Abstract
This paper identifies the factors that influence perception of program benefit of the recipients when a hypothetical public program is implemented in reality. We compare pre- and post-program Willingness to pay (WTP) estimates for improved waste management in Bally Municipality, India, and find that post-program predicted WTP falls by more than 50 % even when if there are substantial improvements in urban environment. We show that this can be explained by the relative strength of leisure cost of effort to participate in the waste management program vis-à-vis the benefit derived from cleaner environment. Our study shows that mismatch between expected and offered service attributes might be a source of disutility and could also dampen households’ perceived value of the program benefits. In such cases, the reduction in WTP might act as an indication of the local bodies regarding the scale of outreach and expansion of the program needed to finance the operation and maintenance expenses by supplementing the property tax bases through user fees.
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Notes
Littering can promote rodent that spreads communicable disease. On the other hand, open dumping of unsorted waste can lead to groundwater pollution from leachate and generation of methane from putrefaction of organic waste.
Instances of such reforms in waste management sector s include Ecological Solid Waste Management Act (2000) in Philippines (Chiu 2010) and National Environmental Management: Waste Act (2010) in South Africa (http://sawic.environment.gov.za/documents/696.pdf). For other country studies, see Sarkhel (2013).
In its simplest form, CV survey offers the respondents an improved level of service in a constructed market against the status quo. The respondents are asked whether they would be willing to avail the improved service by paying an amount that is presented to them through value elicitation questions. The bids are randomized across respondents, and he either agrees to pay it or refuses. The resulting set of “yes–no” responses is used in binary choice models to estimate the average WTP (see Carson and Hanemann (2005) for a detailed account of CV survey method).
The application of CV studies on environmental issues is increasing in low- and middle-income countries. In a review of 250 CV studies, for instance, Biller et al. (2006) find that 71 % of such studies address problems in natural resource management or environmental pollution.
Rapid urbanization in the urban India increased scarcity of dumping grounds. Thus, waste diversion from landfills by using them for compost production is one of the major policy goals of urban planners.
A number of such initiatives are already in operation in collection, transportation and treatment of waste in Bangalore, Chennai, Surat, Guwahati, etc. (GOI 2009).
As of 2011 census, the population figure is 285,486, while in 2001 it was 261,575.
Class I cities are those that have population of 100,000 and above. Estimates show that per capita waste generation in Indian cities ranges between 0.2 and 0.6 kg per capita (Asnani 2006).
See Sarkhel and Banerjee op. cit. for more on survey design.
From the pilot survey in 2006 as well as other official reports like Kolkata Municipal Development Authority (KMDA) reports, we ascertained that the average household size in Bally Municipal area is 5. This gives the total sampled population of 2850. Given the census population of 261,575, our sample constitutes 1 % of the total population. Keeping in mind the budget and time constraint for the project, the sampled population appeared to be a reasonable approximation.
In 2011, administrative boundaries of Bally Municipality had been redefined to keep pace with the increasing population density and the number of wards has gone up from 29 to 35. We identified the family based on their holding number and the household head even though the wards in which they reside have changed.
In fact, this time almost 46 % of the respondents are different from those who responded on behalf of the households in the 2006 survey. To control the respondent effect, we also checked whether the average elicited WTP of the group of households with the same respondents in both the surveys differs significantly from those with different set of respondents. Our one sample mean difference test failed to reject the null hypothesis of equality of WTP at 99 %.
The National Oceanic and Atmospheric Administration (NOAA) panel suggested that single-bounded dichotomous choice (SBDC) needs to be used in CV surveys rather than open-ended questions. Here, the respondents are posed with a single valuation question. In the DBDC format, respondents are asked a follow-up question after the initial bid. Hanemann et al. (1991) show that DBDC format is relatively more efficient than SBDC as it yields a tighter confidence interval around mean WTP.
The bid amounts were estimated from open-ended questions in the pilot survey (Sarkhel and Banerjee op. cit.). In 2006, the conversion rate was 1 US dollar = INR 44–45, while in 2011 1 US dollar = INR 44–52 (http://www.x-rates.com/average/?from=USD&to=INR&amount=1.00&year=2011, accessed on February 24, 2015).
An important distinction between Lancaster (op cit) and Rosen (op cit) is that in the former given characteristics goods are assumed to be infinitely divisible while in the later goods or services are available over a continuum of objective characteristics. In particular, Rosen (op cit) worked out the case for lumpy public goods that are germane to the case we deal here.
In case of choice experiments, choice sets are generated by experimental designs and households are offered simulated program alternatives formed with different combinations of attribute levels to choose from (Hensher et al. 2005; Louviere et al. 2000). Applications in waste management include Caplan et al. (2002), and Czajkowski et al. (2014) are some instances of application.
The major change between the first and second round of survey is in the format of the value elicitation question. In the first round, we offered household a single alternative against the status quo and the alternative program had all the features of satisfying the norms of MSWMHR. In contrast, in the second round we offered the households several program features in which the status quo, i.e., the existing reforms in past practices was always an option to choose from. The households’ choice of each attribute for collection, transport and disposal, when taken together, reveals his “preferred” waste management service.
The general structure for WTP equations for the jth household in the DBDC referendum format is, \( {\text{WTP}}_{1j} = x_{1j} \beta_{1} + \varepsilon_{1j}\quad (1)\) and \({\text{WTP}}_{2j} = x_{2j} \beta_{2} + \varepsilon_{2j}\quad (2).\) The error terms in (1) and (2) can be decomposed into two parts. This type of specification is called the composite error specification (Haab 1997).
\(\begin{aligned} &\varepsilon_{1j} = \varepsilon_{j} + \hat{\varepsilon }_{1j}\\ &\varepsilon_{2j} = \varepsilon_{j} + \hat{\varepsilon }_{2j}\end{aligned} \)where ε j is the individual-specific error and \( \tilde{\varepsilon }_{1j} \) and \( \tilde{\varepsilon }_{2j} \) is equation-specific error. Assuming that, \( \hat{\varepsilon}_{1j} \sim N\left({0,\sigma_{1}^{2}} \right) \), \( \hat{\varepsilon}_{2j} \sim N\left({0,\sigma_{2}^{2}} \right) \), \( \hat{\varepsilon}_{j} \sim N\left({0,\sigma^{2}} \right) \), \( \varepsilon_{1j} \sim N\left({0,\sigma^{2} + \sigma_{1}^{2}} \right) \), \( \varepsilon_{2j} \sim N\left({0,\sigma^{2} + \sigma_{2}^{2}} \right) \). Therefore, we have \( {\text{WTP}}_{1j} = x_{1j} \beta_{1} + \varepsilon_{1j} + \hat{\varepsilon }_{1j} \), and \( {\text{WTP}}_{2j} = x_{2j} \beta_{2} + \varepsilon_{2j} + \widehat{\varepsilon }_{2j} \). This joint distribution will therefore be a bivariate normal distribution with an unknown correlation coefficient ρ. Thus, WTP1j and WTP2j have a bivariate normal distribution with means μ 1 and μ 2, variances σ 21 and σ 22 and correlation coefficient ρ, where \( \mu_{1} = x_{1j} \beta_{1} \) and \( \mu_{2} = x_{2j} \beta_{2} \).
Anchoring bias arises when the respondents in CV survey base their response for the second bid on the amount of first bid preceding it (Herriges and Shogren 1996).
Thus, instead of using the 570 observations in the first round to estimate the WTP we used responses of those households that were interviewed in the repeat survey. However, the t test of mean difference between the average WTP between the full sample (i.e., 570 households) and the truncated one, i.e, 496 households, in the 2006 survey were statistically insignificant.
During field visits, we found that households that are yet to receive a bin have arranged for their own separation containers and practiced source separation. This is also evident from higher rate of recycling for both program recipient and non-recipient compared to the pre-program stage (page 17). We hypothesize that this might be the result of knowledge spillovers and would depend on the extent of community network in the locality.
The households those who prefer separation of waste at source their stated as well as predicted WTP are higher than the households with preference on mixed waste disposal.
Thus, sample evidence did not suggest enhanced recycling demand for plastics.
It is must be noted that predicted WTP from the DBDC shows a greater decline than stated WTP obtained from open-ended responses. Given the susceptibility of open-ended WTP to imprecision and systematic bias (Banerjee and Sarkhel op cit) we consider the divergence in WTP predicted from the econometric model.
We do recognize that cleanliness can have different dimensions in household preference and treating it as homogeneous runs the risk of getting biased estimates of its influence on WTP. We later included Cleanliness as a dummy independent variable in our regression and the results indicated that average WTP is higher for those that think cleanliness of the area has improved after program introduction than those who didn’t. Thus, our measure seems to have correctly captured the average households’ preference.
However, we do not include the choice of the households for the option of service provider, i.e., whether the households would like to avail the service from a private provider or a public provider.
We also find significant differences in median WTP across slum and non-slum wards in the pre-program period. In keeping with our expectations high slum wards pledged a lower amount (INR 25) for the improved service compared to median and low slum areas taken together (INR 32). Interestingly, this difference is no longer significant in the post-program period. Here, the median WTP for the slum and non-slum wards are almost identical. As argued earlier this may be the result of spillovers of service benefits arising from the public good nature of waste management services.
Consider a situation where preferences remain stable over time and are denoted by the utility function \( U = U\left( {c, l, G} \right)\quad (1) \quad U_{c} > 0, \;U_{L} > 0,\; U_{LL} \left\langle { 0, U_{G} } \right\rangle 0, \;U_{GG} < 0\) Here c denotes consumption, l denotes leisure and G denotes garbage disposal services.
Suppose the households work for fixed hours so the choice is really between contributing effort for the public good G in terms of source separation and leisure. Thus, \( l + t = T \quad (2) \)
Here, t denotes time requirement for public good and T denotes aggregate time endowment.
Assume further that provision of garbage disposal service G is given by the individual contribution of segregated waste g and total supply of separated waste G −1 by other members of the community.
Hence, \( G = g\left( t \right) + G_{ - 1} ,g^{\prime} > 0 \quad (3) \)
In that case, suppose further, prior to the implementation of G perceived effort/time requirement is given by t p and post-program actual time requirement is given by t A. Thus given G, WTP over time would be given as \(u\left( {c - {\text{WTP}}^{\text{P}} , T - t^{\text{p}} , G} \right) = u \left( {c - {\text{WTP}}^{\text{A}} , T - t^{\text{A}} , G} \right) \)
How does WTP changes with changes in time required for waste segregation? Assuming that the constraints (2) and (3) are binding we totally differentiate \( u = u\left( {c - {\text{WTP}},T - t,G} \right)\quad (3) \) We get \( \frac{{{\text{dWTP}}^{\text{P}} }}{{{\text{d}}t^{\text{P}} }} = - \frac{{U_{L} }}{{U_{C} }} + \frac{{{\text{d}}G^{\text{P}} }}{{{\text{d}}t^{\text{P}} }}\frac{{U_{G} }}{{U_{c} }} \). Thus, the sign of the L.H.S would depend on the relative strength of \( - \frac{{U_{L} }}{{U_{C} }} \) and \( \frac{{{\text{d}}G^{\text{P}} }}{{{\text{d}}t^{\text{P}} }}\frac{{U_{G} }}{{U_{c} }} \) given the assumptions.
References
Addressing Challenges with Waste Service Provision in South Africa National Domestic Waste Collection Standards, Department of Environmental Affairs Republic of South Africa (2010). http://sawic.environment.gov.za/documents/696.pdf. Accessed 14 September, 2014.
Altaf, M. A., & Deshazo, J. R. (1996). Household demand for improved solid waste management: A case study of Gujranwala, Pakistan. World Development, 24(5), 857–868.
Anand, P. B. (1999). Waste management in Madras revisited. Environment and Urbanization, 11(2), 161–176.
Asnani, P. U. (2006). Solid waste management. India infrastructure report, 570.
Banerjee, S., & Sarkhel, P. (2012). Biases and reliability of WTP estimates from contingent valuation responses: A study based on solid waste management practices in Bally Municipality, India. Arthaniti, 11(1–2), 78–103.
Biller, D., Rogge, K., & Ruta, G. (2006). The use of contingent valuation in developing countries: A quantitative analysis. In A. Alberini & J. R. Khan (Eds.), Handbook of Contingent Valuation. Cheltenham: Edward Elgar.
Brekke, K. A., Kverndokk, S., & Nyborg, K. (2003). An economic model of moral motivation. Journal of Public Economics, 87(9–10), 1967–1983.
Caplan, A. J., Grijalva, T. C., & Jakus, P. M. (2002). Waste not or want not? A contingent ranking analysis of curbside waste disposal options. Ecological Economics, 43(2), 185–197.
Carson, R. T., & Hanemann, W. M. (2005). Contingent valuation. In K.-G. Mäler & J. R. Vincent (Eds.), Handbook of environmental economics (Vol. 2, pp. 821–936). Amsterdam: Elsevier.
Chiu, ASF. (2010). The 3Rs and Poverty Reduction in Developing Countries Lessons from Implementation of Ecological Solid Waste Management in the Philippines, Institute for Global Environmental Strategies.
Choe, C., & Fraser, I. (1998). The economics of household waste management: A review. Australian Agricultural and Resource Economics, 42(3), 269–302.
Czajkowski, M., Kądziela, T., & Hanley, N. (2014). We want to sort! Assessing households’ preferences for sorting waste. Resource and Energy Economics, 36(1), 290–306.
Government of India (GOI) (2009). Position paper on Solid Waste Management Sector in India, Department of Economic Affairs, Ministry of Finance.
Guerrero, L. A., Maas, G., & Hogland, W. (2013). Solid waste management challenges for cities in developing countries. Waste Management, 33(1), 220–232.
Haab, T. C. (1997). Analyzing multiple question contingent valuation surveys: A reconsideration of bivariate probit, Washington DC.
Haab, T. C., & McConnell, K. E. (2002). Valuing environmental and natural resources: The econometrics of non-market valuation. Cheltenham: Edward Elgar.
Hanemann, M., Loomis, J., & Kanninen, B. (1991). Statistical efficiency of double-bounded dichotomous choice contingent valuation. American Journal of Agricultural Economics, 73(4), 1255–1263.
Hensher, D. A., Rose, M. J., & Greene, H. W. (2005). Applied choice analysis: A primer. Cambridge: Cambridge University Press.
Herriges, J. A., & Shogren, J. F. (1996). Starting point bias in dichotomous choice valuation with follow-up questioning. Journal of Environmental Economics and Management, 30(1), 112–131.
Hoornweg, D., & Bhada-Tata, P. (2012). What a waste: A global review of solid waste management. Washington DC: World Bank.
Jin, J., Wang, Z., & Ran, S. (2006). Solid waste management in Macao: Practices and challenges. Waste Management, 26(9), 1045–1051.
Kinnaman, T. C., & Fullerton, D. (2000). Garbage and recycling with endogenous local policy. Journal of Urban Economics, 48(3), 419–442.
Lancaster, K. J. (1966). A new approach to consumer theory. The Journal of Political Economy, 74(2), 132–157.
Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: Analysis and applications. Cambridge: Cambridge University Press.
Ministry of Urban Development (2011). Report on Indian Urban Infrastructure and Services. High Powered Expert Committee. Government of India.
Mitchell, R. C., & Carson, R. T. (1989). Using Surveys to value public goods: The contingent valuation method. Baltimore, MD: Johns Hopkins University Press.
Othman, J. (2003). Household preferences for solid waste management in Malaysia (No. rr2003054). Economy and Environment Program for Southeast Asia (EEPSEA).
Owen, A. L., Videras, J., & Wu, S. (2012). More information is not always better: The case of voluntary provision of environmental quality. Economic Inquiry, 50(3), 585–603.
Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. The Journal of Political Economy, 82(1), 34–55.
Sarkhel, P. (2013). Municipal Solid Waste Management in India: Environmental impact and institutional reforms. In S. Mukherjee & D. Chakraborty (Eds.), Environmental scenario in India: Successes and predicaments. London: Routledge.
Sarkhel, P., & Banerjee, S. (2010). Municipal Solid Waste Management, source-separated waste and stakeholder’s attitude: A contingent valuation study. Environment, Development and Sustainability, 12(5), 611–630.
Un-Habitat, and United Nations Human Settlements Programme. (2010). Solid waste management in the world’s cities: Water and sanitation in the world’s cities. London: Earthscan.
Whitehead, C. John. (2009). A practitioner’s primer on the contingent valuation method. In A. Alberini & J. R. Khan (Eds.), Handbook of Contingent Valuation. Cheltenham: Edward Elgar.
Zhu, D., Ansani, P. U., Zurbruegg, C., Anapolsky, S., & Mani, S. (2008). Improving Municipal Solid Waste Management in India: A sourcebook for policy makers and practicitioners. Washington DC: The World Bank.
Acknowledgments
The Contingent valuation survey reported in this paper was funded by University Grants Commission and DRS, Department of Economics, University of Calcutta. In collection of primary data, assistance extended by the National Field Services was invaluable. The cordial help obtained from the officials of Bally Municipality is gratefully acknowledged. An earlier version of this paper was presented in the Research Scholars’ Workshop, University of Calcutta, held during July 8–9, 2014. Comments of two anonymous reviewers have immensely helped in giving the paper its current shape. We are thankful to the workshop participants and specially Prof. Vivekananda Mukherjee of Jadavpur University for insightful comments and encouragement. The usual disclaimer applies. Funding: This study was funded by the University Grants Commission (Grant No. F. 5-26/2007(SAP-III)).
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Sarkhel, P., Banerjee, S. & Banerjee, S. Willingness to pay before and after program implementation: the case of Municipal Solid Waste Management in Bally Municipality, India. Environ Dev Sustain 18, 481–498 (2016). https://doi.org/10.1007/s10668-015-9659-5
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DOI: https://doi.org/10.1007/s10668-015-9659-5