Eco-label effects on French manufacturing firms’ performance

Abstract

This study examines the causal effects of implementing eco-labels and ethical labels on the corporate performance of French companies, using the matching method. Through matching the 2006 COI survey and the DIANE database, the available data show that ecological or environmental and ethical certification enhances added value, while financial capital productivity and labor productivity might not have any significant effects on corporate revenues.

Keywords

Certification corporate performance matching estimator matching method selection bias

Kais Mtar, A Doctor of Economics at the University of Nice Sophia Antipolis, MTAR Kais, aged 36, got his Master’s degree in 2007 from the same institution. It was in 2015 that he started working as an Assistant Professor on contract basis at the “University of Rennes 2” in France. Apart from taking part in seminars and conferences, Dr. Mtar has also published a research article in the year 2015 and is currently working on some others.

Souad Bannour Ep Sfar, (born in 1983 in Mahdia, Tunisia) holds a Phd in Economic Sciences (2013) from the University of Sousse and the University of Nice Sophia Antipolis, France. She obtained her Master’s degree in 2007 from the same institution. From 2014 till now, Souad has worked as an Assistant Professor on contract basis at the High Institute of Management in Sousse as well as in the Faculty of economic Sciences and Management, Mahdia. In addition to teaching, not only did Souad take an active part in related seminars and conferences but also published 3 articles in the year 2015 and is currently working on some others.

1 Introduction

For years, the economy’s activity has indeed predicated on the belief of relentless industrial progress and a vision of nature where inexhaustible resources have to meet the requirements of production as indicated by [7]. However, due to environmental degradation that marked the seventies [13], this belief has come under fire from a growing body of business leaders in the industrial world as well as environmental experts.

In this respect and according to [2], the reconciliation between ecology and economy could be of particular interest. In the same vein, the themes of organizational ethics, particularly the ethical formalization, have been garnering increasing interest in the French industry since the late 1980s as shown by [27]. In fact, it came about as a response to the rapid changes in the economic and social environment.

While environmental and ethical standards translate an international consensus on the key features that a certification must fulfill in order to ensure the effective functioning of any organization, they simultaneously raise deep concerns between a number of industry stakeholders as well as complex scientific issues as to how to better comprehend and deal with this phenomenon. Literature, especially the theory of sociology of work [33, 34], complains about the cost of certification and cumbersome procedures which may conflict with a company’s new management in terms of flexibility, responsiveness and autonomy. Furthermore, analysis of the impact of implementing these standards is relatively understudied in terms of financial and organizational investments and issues of real benefits of these insurance standards still continue to raise controversy.

In this context, this article attempts to provide some basis for responding to the evaluation of the impact of the companies’ environmental and ethical standards. One approach to addressing this problem is to employ the matching method as originally developed by Rubin [39], credited by offering an interesting alternative to the analysis through the use of nonparametric estimators, whilst maintaining the individual character of the certification on corporate performance. The peculiarity of this method is its ability to estimate the overall effect of the certification only on certified companies and the effect is homogeneous within companies, in contrast with the estimates by the nonlinear least squares method of a Translog or Cobb-Douglas production function, which require estimating the overall effect of certification on all companies.

The present article is divided into five sections. While the first part presents in brief the environmental and ethical standards, the second section provides a literature review of the academic studies that have tried to find a link between the implementation of certification by businesses and the expected benefits. The presentation of the econometric model and estimators are dealt with in the third part. In the fourth section, the data in use from matching the 2006 COI (Organisational Changes and Computerisation) survey and DIANE (France’s Disc for Economic Analysis) database is to be presented and finally, the fifth section provides both the results and comments.

2 The emergence of environmental and ethical management program: A historical overview and presentation

Since the early 1990s, there have been growing calls for mainstreaming environmental protection. The first foundations of this issue were laid at the UN (United Nations) conference in Stockholm in 1972. The environmental management has spread around the world following the publication of the Report of Gro Harlem Brundtland (former Prime Minister of Norway) in 1987 by the UN environment and development world Commission [2]. In 1991, the G7 statement highlighted the importance and urgency of addressing the environmental issues and recognized the seriousness of the threats that planet earth faces nowadays. The decisions of the summit were further advanced by another Earth Summit held in Rio de Janeiro in 1992 under the aegis of United Nations. The conclusions of this conference highlight the need to develop consensus and universal international standards [14].

Similarly, the ethical and organizational concern has become omnipresent in Western companies and an overarching theme in the society as a whole. In the manufacturing industry, more and more French companies are implementing ethical standards in line with the willingness and resolve of the economic authorities to frame and guide employee behavior. This new practice did not come about by chance but as a response to the new managerial initiatives of companies, the emergence of new ICT (Information Communication Technology) along with the pressure of public opinion, media and national and international agencies.

In this perspective, environmental and ethical standards are now looked upon as a managerial discipline in its own right [32]. Thus, the formalization is a symbol of its commitment to promote ethical behavior (ISO 26000) and environmental (ISO 14001). It serves as a baseline for the company when stating its values, principles and beliefs [28]. These standards are not meant to establish the specific environmental performance criteria and, as such, do not present themselves as result-oriented. They are rather aimed more at ensuring a good management of the environmental issues than environmental and ethical quality [2]. They are also tasked with helping harmonize the different cultures, forecasting individual excesses and providing confidence to partners [11].

The environmental and ethical management is a field that is expected to experience rapid growth in most of the industrialized world. In the case of France, the results of the COI survey (2006) show that environmental and ethical management programs are highly disseminated within French companies. Table 1 shows that these standards have evolved significantly: the number of manufacturing companies that have adopted environmental and ethical management certificates rose from 18% to 24% between 2003 and 2006. This tendency has spread across industries. Thus, they are highly disseminated in the coke industry, refining and nuclear fuel (68%), transport equipment manufacturing industry (46%) and chemicals (42%). However, they are poorly used in the leather and footwear industry (5%) and textiles and clothing (10%)

Table 1
Breakdown of French manufacturing firms which adopted eco-labelling (in %)

Manufacturing Industry 2003 2006

Food and agricultural industries 18 26

Textiles and clothing industry 6 10

Leather and footwear industry 5 5

Wood working and wood products 15 24

Manufacture of pulp, paper and paper products; publishing and printing 13 18

Coking, refined petroleum products and nuclear fuel 68 68

Chemical industry 33 42

Manufacture of rubber and plastic products 17 26

Manufacture of other non-metallic mineral products 14 19

Metallurgy and manufacture of metal products 13 18

Manufacture of machinery and equipment 14 18

Manufacture of electrical machinery and optical equipment 25 32

Manufacture of Transportation Equipment 42 46

Other manufacturing industries 14 2

Total 18 24

Manufacturing Industry	2003	2006
Food and agricultural industries	18	26
Textiles and clothing industry	6	10
Leather and footwear industry	5	5
Wood working and wood products	15	24
Manufacture of pulp, paper and paper products; publishing and printing	13	18
Coking, refined petroleum products and nuclear fuel	68	68
Chemical industry	33	42
Manufacture of rubber and plastic products	17	26
Manufacture of other non-metallic mineral products	14	19
Metallurgy and manufacture of metal products	13	18
Manufacture of machinery and equipment	14	18
Manufacture of electrical machinery and optical equipment	25	32
Manufacture of Transportation Equipment	42	46
Other manufacturing industries	14	2
Total	18	24

Field: 1873 manufacturing businesses with 10 or more employees. Source: 2006 COI survey.

3 The benefits of certification: A literature review

Since its creation, most managerial studies show that the development of environmental and ethical certification brings about significant benefits for businesses. Literature offers an increasing number of variables measuring the contribution of the implementation of this new managerial practice. To simplify the analysis, as other researchers do [37], these variables are grouped together in three dimensions: the operational aspect, the financial aspect and the organizational dimension.

3.1 The operational dimension

The Literature review presents the contributions of certification on the operational level of the company, drawing a distinction between the impacts of certification in terms of production process management and the commercial aspect.

A. The production process

Eco-labelling schemes improve the internal production processes of the company in a way that makes the product more appealing and trusted by the public. Increased rigor introduced by environmental and ethical standards is generally described as very positive, constituting a ‘kind of tidying-up exercise’. It eliminates the causes of errors and reduces the variability of the production process and hence becomes easier to control [20]. In parallel, the implementation of the certificate reduces the overall costs by optimizing the waste management costs, water treatment and energy as well as commodity costs [30]. Not only does it helps prevent incidents and mitigate their impact but also detects potential savings deposits as a result of saving much of the energy in use or reducing the amount of waste. Therefore, a better management of environmental and ethical standards can contribute to significant savings in raw materials and energy [2] which allows to significantly improving the competitiveness of enterprises. Consequently, the use of these managerial innovations leads to improved customer satisfaction [35], cost reductions [2] and increased productivity [5]. From these studies, a first research hypothesis is to be held for test.

Hypothesis 1. The environmental and ethical standards increase productivity and reduce costs, hence, enabling it to act positively on the added value (VA)

B. The commercial process

The new economic constraints promote the use of environmental and ethical standards as a means of establishing trust between the partners. In this context, the implementation of certification improves the corporate image with the public authorities, citizens or environmental groups [3, 29] along with their trading partners through a thoughtful management of the environment [30]. In a commercial approach, these constraints are percieved as a key differentiator in the market [12] by reinforcing the brand power of the product [2] thus acting as a competitive edge tool, and are most importantly appealing to customers. In the context [7] describes, said standards become a passport for entry to certain markets. Hence that hypothesis is chosen for this study.

Hypothesis 2. The environmental and ethical standards improve the commercial aspect measured by turnover (CA)

In their empirical studies, [41] show that the implementation of the certificate improves the financial performance of companies. Accordingly, certificates serve as a guarantee to the market that the products offered are manufactured and tested according to a minimum of requirements aimed at meeting the customers’ demands. They are also perceived as an information signal on the stock market, sensitive to communication actions undertaken by companies [9]. With the new law on New Economic Regulations (NER) in 2001, the company tends to adopt the ISO (International Organization for Standardization) 14001 certification to demonstrate its environmental commitment and therefore enhance the value of its shares on the financial markets. In theory, including agency theory, markets should therefore respond positively to the opportunity offered to ‘prove’ the quality of its production [42]. It is seen as a signal towards reducing the information asymmetry between seller and buyer. For a listed company, standards can reassure the financial markets on the quality program, control of environmental aspects and social responsibility [7]. Therefore, the hypothesis 3 is held through this research.

Hypothesis 3. The environmental and ethical standards have a positive impact on the financial aspect of productivity measured by financial capital.

3.2 The organizational dimension

The interest that business executives show for certification is not only limited to operational and financial arguments but also because it is a relevant internal management tool. The new management literature shows that these standards may be potential catalysts for organizational change [2]. These introduce managerial innovations within the business homogenization of ways of working which reduces the uncertainty of coordination and relational program and which is the key contributor to innovation [4]. The prescription of environmental and ethical standards helps to bring more rationality in the coordination of management, collection and knowledge creation [7, 14]. Furthermore, these standards promote knowledge codification, creation and transfer, thereby leading to organizational knowledge clarification [10]. In the same vein, the management literature shows that environmental and ethical standards may be organizational learning devices [8, 31]. They are also seen as a source of motivation, related to concerns that transcend company boundaries and strict financial metrics, thereby enhancing employee engagement and self-esteem [1, 40]. The certificate is viewed as an integrated technique [36] insofar as it tends to mobilize and reinforce the internal cohesion around a common corporate project [2]. Hence the fourth research hypothesis:

Hypothesis 4. The environmental and ethical standards positively influence the internal management which increases labor productivity (PRT).

4 Assessment Methods of the Impact of implementing environmental and ethical standards on the performance of French companies

For the reasons mentioned above, we propose in this work, to test the causality between the use of environmental and ethical standards and business performance, taking into account the selectivity bias. To do that, the Rubin’s causal model [39] will be adopted.

4.1 Rubin’s causal model [39]

Let us consider a set of firms i = 1, 2 …… N making or not the object of a treatment (here implementing an environmental and ethical certificate) and y_i denoting corporate performance variable (here added value, turnover, labor productivity and the productivity of the financial capital). Rubin’s causal model [39] rests mainly on the assumption of two unobserved result variables namely Y_0i and Y_1i. These two variables are random variables associated with each company, and correspond to potential achievements of the performance variable depending on whether the company is certified or not. Potential Y_0i and Y_1i do exist before undergoing the treatment, but only one of them is observed. Thus, for a certified firm, Y_1i is observed while Y_0i is unknown. A straightforward way to gauge the impact of implementing certification is to consider the difference in mean performances of certified and non-certified companies C. This method rests on the assumption of independence between potential performance variables Y_0i and Y_1i and the variable of certification choice T_i = 1. In light of this assumption, it will be deduced:

$\begin{matrix} C = E (Y_{1 i}) - E (Y_{0 i}) \\ = E (Y_{1 i} / T = 1) - E (Y_{0 i} / T = 0) \end{matrix}$ (1)

With:

C: the mean difference of potential outcomes observed in the certified companies and non certified ones.

T_i: the treatment choice, which is a random variable (in our case study, certification), with T_i = 1 when the company is certified and T_i = 0 if that is not the case.

The assumption of interdependence between variables of potential performances and variables of certification choice is very restrictive. Since the previous independence property is not satisfied, the natural estimator formed by the difference in mean outcome variables is affected by a selection bias:

$\begin{matrix} C = E (Y_{1 i} / T = 1) - E (Y_{0 i} / T = 0) \\ = E (Y_{1 i} / T = 1) - E (Y_{0 i} / T = 0) \\ + [E (Y_{0 i} / T = 1) - E (Y_{0 i} / T = 1)] \\ = [E (Y_{1 i} / T = 1) - E (Y_{0 i} / T = 1)] \\ + [E (Y_{0 i} / T = 1) - E (Y_{0 i} / T = 0)] \\ = C_{T = 1} + \end{matrix}$ (2)

With:

C_T=1: the mean causal effect of certification on one of the population of certified companies.

Selection bias is the term β = E (Y_0i/T = 1) - E (Y_0i/T = 0) in the above expression. This bias arises out of the fact that the mean situation of certified companies has not been the same as that of those untreated companies. This difficulty can nevertheless be circumvented only if for each firm, certified or not, one can observe the mean performance of non-certified companies is the same as that of certified and non-certified reorganized firms. This comes down to the condition of independence between the potential performance variable of non-certified companies Y₀i and the treatment choice T: $Y_{0 i} ⊥ T \Rightarrow E (Y_{0 i} / T = 0) = E (Y_{0 i})$ (3) However, there are good grounds to believe that this will not usually be the case. Several essential differences exist among certified and non-certified firms, regarding their: bureaucratic hierarchy, financial performance, pressure from both customers and suppliers, industry-specific market trends, size, strategy and tactics, and so forth. Therefore, (unconditional) independence between latent result variables (Y_1i and Y_0i) and treatment assignment T is very unlikely. A less restrictive condition is to consider that there is a set of conditional observable variables X to which the property of independence between latent results and reorganization is verified. Generally speaking, this property requires a thorough consideration of a large number of conditioning variables. For this reason, matching on observable characteristics can be difficult to achieve in practice. This problem was solved by [38] who proposed an alternative method which consists of estimating for each certified and non-certified company, a probability to be a member of a class held by a counterfactual master based on its observable baseline characteristics X: the propensity score, as noted. $P (X) = Pr (T = 1 / X)$ (4)

4.2 Illustration of different methods and estimation steps

To test the impact of certification on firm performance, there is a wide range of estimators based on assumptions which have varying degrees of rigidity and whose implementation presents variable degrees of complexity [26]. In the present work the following estimates are to be put to the test: the naive estimator, the nearest neighbor estimator, the caliper or Radius estimator and finally the matching estimator with kernel function.

4.2.1 The naive estimate

The naive estimate is the simplest solution according to some authors [15]. It consists in performing the difference between the arithmetic means of Y_1i and Y_0i.

4.2.2 The estimate by the nearest neighbor (NN)

The Nearest Neighbor matching is probably one of the most heavily used matching procedures [17]. A company from the comparison group is chosen as a match for a treated company in terms of the closest propensity score. Matching of the M nearest neighbors (M ≻ 1) can be achieved. In this case study, M = 1 is choose without replacement. The estimator of the average treatment effect of the introduction of certification of certified companies is written as follows: $C_{T = 1} = \frac{1}{N_{1}} \sum_{T_{i} = 1} [Y_{i}^{1} - \sum_{T_{j} = 0} {ω_{N_{0}} (i, j) Y_{j}}^{0}]$ (5)

4.2.3 Caliper or Radius matching

Using this method, a company from the control group is matched with another company from the certified group located inside the caliper (or maximum propensity score distance by which a match can be made) and it is closest in terms of propensity score. In this research, a level of intensity (radius) 0.01 is imposed.

According to [18], Radius or caliper matching may be represented as follows: $C_{T = 1} = \frac{1}{N_{1}} \sum_{T_{i} = 1} [Y_{i}^{1} - \sum_{T_{j} = 0} ω_{N_{0}} (i, j) Y_{j}^{0}]$ (6)

4.2.4 The estimate by matching with kernel function

The kernel matching estimator proposed by [22, 23] offers different estimates following the previous terminologies by [16, 18]. For them, the kernel matching uses all cases in the control group to construct the counterfactual for each reorganized company. The final estimator of the certification impact, certification in this condition, is as follows [23]:

$\begin{matrix} \hat{E} \\ = \sum_{j \in I_{0}} \frac{K [(P (x_{j}) - (P (x_{i})) / h]}{\sum_{j \in I / \circ} K [(P (x_{j}) - (P (x_{i})) / h]} y_{j} \end{matrix}$ (7)

With:

K (): a kernel function of the non-parametric estimation which is constantly differentiable and symmetrically arranged around 0, and while $\int_{- \infty}^{+ \infty} K (u) du = 1$ , h denotes the window that measures the size of the neighbourhood without which the weights are very low.

4.3 The steps of the estimate

The estimation procedure involves three main steps: calculating the propensity score, determining the propensity score and finally the estimate to speak.

4.3.1 Calculating the canonical score

In this stage, it seems convenient to estimate the probability of the certification choice for each company or the canonical score. The purpose of this estimate is to explain the T variable of the certification choice through the X observable characteristics. While assuming that this choice made by the company is only the visible manifestation of a latent variable leading to the certification choice, the logit model allows for testing whether the X observable characteristics suspected of influencing the decision of certification choice have a significant say in the implementation of a new quality assurance program. This step is informative inasmuch as it proposes a description of the treatment assignment.

4.3.2 Determining the propensity score

Once each company’s propensity scores were calculated, the common-support of the densities scores of the two groups of companies is determined. The question of determining the distribution support of the propensity score is essential in this type of analysis [23]. This support indicates the existence of both the density of the propensity score for both certified and non-certified firm groups.

5 Data presentation

Data are sourced from the matching of the 2006 COI survey, jointly produced by DARES (Directorate for Research, Studies and Statistics) and INSEE (The National Institute of Statistics and Economic Studies), with those data related to businesses’ performance from DIANE files.

5.1 Presentation of the 2006 COI survey

The survey conducted by the COI in 2006 is the result of a growing interest by researchers and international organizations, in the late 1980s, in the organizational and technological business dynamics. It is a deep extension of the first 1997 COI survey [19]. The survey was carried out simultaneously on a representative sample of industrial companies and a representative sample of their permanent employees. The development and coordination of investigative device were performed in the France’s Centre for Employment Studies, under the direction of [19] and jointly produced by other statistical agencies. The survey design was developed by a number of researchers from various disciplines. The survey questioned 16,962 companies with 10 or more employees divided into the following segments: sections D (Manufacturing), E (Production and distribution of electricity, water and gas), F (Construction), G (Trade), H (Hotels and restaurants), I (Transport and communication), J (financial intermediation), K (Real estate, renting and business activities) and groups 92.1 and 92.2 (motion picture and video activities, radio and television). Public companies in these sectors are part of the COI field 2006. The overall response rate was 85%.

5.2 DIANE

DIANE files are composed of information available in the tax files, namely, in particular, the balance sheet and income statement of 885.422 French companies in 2011. These companies are divided into four targets: A (140.074 companies), B (139.985 companies), C (133.574 companies) and D (471.816 businesses). This source allows for identifying 113,823 manufacturing companies.

Information is available from the annual financial statements (balance sheet, income statement, statement of assets and liabilities...) that all public limited company (SA), limited liability company (SARL), a general partnership whose partners are SARL or SA and agricultural cooperatives whose turnover exceeds EUR 75,000, are required to file with the clerks of the commercial courts under penalty of a fine of 1500 euro [25]. DIANE files are used in the contribution to measuring the economic and financial performance of companies.

5.3 Matching data

As noted earlier, 16.962 companies of 10 or more employees responded to the 2006 COI survey. These companies spread across 8 fields of economic activity. In this case study, only the manufacturing industry has been included. Of the 3962 manufacturers documented in the file of the COI survey, 2090 were found in DIANE files for the years 2005, 2006 and 2009. Companies which showed negative added value as well as companies in which at least one of the growth rate of variables (value added, turnover, labor productivity or financial performance) shows an outlier, were then excluded from the data set [25]. In total, the selected sample consists of 1873 manufacturing companies with a workforce of 10 or more employees in 2006, only 47.2% of the initial manufacturing companies were retained.

The dependent variable, in this study, is the implementation of the environmental and ethical standards. Therefore, companies are expected to respond to the following question: ‘In its activities of production or service delivery, does your company possess an eco-label or an ethical label? (eg. ISO 14001, l’Agence Bio or AB, fair-trade ...). Of 1873 listed companies, only 511 companies are found to be certified (27.3%) whereas the remaining 1362 companies proved to be non-certified.

6 Analysis of results

6.1 Logistic regression and construction of common support

The estimation results of the logit model presented in Table 2 suggest a significant link between the offshore outsourcing of one part of the activity and the implementation of eco-labels or ethical labels. However, the intervention in the domestic market leads to a lower likelihood of certification.

Table 2
Estimation of the logistical model on the certification determinants

Observable Characteristics Coefficient

The headcount

Logarithm of the personnel 2006 0,45 ^***

Growth rate of the personnel between 2009 and 2006 –0,008

Strategy and tactics

Witnessing a financial restructuring –0,24

Witnessing an offshore outsourcing of one part of the activity 0,44 ^*

Witnessing the implantation of new sites 0,14

Location in the market

Intervention on the regional market 0,07

Intervention on the national market –0,59 ^*

Intervention on the European market 0,23

Intervention on the international market 0,16

Mobilisation of new managerial practices

Group membership 0,34 ^*

Creation of semi-autonomous work teams 0,65 ^*

Use of production systems and JIT delivery 0,30 ^

Use of a logistical tool 0,31 ^**

Setting up a financial profitability chart –0,45

Setting up a Project Management Chart 0,40

Partnership of Human Resources and Development with companies or private laboratories 0,22 ^*

Dissemination of ICTs

Use of the web –0,03

Use of a local network –0,14

Use of the Intranet 0,08 ^***

Use of the Extranet 0,48

Use of Electronic Data Interchange (EDI) –0,14

Customer relations

Largest customer belongs to a network –0,05

Largest customer is located near the firm premises –0,03

Largest customer requires a quality control standard 1,02 ^*

Largest customer has in place a coordinated System Information (SI) 0,49 ^*

Supplier relations

Largest supplier belongs to the same group –0,006

Largest supplier is located abroad 0,006

Largest supplier sells non standard good and services –0,23

Largest supplier complies with quality control standards 0,06

Largest supplier has in place a coordinated system information (SI) 0,11

Industry

Textiles and clothing industry –0,47

leather and footwear industries –2,16 ^

Pulp, paper products; publishing and printing. –0,33

Coking, refined petroleum products and nuclear fuel –0,06

Chemical industry –0,13

Manufacture of other non-metallic mineral products –0,42

Metallurgy and metalworking –0,12

manufacture of machinery and equipment –0,73 ^*

manufacture of electronic and electrical equipment –0,18

Transportation equipment industry 0,18

Observable Characteristics	Coefficient
The headcount
Logarithm of the personnel 2006	0,45 ^***
Growth rate of the personnel between 2009 and 2006	–0,008
Strategy and tactics
Witnessing a financial restructuring	–0,24
Witnessing an offshore outsourcing of one part of the activity	0,44 ^*
Witnessing the implantation of new sites	0,14
Location in the market
Intervention on the regional market	0,07
Intervention on the national market	–0,59 ^*
Intervention on the European market	0,23
Intervention on the international market	0,16
Mobilisation of new managerial practices
Group membership	0,34 ^*
Creation of semi-autonomous work teams	0,65 ^***
Use of production systems and JIT delivery	0,30 ^**
Use of a logistical tool	0,31 ^**
Setting up a financial profitability chart	–0,45
Setting up a Project Management Chart	0,40
Partnership of Human Resources and Development with companies or private laboratories	0,22 ^*
Dissemination of ICTs
Use of the web	–0,03
Use of a local network	–0,14
Use of the Intranet	0,08 ^***
Use of the Extranet	0,48
Use of Electronic Data Interchange (EDI)	–0,14
Customer relations
Largest customer belongs to a network	–0,05
Largest customer is located near the firm premises	–0,03
Largest customer requires a quality control standard	1,02 ^***
Largest customer has in place a coordinated System Information (SI)	0,49 ^***
Supplier relations
Largest supplier belongs to the same group	–0,006
Largest supplier is located abroad	0,006
Largest supplier sells non standard good and services	–0,23
Largest supplier complies with quality control standards	0,06
Largest supplier has in place a coordinated system information (SI)	0,11
Industry
Textiles and clothing industry	–0,47
leather and footwear industries	–2,16 ^**
Pulp, paper products; publishing and printing.	–0,33
Coking, refined petroleum products and nuclear fuel	–0,06
Chemical industry	–0,13
Manufacture of other non-metallic mineral products	–0,42
Metallurgy and metalworking	–0,12
manufacture of machinery and equipment	–0,73 ^***
manufacture of electronic and electrical equipment	–0,18
Transportation equipment industry	0,18

^*Significant at 1% – ^**significant at 5% – ^***significant at 10%. Field: 1873 manufacturing companies with more than 10 employees in the manufacturing industry resulting from matching COI survey and DIANE. Source: COI 2006 survey.

Consistent with prior findings, the mobilization of certain managerial practices such as group membership, creating work teams, the use of JIT (Just In Time) production and delivery programs, along with the implementation of a logistics tool and the development of R&D with companies or private laboratories do have a strong bearing on the likelihood of certification. Similarly, results show that the use of the Intranet also significantly increases the probability of adopting a certification scheme.

Regarding relations with customers and suppliers, results indicate that the requirement of the largest customer to comply with a quality standard or quality control procedures and the procurement of a computer program that is coupled with the largest customers increase significantly the likelihood of certification.

Results also capture a significant positive relation between the headcount in 2006 and the implementation of certification.

Lastly, companies belonging to the leather industry and footwear as well as machine-making industry and equipment industry are less likely to be certified.

From the outcome of the estimate of logistic regression, propensity scores of each certified and non-certified firm have been established while taking into account only the significance variables of 10% or lower. The Fig. 1 shows that the surface of the common propensity score covers a very large distance: the common support region which is obtained is more or less [0.18, 0.95].

Fig.1

Propensity score distribution of certified and non-certified groups of firms. Field: 2.106 manufacturing companies of 10 or more salaried employees as taken from the matched results of both COI&DIANE. Source: COI 2006 survey.

6.2 Results and interpretation of the estimated impact of certification on corporate performance

Once the propensity score and its common support were determined, one can assess the impact of certification of businesses through the growth rate of the added value, the turnover, labor productivity and the profitability of financial capital between 2006 and 2009 through the use of the following three methods: the estimation by the nearest neighbor (1), the radius or caduis estimate (h = 0.01) and, last of all, the weighted estimate. The outcomes of these tests are presented in the Table 3.

Table 3
Outcome of the implementation of environmental and ethical certificates on corporate performances (the growth rate differential between2006 and 2009)

Performance variables Naive estimator NN(1) Radius (h = 0.01) Kernel

Growth rate of added value 4,52 ^*** 5,7 ^* 5,3 ^ 5,09 ^

Growth rate of turnover 3,08 1,6 1,95 1,3

Growth rate of labor productivity 7,48 ^* 8,9 ^* 9,93 ^ 6,5

Growth rate of financial capital productivity 9,23 ^* 2,66 6,35 ^* 5,71 ^**

Performance variables	Naive estimator	NN(1)	Radius (h = 0.01)	Kernel
Growth rate of added value	4,52 ^***	5,7 ^*	5,3 ^**	5,09 ^**
Growth rate of turnover	3,08	1,6	1,95	1,3
Growth rate of labor productivity	7,48 ^*	8,9 ^*	9,93 ^**	6,5
Growth rate of financial capital productivity	9,23 ^***	2,66	6,35 ^*	5,71 ^**

^*Significant at 1% – ^**significant at 5% – ^***significant at 10%. Field: 2106 manufacturing companies with more than 10 employees manufacturing of more than 10 employees of the manufacturing industry; giving rise to the matching COI/DIANE. Source: COI 2006 survey.

First of all, one can easily notice that all the estimators show a positive and significant effect of the implementation of certification on the growth rate of the added value. This effect becomes more prominent when employing the nearest neighbor estimator: certified companies improved their growth rates in added value with 5.7% compared to non-certified companies.

In addition, results show that the implementation of environmental and ethical certificates does not have an adverse effect on the growth rate of revenues. The estimators point to a positive and non-significant impact of the certificate on the turnover. However, this effect seems stronger when estimated using the naive estimator.

It has also been noted that, with the exception of the kernel matching, the remaining estimators such as the naive method, the nearest neighbor and radius estimators do indicate a positive and significant effect of certification on the growth rate of labor productivity. This effect appears even stronger when the radius method is employed: certified companies improved the growth rate of their labor productivity by 9.93% compared to non-certified companies.

Finally, the overall effect of implementing eco-labels or ethical labels on the growth rate of financial capital productivity proves to be positive and significant when the naive estimator, the radius estimator and the kernel estimator are used. By contrast, the results obtained of this causality by the nearest neighbor estimator do not show any significance.

In conclusion, the results demonstrate that the implementation of environmental and ethical certificates has positive effects on the added value, labor productivity and the productivity of financial capital. They are in tune with the theoretical proposals of the new managerial theory. They are also compatible with the analyses proposed by some authors such as [2 , 31], for whom the mobilization of these management practices is highly beneficial: it can contribute to significant savings, reinforce internal cohesion around a common project [35], to enhance the reliability of the process and the entire production chain [4] and to improve the credibility of the firm in the financial market [9].

7 Conclusion

The present study evaluates the effects of implementing eco-labels and ethical labels on the corporate performances of 1873 French manufacturing companies. Inspired by Rubin’s causal model [39], the econometric matching method is employed, along with some contemporary estimation methods. Proposed by some econometricians, such as [23], these methods offer new non-parametric estimation techniques that help assess the mean causal effect of implementing eco-labels or ethical standards through drawing a distinction between certified and non-certified firms as well as proposing efficient and converging estimators while taking into account the selection bias. In this study, the methods in use range from the Naïve estimator to the Nearest Neighbor estimator (M = 1), along with the caliper estimator (h = 0.01) and the Kernel method. The test results were confirmed by most of the hypotheses. At this level of study, implementing eco-labelling schemes has proved very effective in enhancing the added value, labor productivity and the productivity of the financial capital and has no effect on the firm turnover.

Addressing the present article’s inherent limitations, the first has to do with the choice of the performance indicators. We narrowed down the performance of the businesses to one and only dimension, the economic dimension. However, since the 1990’s, the concept of performance has become multidimensional [24]. As a consequence, it would be more appropriate to evaluate the certification/performance in light of other levels of analysis. The second limitation is related to the origin of the data in use which only cover the period up to 2006. This study suffers from a limited time interval during which the relation between implementing an eco-labelling scheme and a company’s performance can be investigated. There is now an increasing body of literature that supports the idea that, unlike longitudinal studies, cross sectional studies cannot offer a dynamic vision and hence, provide less informative data, less variability and more collinearity between two variables [21].

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