Does Weight-based Pricing on Municipal Waste Contribute to Waste Reduction?
Dynamic Panel Analysis in Flanders
Toshiaki Sasao † and Simon De Jaeger ‡1. Introduction
Weight-based pricing (WBP) on municipal solid waste (MSW) is introduced in some European
countries and regions. Although some pioneering studies examine the effect of WBP on MSW (DeJaeger, 2010; De Jaeger and Eyckmans, 2015; Dijkgraaf and Gradus, 2004; Linderhof et al., 2001),
the controversy of its effectiveness is still ongoing. However, the existing literature does notconsider the dynamic relationship between the dependent variables and unobserved individual
heterogeneity among the municipalities. Therefore, this paper analyzes the effects of WBP on MSW
in Flanders, Belgium, by applying the bias-corrected least-squares dummy variables (LSDVC)
model to account for the dynamic relationship.

2. Methodology and Data
When we examine the effects of waste policies on waste generation and composition in detail, weshould consider possible endogeneity in a wide sense (Sasao 2014). First is possible reverse
causality, that is endogeneity in a narrow sense. If common characteristics exist across themunicipalities that have introduced WBP, causal relationships may be misunderstood. Second is afixed effects. If there are unobserved time-invariant municipalities characteristics or omitted
variables, they may be correlated with the explanatory variables. Third is the presence of the lagged
dependent variable resulting in autocorrelation.
We use a balanced panel dataset consisting of 307 (out of 308) Flemish municipalities and 11years (from 2005 to 2015). Each municipality in Flanders either uses a classical price-per-bag (orprice-per-container) or a WBP schedule. Considering the possible econometrical problems noted
above, the model specification is as follows:ln(𝑊𝐴𝑆𝑇𝐸) ) = 𝛼 + 𝛽/ ln(𝑊𝐴𝑆𝑇𝐸)0/ ) + 𝛽1 ln(𝐷𝐸𝑀𝑂𝐺) ) + 𝛽6 ln(𝑃𝑅𝐼𝐶𝐸) ) + 𝛽; ln (𝑊𝐵𝑃) ) + 𝛾? + 𝑌𝐸𝐴𝑅) + 𝜀?)
Where WASTE represents the generated amount of residual waste: all waste (in kg per capita)
generated by households, excluding bulky waste and any selectively collected flows. The data on
waste generation were provided by the Public Waste Agency of Flanders (OVAM). For the DEMOG,
socio-demographic determinant, we consider the following four variables: average income (from†岩 手 大 学 人 文 社 会 科 学 部 Faculty of Humanities and Social Sciences, Iwate University〒 020-8550 盛 岡 市 上 田 3 丁 目 18-34‡

ルーヴェン・カトリック大学

E-mail: tsasao@iwate-u.ac.jp

Center for Economics and Corporate Sustainability, KU Leuven

the Studiedienst van de Vlaamse Regering, http://www4dar.vlaanderen.be/sites/svr (in Dutch));population density (per square km); rate of older people; that of people younger than 5 years of age(from the Statistics Belgium, http://statbel.fgov.be/en). PRICE represents the variable price forresidual waste collection in each municipality. All prices are expressed as prices per kg of residual
waste collected. The fee for the price-per-bag schedule is therefore transformed to a price-per-kg
based on the conversion rate suggested by OVAM. Information on the pricing schedule for eachmunicipality is collected via OVAM databases, municipal websites and provincial archives. We
examine three types of WBP dummy variables: (1) continuous participation in WBP (“generalparticipation effect” in De Jaeger and Eyckmans (2015)); (2) first year dummy “introduction effect”
in addition to that, and (3) yearly WBP dummy variables to clarify the annual effects after
introducing WBP. Furthermore, 𝛾? is the fixed effect of each municipality; YEAR is the fixed effectof each year; and 𝜀?) is the error term that includes all unobserved influences.
This study uses a standard (non-dynamic) fixed effects (FE) model, a Dynamic Ordinary Least
Squares estimation (DOLS) and a dynamic Least Squares Dummy Variables estimation (LSDV) asbenchmark, in addition to the LSDVC estimation. The DOLS estimator is biased upwards while theLSDV estimator is biased downwards (Bond, 2002). Kiviet (1995) removed the bias from the LSDVestimator, proving that the LSDVC was more efficient than various instrumental variable (IV)estimators including the general method of moments (GMM). Judson and Owen (1999) showed that
the LSDVC estimator is the best choice for a balanced panel. However, it should be noted that theLSDVC estimator is only applicable in the presence of strict exogeneity between independentvariables and the error term (Bun and Carree, 2005).

3. Results
First, we focus on the parameters of the lagged dependent variable, ln(𝑊𝐴𝑆𝑇𝐸)0/ ), to check therobustness of each estimation model. Because the order of the parameters by DOLS, LSDV, andLSDVC estimators is LSDV < LSDVC < DOLS for all the models, the estimation results by theLSDVC are more robust than LSDV and DOLS. The square root of the error variance estimate
suggests that the model with first year dummy in addition to general participation in WBP is themost suitable model. The results show that introduction of the WBP decreases the amount of residual
waste significantly (by 3 %), however, the remarkable decrease (24 or 25 %) is observed only inthe first year after the introduction. The percentage is lower than that in the non-dynamic FE modelfor the (continuous) introduction of WBP and is higher for the first year effect. In addition, a 1 %increase of waste bag price decreases the amount of residual waste by 0.02 %. The percentage islower than that in the non-dynamic FE model. This indicates that the estimation in non-dynamicmodels can overestimate the continuous effects of WBP.