نرخ تنزیل ذهنی در جمعیت عمومی و قدرت پیش بینی آنها برای رفتار صرفه جویی در انرژی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|26955||2014||17 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 13795 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Policy, Volume 65, February 2014, Pages 524–540
Why do people sometimes refrain from saving energy even if it would pay off in monetary terms? Subjective discount rates present one possible explanation for this lack of foresight, but little is known about their level and reliability in the general population. With regard to behavior, persons with lower discount rates are expected to accept additional costs upfront more readily than those with higher discount rates. Based on a representative nation-wide study, the Swiss Environmental Survey 2007, and a follow-up survey, our analyses reveal that on average subjective discount rates are well above market interest rates and moderately stable over a time interval of four years. Income and education are negatively correlated with discount rates. Contrary to expectations, we did not find convincing support for an impact of discount rates on energy saving behavior.
Many decisions relevant to environmental conservation involve a trade-off between short- and long-term benefits. For example, when buying a washing machine, less expensive but less energy-efficient devices have to be compared to options with higher purchase prices and lower operating costs. In some of these situations, investments in energy efficiency would result in lower life-cycle costs. But even in such cases, the corresponding option is not always chosen. Hence, more money than necessary is spent on certain goods and services, and on the aggregate level a large potential for energy saving is lost. This lack of investments corresponds to the so-called “energy efficiency gap” (Howarth, 2004). This paper focuses on one possible explanation for such a lack of foresight: subjective discount rates.1 They capture the extent to which a person is present- or future-oriented. Daly and Farley (2011, p. 190) describe discounting as follows: “When evaluating present and future values, intertemporal discounting is the process of systematically weighting future costs and benefits as less valuable than present ones. […] The farther off in time that a cost or benefit occurs, the more we discount its present value.” A high discount rate implies that someone is devaluing future rewards rapidly and thus is present-oriented. In contrast, a low discount rate signifies a higher valuation of future utility and therefore a higher degree of future orientation. Environmentally responsible behavior that pays off financially often only does so in the long run. It therefore requires behaving in a future-oriented manner by delaying utility. For example, the purchase of a fuel-efficient car may initially be more expensive than a less energy-efficient vehicle, but it is profitable in the long term and at the same time causes a lower environmental burden. In such situations, persons with lower discount rates should more readily accept additional costs up front than those with higher discount rates since they put greater value on future utility. While these theoretical considerations might be compelling, little is known about the level of subjective discount rates and their reliability (and hence temporal stability) in the general population. Most previous studies have been conducted with relatively small (student) samples and report average discount rates that are considerably higher than market interest rates (see Frederick et al., 2002). Furthermore, there are only a few studies analyzing subjective discount rates as predictors of actual behavior. So far the results have been heterogeneous. The aim of this paper is fourfold: First, it reports subjective discount rates for a representative population sample in Switzerland. Second, a brief analysis of the reliability of discount rates is conducted. This is noteworthy not only because of the sample properties but also since the measurements were conducted four years apart. Third, the effects of different socio-demographic variables on subjective discount rates are analyzed by means of multivariate methods. Fourth, discount rates are used to predict self-reported behavior. This paper thus presents one of the rare examples that analyzes a representative general population sample and links discount rates to energy saving behavior. 1.1. Estimation of subjective discount rates in the general population So far, only a few studies have reported discount rates for representative population samples. For example, Harrison et al. (2002) have reported a mean of 28% for a Danish sample and Epper et al. (2011) a median of 47% based on an online survey in the German-speaking area of Switzerland. For student samples, a broad range of average discount rates has been reported. For example, in their classic study Benzion et al. (1989) report mean discount rates of 11–46% (average 21%) depending on framing, amounts and delays involved. There is no clear expectation as to whether the average discount rate of a student or a population sample should be higher. On the one hand, students are more educated than the average population and therefore presumably more adept in handling compound interest computations. On the other hand, they might also be more impulsive and hence more tempted to choose earlier payments rather than more delayed ones (see the next section for a discussion of the effects of education and age on discount rates). When interpreting subjective discount rates reported by such studies, it should be kept in mind that the way in which discount rates are typically measured already implies there should be positive discounting and hence may bias discount rates upwards (Frederick et al., 2002): Most studies – including the ones cited above – use choice tasks to capture subjective discount rates (Frederick et al., 2002). Usually, respondents are given a choice between a smaller sooner reward (SSR) and a larger later reward (LLR) – for example, a payment of $100 in one year versus a payment of $125 in two years. In the above example, someone preferring the earlier payment is said to have an annual discount rate of at least 25%. As such a measurement simply yields a lower or upper bound on the discount rate, many studies use series of choice tasks varying the delay as well as the amounts of the reward involved. By doing so, the possible range of each person's discount rate can be narrowed down (for example, Kirby et al., 1999). The absolute level of discount rates found in such experiments depends on several factors (for comprehensive reviews see Frederick et al., 2002 and Manzini and Mariotti, 2007). For example, lower discount rates are reported when higher amounts of a reward are involved (see Kirby, 1997 and Percoco and Nijkamp, 2009). This “magnitude effect” is plausible if the respondents do not only consider the relative but also the absolute height of the amounts involved and behave accordingly (Loewenstein and Prelec, 1991). A related phenomenon is increasing patience with delay (hyperbolic discounting). For example, a decision between a cookie tomorrow and two cookies the day after tomorrow is perceived differently from a decision between a cookie in 60 days and two cookies in 61 days. In both situations, the same additional waiting period (one day) is required to receive a larger instead of a smaller reward. However, the situations differ with regard to when this additional waiting period begins (in one or in 60 days). Hyperbolic discounting conveys that respondents are more likely to wait for LLRs in the second type of decision. The closer to the present the additional waiting period starts, the higher the discount rates are (Benzion et al., 1989 and Thaler, 1981). So far there is no conclusive evidence on whether discount rates are affected by whether hypothetical rewards, real rewards or rewards depending on a lottery are used (Coller and Williams, 1999 and Frederick et al., 2002). It could either be argued that the possibility of actually receiving a reward increases its salience (see for example research on psychological distance; Trope and Liberman, 2010) and hence may lead to more impulsive choices, or it could be assumed that incentivized choices should yield lower discount rates as respondents might be more thoughtful when facing real rewards (see Camerer and Hogarth, 1999). Apart from the methodological factors discussed so far, there are other possible confounding factors such as transaction costs, risk preferences and trust in the paying institution. To reduce these, studies with incentivized choices often use delayed rewards only (see Frederick et al., 2002 and Harrison et al., 2005). In our surveys, the subjective discount rate was measured twice in each wave. All measures are based on choice tasks, some of which were incentivized while some also included a front-end delay. This allows comparing them and testing for possible influences of the magnitude of the rewards, the delays involved and the presence of a lottery. However, one of the main goals of this paper is to estimate the average discount rate for a general population sample, in this case the Swiss population. 1.2. Reliability of subjective discount rates Only a few studies have investigated the reliability or stability of discount rates. Typically, two measurements were conducted with a brief period between them, such as one week or three months, and the findings are based on non-representative and rather small (student) samples. Table 1 gives an overview of correlations between discount rates over time reported by previous studies. Most of them report moderate to high correlations, although the results vary both between and within the studies.Unfortunately, none of these correlation studies is based on a representative population sample. The present paper will, however, report the test–retest reliability of discount rates over a period of four years as well as the parallel form reliability for a representative Swiss population sample. 1.3. Socio-demographic determinants of subjective discount rates Although several socio-demographic variables have been shown to co-vary with subjective discount rates, the evidence regarding the causes of individual differences in discount rates is still very limited (see Kirby et al., 2002). Stable effects have mainly been found for education and income. Most studies report a negative relationship – thus, higher income and higher education both accompany lower discount rates (for example Burks et al., 2011, de Wit et al., 2007, Green et al., 1996, Harrison et al., 2002, Hausman, 1979 and Reimers et al., 2009). For gender and age the existing studies report diverging results. Many studies do not find any relationship between discount rates and gender (for example Anderson and Stafford, 2009, Coller and Williams, 1999, Daly et al., 2008, de Wit et al., 2007 and Harrison et al., 2002). A few point to females being more present-oriented (Cairns et al., 2000, Read and Read, 2004 and Reimers et al., 2009) and others on the delay of gratification generally conclude females were slightly more future-oriented (Silverman, 2003). Studies on the effect of age are mainly based on cross-sectional data (see Borghans et al., 2008, Frederick et al., 2002 and Khwaja et al., 2007). To our knowledge, there are no panel studies over substantial periods. Different theoretical positions either suggest a decline (Rogers, 1994), a steady increase (Trostel and Taylor, 2001) or a curvilinear relationship (u-shaped; Sozou and Seymour, 2003) over adulthood. Each of these positions comes with limited empirical support only (for example Cairns et al., 2000, Harrison et al., 2002, Kirby et al., 2002, Read and Read, 2004 and Reimers et al., 2009). Our analyses focus on the four predictors discussed above – education, income, gender and age – as they are believed to be relevant for many other individual differences, and attempt to shed further light on their role in discounting. 1.4. Subjective discount rates as a predictor of behavior So far, discounting and pro-environmental behavior have not been linked very often. A number of mainly older studies from the US infer (aggregate market) discount rates from actual energy-saving behavior (e.g. Gately, 1980, Hausman, 1979, Liebermann and Ungar, 1983, Meier and Witthier, 1983 and Ruderman et al., 1987). Their results vary greatly both between and within product types (partially due to model specifications). For example, annual discount rates of 10–32% were found for thermal insulations, 2–36% for space heating, 3–29% for air conditioning systems and 34–300% for refrigerators (Train, 1985, p. 1246ff). Most of these discount rates are considerably higher than market interest rates. This implies that consumers spend more money on certain goods and services than necessary. But as these studies inferred discount rates from behavior, the latter determines the former and not the other way round. One cannot conclude from these studies that the subjective discount rate has a causal impact on the purchasing behavior. Many of the more recent studies, in contrast, use discount rates as predictors of behavior and thus measure individual discount rates independently from behavior. Most of these studies focus on financial savings and debts, substance abuse, sexual intercourse and other health-related behaviors such as nutrition and exercise (e.g. Burks et al., 2011, Chabris et al., 2008, Chapman, 1998, Khwaja et al., 2007, Kirby et al., 1999, Nyhus, 2002, Reimers et al., 2009 and Sutter et al., 2010). The results so far have been ambiguous. Only very few studies have linked discount rates to environmental behavior. To our knowledge, these are the studies by Fehr and Leibbrandt (2008) and Liebermann and Ungar, 1997 and Liebermann and Ungar, 2002. Fehr and Leibbrandt (2008) analyze the impact of discount rates on fishing in rural Brazil. For the particular behaviors in question, the individual rational solution is to free-ride and thus not to behave environmentally friendly. Nonetheless, one of two measures of discount rates – a choice task involving mineral water – had a significant impact: in comparison to respondents with high discount rates, those with low discount rates do their everyday fishing in a more future-oriented, environmentally conscious manner. An analogous measure using a monetary reward did not predict behavior. Both studies by Liebermann and Ungar, 1997 and Liebermann and Ungar, 2002 first measured discount rates and then presented the respondents with a questionnaire including an item on the purchase of an air-conditioning system. This task required a choice between a more energy-efficient while initially more expensive device and a less energy-efficient system with a lower purchase price. The earlier study implied an annual threshold discount rate of roughly 7%, the later a rate of 10%. In both studies, persons with discount rates below this cut-off value were expected to choose the more energy-efficient device whereas respondents with discount rates above this threshold were expected to do the opposite. Behavior in line with these expectations was labeled as “efficient” (Liebermann and Ungar, 2002, p. 732). Roughly 41% of the participants in 1997 and 71% in 2002 made efficient choices in terms of their discount rates. However, note that this is a weak test for the hypothesis of an impact of subjective discount rates on behavior because both studies rely on hypothetical choices. Given these inconsistent results, the question of the relevance of discounting for behavioral outcomes needs further investigation. Our study contributes by applying discount rates as a predictor of various energy-saving behaviors. From this, the question arises as to which behaviors are expected to be influenced by discount rates. Generally, discount rates convey a sense of future orientation and self-control; more narrowly, they imply financial optimization. This supplies us with a criterion for when to expect a relationship: where energy-saving behavior is most cost-effective in the long run.
نتیجه گیری انگلیسی
Our study pursued four main aims, two of which led to results in accordance with expectations: With regard to our aim to report population values for the subjective discount rate, we were able to demonstrate that in a large, nation-wide survey they are on average very high and well above market interest rates. Based on the serial choice tasks, the mean discount rate of the Swiss population is approximately 27% if extreme values of 100% and higher are excluded (both in 2007 and 2011) and 65% (2007) or 40% (2011) if all cases are considered. According to both the singular and the serial choice tasks, more than half of the respondents have a discount rate of 20% and above (singular choice tasks: 58% in 2007 and 56% in 2011; serial choice tasks with / without extreme values: 74%/46% in 2007 and 59%/51% in 2011). Such high figures are a common finding in both experimental and field studies, with students or with general population samples (see Epper et al., 2011, Frederick et al., 2002, Harrison et al., 2002 and Train, 1985).6 Nonetheless, this has serious consequences as it implies that future benefits are devalued very strongly. This means future benefits that come with a particular option need to be high compared to more immediate payoffs by other options for an average decision-maker to choose accordingly. Another aim of this study was to analyze the determinants of subjective discount rates in a general population sample. The respective analyses confirmed the expected effects of education and income: more educated persons and those with higher incomes make more patient choices. In addition, our data indicates that females rather than males choose smaller sooner payments. The relationship between age and discount rates appears curvilinear with its minimum at roughly 49 years and 37 years for the serial choice task in 2007 and 2011, respectively. The remaining variables in our models indicate lower discount rates for those who are economically active, are in a steady relationship, do not have children and live in the German-speaking area of Switzerland. With regard to the remaining two aims of this paper – concerning the reliability (and hence stability) of discount rates and their predictive power for energy-saving behavior – the results are ambiguous at best and the conclusions are sobering. They challenge the classic economic assumption that the subjective discount rate represents a trait, a stable individual difference variable that “applies to all acts of consumption” (Frederick et al., 2002, p. 394). These two properties – stability and relevance to a wide range of behaviors – are essential to the psychological understanding of traits (cf. Frederick et al., 2002 and Roberts, 2009). The next few paragraphs therefore focus on these two aspects. Our analyses indicate a certain reliability and a moderate stability of discount rates. This conclusion is based on several observations. First, the bivariate correlations between the two measures used in 2007 and in 2011, respectively, amount to roughly 0.6 (parallel form reliability). Second, in the singular choice tasks, roughly 70% of the respondents choose the same option both in 2007 and in 2011. The resulting correlation between the two points in time is around 0.4 (test–retest reliability). The test–retest reliabilities between different pairs of measures are around 0.3. Thus, the correlations across time (test–retest reliability) are considerably lower than between different items measured at the same time (parallel form reliability). This might be taken as a hint that discount rates might not be stable over time. In addition, a look into psychological studies on personality measures reveals that one would generally expect higher correlations over an interval of four years in order to assume stability. For example, in an older but frequently cited overview, Costa and McCrae (1994) report that many studies based on adults find test–retest correlations in the range of 0.6 to 0.8 for the Big Five dimensions. A slightly more recent meta-analysis limited to 152 studies with retest intervals of at least one year reports average test–retest correlations of roughly 0.5 (Roberts and DelVecchio, 2000). While clearly lower and limited to four years as opposed to many personality studies with retest intervals as large as 10, 20 or even 40 years, our correlations are roughly comparable to the one-year stability of discount rates reported by Meier and Sprenger (2010, a value of 0.4). They suggest this low value may be unproblematic considering they were using a one-item measure of discount rates as opposed to psychological scales that typically comprise a large number of items – future studies using more precise techniques to measure discount rates may find higher test–retest reliabilities. Hence, the overall conclusion with regard to the stability of discount rates remains ambiguous. Contrary to expectations we did not find convincing evidence for the hypothesis that subjective discount rates have an impact on the degree of energy saving behavior. A lot of research has investigated discount rates in the past decades, yet most of it has been focused on the development of adequate measures, the exploration of methodological aspects and the discounting function itself. While it frequently has been assumed that discount rates are a relevant predictor of various behaviors, only a small portion of all research has actually investigated this link. The results of these studies are heterogeneous in most behavioral domains (see for example Chapman, 1998 and Nyhus and Webley, 2006). We, too, do not find consistent effects of discount rates on behavior, despite our focus on economically efficient behavior. Only five out of 34 expected effects are confirmed (15%). This weakness may be due to the typical measurement technique of discount rates. When asking subjects to compare SSR and LLR, the outcome does not only depend on time preferences. There are several confounding factors such as the uncertainty whether they would receive the LLR, possible transaction costs or risk preferences (Epper et al., 2011 and Frederick et al., 2002). Hence, the discount rate is a multidimensional construct which might partly explain the poor predictive performance of the measure. In addition, our study may be limited by the methodology used as, for instance, we rely on self-reported behavior. But despite all possible deficiencies, our results clearly indicate that subjective discount rates may not be a valid predictor of behavior after all – even given that the behavior pays off financially in the long run. This finding puts into question a second property of subjective discount rates as individual difference variables: their ability to predict behavior across different situations. This is in line with Frederick et al. (2002, p. 392) who conclude their comprehensive review of time discounting and time preferences by noting: “[…] in our view the cumulative evidence raises serious doubts about whether there is, in fact, such a construct – a stable factor that operates identically on, and applies equally to, all sources of utility.” One possible albeit confining answer is to investigate whether discount rates are domain-specific. This might help explaining why the studies that inferred discount rates from actual purchases of energy-using devices have reported a wide range of discount rates (for overviews see DEFRA, 2010 and Train, 1985). If discount rates are domain-specific (as for example suggested by Tsukayama and Duckworth, 2010 and Weatherly et al., 2010), they need to be measured specifically tailored to the behavioral domain at hand. In our case, this raises two problems: first, there is the question of practicability. As energy saving behavior does not fall within one single domain (for example, mobility, diet, household finances), there should be separate measures for each domain involved. Second, when assessing discount rates specifically by domain (and hence following the correspondence principle, see, for example, Ajzen, 1991), framing effects may occur and lead to an overestimation of the relevance of the predictor in question. Studies using broader, more general measures, however, unavoidably report lower effects on specific behaviors and therefore are less prone to overestimation. In this light, domain- or even behavior-specific measures are problematic while the use of general measures appears more favorable. In sum, our study confirms some of the expected findings on discount rates with regard to their absolute level and group differences in the population while calling into question the assumption that discount rates are stable over longer periods and that they are valid predictors of energy saving behavior. This points to a very fundamental theoretical issue: Do discount rates reflect an individual difference variable at all? Frederick et al. (2002, p. 392) argue this may not be the case – however theoretically compelling the idea might be. What adds weight to our results is that our data is based on a representative general population sample and covers a time interval roughly four times larger than that of any of the previous studies on the stability of subjective discount rates known to us. Given these results, it may be advisable to instead examine related concepts such as future orientation, self-control or impulsivity (for similar reasoning, see Duckworth et al., 2013, Frederick et al., 2002 and Nyhus and Webley, 2006). There already are a number of psychological studies on the impact of future orientation – as measured by the Zimbardo Time Perspective Inventory (Zimbardo and Boyd, 1999), the Consideration of Future Consequences scale (Strathman et al., 1994) or its revised version (Joireman et al., 2012) – on pro-environmental behavior. In a recent meta-analysis of 13 studies, Milfont et al. (2012) report an average correlation of r=0.26, p<0.001. This suggests that future orientation is positively associated with pro-environmental behavior (see also Bruderer Enzler, accepted for publication). Yet for the moment, the following question remains: If subjective discount rates do not account for environmental decisions in households, why do several studies report surprisingly high aggregate discount rates for purchases of energy consuming appliances? For example, Ruderman et al. (1987) report aggregate market discount rates for various appliances in the range of 16 to 243%. Our study suggests that instead of subjective discount rates other factors may have contributed to the seemingly high market discount rate.7 For example, it has frequently been argued that consumers may have insufficient information on energy prices and savings from more efficient appliances. Moreover, in Switzerland landlords usually purchase larger household devices while tenants pay the utility bill. This so-called investor-user dilemma decreases the incentive to invest in energy-saving equipment and is, in international comparison, even more pronounced in Switzerland because of the large proportion of tenants (two thirds according to the Swiss Federal Office for Housing, 2005). Energy efficiency labels, as they are now common with many appliances, the creation of further incentives and regulations for home owners and the adoption of third-party contracting may be helpful to promote energy-efficient investments in households. As far as our empirical findings are concerned subjective discount rates do not contribute to an explanation of energy-saving investments.