nd.

The Economist Intelligence Unit Limited (2009) Tangled up in Red Tape. Business India Intelligence April 29

World Bank (2006) Doing Business. The World Bank & World Bank Reforming Through Difficult Times.

World Bank (2006) Doing Business in 2006: Creating Jobs.

http://www.doingbusiness.org/documents/DoingBusines2006_fullreport.pdf

World Economic Forum (WEF) (2005) Global Competitiveness Report 2004/5, http://www.weforum.org/.

www.enterprisesurveys.org

APPENDICES

Appendix 1 Multicollinearity Correlation matrix.

* p < 0.5 (2-tailed)

VIF values.

INDIA IRELAND

Variable VIF 1/VIF VIF 1/VIF

OE 1.02 0.977164 1.06 0.946696

SF 1.02 0.981799 1.31 0.762504

SI 1.02 0.983597 1.11 0.902933

MS 1.01 0.991850 1.29 0.774099

NT 1.00 0.998420 1.02 0.978065

Mean VIF 1.01 1.16

India NT SI OE MS SF

NT 1.0000

SI 0.0120 1.0000

OE 0.0551* 0.0958* 1.0000

MS -0.0346 -0.0531* -0.0550* 1.0000

SF -0.0028 -0.0667* -0.0762* 0.0507* 1.0000

Ireland

NT 1.0000

SI 0.0030 1.0000

OE 0.1914* 0.0719 1.0000

MS 0.0053 -0.1251 0.0246 1.0000

SF -0.0455 -0.1691* -0.1743* -0.2039* 1.0000

Appendix 2 Heteroskedasticity

Scatter plots of fitted values and residuals.

India Ireland

White test

Source Chi2 Df P

India

Heteroskedasticity 78.92 17 0.0000

Skewness 24.43 5 0.0002

Kurtosis 12.35 1 0.0004

Total 115.70 23 0.0000

Ireland

Heteroskedasticity 9.30 17 0.9302

Skewness 6.06 5 0.3007

Kurtosis 2.53 1 0.1117

Total 17.89 23 0.7633