Analysis of Top R&D investorsperformance under a Technology

Heterogeneity Regime

Chara Katsigianni

A dissertation submitted in partial fulfillment of the requirements for the degree

of Master of Science in Applied Economics & Data Analysis

School of Business Administration

Department of Economics

Master of Science in

“Applied Economics and Data Analysis”

September 2017

University of Patras, Department of Economics

Chara Katsigianni

© 2017 − All rights reserved

Three-member Dissertation Committee

Research Supervisor: Kounetas Konstantinos Assistant Professor

Dissertation Committee Member: Tsekouras Kostas Professor

Dissertation Committee Member: Skuras Dimitrios Professor

The present dissertation entitled

«Analysis of Top R&D investors performance under a Technology Heterogeneity

Regime»

was submitted by Chara Katsigianni, Sid 1018104, in partial fulfillment

of the requirements for the degree of Master of Science in «Applied Economics &

Data Analysis» at the University of Patras and was approved by the Dissertation

Committee Members.

Acknowledgments

I am deeply grateful to my supervisor, K. Kounetas, for the guidance and continu-

ous supporting he provided me during the elaboration of my dissertation. I would

also like to acknowledge the two members of the dissertation committee, professors

K. Tsekouras and D. Skuras. Special thanks are given to N. Chatzistamoulou for

enlightening discussions since my undergraduate studies.

I would like to dedicate my dissertation to my family and friends for encouraging

and being patient throughout my years of education.

i

Summary

This dissertation investigates the impact of corporate research and development

(R&D) activities on firms’ performance. A parametric technique of a stochastic

frontier will be used based on a production function that also takes into account

the impact of R&D activities. In this context, this study quantifies the technical

inefficiencies of individual firms. The original of this dissertation refers to the

adoption of meta-technology as a regime that allows the analysis of productivity

taking into account the heterogeneity resulting from the existence of the different

industries.

Keywords: R&D activities, production function, stochastic frontier, heterogeneity

i

Περίληψη

Η συγκεκριμένη διπλωματική εργασία έχει ως κύριο στόχο την εξέταση της επί-

δρασης της απόδοσης των προσπαθειών σε Ε&Α των τοπ επιχειρήσεων του Ηνωμένου

Βασιλείου για μια αρκετά μεγάλη χρονική περίοδο. Μια παραμετρική τεχνική ενός

στοχαστικού ορίου θα χρησιμοποιηθεί στηριζόμενη σε μια συνάρτηση παραγωγής

που λαμβάνει υπόψη και την επίδραση των Ε&Α προσπαθειών. Σε αυτό το πλαίσιο,

η παρούσα μελέτη ποσοτικοποιεί τις τεχνικές αναποτελεσματικότητες των μεμον-

ωμένων επιχειρήσεων. Το πρωτότυπο της συγκεκριμένης διπλωματικής αναφέρεται

στην υιοθέτηση της μετά-τεχνολογίας ως ένα καθεστώς που επιτρέπει την ανάλυση

της παραγωγικότητας λαμβάνοντας υπόψη και την ετερογένεια που προκύπτει από

την ύπαρξη των διαφορετικών κλάδων.

Λέξεις κλειδιά: προσπάθειες σε Ε&Α, συνάρτηση παραγωγής, στοχαστικό όριο,

ετερογένεια

Contents

1 Introduction 1

2 Literature Review 4

3 Data 8

3.1 Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Definitions and organization of the data . . . . . . . . . . . . . . . 9

3.3 Construction of the main variables and descriptive statistics . . . . 10

4 Methodology 14

4.1 Theoretical modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.2 Methodological Approach . . . . . . . . . . . . . . . . . . . . . . . 17

5 Results 19

6 Conclusions 27

7 Appendix 29

Bibliography 32

ii

List of Figures

3.1 Mean R&D intensity the time period 2007-2015 . . . . . . . . . . . 12

3.2 Scatter plots of turnover, physical capital and R&D . . . . . . . . . 13

5.1 Annual Technical Efficiency . . . . . . . . . . . . . . . . . . . . . . 25

iii

List of Tables

3.1 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5.1 Model selection decisions . . . . . . . . . . . . . . . . . . . . . . . . 19

5.2 Additional hypotheses tests . . . . . . . . . . . . . . . . . . . . . . 20

5.3 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.4 Technical efficiency statistics . . . . . . . . . . . . . . . . . . . . . . 24

5.5 Annual technical efficiency statistics . . . . . . . . . . . . . . . . . . 24

7.1 Sector classification using NACE code . . . . . . . . . . . . . . . . . 29

7.2 Technology, industrial and service sectors . . . . . . . . . . . . . . . 31

iv

Chapter 1

Introduction

From an empirical point of view, it is generally accepted that a main factor in

productivity growth is the Research and Development (R&D) activities (Griliches,

1979). The main objective of this study is to investigate the impact of corporate

R&D activities on firm performance, measured by labour productivity. Produc-

tivity is an index that measures output (goods and services) relative to the input

(capital, labor, materials, energy, and other resources) used to produce them. Effi-

ciency is the ratio of actual output generated to the expected (or standard) output

prescribed. In addition, efficiency is a necessary (but not a satisfactory) condition

for productivity. In fact, both effectiveness and efficiency are necessary in order

to be productive.

Production function describes the technical relationship between the inputs

and the outputs of a production process. Gives the maximum output(-s) at-

tainable from a given vector of inputs. The Stochastic Production Function is

a relationship between output and a set of input quantities. A number of dif-

ferent functional forms are used in the literature to model production functions

such as Cobb-Douglas (linear logs of outputs and inputs), Quadratic (in inputs),

Normalized quadratic and Translog function. Translog function is very commonly

used as it is a generalization of the Cobb-Douglas function. It is also a flexi-

ble functional form providing a second order approximation. Both Cobb-Douglas

1

2

and Translog functions are linear in parameters and can be estimated using least

squares methods.

As far as the innovation activities is concerned there are all scientific, tech-

nological, organizational, financial and commercial steps which actually, or are

intended to, lead to the implementation of innovations. Some innovation activi-

ties are themselves innovative, others are not novel activities but are necessary for

the implementation of innovations. Innovation activities also include R&D that is

not directly related to the development of a specific innovation.

In measuring inventive and innovative activity researchers have employed three

main alternative; a) head-counts of the number of patents issued b) expenditure

or employment of personnel on R&D c) head-counts of the number of innovations,

sometimes confined to significant innovations as defined by the researcher or/and

the industry experts.

Impacts of innovations on firm performance range from effects on sales and

market share to changes in productivity and efficiency. Important impacts at

industry and national levels are changes in international competitiveness and in

total factor productivity, knowledge spillovers of firm-level innovations, and an

increase in the amount of knowledge flowing through networks.

In this dissertation we estimate a Translog production function using the

stochastic frontier technique over an unbalanced unique data set of 170top R&D

investors in United Kingdom over the 9-year period 2007-2016 composed of two

data sets. We assume a common production function with three inputs, physical

capital (K), labour (L) and R&D investments (RD) and measuring the output

using the firms’ turnover. We expect our estimations indicate that R&D invest-

ments affect labour productivity and, for this reason, it is necessary to include

R&D activities as a complementary input to capital and labour.

The original of this dissertation refers to the adoption of meta-technology as a

regime that allows the analysis of productivity taking into account the heterogene-

ity resulting from the existence of the different industries. The empirical findings

3

in brief reveal that firstly R&D investments have a statistical significant and posi-

tive effect in labour productivity, secondly blablabla and thirdly R&D investments

affect technical efficiency not only the year the investments occur but also in the

future year(s).

To summarize the organization of this paper, Chapter 2 presents previous the-

oretical and empirical review of studies using the same direction applied in this

dissertation, Chapter 3 present and discuss the data used, Chapter 4 provides the

theoretical framework and the modeling process, Chapter 5 review the estima-

tion results and Chapter 6 concludes about the impact of R&D activities on firm

performance.

Chapter 2

Literature Review

Kumbhakar et al. (2012) investigated the impact of corporate research and devel-

opment activities on firm performance. As mentioned in the paper Kumbhakar

et al. (2012) measured the referent impact by labour productivity. Using the

stochastic frontier analysis and an unbalanced panel data set of top European

R&D investors over the period 2000–2005, their study quantifies technical ineffi-

ciency. The results they end up in implies that firms’ productivity levels up via

supporting corporate R&D in high-tech and medium-tech sectors. As far as the

low-tech sector is concerned, is found to have a minor effect in explaining produc-

tivity in contrast to investment in fixed assets which seems to be more important.

Finally, R&D intensity is found to be a very important factor in explaining firm

efficiency for all industries.

Another contiguous approach made by Hall and Mairesse (1995). In this pa-

per, estimates the contribution of R&D investments to productivity using French

manufacturing firms the time period 1971 to 1987. First of all, as production

function they used a cobb douglas function in the three inputs, physical capital,

labor, and R&D. Their results showed that the choice of depreciation rate, as far

as the construction of R&D capital is concerned, does not make much difference

to the coefficient estimates and the correction for the double-counting of R&D

expenditures in capital and labor is important in the production function.

4

5

Taking a brief look in literature, many studies have been analyzed the impact

of R&D intensity on firm performance. Most of them have commonly found that

R&D activities make a significant contribution in firm productivity. However,

there are several ways of measuring the R&D intensity. There are two main

differentiations; the level of data aggregation, and the type of estimation model

used. A large number of studies uses the number of patents or innovative activities

in order to catch up the impact of R&D intensity. As an example, Chen and Yang

(2005) used a Taiwanese firm level panel data set of 279 manufacturing firms from

1990 to 1997. They found that patents have a very significant contribution to

firms productivity, which implies that innovative activity investment has been very

significant factor for increasing output productivity for Taiwanese manufacturing

firms in the examined time period.

Similarly, Crepon et al. (1998) found that innovation output have a very signif-

icant positive correlation to firms productivity. Moreover, using some econometric

methods tried to correct for selectivity and simultaneity biases. Finding showed

that these two sources of bias should be taken into account in order to give a better

aspect of the complexity of the interaction between innovation and productivity.

Griffith et al. (2004) explored this idea of the two faces of research and devel-

opment entering the R&D intensity in levels, to capture an effect on innovation

and interacted with the relative productivity term, which captured an effect on

the rate of technological transfer. Their sample consisted of twelve countries over

the period 1974–1990. Finally, human capital found to be a major factor in pro-

ductivity growth and R&D found to be statistically and economically important

in both faces, technological catch-up and innovation.

On the other hand, Verspagen (1995) used quite different econometrical meth-

ods in order to examine how R&D activities contribute in firm productivity. The

functional form used in this paper is a translog production function applied to

data for 15 manufacturing sectors and 9 principal OECD countries for 16 years

the time period of 1973-1988. As Kumbhakar et al. (2012) deal with technological

6

sectors, Verspagen (1995) divided the sample in high-tech, medium-tech and low-

tech sectors based on the ratio of R&D to value added. Additionally, because of the

fact that the cross-country analysis introduced heterogeneity in the R&D amongst

countries, a time trend was included to capture the effects of technical changes

without affect the estimations in other sectors or countries. The main result of the

study was that R&D activities have a positive impact on firms’ productivity but

only in the high-tech sectors. In medium- and low-tech sectors elasticities found

to be positive but not statistically significant.

Suer (1995) has estimated the magnitude and nature of technological progress

in a number of manufacturing industries in the United Kingdom where some indus-

tries have traditionally been characterized by high profit margins, above-average

rates of spending on R&D as compared to UK manufacturing firms generally, and

higher-than-average rates of patent activity. Suer found evidence of significant

technological progress in the manufacturing industries he studied. This techno-

logical progress was not neutral. Instead, it resulted in an increase in the marginal

product of capital relative to the marginal product of labor (i.e., it was labor-saving

technological progress).

More recently, Kwon and Inui (2003) examining the relationship between the

R&D and the productivity improvement in more than 3,000 Japanese firms the

period between 1995 and 1998, they estimated a cobb douglas production func-

tion. They used three different estimation techniques (within estimates, first dif-

ferences and 3-years differences) and found out that this relationship is positive

and significant. Although, it seems that for the large sized and high-tech firms the

improvement was larger than the smaller sized and the low-tech firms. Following

approximately the same methods of measurement, Tsai and Wang (2004) used a

sample of 136 Taiwanese firms for the period 1994–2000. Their estimation of R&D

capital elasticity was lying between 0.18 and 0.20.

Two different but interesting approaches are referring to returns to R&D and

the correlation between countries’ per capita income and efficiency. Firstly, Rogers

7

(2006) on the other hand using a production function approach, and a sample of

719 firms estimated the elasticity of, and rate of return to, R&D. The results

of this study imply that high rates of competition in sector based on science

and research are associated with low returns to R&D. Secondly, using stochastic

frontier methods with a translog specification of production function Wang (2007)

estimated the mean of efficiency scores for 30 countries to 0.65. In country level

it has been shown that there is a significant positive correlation between the level

of efficiency (which is affected by R&D investments) and per capita income.

Last but not least, Heshmati and Kim (2011) using a panel data set of Ko-

rean firms from 1986 to 2002, and several estimation methods found something

very interesting. They show that there is a two-way causal relationship between

R&D investment and productivity for their sample. As mentioned also in the

paper, another important study in this area is Griliches and Mairesse (1984) that

presents some problems of measuring mostly. Using data of 133 US firms the

time period of 1966-1977, they found a strong R&D and productivity correlation

in the cross-sectional dimension, something that collapses in the time series ap-

proach. According to the paper this is due to the problems of simultaneity and

R&D measurement error.

Chapter 3

Data

3.1 Sources

The data set used in this dissertation is based on an unbalanced database of 170

top R&D investors in United Kingdom over the 9-year period 2007-2016. This

database was created by merging the companies data extracted directly from each

company’s Annual Report as published in European R&D Scoreboards by the

European Commission’s Joint Research Centre (JRC)1 with a United Kingdom’s

financial database. European R&D Scoreboards provide information about the

R&D intensity of the top R&D investors and the financial database covers mainly

financial data such as the operating revenue (turnover) of each company. Both

databases are published once every year.

In order to make it possible to analyze the data instantly for every year in the

sample, every annual published data set was united with the rest. In the end, the

merging was between two databases each of them was referred to the whole 9-year

period. Companies with missing values were discarded from the data set so as

there is no problem with modeling.1European Commission’s Joint Research Centre (JRC): http://iri.jrc.ec.europa.eu/

home

8

3.2 Definitions and organization of the data 9

3.2 Definitions and organization of the data

After the process of merging, the data set provides information at firm level for all

top R&D investors in United Kingdom. Although there is more information in the

data set, the main variables in use are the operating revenue or for short turnover

(Y), number of employees (L), R&D investments and capital. We used as depen-

dent variable the labour productivity which is defined as turnover per employee.

Respectively, the main impact variables are R&D investments per employee, cap-

ital per employee and labour. In Section 3.3 we present the analytic construction

of the main independent variables we used giving further information.

Every company is active to an industry as Table 7.1 in the "Appendix" in-

dicates. There are 25 different industries in the sample. In order to reduce the

computing load and have a satisfactory number of observations in each sector, we

grouped firms into 9 industrial and services sectors shown in the first part of Ta-

ble 7.2. These sectors were created considering the cohesion of industries included

based on their main business activities. In case there was an irrelevant in a way

sector to every group created, we classified it to a separate sector named "Others".

Another classification we did in this dissertation is referring to the level of

technological intensity. As mentioned also in Kumbhakar et al. (2012), there are

three sectors created in order to categorize how high is the technology level used

in every firm’s production function in contrast to the rest. Based on ISIC REV.3

classification 2 of manufacturing and service industries our sample includes 222

high-tech firms, 188 medium-tech firms and 252 low-tech firms. High-tech firms

is characterized by a high ratio of R&D to turnover, and high growth rates for

the stock of knowledge inputs. The second group of sectors, medium-tech firms,

are characterized by average R&D to turnover ratios. Finally, the low-tech sector

display low R&D intensity. More informations presented in Table 7.2.2OECD (2003), OECD Science, Technology and Industry Scoreboard 2003, OECD Publishing

3.3 Construction of the main variables and descriptive statistics 10

3.3 Construction of the main variables and de-

scriptive statistics

The main topic in this dissertation is related to productivity. Consequently, we

measure it using labour productivity computed by firm’s turnover divided by the

corresponding number of employees. Owing to the fact that there is high de-

viation among the firms’ size3, the effect of the R&D expenditures and capital

expenditures in firm’s productivity are not distinct from the effect of firm’s size.

Therefore, in need of normalizing the data, we used divided per capita values in

order to restrict the effects of size in productivity and gain the real effect of the

impact variables. Via this method the labour factor stays constant as a control

variable allowing the relationship between the other impact variables being better

tested.

A further aspect of productivity includes time. In other words, assuming that

a company invests in R&D or makes capital expenditures, affects not only the

productivity in the same year but also the year, or even years, later. In this

case, measuring firm’s productivity, investments in R&D, for example, of previous

years should be also taken into consideration. Dispite the fact that lagged R&D

expenditure is used in many studies but there is no agreement on the correct length

of the lag (Kwon and Inui (2003), Tsai and Wang (2004), Rouvinen (2002)), in

this dissertation we used the perpetual inventory method (PIM) as in Kumbhakar

et al. (2012) in order to catch this ’learning by doing effect’ (Kenneth J. Arrow,

1962). The corresponding equations are the following:

RDt0 = R&Dt0

g(RD) + δj(3.1)

where R&D = R&D expenditures, g(RD) is the growth rate for R&D expenditures3We measure size by the absolute number of employees.

3.3 Construction of the main variables and descriptive statistics 11

according to the industrial sector, δ is the depreciation rate and t0=2007

Kt0 = Ct0

g(K) + φj(3.2)

where C = Capital expenditures, g(K) is the growth rate for Capital expenditures

according to the industrial sector, φ is the depreciation rate and t0=2007.

As far as the depreciation rates δ and φ for RD and K are concerned, each

of the three sectoral groups (j) has different δ and φ values. As mentioned in

Kumbhakar et al. (2012) higher technologically advanced sectors characterized by

shorter product life-cycles so the depreciation rates are higher, too. In this paper

we applied depreciation rates of 10, 12 and 15% for high-, medium-, and low-tech

sectors respectively.

Consequently, one of our main questions is to examine if R&D activities have

an effect n productivity and efficiency thoughout time variation. In Figure 3.1 we

see the level of R&D investments by year. It seems that there are ups and downs in

the distribution. R&D intensity in 2007 stands in low records comparing to the rest

of the years in the sample. Later in 2010, there is a decline in R&D investments.

In 2012 and 2013 R&D investments surpass with a small difference the 2010 levels.

The question is if the ups and downs affect the firms productivity and efficiency.

We expect that for higher levels of R&D intensity the total productivity and

technical efficiency will increase respectively.

3.3 Construction of the main variables and descriptive statistics 12

Figure 3.1: Mean R&D intensity the time period 2007-2015

Table 3.1. Descriptive statistics

Variable Obs Mean Std. Dev. Min Max P50Y 662 9153.639 43998.18 .0047 372513.4 299.8533K 662 2.39e+08 2.23e+09 .3275 3.30e+10 15143.37L 662 14236.04 45895.59 5 639904 2021R&D 662 447.9438 1104.73 6.998 7604.602 108.8768T 662 2010.36 2.521447 2007 2015 2010Profits 662 9.29e+07 9.45e+08 -1.68e+09 1.44e+10

Tables 3.1 provides general information (some descriptive statistics) on the

sample and variables. The first thing we observe in this table is the huge standard

deviation of inputs and output. This is due to the fact that in the sample there

is a variety of firms in terms of size. There are small firms with 5 employees in

the same data base of Top R&D investors with firms employ more than 600,000

workers. This leads to high level of heterogeneity in our sample. Another thing

that we should focus on is that the distribution of every input is right skewed as

the 50th percentile (median) is much lower than the mean. Due to this and so as

to take trustworthy results of the models we would like to estimate, our data set

is parameterized directly in terms of the median.

3.3 Construction of the main variables and descriptive statistics 13

(a) Turnover - Capital (b) Turnover - R&D

Figure 3.2: Scatter plots of turnover, physical capital and R&D

Figure 3.2 presents two scatter plots showing the relationship between turnover,

physical capital and R&D. There is a strong positive relationship between turnover

and capital but also R&D as well. We observe also a large concentration in lower

R&D and capital expenditure. This may be due to the fact that some of the top

R&D investors scores really high levels of investments, leaving the rest of the firms

in lower levels. That’s the reason why we separate firms as high, medium and low

technological intensity.

Chapter 4

Methodology

4.1 Theoretical modeling

Research on the relationship between input and output of a "knowledge produc-

tion function" is an important contribution towards the understanding of how

firms’ productivity links to innovation. The understanding of the economic process

that leads to product and process innovation is of high interest, as an economy’s

wealthy and growth depend on technological progress. However, the identifica-

tion of innovation output is not trivial, as there is no strong, undisputed variable

measuring innovation success comprehensively. A number of proxies can be used

for knowledge capital, including stocks of R&D expenditure, patent counts and

data on actual innovations. There are cases that these proxies may not be an

accurate reflection of their actual R&D activities, although R&D expenditure is

the most common choice. Furthermore, there are studies which argument that

research component rather than the sum of research and development influences

productivity (Oecd, 1993). All previous microeconomic, empirical studies on the

relationship between patenting and R&D have treated R&D investment as a sin-

gle, homogeneous activity. Thus it is possible that previous studies underestimate

the effect of research activities as the development expenditure is typically larger

than research expenditure in the business sector. Although, it would be reason-

able to split R&D expenditure into its two components, research and development,

14

4.1 Theoretical modeling 15

most databases do not allow to explore this issue since usually only numbers for

aggregated R&D are available. Consequently in this dissertation we assume that

R&D expenditure captures the effect of research and development combined.

Different approaches have been applied in the literature in order to measure

the contribution of R&D to productivity growth using both parametric and non-

parametric methods, production or cost frontier. For this dissertation’s research

question and construction of the data we follow the parametric stochastic frontier

technique. The reason for this seems to be that the parametric approach makes

it possible to test several hypotheses which we will mention below. Moreover,

the parametric approach allows as taking into account the statistical noise and

provide parameter estimates of production factors which are mainly oriented either

to the production mix itself, either to exogenous factors which are accounted as

productive inefficiency factors.

In this Section we present the frontier production function form we used in

order to defining the maximum output achievable, given the current production

technology and available inputs. If all industries produce on the boundary of

the common production function (i.e., on the frontier) with three inputs, physical

capital (K), labour (L) and R&D investments (RD), the output of firm i at time

t can be expressed as:

Y ∗it = f(Kit, Lit, RDit, t; β)exp{vit}, i = 1...662, t = 2007...2015 (4.1)

where Y ∗it is the frontier (maximum) output of firm i at time t. The output

variable (Y ) is the turnover at the firm level. Function f (.) expresses the pro-

duction technology and the unknown parameter vector is the coefficients β which

in this case can be interpreted as elasticities. The time trend variable, t, captures

Hicks-neutral technological change and vit is an independently and identically dis-

tributed (i.i.d.) error term distributed as N(0, σ2v), which reflects the stochastic

nature of the frontier.

As we mentioned above, our sample includes firms from more than 20 different

4.1 Theoretical modeling 16

industries. As a consequence, the firms under investigation are heterogeneous. In

order to catch the effect of each technological sector, we add explanatory variables

to the main function of the model. In this dissertation we focus on whether, to

what extent, and how investments in R&D activities and/or physical capital affect

productivity controlling for both time and industry specific effects.

The hypotheses tested are referred to the specification of functional form of

production process. The first hypothesis need to be tested answers the question

about the significance of the technical inefficiency measured by γ = σ2u/σ2

v, where

σ2u refers to the "noise" error term and σ2

v to the "inefficiency" error term, as

mentioned above. The null hypothesis is H0 : γ = 0 denotes that the deviation

from the frontier is due entirely to noise and the alternative one is H1 : γ > 0

represent that most of the estimated deviation is due to technical efficiency.

The cobb douglas and the translog functional form of production function are

most commonly used. The cobb douglas is based on strong assumption in con-

trast to translog function which relax the restrictions on demand elasticities and

elasticities of substitution. Consequently, the second hypothesis has to do with

the decision between a cobb douglas versus a translog production function model.

The null hypothesis in this case is H0 : β4 = β11 = . . . = β24 = β34 = 0 de-

notes that all the second order coefficients, the cross products and time effects are

statistically insignificant, meaning that the generalized production function form

is not necessary since cobb douglas (the restricted model) is an ideal description

of the production function according to data given. Respectively, the alternative

hypothesis represents that cobb douglas include too strong assumptions.

Last hypotheses examine if any of the explanatory variables (δ′s) have any

effect in firms productivity or their coefficients are jointly zero. In other words,

the null hypothesis is H0 : δ1 = . . . = δ11 = 0 denotes that all δ parameters (except

δ0) are zero and thus the explanatory variables do not affect technical efficiency

levels. Therefore, if null hypothesis is not rejected, the model reduces to the one

proposed by Stevenson (1980). Some additional hypotheses have to do with the

4.2 Methodological Approach 17

technological change.

In order to test the hypotheses mentioned above we run the Likelihood ratio

test assuming that model 1 is our restricted model with Log Likelihood Func-

tion (LLF0) and model 2 is our unrestricted model with Log Likelihood Function

(LLF1), the test form is: λ = −2(LLF0 − LLF1) The hypotheses

are one-sided, therefore it is more secure to use David Kodde and Palm Franz

(1986) critical values (not chi-square) for Likelihood ratio tests.

4.2 Methodological Approach

As far as the form of the production function is concerned, a number of different

functional forms are used in the literature to model production functions. We use

both the well known cobb douglas and translog forms, adding some explanatory

variables in order to catch the effects of profits, technological intensity and in-

dustry. Cobb douglas and translog functions are linear in parameters and can be

estimated using least squares methods. The basic functional form of these two

models are:

lnY = β0 +N∑i=1

βilnxi + v (4.2)

lnY = β0 +N∑i=1

βilnxi + 12

N∑i=1

N∑j=1

βijlnxilnxj + v (4.3)

where x denotes the three inputs, R&D investments (RD), physical capital

(K) and labour (L).

Below we describe the four different adjusted models estimated in this disser-

tation. The model selection decision process has to do with the validation of the

hypothesis presented in Section 4.1.

First: Cobb douglas production function

lnY ∗it = β0 + β1lnKit + β2lnLit + β3lnRDit + vit (4.4)

4.2 Methodological Approach 18

Second: Translog production function

lnY ∗it = β0 + β1lnKit + β2lnLit + β3lnRDit + β11lnK2it + β22lnL

2it + β33lnRD

2it

+ β12lnKitlnLit + β13lnKitlnRDit + β23lnLitlnRDit + β4T + β44T2

+ β41T lnKit + β42T lnLit + β43T lnRDit + vit (4.5)

Third: Translog production function including explanatory variables related to

profits and technological intensity

lnY ∗it = β0 + β1lnKit + β2lnLit + β3lnRDit + β11lnK2it + β22lnL

2it + β33lnRD

2it

+ β12lnKitlnLit + β13lnKitlnRDit + β23lnLitlnRDit + β4T + β44T2

+ β41T lnKit + β42T lnLit + β43T lnRDit + δjzj + vit (4.6)

where zj, j = 1, . . . , 4 are the explanatory variables related to profits and

technological intensity presented in Table 7.2

Forth: Translog production function including explanatory variables related to

profits, technological intensity and industry sectors

lnY ∗it = β0 + β1lnKit + β2lnLit + β3lnRDit + β11lnK2it + β22lnL

2it + β33lnRD

2it

+ β12lnKitlnLit + β13lnKitlnRDit + β23lnLitlnRDit + β4T + β44T2

+ β41T lnKit + β42T lnLit + β43T lnRDit + δ0 + δjzj + vit (4.7)

where zj, j = 1, . . . , 11 are the explanatory variables related to profits, tech-

nological intensity and industry sectors presented in Table 7.2

Chapter 5

Results

The models which are analytically presented in section 4.2 have been estimated

using Frontier 4.1 software (Coelli et al., 2005). The tests relevant to the estimated

models about technical inefficiency effects indicate that the the null hypothesis of

no technical inefficiency effects (γ = 0) in the estimated production frontier are

rejected in each of the four models. As far as the rest of specification tests carried

out for every one of the estimated production frontier the results are presented in

Table 5.1 below, where are including a Log Likelihood Ratio test for the speci-

fication of the production frontier as Cobb Douglas and two tests examining the

hypothesis of inefficiency effects of explanatory variables described in Section 4.1.

The hypothesis of the Cobb Douglas type is rejected over the alternative one of

the Tranlog production function in a significance level of 0.01. Consequently, from

the vertical decision making process the outcome is that in the case that the initial

assumption is that the R&D affect productive efficiency through the deterministic

part of the frontier, the overall preferable model is the one denoted as Model 4.

Table 5.1. Model selection decisions

H0hypothesis

Restrictedmodel

Unrestric-ted model LLF0 LLF1 λ

No. ofrestriction

Criticalvalue (0.01) Decision Preferab-

le modelβ4 = . . . =β34 = 0

(1) (2) -1161.962 -1060.996 201.931 11 24.049 Reject (2)

δ1 = . . . =δ4 = 0

(2) (3) -1060.996 -985.120 151.752 3 10.501 Reject (3)

δ5 = . . . =δ11 = 0

(3) (4) -985.120 -887.031 196.176 7 17.755 Reject (4)

19

20

Some additional hypotheses tested and their results presented in Table 5.2.

The relevant to the nature of technical change tests indicate the presence of a

two-dimensional technical change, one concerning neutral technical change and

the other biased technical change. The neutral technical change leaves the ratio of

inputs constant and shifts the production frontier in parallel and outwards. The

biased technical change is the technical change embedded in at least one of the

inputs; it changes the slope of the production frontier and shifts it outwards.

Table 5.2. Additional hypotheses tests

H0 hypothesis λ Critical value (0.01) DecisionNo technical changeβt = βtt = βtk = 0,∀k 43.112 14.325 Reject

Only neutral technical changeβtk = 0, ∀t 23.036 10.501 Reject

Only biased technical changeβt = βtt = 0 14.696 8.273 Reject

Table 5.3 presents the estimation results for each model. Elasticity of sub-

stitution are the estimated values of βi and β4 especially estimates the annual

percentage change in output resulting from technological change. Based on the-

ory, a production technology exhibits constant returns to scale (CRS) if a Z%

increase in inputs results in Z% increase in outputs (e = 1), increasing returns to

scale (IRS) if a Z% increase in inputs results in a more than Z% increase in outputs

(e > 1) and decreasing returns to scale (DRS) if a Z% increase in inputs results

in a less than Z% increase in outputs (e < 1). According to this, and looking the

first part of Table 5.3 our conclusion is that for Models 2, 3 and 4 the elasticity of

substitution of physical capital and R&D expenditures exhibit increasing returns

to scale since β1 > 1 and β3 > 1, however, the elasticity of substitution of labor

exhibits decreasing returns to scale since β2 < 1. In Model 1 which represents the

Cobb Douglas production function the coefficient of R&D is statistically insignif-

icant and γ value is low. This indicates that our model specification seems to be

deficient. This result is also mentioned and confirmed above where the results of

the tested hypotheses are presented.

21

In Model 4, γ is equal to 80.5%. This estimate is high, meaning that much

of the variation in the composite error term is due to the inefficiency component.

Moreover, as far as the inputs is concerned, we see a positive effect of R&D

activities in firms productivity as the elasticity estimated is 1.213 and statistically

significant in 1% level of confidence. Due to this estimation, our first question

of if and how R&D activities affect firms productivity is answered. In addition,

the coefficient of the interaction terms between R&D and physical capital seems

to have also a positive effect in firms productivity. It means that an increase in

R&D expenditure combined with investments in physical capital by 1%, increase

firms productivity by 0.047%. However the coefficient of the interaction terms

between R&D and labor doesn’t seem to affect significantly firms productivity.

This may also leads to the fact that some industries of our sample illustrates labor

and R&D saving technological progress. Under this form of technological progress

the marginal product of capital increases more rapidly than the marginal product

of labor and R&D combined (Suer, 1995).

Another interesting part of our findings is the time trend effect in firms pro-

ductivity. Time trend in UK firms catch our interest, while in Table 5.3 coefficient

of time shows that getting more time the trend is downward slopping indicating

firm’s disadvantage as getting older. Consequently, the interaction between time

trend and labor and time trend and R&D influence negatively firms efficiency.

Looking at the micro-level descriptive evidence, we don’t find significant dif-

ferences across companies within each sectoral group. The inclusion of industry

effects aims to account for factors which may vary by industry and which have

been omitted from the model. In the present model, there are no variables which

indicate the different economic conditions experienced in each industry. However,

the explanatory variables related to technological intensity seems to tell a story.

The coefficient of the low-tech sector implies that there is a positive effect in firm’s

inefficiency and high-tech sector implies a negative effect in firm’s inefficiency. In

other words, a firm classified in high-tech sector has a great potential to increase

22

its technical efficiency and productivity.

Table 5.3. Estimation Results

Coefficient Variables Model 1 Model 2 Model 3 Model 4

β0 Constant0.108(0.889)

-0.483(-3.077)***

0.265(2.809)***

0.141(1.680)*

β1 lnK0.349(14.966)***

1.782(19.932)***

1.782(19.280)***

1.780(1.806)*

β2 lnL0.190(3.905)***

0.959(10.729)***

0.959(7.082)***

0.957(7.202)***

β3 lnR&D0.016(0.314)

1.213(3.567)***

1.215(1.756)***

1.213(3.232)***

β4 T0.346(7.345)***

0.271(7.177)***

0.217(6.215)***

β11 lnK2 0.811(1.991)***

0.891(1.992)***

0.890(-0.180)

β22 lnL2 0.480(10.715)***

0.481(10.223)***

0.478(7.034)***

β33 lnR&D2 0.608(1.354)

0.606(1.633)**

0.668(1.612)**

β44 T 2 -0.017(-2.065)**

0.005(-0.771)

-0.004(-0.704)

β12 (lnK)(lnL)0.015(1.285)

0.020(1.502)

0.024(2.945)***

β13 (lnK)(lnR&D)0.022(1.468)

0.023(1.469)

0.047(3.909)***

β23 (lnL)(lnR&D)0.130(1.193)

0.032(2.870)***

0.004(0.427)

β14 T lnK0.038(3.871)***

0.016(1.951)**

0.006(0.810)

β24 T lnL-0.135(-7.130)***

-0.121(-8.005)***

-0.095(-6.727)***

β34 T lnR&D-0.040(-2.050)***

-0.043(-2.768)***

-0.038(-2.537)**

δ0-0.421(-6.765)***

Continued on next page

23

Table 5.3 – continued from previous page

Coefficient Variables Model 1 Model 2 Model 3 Model 4

δ1 Profits0.000(0.003)

0.000(0.180)

δ2 Low-tech3.380(-3.068)***

13.453(4.614)***

δ3 High-tech-2.981(-0.3825)***

-7.164(3.200)***

δ4 Sector (2)37.154(4.733)***

δ5 Sector (3)28.318(6.163)***

δ6 Sector (4)6.598(1.785)*

δ7 Sector (5)31.079(4.784)***

δ8 Sector (6)24.942(4.432)***

δ9 Sector (7)9.883(10.999)***

δ10 Sector (8)6.299(6.431)***

δ11 Sector (9)6.665(2.114)**

γ0.353(6.209)***

0.443(4.571)***

0.722(5.047)***

0.805(2.622)***

σ2 2.193(13.521)***

1.604(12.716)***

3.841(13.252)***

18.729(7.143)***

1 Two explanatory variables omitted in Model 4 because of collinearity.2 Numbers in parentheses are the estimated coefficients to their standard errors, t statistic.3 One, two and three asterisks denote statistical significance at 10%, 5% and 1% respectively.

It is not worthless to discuss a bit about Malmquist (Productivity Growth)

Index (MPI) having discussed productivity and efficiency relationship. MPI as

introduced by Douglas Caves et al. (1982), measures the productivity changes

along with time variations and can be decomposed into changes in efficiency and

24

technology. There are two basic methodologies DEA and SFA in order to estimate

Malmquist Productivity Index. In the case of DEA we have to calculate the

corresponding distance functions to measure Total Factor Productivity (TFP) for

two periods. In the SFA case we have to calculate efficiency change using the type:

EFFCH = TEt+1i

TEti

from TEti = exiβ−ui

exiβ= e−ui (5.1)

Table 5.4. Technical efficiency statistics

Model Mean Std. Dev. Min MaxModel 1 0.667 0.109 0.268 0.859Model 2 0.708 0.096 0.388 0.874Model 3 0.443 0.216 0.003 0.900Model 4 0.547 0.227 0.003 0.924

In Table 5.4 some descriptive statistics of technical efficiency estimated by each

model are presented. A firm is said to be technically efficient if it operates on the

frontier of the production technology. Technical efficiency taking values between

zero and one. In Table 5.4 we see that even the data set includes the top R&D

investors, they are not fully efficient regardless of which model describe better the

frontier.

Table 5.5. Annual technical efficiency statistics

Yeat Mean Std. Dev. No. of Obs.2007 0.541 0.230 952008 0.514 0.229 992009 0.539 0.220 952010 0.567 0.226 942011 0.539 0.238 602012 0.544 0.213 572013 0.548 0.202 582014 0.599 0.189 522015 0.560 0.230 52Total 0.547 0.227 662

Focusing on Model 4 due to the fact that among the four models it is the

preferable one, in Figure 5.1 a distribution of estimated technical efficiency illus-

trated by each year examined. Combined with the Figure 3.1, we observe that

25

Figure 5.1: Annual Technical Efficiency

R&D intensity in 2007 stands in low records comparing to the rest of the years in

the sample. Respectively, in Figure 5.1 the density function of technical efficiency

in 2007 is lower than the others and lies wider, too. In years until 2009 R&D

intensity presented to rise to the top level of R&D investments for the sample we

examine.

Forward in time, the period between 2010-2011 in UK was a period of tem-

porary economic decline during which trade and industrial activity were reduced

(economic recession). As a result banks did not sponsored loans as previously.

In that terms, the decline of R&D investments shown in Figure 3.1 seems to be

logical since that R&D investments (mostly among Top R&D investors) suppose

large capital investments accompanied with high risk and uncertainty. Although,

looking to the efficiency distribution, in this period companies seems to be more

efficient than previous years. The reason for this seems to be the path-dependence

of the effects of R&D investments in productivity. In other words, due to the fact

26

that the previous years firms invested in R&D, the efficiency of later years declares

increase. In addition, in 2010 and 2011 more companies seem to be classified be-

tween 0.7 and 0.8 meaning that not only the total estimated efficiency increased

but also many of the companies examines increased their own efficiency score.

This fact implies that this increase in total estimated efficiency is not a result

of only one large company invested huge amount of capital. This fact drives us

assuming effects of R&D investments within companies.

In 2012 and 2013 R&D investments surpass with a small difference the 2010

levels before the recession. In the same time period technical efficiency present a

concentration in 0.5 and 0.7. Using the same direction as above, this increase in

R&D investments leads to the increase of technical efficiency in 2014 where we see

a pic of the distribution of technical efficiency, despite the fact that in this year

the level of R&D investments reaches the lowest of the sample. Our statement

confirmed taking a brief look to the Table 5.5 where we see that in 2014 mean

technical efficiency was almost 0.6, greater than every other year of our sample,

and a standard deviation around 0.19, meaning that there is higher concentration

around the mean than other years. The last yer of the sample is 2015 where R&D

investments seem to take a rising rout again which keeps us positive in terms of

efficiency of 2016. Although, considering the large decrease in 2014 our results

show that the technical efficiency returns to lower levels, even lower than 2013.

Technical efficiency results in total imply that the expenditures that any com-

pany invests in R&D, have effects not only in current year but also the next

year(s). Consequently, the effect of R&D investments in firms productivity fol-

lows a path-dependence for one or more years. Therefore in order to examine the

effects and spillovers of R&D investments in firms productivity we should take

also into account the lags of investments in R&D.

Chapter 6

Conclusions

In this dissertation we examined the effect of physical capital and R&D stocks on

labour productivity and technical efficiency using longitudinal data on a sample

of top UK R&D investors. To address the question of whether supporting policy

measures should target specific industries, three groups were created based on

their average R&D intensity and nine groups based on industry each firm belongs.

Four stochastic frontier models were run for the entire sample as well as for each

group. The main empirical findings are:

i. The R&D elasticity indicates that R&D should be a input in production

function since its effect is positive and statistically significant to firm’s pro-

ductivity.

ii. Our study also demonstrates that R&D investments affects firms’ efficiency

regardless to the technological sector each firm belongs.

iii. Moreover, the estimated rates of return on R&D investment across each in-

dustry sector are not varying significantly.

As there is considerable heterogeneity among firms and large deviation between

variables’ values, in this dissertation we face some problems to catch fully and

successfully every effect of R&D in firms productivity and efficiency. Another

problem was the measurement of R&D activities. In this study we assume that

27

28

R&D activities are represented by the R&D expenditure. Some further research

should be done in order to particularize R&D effects among firms from different

countries and provide evidence using a global data set and a common measurement

of R&D activities.

Supporting companies’ R&D expenditures government would help in increasing

the overall efficiency of UK firms if long-term technological progress is assumed.

From this perspective, the allocation of the R&D efforts is as important as its

overall increase and high-tech sectors should be specifically targeted by the UK

research policy.

Some further important implications are, firstly, that there is a path-dependence

of the effects of R&D investments in productivity. Technical efficiency results in

total imply that the expenditures that any company invests in R&D, have effects

not only in current year but also the next year(s). A second important implication

is the indication that the innovation policy measures adopted by every government

may have a positive impact in terms of enhancing the competitive advantage of

firms in several industries. In order to encourage firms to engage in R&D, the

government could introduce several policy measures, such as R&D tax credits,

technical assistance and venture capital support.

Chapter 7

Appendix

Table 7.1. Sector classification using NACE code

NACEcode

OECDclassification

R&Dintensity

Firms Observations

High-tech

Aerospace & defence 35, 75 High 6.787 4 24

Pharmaceuticals &biotechnology

24, 73 High 10807.79 27 95

Software &computer services

72 30.489 32 104

Technology hardware& equipment

30, 32 High 10.766 2 13

Leisure goods 32, 36 High 64.590 6 16

Medium-tech

Automobiles& parts

25, 34 Medium-high 386.256 3 7

Chemicals 24 Medium-high 29.758 5 32

Fixed linetelecommunications

64 543.116 9 43

General industrials 25, 74 Medium-high .707 5 24

Health care equipment& services

33, 36, 85 80.969 5 15

Continued on next page

29

30

Table 7.1 – continued from previous page

NACEcode

OECDclassification

R&Dintensity

Firms Observations

Household goods 36 Medium-high 1.453 2 11

Industrial engineering 29, 35 Medium-high 2.494 9 38

Industrial metals 27 Medium-low .884 1 1

Media 22, 92 2.276 4 16

Mobiletelecommunications

64 493.896 1 1

Low-tech

Beverages 15 Low .150 2 13

Banks 65 .532 2 7

Construction& materials

26, 45 13.720 3 13

Electricity 40 8.037 17 80

Food Producers 5, 15 Low 1.249 2 12

Gas, water &multiutilities

40, 41 .765 1 9

General retailers 52, 93 1.797 1 3

Mining .224 2 16

Oil & gas producers 11 .325 4 19

Support services 51 5.163 14 50

Total 447.943 163 662

1 R&D intensity measures the average intensity in each sector2 The level of technology intensity is following the OECD classification only if it is avalilable.

31

Table 7.2. Technology, industrial and service sectors

R&D intensity Firms Observations

Industry and service(1) Aerospace & automobiles 92.473 7 31(2) Beverages & food producers .678 4 25(3) Energy and water utilities & producers 6.074 22 108(4) General industrial 1.802 14 62(5) Pharmaceuticals & biotechnology 10807.79 27 95(6) Media & telecommunications 398.072 14 60(7) Mining, chemicals & metals 18.308 11 62(8) Software & technology equipment 28.297 34 117(9) Others 24.816 30 102

Technology intensityHigh 343.840 71 252Medium 507.868 44 188Low 515.368 48 222

Total 163 6621 R&D intensity measures the average investments in R&D in each sector2 Sectors were created considering the cohesion of industries included.

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