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Estimation results from the happiness equation

Table 2.2 shows the results from estimating the happiness equation, for India and South Africa, as a logit model. The equations for India and South Africa were estimated on samples of 5,580 persons and 11,299 persons, respectively, and the results for both countries are shown in the table in terms of the PPs of being happy. The sample was subdivided into two groups: a dominant group and a subordinate group. The dominant group in India comprised the FCs, while for South Africa it consisted of Whites; the subordinate group in India comprised Muslims and persons belonging to the OBCs, the SCs, and the STs (hereafter, non-FCs), while for South Africa it consisted of Blacks, Coloured, and Asians (hereafter, non-White).

As discussed earlier, the predicted probabilities (of being happy) for persons in the FC and non-FC, and White and non-White, groups were obtained in Table 2.2 by assuming that the entire sample of 5,580 persons in India were, respectively, FCs and non-FCs and by assuming that the entire sample of 11,299 persons in South Africa were, respectively, White and non-White. These probabilities are shown in Table 2.2 as 84.7% for the FCs and 83.7% for the non-FCs and as 88.7% for Whites and 79.3% for non- Whites. The next column in Table 2.2 (labelled marginal probability) shows the change in the predicted probability of being happy when group identity was altered from the reference group - the FCs in India and Whites in South Africa, denoted [R] in Table 2.2 - to that of the “target” group - non-FCs in India and non-Whites in South Africa. Dividing the marginal probability by its standard error yields the associated г-value.

The г-value indicates whether the difference in the PP of being happy between the reference and the target group (the marginal probability) was significantly different from zero. The conclusion from Table 2.2 is that, in India, there was no significant difference between the FCs and non-FCs in their predicted probabilities of being happy (respectively, 84.7% and 83.7%) while in South Africa, the PP of being happy was significantly higher for Whites (88.7%) than for non-Whites (79.3%).

In a similar vein, the PPs (of being happy) for men were computed by first assuming that the entire sample of 5,580 persons in India and 11,299 persons in South Africa were male and then assuming that the entire sample, in the respective countries, was female. The results show that while in India there was no gender difference in the PP of being happy, men were significantly less likely to be happy than women in South Africa (Table 2.2: 79.4% vs. 81.6%).

The PPs (of being happy) for persons in good/very good health and in poor health were also computed by first assuming that the entire sample of 5,580 persons in India and 11,299 persons in South Africa were in good health and then assuming that the entire sample, in the respective countries, was in poor health. These probabilities are shown, for India, in Table 2.2 as 95.4% for those in good health and 70.7% for those in poor health, yielding a

Table 2.2 Determinants of happiness in India and South Africa

Predicted Probabilities of Being Very or Quite Happy

India

South Africa

Probability

Marginal

Probability

Standard

Error

z - Value

Probability

Marginal

Probability

Standard

Error

z - Value

Social/Racial Group

Forward Castes [R]/Whitcs [R]

0.847

0.887

Non-Forward Castes/Non-Whites

0.837

-0.010

0.013

-0.8

0.793

-0.094**

0.011

-8.8

Gender

Male [R|

0.841

0.794

Female

0.836

-0.005

0.012

-0.4

0.816

0.023**

0.007

3.1

Friends

Not Very/At All Important [R]

0.819

0.785

Rather Important

0.850

0.031**

0.013

2.4

0.817

0.032**

0.009

3.7

Very Important

0.836

0.018

0.013

1.3

0.809

0.023**

0.009

2.5

Religion

Not Very/At All Important [R]

0.815

0.778

Rather Important

0.827

0.012

0.015

0.8

0.809

0.031**

0.013

2.4

Very Important

0.852

0.037**

0.013

2.8

0.809

0.031**

0.012

2.6

Health (self-assessed)

Good/Very Good [R]

0.954

0.883

Fair

0.883

-0.071**

0.009

-8.1

0.815

-0.069**

0.007

-9.3

Poor

0.707

-0.246**

0.013

-19.6

0.639

-0.244**

0.011

-21.4

(Continued)

Predicted Probabilities of Being Very or Quite Happy

India

South Africa

Probability

Marginal

Probability

Standard

Error

Z -Value

Probability

Marginal

Probability

Standard

Error

Z - Value

Age

15-30 Years [R]

0.802

0.807

30-45 Years

0.854

0.052**

0.012

4.2

0.794

-0.014

0.010

-1.4

45-60 Years

0.857

0.055**

0.015

3.7

0.807

-0.001

0.012

-0.1

60+ Years

0.850

0.048**

0.020

2.4

0.841

0.034*

0.019

1.8

Marital Status

Married/Living Together

0.841

0.017

0.021

0.8

0.818

0.025**

0.010

2.5

Divorced/Separated/Widowed

0.824

0.000

0.031

0.0

0.793

0.000

0.015

0.0

Single Never Married [R]

0.824

0.793

Number of Children

None [R]

0.858

0.807

1-2

0.849

-0.009

0.017

-0.5

0.808

0.001

0.010

0.1

3+

0.824

-0.034**

0.017

-2.0

0.800

-0.006

0.012

-0.5

Education

Elementary Education (partial/ complete) [R]

0.807

0.798

Secondary Education (vocational)

0.864

0.057**

0.013

4.4

0.821

0.023*

0.014

1.6

Secondary Education (academic)

0.853

0.045**

0.014

3.2

0.806

0.008

0.009

0.9

University Education (partial/ complete)

0.860

0.053**

0.015

3.5

0.804

0.006

0.018

0.4

Economic Status

Full-Time Employed [R]

0.803

0.830

Part-Time Employed

0.827

0.024

0.019

1.3

0.777

-0.053**

0.015

-3.6

Self-Employed

0.853

0.049”

0.015

3.3

0.805

-0.025

0.018

-1.4

Housewife

0.859

0.056”

0.017

3.2

0.814

-0.015

0.015

-1.0

Student

0.875

0.072”

0.019

3.7

0.843

0.013

0.014

1.0

Unemployed

0.806

0.003

0.020

0.1

0.765

-0.065**

0.010

-6.5

Retired/Other

0.836

0.033 *

0.021

1.6

0.841

0.012

0.016

0.7

Social Class

Upper/Upper Middle [R]

0.882

0.878

Lower Middle

0.862

-0.021*

0.011

-1.8

0.855

-0.023**

0.011

-2.1

Working Class

0.803

-0.079**

0.015

-5.4

0.846

-0.031**

0.010

-3.0

Lower Class

0.769

-0.113**

0.018

-6.3

0.717

-0.160**

0.012

-13.2

Period

1994-98

0.815

-0.072**

0.016

-4.6

0.805

0.007

0.010

0.6

1998-2004

0.789

-0.098**

0.015

-6.5

0.797

-0.001

0.011

-0.1

2005-09

0.823

0.025**

0.010

2.6

2010-14 [R]

0.887

0.798

Source: Own calculations from WVS Longitudinal.

Note: Logit estimates based on 5,580 observations for India and 11,299 observations for South Africa. [R] denotes reference category. ” Marginal probability significant at 5% level; * Marginal probability significant at 10% level.

difference of 24.6 points which, with an associated г value of 19.6, was significantly different from zero. Similarly, for South Africa, Table 2.2 shows the predicted probability of being happy as 88.3% and 63.9% for those in, respectively, good and poor health and this difference 24.4 points, with an associated z value of 21.4, was also significantly different from zero. The conclusion from this is that in both India and South Africa, there was a significant difference between persons in good and in poor health in their PPs of being happy. Similarly, in both India and South Africa, the PP of being happy was significantly higher for persons in good health than for those in fair health. Last, in both countries, the PP of being happy was significantly higher for persons in fair health than for those in poor health.

The strong link between health and happiness evidenced in Table 2.2 is consistent with the findings of most researchers. Gerdtham and Johannes- son (2001) analysed a random sample of 5,000 individuals from Sweden to show that happiness increases with health. Angner et al. (2009) explored the link between health and happiness for 383 older adults in primary care centres across the state of Alabama in the US to show that very often subjective health measures (such as those used in the WVS) are better predictors of happiness than objective measures. Borooah (2006), in a study for Northern Ireland, showed that while bad health had a negative effect on happiness, the strongest link between health and happiness was through mental illness: compared to not having any health problem, mental illness directly reduced the probability of being happy by 39.8 points.

In both India and South Africa, the PP of being happy increased significantly as one moved up the social ladder, although this increase was more marked in South Africa than in India. Table 2.2 shows that the PP of persons in the lowest social class being happy was 76.9% in India and 71.7% in South Africa, rising to 88.2% in India and 87.8% in South Africa for persons in the upper middle classes. Social class is, of course, a proxy for income, and so to say that happiness increases with social class is to say that money was capable of buying happiness. This broad conclusion needs, however, to be nuanced. In both India and South Africa, while the PP of happiness rises sharply between persons in the lowest class and those in the next highest (working class) - from 76.9% to 80.3% in India and from 71.7% to 84.6% in South Africa - it rises by very little between the lower and the upper middle classes, climbing from 86.2% to 88.2% in India and from 85.5% to 87.8% in South Africa.

These results are consistent with those of Kahneman and Deaton (2010), who found on the basis of 450,000 survey responses that, beyond a certain level, more income (estimated by them as an annual income of US$75,000) led to very small increases in happiness, although it did lead to increases in life satisfaction. Social class, however, is indicative of more than income: it reflects status, position, and power in society. Islam et al. (2009), in a study for Brazil, suggested that while income was an important consideration in the prediction of happiness, the effects might be channelled through proximal lifestyle mechanisms such as how individuals perceived themselves to be placed in society and how their objective consumption patterns reflected their high status.

The acquisition of education above an elementary level increased the predicted probability of happiness in India. As Table 2.2 shows, compared with the predicted happiness probability of 80.7% for those whose highest education level was elementary education, people with secondary education - vocational (86.4%) or academic (85.3%) - and those with university education (86%) all had significantly higher probabilities of being happy. However, there was no significant difference in the PP of being happy between those whose highest level was secondary vocational and secondary academic, nor was there any significant difference between those with secondary, whether vocational or academic, and university education. In the South African context, there was no significant relation between education levels and the predicted probability of being happy. These results are again consistent with the findings of Kahneman and Deaton (2010): education had, perhaps, more to do with life evaluation than with emotional well-being.

Both in India and South Africa, social relationships - as expressed in the importance attached to friends - were significantly important in terms of the PP of happiness. Persons who thought that friends were important (either “rather important” or “very important”) had significantly higher probabilities of happiness than those for whom friends were not important. In India, the PP of being happy rose from 81.9% for those for whom friends were not important to 85% for those for whom friends were rather important while, in South Africa, the corresponding rise was from 78.5% to 81.7% (Table 2.2). Neither in India nor in South Africa was there, however, a significant difference in the PP of being happy between those who regarded friends as “rather important” and those who regarded friends as “very important”.

Demir et al. (2007), in a study of 280 persons at a midwestern university in the US, found that friendships were an important source of happiness and what mattered particularly was the quality of friendship: people were happiest when they experienced high-quality, close friendships in conjunction with best friendship. In another study, Helliwell and Huang (2013) focused on the number of friends rather than on the quality of friendship. Using a sample of 5,000 persons in Canada, they compared the effects of “real” friends and of “online” friends on happiness. Their conclusion was that while the number of real-life friends was positively correlated with happiness, the size of online networks left happiness unaffected.

In terms of religion, the PP of being happy, in India and South Africa, was lowest amongst those for whom religion was not important and highest, and significantly so, for those who regarded religion as very important. Table 2.2 shows that in India, the PP of being happy rose from 81.5% for those for whom religion was not important to 85.2% for those for whom religion was very important while, in South Africa, the corresponding rise was from 77.8% to 80.9%. In India, the PP of being happy was significantly higher for those who regarded religion as “very important” than for those who regarded it as “rather important” (85.2% vs. 82.7% in Table 2.2). In South Africa, however, there was no significant difference between these two groups in the predicted probability of being happy.

Stark and Maier (2008), in a study of 24 years of the General Social Survey for the US, found that religion was positively related to happiness but that the link between the two was primarily social rather than doctrinal and was due largely to the fact that religion provided more accessible and supportive social networks, centred on a place of worship, than did its secular alternatives. Lewis and Cruise (2006), however, pointed to a contradiction within the genre of religiosity-happiness studies: while research using the Oxford Happiness Inventory (Argyle, 1987) consistently found religiosity to be positively associated with happiness, research employing the Depression- Happiness Scale (Joseph and Lewis, 1997) consistently found that there was no association between the two. Lewis and Cruise (2006) surmised that it was because there was little theoretical guidance on the relationship between religion and happiness. While, as Stark and Maier (2008) argued, religion might provide supportive social networks or provide a purpose in life (Seligman, 1988) and hope (Soloman et al., 1991), it might also cause anxiety (Pressman et al., 1992) and promote guilt (Hood, 1992).

In the context of age, the PP of being happy in India was lowest for persons in the youngest, 15-30, age group (Table 2.2: 80.2%), and although this probability was significantly higher for the older age groups, there was no significant difference between the three older groups in their predicted probability of being happy. For South Africa, persons in the oldest age group (60+) had a significantly higher probability of being happy than those in the preceding three age groups (Table 2.2: 84.1%); there was, however, no significant difference between the three earlier groups in their PPs of being happy.11

 
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