A Stochastic Frontier Analysis of Estimates and Correlates of the Efficiency of Solid Waste Management in Welsh SMEs : James Cordeiro , State University of New York (Brockport), USA
Joseph Sarkis, Clark University, USA
Diego Vazquez & Jeroen Dijkshoorn, BRASS Center, Cardiff University, Wales, UK
GIN CONFERENCE 2008 (Leeuwarden, Netherlands) A Stochastic Frontier Analysis of Estimates and Correlates of the Efficiency of Solid Waste Management in Welsh SMEs
Purpose of the Study : Purpose of the Study Primary Purpose: To identify the technical efficiency of 299 solid waste recycling efforts of Welsh SMEs in 2003 by using a sophisticated econometric technique called Stochastic Frontier Analysis (SFA)
Secondary purposes:
To identify the extent to which environmental practices such as waste monitoring, auditing, publication of environmental policies, and use of local support groups are related to the efficiency scores
To check whether the SFA efficiency scores correlate significantly with those obtained using Data Envelopment Analysis (DEA) approaches
Efficiency Analyses : Efficiency Analyses Efficiency analyses focus on the efficiency of some production process (e.g. solid waste processing) in transforming inputs into outputs.
Frontier methods use an efficient frontier to identify the efficiency of individual organizations relative to a reference set of organizations
DEA is a non-parametric approach that uses mathematical programming to identify the efficient frontier.
SFA is a parametric approach that hypothesizes a functional form and use the data to econometrically estimate the parameters of that function using the entire set of DMUs
The measure of efficiency is normally one of either:
The distance between observed and maximum possible output for given inputs (output efficiency)
The distance between observed and minimum possible input for given outputs (input efficiency)
Overview of Study Sample and Variables : Overview of Study Sample and Variables Study Focus: Efficiency of solid waste management (a subject of major environmental significance and legislation in Wales)
Study sample: 299 Welsh Small and Medium-sized manufacturing Organizations (SMES) in 19 industries who responded to a BRASS survey in 2003
Methodology: Stochastic Frontier Analysis (SFA)
Study Variables:
Output: Ln (Proportion of total solid waste recycled)
Inputs: (a) Ln (Total recycling cost expended per ton)
(b) Ln(Organization size i.e.,#employees))
Variables use to explain inefficiencies: Dummy indicators (0,1) of waste auditing, waste monitoring, publication of environmental policies, use of local support groups
Methodology: SFA and DEA compared to each other and to OLS Regression : Methodology: SFA and DEA compared to each other and to OLS Regression DEA creates virtual units that serve as benchmarks for measuring DMUs comparative efficiency
SFA uses an hypothesized function to calculate estimates of the efficiencies of individual DMUs
Only SFA can separate random noise from efficiency; DEA incorporates noise as part of the efficiency score.
SFA and OLS regression methods reveal overall sample-based information. DEA reveals unit-specific data type of returns to scale, productivity change 2 4 6 8 10 2 4 6 8 10 12 0 Input A B E F D C G H DEA OLS Regression Output SFA I1 I2 O2 O1 X Y Output Efficiency of F: F0/YO Input Efficiency of E: XI/XF
SFA Results (Base Model) : SFA Results (Base Model) Model Parameters:
Constant 0.031 ***
Ln (Treatment cost per ton of solid waste) -0.002***
Ln (Number of Employees) -0.010 ***
Model Statistics:
Lambda 0.000
Log-Likelihood -527.27
Wald Chi2 (Prob. > Chi2) 460280.49(.000)
Model of Predictors of Variance of Inefficiency Scores
(this section of the model is re-estimated as part of the original model):
Constant 2.493 ***
Firm Monitors Waste 0.159
Firm Audits Waste -0.418 *
Firm Publishes Environmental Policy 0.192
Firm Uses Local Business Support Group -0.495 ***
Rank Correlations of SFA with DEA Efficiency Rankings : Rank Correlations of SFA with DEA Efficiency Rankings SFA technical efficiency score rankings were correlated (using Kendall’s Tau) with the DEA efficiency score rankings for two different DEA approaches:
CCR approach (correlation is .87)
Slack-based CCR approach (correlation is .87)
Thus our confidence that SFA and DEA provide similar efficiency rankings of the Welsh SMEs is very high this is encouraging given the different assumptions underlying these two approaches
Summary of Findings : Summary of Findings SFA was used to obtain technical efficiency scores for 299 Welsh SMEs and the two inputs specified were both found to be significant
The SFA scores correlate with those obtained by DEA increasing our confidence in our estimates of the SMEs efficiency rankings
Some initial evidence that waste auditing and use of local business support groups may impact efficiencies of the SMEs
Further research: We are focusing on panel data collection of SME inputs and outputs to answer important questions like:
Which industries have been improving their efficiencies faster over time?
Which waste management practices have the most impact over time?
Can more sophisticated input and output models be developed for the rankings?
Can the model be successfully applied to other waste management approaches?
How do SMEs compare to larger firms? Different countries?
Thanks! : Thanks!
DEA Numeric Example : DEA Numeric Example
Five LP models are run (for each firm A to E) to find the efficient frontier for DEA : Five LP models are run (for each firm A to E) to find the efficient frontier for DEA
CRS Input-Oriented DEA Solution : CRS Input-Oriented DEA Solution
CRS Input-Oriented Example of DEA Frontier : CRS Input-Oriented Example of DEA Frontier 1) TE (technical efficiency) of Firm 3 is 0.833, i.e., Firm 3 could reduce all inputs (x1,x2) by 16.7% to produce the same amount of y.2) Thus, firm 3 project to 3’ ,on a line joining 2 and 5 and firm 4 to 4’3) The line joining 2 and 5 is called the “Frontier” and firm 2 and 5 are referred to as “targets” or “peers” for firms 3 and 4.
Start with a Set of Observed DMUS and their Input -Output Correspondences : Start with a Set of Observed DMUS and their Input -Output Correspondences
By virtue of interpolation between C and D all input - output correspondences on CD are feasible : By virtue of interpolation between C and D all input - output correspondences on CD are feasible
Using interpolations between observed units the set of all feasible input -output correspondences is constructed and its boundary identified : Using interpolations between observed units the set of all feasible input -output correspondences is constructed and its boundary identified
Slide 17: Using the set of all feasible input output correspondences the comparative efficiency and other information in respect of a unit (e.g. unit E) is derived as illustrated here: Output Efficiency of E: FE/FG
Output benchmarks for E: Units C and D
Scope for output augmentation at E: EG
Returns to scale (increasing, decreasing, constant): Revealed by the intercepts of the segments of the efficient boundary.
Scale elasticity revealed by the slope of the segments on the efficient boundary. Examining DMU Efficiencies in DEA
Slide 18: Consider a production function for I DMUs and K inputs: PARAMETRIC METHODS FOR COMPARATIVE EFFICIENCY MEASUREMENT Where y is output, xik are inputs, and ei is the residual for DMU I It is the residual ei the captures any inefficiency in this model The residual also captures other noise or random effects (e.g. omitted variables, measurement error, etc.)
SFA attempts to decompose the error term into inefficiency and noise components for each DMU i [1]
SFA Model for I DMUs and K inputs : SFA Model for I DMUs and K inputs We decompose the error term into two components: v is an identically distributed conventional two-sided error term with zero mean. It stands for random noise, omitted variables etc.
u is an identically distributed one-sided error term with a non-zero mean. It stands for inefficiency. u is typically assumed to be exponential, half-normal or truncated normal
Stochastic Frontier Example : Stochastic Frontier Example Frontier:
y= exp(xβ)
yi= exp(xβ +vi)
vi is noise due to random events
if vi>0 above frontier
if vi<0 underfrontier;
Slide 21: The exponential or half-normal distributions often assumed for SFA inefficiencies (u) acknowledge that larger inefficiency values are less likely
The Stochastic Frontier : The Stochastic Frontier
Slide 23: The SFA model is usually fitted using Maximum Likelihood estimation We need to estimate the inefficiency of the ith producer (ui) by using its composed residual ei = vi - ui . Depending on the assumption we make about the distribution (e.g. half-normal, exponential) of the inefficiency ui we arrive at a different formula for the conditional value We plug into this formula the values of ei and other values we derive from the ei to arrive at an estimate of the conditional inefficiency ui of the ith DMU. The formulae for differs depending on the distribution assumed for ui and are coded in software such as Limdep and STATA.
Summary Comparison of DEA v/s SFA Approaches : Summary Comparison of DEA v/s SFA Approaches
Overview of Study Sample and Variables : Overview of Study Sample and Variables Study Focus: Efficiency of solid waste management (a subject of major environmental significance and legislation in Wales)
Study sample: 299 Welsh Small and Medium-sized manufacturing Organizations (SMES) in 19 industries who responded to a BRASS survey in 2003
Study Variables:
Output: Ln (Proportion of total solid waste recycled)
Inputs: (a) Ln (Total recycling cost expended per ton)
(b) Ln(Organization size i.e.,#employees))
Variables use to explain inefficiencies: Dummy indicators (0,1) of waste auditing, waste monitoring, publication of environmental policies, use of local support groups
SFA Results (Base Model) : SFA Results (Base Model) Model Parameters:
Constant 0.031 ***
Ln (Treatment cost per ton of solid waste) -0.002***
Ln (Number of Employees) -0.010 ***
Model Statistics:
Lambda 0.000
Log-Likelihood -527.27
Wald Chi2 (Prob. > Chi2) 460280.49(.000)
SFA Results -- 2 : SFA Results -- 2 Model of Predictors of Variance of Inefficiency Scores
(this section of the model is re-estimated as part of the original model):
Constant 2.493 ***
Firm Monitors Waste 0.159
Firm Audits Waste -0.418 *
Firm Publishes Environmental Policy 0.192
Firm Uses Local Business Support Group -0.495 ***
Rank Correlations of SFA with DEA Efficiency Rankings : Rank Correlations of SFA with DEA Efficiency Rankings SFA technical efficiency score rankings were correlated (using Kendall’s Tau) with the DEA efficiency score rankings for two different DEA approaches:
CCR approach (correlation is .87)
Slack-based CCR approach (correlation is .87)
Thus our confidence that SFA and DEA provide similar efficiency rankings of the Welsh SMEs is very high this is encouraging given the different assumptions underlying these two approaches
Summary of Findings : Summary of Findings SFA was used to obtain technical efficiency scores for 299 Welsh SMEs and the two inputs specified were both found to be significant
The SFA scores correlate with those obtained by DEA increasing our confidence in our estimates of the SMEs efficiency rankings
Some initial evidence that waste monitoring and use of local business support groups may impact efficiencies of the SMEs
Further research: We are focusing on panel data collection of SME inputs and outputs to answer important questions like:
Which industries have been improving their efficiencies faster over time?
Which waste management practices have the most impact over time?
Can more sophisticated input and output models be developed for the rankings?
Can the model be succesfully applied to other waste management approaches?
How do SMEs compare to larger firms? Different countries?