Social connections and tertiary health-care utilization

Pandemics and technology engagement: New evidence from m-Health intervention during COVID-19 in India

Information provision for social welfare via cheap technological media is now a widely available tool used by policymakers. Often, however, an ample supply of information does not translate into high consumption of information due to various frictions in demand, possibly stemming from the pecuniary and non-pecuniary cost of engagement, along with institutional factors. We test this hypothesis in the Indian context using a unique data set comprising 2 million call records of enrolled users of ARMMAN, a Mumbai-based nongovernmental organization that sends timely informational calls to mobile phones of less-privileged pregnant women. The strict lockdown induced by COVID-19 in India was an unexpected shock on engagement with m-Health technology, in terms of both reductions in market wages and increased time availability at home. Using a difference-in-differences design on unique calls tracked at the user-time level with fine-grained time-stamps on calls, we find that during the lockdown period, the call durations increased by 1.53 percentage points. However, technology engagement behavior exhibited demographic heterogeneity increasing relatively after the lockdown for women who had to borrow the phones vis-à-vis phone owners, for those enrolled in direct outreach programs vis-à-vis self-registered women, and for those who belonged to the low-income group vis-à-vis high-income group. These findings are robust with coarsened exact matching and with a placebo test for a 2017-2018 sample. Our results have policy implications around demand-side frictions for technology engagement in developing economies and maternal health.

Does social network impact rising out-of-pocket health care expenditure in India? A propensity score matching approach

BACKGROUND

The out-of-pocket expenditure (OOPE) on health care in India is more than two-third as per the recent estimates and is catastrophic. Literature identifies different factors, but little attention has been given to social network or social relationships as an essential factor in dealing with OOPE on health care.

OBJECTIVES

This paper targets to find the link or association between social network and OOPE on health care through access to information.

METHODS

The paper uses India Human Development Survey (2011-12). Among the standard methods of impact evaluation, the Propensity Score Matching (PSM) method is identified as the best fit to explain the role of social network on health care outcomes. Social network is considered as the treatment variable. The independent variables include age, sex, education, place of residence, household expenditure per capita and information index. The paper also applies Principal Component Analysis method to estimate information index with equal weight using variables such as newspapers, mobile phones, and Internet facilities.

RESULTS

PSM result shows that direct social network reduces OOPE on health care by ₹1417 in a year. In balancing test, we see that for each of the covariate, the bias is actually reduced by applying PSM. The Information index has a positive relationship with both direct and indirect social network.

CONCLUSION

The paper suggests policy-based interventions to enrich the social relationships and spread of information, in order to curtail the rising OOPE on health care in India.

Family health spillovers: evidence from the RAND health insurance experiment

I study how family spillovers shape healthcare consumption through two main sources: a learning channel whereby family members share information about their health insurance and the effectiveness of healthcare, and a behavioral channel whereby risk perception and habits are shared and transmitted. I exploit two types of sudden health shocks to identify a causal effect operating through each channel: a spouse's non-fatal heart attack or stroke and a severe injury to a child. I incorporate these shocks into an event-study framework to quantify the effect of spillovers on healthcare consumption of a non-injured adult family member. I find a significant behavioral spillover effect of an increase of more than 200% in medical expenditure of preventive care over a four-year horizon. Moreover, I find a strong and persistent learning spillover that amounts to an average increase of more than 150% in medical expenditure relative to prior to the health shock, and I demonstrate that this effect promotes health investment. While the first result is in line with previous findings in the literature, the second is novel.