# 8. Measuring the non-monetary cost of unemployment Solutions

These are not model answers. They are provided to help students, including those doing the project outside a formal class, to check their progress while working through the questions using the Excel, R, or Google Sheets walk-throughs. There are also brief notes for the more interpretive questions. Students taking courses using Doing Economics should follow the guidance of their instructors.

## Part 8.1 Cleaning and summarizing the data

• No solution is provided.
• One way to rename the variables is given in Solution figure 8.1.
Variable New name Variable description
S002EV EVS-wave EVS-wave
S003 Country/region Country/region
S006 Respondent number Original respondent number
S009 Country abbreviation Country abbreviation
A009 Health State of health (subjective)
A170 Life satisfaction Satisfaction with your life
C036 Work Q1 To develop talents you need to have a job
C037 Work Q2 Humiliating to receive money without having to work for it
C038 Work Q3 People who don’t work become lazy
C039 Work Q4 Work is a duty towards society
C041 Work Q5 Work should come first even if it means less spare time
X001 Sex Sex
X003 Age Age
X007 Marital status Marital status
X011_01 Number of children How many children you have—deceased children not included
X025A Education Educational level respondent: ISCED-code one digit
X028 Employment Employment status
X047D Monthly household income Monthly household income (× 1,000), corrected for ppp in euros

Solution figure 8.1 Completed data dictionary.

1. Examples of answers on how some of the variables were measured are provided:
• To use reported life satisfaction in interpersonal and cross-country comparisons, we need to assume that all individuals share similar presuppositions and answer the questions in similar environments and contexts.

These assumptions are not entirely plausible. Perceived social norms, beliefs, culture, and many other characteristics vary across individuals and across countries, affecting individuals’ responses. As a result, differences in answers may reflect differences in these factors rather than the true level of satisfaction.

There are a large number of environmental factors that can influence the answers. The day of the week, the weather, and major news stories are all examples of such factors. It is impossible to control for all these factors.

Although the measure has limitations, it may be the best we can obtain.

• Employment status: Social norms, beliefs, culture, and other factors which vary across individuals and countries can affect people’s willingness to report truthfully on their employment status. Many people probably do not know accurately the technical definitions of the terms associated with employment status, resulting in misreports. Misreporting of employment status is therefore likely to be an issue.
• An example answer is given below.

• No-opinion responding: A tendency to select the response category that is most neutral in its meaning (for example ‘neither agree nor disagree’). Some people may consider the information being asked for as private and therefore will not want to give opinions. We can check for respondents who choose neutral answers for most questions.
• Primacy effects: A tendency to select one of the first response categories presented on a list. When people want to spend minimal time on a questionnaire, they may just choose the first category given for each question. We can check for respondents who choose the first category for most questions. We can include in the questionnaire similar questions with the categories ordered differently so that if individuals select the first categories even for similar questions, we would know that they are probably not paying attention.
• Socially desirable responding: Conscious or subconscious tendency to select response options more likely to conform with social norms or present the respondent in a good light. The answers reveal the values, attitudes, and beliefs of the respondents. Respondents may engage in socially desirable responding because they are afraid of the risk that their employers or potential employers may learn about their answers and penalize them. We can identify a set of answers complying with social norms in any given region and identify respondents who chose almost the same set of answers.
1. (a)(e) No solution is provided. For Excel users: Refer to Excel walk-through 8.1 for guidance on answering Question 3(a) and (e). For R users: Refer to R walk-through 8.2. For Google Sheets users: Refer to Google Sheets walk-through 8.1.
1. No solution is provided.
• No solution is provided.
• The long right tail means the mean is skewed to the right. Using deviations from the average income in this case would underestimate the living standards of many people.
• The table in Solution figure 8.2 shows the breakdown of each country’s population according to employment status. There are many possible features to comment on, for example:

• the differences in percentages of students surveyed
• the percentages in full-time employment, part-time employment, and self-employment, making a comparison between developing and developed countries
• variations in unemployment across countries.
Country Full-time Housewife Other Part-time Retired Self-employed Students Unemployed
Albania 29.42 7.42 1.50 5.50 9.08 22.08 7.33 17.67
Armenia 23.86 20.92 1.14 8.09 18.38 5.96 6.70 14.95
Austria 39.80 7.24 1.89 9.95 25.49 5.02 8.39 2.22
Belarus 57.88 2.43 1.21 6.95 18.59 3.40 6.87 2.67
Belgium 42.89 5.96 3.72 8.94 23.01 3.57 5.21 6.70
Bosnia Herzegovina 34.06 9.33 0.82 2.90 14.67 3.08 8.15 26.99
Bulgaria 46.32 2.62 0.76 2.79 31.28 5.58 2.37 8.28
Croatia 41.58 3.37 0.93 2.78 26.01 2.86 8.75 13.72
Cyprus 46.32 13.68 1.29 2.84 24.39 6.58 1.68 3.23
Czech Republic 46.56 3.06 4.66 1.68 31.27 3.82 5.43 3.52
Denmark 55.89 0.28 1.32 6.69 24.32 5.94 4.15 1.41
Estonia 50.35 4.08 2.20 5.11 28.52 3.61 3.38 2.75
Finland 52.34 1.38 3.94 5.11 22.77 6.17 3.72 4.57
France 46.83 5.59 1.94 6.04 28.78 2.76 3.13 4.92
Georgia 19.46 11.60 0.81 6.57 19.38 7.06 2.60 32.52
Germany 38.44 4.58 3.03 8.44 28.64 2.97 2.67 11.23
Great Britain 33.50 7.32 4.01 11.23 28.99 5.72 1.40 7.82
Greece 28.49 17.42 0.40 2.97 26.73 13.72 6.18 4.09
Hungary 46.39 1.20 7.21 2.00 24.04 3.53 6.57 9.05
Iceland 54.50 2.25 6.01 9.91 7.06 11.41 4.95 3.90
Ireland 41.87 19.84 1.59 9.72 13.89 4.96 1.59 6.55
Italy 32.88 8.33 0.46 9.13 23.06 13.70 7.08 5.37
Kosovo 19.57 11.65 0.52 5.23 5.83 9.41 18.00 29.80
Latvia 52.05 6.18 2.34 3.76 23.22 3.26 4.26 4.93
Lithuania 50.13 4.11 2.89 5.16 23.97 3.41 6.04 4.29
Luxembourg 51.33 9.36 1.12 7.38 15.36 3.00 9.87 2.58
Macedonia 35.74 4.34 1.24 1.71 16.74 3.72 8.53 27.98
Malta 33.84 32.33 0.68 3.84 23.42 2.33 0.55 3.01
Moldova 30.49 7.24 1.87 7.58 25.64 4.86 4.43 17.89
Montenegro 39.02 4.63 0.60 2.14 16.64 4.97 4.80 27.19
Netherlands 32.40 9.52 3.68 18.24 27.76 6.48 0.80 1.12
Northern Cyprus 31.19 19.55 2.23 5.20 8.91 8.91 13.61 10.40
Northern Ireland 30.10 10.36 4.53 8.74 29.45 3.56 1.29 11.97
Norway 53.23 2.22 6.55 9.48 12.60 8.17 7.06 0.71
Poland 41.81 6.00 0.10 3.14 28.00 5.81 7.62 7.52
Portugal 46.20 5.24 1.57 3.27 33.51 1.70 1.05 7.46
Romania 41.07 10.54 1.95 3.22 33.95 3.02 3.61 2.63
Russian Federation 54.36 5.81 2.72 5.08 23.77 1.27 2.72 4.26
Serbia 34.21 5.02 1.07 2.38 25.16 6.91 4.11 21.13
Slovakia 40.98 1.73 4.89 2.21 39.73 3.55 1.25 5.66
Slovenia 47.44 2.50 2.75 1.25 31.59 4.37 6.99 3.12
Spain 41.52 16.30 0.11 4.63 19.93 6.28 3.19 8.04
Sweden 54.82 0.38 6.60 7.36 15.36 7.23 4.06 4.19
Switzerland 48.50 6.42 3.21 14.03 21.31 2.89 1.39 2.25
Turkey 16.42 42.39 0.60 2.14 10.00 7.66 5.92 14.88
Ukraine 40.92 6.79 1.02 4.84 32.09 4.41 3.23 6.71

Solution figure 8.2 Self-reported employment status in each country (per cent of sample).

Male Female
Mean Standard deviation Mean Standard deviation
Life satisfaction 6.98 2.30 6.98 2.30
Self-reported health 3.68 0.95 3.68 0.95
Work ethic 3.68 0.76 3.68 0.76
Age 47.10 17.42 47.09 17.42
Education 3.09 1.36 3.09 1.36
Number of children 1.63 1.41 1.63 1.41

Solution figure 8.3 A summary table for the EVS data.

## Part 8.2 Visualizing the data

1. Germany is used as an example.
• Solution figures 8.4 and 8.5 provide frequency tables for work ethic in Germany for Wave 3 and Wave 4.
Range of work ethic score Frequency Percentage of individuals (%)
1.00 0 0.00
1.20 3 0.21
1.40 6 0.42
1.60 9 0.63
1.80 15 1.05
2.00 18 1.26
2.20 21 1.47
2.40 47 3.28
2.60 68 4.75
2.80 79 5.52
3.00 114 7.96
3.20 130 9.08
3.40 166 11.59
3.60 171 11.94
3.80 185 12.92
4.00 164 11.45
4.20 106 7.40
4.40 55 3.84
4.60 34 2.37
4.80 20 1.40
5.00 21 1.47

Solution figure 8.4 Frequency table for work ethic (Germany, Wave 3).

Range of work ethic score Frequency Percentage of individuals (%)
1.0 1 0.06
1.2 1 0.06
1.4 0 0.00
1.6 6 0.36
1.8 9 0.53
2.0 18 1.07
2.2 22 1.31
2.4 35 2.08
2.6 44 2.61
2.8 75 4.46
3.0 90 5.35
3.2 125 7.43
3.4 152 9.03
3.6 171 10.16
3.8 180 10.70
4.0 207 12.30
4.2 191 11.35
4.4 166 9.86
4.6 79 4.69
4.8 37 2.20
5.0 74 4.40

Solution figure 8.5 Frequency table for work ethic (Germany, Wave 4).

Solution figure 8.6 Distribution of work ethic score in Germany: Waves 3 and 4.

• The work ethic scores have increased over time, as shown by the rightward shift in the distribution in Wave 4.
Country Wave 1 Wave 2 Wave 3 Wave 4
Belgium 7.37 7.60 7.42 7.63
Denmark 8.21 8.17 8.31 8.41
France 6.71 6.77 6.98 7.05
Germany 7.22 7.03 7.43 6.77
Iceland 8.05 8.01 8.08 8.07
Italy 6.65 7.30 7.18 7.40
Netherlands 7.75 7.77 7.83 7.99
Northern Ireland 7.66 7.88 8.07 7.82
Spain 6.60 7.15 6.97 7.29
Sweden 8.03 7.99 7.62 7.68

Solution figure 8.7 Average life satisfaction across countries and survey waves.

• Solution figure 8.8 provides the line chart of average life satisfaction across countries and survey waves. Note: When producing a chart with many lines, the key produced by Excel/R/Google Sheets is often inadequate in allowing the reader to distinguish between the series. You may need to add labels to the series.

Solution figure 8.8 Line chart of average life satisfaction (wellbeing) across countries and survey waves.

• Average life satisfaction increased slightly over the survey periods for most countries. Also, instead of looking at averages, we could look at deciles, which would allow us to assess whether the changes in average life satisfaction are due to a shift in the entire distribution or an increase in reporting of extreme values.
Variable life satisfaction Work ethic
Age –0.08 0.13
Education 0.09 –0.15
Full-time employment 0.18 –0.03
Gender (= 0 if male, = 1 if female) –0.02 –0.05
Self-reported health 0.38 –0.07
Income 0.24 –0.15
Number of children –0.02 0.09
Relative income 0.19 –0.05
Life satisfaction 1.00 –0.03
Work ethic –0.03 1.00

Solution figure 8.9 Correlation between life satisfaction, work ethic and other variables.

• The correlation coefficient, ranged between –1 and 1, measures the strength and direction of a linear relationship between two variables. A negative coefficient means the two variables are negatively linearly correlated, and a positive coefficient means the two variables are positively linearly correlated. The coefficient of 0.18 between employ­ment status and life satisfaction, for example, means that people who are full-time employed tend to be more satisfied with their lives relative to those who are unemployed. The coefficients between life satisfaction and other variables have the expected sign. The results that work ethic is negatively linearly related to education, employment status, and relative income, however, are somewhat surprising.
• Solution figure 8.10 shows average life satisfaction according to employment status.

Employed respondents are more satisfied with their lives compared with the unemployed. The retired, like the unemployed, do not work, but they do not suffer nearly as large a drop in life satisfaction. This suggests that social norms that discriminate against the unemployed is a plausible explanation for the relatively low life satisfaction of the unemployed.

Average life satisfaction Employment status
Country/Region Full-time Retired Unemployed
Albania 6.63 5.81 6.07
Armenia 6.04 4.85 5.46
Austria 7.44 7.74 6.07
Belarus 6.10 5.62 5.61
Belgium 7.72 7.83 6.37
Bosnia Herzegovina 7.33 7.01 6.77
Bulgaria 6.18 4.97 4.69
Croatia 7.31 6.48 7.17
Cyprus 7.38 7.03 6.56
Czech Republic 7.30 6.89 6.07
Denmark 8.54 8.21 7.20
Estonia 6.93 6.25 4.97
Finland 7.82 8.02 5.77
France 7.20 6.97 6.24
Georgia 6.12 4.69 5.42
Germany 7.26 6.85 4.61
Great Britain 7.53 7.93 6.03
Greece 7.15 6.62 5.98
Hungary 6.65 5.89 4.86
Iceland 8.20 8.45 7.23
Ireland 7.90 7.83 7.18
Italy 7.43 7.44 6.60
Kosovo 6.30 6.04 6.78
Latvia 6.52 5.93 5.31
Lithuania 6.59 5.63 4.53
Luxembourg 7.87 8.24 5.50
Macedonia 7.19 6.67 6.61
Malta 7.70 7.76 5.95
Moldova 7.12 5.98 6.07
Montenegro 7.63 7.22 7.47
Netherlands 8.04 7.95 7.00
Northern Cyprus 6.74 6.44 5.64
Northern Ireland 7.68 7.77 7.54
Norway 8.19 8.26 8.00
Poland 7.46 6.57 7.06
Portugal 6.84 5.88 5.44
Romania 7.14 6.57 7.41
Russian Federation 6.86 5.70 6.40
Serbia 7.17 6.67 6.73
Slovakia 7.47 6.71 6.12
Slovenia 7.83 7.13 6.76
Spain 7.34 7.21 7.19
Sweden 7.88 8.17 6.52
Switzerland 8.04 8.12 5.76
Turkey 6.50 6.61 5.76
Ukraine 6.34 5.44 4.95

Solution figure 8.10 Average life satisfaction according to employment status and country.

• The calculation of the difference in average life satisfaction is provided in Solution figure 8.11.
Country/Region Difference between full-time employed and unemployed Difference between full-time employed and retired
Albania 0.57 0.83
Armenia 0.58 1.18
Austria 1.36 –0.31
Belarus 0.49 0.48
Belgium 1.35 –0.12
Bosnia Herzegovina 0.56 0.33
Bulgaria 1.48 1.20
Croatia 0.14 0.83
Cyprus 0.82 0.35
Czech Republic 1.24 0.42
Denmark 1.34 0.34
Estonia 1.95 0.67
Finland 2.05 –0.20
France 0.96 0.23
Georgia 0.70 1.43
Germany 2.65 0.41
Great Britain 1.51 –0.40
Greece 1.17 0.53
Hungary 1.79 0.76
Iceland 0.97 –0.24
Ireland 0.71 0.07
Italy 0.83 –0.01
Kosovo –0.49 0.26
Latvia 1.21 0.59
Lithuania 2.06 0.96
Luxembourg 2.37 –0.37
Macedonia 0.58 0.52
Malta 1.75 –0.06
Moldova 1.05 1.14
Montenegro 0.16 0.42
Netherlands 1.04 0.09
Northern Cyprus 1.10 0.29
Northern Ireland 0.14 –0.09
Norway 0.19 –0.07
Poland 0.40 0.89
Portugal 1.40 0.96
Romania –0.27 0.56
Russian Federation 0.46 1.16
Serbia 0.44 0.50
Slovakia 1.35 0.76
Slovenia 1.07 0.70
Spain 0.15 0.13
Sweden 1.36 –0.30
Switzerland 2.28 –0.08
Turkey 0.74 –0.11
Ukraine 1.40 0.90

Solution figure 8.11 Difference in average life satisfaction: full-time employed minus unemployed, and full-time employed minus retired.

• Solution figures 8.12 and 8.13 provide column charts showing both differences in life satisfaction.

Solution figure 8.12 Difference in life satisfaction between the full-time employed and the unemployed vs average work ethic.

Solution figure 8.13 Difference in life satisfaction between the full-time employed and the retired vs average work ethic.

• The correlation coefficient for full-time employed vs unemployed is -0.158, which indicates a weak negative correlation. Looking at the scatterplot (Solution figure 8.12), it is difficult to see a clear relationship between the two variables. The correlation coefficient for full-time employed vs retired is 0.4843, which indicates a moderate positive correlation (the gap in life satisfaction between employed and retired is positive and wider in countries with higher average work ethic).

## Part 8.3 Confidence intervals for difference in the mean

1. Choice of countries for this example:

• top third: Turkey
• middle third: Spain
• lower third: Great Britain.
Full-time Retired Unemployed
Country Average SD Count Average SD Count Average SD Count
Great Britain 7.53 1.85 334 7.93 1.98 289 6.03 2.19 78
Spain 7.34 1.70 377 7.21 1.93 181 7.19 2.14 73
Turkey 6.50 2.55 330 6.61 2.60 201 5.76 3.13 299

Solution figure 8.14 Summary table of life satisfaction, by employment status.

• The calculations are provided in Solution figures 8.15 and 8.16.
Country Difference in means SD of difference in means Number of observations
Great Britain –0.40 2.71 623
Spain 0.13 2.58 558
Turkey –0.11 3.64 531

Solution figure 8.15 Calculated values for differences in life satisfaction (full-time vs retired).

Country Difference in means SD of difference in means Number of observations
Great Britain 1.51 2.86 412
Spain 0.15 2.73 450
Turkey 0.74 4.04 629

Solution figure 8.16 Calculated values for differences in life satisfaction (full-time vs unemployed).

• The calculations are provided in Solution figures 8.17 and 8.18.
Country Difference in means SD of difference in means Number of observations CI distance to sample mean Total width of CI
Great Britain –0.40 2.71 623.00 0.21 0.43
Spain 0.13 2.58 558.00 0.21 0.43
Turkey –0.11 3.64 531.00 0.31 0.62

Solution figure 8.17 Calculated width of 95% confidence interval for differences in life satisfaction (full-time vs retired).

Country Difference in means SD of difference in means Number of observations CI distance to mean Total width of CI
Great Britain 1.51 2.86 412.00 0.28 0.55
Spain 0.15 2.73 450.00 0.25 0.51
Turkey 0.74 4.04 629.00 0.32 0.63

Solution figure 8.18 Calculated width of 95% confidence interval for differences in life satisfaction (full-time vs unemployed).

• Solution figures 8.19 and 8.20 provide column charts for the example countries showing the difference in life satisfaction and confidence intervals.

Solution figure 8.19 Difference in life satisfaction (wellbeing): full-time and retired.

Solution figure 8.20 Difference in life satisfaction (wellbeing): full-time and unemployed.

• For full-time and retired: In Great Britain, the retired have higher life satisfaction than full-time workers on average (0.4 points higher on a 1–5 scale), and this difference has been estimated with reasonable precision, so we can be fairly confident that this difference is unlikely to have occurred if really there was no difference in wellbeing between the two groups. In Turkey, the retired have slightly higher life satisfaction but this difference is both small (0.1 points) and not precisely estimated (the confidence interval is quite wide), so it is well possible that the difference we observed is consistent with there actually being no difference between the two groups. The same conclusions can be drawn for Spain, except that full-time workers have a slightly higher life satisfaction than the unemployed on average.

For full-time and unemployed: In all three countries, full-time workers have higher life satisfaction than the unemployed on average. The difference is largest for Great Britain (1.5 points on a 1–5 scale, which is considerable), and is precisely estimated, so we can be fairly confident that this difference is unlikely to have occurred if really there was no difference in wellbeing between the two groups. While we find a smaller difference for Turkey, it is estimated with precision and we come to the same conclusion as for Great Britain. For Spain, the difference is small and not very precisely estimated, so it is well possible that the difference we observed is consistent with there actually being no difference between the two groups.

Recall that 95% confidence intervals may not always contain the respective population means, so the above conclusions are never definite.

• A natural experiment can control for variables that affect both the dependent and independent variables. The method allows us to isolate the effect of employment status on life satisfaction. The ceteris paribus effect of employment status on life satisfaction, if obtained, has a causal interpretation.
• If we have data on the same individual over time, then we can calculate the difference in life satisfaction for the same person under different employment statuses, instead of comparing different individuals (as we did in this project).