Employment effect on happiness: Evidence from Punjab

Introduction

Happiness is usually defined as how people experience and evaluate their lives (OECD, 2013) [1] . Since most of life is spent at work, it becomes imperative to understand the role of employment and in particular workplace in shaping an individual's happiness. Employment not only ensures economic security but also results in happiness through income. The experience of unemployment is, however, the most significant cause of unhappiness (Chadi, 2014) [2] . Wolfers (2003) [3] analysed the adverse effect of unemployment over happiness and found that it was 4.7 times more than inflation's. The literature related to this aspect explains the importance of employment for subjective well-being worldwide (Helliwell, 2017) [4] . Employment seems to have a more significant effect on happiness and similar results have been observed by Oswald (1997) [5] . Nevertheless, out of the coefficient values of income and employment, employment had a larger coefficient value, implying a larger effect on happiness. Employment improves the quality of life by providing economic security, social status, a better standard of living, life goals, etc. Being employed adds to income and consumption, which makes an individual happier. Recent research also shows that employment drives happiness, which further leads to better outcomes at work, performance, and productivity (Neve & Oswald, 2012) [6] . As per Oswal (2014) [7] , happier workers are more productive. Happy employees are an asset for the company as they are more effective, recover faster from illness, have a higher life expectancy, and thus earn more. Over the years, the job concept underwent modifications, and individuals must be more dynamic, proactive, and responsible for fulfilling everyday demands in professional areas (Santos et. al., 2010) [8] . Developing and transitional countries are rarely studied in job quality analysis, and broader definitions of well-being are seldom used (Houseman, 1995) [9] ; (Goos & Manning 2007) [10] . To evaluate the quality of the job, a more comprehensive definition shall be considered for the present study, and more domains to the quality of the job shall be added.

Data, Sample & Research Methodology

To conduct the study, primary data was collected in the form of questionnaires from the state of Punjab. The data was collected from 1205 respondents aged 18-60 years from 6 state districts. The mean age of the respondents was 32.2 years. The data were collected from these selected districts using a random sample approach. To create a sample, items were chosen from each stratum after the population had been separated into several sub-populations that were all more homogenous than the whole population. According to income levels and the industry in which they work, such as agriculture, industry, and services, the population was split into urban and rural, male and female. A sample size of a total of 1205 individuals was selected according to the size of the population of the selected districts. An average sample of 200 individuals was taken from each of the 6 districts, out of which 100 were taken from urban areas and 100 from rural areas. An index of happiness has been calculated based on respondents' responses to rate their happiness levels in many areas, including well-being, life satisfaction, time usage, standard of living, etc., The results were then transformed into index scores, ranging from 0 to 10. A censored dependent variable has been provided since the happiness index ranges from 0-10. When there is left-or rightcensoring in the dependent variable, the Tobit model, also known as a censored regression model The data collected with the help of the questionnaire allows us to calculate the relationship between having a job and happiness and an association between the quality of a job and happiness. Descriptive statistics of responses for this are presented in Table 1 below: Hypothesis: Happiness is significant among various occupational groups.

Subjective well-being and employment status

Unemployment is connected with poorer levels of wellbeing in both cross-sectional and panel data. This discovery was an early mainstay of psychology (Jahoda, 1982) [11] , and has repeatedly appeared in the economic literature. Scholars suggest that unemployment lowers people's happiness in two ways. Firstly, in a direct effect, the truly unemployed have a lower sense of happiness. Secondly, in an indirect effect, a high unemployment rate makes individuals apprehensive and stressed because they are at risk of being fired in the future (Di Tella et. al., 2003) [12] . The general belief of available literature on happiness is that employment adds to income and capacity to consume in the market, thereby leading to higher happiness levels. Primary data relating the happiness index to employment status is presented in Fig 1.

Subjective well-being and employment status by gender

Literature indicates that unemployment has a devastating effect on happiness for both genders, but the impact of being unemployed is felt more strongly by males than females [13] . Females working only part-time experience a lesser fall in happiness index than males. Gender inequality in job prospects, work-life imbalance, the gender wage gap, and the existence of the glass ceiling are workplace realities that generate failures that can lower women's satisfaction at work and impair organizational happiness [14] . Among gender equality indices, a low rate of female nonagricultural work is related to higher female happiness [15] . Based on primary data, the average level of happiness of male and female respondents is presented in Fig. 2. www.extensionjournal.com Figure 2 above depicts that being employed, on average, has a higher happiness level for both males and females. Employed males show a higher average happiness level as compared to females. However, unemployment has an adverse impact on male's happiness as unemployment significantly lowers the average happiness level of males compared to their female counterparts.

Subjective well-being and employment concerning region:

The average level of happiness of people in urban and rural areas is represented in Fig. 3. Similar results are obtained for an average level of happiness derived from employment for both rural and urban areas. Employed people are generally happier, and the region of work is insignificant. But World Happiness Report 2020 [16] , established that relatively more singles, migrants, and higher cost of living found in urban areas reduce people's happiness level. 3 above shows that, on average, employed are happier than unemployed on the happiness Index, irrespective of whether they belong to urban or rural areas. But when we compare the average employed happiness level in rural areas with urban, it can be seen that rural average employed happiness is higher than urban. Previous chapters show similar results of variation of happiness with development. Development lowers the happiness level.

Type of employment and Happiness

There is insufficient evidence to draw clear conclusions about the impact of the type of work on well-being. Given the time people spend at work, this area requires more investigation. Some evidence from the UK suggests that casual employment is detrimental to SWB and that belonging to a union is beneficial to life satisfaction (Blanch flower & Oswald, 1998) [17] . Self-employment has a complex relationship with happiness (Binder, M., & Coad, A. 2013) [18] . However, in some studies, self-employment and happiness show a strong positive relationship (Oswald, 1997) [19] . World Happiness Report (2017) found that being self-employed leads to higher life satisfaction in developed countries, but self-employed also experience negative emotions such as stress and worry. Graham (2004) [20] , in a study based on 17 countries in Latin America and Russia, found that unemployed people are less happy than others. Self-employed people, meanwhile, are happier in the U.S. and Russia on average, while in Latin America, they are less happy. Being in the public sector derives a sense of security and adds to happiness. On average, people working in the government sector are happier than those working in the private sector (Arampatzi, 2018) [21] . Senior professionals report the highest level of happiness, whereas office workers report lower life satisfaction. To study this relationship, fig 5 .4 presents an average level of happiness of various types of employment. www.extensionjournal.com 4 shows that the average happiness of self-employed people is maximum. Being self-employed derives maximum happiness, followed by a sense of security from the public sector. The private sector derives the least average happiness out of the three.

Sector of Work and Happiness

Blue-collar jobs and white-collar jobs reveal a significant difference in happiness. Considering these categories of jobs, it was found that blue-collared jobs are correlated with a lower level of happiness, as labour-intensive industries, farming, fishing, and forestry, show a lower level of happiness. People working in agriculture, which is labour intensive, derive less average happiness than the services sector [22] . The average happiness level in various employment is illustrated in Fig 5. Figure 5 provides the average happiness of people in various sectors. People working in the service sector have the highest average happiness level. It is also evident from the literature that high-paying, less labour-intensive jobs add to happiness. On the contrary, the agriculture sector being labour intensive has higher average happiness as compared to the industrial sector which, shows the lowest average happiness.

Income and Happiness

As expected, it has been seen that well-paying jobs make people happier and more satisfied with their jobs and life [23] . People working high-level jobs are paid well, which impacts their happiness level. The following figures (Fig 6) present the average level of happiness at various income levels. Respondents with income above Rs.10 lakhs have the highest average happiness index, followed by respondents from the income bracket of Rs. 6-10 lakhs. Hence, betterpaying jobs lead to more income and enhance average happiness. However, the marginal difference between the happiness index between income 6-10 lakhs and above ten lakhs represents a diminishing rate of increase in happiness level with an increase in income.

Hours worked and Happiness

For other factors remaining constant, life satisfaction increases with increased working hours (Meir & Stutzer, 2008) [24] . Part-time work lowers life satisfaction compared to full-time work (Schoon & Hanson 2005) [25] . However, Dong et al.., (2021) [26] found that hours worked have an inverse U-shape curve with life satisfaction. The average level of happiness at the number of hours worked in a day is presented in Fig. 7. People working for fewer hours, i.e., between 4-6 hours, are usually considered part-time job holders. Figure 7 above shows that those in part-time jobs show the lowest average happiness, while full-time overworked people, i.e., those working above 12 hours, also have low average happiness. People working for 8-10 hours show a maximum average happiness index. The above backward bending curve shows the work-leisure relationship where happiness is also less when the number of working hours is less. Still, maximum happiness is derived at the threshold level of 8-10 hours of work. But beyond this point, with the disturbance in worklife balance and fall in leisure time, the happiness level falls. This shows a more substantial substitution effect, and after 8 -10 hours of work, leisure is treated as a normal good. That's why happiness falls beyond this point. After considering all the available parameters of the quality of job and employment status of individuals and their relationship with happiness, the correlation has been run with the happiness index, and the following results are found. Table 2 shows the correlation coefficient and level of significance of various employment parameters. As evaluated in the previous chapter, happiness and employment show a significant positive relationship. So, taking all the employed respondents from the data and evaluating its relationship with various other parameters related to employment, it is found that better-paid jobs add to income and have a positive relationship with happiness. Further looking at the quality of jobs, the hours worked show a negative relationship. It signifies that more work hours reduce one's happiness. Working in public, private and self-employed sectors shows negative relation, signifying that people working as selfemployed are the happiest. The correlation coefficient was positive, showing better happiness levels of people working in the services sector. After considering all the above domains, it becomes essential to understand the quality of the job. For this purpose, individuals rated their job satisfaction on a scale of 0-10. The correlation coefficient is positive and significant. Since the reliability and consistency of various questions in the questionnaire were checked in the last chapter, it becomes necessary to undertake the ANOVA test to run the regression model further. ANOVA determines whether the groups created by independent variable levels are statistically different by calculating whether the means of treatment levels are different from the overall mean of the dependent variable. To determine whether any of the differences between the means are statistically significant, the p-value is compared to the significance level to assess the null hypothesis. The null hypothesis states that the population means are all equal. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5 per cent risk of concluding that a difference exists when there is no actual difference. From Table 3. it can be gauged that the p-value of all the independent variables is statistically significant (p<0.05), and all these aspects likely have a significant effect on happiness. Since it has been evaluated that these independent variables do affect happiness, it is important to understand the relationship of these variables with happiness. For that, the Tobit model has been used by taking the happiness index of employed respondents as a dependent variable and income, sector of work, type of work, gender, hours worked, and job satisfaction as www.extensionjournal.com independent variables: Hypothesis: Happiness is significant among various occupational groups.

Results

The model used for Tobit is;

Happiness Index of employed = a +b2 hours worked+ b3 type of work+ b4 sector of job+b5job satisfaction 5 shows the cause-and-effect relationship between the happiness index and various employment dimensions. While looking at job quality, starting with job satisfaction again, the coefficient value 0.325 is significant at a 1 per cent significance level. However, the number of hours one works significantly negatively affects the happiness index, as the coefficient value is -0.151. The coefficient is negative for the type of work, signifying that self-employed people are happiest as the relationship is significant. Looking at the employment sector, people working in the service sector are happier than those working in the agriculture sector. Income is always regarded as impacting happiness positively for employed people. Still, while keeping other things constant, the coefficient is significant in the Tobit model. Concerning gender, the results are insignificant. The above results show that though employment is essential for happiness, job satisfaction is more important. After job satisfaction, income earned plays a very significant role. Being overworked leads to unhappy situations that disturb the work-life balance and reduce leisure time. Gender has no role to play in employment happiness, while type of work and sector of work impact happiness. Hence, there is enough statistical evidence that Happiness is significantly related to employment and among various occupational groups. So, along with providing employment, quality of work should also be considered.

Conclusion

In 2017, the Economic Advisory Council chalked out a clear road map for stepping up skill development, job creation, and enhancing resource investment. Linking economic growth indicators with social indicators and evolving a design for tracking the economy needs to be stressed. An appropriate joint strategy involving both the public and private sectors must be considered to tackle both rural and urban unemployment.

Government policies can play an essential role in generating more employment and protecting people from the damaging effects of unemployment. With the introduction of a new economic policy, India initiated the technological upgradation, leading to capital-intensive technologies. In India, faster economic growth would only be relevant if it is job-oriented, and that too in the secondary or tertiary sector as the primary sector employed people are less happy. Jobless growth will not only lead to economic and social tensions but will also impact the well-being of the individuals [27] . It is evident from the results that employment is significant for happiness. Promoting initiatives that keep people occupied and motivated is advisable. Volunteer action and social entrepreneurship are recommended, and it has been concluded in the study that self-employed people are happier. Policies should be geared towards quantity and the quality of jobs to support the well-being of the people of Punjab. Higher-paying jobs, higher job satisfaction, and not working in labour-intensive sector enhances well-being. Creating more employment against vacancies in Punjab should be the focus as, after self-employed, people in the public sector are happier than the private sector. Successful jobless assistance initiatives must address the negative consequences of unemployment on emotions of life satisfaction. It not only has economic effects, but it adversely affects society as well. As seen in the chapter, unemployed people are associated with more criminal activities, drug abuse, and suicides. Hence, ensuring that the unemployed get work does more for their happiness than compensating them through welfare alone.

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