Abstract
Trickle-down has been addressed, so far, using income-based measures of inequality and poverty. However concerns over the inequality in access to other dimensions important for quality of life remains. I revisit trickle-down using the Alkire and Foster (J Public Econ 95(7–8), 2011) class of measures to estimate multidimensional poverty in India. Using NSS data estimates are presented for the 16 major states and are compared to income-based measures. Adding dimensions in poverty measurement results in the reversal of several income-based conclusions about poverty across regions. The paper also finds that contrary to income-based findings, Muslims are less poor than Hindus under the multidimensional criteria.
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Notes
Over 70% of India’s population can be classified as rural.
Banerjee and Somanathan (2007) look at the location of public goods between 1971 and 1991 in about 500 parliamentary constituencies in rural India to assess the differences in the allocation of public resources over the period for the various ethnic groups. They find that the allocation in areas with large Scheduled Caste populations has increased whereas the access is reduced in areas with Scheduled Tribes and Muslims.
See the World Bank report, India’s Undernourished Children: Call for Reform and Action.
57% of the population in India was literate according to the Census of India, 2001 which is well below Thailand’s 96%, Sri Lanka’s 92%, Indonesia’s 87 percent, and China’s 84% (See Barooah and Iyer 2005).
Chaudhuri et al. (2006) show that literacy and increased growth of newspapers translates into better governance. Gamper-Rabindran et al. (2009) discuss the effect of piped water on infant mortality in Brazil. In Datt and Ravallion (2002) initial conditions including indicators of health, education and standard of living is recognized as one of the factors explaining regional differences in pro-poor growth. Ferreira et al. (2009) show that for Brazil impact of growth on poverty reduction is primarily explained by differences in macroeconomic factors however initial conditions have a significant albeit small effect.
However for paucity, I will continue calling it income as is also the norm in the literature.
The case of multidimensional poverty for the nation as a whole using a dimension cutoff of four.
Esteban and Ray (2008) refers to the linkages between economic inequality and ethnic conflict.
In India, 85% of the population is Hindu and every Hindu is assigned a caste. Scheduled castes form about 16% of the Indian population. Their economic backwardness is a direct result of the caste system which has been ingrained into the Indian social fabric for a long time. Caste membership is hereditary and is reflective of the person’s traditional occupation choice. Particularly, members of the scheduled castes were traditionally assigned low-paying, labor-intensive jobs. In fact, they were denied education and barred from high-paying tasks.
Such an intra-group distinction does not apply to Muslims, since traditionally there were no castes associated with them and the Constitution of India does not recognise Muslims to be part of the Scheduled Castes.The following is a link to an extract from the Constitution of India available on the Ministry of Law and Justice (Government of India) web page, which clearly states that no Muslim group can be declared a member of the Scheduled Castes in India: http://lawmin.nic.in/ld/subord/rule3a.htm.
However, the sets of individuals who are identified as poor by the two criteria may well be very different.
A discussion of the more general case can be found in Alkire and Foster (2011).
This is much closer to the World Bank estimate of 42% of the population living below $1.25 a day in India than the official income poverty estimates for India.
The choice of cutoffs here is somewhat arbitrary and represents the author’s best estimate of minimal criteria.
A person is considered literate if she can sign her name.
Kutcha in India means not firm/solid.
It is shown in Jalan and Ravallion (2003) that access to piped drinking water reduces the chances of diarrhoea among infants in India.
Of course if the cooking is done in a separate room or in the open then the problem is less severe. However, the NSS is not able to give information on the arrangements of cooking beyond the cooking medium.
When calculating the multidimensional poverty measure for the union approach (that is k=1) almost 90% of the population is identified as poor. In other words, 90% of the population is deprived in at least one of the seven dimensions. For the intersection approach, when k takes the value of seven, the number of people identified as poor is less than 3%. This suggests that looking at an intermediate dimension cutoff may yield more interesting insights.
For income poverty the headcount ratio is the same as the FGT measure with alpha equal to zero. For the World Bank estimates see Poverty Data: A supplement to World Development Indicators 2008. Note for multidimensional poverty, the headcount ratio and the actual estimate of multidimensional poverty with \(\alpha\) set to zero diverge. The multidimensional headcount is an indicator of the incidence of poverty but to gauge the depth of poverty in the multidimensional sense I look at the \(P_{0}(k,z),\) the AF measure with alpha equal to zero. I have discussed the multidimensional headcount and how it varies with changes in k (Table 3). \(P_{0}(k,z)\) for India is also described in Table 3. For the union approach (with k equal to 1) I have that \(P_{0}(k,z)\) is equal to 0.466 and with k as four this estimate is 0.349.
This is done as these excluded regions have special features which need to be accounted for separately because of the terrain and the special treatment some of these regions receive from the central government. This, though interesting, is tangential to the main questions being addressed here.
This is perhaps not unexpected. Gujarat has witnessed severe outbreaks of Hindu-Muslim violence over the decades (which seems to have intensified in the last 20 years); this clearly results in destruction of social capital apart from creating widespread mistrust between religious groups. Also, successive state governments in Andhra Pradesh have been accused of favouring urban development (especially by promoting IT-led growth) at the expense of rural agriculture.
Table 8 in the online appendix gives the estimates of poverty levels as the weight on income is progressively increased from 20 to 45% (and the other weights are proportionately decreased). Each column of this table represents a different weighting scheme. For ease of comparison, in this table the k value is fixed at 3.5. Table 9 (in the online appendix) provides the rank of each state corresponding to the poverty levels in Table 10. What is notable is that the ranking of the states remains unaltered as one moves from equal weights to 30% weight on income alone. When more than 30% weight is given to income, the ranking gradually start resembling the ranking generated by the income headcount. This is hardly unexpected.
Now looking at the multidimensional head count (in the left portion of the table), which gives the proportion of the population which is multidimensional poor, I see that again, a higher proportion of Hindus (compared to Muslims) are multidimensional poor. At k equal to one (which gives the union approach) I find a higher proportion of Muslims to be poor. However at this k value almost 90% of the population is poor, which seems very unrealistic according to standard notions of poverty.
There is anecdotal evidence suggesting that in rural India low caste Hindus engage in more social interactions with Muslims than with high caste Hindus. Often they reside in contiguous neighborhoods which are some distance away from the neighborhoods populated by high caste Hindus. The population can be split on the basis of different markers as well. For example I can compare Hindu and Muslim poverty across urban and rural areas. Table in the online appendix gives the estimates of rural and urban poverty of Hindus and Muslims separately. It should come as no surprise that for both religions the rural poverty is higher than urban poverty. The overall differences (urban and rural) between Hindus and Muslims is probably coming from the fact that rural poverty is greater than urban poverty and a greater proportion of Muslims reside in urban areas than in rural areas as compared to Hindus. Once the Hindu population is decomposed along the caste dimension, it is evident that high caste Hindus are the least poor, followed by Muslims. Also, low caste Hindus are the poorest in both rural and urban areas (see table in the online appendix).
Tables in the online appendix represent the results for the alternate weighting schemes.
The state government in Andhra Pradesh during that period stressed “IT (information technology)-led” growth which was primarily geared towards urban areas and possibly came at the expense of the rural sector.
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I would like to thank Sabina Alkire, James Foster and Yanqin Fan whose comments greatly improved the paper. I have also greatly benefited from discussions with T. M. Tonmoy Islam. I would also like to thank Anirban Mitra for reading multiple drafts. All remaining errors are solely mine.
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Mitra, S. Re-Assessing “trickle-down” Using a Multidimensional Criteria: The Case of India. Soc Indic Res 136, 497–515 (2018). https://doi.org/10.1007/s11205-017-1568-6
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DOI: https://doi.org/10.1007/s11205-017-1568-6