Direct and Indirect Effects of Covid-19 On Life Expectancy and Poverty in Indonesia

Abstract

The spread and threat of Covid-19 have resulted in unprecedented economic and public health responses in Indonesia and elsewhere. We analyse the direct and indirect effects of Covid-19 on life expectancy and poverty in Indonesia, and the responses to the virus. We view life expectancy and poverty as indicators of quantity and quality of life. Our analysis shows that the indirect effects on life expectancy, which operate through lower future income, exceed the direct effects of Covid-19-related deaths by at least five orders of magnitude. The reduction in long-run real income due to the Covid-19 shock may reduce life expectancy by up to 1.7 years, compared with what could otherwise be expected. In contrast, even if the Covid-19 death toll to date were 40 times worse, life expectancy would fall by just two days. Given this imbalance between direct and indirect effects, any interventions to reduce the risk of Covid-19 must be finely targeted and must consider indirect effects. Our analysis of the geographic pattern of poverty effects, which is based on near real-time mobility data, discusses how targeted interventions that are less fiscally costly could be developed. Such interventions should pose less of a threat to future growth and may help to reduce the indirect effects of the Covid-19 shock.

Penyebaran dan tantangan Covid-19 telah menghasilkan respon-respon ekonomi dan kesehatan publik yang belum pernah dilakukan sebelumnya, baik di Indonesia, maupun di banyak tempat lain. Kami menelaah dampak langsung dan tak langsung dari Covid-19 dan respon-respon terhadapnya pada harapan hidup dan kemiskinan di Indonesia. Kami melihat harapan hidup dan kemiskinan sebagai indikator kuantitas dan kualitas hidup. Analisis kami menunjukkan bahwa dampak tak langsung pada harapan hidup, yang beroperasi melalui rendahnya pendapatan di masa depan, melebihi dampak langsung dari kematian Covid-19, paling tidak sebanyak lima tingkat besaran. Penurunan pendapatan riil jangka panjang sebagai dampak Covid-19 dapat mengurangi angka harapan hidup sampai 1,7 tahun di bawah yang proyeksi-proyeksi sebelumnya. Padahal, bahkan jika korban kematian karena Covid-19 saat ini 40 kali lipat lebih banyak, harapan hidup hanya turun dua hari. Karena ketidakseimbangan dari efek langsung dan tidak langsung ini, setiap intervensi untuk mengurangi risiko Covid-19 harus ditargetkan dengan baik dan mempertimbangkan dampak tidak langsung. Analisis kami mengenai pola geografi dari dampak kemiskinan, yang didasarkan oleh data mobilitas langsung, membicarakan bagaimana intervensi yang ditargetkan dan lebih murah secara fiskal dapat dikembangkan. Intervensi yang demikian seharusnya tidak lebih membebani pertumbuhan di masa depan dan dapat membantu mengurangi dampak langsung dari kejutan Covid-19.

Keywords:

• Covid-19
• Indonesia
• life expectancy
• poverty
• mobility

JEL classifications:

• I15
• I32
• O15

ACKNOWLEDGMENTS

We are grateful to Lydia Napitupulu and Rus’an Nasrudin for their assistance with the data used for this article. We thank the BIES editors, as well as Anne Booth and Howard Dick, for their useful comments.

Notes

1 For example, New York governor Andrew Cuomo tweeted: ‘If it’s public health versus the economy, the only choice is public health. You cannot put a value on human life. You do the right thing. That’s what Pop taught us.’ See https://twitter.com/NYGovCuomo/status/1242264009342095361.

2 A similar pattern is evident for all-cause mortalities, which would capture the indirect effects that can occur if life-saving procedures for treating other diseases are not carried out because hospital beds are full of (or being reserved for) patients with Covid-19. Docherty et al. (2020) found that the number of deaths from all causes between February and early May in England and Wales was about 11% above usual for people younger than 45 but 65% above usual for 65–74 year olds and 82% above usual for those aged 75 or older.

3 See https://www.worldometers.info/coronavirus/#countries.

4 Testing the statistical relationships between trending variables can be difficult. But for Indonesia (and Cambodia, Myanmar and Malaysia), life expectancy and real per capita GDP appear to be cointegrated. This implies the existence of a stable long-run relationship between the two variables that can be estimated with an ordinary least squares method.

5 McKibbin and Fernando (2020) considered six scenarios resulting from the economic effects of Covid-19. We use the results from their third scenario. In this scenario, countries that have used lockdowns to reduce infection transmission begin to relax these restrictions. But we allow for the possibility of a second wave emerging in 2020 as a result of being too early in easing the lockdowns after the first wave (as seems to have happened in Europe).

6 We use an interest rate based on the lending rate charged to commercial banks. See https://www.ojk.go.id/id/kanal/perbankan/Pages/Suku-Bunga-Dasar.aspx.

7 We compare 2.2 days with 1.7 years to derive a factor of about 280. The 2.2 days in turn are based on 40 times the actual death toll; 280 multiplied by 40 gets us to five orders of magnitude.

8 The National Labour Force Survey (Sakernas) found that over half of all workers in Indonesia’s agricultural sector were aged 45 or older in August 2019.

9 A World Bank (2020b) survey found in May 2020 that 31% of Indonesian households experienced a shortage of food and 38% admitted to eating less than needed over the previous week because of a lack of money. Poorer households, as well as households experiencing a loss of income, reported a higher prevalence and severity of food insecurity.

10 Williams et al. (2020) used a different approach based on time series Poisson modelling. They found that the lockdown in the United Kingdom resulted in more deaths, because of (non-income-related) indirect effects operating through mortality not directly caused by Covid-19.

11 Sumner, Hoy and Ortiz-Juarez (2020) estimate a scenario in which household income or consumption falls by 20%. Laborde, Martin and Vos (2020) estimate that each percentage point of global economic slowdown increases the number of poor and food-insecure people by between 14 million and 22 million in Sub-Saharan Africa and South Asia—the two regions that contribute most to global poverty.

12 See ‘Estimating the Impact of Covid-19 on Poverty in Indonesia’ in the August BIES.

13 Pradesha et al. (2020) estimate increases in the poverty rate of about 13 percentage points, following the large-scale social restrictions in regions of Indonesia with high numbers of Covid-19 cases.

14 The Australian Bureau of Statistics uses high-frequency data to produce an interactive map of the impacts of Covid-19 on employment in Australia. See https://bit.ly/33wvSnQ. Similarly, the United Kingdom releases weekly bulletins, including online price indices and daily shipping data, to measure the economic impact of Covid-19 on inflation and trade. See https://bit.ly/2GksVy9.