Role of innovation in linking environmental and financial performance: An analysis combining DEA and statistics 12 March 2015; International Symposium in honor of Prof R Ravindran, Bangalore Professor Ram Ramanathan University of Bedfordshire Business School, UK E: ram.ramanathan@beds.ac.uk
Co-author Andrew Black, ex-research Associate, Nottingham University Business School Study partly supported by research funding from Nottingham Innovative Manufacturing Research Centre.
Data Envelopment Analysis (DEA) A methodology based upon an interesting application of linear programming Originally developed for assessing the relative performance of a set of firms (decision making units DMU) that use a variety of identical inputs to produce a variety of identical outputs. Basic ideas from Farrell (1957) Charnes, Cooper and Rhodes (1978) Introductory books - Norman and Stoker (1991), and Ramanathan (2003). Also, Cooper, Seiford and Tone (2007).
Basic Concepts Efficiency Output Input 100 (.091/.209) Firm Input: Capital Employed (CAP) ($ millions) Output: Value added (VA) ($ millions) Efficiency: Value added per capital employed Relative Efficiency )%( A 8.6 1.8 0.209 100 B 2.2 0.2 0.091 43.4 C 15.6 2.8 0.179 85.8 D 31.6 4.1 0.130 62.0 Dr. R. Ramanathan 4
Single Output and Two Inputs Firm Input 1: CAP A B C D 8.6 2.2 15.6 31.6 Input 2: No. of Employees (EMP) ( 000) 1.8 1.7 2.6 12.3 Output: VA 1.8 0.2 2.8 4.1 Firm A B C D VA/ CAP 0.209 0.091 0.179 0.130 VA/ EMP 1 0.118 1.077 0.333 Dr. R. Ramanathan 5
Frontier analysis in DEA F (0.0909, 1.0769) Efficiency Frontier Efficiency Frontier E (0.2093, 0.2709) G (0.2093, 0.1176) 6
Mathematical Programming Aspects of DEA Virtual Input I i 1 u i x i x i is the i th input u i is its weight I is the number of inputs Virtual Output J j 1 v j y j y j is the j th output v j is its weight J is the number of outputs Efficiency Virtual Output Virtual Input J j 1 I i 1 v u j i y x j i 7
Mathematical Programming Aspects of DEA For m th DMU, Efficiency J j 1 I v u i 1 jm im y x jm im In DEA, Efficiency of m th DMU is maximized subject to the condition that efficiencies of other DMUs using the same weights is restricted to be between 0 and 1. Let there be N decision-making units. 8
Fractional DEA Program max E v jm J j 1 I, u m subject to 0 v u i 1 im jm im j 1 I y x J v u i 1 jn in jm im y x jm im 1; n 1,2,, N 0; i 1,2,, I ; j 1,2,, J E m is the efficiency of the m th DMU, y jm is j th output of the m th DMU, v jm is the weight of that output, x im is i th input of the m th DMU, u im is the weight of that input, and y jn and x in are j th output and i th input of the n th DMU 9
Fractional DEA Program for the Efficiency of Firm A max E A subject to 0 E A 1.8v VA, A 8.6uCAP, A 1.8u EMP, A 1.8v VA, A 1 8.6uCAP, A 1.8u EMP, A 0.2v VA, A 0 E B 1 2.2u CAP, A 1.7u EMP, A 2.8v VA, A 0 EC 1 15.6u CAP, A 2.6u EMP, A 4.1v VA, A 0 E D 1 31.6u CAP, A 12.3u EMP, A vva, A, ucap, A, uemp, A 0 If the efficiency is unity, then the firm is said to be efficient, and will lie on the frontier. Otherwise, the firm is said to be relatively inefficient. Use the program to get the efficiency of only one firm (the reference firm Firm A here). 10
Fractional DEA Program for the Efficiency of Firm B To get the efficiency scores of other firms, more such mathematical programs have to be solved, keeping each of them as the reference firm. 0.2v VA, B max E B 2.2u CAP, B 1.7u EMP, B subject to 1.8v VA, B 0 E A 1 8.6uCAP, B 1.8u EMP, B 0.2v VA, B 0 E B 1 2.2u CAP, B 1.7u EMP, B 2.8v VA, B 0 EC 1 15.6u CAP, B 2.6u EMP, B 4.1v VA, B 0 E D 31.6u CAP, B 12.3u EMP, B vva, B, ucap, B, uemp, B 0 11 1
Converting Fractional DEA Program into LP format Output maximization DEA program Normalize the denominator by forcing it to be equal to one, and maximize virtual output. Input minimization DEA program Normalize the numerator by forcing it to be equal to one, and minimize virtual input. Dr. R. Ramanathan 12
DEA Results for the Four Firms If the DEA models for Firms A, B, C and D are solved using LINDO or any other LP software, we get the following efficiency values. Efficiency of Firm A: 1 or 100% Efficiency of Firm B: 0.434 or 43.4% Efficiency of Firm C: 1 or 100% Efficiency of Firm D: 0.62 or 62% Dr. R. Ramanathan 13
Advanced Features Positivity restrictions on decision variables Dual DEA models Multiplier and Envelopment Versions Returns to Scale CRS, VRS, etc. CCR and BCC models Dr. R. Ramanathan 14
Advanced Features CCR and BCC models CRS and VRS efficiencies Technical and scale efficiencies Weight restrictions Super-efficiency scores Categorical variables Time-series Analysis Malmquist Indices Dr. R. Ramanathan 15
DEA as a MCDM Tool DEA has been recognised as a MCDM tool All criteria to be maximised are treated as outputs All criteria to be minimised are treated as inputs Dr. R. Ramanathan 16
Using DEA in research studies Simple applications of DEA DEA with regression (simple OLS or Tobit) DEA as part of empirical research in statistical studies (ANOVA, multi-group analysis, etc.) This research is about a more rigorous application of DEA in an empirical study Dr. R. Ramanathan 17
AN APPLICATION OF DEA FOR ENVIRONMENTAL POLICY IN THE UK Impact of environmental regulations on performance Manufacturing firms in the UK Sector level data from the Office for National Statistics Primary questionnaire survey is being carried out. Study based on a research project in Nottingham, funded by Nottingham Innovative Manufacturing Research Centre, UK. 18
The influence of environmental regulations on performance Highly debated issue Traditional view: Detrimental to growth Porter s hypothesis When properly formulated, environmental regulations could have a positive impact on performance Induce firms to identify best ways of utilising their efforts in satisfying regulations, usually in the form of new and innovative products or processes. 19
Porter s Win-Win hypothesis Properly and not-so-properly formulated environmental regulations Issue of flexible regulations Pollution control expenditure in satisfying flexible regulations have a positive impact on performance (Majumdar and Marcus, 2001) Expenditure in adhering to sold waste regulations (which is a flexible regulation that focuses on outcomes) is positively related to productivity Expenditure in meeting air and water regulations (which are not-so flexible and stipulate the use of best available technologies) were negatively related to productivity. DEA to measure firm performance 20
Research Question Impact of different kinds of regulations (flexible or otherwise) on performance will be moderated by innovation. More innovative sectors will be able to utilise their expenditure in flexible regulation in improving their performance more than less innovative sectors. Sector level data for the UK. 21
Environmental Regulations in the UK (Solid) Waste Regulations Packaging (Essential Requirements) Regulations 1998 Producer Responsibility Obligations (Packaging Waste) Regulations 1997) End-of-Life Vehicles Regulations 2003 and 2005 Waste Electrical and Electronic Equipment Regulations 2006 Environmental Protection (Duty of Care) Regulations 1991 Hazardous Waste (England and Wales) Regulations 2005 22
Environmental Regulations in the UK Air Pollution Regulations (Traditional command and control) Pollution Prevention and Control Act 1999 Integrated Pollution Prevention and Control (IPPC) permit Stipulate the use of Best Available Techniques (BAT) to control air pollution Environmental Protection (Prescribed Processes and Substances) Regulations 1991 Clean Air Act 1993 Pollution Prevention and Control (England and Wales) Regulations 2000 Solvent Emissions (England and Wales) Regulations 2004 Volatile Organic Compounds in Paints, Varnishes and Vehicle Refinishing Product Regulations 2005 More recent regulations (flexible) Non-Road Mobile Machinery (Emission of Gaseous and Particulate Pollutants) Regulations 1999 European Union-wide greenhouse gas Emissions Trading Scheme Regulations 2003/05 Environmental Protection (Controls on Substances that Deplete the Ozone Layer) Regulations 1996 23
Environmental Regulations in the UK Water Pollution Regulations Water Resources Act 1991 Groundwater Regulations 1998 Anti-Pollution Works Regulations 1999 Pollution Prevention and Control (England and Wales) Regulations 2000 Control of Pollution (Oil Storage) (England) Regulations 2001 Water Resources (Abstraction and Impounding) Regulations 2006 Specify BATs and inflexible 24
DATA Performance data from the Office for National Statistics (ONS), UK Pollution expenditure data from the Department for Environment, Food and Rural Affairs (DEFRA) Innovation from the UK Community Innovation Surveys available at the Department of Business, Enterprise and Regulatory Reform (BERR) Product innovation, process innovation, innovation expenditure Time period 2002-2006 (five years) 25
Three Hypotheses H1: Expenditure in pollution control to meet environmental regulation is significantly related to performance. H2: Compared to pollution abatement expenditure to meet less flexible regulations, pollution abatement expenditure in more flexible ones will be more positively (or less negatively) associated with higher productivity levels. H3: Existing innovation capabilities moderate the relationships between pollution abatement expenditure and productivity for flexible regulations. The moderating role of innovation will be lower or non-existent for inflexible regulations. 26
Sectors analysed SIC Description SIC Description Code Code 10-14 Mining and quarrying 26 Manufacture of other nonmetallic mineral products 15-16 Manufacture of food, beverages 27-28 Manufacture of basic metals & tobacco products and fabricated metal products 21-22 Manufacture of pulp, paper and 29 Manufacture of machinery and paper products publishing and printing equipment not elsewhere classified 23 Manufacture of coke, refined petroleum products and nuclear fuel 24 Manufacture of chemicals, chemical products and manmade fibres 25 Manufacture of rubber and plastic products 30-33 Manufacture of electrical and optical equipment 34-35 Manufacture of transport equipment 40-41 Electricity, gas and water supply 27
DEA inputs and outputs and their descriptive statistics Inputs or outputs Mean Stdev. Min. Max. Inputs Compensation of employees ( millions) 8704 4332 2048 14870 Net capital stock ( billions) 24.96 21.78 6.1 88.6 Intermediate consumption 25035 12110 6429 45790 ( millions) Outputs Gross value added 14596 6597 2377 32202 ( millions) Gross fixed capital formation ( millions) 4816 7221 0 20917 28
DEA inputs and outputs and their descriptive statistics Mean Std. Dev. Min Max 1. Technical Efficiency (CRS, inputminimisation DEA) 77.733 19.621 32 100 2. Number of Employees (hundred thousands) 6.506 3.578 1.217 15.746 3. Energy Consumption (million tonnes of oil equivalent) 2.845 2.416 0.382 9.153 4. Other Pollution Abatement Expenditure ( hundred millions) 0.449 0.385 0.062 1.8 5. Waste Pollution Abatement Expenditure ( hundred millions) 0.77 0.532 0.03 2.967 6. Air Pollution Abatement Expenditure ( hundred millions) 0.38 0.311 0.044 1.265 7. Water Pollution Abatement Expenditure ( hundred millions) 0.83 1.008 0.127 5.094 8. Innovation 0.017 1.042-2.585 2.568 29
Simple Regression Analysis with DEA Efficiency as the Dependent Variable Controls Energy consumption -3.63*** Other pollution expenditure -17.08*** Number of employees 2.69*** Direct Effects Waste expenditure -8.99*** Air expenditure -11.55** Water expenditure -1.71 R 2 0.73 F 23.45*** df 6, 52 *** p<0.01, ** p<0.05, * p<0.10 (N = 59) 30
Moderated Regression Analysis with DEA Efficiency as the Dependent Variable Stage 1 Stage 2 Controls Energy consumption -3.77*** -4.37*** Other pollution -16.54*** -6.80** expenditure Number of employees 2.56*** 2.36*** Direct Effects Waste expenditure -8.11*** 0.21 Air expenditure -10.84** -8.66** Water expenditure -1.63-6.64*** Innovation 1.25-7.15*** Moderating Effects Innovation x waste 2.49* Innovation x air 27.59*** Innovation x water -2.29 R 2 0.73 0.88 R 2 0.15*** F 20.02*** 34.35*** df 7, 51 10, 48 *** p<0.01, ** p<0.05, * p<0.10 31
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Conclusions H1 supported Expenditure in pollution control to meet environmental regulation is significantly related to performance. H2 inconclusive Compared to pollution abatement expenditure to meet less flexible regulations, pollution abatement expenditure in more flexible ones will be more positively (or less negatively) associated with higher productivity levels. H3 supported Existing innovation capabilities moderate the relationships between pollution abatement expenditure and productivity for flexible regulations. The moderating role of innovation will be lower or non-existent for inflexible regulations. 34
Subsequent analyses A larger statistical study of the same problem with larger firm-level data set has supported these findings for three subsequent years. A qualitative study of the same problem seems to support these results. The qualitative study has been repeated recently in Chinese context and supports similar results. 35