The Causal Effect of Education on Sick Leave
How an increase in compulsory schooling affects future sick leave
Oscar Fredrik Erichsen
Thesis submitted for the degree of Master of Philosophy in Economics
30 Credits
Department of Economics Faculty of Social Science
University of Oslo, May 2020.
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Abstract
The sick leave level in Norway is known to be high compared to other countries with similar welfare systems. One of the most important indicators for sick leave is socio-economic standing, and education is an essential determinant for socio-economic position. Furthermore, education is positively interrelated with other socio-economic measures, like income and occupational class, which again are measures that affects the risks for sick leave. This highlights the importance for education as an indicator for sick leave. This thesis aims to understand the causal effect of increased education on long- term (>16 days) sick leave.
To do this, I assess an educational reform that was implemented in 1959, which increased compulsory schooling from seven to nine years. Using register data from Statistics Norway, I analyze the causal effect of an increase in years of education on the number of years one received payments from the Norwegian Labor and Welfare
Administration (NAV) between 2006 and 2010. The approach used is instrumental variable analysis, where the compulsory schooling reform is used as an instrument for years of education. I include fixed effects for birth year and municipality in the
regression. The analysis is conducted on both a full and a restricted sample, as well as for each gender separately. I find no significant evidence that an additional year of education affected the number of years one received payment from NAV for being on sick leave, neither for the full nor for the restricted sample. However, implementing the reform led to a significant increase in educational attainment, for both men and women.
The effect of the reform on educational attainment was especially strong for individuals in the restricted sample.
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Preface
This thesis marks the end of my five years at the University of Oslo. The writing process has been both challenging and inspiring. I would like to thank my supervisor Monique de Haan at the University of Oslo. She has contributed with valuable guidance and necessary advice. I would also like to thank Manudeep Bhuller at the University of Oslo for insight in how to assign numerical values to educational data provided by OFS.
I am forever grateful to fellow students at the Department of Economics, who have contributed with advice and cooperation during these five years.
To my friends, family members and girlfriend, thank you for your continuous support during these last 5 months. I am looking forward to spending time with you as a graduate.
Any remaining errors or inaccuracies are solely my own.
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Content
Abstract ii
Preface iii
1. Introduction 1
2. Background 5
2.1 The school reform and educational system in Norway 5
2.2 Sick leave and education 8
2.2.1 Sick leave in Norway 8
2.2.2 The causal effect of education on sick leave 11
3. Empirical approach 15
4. Data selection & descriptive statistics 18
5. Analysis 24
5.1 Results for full sample 24
5.1.1 The first-stage effect of the reform on educational attainment 24 5.1.2 The effect of an increase in years of education on sick leave 27
5.2 Restricted sample 29
5.2.1 The first-stage effect of the reform on educational attainment 30 5.2.2 The effect of an increase in years of education on sick leave 31
5.3 Test of assumptions 31
5.3.1 Instrumental relevance 32
5.3.2 Endogeneity of education 33
5.3.3 Instrumental exogeneity 33
5.4 Discussion, limitations and robustness tests 35
6. Conclusion 41
References 42
Appendix 49
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List of figures
Figure 1: Overview of implementation 7
Figure 2: Absence of at least 1 week as percentage of all employed 9 Figure 3: Costs to paid sick leave per capita and as a percentage of GDP 10
List of tables
Table 1: Years of education for each educational level 20
Table 2: Summary statistics 22
Table 3: Share of individuals who experienced the reform for each cohort 23 Table 4: Effect of the reform on educational attainment 25 Table 5: The first stage effects of the reform on educational attainment 26 Table 6: The effects from one additional year of education on number of years one
received sickness benefits 28
Table 7: Tests of robustness 32
A. 1: Results from different robustness tests of the joint sample 49 A. 2: Results from different robustness tests for only men 49 A. 3: Results from different robustness tests for only men 51
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1. Introduction
The importance of education is communicated all over the world. The United Nations (UN) have quality education for all children as one of their sustainability goals, and they state that education is “the single best investment countries can make to build prosperous, healthy equitable societies” and “education empowers people everywhere to live healthy and sustainable lives” (UN, 2020a; UN, 2020b). Michael Grossman acknowledged the value of education by stating “that years of formal schooling
completed is the most important correlate of good health” (Grossman, 2005, p. 32). In previous literature, there is a clear and documented correlation between education and health measures, often referred to as the health-education gradient. Several studies have shown that higher educated people statistically have fewer health related problems than people with less education, and to a greater extent devotes themselves to healthy
behavior, such as exercising regularly and refrain from smoking. For instance, Cutler and Lleras-Muney (2006) show that people with higher education tend to self-report better health conditions and are less likely to report problems regarding anxiety and
depression. It has also been shown that there is a significant negative correlation between education and chronic diseases such as diabetes and heart diseases, where highly educated individuals suffers less from these diseases (Fonseca, Michaud and Zheng, 2019).
One aspect of the public health, which is highly relevant for government,
companies and employees, is sickness absence. The financial and personal impact that may occur due to sickness absence are tremendous. Due to absence from work, the employer could suffer from costs associated with replacement staff, reduced quality of services and low productivity, while costs for the employee from sickness absence could be reduced future earnings and increased probability of early retirement (Whitaker, 2001; Markussen, 2012; Salonen, Arola, Nygård, Huhtala & Koivisto, 2003). The
Norwegian Labour and Welfare Administration (NAV) spent NOK 42,362 million on sick leave compensation in 2019 (NAV, 2020). It is therefore important to reduce workers’
risk for sickness absence. Ziebarth & Karlson (2010) points out that the healthiness of individuals is positively correlated with sickness absence. Bad health reduces each worker’s capability of working, which again increases sick leave. The witnessed
2 correlation between higher education and better health therefore indicates that higher education could help to prevent sick leave.
Sick leave comes into play due to a composition of personal and work related factors. For instance, Kangas, Muotka, Huhtala, Mäkikangas & Feldt (2015) examines how differences in the ethical culture at the work place affects sickness absence. They found that a higher perception of strong ethical culture at work was associated with a decrease in the number of absence days due to own illness. Ybema & van den Bos (2010) show that distributive justice within the organization led to a decrease in sickness
absence, while Virtanen, Kivimäki, Elovainio, Vahtera & Ferrie (2003) shows that increased job security as a result of permanent rather than fixed term employment led to higher sick leave. Others have pointed out that key socio-economic measures like occupational class, income and education are strong predictors of sickness absence. For instance, Piha, Laksonen, Martikainen, Rahkonen & Lahelma (2009) shows that high levels of these three measures of socio-economic position were consistently correlated with lower sick leave rates. Both education and occupational class turned out to be strong independent determinants of sick leave after excluding any interrelated effects between the three measures.
If sick leave is determined by factors such as job satisfaction, work culture and job security, then the problem is related to how organizations and industries are
designed. However, if sick leave is a reflection of differences in socio-economic standing or healthiness, then it can be considered an inequality problem with severe effects on nations’ economy and the public health. Understanding the determinants of sick leave is therefore of high importance to policy makers, organizations and individuals, so that they are able to implement changes to prevent sickness absence and not experience the financial and personal costs that may occur due to high sick leave. If education
attainment is a strong preventer of sick leave, then the returns to education could be more substantial than first believed. This would therefore be an incentive for policy makers to reallocate resources to increase the educational attainment of individuals to reduce their risk of sickness absence.
This study concentrates on estimating the causal effect of years of education on sick leaves longer than 16 days. If higher educated men and women tend to engage in healthier behavior, reduce their exposure to factors that predicts sickness absence,
3 suffer less from illness, stress and depression, then it is reasonable to believe that they would have lower levels of sick leave than those with less education. To be able to
identify a possible causal effect of education on sick leave, this thesis uses an educational reform that was implemented in Norwegian municipalities in the 1950s and 1960s. The reform led to an exogenous increase in compulsory schooling from seven to nine years and the municipalities had to implement the reform before 1975 (Lie, 1973). This led to variation in the length of the compulsory schooling for same cohorts between
municipalities and variations in compulsory schooling for cohorts within each municipality. This thesis uses the last two cohorts that completed their compulsory schooling before the reform was implemented and the two first cohorts that
experienced the reform in each municipality. By using the compulsory schooling reform as instrument for years of education, I estimate the causal effect of an additional year of education on the number of years an individual received sickness benefits between 2006 and 2010.
This thesis finds no significant effects of increased education on the number of years one received sickness benefits, for neither a full sample nor a restricted sample.
The results are consistent even for various robustness test like different definitions of the dependent variable, for longer spell of years and controlling for disability pensions and retirement pensions. The analysis do find substantial evidence that implementation of the reform had significant impact on educational attainment. The effects are stronger for the restricted sample, implying that the effects of the reform were strongest at the bottom of the educational distribution.
This thesis contributes to the literature by investigating the effect of increased education on sick leave. There is broad research on the association between higher education, especially as a measure of socio-economic position, and reduced sick leave, but there are no research, to the best of my knowledge, that have used the increase in compulsory schooling from seven to nine years as a tool for exploring differences in sickness benefits. Since the most impact of the reform on educational attainment came at the lowest levels of education, then this thesis can hopefully shed some light on the effects of education on sick leave for low educated.
The thesis has the following structure. Section 2 consists of two main parts.
Firstly, I will elaborate on how the education system was both prior to and after the
4 reform was implemented. Then, I will present the sick leave levels in Norway and
discuss the possible ways through which increased education might affect sickness absence. Following this, I will present the instrumental variable approach and discuss the necessary assumptions, in addition to the presentation of the technical set up of the analysis. The dataset, the descriptive statistics and the definition of the sample is
presented in Section 4, while Section 5 presents the results, discussions, robustness tests and limitations of the study. In the final section, the results is summarized and a
conclusion is provided. The statistical software that is used to analyze the research question is STATA 16.
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2. Background
2.1 The school reform and educational system in Norway
Before 1959, children started their compulsory schooling the year they turned seven and stayed until the year they turned 14. After completing the 7th year of
schooling, the students could either continue their education at junior high (“Realskole”) or the continuation school (“Framhaldsskolen”), or they could drop out of the school system altogether. Junior had a more theoretical approach and prepared the children for further studies at high school and university level, while the continuation school was more practically orientated and prepared the children for vocational training.
In 1959, the Norwegian Parliament legislated the law of an increase to nine years of compulsory schooling. The reform did not only bring structural changes to the
educational system, but also changes in the curriculum. The children would still start school the year they turned seven, but the potential dropout age was postponed to the year they turned 16. The most common way to structure the new educational system was six years in elementary school before three additional years in secondary school.
The local authorities was given the authority to decide themselves if they wanted to implement the new reform. (Telhaug, 1970; Lie, 1974). To incentivize implementation of the reform in the municipalities, the government provided subsidies to municipalities in need. This could be paying a share of one’s salary to new teachers or being responsible for some of the increased costs related to expansion of school transportation. The subsidies were primarily for municipalities with poor economy and municipalities who had suffered great damages during World War II and therefore were in greater need for renovations of their schools (Kirke- og undervisningsdepartementet, 1959).
Before the reform took place, it was only possible to continue academic studies if you finished junior high, which forced children into taking an important decision
regarding their future at the age of 13-14. After completing compulsory schooling, children had to choose between continuation school, junior high or starting to work, and an astonishing 2/3 of all children chose to leave school and start working after
completing the their seven years in school (Næss, 1989). Substituting junior high and continuation school with two additional years of compulsory schooling postponed the
6 decision between further education and dropping out. This made it possible for all
children to continue their education after the 9th year of schooling, also for those who had not completed junior high (Telhaug, 1970). Thus, the increase in compulsory schooling was also believed to reduce both economic and social inequalities (Briseid, 2009). Since the reform also brought a common curriculum for all schools and mandated that each municipality should provide compulsory schooling, it is also believed that the reform increased the average quality of schooling in Norway (Black, Devereux &
Salvanes, 2005). The increased quality of schooling was especially important for municipalities in the northernmost part Norway, which suffered great damages during the war, and hence was in greater need of an improved schooling system (Næss, 1989).
It took ten years from the government legislated the law of nine years of compulsory schooling in 1959, before the government chose to make the new reform compulsory for the municipalities. The local authorities was given up until 1975 to implement nine years of compulsory schooling (Telhaug, 1991). At the time when the reform was made mandatory in 1969, most of the municipalities had already initiated the reform. Since the municipalities themselves decided when they wanted to
implement the new reform, the educational system varied between municipalities during this period. While only 123 municipalities had implemented the reform in 1964, this number had grown to 379 five years later (Telhaug, 1991). The timing of
implementation for all municipalities in this study is displayed in figure 1 below, where we see the total number of municipalities who implemented the reform for each year1.
1 28 municipalities started experimenting with their educational system between 1954 and 1959 by for example increasing the compulsory schooling to nine years. It is therefore some municipalities who had implemented the new educational system before the reform was implemented in 1959. (Lie, 1974).
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Figure 1: Overview of implementation2.
Because of how the new educational system was implemented, the Norwegian educational system was divided into two different systems for almost 15 years. One system where the children had seven years of compulsory schooling, and one where the children had nine years of compulsory schooling, before deciding whether to continue their education or simply drop out. The decisive factors in determining what system a child would participate in were their year of birth and the municipality in which they grew up.
The earliest cohort that could be affected by the reform after it was implemented in 1959 were those who were born in 1947, started school in 1954 and were born in a municipality that implemented the reform in 1960. The latest cohort that could have gone through their compulsory schooling and not be affected by the increase in
compulsory schooling, were born in 1958, started in school in 1965 and completed their compulsory schooling in 1972. The exogenous effect that the reform had on educational attainment naturally splits the children into treatment and control groups. The
treatment group consists of children who obtained nine years of compulsory schooling while the control group consists of the ones who only had seven years of compulsory schooling.
2 The figure represent the implementation of municipalities that are included in this thesis, and is therefore not representative for the actual implementation for Norwegian municipalities.
0 10 20 30 40 50 60 70
1958 1960 1962 1964 1966 1968 1970 1972
Number of municipalities
Year of implementation
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2.2 Sick leave and education
2.2.1 Sick leave in Norway
To deal with high levels of sickness absence from work, many governments have imposed policies aiming to reduce it (Whitaker, 2001). One of the most important measures that has been implemented in Norway is the intentional deal for including work life (IA). The first agreement came in 2001, and it has been renegotiated four times since then (The Norwegian government, 2019a). The latest agreement was reached upon in 2019. Its main purpose is to create a better and healthier work environment, reduce the sick leave and make it easier to get back to work after long period of absence due to sickness or injuries (The Norwegian government, 2019a). However, even though the Norwegian government has spent a lot of effort and money on the IA-agreement and other measures to prevent sickness absence and increase job satisfaction, it has not yet been witnessed a substantial reduction in the sickness absence. According to Statistics Norway, the sickness absence rate has only come down from 6.9% in 2001 to 5.8% in 2019 (Statistics Norway, 2020a).
Since Norway has such a high level of sickness absence, there has been a significant amount of research devoted to comparing it to that of other countries, as a way of understanding why Norway is standing out. In 2013, the OECD published a report on mental health and sick leave where they state that Norway has the highest sickness absence compared to the other countries, even after controlling for differences in methods for measuring sickness (OECD, 2013). These results are supported by a report published by Proba on behalf of the Ministry of Labor and Social affairs, who concludes that the differences cannot be explained by differences in measuring, maximal length of sick leave or other country-specific rules for sick leave (Proba, 2014). Statistics Norway more recently studied the differences between Norway and six other European
countries with similar welfare systems, and found that that the absence level in Norway was greater than the other countries (Statistics Norway, 2019). The results were still consistent after controlling for different country-specific characteristics like average age of the workers, number of disabled people in the workforce and how long it took before a sick person was declared incapacitated and therefore removed out of the potential
9 workforce. A comparison of the sick leave levels between Norway and the six other countries are presented in figure 2 below.
Figure 2: Absence of at least 1 week as percentage of all employed (Statistics Norway, 2019)
As a consequence of the high level of sickness absence from work, Norway also has high costs pertaining to paid sick leave. According to the state budget for 2020, the Norwegian government plans to spend NOK 39.9 billion on paid sick leave, which is an increase of NOK 2.7 billion compared to the budget for 2019 (The Norwegian
government, 2019b). Figure 3 shows that the cost level, both measured as costs per capita and as a percentage of GDP is significantly higher in Norway compared to the same six countries in the mentioned report by Statistics Norway.
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Figure 3: Costs to paid sick leave per capita and as a percentage of GDP (Eurostat, 2020)
Since both the level and the expenditures of sick leave are substantially higher in Norway than in other similar countries, there is likely room for more efficient solutions to deal with the high sick leave problem. If there is in fact a genuine causal effect of increased education on sickness absence, increasing the years of compulsory schooling or subsidizing education in other ways may be one way to reduce the high level of sick leave that Norway has today, and thus reduce the associated expenditures. If this causal effect of education on sick leave exists, then the returns of education could be higher than first estimated. Not only would an increase in educational attainment have monetary returns from increased earnings in the future, but it would also have
monetary benefits for the government and companies by reducing the costs related to sickness benefits, lost productivity and replacing workers. It would therefore be an incentive for politicians to shift public spending to incentivize educational attainment since this could be more efficient in both improving the educational level and reducing the risk for sickness absence. However, if there is no causal effect, then the government should reallocate their spending to other measures that could prevent sick leave. The possible causal effect of education on sick leave may come through several different channels, and in the following section, I will elaborate on some of the possible ways the causal effect can proceed.
11 2.2.2 The causal effect of education on sick leave
As previously mentioned, socio-economic measures are highly important
determinants for sick leave. If higher education increases the socio-economic status for each individual, then it is reasonable to assume that higher education also affects absence from work due to sickness. Mastekaasa (2016) examines sick leave in both the public and private sector in Norway. He shows that educational differences between those sectors are one of the reasons behind differences in the estimated level of sick leave. He points out that the public sector in general have a higher levels of education compared to the private sector, and that sick leave dramatically decreases with higher education. Differences in educational levels would most likely also have different effects on short-term sick leave than long-term. The probability of long-term absence due to sickness is significantly higher for individuals with primary schooling as highest
completed level of education compared to individuals with a master’s degree, according to Mastekaasa (2015). However, he finds no association between differences in
educational attainment and the probability for short-term sickness absence.
Education is typically obtained at early stages of adulthood and could therefore have a strong impact on other measures of one’s socio-economic position that are most likely determined at later stages in life. Piha et. al (2009) shows that education is interrelated with both occupational class and income while Bhuller, Mogstad and Salvanes (2017) finds that an additional year of education increases the expected lifetime earnings of an individual. At the same time, specific occupations require certain levels of education, and educational attainment does therefore strongly affect the occupational class that individuals end up in. Education is therefore arguably the most important determinant for socio-economic position. The fact that obtaining education at a young age is decisive for future socio-economic standing, at the same time as socio- economic position is an important predictor for the risk of leave, shows the importance of education as a determinant for sick leave. This is strengthened by the results of Hoff et. al (2018). They found that Norwegian men who had not completed upper secondary education reported higher rates of health-related absence.
Other likely determinants for sick leave are work-related factors. Exposure to more physical work at the workplace, like uncomfortable working positions, heavy
12 lifting and pulling, increases the risk of long durations of sick leave (Lund, Labriola, Christensen, Bültmann and Villadsen, 2006). A study conducted on the general working population in Norway showed that the risk of being exposed to physical work and awkward working positions were substantially lower for higher educated individuals (Sterud, 2013). This evidently led to fewer long-term sick leave for high-educated individuals than for low-educated individuals.
Another work-related factor that could affect sickness absence is the level of stress experienced at work. A French study showed that work-related stress was the main driver behind almost half of all absence caused by psychiatric factors (Melchior et.
al, 2005). Lunau, Siegrist, Dragano and Wahrendorf (2015), analyses the correlation between education and work stress by using a sample from sixteen European countries.
Their main finding illustrates a consistent negative correlation between education and experienced stress at work. The fact that physical work and work-related stress are important determinants for sick leave, and that higher education minimizes the possibility of exposure to these two determinants, makes it reasonable to assume that higher education also should lead to reduction in sickness absence.
Two other determinants for sick leave could be the feeling of social support and control over your own life, both at the work place and in private. Väanänen et. al (2003) shows that low support from co-workers led to an increase in the frequency of long sick leave periods for men, while low support from supervisors was associated with higher risk of long sickness absence for women. They also find that low levels of control over their own schedule and how the work should be conducted was associated with higher risk of sickness absence. This is consistent with the findings in North, Syme, Feeney, Shipley and Marmot (1996), where low levels of control over your own life and social support were correlated with higher rates of sickness absence. According to Ross and Wu (1995), individuals with higher levels of education tend to have higher levels of social support and more control over their life. This indicates again that higher education will reduce the effects of these determinants on sick leave.
Educational attainment is also highly correlated with different measures of health that could affect sick leave. For example, lower educated individuals tends to smoke more, exercise less, and be more risk prone and impatient (Pampel, Krueger & Denney, 2010; Perez-Arce, 2017; Jung, 2015). It has been shown that higher educated individuals tend to have more knowledge about the damages that occur from smoking and excessive
13 alcohol consumption, and therefore reduce their overall intake (Cutler and Lleras-
Muney, 2006). These are all factors that most likely would lead to generally better health for higher educated individuals compared to lower educated, and Amiri and Behnezhad (2020) show that high levels of alcohol consumption increases the
probability of sick leave. Higher educated individuals could therefore have lower risk of sick leave due to lower consumption of alcohol. The theory that health affects sick leave is strengthened by the findings in Andrea et. al (2003) and Roelen, van der Pol,
Koopmans and Groothoff (2006). They show respectively that the presence of long-term diseases (for example asthma, diabetes, ulcer and migraine) is a strong predictor for sick leave for at least a month, and that health complaints like concentration problems and nervous complaints is a predictor of sickness absence even without the presence of a chronic disease.
Furthermore, a study conducted on young Swedish men estimated the effect of obesity on future sick leave. They found that obesity and overweight were positively correlated with the risk of sick leave, especially for sickness absence of longer duration (Neovius, Neovius, Kark and Rasmussen, 2010). This conclusion is supported by a qualitative review conducted on thirteen different studies (van Duijvenbode,
Hoozemans, Poppel and Proper, 2009). They concluded that being overweight predicts long terms of sickness absence, while obesity in general was a significant indicator for long term sick leave. Davison et. al (2015) shows that children who left school before the age of 16 in Northern Ireland chose to eat junk food more frequently compared to the ones who stayed in school, and consumption of junk food is positively correlated with obesity (Currie, Vigna, Moretti and Pathania, 2010). Since higher educated tends to have better health because of lower consumption of both alcohol and junk food, it is therefore reasonable to believe that higher educated individuals would have a lower risk of sick leave due to alcohol-related problems and obesity. If education also reduces the risk of both long-term diseases and general health complaints, then higher educated individuals would also have a lower risk of long durations of sickness absence than individuals with low education would.
Some have argued that sick leave could be used as a global measure for the health of individuals, but the assessment of sick leave as a measure of health is controversial.
Since it is possible that the risk of sick leave is determined by work culture and job
14 satisfaction, it could therefore be a measure of job security, social cohesion and
contentment at work. However, WHO defines health as a “state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (WHO, 2020). If absence from work were a state without complete physical, mental and social well-being, then sickness absence would, by definition, be an indicator of ill health.
Marmot, Feeney, Shipley, North & Syme (1995), who found a strong association between ill health and sick leave, support this view. The association is particularly strong for long-term absence. Additionally, Kivimäki et. al (2003) finds that men and women with multiple cases of medically certified absence had higher risk of mortality than those with less certified absence. Moreover, medically certified sick leave predicted mortality at least as well as other established measures of health, like self-reported health or longstanding physical illness. Vahtera, Pentti and Kivimäki (2004) contributes by showing that sick leave and sick days are strong predictors for mortality, especially for mortality due to cardiovascular diseases, cancer, alcohol and suicide. These studies indicate that sick leave can in fact be considered as a measure of health.
It is however important to be cautious when attempting to use sick leave as a global measure of health. Differences in social security between countries would most likely have an effect on the sick leave rate and it would therefore not be a comparable measure across countries. It is also important to remember that sick leave can be an indicator for bad health at the same time as sick leave could improve the health of individuals. Taking out sick leave could reduce the recovery period from certain health problems, as well as minimizing the possibility of infecting other individuals. Kumar, Grefenstette, Galloway, Albert and Burke (2013) showed that staying home from work reduced the burden of influenza illness, while Kivimäki et. al (2008) finds that low levels of sick leave led to an improvement in health for workers who originally reported poor health. It is therefore important to keep in mind that sick leave could be a measure of bad health and illness at the same time as it can improve health.
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3. Empirical approach
As previously mentioned, education is one of the most important measures of socio-economic position, which again is one of the most important determinants of sick leave. Since education interrelates with other socio-economic measures as well, such as occupational class and income, it is hard to conclude that an effect of higher education on sick leave is in fact a causal effect due to higher education and not effects from increasing other socio-economic measures. It is also possible that there are factors that affect years of education and an individual’s sickness absence. This could for instance be attitudes, personal characteristics, or the characteristics and socio-economic status of their parents, which all could make people more prone to take on higher education as well as having low levels of sick leave. Due to the witnessed correlation between healthiness and sick leave, there is also a possibility that the effect of education on sick leave could suffer from the same existence of simultaneous causality as between education and health. Higher educated individuals tend to have better health, but healthier individuals tends to obtain higher education. Since better health is associated with lower sick leave, there is a possibility that higher educated individuals have lower risks of sick leave at the same time as individuals with low risk of sick leave obtains higher education.
An ordinary least square (OLS) estimate would not recognize the potential effects from increased other socio-economic measures, omitted variables and simultaneous causality. This could lead to the OLS-estimate of the effect of education on sick leave being upward-biased, because some of the estimated effect could in fact capture the effect of sick leave on education, or that the estimate is catching up effects of omitted variables from the error term of the dependent variable. One way to minimize these problems is to use an instrumental variable (IV) analysis, and the sharp exogenous increase in compulsory schooling that occurred because of the reform provides a good opportunity to estimate the causal effect by using IV.
Using the compulsory schooling reform as an instrument in estimating the causal effect of education on the number of years one received sickness benefits for, makes sense for several reasons. Since the reform was implemented at different years in different municipalities, there is an exogenous variation in people’s education level
16 depending entirely on the year and municipality in which they were born. The reform led to potential dropouts after the initial seven years of compulsory schooling to at least stay in school for an additional two years, and thereby increasing their educational attainment. The design of the IV-analysis makes it possible to compare children who were not exposed to the reform with children who did, and thereby potentially
discovering a causal effect of education on sick leave. Since which year the reform was implemented varied between municipalities, I was able to include fixed effects for both municipality and birth year. Unobserved variables across municipalities and cohorts most likely had an effect on both educational attainment and the level of sick leave for individuals and including fixed effects in the regression cancels out these potential effects.
This specific reform has been used before in several studies on different subjects in the past. Black et. al (2005) used the educational reform to study if there is any causal effect of parental education on children’s education. Aakvik, Salvenes and Vaage (2010) uses this reform as an instrument to study the returns to education. It has also been used to estimate the effects of increased education on lifetime earnings and on the internal rate of return (Bhuller et. al, 2017). The education reform is also considered a relevant instrument with respect to research on health. Health related studies, like Leuven, Plug and Rønning (2016) and Grytten and Skau (2017), use the reform to estimate the causal effect of education on the probability of receiving periodontal treatment and various cancer outcomes, respectively. These studies did not report any problems with weak instruments, which strengthens the belief that the reform works as a valid instrument.
One could argue for the possibility that the timing of implementation is related to specific municipality characteristics, which again could affect the estimation. If the implementation of the reform is correlated with specific characteristics at municipality level, it would have been possible for families to move to municipalities who
implemented the reform at an earlier stage and the increase in compulsory schooling could then not be considered exogenous. Black et. al (2005) ran a regression where they controlled for such municipality specific characteristics (average earninge, educational levels and unemployment rates for example), and found that there were no systematic relationship between these characteristics and the time of implementation of the
17 reform. In another study, Aakvik et. al (2010) used various municipality specific data, such as age, population, average parental income, level of education, and the
unemployment rate, and found that there were no significant patterns between the time of implementation and these variables. This makes it reasonable to assume that the year each municipality implemented the reform was exogenous. Considering how the
government also provided economical subsidies to allow the municipalities to offer better schooling possibilities, it also limited the economic differences between the rich and poor municipalities, which underscores this assumption.
This thesis will use the method of two-staged-least-squares (2SLS) to estimate the effect of years of education on the number of years one received payments from NAV for sick leave. The first-stage regression is a regression on equation (2) and the second- stage is a regression of equation (1)3. Since the assumption is that the instrumental variable is uncorrelated with the error term and have no direct effect on the dependent variable, the effect from increasing compulsory schooling on the number of years one received sickness benefits for, will only be visible through the effect of increased
education. This would be the true causal effect of education on sick leave. The empirical approach is summarized by the following two equations:
(1) Sickleaveijk = β0 + β1Years_educationijk + β2Genderijk + δj + λk + ɛijk
(2) Years_Educationijk = ϒ0 + ϒ1Reformjk + ϒ2Genderijk + δj + λk + νijk
In equation (1), Sickleave denotes the number of years between 2006 and 2010 in which an individual received sick leave payments from NAV. Years_education denotes the length of an individual’s education and Gender is a binary variable equaling one for women, and zero for men. Reform is a binary variable, which equals one for every individual who experienced the reform, and zero for everyone else. δj and λk control for fixed effects at municipality level and birth year, respectively, and the subscripts j and k denotes birth cohort and municipality, respectively. Since we are interested in the causal effect of education on sick leave, our parameter of interest is β1.The method of 2SLS addresses the problem of bias that an OLS-regression of equation (1) would suffer from.
3 To complete the IV-analysis, the command “ivregress” in Stata 16 is used. This command automatically provides correct standard errors at the second stage.
18
4. Data selection & descriptive statistics
The aim of this section is to describe and present the data and the sample that is used for this study. The data that has been used comes from two sources, and they will be presented in order. The main data source is register data provided by Statistics Norway. In each dataset, individuals are recognized by their personal ID-number, which conjoin different observational variables for each person. The first data set consists of population statistics, and provides information about each person’s time and place of birth, relationship status and relatives, among others. The second data set contains information about income, wealth and financial support from the government, which in turn makes it possible to see how much each individual received from the government due to sick leave between 2006 and 2016, in addition to payments for being disabled or retired. The third data set contains educational statistics, and shows, among other things, the highest level of completed education for any individual in the years between 1980 and 2018, following a specific system for classification
In this thesis, I have chosen to look at individuals in the last two cohorts that went through the old educational system and the first two cohorts that experienced the reform in each municipality. Therefore, the data set contains individuals who are born between 1944 and 1960. Individuals who did not have available data on received payment for sick leave for all these five years were excluded. Further, individuals who received retirement pensions or disability pensions during these five years are also excluded from the analysis.
I have decided to look at the number of years any given person received sickness benefits from NAV from 2006 to 2010. Since the youngest individuals are born in 1944 and the usual retirement age in Norway is 67 (people born in 1944 turned 67 in 2011), I chose to use 2010 as the latest year (Statens Pensjonskasse, 2020)4. This is to minimize the number of subjects in the population who have retired, as this would make them ileligible for sickness benefits from NAV after their retirement. Myhr, Haugan, Lillefjell and Halvorsen (2018) finds that non-completion of secondary education is strongly correlated with the probability of receiving disability pensions. Thus, by excluding
4 The retirement age were workers can take their state pension, is flexible in Norway and varies from 62 – 75 years. However, 67 years is the normal retirement age (Trading Economics, 2020).
19 individuals who received disability benefits, I would therefore most likely exclude
relatively more individuals who would have been at the bottom of the educational distribution. This could reduce the effect of education on sick leave and the estimate could be understated. The sample most likely contains some individuals who are
unemployed and therefore cannot receive benefits for sickness absence. Since a greater share of the unemployed in Norway are individuals with low levels of education, the estimate would understate the effect of education on sick leave (Statistics Norway, 2020b). It is also possible that the sample contains individuals who are not even in the labor force. This also imposes a threat to the precision of the estimates.
Regarding paid sick leave, a person has to be registered on sick leave for at least 16 days before NAV starts paying out sickness benefits. Before that, the employer is responsible for paying the sickness benefits to its employees. However, it is only
possible to be registered on sick leave for a maximum length of one year (NHO, 2020). If the sick leave period exceeds one year, then the individuals end up in a different benefit program before potentially receiving disability benefits. The dependent variable in this thesis is defined as the number of years between 2006 and 2010 that an individual received sickness benefits from NAV. Every year one individual received payment for sick leave are assigned a value of one, while a year is assigned the value zero if the individual did not receive sickness benefits from NAV that specific year. Receiving payments from NAV for sick leave on two different occasions within the same year would still only count as one. This could also lead to the estimate being understated. The dependent variable is the sum of all years in which an individual received payments from NAV for sick leave. Since you have to be on sick leave for more than 16 days to be eligible for sickness benefits from NAV, this study does not contribute with any
estimation of the effect of education on shorter terms of sick leave.
The register data for education from Statistics Norway is divided into different education categories, where each category reflects the highest level of completed
education. The system reflects the schooling system and the categories are given a value from zero to nine following Statistics Norway’s definitions. The different categories define whether someone have no education, primary school, lower secondary school, vocational school, a college degree with a duration of four years or less, a college degree with a duration of four years or more, or a Ph.D. degree. These educational levels are
20 then converted into years of education, meaning that those who did not experience the reform are assigned seven years for their compulsory schooling, while those who did experience it are assigned nine years for their compulsory schooling.
Table 1 shows specifically how many years that are assigned to each level of completed education. The column “Before” represents the individuals who completed their compulsory schooling before the reform was implemented in their municipality, and the column “After” represents those who completed it after the implementation. The number of years that is assigned to each education category follows the NUS-2000 code of Statistics Norway. However, that code includes an additional education reform from 1994, which increased compulsory schooling with one additional year. Each category is therefore assigned with one year less compared to the NUS-2000 code5. The highest level of completed education is registered for all individuals who live in Norway and is updated once a year, thereby minimizing the possible threat of measurement error. I have used the highest reported level of completed education by 2004 for all the individuals included in this study. Note that continuation school and junior high are assigned no value in the last column. This is because these two educational levels were substituted by the new compulsory schooling and thus did not exist after the reform was implemented.
Table 1: Years of education for each educational level
5 Educational category 6 and 7 are for simplicity assumed to be respectively a three-year bachelor’s degree and five-year master’s degree.
Years of education Level of completed
education Before After
0 No education 0 0
1 Primary school 6 6
2 Lower secondary school 7 9
2 Continuation school 8 -
3 Junior high 9 -
3 Upper secondary, basic 11 11
4 Upper secondary, final
year 12 12
5 Vocational school 13 13
6 Bachelor’s degree 15 15
7 Master’s degree 17 17
8 Ph.D. 20 20
21 It is possible that the years of education that are assigned to each level in this study may not reflect the true number of years of education for each individual. It is possible to complete the different levels in fewer or more years than what is assigned here, but the conversion from highest completed level of education to years of education is based on how many years you normally would have to use to complete each level of education. Individuals who either had undefined or unavailable data for their
educational attainment were removed.
The second part of the data is information about the implementation of the reform in different municipalities. The information comes from applications and letters saved at The National Archives in Oslo6. For each municipality, there is information about when they applied for acceptance to implement the reform, which schoolyear they wanted to start with the new educational system and for which grades the reform would apply. The numbering of municipalities in the archive is similar to Statistics Norway’s numbering system. This makes it possible to link the implementation of the reform to each individual, thereby finding out whether they experienced the reform or not depending on year and municipality of birth.
From the information in The National Archives, I successfully obtained data from 392 municipalities regarding when the reform was implemented and what individuals were included in the first cohorts to experience it. The municipalities where I did not find any information about the implementation of the reform were removed from the study. The same goes for the municipalities where I was unable to connect the reform data to the place of birth in the register data. In the end, I was successfully able to link the reform and individual data together for 387 municipalities. I created a binary variable that equals one for each individual who experienced the reform, and zero for everyone else. The youngest individuals to experience the reform were born in 1946 and started their compulsory schooling in 1953 in a municipality that implemented the reform in 1959. The oldest individuals who went through compulsory schooling without being affected by the reform were born in 1958 in municipalities which did not
implement the reform before 1969.
6 The information is found in the archive “RA/S – 1587 – Kirke- og Undervisninsdepartementet, Forsøksrådet for skoleverket”. Underlying files are “RA/S – 1587/F/Fc – Kommunearkivet – 9. Årig skole, tilrådinger og avtaler»
and «RA/S – 1587/F/Fd – Innføring av 9-årig skole. Ordnet etter kommunenr. Ca 1959 – 1968».
22 The final data consists of municipalities which implemented the reform from 1959 to 1970, and summary statistics are presented in table 2. In total, there is register data for 122,909 individuals who are born between 1944 and 1960. There are 57,670 women and 65,239 men in the dataset, giving an equal distribution of gender. The average education are 12.269 years, which is just above high school level. Men have a slightly higher average than women, both in the full and restricted sample.
Table 2: Summary statistics Full sample
n = 122,909
Restricted sample n = 24,036 Women
n = 57,670
Men n = 65,239
Women n = 12,366
Men n = 11,670 Average years received
payment for sick leave
1.682 (1.725)
1.175 (1.531)
1.729 (1.761)
1.51 (1.67) Individuals who received
payment for sick leave = 0
23,617 (40.95%)
35,220 (53.99%)
5,054 (40.87%)
5,225 (44.77%) Average years of
education
12.131 (2.681)
12.42 (2.808)
8.452 (0.945)
8.349 (1.079)
Note: The parenthesis in the first and third row shows the standard deviation of the average. The parenthesis in the second row is the percentage share within each gender.
Table 3 below shows the distribution of individuals between cohorts and the share of individuals within each cohort that experienced the reform. In total, there were 58,671 individuals who completed their compulsory schooling before the reform was implemented and 64,238 children who completed nine years of compulsory schooling. It is worth mentioning that no children who were born in 1944 and 1945 experienced the reform, and that all children born in 1959 and 1960 experienced it.
23 Table 3: Share of individuals who experienced the reform for each cohort
All
(1) Non-treated
(2) Treated
(3)
Cohort: (% of all) (% of all)
1944 68 68 (100.00) 0 (0.00)
1945 1,820 1,820 (100.00) 0 (0.00)
1946 3,788 3,695 (97.54) 93 (2.46)
1947 4,166 1,687 (40.99) 2,429 (59.01)
1948 5,404 1,547 (28.63) 3,857 (71.37)
1949 5,650 3,814 (67.50) 1,836 (32.50)
1950 8,036 6,511 (81.02) 1,525 (18.98)
1951 10,850 6,675 (61.52) 4,175 (38.48)
1952 17,011 9,950 (58.49) 7,061 (41.51)
1953 17,196 9,531 (55.43) 7,665 (44.57)
1954 17,078 6,189 (36.24) 10,880 (63.76)
1955 15,455 4,715 (30.51) 10,740 (69.49)
1956 8,653 1,824 (21.08) 6,829 (78.92)
1957 5,453 582 (10.67) 4,871 (89.33)
1958 1,793 63 (3.51) 1,730 (96.49)
1959 510 0 (0.00) 510 (100.00)
1960 28 0 (0.00) 28 (100.00)
N 122,909 58,671 (47.74) 64,238 (52.25)
Note: The numbers in the parenthesis is the percentage share within each cohort.
24
5. Analysis
First, I will present the first-stage effects of the reform on educational attainment.
Then, I present the OLS-estimate of years of schooling on years of received payment for sick leave between 2006 and 2010. This is done to set a benchmark for comparison.
Following this, I present the results from the IV-regression. The analysis is also conducted on a restricted sample of those with nine years or less of education, before the robustness, results and limitations of the analysis are discussed. Throughout the analysis, all standard errors are clustered at municipality level. All analyses is conducted on both men and women jointly and on each gender separately. This is done to control for possible gender-specific differences in sick leave or other omitted variables that might affect each gender differently.
5.1 Results for full sample
5.1.1 The first-stage effect of the reform on educational attainment
To show the results of the implementation of the new educational reform on years of education, I start by presenting how the distributions of individuals for each educational level changed from experiencing the reform. The changes are presented in Table 4. The share of individuals with nine years or less of education drastically
decreased after the reform was implemented. Before the reform was implemented, 22.44% of all individuals had nine years or less of education compared to 16.93% for those who experienced the reform. The share of individuals who have upper secondary basic or upper secondary final as their highest level of completed education increased with just above 5.00% as a result of implementing the reform.
Note that there are individuals registered with zero years of education in both groups, and that there are individuals who completed less than the compulsory schooling. This could reflect the possibility that not everybody complied with the new law. Another reason could be misreporting or troubles pinpointing exactly when each municipality initiated the reform, for instance if some municipalities spent years gradually implementing the new system. It is therefore possible that there are
individuals who did not experience the reform, but are registered as someone who did.
The existence of individuals registered with zero years of education is a threat to the
25 Table 4: Effect of the reform on educational attainment
Note: Mean is the average years of education for each specific column, and the corresponding parenthesis is the standard error. N = 122,909.
Non-
treated Treated Male Female
(% of all) (% of all) Non-
treated Treated Non-
treated Treated Years of
education
(% of non- treated)
(1)
(% of treated)
(2)
(% of non- treated)
(3)
(% of treated)
(4)
(% of non- treated)
(5)
(% of treated)
(6)
0 46
(0.04) (0.08)
91 (0.07) (0.14)
(0.07) 23 63
(0.18) 23
(0.08) 28 (0.09)
6 1
(0.00) (0.00)
1 (0.00) (0.00)
0
(0.00) 0
(0.00) 0
(0.00) 1
(0.00)
7 4,521
(3.68) (7.71)
0 (0.00) (0.00)
2,369
(7.61) 0
(0.00) 2,152
(7.82) 0
(0.00)
8 4,103
(3.34) (6.99)
0 (0.00) (0.00)
2,091 (6.71)
0 (0.00)
2,012 (7.31)
0 (0.00)
9 4,493
(3.66) (7.66)
10,781 (8.77) (16.78)
1,682
(5.40) 5,442
(15.96) 2,811
(10.21) 5,339 (17.71)
11 13,090
(10.65) (22.31)
16,368 (13.32) (25.48)
6,442
(20.68) 7,582
(22.24) 6,648
(24.15) 8,785 (29.14)
12 10,920
(8.88) (18.61)
13,164 (10.71) (20.49)
6,977
(22.40) 8,359
(24.52) 3,943
(214.33) 4,805 (15.94)
13 16,936
(1.58) (3.30)
2,469 (2.01) (3.84)
1,233
(3.96) 1,673
(4.91) 703
(2.55) 796 (2.64) 15
14,372 (11.69) (24.50)
16,056 (13.06) (24.99)
6,527
(20.95) 7,157
(20.99) 7,845
(28.50) 8,899 (29.52)
17 4,760
(3.87) (8.11)
4,836 (3.93) (7.53)
3,492 (11.21)
3,458 (10.14)
1,268 (4.61)
1,378 (4.57)
20 430
(0.35) (0.73)
472 (0.38) (0.73)
312
(1.00) 356
(1.04) 118
(0.43) 116 (0.38) Mean 12.105
(0.012) 12.449
(0.01) 12.282
(0.017) 12.546
(0.014) 11.904
(0.018) 12.337 (0.014) Total 58,671
(47.74) 64,238
(52.26) 31,148
(47.74) 34,091
(52.26) 27,523
(47.72) 30,147 (52.28)
26 the precision of the estimates in this study. However, the total number of deviations is quite low, and therefore poses a small risk to the estimates.
Table 5: The first stage effects of the reform on educational attainment Dependent variable: Years of education
Full sample All
(1) Male
(2) Female
Panel A: (3)
Reform 0.310***
(0.039) 0.198***
(0.057) 0.437***
(0.05)
Gender -0.299***
(0.04) - -
N 122,909 65,239 57,670
Years of education < 10 All
(1)
Male (2)
Female (3) Panel B:
Reform 1.209***
(0.045) 1.256***
(0.056) 1.434***
(0.051)
Gender 0.136**
(0.014) - -
N 24,036 11,670 12,366
Note: *** p<0.01, ** p<0.05 and * p<0.1. Gender shows the estimated of being a female (Gender = 1). The standard errors are robust and clustered at municipality level. Fixed effects for municipality and year of birth are included in the regression.
The first-stage estimations of the effect of experiencing the reform on years of education are reported in Table 5 Panel A with robust standard errors in the
corresponding parenthesis. There is a significant increase of 0.310 years of schooling by experiencing the reform.
There are two main points from implementation of the reform that are worth discussing. Firstly, the effect of experiencing the reform on years of education is precisely estimated. The t-statistic of the first-stage regression of experiencing the reform on years of education is in excess of eight. The strength of the t-statistic indicates
27 that the reform is a strong instrument for years of education. Secondly, the
implementation of the reform had most of its impact on the lower end of the educational distribution. At the highest levels of education, there are barely any changes in the distribution of individuals for each educational level. The first-stage estimation is also conducted on each gender separately, and the estimated effect of experiencing the reform on years of education for men and women are reported in column 2 and 3 in Panel A in Table 5. The effect of the reform on educational attainment for men is an increase of 0.198 years, whereas if for women is 0.437 years. These effects are also precisely estimated with t-statistics at 3.45 for men and 8.75 for women.
5.1.2 The effect of an increase in years of education on sick leave
First, I begin with an OLS-regression of equation (1) to set a benchmark for the rest of the study. The results are presented in columns 1-3 Panel A in Table 6. One additional year of education leads to a statistically significant decrease of -0.062 of years of paid sick leave. The OLS-regression is also performed on each gender separately. For men, the OLS-estimated effect from one additional year of schooling is -0.095 years of received sick leave benefits. This is a bit stronger than the estimated effect of -0.022 for women. Both estimates are statistically significant. However, as discussed previously, due to the existence of interrelationship between education and other socio-economic measures, possibility of simultaneous causality and the risk of omitted variables, these estimates could be biased and the reported effect is most likely too strong. Therefore, we cannot yet fully trust the regression results conducted by OLS.
The IV-estimates are reported in columns 4-6 in Panel A Table 6. The estimated effect of one additional year of education on the number of years in which one received payment for sick leave, is 0.056. However, the estimate neither significant, nor very precise. As the OLS-regression, the IV-analysis is also conducted on each gender. The effect of one more year of education is 0.203 for men and -0.008 for women. These results indicates that there is a positive association between years of education and sick leave for men and a negative association for women. However, both estimates are insignificant and imprecise. Overall, there seems to be no significant causal effect of increased education on the number of years between 2006 and 2010 that one received payment from NAV for being on sick leave.
28 The fact that the estimated effect of years of education on years received payment for sick leave was significant negative in the OLS-regression and shifted to insignificant in the IV-analysis for supports the suggestion that an IV-analysis was necessarily. The OLS-estimate were believed to capture effects from omitted variables and simultaneous causality. It was therefore expected that the estimated effect would be stronger
compared to the IV-estimate.
Table 6: The effects from one additional year of education on number of years one received sickness benefits
Dependent variable: Years of received payment for sick leave
Panel A: Full sample
OLS IV
All
(1) Male
(2) Female
(3) All
(4) Male
(5) Female (6) Years of
education -0.062***
(0.003) -0.095***
(0.003) -0.022***
(0.004) 0.056
(0.072) 0.203
(0.154) -0.008 (0.076) Mean 12.284
(0.008) 12.42
(0.011) 12.131
(0.011) 12.284
(0.008) 12.42
(0.011) 12.131 (0.011) Reform 64,238
(52.23%) 34,091
(52.26%) 30,147
(52.28%) 64,238
(52.23%) 34,091
(52.26%) 30,147 (52.28%)
N 122,909 65,239 57,670 122,909 65,239 57,670
Panel B: Years of education < 10
OLS IV
All (1)
Male (2)
Female (3)
All (4)
Male (5)
Female (6) Years of
education -0.055 ***
(0.012) -0.059***
(0.013) -0.052***
(0.022) -0.007
(0.036) -0.025
(0.048) -0.003 (0.057) Mean 8.402
(0.007) 8.349
(0.01) 8.469
(0.009) 8.402
(0.007) 8.349
(0.01) 8.469 (0.009) Reform 10,873
(45.24%)
5,505 (47.17%)
5,368 (43.41%)
10,873 (45.24%)
5,505 (47.17%)
5,368 (43.41%)
N 24,036 11,670 12,366 24,036 11,670 12,366