Time Preferences, Health Behaviors, and Energy Consumption
David Bradford, University of Georgia, bradfowd@gmail.com
Charles Courtemanche, Georgia State University and NBER, ccourtemanche@gsu.edu
Garth Heutel, University of North Carolina at Greensboro and NBER, gaheutel@uncg.edu
Patrick McAlvanah, Federal Trade Commission, pmcalvanah@ftc.gov
Christopher Ruhm, University of Virginia and NBER, ruhm@virginia.edu
Growing evidence from economics and from psychology suggests that people make
intertemporal trade-offs in a way that is not found in standard economic models. The standard
assumption made in economic models is that consumers discount the future in a time-consistent
manner; the relative weight that I place on consumption one year from now relative to now is
equal to the weight that I place on consumption ten years from now relative to nine years from
now. However, many laboratory and econometric studies suggest that consumers are timeinconsistent, and in particular exhibit present bias: the weight I place on consumption now
relative to a year from now is greater than the weight I place on consumption nine years from
now relative to ten years from now.
If consumers are time inconsistent and exhibit present bias, important policy implications
arise across a broad range of issues. Environmental and energy policy is affected, since
consumers may be less willing to invest in energy-efficiency technologies (hybrid cars, home
energy improvements) if they do not account for future savings as much as the standard model
assumes that they do. Health policies are affected by the fact that consumers may underinvest in
health, e.g. eating too much, exercising too little, or failing to buy insurance, since they are
present biased. Policies designed to encourage retirement savings need also to accommodate the
fact that consumers are time-inconsistent. In each of these examples, economists have suggested
that an explanation for consumer behavior (e.g. underinvestment in energy efficiency, high
obesity rates, lack of retirement savings) may be present bias.
The purpose of this paper is to provide direct evidence of the link between timeinconsistent preferences and consumer choices over health behaviors and energy consumption
behaviors. We develop a survey that elicits each individual's time preferences and behavior in
these markets that have been predicted to be impacted by present bias. We measure time
preferences by asking questions about intertemporal tradeoffs, paying out one of the choices for
randomly-selected respondents to mitigate hypothetical bias. We compute both the “beta”
(present bias) and “delta” (time consistent) components of a quasi-hyperbolic specification,
allowing for an empirical test of the association between time inconsistency and outcomes
including body mass index, obesity status, dietary and exercise habits, smoking, drinking, health
insurance status, preventive health care utilization, car model (to see its fuel efficiency), use of
energy-efficient technologies in the home, and amount of retirement savings.
A prior literature exists that merely tests whether or not consumers' time preferences are
time-consistent. Laboratory evidence goes back to Thaler (1981), and laboratory evidence
combined with neurological evidence in McClure et. al. (2004) support the existence of present
bias in preferences. Other studies look at consumer behavior in the market for evidence of
present bias. Individuals' choices about exercising (Dellavigna and Malmendier 2006), doing
homework (Ariely and Wertenbroch 2002), participating in welfare programs (Fang and
Silverman 2009), and eating (Ruhm 2012) all show evidence of time inconsistency and present
bias. Buyers of cars seem to underweight future gasoline costs (Allcott and Wozny 2012).
Obesity rates and body-mass index (BMI) seem to be correlated with present bias
(Courtemanche, Heutel and McAlvanah 2011).
A paper similar in scope to this proposal is Chabris et. al. (2008), who measure discount
factors of subjects and test for correlations between the discount factor and market outcomes.
They find that discount factor predicts behavior like exercise, BMI and smoking. However, they
do not attempt to separate out a present bias in discounting from a time consistent discount
factor. The distinction between these two types of discounting can have important policy
implications. In particular, present bias may justify government intervention in a way that is not
justified if decisions are based on time-consistent preferences (O'Donoghue and Rabin 2006).
Chabris et. al. (2008), along with another paper that similarly connects laboratory-calculated
discount factors with market outcomes, Burks et. al. (2012), do not examine individuals' choices
about energy efficiency investments, retirement savings, or health insurance.
Preliminary results from a small pilot of the survey provide some evidence of a link
between time inconsistency and these behaviors: smokers are significantly more likely to exhibit
present bias, and those with present bias are significantly less likely to have well-insulated
homes.
Survey Design
An online survey was conducted using Qualtrics software. A panel of 200 respondents
was purchased from Qualtrics Panels. The survey was conducted online December 5-7, 2012.
We asked four sets of questions of the respondents. The first set consists of demographic
questions (e.g. age, income, race, education).
The second set of questions is used to calculate individual discount factors and test for
present bias. We asked each respondent whether he would prefer a smaller payment more
quickly or a larger payment after a longer wait (these questions are called multiple price list
(MPL) questions). There were three payout time pairs to choose among: now vs. one month
from now, now vs. six months from now, and six months from now vs. seven months from now.
For each of the three payout time pairs, the larger payment (after the longer wait) was $30, and
the smaller payment ranged from $29 to $8. Each respondent was asked 22 such questions. 1
Using these questions, we can calculate a monthly discount factor for each of the three payout
time pairs; call these 𝛿0,1 , 𝛿0,6 , and 𝛿6,7. (That is, 𝛿0,1 is the discount factor calculated using the
respondent's answer to the MPL questions about payoffs now vs. one month from now.) 2
Several individuals' responses to the MPL questions are missing or are such that we cannot
calculate one or more of the discount factors; these individuals are dropped. 3 We take the
average of all of these three discount factors and call it 𝛿𝑎𝑣𝑔 . This assumes time-consistent
discounting. However, we allow for time-inconsistent discounting by noting that a respondent
can have a different value for 𝛿0,1 and 𝛿6,7. Under time-consistent discounting, these two values
are equal to each other. If 𝛿0,1 < 𝛿6,7, then consumers are present-biased. Imposing a quasi𝛿
hyperbolic structure to the subjects' discounting, the present bias discount factor 𝛽𝑞ℎ = 𝛿0,1 and
6,7
the long-run discount factor 𝛿𝑞ℎ = 𝛿6,7 . To combat hypothetical bias, we paid a random subset
of respondents based on their responses to these questions. 4
The third set of questions asks respondents about health-related economic decisions.
These include questions about smoking (have you ever smoked, do you currently smoke, how
many cigarettes per day do you currently smoke), wearing sunscreen, sexual behavior, and health
1
The time periods of the questions were the same as those used in Meier and Sprenger (2010). We adjusted the
dollar values of the payments downward to reflect our budget (and rounded each to the nearest dollar integer).
2
The discount factor is calculated based on the question where the respondent switched from preferring the later
payout to preferring the earlier payout. This method is described in more detail in Meier and Sprenger (2010, 199200).
3
For example, some respondents switched from the earlier to the later payments more than one time within a single
price list.
4
20% of respondents were randomly selected to receive a payment. For each respondent chosen, one of the 22 MPL
questions was randomly chosen to be the payout question. Payments were Amazon.com gift cards.
insurance. These questions were predominantly drawn from the US Center for Disease Control
and Prevention's Behavioral Risk Factor Surveillance System (BRFSS) 2011 questionnaire. 5
The fourth and final set of questions asks respondents about energy use decisions. These include
questions about transportation (what is your primary form of transportation, what is the fuel
economy of your car) and home energy use (does your home have energy-efficient light bulbs,
have you had an energy audit of your home conducted). These questions were predominantly
drawn from questions asked in the US Energy Information Administration's 2009 Residential
Energy Consumption Survey. 6
Preliminary Results
The primary empirical question is to identify statistically significant correlations between
time preferences and these decisions or behaviors. Table 1 provides summary statistics of
several of the variables described above (means, standard deviations, and counts). We present
the statistics for the entire sample (of those who responded to the question), and separately for
those who are present-biased (𝛽𝑞ℎ < 1) and those who are not present-biased (𝛽𝑞ℎ ≥ 1). We
present a t-statistic for a test of a difference in means in the fourth column. The sum of the count
of present-biased responses and non-present-biased responses is not the count of all responses
because some respondents' MPL responses were inconsistent and did not yield a value for 𝛽𝑞ℎ .
The first panel of Table 1 presents the demographic variables. Age is in years, income is
in thousands of dollars, and the rest of the variables are binary indicators. White is one if the
race/ethnicity question was answered "White (non-Hispanic)." (The other options were AfricanAmerican (non-Hispanic), Hispanic, Asian, and Other.) College is one if the highest level of
5
That survey is available here: http://www.cdc.gov/brfss/.
That survey is available here: http://www.eia.gov/consumption/residential/. Some additional questions were taken
from the survey designed for Attari et. al. (2010).
6
education completed is college graduate or postgraduate degree. There are no significant
differences in means across present-biased and non-present-biased individuals.
The second panel of Table 1 presents the discount factor variables described above. The
average monthly discount factor using all three sets of MPL questions 𝛿𝑎𝑣𝑔 is 0.8779, which is
quite low for a monthly discount factor but consistent with previous literature finding low
discount factors when using MPLs (Meier and Spregner 2010) (Frederick, Loewenstein and
O'Donoghue 2002). There is no significant different in 𝛿𝑎𝑣𝑔 between present-biased and non-
present-biased individuals. 7 The long-run discount factor 𝛿𝑞ℎ is significantly higher for present-
biased individuals. This is not what we would expect, and it is likely a result of the way in which
present bias was calculated (𝛿𝑞ℎ is in the denominator of the calculation for 𝛽𝑞ℎ ). Of course, 𝛽𝑞ℎ
is significantly lower for present-biased individuals.
The next panel of Table 1 examines differences in health outcomes and behaviors
between the two groups. Respondents were asked if they would say that their health "in general"
is excellent, very good, good, fair, or poor. The variable "health status" is that response on a
scale from 0 to 4 where 4 is excellent and 0 is poor. The variable "good health" is a binary
indicator equal to one if the response is excellent, very good, or good. Neither of these variables
is significantly different between present-biased and non-present-biased individuals. "Ever
smoked" is one if the respondent has smoked at least 100 cigarettes in his or her entire life;
"regular smoker" is one if the respondent now smokes every day or some days (vs. not at all).
Present-biased individuals are twice as likely to be regular smokers as are non-present-biased
individuals, significant at the 95% level. Non-present-biased individuals are more likely to have
7
Although not reported in Table 1, we find that 50.6% of our respondents are present-biased, somewhat higher than
the 36% found in Meier and Sprenger (2010). We find future bias (𝛽𝑞ℎ > 1) in 19.8% of the sample, compared with
9% in Meier and Sprenger (2010).
health insurance and to answer that they use sunscreen always or nearly always when they are
outside, but neither difference is significant. This panel supports our intuitive priors about how
present bias affects health behaviors and outcomes, but statistical significance is limited because
of small sample size.
The last panel of Table 1 examines energy consumption behaviors. Each of these
variables is a binary indicator. "Alternate transportation" equals one if the respondent's primary
method of transportation to work or to school is anything other than "car (driving by yourself)"
(i.e. car (driving with others/carpooling), public transportation, bicycle, walk, or other). Presentbiased individuals are slightly more likely to use alternate transportation, but the difference is not
quite statistically significant. "High fuel economy" is one if the respondent's vehicle's fuel
economy is 31 miles per gallon or greater. "Large vehicle" is one if the respondent's vehicle is
anything other than "car" (i.e. station wagon, pickup truck, sport utility vehicle, minivan, or
other). Present-biased individuals are less likely to have a high fuel economy vehicle and more
likely to have a large vehicle, but the differences are not quite significant (although the
difference for fuel economy is almost significant at the 90% level). "Home well insulated"
equals one if the respondent says that his or her home is well insulated, rather than adequately
insulated or poorly insulated. "Less energy than average" is one if the respondent claims that his
or her residence consumes less energy than the average residence in the neighborhood. Presentbiased individuals are about 14 percentage points less likely to live in a well-insulated home, and
this difference is significant at the 95% level. There is no significant difference between presentbiased and non-present-biased individuals in whether they use less energy than average. As with
the results about health behaviors, the summary statistics on energy consumption largely confirm
our priors (present-biased individuals are less likely to make energy-saving investments),
although statistical significance is limited.
We next turn to regression analysis. Table 2 presents OLS regression results for the
health outcomes and behaviors. The dependent variable in each regression is one of the health
outcomes from Table 1. The right-hand-side variable of interest is a binary indicator for present
bias. Odd-numbered columns include just this indicator and a constant; even-numbered columns
also add in demographic controls. As was seen in the table of summary statistics, the only
outcome for which present bias enters significantly is being a regular smoker. Adding in
demographic controls does not substantially affect the coefficient or its significance. The
coefficient on present bias in all other columns follows intuition but is insignificant.
Table 3 presents similar regressions, this time looking at the energy consumption
variables that appear in Table 1. Again, most of these coefficients on present bias are
insignificant although they generally follow intuition. Only the coefficients on present bias in
the regressions for having a well-insulated home are significant. (The coefficients on present
bias in the high fuel economy regressions are significant at the 85% level.)
Table 4 explores alternate measures of time preferences and how they affect the
significant results found in Tables 2 and 3. We focus just on the indicators for being a regular
smoker (columns 1–4) and having a well-insulated home (columns 5–8). The first two columns
regress the indicator variable on 𝛿𝑎𝑣𝑔 , the measure of the discount factor that imposes time-
consistency. It does not enter significantly in any column, although it is of the expected sign in
columns 1, 2, and 5 (we expect more patient individuals to be less likely to smoke and more
likely to have well-insulated homes). The next pair of columns controls for both 𝛿𝑞ℎ and 𝛽𝑞ℎ ,
the discount factors of a quasi-hyperbolic specification. In these regressions, only the
coefficients on 𝛽𝑞ℎ enter significantly, and their significance drops when demographic controls
are included (the p-value on the coefficient on 𝛽𝑞ℎ in column 4 is 0.192; in column 8 it is 0.196).
The results of these alternate specifications imply that when time preferences are measured the
standard way (i.e. ignoring present bias), there is no relationship found between them and these
behaviors. Only when time-inconsistency is allowed do we see the expected relationships, and
there is only a significant relationship between the present bias discount factor 𝛽𝑞ℎ and the
behavior, not between the long-run discount factor 𝛿𝑞ℎ and the behavior. Of course, these results
could also arise simply because the sample sizes are so small.
All of the other columns of Tables 2 and 3 can be replicated using these alternative
measures of time preferences. Most of these regressions yield insignificant coefficients. One
interesting exception is the regression for alternate transportation when controlling for 𝛿𝑎𝑣𝑔 . In
those regressions (whether controlling for demographics or not), the coefficient on 𝛿𝑎𝑣𝑔 is
significantly negative, indicating that more patient individuals are less likely to use alternate
transportation (anything other than a car).
References
Allcott, Hunt, and Nathan Wozny. "Gasoline Prices, Fuel Economy, and the Energy Paradox."
NBER Working Paper, November 2012.
Ariely, Dan, and Klaus Wertenbroch. "Procrastination, Deadlines, and Performance: SelfControl by Precommitment." Psychological Science 13, no. 3 (May 2002): 219-224.
Attari, Shahzeen, Michael DeKay, Cliff Davidson, and Wändi Bruine de Bruin. "Public
Perceptions of Energy Consumption and Savings." Proceedings of the National Academy
of Sciences 107, no. 7 (September 2010): 16054–16059.
Burks, Stephen, Jeffrey Carpenter, Lorenz Gotte, and Aldo Rusticini. "Which Measures of Time
Preference Best Predict Outcomes? Evidence from a Large-Scale Field Experiment."
Journal of Economic Behavior and Organization 84, no. 1 (September 2012): 308-320.
Chabris, Christopher, David Laibson, Carrie Morris, Jonathon Schuldt, and Dmitri Taubinsky.
"Individual laboratory-measured discount rates predict field behavior." Journal of Risk
and Uncertainty 37 (2008): 237-269.
Courtemanche, Charles, Garth Heutel, and Patrick McAlvanah. "Impatience, Incentives and
Obesity." NBER Working Paper, 2011.
Dellavigna, Stefano, and Ulrike Malmendier. "Paying Not to Go to the Gym." American
Economic Review 96, no. 3 (June 2006): 694-719.
Fang, Hanming, and Dan Silverman. "Time-Inconsistency and Welfare Program Participation:
Evidence from the NLSY." International Economic Review 50, no. 4 (November 2009):
1043-1077.
Frederick, Shane, George Loewenstein, and Ted O'Donoghue. "Time Discounting and Time
Preference: A Critical Review." Journal of Economic Literature 40, no. 2 (June 2002):
351-401.
McClure, Samuel, David Laibson, George Loewenstein, and Jonathan Cohen. "Separate Neural
Systems Value Immediate and Delayed Monetary Rewards." Science 306 (October
2004): 503-507.
Meier, Stephan, and Charles Spregner. "Present-Biased Preferences and Credit Card Borrowing."
American Economic Journal: Applied Economics 2, no. 1 (January 2010): 193-210.
O'Donoghue, Ted, and Matthew Rabin. "Optimal Sin Taxes." Journal of Public Economics 90,
no. 10-11 (November 2006): 1825-1849.
Ruhm, Chris. "Understanding Overeating and Obesity." Journal of Health Economics 31, no. 6
(December 2012): 781-796.
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no. 3 (1981): 201-207.
Table 1 – Summary Statistics
All
Present Biased
Not Present
t-test of
Biased
difference of
means (p-value)
Demographics
Age
Female
White
College
Income ($1000)
51.46
51.02
52.88
0.9295
(13.36) [200]
(12.38) [87]
(13.83) [85]
(0.3539)
0.5330
0.5581
0.5238
–0.4467
(0.5002) [197]
(0.4995) [86]
(0.5024) [84]
(0.6557)
0.7990
0.8160
0.8118
–0.0725
(0.4018) [199]
(0.3897) [87]
(0.3932) [85]
(0.9423)
0.3850
0.3678
0.3647
–0.0421
(0.4879) [200]
(0.4850) [87]
(0.4842) [85]
(0.9665)
53.45
55.90
53.43
–0.3451
(43.21) [185]
(38.25) [83]
(51.58) [77]
(0.7305)
Discounting
𝛿𝑎𝑣𝑔
0.8779
0.8801
0.8756
–0.2780
(0.1037) [165]
(0.0787) [84]
(0.1249) [81]
(0.7814)
𝛿𝑞ℎ
0.8723
0.9402
0.8071
–5.980***
(0.1634) [183]
(0.0905) [87]
(0.1865) [85]
(0.0000)
𝛽𝑞ℎ
0.9702
0.8267
1.117
9.195***
(0.2527) [172]
(0.1349) [87]
(0.2611) [85]
(0.0000)
Health Outcomes and Behaviors
Health Status
Good Health
Ever Smoked
Regular Smoker
2.370
2.299
2.318
0.1284
(0.9474) [200]
(0.9358) [87]
(0.9662) [85]
(0.8980)
0.8250
0.7816
0.8235
0.6871
(0.3809) [200]
(0.4155) [87]
(0.3835) [85]
(0.4929)
0.5303
0.5698
0.5357
–0.4441
(0.5003) [198]
(0.4980) [86]
(0.5017) [84]
(0.6575)
0.2525
0.3448
0.1786
–2.499**
(0.4356) [198]
(0.4781) [87]
(0.3853) [84]
(0.0134)
Have Health
0.8050
0.7816
0.8235
0.6871
Insurance
(0.3972) [200]
(0.4155) [87]
(0.3835) [85]
(0.4929)
Regularly Use
0.2929
0.2644
0.2892
0.3599
Sunscreen
(0.4563) [198]
(0.4436) [87]
(0.4561) [83]
(0.7199)
Energy Consumption Behaviors
Alternate
0.2261
0.2759
0.2143
–0.9321
Transportation
(0.4194) [199]
(0.4995) [87]
(0.4128) [84]
(0.3526)
High Fuel
0.2112
0.1571
0.2647
1.553
Economy
(0.4094) [161]
(0.3666) [70]
(0.4445) [68]
(0.1228)
Large Vehicle
0.4506
0.4789
0.4118
–0.7917
(0.4991) [162]
(0.5031) [71]
(0.4951) [85]
(0.4299)
Home Well
0.3618
0.2644
0.4118
2.058**
Insulated
(0.4817) [199]
(0.4436) [87]
(0.4951) [85]
(0.0412)
Less Energy than
0.4700
0.4713
0.4824
0.1447
Average
(0.5004) [200]
(0.5021) [87]
(0.5027) [85]
(0.8851)
Notes: In the first three columns, the mean is in no parentheses, the standard deviation is in
parentheses, and the count of nonmissing values is in brackets. ** indicates significance at 5%
level; *** indicates significance at 1% level. Variables are described in the text.
Table 2 – Health Outcomes and Behaviors Regressions
(1)
(2)
Health Status
Health Status
Present Bias
-0.0188
(0.146)
172
0.000
158
0.097
172
0.003
158
0.037
170
0.001
156
0.138
(7)
Regular Smoker
(8)
Regular Smoker
(9)
Have Health
Insurance
(10)
Have Health
Insurance
(11)
Regularly
Use
Sunscreen
(12)
Regularly
Use
Sunscreen
0.166**
(0.0663)
0.174**
(0.0670)
-0.00194
(0.00258)
-0.0205
(0.0683)
-0.0419
(0.0610)
-0.0591
(0.0638)
0.000694
(0.00196)
0.100
(0.0640)
-0.0248
(0.0691)
-0.0268
(0.0686)
-0.00701**
(0.00281)
-0.198***
(0.0710)
Income ($1000)
Female
0.536***
(0.0547)
0.0539
(0.0768)
0.00608*
(0.00309)
0.129*
(0.0768)
0.229**
(0.102)
-0.191**
(0.0841)
-0.00108
(0.00110)
0.0712
(0.169)
0.824***
(0.0416)
College
Age
-0.0419
(0.0610)
2.318***
(0.105)
White
Present Bias
(5)
(6)
Ever Smoked Ever Smoked
-0.0531
(0.0640)
-0.00392*
(0.00230)
-0.0234
(0.0664)
-0.0427
(0.0817)
0.0520
(0.0657)
0.000946
(0.000615)
0.999***
(0.112)
Female
Observations
R-squared
(4)
Good Health
-0.0850
(0.150)
-0.0162***
(0.00617)
-0.102
(0.154)
0.0161
(0.226)
0.345**
(0.170)
0.00300
(0.00198)
2.935***
(0.341)
Age
Constant
(3)
Good Health
0.0341
(0.0767)
White
College
Income ($1000)
Constant
0.179***
(0.0420)
0.118
(0.0934)
-0.148**
(0.0735)
-0.000168
(0.00100)
0.246*
(0.131)
0.824***
(0.0416)
0.0978
(0.0921)
0.106
(0.0645)
0.00124
(0.00129)
0.560***
(0.124)
Observations
171
157
172
158
R-squared
0.036
0.087
0.003
0.076
Notes: Heteroskedasticity-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
0.289***
(0.0501)
0.195**
(0.0796)
0.124
(0.0748)
0.000781
(0.000984)
0.519***
(0.155)
170
0.001
156
0.131
Table 3 – Energy Consumption Behaviors Regressions
(1)
(2)
Alternate
Alternate
Transportation
Transportation
Present Bias
0.0616
(0.0660)
(3)
(4)
(5)
(6)
High Fuel Economy High Fuel Economy Large Vehicle Large Vehicle
0.214***
(0.0450)
0.0684
(0.0702)
-0.00434
(0.00307)
-0.0481
(0.0689)
-0.127
(0.0997)
0.0336
(0.0772)
-0.000737
(0.00120)
0.593***
(0.175)
0.265***
(0.0539)
-0.106
(0.0742)
-0.00439
(0.00343)
-0.0674
(0.0750)
-0.0380
(0.112)
0.0809
(0.0894)
-1.42e-05
(0.00108)
0.541**
(0.227)
Observations
R-squared
171
0.005
(7)
Home Well
Insulated
157
0.057
(8)
Home Well
Insulated
138
0.017
(9)
Less Energy than
Average
126
0.047
(10)
Less Energy than
Average
Present Bias
-0.147**
(0.0717)
-0.167**
(0.0750)
0.00750**
(0.00295)
0.0823
(0.0733)
-0.120
-0.0111
(0.0766)
0.000989
(0.0789)
0.00743**
(0.00298)
0.119
(0.0790)
0.0922
Age
Female
White
College
Income ($1000)
Constant
Age
Female
White
-0.108
(0.0695)
0.0671
(0.0847)
0.412***
(0.0601)
0.0727
(0.0910)
0.00128
(0.00361)
-0.0412
(0.0908)
0.113
(0.122)
-0.0688
(0.0942)
-0.00127*
(0.000687)
0.362
(0.220)
139
0.005
127
0.033
College
Income ($1000)
Constant
0.412***
(0.0537)
(0.106)
-0.0106
(0.0769)
-0.000555
(0.000664)
0.138
(0.183)
0.482***
(0.0545)
(0.0981)
-0.0446
(0.0823)
0.000752
(0.000918)
-0.0603
(0.169)
Observations
172
158
172
158
R-squared
0.024
0.088
0.000
0.072
Notes: Heteroskedasticity-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 4 – Alternate Specifications
(1)
(2)
Regular
Regular
Smoker
Smoker
𝛿𝑎𝑣𝑔
-0.225
(0.323)
-0.176
(0.258)
-0.252*
(0.137)
𝛽𝑞ℎ
Female
White
College
Income ($1000)
Constant
0.459
(0.288)
(4)
Regular
Smoker
-0.0364
(0.341)
𝛿𝑞ℎ
Age
(3)
Regular
Smoker
-0.00271
(0.00277)
0.0207
(0.0711)
0.130
(0.0930)
-0.161**
(0.0733)
-0.000122
(0.000938)
0.378
(0.325)
0.662*
(0.336)
0.0257
(0.272)
-0.180
(0.138)
-0.00274
(0.00267)
-0.0133
(0.0698)
0.121
(0.0919)
-0.158**
(0.0733)
-6.68e-05
(0.000951)
0.520
(0.381)
(5)
Home Well
Insulated
(6)
Home Well
Insulated
0.184
(0.386)
-0.391
(0.435)
0.172
(0.341)
0.00837***
(0.00314)
0.0387
(0.0778)
-0.143
(0.107)
-0.00198
(0.0801)
-0.000409
(0.000668)
0.380
(0.396)
Observations
164
150
171
157
165
151
R-squared
0.003
0.055
0.014
0.060
0.002
0.057
Notes: Heteroskedasticity-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
(7)
Home Well
Insulated
(8)
Home Well
Insulated
0.0994
(0.313)
0.300*
(0.172)
-0.0406
(0.409)
-0.349
(0.332)
0.229
(0.176)
0.00867***
(0.00288)
0.0915
(0.0733)
-0.121
(0.103)
0.00680
(0.0777)
-0.000616
(0.000639)
0.0708
(0.437)
172
0.020
158
0.099