The social costs of unemployment, in terms of unemployment's impact on European citizens' life satisfaction, relate strongly to unemployment duration. At any level of general joblessness, reducing long-term unemployment is more important than reducing the number of people unemployed at any point in time.unemployment; unemployment duration; life satisfaction; happiness; social costs

Carsten Ochsen

Heinz Welsch

English

Research Papers in Economics

 
 
Thünen-Series of Applied Economic Theory 
Thünen-Reihe Angewandter Volkswirtschaftstheorie 
 
 
Working Paper No. 60 
 
 
 
The Social Costs of Unemployment: Accounting for 
Unemployment Duration  
 
 
  
Carsten Ochsen 
Heinz Welsch 
 
 
 
 
Universität Rostock 
Wirtschafts- und Sozialwissenschaftliche Fakultät 
Institut für Volkswirtschaftslehre 
2006  
 
The Social Costs of Unemployment: Accounting for 
Unemployment Duration 
 
Carsten Ochsen
a), Heinz Welsch
b) 
 
 
 
 
 
 
Abstract:  The social costs of unemployment, in terms of unemployment’s impact on 
European citizens’ life satisfaction, relate strongly to unemployment duration. At any level of 
general joblessness, reducing long-term unemployment is more important than reducing the 
number of people unemployed at any point in time. 
 
JEL classification: J64, I31 
Keywords: unemployment; unemployment duration; life satisfaction; happiness; social costs 
 
 
 
a)  Department of Economics, University of Rostock, 18057 Rostock, Germany; e-mail: 
carsten.ochsen@uni-rostock.de 
b) Department of Economics, University of Oldenburg, 26111 Oldenburg, Germany; e-mail: 
heinz.welsch@uni-oldenburg.de   - 1 -
1. Introduction 
Over the past decade or so it has become a familiar approach to assess the welfare 
consequences of economy–wide phenomena in terms of their impact on subjective well-being 
(happiness, life satisfaction).
1 Especially, the happiness consequences of unemployment have 
become an important field of research.
2 According to this literature, being unemployed 
features among the strongest individual determinants of unhappiness, and the non-pecuniary 
effect of being unemployed may be larger than the effect that stems from the associated loss 
of income (see e.g. Clark and Oswald 1994, Blanchflower 1996, Winkelmann and 
Winkelmann 1998). Moreover, people’s happiness does not seem to adapt to the status of 
being unemployed (Winkelmann and Winkelmann 1998), in contrast to the adaptation noticed 
with respect to other circumstances. 
In addition to these individual effects, unemployment may act as a social bad, that is, people 
may be unhappy about unemployment even if they are not themselves out of work. For 
instance, they may worry about the possibility of becoming unemployed themselves when the 
general unemployment level is high, or they may fear that social tension may increase. 
Consistent with these and related hypotheses, an important study by Di Tella et al. (2001) 
finds a considerable effect of the general unemployment rate on self-rated life satisfaction in 
several European countries, even when controlling for the individual employment status. 
Similar evidence has been presented for Latin America (Graham and Pettinato 2001). There is 
thus not only a private, but also a social cost to unemployment. 
Given these findings, the aim of the present paper is to look in more detail into the linkage 
between general unemployment and life satisfaction. Our point of departure is the simple 
observation that any given level of the annual unemployment rate can arise from a certain 
number of persons unemployed for a short period of time or a smaller number of persons 
unemployed for a longer period. The question then arises whether or not life satisfaction is 
affected not just by the level of unemployment, but in addition by its quality in terms of how 
many people are long-term unemployed. If so, this would indicate that the fear of losing one’s 
job is more pronounced when the prospect is to stay unemployed for a long time. 
Using data for eleven European countries, 1992-2002, we find that the percentage of jobless 
people that are unemployed for more than one year affects self-rated life satisfaction in a 
                                                 
1 See Frey and Stutzer (2002) or Layard (2005) for a discussion. 
2 Other phenomena recently studied in this way include income inequality (Alesina et al. 2004), environmental 
pollution (Welsch 2002, 2006a), climate (Rehdanz and Maddison 2005), aircraft noise (van Praag and Baarsma 
2005) and the extent to which civil rights and liberties are held in respect (Welsch 2003).   - 2 -
sizable and significant way, in addition to the mere impact of the general unemployment rate. 
What seems to bother people is thus not just the risk of becoming unemployed, but especially 
the risk of permanently staying out of work. This then suggests that society may have an 
interest in reducing the share of long-term unemployment, even if this did not affect the 
overall unemployment rate. 
Our life satisfaction regressions control for the inflation rate. With respect to inflation, we 
find that introducing unemployment duration as an additional explanatory variable raises the 
unhappiness from inflation. Estimates that omit unemployment duration may thus 
underweight the unhappiness from inflation, as inflation to some extent seems to act as a 
proxy for a low risk of being long-term unemployed. 
The paper is organized as follows. Section 2 discusses the model and the data. Section 3 
presents the results. Section 4 concludes. 
2.  The Model and Data 
We consider a life satisfaction regression of the following form:  
 
kit kit i it GR it UD it IR it UR kit D TIME GR UD IR UR LS ε δ γ β α α α α + + + + + + + =    (1) 
 
where LSkit denotes life satisfaction of individual k in country i and year t and Dkit is a vector 
of socio-demographic characteristics. UR,  IR,  UD and GR are the unemployment rate, 
inflation rate, unemployment duration and the growth rate, respectively, and αUR, αIR, αUD and 
αGR the associated coefficients. The βi are country dummies, and εkit is an error term. The 
regression includes a common time trend, denoted by TIME.
3 
The data on life satisfaction and socio-demographic characteristics are taken from the 
Eurobarometer survey series. They cover the period 1992 – 2002 and refer to the following 
countries: Belgium, Denmark, France, Germany, Greece, Ireland, Italy, the Netherlands, 
Portugal, Spain and the UK. The Eurobarometer survey is a representative survey of 
approximately 1000 persons per country (Germany: 2000).
4 Given that not all of the required 
                                                 
3 Alternatively, we experimented with year dummies as well as country-specific time trends. Inclusion of year 
dummies implied near-singularity, due to the large number of dummy variables used to capture socio-
demographic characteristics. Country-specific time trends turned out to be rather uniform and not to affect our 
findings. 
4 The Eurobarometer public opinion surveys are conducted on behalf of the European Commission, DG Press 
and Communication. Each consists of approximately 1000 face-to-face interviews per Member State of persons 
aged 15 and over.   - 3 -
socio-demographic characteristics are available in all years for all countries, the regressions 
refer to 57533 individuals. 
The life satisfaction question reads as follows: "On the whole, are you very satisfied, fairly 
satisfied, not very satisfied or not at all satisfied with the life you lead." The responses are 
rated as follows: "very satisfied" = 4, "fairly satisfied" = 3, "not very satisfied" = 2, "not at all 
satisfied" = 1. 
The rates of growth, unemployment, and inflation are taken from the EU’s Annual 
Macroeconomic Data Base (AMECO).
5 They are entered in our data as percentages. The time 
frame considered is restricted to the post-1991 period because AMECO data prior to that date 
show West Germany only, not unified Germany. Unemployment duration is captured by the 
persons unemployed for more than one year as a percentage of the total number of 
unemployed people. This variable captures the risk of long-term unemployment. It is taken 
from the OECD Labour Market Statistics.
6  
Given the ordinal character of our dependent variable the model is estimated using an ordered 
probit maximum likelihood estimator. Huber/White robust standard errors are used to control 
for heteroskedasticity.  
 
3. Results 
Table 1 shows the results of several versions of the life satisfaction regression stated in equ. 
(1). The regressions include country dummies and a time trend; they control for individual 
characteristics of the respondents. In common with virtually all of the literature in happiness 
economics, being unemployed is the single most powerful individual determinant of life 
satisfaction, even controlling for income (see Table A1 in the Appendix). With respect to the 
macroeconomic variables, regression (A) shows that, over and above individual 
unemployment, the general unemployment rate significantly affects life satisfaction, as does 
inflation. Consistent with earlier findings (Di Tella et al. 2001) the weight placed on inflation 
is less than the weight placed on unemployment.  
Regression (B) introduces our indicator for unemployment duration as an explanatory 
variable, in addition to the standard formulation (A). The coefficients on all three variables – 
unemployment rate, inflation rate, and unemployment duration – are negative and significant. 
In comparison to (A), the coefficient on the unemployment rate gets reduced. Regression (B) 
                                                 
5 See http://europa.eu.int/comm/economy_finance/indicators/annual_macro_economic_database/ameco_en.htm. 
6 See http://www1.oecd.org/scripts/cde/members/lfsdataauthenticate.asp.   - 4 -
thus yields an estimate of how strongly the pure level of unemployment affects life 
satisfaction, controlling for the quality of unemployment in terms of the general duration of 
joblessness.  
Another result from including unemployment duration is that the coefficient on inflation 
increases in magnitude, compared to regression (A). A possible explanation is that (A) 
involves an omitted variable bias, in the sense that inflation to some extent acts as a proxy for 
low risk of long-term unemployment.
7 When unemployment duration is explicitly accounted 
for, the disutility from inflation is shown in a purer way and turns out to be larger.  
For the purpose of sensitivity analysis, regressions (C) and (D) augment regressions (A) and 
(B), respectively, by including the growth rate as an additional regressor. In (C), which omits 
unemployment duration, the coefficients on the unemployment and inflation rates remain 
negative and significant, while the coefficient on the growth rate is positive and significant. 
There is thus some autonomous value placed on growth. When we compare this regression 
with its counterpart (A), we see that the coefficients on unemployment and inflation are now 
smaller. However, the drop in the unemployment coefficient is more pronounced than that in 
the inflation coefficient. The sharp drop in the unemployment coefficient when growth is 
accounted for may indicate that, in (A), the unemployment coefficient to some extent captures 
the disutility from a lack of growth. The “pure” disutility from unemployment as shown in (C) 
thus appears to be smaller than the composite effect shown in (A). Overall, regression (C) 
suggests that the disutility from inflation may be - at least - of a similar magnitude as that 
from unemployment, a result already noted by Welsch (2006b).  
Finally, regression (D) augments (C) by adding unemployment duration. Similar as in (B), the 
unemployment and inflation rates as well as unemployment duration retain their negative and 
significant coefficients, while the coefficient on growth is positive and significant. The effects 
from including unemployment duration noted above turn out to be robust: In the first place, 
the coefficient on the unemployment rate gets reduced in comparison with (C), and part of the 
effect of the unemployment rate is now assigned to unemployment duration. In the second 
place, similar as above when switching from (A) to (B), the coefficient on inflation rises, 
suggesting that in (C) part of the disutility from inflation is mitigated by inflation being 
associated with lower unemployment duration. 
                                                 
7 There exists a small but significant negative correlation between long-term unemployment and inflation (r = -
0.12).   - 5 -
4. Conclusions 
This paper has examined the linkage between the general unemployment level and life 
satisfaction, placing the emphasis on the duration of unemployment. Our main finding is that 
the social costs of unemployment, in terms of general unemployment’s impact on life 
satisfaction, relate significantly and to a considerable extent to unemployment duration. It is 
thus not just the risk of becoming unemployed that people worry about, but especially the risk 
of staying long-term unemployed. This is consistent with earlier evidence that people’s 
happiness does not adapt to the status of being unemployed. 
Observing that any given level of the annual unemployment rate can arise from (i) a large 
number of persons unemployed for a short period or (ii) a smaller number of persons 
unemployed for a longer period, our results suggest that society dislikes (ii) more than (i). 
Given the same level of general joblessness, reducing long-term unemployment thus seems to 
be more important than reducing the number of people unemployed at any point in time. This 
can be viewed as a strong point in favor of increased labor market flexibility.   - 6 -
References 
Alesina, A., Di Tella, R., MacCulloch, R. (2004), Inequality and Happiness: Are Europeans 
and Americans Different?, Journal of Public Economics 88, 2009-2042. 
Blanchflower, D.G. (1996), Youth Labor Markets in Twenty-Three Countries: A Comparison 
Using Micro Data. In: D.Stern (ed.), School to Work Policies and Practices in Thirteen 
Countries, Cresskill: Hampton Press. 
Clark A.E., Oswald, A.J. (1994), Unhappiness and Unemployment, Economic Journal 104, 
648-659. 
Di Tella, R., MacCulloch, R.J., Oswald, A.J. (2001), Preferences over Inflation and 
Unemployment: Evidence From Surveys of Happiness, American Economic Review 91, 
335-341. 
Frey, B.S., Stutzer, A. (2002), What Can Economists Learn from Happiness Research?, 
Journal of Economic Literature XL, 402-435. 
Graham, C., Pettinato, S. (2001), Happiness, Markets, and Democracy: Latin America in 
Comparative Perspective, Journal of Happiness Studies 2, 237-268. 
Layard, R. (2005), Happiness: Lessons from a New Science, Penguin Press. 
Rehdanz, K., Maddison, D. (2005), Climate and Happiness, Ecological Economics 52, 111-
125. 
Van Praag, B., Baarsma, B. (2005) Using Happiness Surveys to Value Intangibles: The Case 
of Airport Noise, Economic Journal 52, 111-125. 
Welsch, H. (2002), Preferences over Prosperity and Pollution: Environmental Valuation 
Based on Happiness Surveys, Kyklos 55(4), 473-494. 
Welsch, H. (2003), Freedom and Rationality as Predictors of Cross-National Happiness 
Patterns, Journal of Happiness Studies 4, 295-321. 
Welsch, H. (2006a), Environment and Happiness: Valuation of Air Pollution Using Life 
Satisfaction Data, Ecological Economics 58, 801-813.. 
Welsch, H. (2006b), Is the “Misery Index” Really Flawed”, Discussion Paper, Department of 
Economics, University of Oldenburg. 
Winkelmann, L., Winkelmann, R. (1998), Why are the Unemployed so Unhappy? Evidence 
from Panel Data, Economica 65, 1-15. 
   - 7 -
Table 1: Main regression results, 1992 – 2002, dependent variable: life satisfaction (LS)  
 
Regression 
(A) 
Regression 
(B) 
Regression 
(C) 
Regression 
(D) 
unemployment  rate  -0.0456 -0.0378 -0.0329 -0.0287 
  (-9.944) (-7.458) (-6.053) (-5.096) 
inflation  rate  -0.0412 -0.0459 -0.0358 -0.0404 
 (-9.424)  (-10.053)  (-7.928)  (-8.375) 
unemployment  duration   -0.0068  -0.0054 
   (-3.950)    (-3.026) 
growth rate      0.0057  0.0048 
       (4.338)  (3.536) 
country fixed effects  9  9  9  9 
common time trend  9  9  9  9 
demographic control variables  9  9  9  9 
countries    11 11 11 11 
total  observations  57533 57533 57533 57533 
Pseudo-R
2  0.12111 0.12122 0.12125 0.12132 
Considered countries: EU-12 without Luxembourg; estimation method: ordered probit; t-
values in parentheses. 
   - 8 -
Appendix 
 
Table A1: Complete regression results, 1992 – 2002, dependent variable: life satisfaction (LS) 
  
Regression 
(A) 
Regression 
(B) 
Regression 
(C) 
Regression 
(D) 
unemployment  rate  -0.0456 -0.0378 -0.0329 -0.0287 
  (-9.944) (-7.458) (-6.053) (-5.096) 
inflation  rate  -0.0412 -0.0459 -0.0358 -0.0404 
 (-9.424)  (-10.053)  (-7.928)  (-8.375) 
unemployment  duration     -0.0068  -0.0054 
   (-3.950)  (-3.026) 
growth rate      0.0057  0.0048 
     (4.338)  (3.536) 
household  income  0.0620 0.0615 0.0617 0.0614 
  (35.844) (35.491) (35.638) (35.386) 
household  size  -0.0336 -0.0333 -0.0334 -0.0332 
  (-8.188) (-8.129) (-8.146) (-8.106) 
age  -0.0323 -0.0323 -0.0322 -0.0322 
  (-16.293) (-16.269) (-16.242) (-16.230) 
age
2  0.0004 0.0004 0.0004 0.0004 
  (17.042) (17.019) (17.001) (16.989) 
male  reference group 
female  0.0525 0.0526 0.0525 0.0526 
  (4.933) (4.941) (4.928) (4.935) 
education ≤ 15 years  reference group 
education > 15 ≤  19  years  0.0706 0.0712 0.0714 0.0718 
  (5.491) (5.543) (5.554) (5.585) 
education  >  19  years  0.1713 0.1722 0.1724 0.1730 
  (11.083) (11.135) (11.154) (11.184) 
education  still  0.0626 0.0624 0.0658 0.0652 
  (1.556) (1.552) (1.635) (1.619) 
single  reference group 
married  0.1201 0.1205 0.1199 0.1203 
  (7.624) (7.650) (7.610) (7.632) 
living  together  0.0241 0.0250 0.0243 0.0250 
  (0.983) (1.018) (0.992) (1.018) 
divorced  -0.2468 -0.2479 -0.2477 -0.2485 
  (-8.386) (-8.421) (-8.420) (-8.443) 
separated  -0.3265 -0.3276 -0.3281 -0.3287 
  (-7.500) (-7.529) (-7.538) (-7.555) 
widowed  -0.1548 -0.1553 -0.1565 -0.1566 
  (-6.337) (-6.356) (-6.405) (-6.409) 
employed  reference group 
unemployed  -0.4437 -0.4446 -0.4447 -0.4453   - 9 -
  (-19.546) (-19.575) (-19.590) (-19.606) 
retired  0.0214 0.0211 0.0211 0.0210 
  (1.113) (1.101) (1.100) (1.092) 
housewife  0.0430 0.0424 0.0431 0.0426 
  (2.639) (2.601) (2.644) (2.613) 
other  occupation  0.2515 0.2519 0.2489 0.2496 
  (6.488) (6.499) (6.419) (6.438) 
rural  reference group 
small  town  -0.0735 -0.0734 -0.0736 -0.0735 
  (-6.382) (-6.375) (-6.387) (-6.381) 
big  town  -0.1583 -0.1581 -0.1579 -0.1578 
  (-12.765) (-12.747) (-12.737) (-12.727) 
common  time  trend  -0.0271 -0.0201 -0.0275 -0.0219 
  (-7.088) (-4.758) (-7.204) (-5.141) 
limit  point  2  -2.4171 -2.7367 -2.2603 -2.5385 
  (-23.907) (-21.051) (-21.088) (-17.769) 
limit  point  3  -1.4549 -1.7741 -1.2978 -1.5757 
  (-14.434) (-13.687) (-12.140) (-11.057) 
limit point 4  0.3110  -0.0080  0.4684  0.1906 
   (3.090)  (-0.061)  (4.386)  (1.339) 
country fixed effects  9  9  9  9 
countries  11 11 11 11 
total  observations  57533 57533 57533 57533 
Pseudo-R
2  0.12111 0.12122 0.12125 0.12132 
Considered countries: EU-12 without Luxembourg; estimation method: ordered probit; t-
values in parentheses. 
 

(2006b), Is the “Misery Index” Really Flawed”,

Environment and Happiness: Valuation of Air Pollution Using Life Satisfaction Data,

Freedom and Rationality as Predictors of Cross-National Happiness Patterns,

Happiness, Markets, and Democracy: Latin America in Comparative Perspective,

Happiness: Lessons from a New Science,

Inequality and Happiness: Are Europeans and Americans Different?,

Preferences over Inflation and Unemployment: Evidence From Surveys of Happiness,

Preferences over Prosperity and Pollution: Environmental Valuation Based on Happiness Surveys,

Using Happiness Surveys to Value Intangibles: The Case of Airport Noise,

What Can Economists Learn from Happiness Research?,

Why are the Unemployed so Unhappy? Evidence from Panel Data,

Youth Labor Markets in Twenty-Three Countries: A Comparison Using Micro Data. In:

The Social Costs of Unemployment: Accounting for Unemployment Duration

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The Social Costs of Unemployment: Accounting for Unemployment Duration

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