Dataset Information

Labour Incomes in India: A Comparison of Two National Household Surveys.

ABSTRACT: The COVID-19 pandemic created a need for high-frequency employment and income data. Policy-makers and researchers of developing countries typically have not had access to such data. In India, a new private high-frequency panel dataset has recently emerged as the dataset of choice for analysis of the economic impact of COVID-19. This is the Consumer Pyramids Household Survey (CPHS) conducted by the Centre for Monitoring the Indian Economy (CMIE). But the CPHS has also been criticised for being inadequately representative nationally by missing poor and vulnerable households in its sample. We examine the comparability of monthly labour income estimates for the pre-pandemic year (2018-19) for CPHS and the official Periodic Labour Force Survey (PLFS). Across different methods and assumptions, as well as rural/urban locations, CPHS mean monthly labour earnings are anywhere between 5 percent and 50 percent higher than corresponding PLFS estimates. In addition to the sampling concerns raised in the literature, we point to differences in the way employment and income are captured in the two surveys as possible causes of these differences. While CPHS estimates are always higher, it should also be emphasised that the two surveys agree on some stylised facts regarding the Indian workforce. An individual earning ₹50,000 per month lies in the top 5 percent of the income distribution in India as per both surveys. Second, both PLFS and CPHS show that half the Indian workforce earns below the recommended National Minimum Wage.

Supplementary information

The online version contains supplementary material available at 10.1007/s41027-023-00427-8.

SUBMITTER: Jha M

PROVIDER: S-EPMC9961298 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Labour Incomes in India: A Comparison of Two National Household Surveys.

Jha Mrinalini M Basole Amit A

The Indian journal of labour economics : the quarterly journal of the Indian Society of Labour Economics 20230225 1

The COVID-19 pandemic created a need for high-frequency employment and income data. Policy-makers and researchers of developing countries typically have not had access to such data. In India, a new private high-frequency panel dataset has recently emerged as the dataset of choice for analysis of the economic impact of COVID-19. This is the Consumer Pyramids Household Survey (CPHS) conducted by the Centre for Monitoring the Indian Economy (CMIE). But the CPHS has also been criticised for being i ...[more]

PMID: 37124123

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Project description:BackgroundEmerging data show that many low-income and middle-income country (LMIC) health systems struggle to consistently provide good-quality care. Although monitoring of inequalities in access to health services has been the focus of major international efforts, inequalities in health-care quality have not been systematically examined.MethodsUsing the most recent (2007-16) Demographic and Health Surveys and Multiple Indicator Cluster Surveys in 91 LMICs, we described antenatal care quality based on receipt of three essential services (blood pressure monitoring and urine and blood testing) among women who had at least one visit with a skilled antenatal-care provider. We compared quality across country income groups and quantified within-country wealth-related inequalities using the slope and relative indices of inequality. We summarised inequalities using random-effects meta-analyses and assessed the extent to which other geographical and sociodemographic factors could explain these inequalities.FindingsGlobally, 72·9% (95% CI 69·1-76·8) of women who used antenatal care reported blood pressure monitoring and urine and blood testing; this number ranged from 6·3% in Burundi to 100·0% in Belarus. Antenatal care quality lagged behind antenatal care coverage the most in low-income countries, where 86·6% (83·4-89·7) of women accessed care but only 53·8% (44·3-63·3) reported receiving the three services. Receipt of the three services was correlated with gross domestic product per capita and was 40 percentage points higher in upper-middle-income countries compared with low-income countries. Within countries, the wealthiest women were on average four times more likely to report good quality care than the poorest (relative index of inequality 4·01, 95% CI 3·90-4·13). Substantial inequality remained after adjustment for subnational region, urban residence, maternal age, education, and number of antenatal care visits (3·20, 3·11-3·30).InterpretationMany LMICs that have reached high levels of antenatal care coverage had much lower and inequitable levels of quality. Achieving ambitious maternal, newborn, and child health goals will require greater focus on the quality of health services and their equitable distribution. Equity in effective coverage should be used as the new metric to monitor progress towards universal health coverage.FundingBill & Melinda Gates Foundation.

Project description:BackgroundAccurate national information on dietary intakes, including heterogeneity among individuals, is critical to inform health implications and policy priorities. In low- and middle-income countries, household expenditure surveys constitute the major source of food data, but with uncertain validity for individual-level intakes.ObjectiveTo investigate how individualized dietary consumption estimated from household survey data compared with individual-level 24-hr dietary recalls (24hR); and to assess potential heterogeneity by method for individualizing household intakes, dietary indicator, and individual characteristics (age, sex, education, religion, household income).MethodsWe evaluated data from the 2011-2012 Bangladesh Household Integrated Survey (BIHS), which included household-level consumption data (5,503 households) and individual-level dietary data based on 24hR from these households (22,173 participants). Household and 24hR estimates were standardized and harmonized for 33 dietary indicators, including 9 food groups, total energy, 8 macronutrients, and 15 micronutrients. Individual consumption was estimated from household data using two approaches, the Adult Male Equivalent (AME) and per capita (PC) approach. For each dietary indicator, differences in household vs. individual mean estimates were evaluated overall and by strata of individual characteristics, using Spearman's correlations and univariate and multivariate linear regression models.ResultsIndividualized household estimates overestimated individual intakes from 24hR for all dietary factors using either estimation method (P<0.001 for each), except for starchy vegetables (AME: P = 0.15; PC: P = 0.85). For foods, overestimation ranged from 4% for seafood to about 240% for fruits, and for nutrients from 11% for carbohydrates and poly-unsaturated fats to 55% for vitamin C, with similar overestimation for the AME and the PC method. By strata, overestimation was modestly higher in men vs. women, in children (0-10y) vs. adolescents (11-19y) and adults (20-44y, ?45y), among adults of higher (?6y) vs. lower (<6y) education, in Muslims vs. other religions (Christians, Hindus), and for the lowest vs. all other income groups. This overestimation was notably higher in young children (0-5y) vs. all other age groups and in the lowest vs. all other income groups. Underestimation was rarely observed, for example for milk intake (-56%) in young children (0-5y). The PC approach did not capture heterogeneity in validity of estimation of different dietary factors by age, mainly in children (0-5y, 6-10y). Spearman's correlations between individualized household estimates and 24hR data were higher for the AME (0.30-0.70) than PC (0.20-0.50) approach. Findings were similar with and without multivariate regression, with proportions of variance (R2) in 24hR intakes explained by the AME being generally greater than PC estimates, yet still low to modest.ConclusionsIn this national survey, established methods for estimating individual level intakes from household surveys produce overestimation of intakes of nearly all dietary indicators, with significant variation depending on the dietary factor and modest variation depending on individual characteristics. These findings suggest a need for new methods to estimate individual-level consumption from household survey estimates.

Dataset Information

Labour Incomes in India: A Comparison of Two National Household Surveys.

Supplementary information

Publications

Labour Incomes in India: A Comparison of Two National Household Surveys.

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