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Using data from the 60(th) round of the National Sample Survey of India (2004), the study investigates the incidence and correlates of 'catastrophic' maternal expenditure (ME) in India. Data on ME come from 6879 births that took place during 365 days prior to the survey. The study adapts earlier definitions and methods for catastrophic total health care expenditure to measure 'catastrophic' ME as: (i) maternal health care expenditure more than 10% of the annual normative household consumption expenditure (ME-1), and (ii) maternal health care expenditure more than 40% of the annual 'capacity to pay' (ME-2). The 'capacity to pay' was derived by subtracting state-wise poverty-line household expenditure from household consumption expenditure. The average maternal expenditure varied by place of delivery: US dollar 9.5, US dollar 24.7 and US dollar 104.3 for birth at home, in a public facility and in a private facility, respectively. Sixteen per cent of households incurred ME of more than 10% of total household consumption expenditure (ME-1), while 51% households incurred ME of more than 40% of household 'capacity to pay' (ME-2). While incidence of ME-1 increased with income decile, the reverse was observed for ME-2, reflecting higher non-utilization of institutional maternal care and its non-affordability among poorer households. All the households from the poorest decile and 99% from the second poorest decile paid more than 40% of their capacity to pay. Multivariate regression results indicate that antenatal care and delivery care in private facilities increased the chances of ME-1 and ME-2 (P < 0.001). Measuring maternal expenditure against 'capacity to pay' (ME-2) may be better than measuring it as a proportion of overall household expenditure when assessing financial constraints in the use of maternal services. Improving the performance of the public sector, appropriate regulation of and partnership with the private sector, and effective direct cash transfers to pregnant women in the poorest households may increase utilization of maternal services and reduce the financial distress associated with ME.

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Advances in digital technology have put music libraries at people’s fingertips, giving them immediate access to more music than ever before. Here we overcome limitations of prior research by leveraging ecologically valid streaming data: 17.6 million songs and over 662,000 hr of music listened to by 5,808 Spotify users spanning a 3-month period. Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. Findings from machine learning showed that the Big Five personality traits are predicted by musical preferences and habitual listening behaviors with moderate to high accuracy. Importantly, our work contrasts a recent self-report-based meta-analysis, which suggested that personality traits play only a small role in musical preferences; rather, we show with big data and advanced machine learning methods that personality is indeed important and warrants continued rigorous investigation.

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