Antecedentes de Investigación Sobre el Tema

An attempt was made to analyse the correlation between education and objective poverty as determined by MPCE, as well as that between education and subjective wellbeing. An econometric analysis was conducted using ordered probit regression. There were two dependent variables. The first was MPCE quintile: (1) lowest MPCE

group to (5) highest MPCE group. The second was the subjective assessment of relative household wealth: (1) lowest to (4) highest.

In order to ensure comparability across estimations, explanatory variables were kept exactly the same as MPCE and other indices in this chapter: employment ratio, LPG, out-remittance, household size, asset index, house index, male ratio, debt, and microfinance participation. Education-related variables also remained the same: (1) average education level of household members of 15 years and above; (2) household head’s years of education; (3) years of education of most highly educated member of household; (4) proportions of household members having completed primary school, middle school, secondary school, higher secondary school, and tertiary education respectively; and (5) proportion of household members having completed at least primary school.

Table 6-17 shows the results of the analysis. In terms of MPCE quintile, the result is very similar to the MPCE regression analysis (Table 6-6). Employment ratio, LPG, out-remittance, asset index, house index, debt, and microfinance participation all have a significant positive correlation with expenditure, while a significant negative correlation with expenditure is shown amongst larger households. With regard to the education variables, average household education level, household head’s education, most highly educated household member’s education, the proportion of household members having completed secondary school, and proportion of household members having completed at least primary school are all significantly positive.

In contrast to MPCE quintile, some coefficient signs relating to subjective assessment of relative wealth differ slightly. Debt does not have a positive correlation with relative

wealth (the coefficient is negative but statistically insignificant) but a significant positive correlation with MPCE. Similarly, household size has a significant negative correlation with MPCE while it does not have such a significant correlation with relative household wealth. The coefficients for out-remittance, asset index, and microfinance participation are also positive with statistical significance. It thus seems that tangible resources such as consumer durables, other assets, and participation in a microfinance scheme are closely related not only with monetary poverty but also subjective assessment of relative wealth.

Education variables in subjective assessment of relative wealth are positive and largely statistically significant. In this regard, there is little difference in terms of coefficient sign between MPCE quintile and subjective assessment of relative wealth. This implies that education is positively correlated with both indices. This result confirms the correlation between household level of education and expenditure/income, as well as that between education and relative wealth, which together indicate that the proportion of those having completed at least primary school – secondary school in particular – has a significant correlation with relative wealth at household level (see Equations 7 to 10).

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Table 6-17 Ordered probit estimates for MPCE quintiles and subjective assessment of relative wealth

Dependent variable Eq (1) Eq (2) Eq (3) Eq (4) Eq (5) Eq (6) Eq (7) Eq (8) Eq (9) Eq (10) Employment ratio 1.1545 *** 0.2467 1.2244 *** 0.3873 1.2172 *** 0.3635 1.0753 *** 0.1304 1.0461 *** 0.0934 (0.3447) (0.2958) (0.3545) (0.3003) (0.3468) (0.2950) (0.3425) (0.3016) (0.3401) (0.2981) LPG 0.6098 *** -0.0270 0.6483 *** 0.0477 0.5656 *** -0.0871 0.6097 *** 0.0453 0.6047 *** 0.0438 (0.1400) (0.1309) (0.1384) (0.1296) (0.1412) (0.1355) (0.1427) (0.1300) (0.1403) (0.1277) Out-remittance 0.7439 *** 0.6043 *** 0.7638 *** 0.6315 *** 0.7627 *** 0.6380 *** 0.7560 *** 0.7034 *** 0.7707 *** 0.7147 *** (0.1794) (0.1661) (0.1811) (0.1631) (0.1798) (0.1644) (0.1821) (0.1659) (0.1783) (0.1632) Household size -0.4003 *** 0.0131 -0.3916 *** 0.0259 -0.4171 *** -0.0147 -0.4192 *** -0.0113 -0.4263 *** -0.0163 (0.0462) (0.0432) (0.0465) (0.0440) (0.0465) (0.0426) (0.0473) (0.0439) (0.0473) (0.0432) Asset index 0.0131 *** 0.0184 *** 0.0135 *** 0.0187 *** 0.0136 *** 0.0189 *** 0.0133 *** 0.0196 *** 0.0134 *** 0.0194 *** (0.0028) (0.0034) (0.0028) (0.0036) (0.0027) (0.0034) (0.0027) (0.0034) (0.0027) (0.0033) House index 0.0023 *** 0.0009 0.0024 *** 0.0010 * 0.0022 *** 0.0008 0.0028 *** 0.0014 ** 0.0029 *** 0.0015 ** (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) Male ratio 0.3108 0.1956 0.3409 0.2790 0.2550 0.1388 0.3302 0.2865 0.3032 0.2679 (0.3589) (0.3599) (0.3572) (0.3536) (0.3652) (0.3664) (0.3652) (0.3672) (0.3591) (0.3609) Debt 0.4777 ** -0.1525 0.4975 ** -0.1195 0.4853 ** -0.1411 0.4479 ** -0.1410 0.4536 ** -0.1456 (0.2170) (0.1695) (0.2193) (0.1746) (0.2121) (0.1673) (0.2204) (0.1734) (0.2165) (0.1720) Microfinance 0.6564 *** 0.4934 ** 0.6319 *** 0.4396 ** 0.6509 *** 0.4806 ** 0.6543 *** 0.4824 ** 0.6588 *** 0.4813 ** (0.1819) (0.2268) (0.1799) (0.2232) (0.1784) (0.2233) (0.1785) (0.2215) (0.1784) (0.2191) Average education 0.0691 *** 0.1188 *** (0.0218) (0.0224)

Household head's education 0.0385 *** 0.0712 ***

(0.0147) (0.0162) 0.0628 *** 0.1049 *** (0.0189) (0.0188) 5.1450 4.6247 (3.7821) (3.5149) 5.5566 2.2895 (4.8813) (3.5806) 13.3097 * 7.2311 *** (7.1395) (2.7809) -1.3975 29.7544 (18.2646) (30.7953) 38.5221 13.6560 (25.3665) (29.9075) 7.2736 *** 5.52723 *** (2.2034) (1.6169) Pseudo R2 _{0.2419 0.1855 0.2396 0.1786 0.2439 0.1908 0.2439 0.1657 0.2430 0.1649}

Higher secondary ratio Tertiary education ratio

MPCE quintile Relative wealth

Most highly educated Primary school ratio Middle school ratio Secondary school ratio

MPCE quintile Relative wealth

At least primary school ratio

MPCE quintile Relative wealth MPCE quintile Relative wealth MPCE quintile Relative wealth

Next, subjective wellbeing was analysed by ordered probit regression. The dependent variable was subjective assessment of overall satisfaction with life according to a Likert scale of (1) very dissatisfied to (5) very satisfied. The explanatory variables were the same as other estimates in this section. Additionally, the logarithm for monthly household per capita income and relative income variables – i.e. income quintiles one (the lowest) to four (higher), with reference to the highest (five) – was added.

The results are given in Table 6-18.63 Again, out-remittance, asset index, and microfinance participation coefficients are positive with statistical significance, being correlated with both objective and subjective wellbeing. In contrast to the ordered probit regression on MPCE (Table 6-17), the household size sign generally changes to a positive indicator of subjective wellbeing.

This is because larger sample households tend to comprise extended families or, in some cases, nuclear families with an above average number of children. Extended families are a traditional way of living in India (Minault, 1981). However, it is not easy for slum households to accommodate many members in a limited space; therefore, a larger household could mean that it can afford to accommodate more people. This might lead to satisfaction with life due to the fact that family members can live together.

Indeed, break-up of the family was cited as a major reason why some slum dwellers were not satisfied with life. For example:

I am very sad, because my father is separated from the family (Prakash – never attended school – engages in any type of daily labour – dissatisfied with life).

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It is noted, as Graham (2011) points out, that regression on ordered logistics or probability equations for happiness generally yields lower R-squared values, reflecting the extent to which feelings, emotions, and other components of true wellbeing drive the results.

Due to some misunderstanding in the family, my wife and two sons live separately in our home village. They live without me over there; I am alone here (Suresh – five years of education – repairs electrical items – dissatisfied with life).

My father takes care of the land and house in our village. I brought my children here from the village for a better education. After all, however, our family is not in one place (Harilal – five years of education – machine fitter in water tank factory – dissatisfied with life).

The literature suggests that in developed countries, social relationships constitute an important element of happiness (Diener and Seligman, 2004); the present thesis contends that the psychological effects of family ties are just as strong among low-income groups in a developing country context.

It should be noted that adding the income variable tends to reduce the other variable coefficients. In particular, the sign for proportion of employed household members changes from positive to negative (although statistically insignificant) when the income variable is added. Per capita household income tends to be higher in households in which more members are engaged in paid work. This leads to a negative assessment of satisfaction if the number increases, since the standard of living to which the household has become accustomed cannot be sustained by fewer working members.

In most cases, females are likely to go out to work in order to supplement and maintain household income. However, it seems that having a working wife carries a certain stigma among male slum dwellers. As discussed in Chapter 3, the norm regarding women’s behaviour that dictates that they should be withdrawn from the labour market is common in India (Chen and Drèze, 2005), although such a custom is in reality

difficult for economically deprived households to observe.

While there is no significant positive correlation between the proportion of male household members and MPCE quintile (Table 6-17), the proportion of males in the household is positively correlated with subjective wellbeing. To put it the other way round, a higher proportion of females – unmarried girls in particular – imposes a psychological burden on the household. During the survey, it was often reported that households worried about having young girls. In particular, parents were extremely concerned about their daughters’ safety, which led to a lower ranking in terms of subjective wellbeing:

My young daughters are suffering from teasing by boys in this slum. I have to get them married off as soon as possible (Bimala – never attended school – very dissatisfied with life).

My daughters are young. I fear for their safety because the environment here is not good at all (Sumita – never attended school – maid servant – very dissatisfied with life).

My daughter got married to a drug addict and he died from an overdose. Now, she is staying with me (Mansuba – never attended school – very dissatisfied with life).

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Table 6-18 Ordered probit estimates for subjective wellbeing

Eq (1) Eq (2) Eq (3) Eq (4) Eq (5) Eq (6) Eq (7) Eq (8) Eq (9) Eq (10) Employment ratio 0.0577 -0.5055 0.1038 -0.4705 0.0531 -0.5373 0.0289 -0.5712 0.0233 -0.5758 (0.3159) (0.3758) (0.3186) (0.3783) (0.3147) (0.3716) (0.3189) (0.3784) (0.3150) (0.3745) LPG 0.0285 -0.0400 0.0431 -0.0351 0.0354 -0.0302 0.1042 0.0207 0.0852 0.0023 (0.1219) (0.1248) (0.1195) (0.1228) (0.1237) (0.1264) (0.1220) (0.1255) (0.1201) (0.1235) Out-remittance 0.4547 *** 0.3767 ** 0.4543 ** 0.3708 ** 0.4780 *** 0.3915 ** 0.5143 *** 0.4247 ** 0.5229 *** 0.4225 ** (0.1771) (0.1791) (0.1777) (0.1792) (0.1757) (0.1782) (0.1796) (0.1805) (0.1769) (0.1777) Household size 0.0543 0.0988 ** -0.0587 * 0.1020 *** 0.0492 0.0985 ** 0.0667 * 0.1187 *** 0.0558 0.1092 *** (0.0353) (0.0389) (0.0355) (0.0393) (0.0354) (0.0390) (0.0363) (0.0398) (0.0356) (0.0393) Asset index 0.0111 *** 0.0089 *** 0.0111 *** 0.0088 *** 0.0115 *** 0.0091 *** 0.0125 *** 0.0100 *** 0.0123 *** 0.0097 *** (0.0025) (0.0026) (0.0026) (0.0026) (0.0025) (0.0026) (0.0027) (0.0027) (0.0026) (0.0026) House index 0.0010 * 0.0008 0.0010 * 0.0008 0.0010 * 0.0008 0.0008 0.0005 0.0009 0.0006 (0.0006) (0.0005) (0.0006) (0.0005) (0.0006) (0.0005) (0.0006) (0.0005) (0.0006) (0.0005) Male ratio 0.6789 ** 0.5141 0.6981 ** 0.5242 0.6802 *** 0.5156 0.7646 ** 0.5755 * 0.6959 ** 0.5069 (0.3461) (0.3411) (0.3447) (0.3408) (0.3465) (0.3412) (0.3499) (0.3456) (0.3454) (0.3405) Debt -0.3223 -0.2962 -0.3173 -0.2949 -0.3151 -0.2904 -0.3141 -0.2800 -0.2865 -0.2568 (0.1995) (0.2080) (0.1997) (0.2084) (0.1982) (0.2076) (0.2026) (0.2123) (0.2001) (0.2107) Microfinance 0.4500 ** 0.4403 ** 0.4363 ** 0.4316 ** 0.4499 ** 0.4412 ** 0.4493 ** 0.4439 ** 0.4642 *** 0.4548 ** (0.1784) (0.1845) (0.1776) (0.1844) (0.1782) (0.1848) (0.1788) (0.1854) (0.1779) (0.1843)

Log of household head's per capita income 0.5184 *** 0.5166 *** 0.5351 *** 0.5721 *** 0.5692 ***

(0.1709) (0.1709) (0.1683) (0.1739) (0.1703)

Average education 0.0259 0.0118

(0.0198) (0.0199)

Household head's education 0.0194 0.0119

(0.0147) (0.0146) 0.0114 0.0002 (0.0162) (0.0157) -7.1800 ** -7.1308 ** (3.4456) (3.4217) -4.5110 -6.5865 ** (3.5071) (3.4618) 3.0475 2.0943 (2.2181) (2.1246) 8.6280 15.0870 (17.3591) (14.9814) 4.7198 -8.1785 (19.8342) (21.4600) -2.7758 * -3.6273 ** (1.5808) (1.7051) Pseudo R2 _{0.0895 0.0993 0.0897 0.0996 0.0885 0.0990 0.0929 0.1047 0.0899 0.1020}

At least primary school ratio Most highly educated

Primary school ratio

Middle school ratio

Secondary school ratio

Higher secondary ratio

Tertiary education ratio

Women in female-headed households were also concerned about their future, security and safety, for example:

I am a young lady; without a husband, I feel insecure (Meena – never attended school – maid servant at a private school – very dissatisfied with life).

I am not happy because I do not have a husband (Renu – never attended school – currently maid servant – very dissatisfied with life).

My husband ran off, leaving me behind. It has been one and half years since then; he has not come back. I have been facing an uneasy time (Sushima – never attended school – dissatisfied with life).

When income variables are added, the coefficient for proportion of males in the household transpires to be insignificant. Thus, it may be concluded that income weakens the correlation between the male ratio and subjective wellbeing to some extent.

All education-related variables have a largely positive correlation with subjective wellbeing. However, unlike the education variables in the ordered probit models of MPCE quintile and subjective assessment of relative wealth (Table 6-17), most coefficients are statistically insignificant. Moreover, a distinctive departure from the results in previous sections is that the proportion of household members having completed primary and middle school has a significant negative correlation with subjective wellbeing. Similarly, the ratio for household members having completed at least primary school also has a significant negative correlation with subjective wellbeing.

In general, sample slum households with greater proportions of educated members were not necessarily satisfied with their lives. In particular, those that had more members

with some schooling but not a very high level of education tended to be dissatisfied. Why was this so? In the literature, adjustment is identified as a key mechanism that affects anomalies in the correlation between economic wealth and subjective wellbeing, that is, subjective wellbeing tends to be enhanced with an increasing income; however, a rising income also tends to be accompanied by higher aspirations (Brickman and Campbell, 1971 cited in Clark, 2012; Graham, 2012).

When conducting the survey, a young unmarried girl who had been educated to postgraduate level and was currently providing private tuition to children told me, “We should not be satisfied with what we are now; we should continuously make an effort to improve ourselves.” This is a good example of the perception that emerged from the study: the educated are more aware that they do not have to be content with living in a slum for the rest of their lives. Schooling develops the capacity for introspective contemplation and constructive thought, playing a role in the judgment of subjective wellbeing. Educated slum dwellers are thus able to set a goal, attain it, and try to set further goals.

However, it is comparatively difficult to attain goals when one is constrained by various disadvantages, including low income, discrimination, a low social position, and few economic opportunities. Even if the slum dwellers under study did accomplish their goals, they were invariably obliged to compare and contrast them with those of educated people who did not live in slums (such a comparison is assumed to be different from that of the relatively uneducated who compared themselves with others who had attained a similar level of education). For example:

My brothers are all educated and in good posts. One is a doctor and the other three are all [medical] compounders (Sizauddin – graduate – tailor in an export

factory – dissatisfied with life).

My father is a property dealer. My younger brother has a BA. I am here as a carpenter (Atar – educated to 10th grade – carpenter – neither satisfied nor dissatisfied).

As discussed in Chapter 2, the existing literature suggests that rural–urban migrants tend to compare their circumstances with those of their new urban neighbours rather than the standard of living of those they left behind in the countryside (Fafchamps and Shilpi, 2008; Knight and Gunatilaka, 2011). However, as the above examples demonstrate, the present study found that comparatively highly educated residents of slums did not seem to compare themselves with either their present or former neighbours; their point of reference was the individual who had received a similar level of schooling but did not live in a slum. It is likely that they believed that it was unfair that they should end up living in such conditions after being educated for longer years.

It is also noteworthy that when monthly per capita income variable or relative income dummy variables are included in explanatory variables, the correlation between education and subjective wellbeing generally becomes weak. This implies that the strength of the relationship between schooling and subjective wellbeing is determined mainly by the education–income dynamic rather than any direct correlation between education per se and subjective wellbeing. This finding is corroborated by a South African case that to some extent attributes subjective wellbeing to income level (Kingdon and Knight, 2006). Such findings re-emphasise the hypothesis that better employment opportunities – and thus higher earnings – are more important than education itself in the judgement of subjective wellbeing, particularly with regard to the relatively highly educated.

The analysis in this chapter has shown that there is a significant positive correlation between income and level of subjective wellbeing: when income variables are added to the equation, it is clear that subjective wellbeing increases with income. This position is corroborated by the existing literature (e.g. Oswald, 1997; Frey and Stutzer, 2002; Sacks et al., 2010).

When household per capita income is replaced by relative income (household per capita monthly income quintile) dummies, the coefficients for the lowest three quintiles prove to be significantly negative, but this is not the case with the second highest quintile in comparison with the highest income group (Table 6-19). Indeed, income quintile coefficients mostly show monotonic functionality. This means that for the poor, both absolute and relative income is critical, but only the former is relevant to the non-poor. This result is corroborated by the contention in the existing literature that relative income is a vital consideration for poor households (e.g. Fafchamps and Shilpi, 2008). Furthermore, in comparison with the different education variable coefficients in Table 6-18, most of the corresponding coefficients in Table 6-19 are larger in most of the cases. This implies that the correlation between education and subjective wellbeing is stronger in the linkage of education–relative income with subjective wellbeing, than in that of education–absolute income with subjective wellbeing.

Table 6-19 Subjective wellbeing and household relative income

Eq (1) Eq (2) Eq (3) Eq (4) Eq (5)

Average education 0.0154 (0.0200)

Household head's education 0.0142

(0.0148) 0.0045 (0.0163) -7.3714 ** (3.4073) -7.0428 ** (3.4108) 2.6335 (2.1312) 12.0482 (15.6280) 1.3710 (22.4904) -3.6317 ** (1.7614) Lowest income -0.7015 *** -0.6984 *** -0.7194 *** -0.7767 *** -0.7819 *** (0.2280) (0.2288) (0.2257) (0.2339) (0.2298) Low income -0.5182 ** -0.5092 ** -0.5287 ** -0.5340 ** -0.5609 ** (0.2359) (0.2354) (0.2359) (0.2379) (0.2355) Middle income -0.5082 ** -0.5036 ** -0.5225 ** -0.5477 *** -0.5470 *** (0.2059) (0.2058) (0.2056) (0.2105) (0.2077) High income -0.2004 -0.1900 -0.2079 -0.1982 -0.2268 (0.1957) (0.1963) (0.1953) (0.2003) (0.1968) At least primary school ratio

Most highly educated Primary school ratio Middle school ratio Secondary school ratio Higher secondary ratio Tertiary education ratio

Notes: N = 412. All explanatory variables in Table 6-18 are included but not individually

shown. Figures in parentheses indicate robust standard errors. ***, ** and ** indicate significance at 1%, 5% and 10% respectively.

6.6. Conclusion

This chapter has examined the correlation of education and multidimensional poverty at the household level. It transpires that 75.3% of sample slum households fall below the poverty line. The correlation between monetary poverty and education tends to be strong, while that between other forms of deprivation – as determined by basic needs and capabilities, relative wealth, and subjective wellbeing – and education is weak.

On the one hand, schooling has a largely positive correlation with monetary poverty. Not only the level of the most highly educated member of the household, but other education-related variables – such as the average household, household head’s schooling levels– have a significant positive correlation.

On the other hand, a higher level of education does not necessarily have a positive correlation with one’s basic needs and capabilities or subjective wellbeing. Anomalies associated with the correlation between education and subjective wellbeing can be explained by the role of schooling in creating aspirations, deepening individuals’ insight, widening horizons, and enhancing introspective contemplation. It is hypothesised that educated slum dwellers tend to compare themselves with others who have attained similar levels of education but do not live in slum areas.

Finally, education seems to have a stronger positive correlation with subjective wellbeing in combination with income than it does by itself. Moreover, amongst poorer slum households in particular, both absolute income and relative income in the slum have a positive correlation with subjective wellbeing.However, as absolute income rises, relative income becomes largely insignificant. Nevertheless, the correlation between education and poverty seems to be more clearly manifested in terms of monetary poverty than it is in respect of non-monetary poverty.