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.
Managed care and insurance have penetrated ophthalmic services for patient care and continue to affect outof-pocket spending related to eye care. In such a scenario, understanding technology diffusion is crucial from the perspective of both technology developers as well care providers. Should healthcare organizations choose cheaper technologies or the ones that can be quickly diffused? In this paper, we try to answer this question by reviewing technologies used in the past 10 years across different services in ophthalmology. We generate a framework that bifurcates technologies and provides nomenclature to them based on cost and speed of diffusion. Our work, within its limitations, can provide insights into contemporary technology producers and healthcare organizations.
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