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The relationship between happiness, health, and socio-economic factors: results based on Swedish microdata
Journal of Socio-Economics 30 (2001) 553–557
The relationship between happiness, health, and socioeconomic factors: results based on Swedish microdata Ulf-G Gerdthama,*, Magnus Johannessonb a
Department of Community Medicine and Lund University Centre for Health Economics, Lund University, Lund, Sweden b Department of Economics, Stockholm School of Economics, Stockholm, Sweden
Abstract This paper investigates the relationship between happiness (utility) and a host of socio-economic variables in a random sample of over 5,000 individuals from the Swedish adult population. The results show that happiness increases with income, health and education and decreases with unemployment, urbanisation, being single, and male gender. The relationship between age and happiness is U-shaped, with happiness being lowest in the age-group 45– 64 years. © 2002 Elsevier Science Inc. All rights reserved. JEL classification: D60, I31, I12 Keywords: Happiness; Utility; Health; Socioeconomic factors
1. Introduction The realization that interpersonal comparisons are necessary for normative issues of economic policy has led to renewed interest in Bentham’s concept of measurable and interpersonally comparable utility (Kahneman et al., 1997). In line with this interpretation of utility there has been an increased interest in studies assessing the relationship between socio-economic factors and happiness (Theodossiou, 1998; Winkelmann and Winkelmann, 1998; Clark and Oswald, 1994). The aim of this note is to assess the relationship between happiness and socio-economic
* Corresponding author. Tel.: ⫹46-40-331969; fax: ⫹46-40-336215. E-mail address: [email protected] (U.-G. Gerdtham). 1053-5357/01/$ – see front matter © 2002 Elsevier Science Inc. All rights reserved. PII: S 1 0 5 3 - 5 3 5 7 ( 0 1 ) 0 0 1 1 8 - 4
554
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
variables, with a special emphasis on health.1 Health status is a factor that can be expected to be an especially important determinant of happiness. Since a number of socio-economic variables may be important for both health status and happiness, we estimate both the direct effect of a variable on happiness and the indirect effect through its impact on health status.
2. Data and methods Health is likely to be an important determinant of happiness (utility) and since socioeconomic factors like education and income may affect both health and happiness, we estimate both the direct and the indirect effects of the socio-economic factors on happiness. To estimate the direct effects of the socio-economic factors on happiness we estimate one equation controlling for health status (structural form model) and to estimate the total effects we estimate one model excluding health status (reduced form model). The analysis is based on data from a random sample of the Swedish population, the Level of Living Survey (LNU) from 1991 (Institutet fo¨ r Social Forskning, 1992). The total sample consists of 6,773 individuals, between the ages 18 –76 years. After correcting for missing values, the sample was reduced to 5,106 individuals for the total sample. Utility (happiness) is measured by a categorical question about life-satisfaction included in the LNU. In this question the individual’s rated their personal satisfaction on a three-point scale (the daily life is never a source of personal satisfaction, the daily life is sometimes a source of personal satisfaction, the daily life is a source of personal satisfaction most of the time.) The health status is also measured by a categorical measure. In the categorical health rating question the individual’s rated their own current health status on a three-point scale (bad health, fair health, good health). We include the following socio-economic variables as independent variables: gross annual income, age, gender, education, civil status (being single), unemployment and urbanisation. In the reduced form model we also include two proxy variables for the initial inherited stock of health capital: a dummy variable for overweight measured as a body mass index over 30, and we also include a dummy variable for if the parents or siblings of the respondent had any health problems. Since our dependent variable for happiness is an ordered categorical measure the ordered probit model is used to estimate the model (Greene, 1993).
3. Results In Table 1 we report the estimation results for the structural and reduced form happiness models. As expected health status has a highly significant positive effect on happiness. The predicted probability of being happy most of the time is 0.42 with a bad health status and 0.60 with a good health status. Happiness also as expected significantly increases with income: the predicted probability of being happy most of the time increases from 0.53 in the lowest income quartile to 0.61 in the highest income quartile. The relationship between age and happiness is U-shaped, with happiness being lowest in the age-group 45– 64 years. The
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
555
Table 1 Ordered Probit Results: Dependent Variable: Happiness. Variable Variable Constant Male Age 18-34 yearsa Age 35–44 years Age 45-64 years Age ⬎ 64 years Body mass index BMI ⬎ 30b Parents or siblings had health problems Unemployed Living alone (single) Less than high schoola High school education University education First quartile of the income distributiona Second quartile of the income distributiona Third quartile of the income distribution Fourth quartile of the income distribution Living in the countryside or in cities with ⬍30,000 inhabitantsa Living in cities with ⬎30,000 inhabitantsc Living in Stockholm, Gothenburg or Malmo Health status rated as poor healtha Health status rated as fair health Health status rated as good health 1 Number of observation Iterations completed -Log-L Pseudo R2d Pseudo R2e % correct predictions
Structural form coeff. 0.855*** ⫺0.115*** ⫺ 0.167E-01 0.652E-01 0.252*** ⫺ ⫺ ⫺0.220*** ⫺0.341*** ⫺ 0.120 0.242*** 0.944E-01* 0.852E-01 0.139** ⫺ 0.401E-02 ⫺0.761E-01* ⫺ 0.333*** 0.824*** 1.325*** 5106 20 ⫺4247.116 0.042 0.070 0.587
t-value
8.532 ⫺3.172 0.287 1.022 3.740 ⫺2.659 ⫺8.845 ⫺ 2.538 3.499 ⫺ 1.800 1.621 2.366 ⫺ 0.092 ⫺1.878 ⫺ 4.171 10.917 45.650
Reduced form coeff. 1.604*** ⫺0.124*** ⫺ ⫺0.374E-01 ⫺0.105* 0.303E-01 ⫺0.492E-02 0.147E-01 ⫺2.18*** ⫺0.365*** 0.165*** 0.307*** ⫺ 0.875E-01* 0.108** 0.206*** ⫺0.101E-04 ⫺0.934E-01** ⫺ ⫺ 1.286*** 5106 14 ⫺4339.038 0.021 0.036 0.584
t-value
23.685 ⫺3.443 ⫺0.648 ⫺1.682 0.465 ⫺0.073 0.309 ⫺2.670 ⫺9.572 3.483 4.429 1.674 2.087 3.534
0.000 ⫺2.331
46.132
* p ⬍ 0.10, ** p ⬍ 0.05, *** p ⬍ 0.01. Baseline category. b BMI is defined as: kg/m2, where kg is the weight in kilograms and m is the height in meters. c Subjects living in Stockholm, Gothenburg or Malmo are not included in this category. d Pseudo R2 ⫽ 1 ⫺ LU/LR, where LU is the unrestricted log likelihood values and LR is the restricted log likelihood values (likelihood ratio index). e Pseudo R2 ⫽ 1⫺ exp((⫺2*(LU⫺LR/n)), where LU is the unrestricted log likelihood values and LR is the restricted log likellihood values. a a
556
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
predicted probability of being happy most of the time is 0.59 in the age-group 18 –34 years, 0.57 in the age-group 35– 44 years, 0.55 in the 45– 64 years age-group and 0.60 in the age-group over 65 years. Happiness furthermore increases with education and decreases with unemployment, urbanisation, being single, and male gender. 4. Concluding remarks We have investigated the relationship between happiness and various socio-economic variables, based on data from a random sample of over 5,000 individuals in the Swedish adult population. Both a structural form and a reduced form model are estimated, to distinguish between the direct effect on utility and the indirect effect through the intervening health status variable. Our results show the importance of taking into account the fact that many variables affect utility both directly and indirectly through the effect on health status. The indirect effect was shown to be very important for the effect of variables such as income, education and being single. It is interesting to compare our results with the studies by Clark and Oswald (1994), Theodossiou (1998) and Winkelmann and Winkelmann (1998). The effects of unemployment, health status, and age are consistent between the studies. There are, however, also some important differences in the results between the studies. As predicted by economic theory happiness increase with income in this study and the study by Winkelmann and Winkelmann (1998), but Clark and Oswald (1994) and Theodossiou (1998) find no systematic relationship between income and happiness. Education has a positive effect on happiness in this study, a negative effect in the study by Clark and Oswald (1994), and no significant effect in the study by Theodossiou (1998) (education is not included in the study by Winkelmann and Winkelmann (1998)). Clark and Oswald (1994) and Theodossiou (1998) find that men are happier than women, whereas we find the opposite result (The study by Winkelmann and Winkelmann (1998) include only men). Notes 1. See Gerdtham and Johannesson (1997) for a more detailed description of the methods and results of this study.
Acknowledgments We are grateful to Professor William H. Greene for valuable comments on the econometric methods. References Clark, A.E., Oswald, A.J., 1994. Unhappiness and unemployment. Economic Journal 104, 648 – 659. Gerdtham, U.-G., Johannesson, M., 1997. The relationship between happiness, health and socio-economic
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
557
factors: results based on Swedish micro data. Working Paper No. 207, Working Paper Series in Economics and Finance, Stockholm School of Economics. Greene, W., 1993. Econometric Analysis, second edition. Macmillan Publishing Company, New York. Institutet fo¨ r Social Forskning, 1992. Level of Living Survey 1991. Stockholm University, Stockholm. Kahneman, D., Wakker, P.P., Sarin, R., 1997. Back to Bentham? Explorations of experienced utility.Quarterly Journal of Economics 112, 375– 405. Theodossiou, I., 1998. The effects of low-pay and unemployment on psychological well-being: a logistic regression approach. Journal of Health Economics 17, 85–104. Winkelmann, L., Winkelmann, R., 1998. Why are the unemployed so unhappy? Evidence from panel data. Economica 65, 1–15.
The relationship between happiness, health, and socioeconomic factors: results based on Swedish microdata Ulf-G Gerdthama,*, Magnus Johannessonb a
Department of Community Medicine and Lund University Centre for Health Economics, Lund University, Lund, Sweden b Department of Economics, Stockholm School of Economics, Stockholm, Sweden
Abstract This paper investigates the relationship between happiness (utility) and a host of socio-economic variables in a random sample of over 5,000 individuals from the Swedish adult population. The results show that happiness increases with income, health and education and decreases with unemployment, urbanisation, being single, and male gender. The relationship between age and happiness is U-shaped, with happiness being lowest in the age-group 45– 64 years. © 2002 Elsevier Science Inc. All rights reserved. JEL classification: D60, I31, I12 Keywords: Happiness; Utility; Health; Socioeconomic factors
1. Introduction The realization that interpersonal comparisons are necessary for normative issues of economic policy has led to renewed interest in Bentham’s concept of measurable and interpersonally comparable utility (Kahneman et al., 1997). In line with this interpretation of utility there has been an increased interest in studies assessing the relationship between socio-economic factors and happiness (Theodossiou, 1998; Winkelmann and Winkelmann, 1998; Clark and Oswald, 1994). The aim of this note is to assess the relationship between happiness and socio-economic
* Corresponding author. Tel.: ⫹46-40-331969; fax: ⫹46-40-336215. E-mail address: [email protected] (U.-G. Gerdtham). 1053-5357/01/$ – see front matter © 2002 Elsevier Science Inc. All rights reserved. PII: S 1 0 5 3 - 5 3 5 7 ( 0 1 ) 0 0 1 1 8 - 4
554
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
variables, with a special emphasis on health.1 Health status is a factor that can be expected to be an especially important determinant of happiness. Since a number of socio-economic variables may be important for both health status and happiness, we estimate both the direct effect of a variable on happiness and the indirect effect through its impact on health status.
2. Data and methods Health is likely to be an important determinant of happiness (utility) and since socioeconomic factors like education and income may affect both health and happiness, we estimate both the direct and the indirect effects of the socio-economic factors on happiness. To estimate the direct effects of the socio-economic factors on happiness we estimate one equation controlling for health status (structural form model) and to estimate the total effects we estimate one model excluding health status (reduced form model). The analysis is based on data from a random sample of the Swedish population, the Level of Living Survey (LNU) from 1991 (Institutet fo¨ r Social Forskning, 1992). The total sample consists of 6,773 individuals, between the ages 18 –76 years. After correcting for missing values, the sample was reduced to 5,106 individuals for the total sample. Utility (happiness) is measured by a categorical question about life-satisfaction included in the LNU. In this question the individual’s rated their personal satisfaction on a three-point scale (the daily life is never a source of personal satisfaction, the daily life is sometimes a source of personal satisfaction, the daily life is a source of personal satisfaction most of the time.) The health status is also measured by a categorical measure. In the categorical health rating question the individual’s rated their own current health status on a three-point scale (bad health, fair health, good health). We include the following socio-economic variables as independent variables: gross annual income, age, gender, education, civil status (being single), unemployment and urbanisation. In the reduced form model we also include two proxy variables for the initial inherited stock of health capital: a dummy variable for overweight measured as a body mass index over 30, and we also include a dummy variable for if the parents or siblings of the respondent had any health problems. Since our dependent variable for happiness is an ordered categorical measure the ordered probit model is used to estimate the model (Greene, 1993).
3. Results In Table 1 we report the estimation results for the structural and reduced form happiness models. As expected health status has a highly significant positive effect on happiness. The predicted probability of being happy most of the time is 0.42 with a bad health status and 0.60 with a good health status. Happiness also as expected significantly increases with income: the predicted probability of being happy most of the time increases from 0.53 in the lowest income quartile to 0.61 in the highest income quartile. The relationship between age and happiness is U-shaped, with happiness being lowest in the age-group 45– 64 years. The
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
555
Table 1 Ordered Probit Results: Dependent Variable: Happiness. Variable Variable Constant Male Age 18-34 yearsa Age 35–44 years Age 45-64 years Age ⬎ 64 years Body mass index BMI ⬎ 30b Parents or siblings had health problems Unemployed Living alone (single) Less than high schoola High school education University education First quartile of the income distributiona Second quartile of the income distributiona Third quartile of the income distribution Fourth quartile of the income distribution Living in the countryside or in cities with ⬍30,000 inhabitantsa Living in cities with ⬎30,000 inhabitantsc Living in Stockholm, Gothenburg or Malmo Health status rated as poor healtha Health status rated as fair health Health status rated as good health 1 Number of observation Iterations completed -Log-L Pseudo R2d Pseudo R2e % correct predictions
Structural form coeff. 0.855*** ⫺0.115*** ⫺ 0.167E-01 0.652E-01 0.252*** ⫺ ⫺ ⫺0.220*** ⫺0.341*** ⫺ 0.120 0.242*** 0.944E-01* 0.852E-01 0.139** ⫺ 0.401E-02 ⫺0.761E-01* ⫺ 0.333*** 0.824*** 1.325*** 5106 20 ⫺4247.116 0.042 0.070 0.587
t-value
8.532 ⫺3.172 0.287 1.022 3.740 ⫺2.659 ⫺8.845 ⫺ 2.538 3.499 ⫺ 1.800 1.621 2.366 ⫺ 0.092 ⫺1.878 ⫺ 4.171 10.917 45.650
Reduced form coeff. 1.604*** ⫺0.124*** ⫺ ⫺0.374E-01 ⫺0.105* 0.303E-01 ⫺0.492E-02 0.147E-01 ⫺2.18*** ⫺0.365*** 0.165*** 0.307*** ⫺ 0.875E-01* 0.108** 0.206*** ⫺0.101E-04 ⫺0.934E-01** ⫺ ⫺ 1.286*** 5106 14 ⫺4339.038 0.021 0.036 0.584
t-value
23.685 ⫺3.443 ⫺0.648 ⫺1.682 0.465 ⫺0.073 0.309 ⫺2.670 ⫺9.572 3.483 4.429 1.674 2.087 3.534
0.000 ⫺2.331
46.132
* p ⬍ 0.10, ** p ⬍ 0.05, *** p ⬍ 0.01. Baseline category. b BMI is defined as: kg/m2, where kg is the weight in kilograms and m is the height in meters. c Subjects living in Stockholm, Gothenburg or Malmo are not included in this category. d Pseudo R2 ⫽ 1 ⫺ LU/LR, where LU is the unrestricted log likelihood values and LR is the restricted log likelihood values (likelihood ratio index). e Pseudo R2 ⫽ 1⫺ exp((⫺2*(LU⫺LR/n)), where LU is the unrestricted log likelihood values and LR is the restricted log likellihood values. a a
556
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
predicted probability of being happy most of the time is 0.59 in the age-group 18 –34 years, 0.57 in the age-group 35– 44 years, 0.55 in the 45– 64 years age-group and 0.60 in the age-group over 65 years. Happiness furthermore increases with education and decreases with unemployment, urbanisation, being single, and male gender. 4. Concluding remarks We have investigated the relationship between happiness and various socio-economic variables, based on data from a random sample of over 5,000 individuals in the Swedish adult population. Both a structural form and a reduced form model are estimated, to distinguish between the direct effect on utility and the indirect effect through the intervening health status variable. Our results show the importance of taking into account the fact that many variables affect utility both directly and indirectly through the effect on health status. The indirect effect was shown to be very important for the effect of variables such as income, education and being single. It is interesting to compare our results with the studies by Clark and Oswald (1994), Theodossiou (1998) and Winkelmann and Winkelmann (1998). The effects of unemployment, health status, and age are consistent between the studies. There are, however, also some important differences in the results between the studies. As predicted by economic theory happiness increase with income in this study and the study by Winkelmann and Winkelmann (1998), but Clark and Oswald (1994) and Theodossiou (1998) find no systematic relationship between income and happiness. Education has a positive effect on happiness in this study, a negative effect in the study by Clark and Oswald (1994), and no significant effect in the study by Theodossiou (1998) (education is not included in the study by Winkelmann and Winkelmann (1998)). Clark and Oswald (1994) and Theodossiou (1998) find that men are happier than women, whereas we find the opposite result (The study by Winkelmann and Winkelmann (1998) include only men). Notes 1. See Gerdtham and Johannesson (1997) for a more detailed description of the methods and results of this study.
Acknowledgments We are grateful to Professor William H. Greene for valuable comments on the econometric methods. References Clark, A.E., Oswald, A.J., 1994. Unhappiness and unemployment. Economic Journal 104, 648 – 659. Gerdtham, U.-G., Johannesson, M., 1997. The relationship between happiness, health and socio-economic
U.-G. Gerdtham, M. Johannesson / Journal of Socio-Economics 30 (2001) 553–557
557
factors: results based on Swedish micro data. Working Paper No. 207, Working Paper Series in Economics and Finance, Stockholm School of Economics. Greene, W., 1993. Econometric Analysis, second edition. Macmillan Publishing Company, New York. Institutet fo¨ r Social Forskning, 1992. Level of Living Survey 1991. Stockholm University, Stockholm. Kahneman, D., Wakker, P.P., Sarin, R., 1997. Back to Bentham? Explorations of experienced utility.Quarterly Journal of Economics 112, 375– 405. Theodossiou, I., 1998. The effects of low-pay and unemployment on psychological well-being: a logistic regression approach. Journal of Health Economics 17, 85–104. Winkelmann, L., Winkelmann, R., 1998. Why are the unemployed so unhappy? Evidence from panel data. Economica 65, 1–15.