Individual time preference determines schooling enrolment. Moreover, smoking behavior in early ages has been shown to be highly related to time preference rates. Accordingly, we use smoking at age 16 as an instrument for schooling in order to cope with ability bias in a returns to education regression. Doing this for Austrian cross-sectional data, we find no evidence of ability bias.returns to education, instrumental variables, ability bias, discount rates

Josef Fersterer

Rudolf Winter-Ebmer

English

Research Papers in Economics

 
 
 
 
Smoking, Discount Rates, and Returns to Education 
  
 
 
 
Josef Fersterer 
University of Linz, Austria 
 
 
Rudolf Winter-Ebmer 
University of Linz, Austria  
CEPR, London, IZA, Bonn and WIFO, Vienna 
 
 
January 2000 
 
 
 
 
 
 
This research was inspired by very helpful discussions with Ian Walker and comments by Andrea Ichino. 
Financial support from the European Commission under the TSER program PL980182 for the PURE project as 
well as helpful comments by seminar participants at Linz is gratefully acknowledged.   
 
 
 
 
   2
 
 
 
 
Abstract 
 
Individual time preference determines schooling enrolment. Moreover, smoking behavior in 
early ages has been shown to be highly related to time preference rates. Accordingly, we use 
smoking at age 16 as an instrument for schooling in order to cope with ability bias in a returns 
to education regression. Doing this for Austrian cross-sectional data, we find no evidence of 
ability bias. 
 
 
 
JEL: J31, I22 
 
Keywords: Returns to education, Instrumental Variables, Ability bias, Discount rates.   3
1.  Introduction 
 
The estimation of returns to education suffers from a typical problem of evaluation 
research: as an individual cannot be observed in the counterfactual situation with different 
educational attainment, wages of different persons with similar characteristics  - but different 
schooling  - have to be compared. However, forward looking individuals will choose the 
optimal amount of schooling based on potential earnings and costs of schooling, leading to an 
endogeneity problem of the schooling variable. A particular concern is "ability  bias": 
individuals with high ability may choose more years of schooling. If ability as such fosters 
earnings, the OLS returns to education can be seriously overestimated. 
Apart from controlling in the wage regression for ability by IQ and other test-based 
measures, researchers have increasingly used instrumental variables estimates to cope with 
the ability bias.
1 Often variables are used as instruments who represent taste for schooling, the 
discount rate or budget constraints of the individuals. Whereas educational costs (distance-to-
college, tuition costs, liquidity constraints caused by wars, etc.) have been extensively 
discussed, direct reliance on discount rates is rare
2. Evans and Montgomery (1994) as well as 
Chevalier and Walker (1999) suggest that smoking habits are a good predictor for discount 
rates and use them as instruments for schooling. This goes back to the analysis of Fuchs 
(1982) who found that health habits like smoking are related to implicit and explicit discount 
rates. 
In this paper we pursue a similar strategy for Austria. Our results are the first IV-
estimates on returns to education for Austria. Moreover, we use smoking habits at a very early 
stage in life -  age 16 – , an age, where actually schooling decisions are taken. 
 
 
1.  Schooling choice and ability bias 
 
In the following we present an adaptation of Card´s (1999) model for the choice of 
schooling. Individuals will invest in schooling until the marginal return in discounted future 
incomes equates to marginal costs of schooling. 
For simplicity assume that individuals earn and pay nothing while in school and get 
) (S g y =  per year thereafter. With r as the discount rate and S as years of education, 
individuals maximize the discounted present value of future earnings:  
                                                                 
1 See Card (1999) for a recent assessment of IV-studies.   4
 
(1)  ￿
¥ -
- =
S
rS
rt
r
e S g
dt e S g
) (
) (  
 
In logs we can present the problem as a utility function approach in y and S 
 
(2)  ) ( log log )) ( log( ) , ( S h y rS r S g S y U - ” - - =  
 
The optimal level of schooling is defined by the first order condition 
 
(3)  ) (
) (
) (
S h
S g
S g ¢ =
¢
 
 
Marginal returns and marginal costs can be individual specific and be determined by 
observable characteristics vectors X and Z. 
 
(4)  i i i i X b c cS b
S g
S g
x b + = ‡ - =
¢
; 0 ;
) (
) (
 
(5)  i i i i Z r r S h h g + = = ¢ ; ) (  
Here, marginal returns decline with schooling, whereas marginal costs are determined by the 
individual discount rate r i  - directly from (2). We get an explicit solution for the optimal level 
of schooling: 
 
(6)  i i i
i i
i Z X
c
r b
S n g b + + =
-
= ~ ~ *  
 
A typical Mincerian earnings function with schooling and age as a proxy for work experience 
would look like 
 
(7)  i i i i S y e a a a a + + + + =
2
3 2 1 0 age age log  
 
The potential ability bias is now evident because individuals base their schooling decision 
on individual returns b i in equation (4). If b i has a direct  – positive  - influence on earnings, 
                                                                                                                                                                                                           
2 Belzil and Hansen (1999) use a dynamic programming model to look at subjective discount rates.   5
OLS estimates will be biased upwards. The available information on smoking habits gives us 
an instrumental variables strategy, because smoking is highly correlated with the discount 
rate, but it does not influence earnings prospects directly, i. e. smoking is part of Z but not of 
X. Explaining schooling only by Z, therefore, gives us variation in schooling which is 
orthogonal to ability which eliminates the ability bias. 
Fuchs (1982) found that health habits like smoking are correlated with schooling even 
controlling for income.
3 Moreover, he showed  that the time preference rate  - obtained by a 
questionnaire with hypothetical situations involving different sums of money at different 
points in time  - correlated positively with smoking habits, even  controlling for schooling. The 
exclusion restriction  - why smoking has no direct influence on earnings  - is impossible to 
prove directly. Ault et al (1991) present evidence that smokers are not more absent from work 
than non-smokers. On the other hand, Levine et al (1997) show for NLSY data that smokers 
do in fact have between 4 -8% lower wages. Because of evident collinearity between smoking 
and schooling variables this is not a conclusive test of the exclusion restriction. Moreover, in 
our empirical analysis we use smoking at age 16, which should in any case  be uncorrelated 
with current earnings. 
 
 
2.  Data and results 
 
We use male workers from the Austrian Mikrozensus 1997 - a quarterly 1% sample of the 
Austrian population.
4 We use the parsimonious specification (7) for estimation, which 
includes no potentially e ndogenous regressors except schooling. Wages are net hourly wages. 
Table 1 presents OLS results along with several variants of instrumental variables estimates. 
The OLS model yields a return to education of 5.6% per year, which is somewhat lower than 
comparable results for Austria, because we use age here instead of potential experience. 
In column (2) we use information about smoking habits at age 16 of the individual to 
construct a dummy instrumental variable for years of schooling. 24.8% of males smoked a t 
that age. Returns to education are lower, but imprecisely estimated. Having smoked at age 16 
reduces educational attainment by 0.44 years. As our sample contains different cohorts, and 
information about smoking risks was not so widespread in the earlier  years we proceed by 
introducing a cohort dummy for those born after 1952 and interact it with the smoking 
                                                                 
3 Evans and Montgomery (1994) present results for larger data sets as Fuchs (1982) used and strongly confirm 
his findings. 
4 See Fersterer and Winter-Ebmer (1999) for a more comprehensive analysis of returns to education in Austria.   6
indicator.
5 Now we get a return to education of 5.3% - very close to the OLS result - which is 
now very precisely estimated (column (3)). Whereas teenage smokers who were born before 
1952 have 0.22 less years of schooling, smokers born after 1952 quit schooling 0.53 years 
earlier  - always compared to non-smokers. As more information on smoking risks was 
available, the decisions of the younger cohorts are more likely to reflect discount rate 
differences. 
A major problem of the instrumental variables approach is that smoking habits could in 
turn be caused by education itself. More highly educated individuals could have more 
information and therefore refrain from smoking. This problem is mitigated because we use 
age 16 as our smoking indicator, an age where most workers were still in school. Moreover, if 
there is a knowledge effect of education on smoking behavior, it should show up in changes in 
smoking among those with more education after age 16. For those who acquire more 
education but do not change smoking behavior, the knowledge effect must be limited.
6 In 
columns (4) and (5) we therefore concentrate on persons who did not change their smoking 
habits since age 16. Regardless of the inclusion of cohort interactions, we get a return to 
education of 5% which is 10 percent below the OLS return. 
For comparison, in columns (6) and (7) we present results using current smoking behavior 
as instruments. Here, five indicators for smoking intensity
7 are available. Using current 
smoking results in considerably higher returns to education. For several reasons these results 
are not reliable. If smoking is a normal good, current smoking can be influenced by current 
income. As shown by Levine et al (1997) (current) smokers may face lower wages, thus 
invalidating the exclusion restriction. Finally, current smoking behavior is not necessarily 
related to the schooling decision taken many years in the past. In fact, the over-identification 
test shown at the bottom of Table 1 rejects instrumental validity clearly in both cases. This is 
not the case for the other instruments: in columns (3) and (5) the over-id test cannot reject the 
null of validity of the instruments. 
In the bottom panel we also present further information concerning our instrumental 
variables estimates. The marginal R
2 gives the marginal contribution of our instrument(s) to 
R
2 after including the other exogenous variables. The marginal R
2 between 0.008 and 0.034 is 
relatively low, but F -tests show that the instruments are highly significant in all first stage 
regressions. Finally a Hausman exogeneity test is performed to check if the IV-results are 
                                                                 
5 The dummy for 1952 coincides also with the lengthening of compulsory schooling in 1966 from 8 to 9 years. 
We experimented also with other dates and two cohort interactions, and got very similar results. 
6 See also Evans and Montgomery (1994, p. 24). 
7 Daily, regularly, from time to time, former smoker, non-smoker. Similar results were also received by using 
daily consumption of cigarettes as instruments.   7
significantly different from the OLS estimates. In all cases except column (6) they are not 
significantly different. 
 
3.  Interpretation 
 
In our instrumental variables estimates we find slightly lower returns to education in 
Austria as compared to OLS. This is as expected, if IV methods are to correct for ability bias. 
Other researchers using also smoking as an instrument find slightly higher (Evans and 
Montgomery, 1994) and substantially higher returns (Chevalier and Walker, 1999). In some 
studies higher IV returns are reconciled by arguing that the instrument affects schooling only 
at the lower end of the schooling distribution (Card, 1999, Ichino and Winter-Ebmer, 1998),
8 
i.e.  a Local Average Treatment Effect interpretation of IV estimates (Imbens and Angrist, 
1994).  This would mean that IV is measuring the returns to education for those people  who 
change educational attainment in case they have  a higher (lower) discount rate (the 
compliers).  But this does not necessarily mean that we are measuring returns  for individuals 
with  inherently  high discount rates and that we should  expect,  therefore,  high returns. To be 
specific: IV measures the return to education by comparing wage levels of low and high 
discount rate individuals and dividing by the schooling difference of the two groups. 
There is one problem which might impair this interpretation. If the discount rates are 
sensible to borrowing constraints, individuals from low income families might have higher 
discount rates. For a fifth of our sample we have information on parental background. We 
included four dummies for father´s education in the wage equation to control for parental 
background. In a regression corresponding to our preferred specification (column (5) in Table 
1) we get returns of 0.058 (0.033 standard errors), very close to the results without family 
background.
9 Controlling for family background, therefore, does not change our basic 
conclusions, that ability bias is not a big problem in Austria. 
 
                                                                 
8 See Ichino and Winter-Ebmer (1999) for an attempt to bound returns to education from below and above by 
using different instruments. 
9 Most other specifications are imprecisely estimated because of low sample size.   8
References 
 
Ault, Richard W. et al (1991): Smoking and Absenteeism, Applied Economics 23, 743-754. 
Belzil, Christian and Jörgen Hansen (1999):  Subjective Discount Rates, Intergenerational 
Transfers and the Return of Schooling, IZA Working Paper 60, Bonn. 
Card, David (1999): The Causal Effect of Education on Earnings, in: Orley Ashenfelter and 
David Card (eds.),  Handbook of Labor Economics, Volume 3, Amsterdam (North-
Holland), forthcoming. 
Chevalier, Arnaud and Ian Walker (1999): Further Results on the Returns to Education in the 
UK, mimeo, University of Warwick. 
Evans, William N. and Edward Montgomery (1994): Education and Health: Where There´s 
Smoke There´s an Instrument, WP 4949, NBER. 
Fersterer, Josef and Rudolf Winter-Ebmer (1999): Are Austrian Returns to Education Falling 
over Time? DP 2313, Centre for Economic Policy Research, London. 
Fuchs, Victor R. (1982): Time Preference and Health: An Exploratory Study, in: Fuchs, 
Victor R. (ed.), Economic Aspects of Health, Chicago: University of Chicago Press. 
Ichino, Andrea and Rudolf Winter-Ebmer (1998):  The Long-Run Educational Cost of World 
War II: An Example of Local Average Treatment Estimation, WP 9802, University of 
Linz.  
Ichino, Andrea and Rudolf Winter-Ebmer (1999): Lower and Upper Bounds of Returns to 
Schooling: An Exercise in IV Estimation with Different Instruments,  European Economic 
Review 43/4-6, 889-902. 
Imbens, Guido and Joshua D. Angrist (1994): Identification and Estimation of Local Average 
Treatment Effects, Econometrica 62, 467-475. 
Levine, Phillip B., Gustafson, Tara A. and Ann D. Velenchik (1997): More Bad News for 
Smokers? The Effects of Cigarette Smoking on Wages,  Industrial and Labor Relations 
Review 50, 3, 493-509. 
Oosterbeek, Hessel and Hans van Opham (1999):  Schooling Choices: Preferences, Discount 
Rates, and Rates of Return, mimeo, University of Amsterdam. 
 
 
 
   9 
Table 1: Instrumental variable estimates 1997  
 
  OLS  Two-Stage Least Square Estimates 
Instruments used     Smoked at  
Age 16 
Smoked at Age 16, 
born after 1952 
Interaction 
Smoked at  
Age 16 
a) 
Smoked at Age 16, 
born after 1952 
Interaction 
a) 
Current Smoking  
b) 
Current Smoking, 
born after 1952 
Interaction 
b) 
  (1)  (2)  (3)  (4)  (5)  (6)  (7) 
Years of Schooling 
0.056 
(0.002) 
0.034 
(0.030) 
0.053 
(0.012) 
0.049 
(0.021) 
0.050 
(0.012) 
0.084 
(0.015) 
0.070 
(0.010) 
Age  0.028 
(0.003) 
0.030 
(0.004) 
0.028 
(0.003) 
0.029 
(0.004) 
0.028 
(0.004) 
0.024 
(0.003) 
0.026 
(0.003) 
Age
2 ·100 
-0.021 
(0.004) 
-0.024 
(0.005) 
-0.021 
(0.004) 
-0.023 
(0.006) 
-0.023 
(0.005) 
-0.015 
(0.005) 
-0.018 
(0.004) 
Constant  3.370 
(0.055) 
3.560 
(0.020) 
3.388 
(0.117) 
3.423 
(0.184) 
3.421 
(0.117) 
3.119 
(0.145) 
3.243 
(0.104) 
R-sq. adj.  0.276  0.254  0.276  0.266  0.266  0.237  0.267 
First Stage               
Marginal R-square
c)    0.008  0.025  0.010  0.028  0.016  0.034 
Hausman (p-value)    0.314  0.861  0.820  0.710  0.054  0.147 
Overid (p-value)    --  0.351  --  0.992  0.001  0.002 
# Obs.  4302  4302  4302  3537  3537  4302  4302 
Note: Standard errors in parenthesis 
a) without former smokers 
b) The following indicators are used: daily, regularly, from time to time, former smoker, non-smoker 
c) F-tests of joint significance of the instruments are highly significant in all cases. 

Are Austrian Returns to Education Falling over Time? DP 2313, Centre for Economic Policy Research,

Education and Health: Where There´s Smoke There´s an Instrument,

Further Results on the Returns to Education in the UK, mimeo,

Identification and Estimation of Local Average Treatment Effects,

Lower and Upper Bounds of Returns to Schooling: An Exercise in IV Estimation with Different Instruments,

More Bad News for Smokers? The Effects of Cigarette Smoking on Wages,

Schooling Choices: Preferences, Discount Rates, and Rates of Return, mimeo,

Smoking and Absenteeism,

The Causal Effect of Education on Earnings, in: Orley Ashenfelter and

The Long-Run Educational Cost of World War II: An Example of Local Average Treatment Estimation, WP 9802,

Time Preference and Health: An Exploratory Study,

Smoking, discount rates, and returns to education

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Smoking, discount rates, and returns to education

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