Evidence map›Paper›PMID 35942311›Full record

ArticleReview of development economics2022

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

Sawan Rathi, Anindya S Chakrabarti, Chirantan Chatterjee, Aparna Hegde

Abstract read
In one paragraph

Article in Review of development economics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sawan RathiIndian Institute of Management Ahmedabad Gujarat India.
Anindya S ChakrabartiEconomics Area, Indian Institute of Management Ahmedabad Gujarat India.
Chirantan ChatterjeeScience Policy Research Unit University of Sussex Business School Brighton UK.ORCID https://orcid.org/0000-0002-4684-2858
Aparna HegdeARMMAN and Cama Hospital Mumbai India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

genderinformation acquisitioninformation provisionm‐Healthpandemictechnology engagement

Identifiers

PMID35942311
PMCPMC9350278

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.