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Embedding effect and the consequences of advanced disclosure: evidence from the valuation of cultural goods

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Abstract

This study revisits the embedding effect, a long-standing problem in the nonmarket valuation literature. The embedding effect was a popular research topic during the 1990s, especially following the Exxon Valdez oil spill in Alaska. It has resurfaced after a special issue of The Journal of Economic Perspectives in 2012 in which Jerry Hausmann asserts that among the three long-standing problems with contingent valuation, the embedding effect is the most challenging. In this study, we focus on how information disclosure regarding the nested structure of goods affects both the willingness to pay and the presence of the embedding effect. Our results suggest that the level of embedding can be reduced with a more complete description of the nested structure of the goods under valuation. Therefore, it is highly important for each valuation study to test whether sufficient information is provided on the goods’ nested structure to ensure that the relationships among the goods’ subsets are correctly understood by respondents. We show that by providing respondents with more high-quality information, it is possible to mitigate the embedding effect.

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Notes

  1. The academic interest in embedding in CV declined in part due to irreconcilable differences regarding this issue and in part due to the eruption of choice experiments in nonmarket valuation (Adamowicz et al. 1998; Alberini et al. 2006; Ryan 2004) and the belief that choice experiments are less prone to the embedding effect (Foster and Mourato 2003; Goldberg and Roosen 2007)). Nevertheless, several papers addressing the EE in choice experiments show mixed results (Czajkowski and Hanley 2009; Jacobsen et al. 2008; Jacobsen et al. 2012).

  2. The role of information in consumer responses has a long history in the economic literature. Green and Blair (1995) show that emphasizing certain aspects, such those that are political, rather than the instrumental characteristics of goods affects the variance of the WTP, while Adaval and Wyer (1998) show that the way in which information is transmitted to consumers affects decisions made in the realm of tourism. In reference to a private good, Magistris et al. (2015) show that the inclusion of information on quality certification affects the WTP for a product. Their results suggest that the use of information significantly affects WTP, while the provision of neutral information does not produce changes in WTP..

  3. A review of CV applications on cultural goods can be found in Noonan (2002), Noonan (2003), and Navrud and Ready (2002).

  4. We used this complement in the way it is used under set theory and not in an economic sense. Rather, Bc = C and B U C = A.

  5. This setting implies that the value of the WTP for each good in the sequence depends on the overall WTP. This may be problematic when drawing a split-sample comparison. However, we followed KK’s approach as closely as possible, such that differences in findings could be attributed exclusively to the treatment of information; therefore, we maintained their design.

  6. We chose to describe the temporary exhibits by highlighting big-named artists since this was meaningful to the interviewees. While we do not expect this to modify the embedding effect, it may have increased the total WTP.

  7. For readers interested in a full explanation of the advantages and disadvantages of Bayesian estimation, please see Ntzoufras (2011) and Bernardo and Smith (2001).

  8. The analysis of explanatory variables in the Bayesian econometrics approach is analogous to the analysis of those variables in a classical econometric approach. We first tested for homogeneity of the demographic characteristics among treatments, and control for them in the regression analysis. The results of the regressions are available upon request.

  9. Given the censoring at zero of open-ended WTP questions, we tested both the Tobit and Weibull models. To select between these two approaches we compared the Akaike information criterion (AIC), the deviation information criterion (DIC), and the Bayesian information criterion (BIC). We used WinBUGS14 for the estimation.

  10. We do not claim that the EE is exclusive for small samples, as a referee correctly pointed out that there is also evidence of the EE using larger sample sizes. Furthermore, the purpose of this study is to test how the information about the nesting structure could affect the value assigned by the respondents, and we do not mean to use this result in a cost–benefit analysis.

  11. The 12 tables (Table 6) of results are given in the appendix.

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Authors and Affiliations

  1. Departamento de Gestión Empresarial, Universidad de Concepción, Juan Antonio Coloma 0201, Los Ángeles, Chile

    Moisés Carrasco Garcés

  2. School of Business and Economics, Universidad del Desarrollo, Ainavillo 456, Concepcion, Chile

    Felipe Vasquez-Lavin & Roberto D. Ponce Oliva

  3. Millennium Nucleus Center for the Socioeconomic Impact of Environmental Policies (CESIEP), Santiago, Chile

    Felipe Vasquez-Lavin

  4. Center for Climate and Resilience Research (CR2), Santiago, Chile

    Felipe Vasquez-Lavin

  5. Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile

    Moisés Carrasco Garcés & Roberto D. Ponce Oliva

  6. Departamento de Economía, Universidad de Concepción, Concepción, Chile

    José Luis Bustamante Oporto

  7. Facultad Economía y Negocios, Universidad San Sebastián, Concepción, Chile

    Manuel Barrientos

  8. Faculty of Economics and Business, Universidad de Talca, Casilla: 721, Campus Lircay, Av. Lircay s/n, Talca, Chile

    Arcadio A. Cerda

  9. Water Research Center for Agriculture and Mining, Victoria 1295, Concepción, Chile

    Roberto D. Ponce Oliva

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  1. Moisés Carrasco Garcés
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  2. Felipe Vasquez-Lavin
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  3. Roberto D. Ponce Oliva
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Correspondence to Moisés Carrasco Garcés.

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Appendix A

Appendix A

See Tables 4, 5 and 6.

Table 4 Descriptive Statistics (mean and standard deviation)
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Table 5 Distribution of education levels among samples
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Table 6 Econometric regressions*
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Carrasco Garcés, M., Vasquez-Lavin, F., Ponce Oliva, R.D. et al. Embedding effect and the consequences of advanced disclosure: evidence from the valuation of cultural goods. Empir Econ 61, 1039–1062 (2021). https://doi.org/10.1007/s00181-020-01897-1

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