Abstract
Stated preference studies are often based on the assumptions that proposed outcomes would realize with certainty and respondents believe their survey responses are consequential. This paper uses split sample treatments to test whether survey consequentiality and outcome uncertainty lead to differences in welfare measures, focusing on a discrete choice experiment on improving quality of electricity supply among business enterprises in Tanzania. Our results show that incorporating uncertainty not only affects the preferences for the attribute with uncertainty (duration of power outage) but also for a choice attribute with a precautionary feature (advanced outage notification). While outcome uncertainty and an additional survey script (a formal letter from a state-owned electric utility) to strengthen consequentiality have some influence on preferences and willingness to pay (WTP) estimates for certain attributes, we do not find significant implications on overall welfare estimates.
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Notes
Depending on electricity usage capacity (e.g., high versus low voltage), the existing electricity tariff rate contains five categories: 350 TZS/kWh, 292 TZS/kWh, 195 TZS/kWh, 157 TZS/kWh, and 152 TZS/kWh.
We use the DCREATE command in Stata 17 which is made available by Arne Risa Hole: https://sites.google.com/view/arnehole/publications
The number of respondents randomly assigned to the standard treatment is relatively large, comprising about 40% of the total sample. This is due to the initial plan to write a standalone research paper with sufficient statistical power for analysis.
Considering respondents' engagement in business activities, their managerial positions, and educational background (see the descriptive statistics in Table 3), concern about respondents' familiarity and understanding of the probabilities of 80% and 20% is minimal. Nevertheless, we acknowledge a limitation in our study of not conducting a comprehensive test to assess respondents' ability to understand these probabilities. We suggest future research to incorporate a simple comprehensive test in their survey designs to address this issue.
The ‘mixlogitwtp’ package is based on ‘mixlogit’ Stata package (Hole 2007), which we use to estimate the coefficients from models in preference space.
The individual marginal WTP estimates from models in WTP space are obtained using the command ‘mixlbeta’ in Stata, after estimating coefficients of the model using ‘mixlogitwtp’ Stata package (Hole 2007).
Similar specifications to Eq. (5) have been employed in other split-sample designs of stated preference studies (e.g., Ishihara and Ida 2022; Venus and Sauer 2022). We also check the robustness of our results using the double-selection LASSO approach (Belloni et al. 2014), which addresses concerns regarding variables that are potentially correlated with the treatments and outcomes.
1US$ was approximately 2,300 TZS (Tanzanian shilling) during the survey period (September 2019).
See Table A.1 in the appendix for model results with different specifications, including conditional logit model and mixed logit models with different distributions of the attributes’ coefficients. The estimated results remain similar across the different specifications, albeit with a few minor differences.
It is important to note that an estimated parameter of a natural logarithm of a coefficient with mean \({\widehat{\mu }}_{k}$$and standard deviation$${\widehat{\sigma }}_{k}$$, the mean and standard deviation of the coefficient itself (without natural logarithm) is given by$$\mathrm{e}\mathrm{x}\mathrm{p}({\widehat{\mu }}_{k}+\frac{{{\widehat{\sigma }}_{k}}^{2}}{2})$$and$$\mathrm{e}\mathrm{x}\mathrm{p}({\widehat{\mu }}_{k}+\frac{{{\widehat{\sigma }}_{k}}^{2}}{2})\sqrt{\mathrm{exp}\left({{\widehat{\sigma }}_{k}}^{2}\right)-1}\), respectively (Train 2003; Hole 2008).
The estimated results also remain similar with different model specifications except for ASC in the conditional logit model, which has a negative sign. But, it does not account for individual heterogeneity (see, results in Table A.1 in the Appendix). This contradicts the estimated parameters on ASC from mixed logit model specifications, which are positive and account for taste heterogeneity across respondents. The high and strongly significant standard deviations highlight the presence of respondents with positive and negative estimated ASC coefficients.
Results of the treatment effects on preferences are robust to different model specifications; see columns (5–8) of Table 5.
The results remain insignificant with total marginal WTP estimates as well. For the sake of saving space, we reported only the effects on marginal WTP estimates.
References
Aanesen M, Armstrong C, Borch T, Fieler R, Hausner V, Kipperberg G, Lindhjem H, Navrud S (2023) To tell or not to tell: preference elicitation with and without emphasis on scientific uncertainty. Land Econ. https://doi.org/10.3368/le.99.3.021122-0011R
Abdullah S, Mariel P (2010) Choice experiment study on the willingness to pay to improve electricity services. Energy Policy 38(8):4570–4581
Andresen A, Kurtz LC, Hondula D, Meerow S, Gall M (2023) Understanding the social impacts of power outages in North America: a systematic review. Environ Res Lett 18(5):053004
Belloni A, Chernozhukov V, Hansen C (2014) Inference on treatment effects after selection among high-dimensional controls. Rev Econ Stud 81(2):608–650
Blackman A, Dissanayake S, Cruz ALM, Corral L, Schling M (2023) Benefits of titling indigenous communities in the Peruvian Amazon: a stated preference approach. Land Econ. https://doi.org/10.3368/le.100.2.092822-0075R
Börger T, Abate TG, Aanesen M, Zawojska E (2021) Payment and policy consequentiality in dichotomous choice contingent valuation: experimental design effects on self-reported perceptions. Land Econ 97(2):407–424
Bujosa A, Torres C, Riera A (2018) Framing decisions in uncertain scenarios: an analysis of tourist preferences in the face of global warming. Ecol Econ 148:36–42
Bulte E, Gerking S, List J, de Zeeuw A (2005) The effect of varying the causes of environmental problems on stated wtp values: evidence from a field study. J Environ Econ Manag 49(2):330–342
Campbell D (2007) Willingness to pay for rural landscape improvements: combining mixed logit and random-effects models. J Agric Econ 58(3):467–483
Carlsson F, Martinsson P (2007) Willingness to pay among Swedish households to avoid power outages: a random parameter Tobit model approach. Energy J 28:75–89
Carlsson F, Martinsson P (2008) Does it matter when a power outage occurs? A choice experiment study on willingness to pay to avoid power outages. Energy Econ 30:1232–1245
Carlsson F, Kataria M, Lampi E (2010) Dealing with ignored attributes in choice experiments on valuation of Sweden’s environmental quality objectives. Environ Resour Econ 47(1):65–89
Carlsson F, Demeke E, Martinsson P, Tesemma T (2020) Cost of power outages for manufacturing firms in Ethiopia: a stated preference study. Energy Econ 88:104753
Carson R, Groves T (2007) Incentive and informational properties of preference questions. Environ Resour Econ 37(1):181–210
Cohen J, Moeltner K, Reichl J, Schmidthaler M (2018) Valuing electricity-dependent infrastructure: an essential-input approach. Energy Econ 73:258–273
Cummings RG, Taylor LO (1999) Unbiased value estimates for environmental goods: a cheap talk design for the contingent valuation method. Am Econ Rev 89(3):649–665
Czajkowski M, Budziński W, Campbell D, Giergiczny M, Hanley N (2017) Spatial heterogeneity of willingness to pay for forest management. Environ Resour Econ 68:705–727
Daly A, Hess S, Train K (2012) Assuring finite moments for willingness to pay in random coefficient models. Transportation 39:19–31
Dohmen T, Falk A, Huffman D, Sunde U, Schupp J, Wagner GG (2011) Individual risk attitudes: measurement, determinants, and behavioral consequences. J Eur Econ Assoc 9(3):522–550
Faccioli M, Kuhfuss L, Czajkowski M (2019) Stated preferences for conservation policies under uncertainty: insights on the effect of individuals’ risk attitudes in the environmental domain. Environ Resour Econ 73(2):627–659
Ghosh R, Goyal Y, Rommel J, Sagebiel J (2017) Are small firms willing to pay for improved power supply? Evidence from a contingent valuation study in India. Energy Policy 109:659–665
Glenk K, Colombo S (2011) How sure can you be? A framework for considering delivery uncertainty in benefit assessments based on stated preference methods. J Agric Econ 62(1):25–46
Groothuis PA, Mohr TM, Whitehead JC, Cockerill K (2017) Endogenous consequentiality in stated preference referendum data: The influence of the randomly assigned tax amount. Land Econ 93(2):258–268
Hanley N, Czajkowski M (2019) The role of stated preference valuation methods in understanding choices and informing policy. Rev Environ Econ Policy 13:248–266
Herriges J, Kling C, Liu CC, Tobias J (2010) What are the consequences of consequentiality? J Environ Econ Manag 59(1):67–81
Hole AR (2007) Fitting mixed logit models by using maximum simulated likelihood. Stand Genomic Sci 7(3):388–401. https://doi.org/10.1177/1536867x0700700306
Hole AR (2008) Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment. J Health Econ 27(4):1078–1094
IEA (2019) Tanzania Energy Outlook, IEA, Paris. https://www.iea.org/articles/tanzania-energy-outlook. Accessed 07 Oct 2022
Ishihara T, Ida T (2022) The effect of information provision on stated and revealed preferences: a field experiment on the choice of power tariffs before and after Japanese retail electricity liberalization. Environ Resour Econ 82:573–599
Johansson-Stenman O, Mahmud M, Martinsson P (2013) Trust, trust games and stated trust: evidence from rural Bangladesh. J Econ Behav Organ 95:286–298
Johnston RJ, Boyle KJ, Adamowicz W, Bennett J, Brouwer R, Cameron TA, Hanemann WM, Hanley N, Ryan M, Scarpa R, Tourangeau R (2017) Contemporary guidance for stated preference studies. J Assoc Environ Resour Econ 4(2):319–405
Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–292
Kassahun HT, Jacobsen JB, Nicholson CF (2020) Revisiting money and labor for valuing environmental goods and services in developing countries. Ecol Econ 177:106771
Kassahun HT, Swait J, Jacobsen JB (2021) Distortions in willingness-to-pay for public goods induced by endemic distrust in institutions. J Choice Model 39:100271
Layton D, Moeltner K (2005) The cost of power outages to heterogeneous households. Applications of simulation methods in environmental and resource economics. Springer, pp 35–53
Lewis KE, Grebitus C, Nayga RM Jr (2016) US consumers’ preferences for imported and genetically modified sugar: Examining policy consequentiality in a choice experiment. J Behav Exp Econ 65:1–8
Liebe U, Glenk K, von Meyer-Höfer M, Spiller A (2019) A web survey application of real choice experiments. J Choice Model 33:100150
Lloyd-Smith P, Adamowicz W, Dupont D (2019) Incorporating stated consequentiality questions in stated preference research. Land Econ 95(3):293–306
Louviere JJ, Hensher DA, Swait JD (2000) Stated choice methods: analysis and applications. Cambridge University Press
Lundhede T, Jacobsen JB, Hanley N, Strange N, Thorsen BJ (2015) Incorporating outcome uncertainty and prior outcome beliefs in stated preferences. Land Econ 91(2):296–316
Mattmann M, Logar I, Brouwer R (2019) Choice certainty, consistency, and monotonicity in discrete choice experiments. J Environ Econ Policy 8(2):109–127
Meles TH (2020) Impacts of power outages on households in developing countries: evidence from Ethiopia. Energy Econ 91:104882
Meles TH, Mekonnen A, Beyene AD, Hassen S, Pattanayak SK, Sebsibie S, Klug T, Jeuland M (2021) Households’ valuation of power outages in major cities of Ethiopia: an application of stated preference methods. Energy Econ 102:105527
Morrison M, Nalder C (2009) Willingness to pay for improved quality of electricity supply across business type and location. Energy J 30(2):117–133
Needham K, Hanley N (2020) Prior knowledge, familiarity and stated policy consequentiality in contingent valuation. J Environ Econ Policy 9(1):1–20
Oseni M (2017) Self-generation and households’ willingness to pay for reliable electricity service in Nigeria. Energy J 38:165–194
Oehlmann, M, Meyerhoff, J (2017) Stated preferences towards renewable energy alternatives in Germany–do the consequentiality of the survey and trust in institutions matter? J Environ Econ Policy 6(1):1–16
Ozbafli A, Jenkins GP (2016) Estimating the willingness to pay for reliable electricity supply: a choice experiment study. Energy Econ 56:443–452
Ready RC, Champ PA, Lawton JL (2010) Using respondent uncertainty to mitigate hypothetical bias in a stated choice experiment. Land Econ 86(2):363–381
Revelt D, Train K (1998) Mixed logit with repeated choices. Rev Econ Stat 80(4):647–657
Roberts DC, Boyer TA, Lusk JL (2008) Preferences for environmental quality under uncertainty. Ecol Econ 66(4):584–593
Rolfe J, Windle J (2015) Do respondents adjust their expected utility in the presence of an outcome certainty attribute in a choice experiment? Environ Resour Econ 60(1):125–142
Rose JM, Masiero L (2010) A comparison of the impacts of aspects of prospect theory on WTP/WTA estimated in preference and WTP/WTA space. Eur J Transp Infrastruct Res. https://doi.org/10.18757/ejtir.2010.10.4.2898
Scarpa R, Thiene M, Train K (2008) Utility in willingness to pay space: a tool to address confounding random scale effects in destination choice to the Alps. Am J Agr Econ 90(4):994–1010
Scarpa R, Zanoli R, Bruschi V, Naspetti S (2013) Inferred and stated attribute non-attendance in food choice experiments. Am J Agr Econ 95(1):165–180
Sullivan M, Schellenberg J, Blundell M (2015) Updated value of service reliability estimates for electric utility customers in the United States. Web. https://doi.org/10.2172/1172643
Torres C, Faccioli M, Font AR (2017) Waiting or acting now? The effect on willingness-to-pay of delivering inherent uncertainty information in choice experiments. Ecol Econ 131:231–240
Train KE (2003) Discrete choice methods with simulation. Cambridge University Press
Train K, Weeks M (2005) Discrete choice models in preference space and willingness-to-pay space. In: Scarpa R, Alberini A (eds) Applications of simulation methods in environmental and resource economics. Springer, Dordrecht, pp 1–16
Venus TE, Sauer J (2022) Certainty pays off: the public’s value of environmental monitoring. Ecol Econ 191:107220
Vossler C, Watson S (2013) Understanding the consequences of consequentiality: testing the validity of stated preferences in the field. J Econ Behav Organ 86:137–147
Welling M, Zawojska E, Sagebiel J (2023) Information, consequentiality and credibility in stated preference surveys: a choice experiment on climate adaptation. Environ Resour Econ 82(1):257–283
Whittington D (2010) What have we learned from 20 years of stated preference research in less-developed countries? Ann Rev Resour Econ 2(1):209–236
Wielgus J, Gerber LR, Sala E, Bennett J (2009) Including risk in stated-preference economic valuations: experiments on choices for marine recreation. J Environ Manag 90(11):3401–3409
Williams G, Rolfe J (2017) Willingness to pay for emissions reduction: application of choice modeling under uncertainty and different management options. Energy Econ 62:302–311
Wilson RK, Eckel CC (2011) Trust and social exchange. In: Druckman JN, Green DP, Kuklinski JH, Lupia A (eds) The Handbook of experimental political science. Cambridge University Press, Boston, pp 243–257
World Bank (2020) Enterprise surveys. World Bank, Washington, DC. https://data.worldbank.org/indicator/IC.ELC.OUTG.ZS?locations=ZG. Accessed 07 Oct 2022
Wu H, Mentzakis E, Schaafsma M (2022) Exploring different assumptions about outcome-related risk perceptions in discrete choice experiments. Environ Res Econ 81:531–572
Zawojska E, Bartczak A, Czajkowski M (2019) Disentangling the effects of policy and payment consequentiality and risk attitudes on stated preferences. J Environ Econ Manag 93:63–84
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Research funding is provided by Styrelsen för Internationellt Utvecklingssamarbete (Sida) through the Environment for Development (EfD).
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We gratefully acknowledge the research fund from the Swedish International Development Agency (Sida) through the Environment for Development (EfD). We would like to thank the Tanzania Electric Supply Company (TANESCO) for providing us with the required data for our study. We are grateful to two anonymous reviewers for their valuable comments and suggestions, which have greatly improved the quality of the paper.
Appendices
Appendix A. Tables
See Tables A.1, A.2 and A.3
Appendix B. Scenario Description
2.1 Appendix B.1. Scenario Description for the Survey Consequentiality Treatment (Translated from Swahili)
Now we will ask you for information about the value that your enterprise places on improved electricity service.
This study is being conducted in collaboration with TANESCO.
Enumerator: Please show the formal letter from TANESCO regarding the study on the quality of electricity supply. In case, the respondent does not read, please read the content of the letter to the respondent.
As you might know, there are electric power outages in many parts of Tanzania, including Dar es Salaam. The current outages are mainly caused due to aged and poor physical conditions of the power distribution and transmission systems, lack of regular maintenance of the systems, and limited capacity of the systems relative to power demand.
To address the outages, TANESCO is considering investments to upgrade and replace the existing power distribution and transmission systems. These investments are expected to reduce the frequency and duration of power outages observed during your enterprise’s operation hours. However, such investments are costly and would result in a rise in electricity prices.
In order to obtain information on how customers think about power outages, alternatives including the current typical situations are presented to you and you will be asked to choose among the different options. The features of each option will be described by the frequency and average duration of outages (in hours) in a typical month, notification of the outages, and increase in the cost of electricity in TSZ per kWh.
Let me show you an example [enumerator shows the example and explains it to the respondent as follows].
Attributes | Current situation | Option A | Option B |
|---|---|---|---|
Number of power outages in a typical month | Four times | One time | Three times |
Duration of the outages in hours | Two and a half hours | Two and a half hours | One hour |
Prior notification about the outages | No prior notification | 24 h prior notification via radio/TV | No prior notification |
Increment in cost of electricity per kWh (in TZS) | 0 TZS | 60 TZS | 5 TZS |
Your choice |
|
|
|
If no action is taken to improve electricity services, in the current situation, it is expected that, on average, your enterprise will face power outages four times per month with an average duration of two hours and 30 min each. You will not receive prior notification about the power outages and the cost of electricity will be the same as now.
If action is taken to improve electricity service, two possible options are presented. In Option A, the number of outages will be reduced to one time per month, but the average duration of outage remains the same as the current situation. You will receive notification about the outages 24 h in advance via radio/TV. However, the cost of electricity will be increased by 60 TZS per kWh from the current unit cost.
In Option B, the number of outages will be reduced to 3 times per month and the duration of each outage will be also reduced to 1 h. However, you will not receive any prior notification about the outages and the cost of electricity will be increased by 5 TZS per kWh from the current unit cost.
Which alternative do you prefer? You will be asked to make 5 such choices. Please note that the choice you make only affects the attributes identified and everything else remains as it is now. Note also that money obtained from increasing electricity prices will be only allocated to improve the quality of electricity service by TANESCO.
Experience from previous similar studies shows that some respondents state their unwillingness to pay for improved electricity service not because they do not want improvements from the current situation but for other reasons. The reasons could be a belief that respondents have the right to uninterrupted electricity supply or that the money collected would not be used for the intended purposes. When choosing from the alternatives, we kindly request you not to think this way. But you might have other reasons and we would like you to tell us the reasons for this after making each of your choices.
Note that the project of improving the quality of the electricity supply will be implemented if the majority of the customers support it. When making decisions, please consider your current situation and how valuable is an improved electricity supply for your enterprise.
2.2 Appendix B.2. TANESCO Letter on Survey Consequentiality (Translated from Swahili)
Dear survey participant,
Manufacturing enterprise,
Dar es Salaam.
RE: Electricity Supply in Manufacturing Enterprise in Dar Es Salaam, Tanzania
Kindly refer to the above heading,
TANESCO in collaboration with researchers from the University of Dar es Salaam is conducting a survey on electricity services as well as the value that micro and small-scale manufacturing enterprises place on improved electricity supply.
The researchers are now collecting information from micro and small enterprises as part of the efforts of TANESCO to improve electricity services in the country. In this research, your identity will not be released in any form that you could be identified. Based on your responses and the results from the analysis, TANESCO will receive the final report and will consider the results of the research in its efforts to improve the electricity supply in Tanzania in the future.
Thank you for your participation.
Regards,
TANESCO
2.3 Appendix B.3. Scenario Description for the Outcome Uncertainty Treatment (Translated from Swahili)
Now we will ask you for information about the value that your enterprise places on improved electricity service.
As you might know, there are electric power outages in many parts of Tanzania, including Dar es Salaam. The current outages are mainly caused due to aged and poor physical conditions of the power distribution and transmission systems, lack of regular maintenance of the systems, and limited capacity of the systems relative to power demand.
To address the outages, TANESCO is considering investments to upgrade and replace the existing power distribution and transmission systems. These investments are expected to reduce the frequency and duration of power outages observed during your enterprise’s operation hours. However, such investments are costly and would result in a rise in electricity prices.
In order to obtain information on how customers think about power outages, alternatives including the current typical situations are presented to you and you will be asked to choose among the different options. The features of each option will be described by the frequency and average duration of outages (in hours) in a typical month, notification of the outages, and increase in the cost of electricity in TSZ per kWh.
For unforeseen reasons, the duration of the power outages could be differed from what would be expected. To capture this, we have introduced a different possible duration of outages with some probabilities.
Let me show you an example [enumerator shows the example and explains it to the respondent as follows].
Attributes | Current Situation | Option A | Option B |
|---|---|---|---|
Number of power outages in a typical month | 4 | 1 | 3 |
Duration of the power outages in hours | 2.5 | 20% chance, six and half hours | 20% chance, three hours |
80% chance, one and half hour | 80% chance, half-hour | ||
Prior notification about the outages | No prior notification | 24 h prior notification via radio/TV | No prior notification |
Increment in cost of electricity per kWh (in TZS) | 0 TZS | 60 TZS | 5 TZS |
Your choice |
|
|
|
If no action is taken to improve electricity services, in the current situation, it is expected that on average, your enterprise will face power outages four times per month with an average duration of two hours and 30 min each. You will not receive prior notification about the power outages and the cost of electricity will be the same as now.
If action is taken to improve electricity service, two possible options are presented. In Option A, the number of outages will be reduced to one time per month and the duration of outage could be six and half hours with a 20% chance or one and half-hour with an 80% chance. You will receive notification about the outages 24 h prior notification via radio/TV. However, the cost of electricity will be increased by 60 TZS per kWh from the current unit cost.
In Option B, the number of outages will be reduced to 3 times per month and the duration of each outage could be three hours with a 20% chance or half-hour with an 80% chance. However, you will not receive any prior notification about the outages and the cost of electricity will be increased by 5 TZS per kWh from the current unit cost.
Which alternative do you prefer? You will be asked to make 5 such choices. Please note that the choice you make only affects the attributes identified and everything else remains as it is now. Note also that money obtained from increasing electricity prices will be only allocated to improve the quality of electricity service by TANESCO.
Experience from previous similar studies shows that some respondents state their unwillingness to pay for improved electricity service not because they do not want improvements from the current situation, but for other reasons. The reasons could be a belief that respondents have the right to uninterrupted electricity supply, or the money collected would not be used for the intended purposes. When choosing from the alternatives, we kindly request you not to think this way. But you might have other reasons and we would like you to tell us the reasons following your choices.
Note that the project of improving the quality of the electricity supply will be implemented if the majority of the customers support it. When making decisions, please consider your current situation and how valuable is an improved electricity supply for your enterprise.
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Meles, T.H., Lokina, R., Mtenga, E.L. et al. Stated Preferences with Survey Consequentiality and Outcome Uncertainty: A Split Sample Discrete Choice Experiment. Environ Resource Econ 86, 717–754 (2023). https://doi.org/10.1007/s10640-023-00810-5
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DOI: https://doi.org/10.1007/s10640-023-00810-5
Keywords
- Stated preferences
- Survey consequentiality
- Outcome uncertainty
- Discrete choice experiment
- Power outages
- Business enterprises
- Tanzania