Abstract
This study investigates the existence of a calorie gap (CG) within gender, caste, and religion groups utilising an entitlement framework. We employed National Sample Survey data on consumer expenditure spanning from 2004–05 to 2011–12 and applied the Oaxaca-Blinder decomposition technique for our analysis. The results of the Oaxaca decomposition indicate that entitlement failure plays a significant role in expanding the CG amongst gender and caste groups in both rural and urban areas, while its impact is only seen within religious groups in urban areas. The failure of exchange entitlement intensifies the CG amongst caste and religious groups in rural areas. Moreover, our quantile-specific Oaxaca decomposition findings reveal that the contributions of entitlement and exchange entitlement gaps to the overall CG fluctuate across quantiles. We propose that increased social awareness and public exposure aimed at reducing caste and religion-based discrimination, alongside programmes and policies designed to enhance the resource base of female-headed households, minority castes, and religious minorities, may aid in the reduction of the CG.
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Notes
Caste serves as a key stratification element in Indian society, which is categorized into Scheduled Tribes (STs), Scheduled Castes (SCs), Other Backward Classes (OBCs), and Others or Majority Caste. STs, constitutionally referred to as "Adivasis", primarily reside in forest areas and are largely isolated from the mainstream economic processes. Conversely, SCs, often referred to as "Dalits", sit at the lower echelons of the social hierarchy in the caste system, often experiencing exclusion in the form of "untouchability". The Mandal Commission classified certain groups as OBCs, with a particular emphasis on economic backwardness. The "Others" category, also known as the general or majority caste, occupies the highest social order.
The formulation of the Entitlement Theory was predicated on the utilization of descriptive statistics and cogent arguments, proposing that the primary precipitant of the Bengal famine was the inequitable distribution of food resources, stemming from a deficient level of exchange entitlement (Sen 1981a). Nevertheless, this theory lacked a rigorous, quantitative regression analysis to ascertain the degrees of entitlement and exchange entitlement. In our present investigation, we endeavour to surmount these limitations by applying an innovative methodology, specifically the Oaxaca-Blinder decomposition.
Oaxaca-Blinder decomposition assumes the distributional impact as a whole. However, the impact of entitlement and exchange entitlement gaps may be different for the lower CI gap than the higher CI gap. The preliminary evidence shows that the dependent variable in our study rejects the null of normality and homoscedasticity (see Table 1). Therefore, in such a situation, it is necessary to examine the impact across the different quantiles of the CI gap, which will provide better information to policymakers. Most of the previous studies have used simple Oaxaca-Blinder decomposition without examining normality of the dependent variable.
The public distribution system (PDS) functions as a governmental entity dedicated to the provision of food grains at a subsidized rate to underprivileged households. The operationalization of the PDS is a shared responsibility between the central and state governments. Tasks such as procurement, storage, transportation, and allocation of food grains fall under the jurisdiction of the central government. In contrast, the state government oversees operational duties, including intra-state distribution, identification of impoverished households, issuance of ration cards, and supervision of resource allocation. Households that have been issued ration cards by the PDS are eligible to receive subsidiary entitlements comprising calorie-dense food items such as rice, wheat, and coarse grains. Consequently, caloric intake serves as an optimal indicator for assessing food security within the context of the Indian food security policy framework.
In this study, entitlement, endowment, and explained component are used interchangeable. Similarly, exchange entitlement, coefficient, and unexplained component are used interchangeable.
The principal aim of the Millennium Development Goals (MDGs) was the elimination of food insecurity and hunger by the conclusion of 2015. However, having only achieved partial success, this led to the conception of the more comprehensive Sustainable Development Goals (SDGs) with an aim to abolish all forms of poverty and hunger by the year 2030. In this study, we analyse three large-scale sample surveys, undertaken during the MDGs era. We believe that the outcomes of our exercise will yield critical insights, potentially instrumental in the establishment of effective policy strategies, thereby facilitating the fulfilment of the targets delineated within the SDGs.
The term "balwadi" is derived from the words "Bal" (children) and "Wadi" (home or center). It refers to a "Special Nutrition Programme" implemented since 1970–71 by the Central Social Welfare Board and national non-governmental voluntary organizations, such as the Indian Council for Child Welfare, Harijan Sevak Sangh (Scheduled Castes Service Board), Bharatiya Adimjati Sevak Sangh, and Kasturba National Memorial Trust. This segment of the nutrition programme is primarily executed by non-governmental organizations. The Central Social Welfare Board, a semi-government organization specializing in social work, dispenses grants-in-aid to voluntary organizations to run the programme. Simultaneously, other national-level voluntary organizations provide assistance to various voluntary organizations while also directly managing some centers. The Special Nutrition Programme primarily benefits disadvantaged societal sections, including tribal or scheduled caste individuals, urban slum dwellers, and migrant labourers. Balwadis not only offer supplemental nutrition but also cater to the social and emotional development of the attending children. Source: http://www.fao.org/3/x0172e/x0172e08.htm—Accessed on July 5, 2020, at 07.58 p.m.
The adjusted equivalence table can be obtained upon request.
The Consumer Price Index for Agriculture (CPIA) and the Consumer Price Index for Industrial Workers (CPIIW) were used for rural and urban areas, respectively. The index data were obtained from the Reserve Bank of India's annual time series publication. Prior to measuring real Monthly Per Capita Expenditure (MPCE), the index was converted to a 1960 base year.
The theory of wage-efficiency advocates that wage/income and calorie intake affects each other. As income increases, there is a possibility of an increase in calorie intake. In reverse, an increase in calorie intake leads to more physical strength to work longer duration. When labour is increasing his/her working duration, his/her wages/income increases. Therefore, the simultaneity exists between income and calorie intake, creating simultaneity bias, in turn, estimates become inconsistent. Therefore, MPCE is an endogenous variable.
In the second stage quantile regression, the instrument omitted from the specification.
We have follows control variable approach to capture endogeneity while doing the quantile decomposition.
The first stage regression results of control function are similar to the first stage result of IV regression, which reported in the appendix.
The positive sign of the endowment and coefficient gap indicates that the rise in the endowment or coefficient gap will increase the CG, vice-versa. The interpretation remains similar for all decomposition results.
We have estimated the detailed quantile decomposition results, which suggest that most of the key predictors vary over the quantiles for gender, caste, and religious groups. These results are not reported here, but they can be obtained on request.
References
Audretsch DB, Bönte W, Tamvada JP (2013) Religion, social class, and entrepreneurial choice. J Bus Ventur 28(6):774–789. https://doi.org/10.1016/j.jbusvent.2013.06.002
Averett SL, Stacey N, Wang Y (2014) Decomposing race and gender differences in underweight and obesity in South Africa. Econ Hum Biol 15:23–40. https://doi.org/10.1016/J.EHB.2014.05.003
Bhuyan B, Sahoo BK, Suar D (2020a) Food insecurity dynamics in India: a synthetic panel approach. Soc Sci Human Open 2(1):100029. https://doi.org/10.1016/j.ssaho.2020.100029
Bhuyan B, Sahoo BK, Suar D (2020b) Nutritional status, poverty, and relative deprivation among socio-economic and gender groups in India: Is the growth inclusive? World Dev Perspect 18:1–15. https://doi.org/10.1016/j.wdp.2020.100180
Bhuyan B, Sahoo BK, Suar D (2020c) Quantile regression analysis of predictors of calorie demand in India: an implication for sustainable development goals. J Quant Econ 18(4):825–859. https://doi.org/10.1007/s40953-020-00200-4
Bhuyan B, Mohanty RK, Patra S (2023) Impact of climate change on food security in India: an evidence from autoregressive distributed lag model. Env Dev Sust 1–21
Blinder AS (1973) Wage discrimination: reduced form and structural estimates. J Human Resour 8(4):436–455
Breusch TS, Pagan AR (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47(5):1287–1294
Chandrasekhar CP, and Ghosh J (2003) The calorie consumption puzzle. The Hindu Business Line. http://www.thehindubusinessline.com/2003/02/11/stories/2003021100210900.htm
Cook RD, Weisberg S (1983) Diagnostics for heteroscedasticity in regression. Biometrika 70(1):1–10
Cragg JG, Donald SG (1993) Testing identifability and specifcation in instrumental variable models. Econ Theory 9:222–240
Deaton A, Drèze J (2009) Food and nutrition in india: facts and interpretations. Econ Pol Wkly 47(7):42–65
Deshpande A, Goel D, Khanna S (2018) Bad karma or discrimination? Male-female wage gaps among salaried workers in India. World Dev 102:331–344. https://doi.org/10.1016/j.worlddev.2017.07.012
Duflo E (2003) Grandmothers and granddaughters: old-age pensions and intrahousehold allocation in South Africa. The World Bank Econ Rev 17(1):1–25. https://doi.org/10.1093/wber/lhg013
Dunn RA, Tan AKG, Nayga RM (2012) Obesity inequality in Malaysia: decomposing differences by gender and ethnicity using quantile regression. Ethn Health 17(5):493–511. https://doi.org/10.1080/13557858.2012.661407
Firpo S, Fortin NM, Lemieux T (2009) Unconditional quantile regressions. Econometrica 77(3):953–973. https://doi.org/10.3982/ECTA6822
Font JC, Fabbri D, Gil J (2010) Decomposing cross-country differences in levels of obesity and overweight: Does the social environment matter? Soc Sci Med 70(8):1185–1193. https://doi.org/10.1016/J.SOCSCIMED.2009.12.011
Fortin N, Lemieux T, Firpo S (2011) Decomposition methods in economics. Handbook Labor Econ 4:1–102. https://doi.org/10.1016/S0169-7218(11)00407-2
Gang IN, Sen K, Yun M-S (2008) Poverty in rural India: caste and tribe. Rev Income Wealth 54(1):50–70. https://doi.org/10.1111/j.1475-4991.2007.00259.x
Gibson J (2016) Poverty measurement: we know less than policy makers realize. Asia Pacific Policy Stud 3(3):430–442. https://doi.org/10.1002/app5.141
GOI (2007) Nutritional Intake in India,2004–05. https://164.100.161.63/sites/default/files/publication_reports/513_final.pdf accessed on 06/June/2023 at 1.05 p.m
GOI (2008) National Industrial Classification. https://www.mospi.gov.in/sites/default/files/main_menu/national_industrial_classification/nic_2008_17apr09.pdf
GOI (2012) Nutritional Intake in India,2009–10. https://164.100.161.63/sites/default/files/publication_reports/nss_rep_540.pdf, 06/June/2023 at 1.05 p.m
GOI (2014a) Nutritional Intake in India, 2011–12. https://164.100.161.63/sites/default/files/publication_reports/nss_report_560_19dec14.pdf accessed on 06/June/2023 at 1.05 p.m
GOI (2014b) Report of the Expert Group to Review the Methodology for Measurement of Poverty. http://planningcommission.nic.in/reports/genrep/pov_rep0707.pdf on 06/03/2017 at 12.40 p.m.
Gopalan C, Ramasastri BV, Balasubramanian SG. Revised and updated by Rao BSN, Deosthale YB and Pant KC (1993) Nutritive value of Indian foods. National Institute of Nutrition, Hyderabad, India. National Institute of Nutrition, Hyderabad
Hjelm L, Mathiassen A, Wadhwa A (2016) Measuring poverty for food security analysis: consumption-versus asset-based approaches. Food Nutr Bull 37(3):275–289. https://doi.org/10.1177/0379572116653509
Imbens GW, Newey WK (2009) Identification and estimation of triangular simultaneous equations models without additivity. Econometrica 77(5):1481–1512
Jha R (2008) Economic reforms and human development indicators in India. Asian Econ Policy Rev 3(2):290–310. https://doi.org/10.1111/j.1748-3131.2008.00115.x
Jodha NS (1975) Famine and famine policies: some empirical evidence. Econ Pol Wkly 10(41):1609–1623
Joe W, Mishra US, Navaneetham K (2009) Inequalities in childhood malnutrition in India: some evidence on group disparities. J Human Dev Capab 10(3):417–439. https://doi.org/10.1080/19452820903048886
Johnston DW, Lee W-S (2011) Explaining the female black-white obesity gap: a decomposition analysis of proximal causes. Demography 48(4):1429–1450. https://doi.org/10.1007/s13524-011-0064-x
Kaul T (2018) Intra-household allocation of educational expenses: gender discrimination and investing in the future. World Dev 104:336–343. https://doi.org/10.1016/j.worlddev.2017.12.017
Kijima Y (2006) Caste and tribe inequality: evidence from India, 1983–1999. Econ Dev Cult Change 54(2):369–404. https://doi.org/10.1086/497008
Lin JY, Yang DT (2000) Food availability, entitlements and the Chinese famine of 1959–61. Econ J 110(460):136–158
Lipton M (1984) Poverty, Undernutrition, and Hunger (No. 597; World Bank Staff Working Papers). http://documents.worldbank.org/curated/en/892041468766760990/Poverty-undernutrition-and-hunger
Lundberg SJ, Pollak RA, Wales TJ (1997) Do Husbands and wives pool their resources ? Evidence from the United Kingdom Child Benefit. J Hum Resour 32(3):463–480
Maity B (2017) Comparing health outcomes across scheduled tribes and castes in India. World Dev 96:163–181. https://doi.org/10.1016/j.worlddev.2017.03.005
Mango N, Zamasiya B, Makate C, Nyikahadzoi K, Siziba S (2014) Factors influencing household food security among smallholder farmers in the Mudzi district of Zimbabwe. Dev South Afr 31(4):625–640. https://doi.org/10.1080/0376835X.2014.911694
Meenakshi JV, Viswanathan B (2017) Estimation of calorie norms and measurement of food intakes : some implications for the magnitudes of the prevalence of undernutrition in India. Indian Econ Rev 48(1):167–188
Minhas BS (1991) On estimating the inadequacy of energy intakes: revealed food consumption behaviour versus nutritional norms (nutritional status of Indian people in 1983). The J Dev Stud 28(1):1–38. https://doi.org/10.1080/00220389108422220
Mittal S (2007) What affects changes in Cereal Consumption? Econ Polit Wkly 42(5):444–447
Oaxaca R (1973) Male-female wage differentials in urban labor markets. Int Econ Rev 14(3):693–709
Palmer-Jones R, Sen K (2001) On India’s poverty puzzles and statistics of poverty. Econ Polit Week 36(3):211–217
Panagariya A (2013) Does India really suffer from worse child malnutrition than sub-Saharan Africa? Econ Pol Wkly 48(18):98–111
Patnaik U (2004) The republic of hunger. Soc Sc 32(9/10):9–35
Patnaik U (2007) Neoliberalism and rural poverty in India. Econ Polit Wkly 42(30):3132–3150
Poel E Van de and Speybroeck N (2009) Decomposing malnutrition inequalities between Scheduled Castes and Tribes and the remaining Indian population. Ethn Health, 14(3):271–287. https://doi.org/10.1080/13557850802609931
Ramaiah A (2015) Health status of dalits in India. Econ Pol Wkly 50(43):70–74
Rao N (2006) Land rights, gender equality and household food security: exploring the conceptual links in the case of India. Food Policy 31(2):180–193. https://doi.org/10.1016/j.foodpol.2005.10.006
Rao N (2017) Assets, agency and legitimacy: towards a relational understanding of gender equality policy and practice. World Dev 95:43–54. https://doi.org/10.1016/j.worlddev.2017.02.018
Rothe C (2010) Identification of unconditional partial effects in nonseparable models. Econ Lett 109(3):171–174. https://doi.org/10.1016/j.econlet.2010.08.028
Sen A (1981a) Ingredients of famine analysis : availability and entitlements. Q J Econ 96(3):433–464
Sen A (1981b) Poverty and famine. Clarendon Press, Oxford
Singh A, Kumar K, Singh A (2016) Trends in inequality in food consumption and calorie intake in India: evidence from the last three decades, 1983–2012. Soc Indic Res 128(3):1319–1346. https://doi.org/10.1007/s11205-015-1081-8
Smith LC (2015) The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy 50:53–67. https://doi.org/10.1016/j.foodpol.2014.10.011
Stock JH, Yogo M (2005) Testing for weak instruments in Linear Iv regression. In: Andrews DWK, Stock JH (eds) Identifcation and inference for econometric models: essays in honor of Thomas Rothenberg. Cambridge University Press, Cambridge. pp 80–108. https://doi.org/10.1017/CBO9780511614491.006
Subramanian S, Deaton A (1996) The demand for food and calories. J Polit Econ 104(1):133–162. https://doi.org/10.1086/262020
Thomas D (1990) Intra-household resource allocation : an inferential approach. J Hum Resour 25(4):635–664
Wooldridge J (2007) What ’ s New in Econometrics ? Lecture 6 Control Functions and Related Methods. NBER Summer Institute, 1–31. http://www.nber.org/WNE/Slides7-31-07/slides_6_controlfuncs.pdf
Wooldridge JM (2013) Introductory econometrics: a modern approach. In South-West, Cengage Learning (Fifth). https://doi.org/10.1016/j.jconhyd.2010.08.009
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We sincerely thank the Minister of Human Resource Development, Government of India, for providing financial support to conduct this research at the Indian Institute of Technology Kharagpur. Any errors in this paper are the sole responsibility of the authors.
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Appendix
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“The Anderson canon. Corr. LM statistic applied for the checking of the underidentification of the structural equation. The test of whether the equation is identified, i.e., that the excluded instruments are "relevant", meaning that it is correlated with the endogenous variable. The significance of the test statistics suggested the rejection of the null of the equation is under-identified. So, the instrument is relevant. We used Cragg–DonaldWald F-statistic and Stock-Yogo weak ID test critical values to examine the weak identification of the instruments (Cragg and Donald 1993; Stock and Yogo 2005). The weak identification test is significant and suggested to rejects the null of weak identification. In our case, the over-identification test using the Sargan statistic and the LM test for redundancy test are not required, because our model is exactly identified. The coefficients derived from the 2SLS are similar to the control function results” (Bhuyan et al. 2020c).
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Bhuyan, B., Sahoo, B.K. & Suar, D. Calorie decomposition by gender, caste, and religion in India: an entitlement approach. Empir Econ (2024). https://doi.org/10.1007/s00181-024-02598-9
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DOI: https://doi.org/10.1007/s00181-024-02598-9