Health or economy in Indonesia? Making the best impossible decision during COVID-19
This report grapples with one of the most difficult decisions faced by governments during the coronavirus pandemic – how to look after people’s health and maintain the economy.
It takes this impossible question and presents a solution, a model that can help with decisions around maximising the economy while working to bring COVID-19 under control.
Choosing measures that are biassed to health or economy is challenging in a country like Indonesia where more than 60 percent of workers are in the informal sector. This includes micro and small enterprise entrepreneurs, roadside stalls, drivers and service providers. This vibrant part of the economy underpins jobs and income, and putting it on hold creates a significant strain for those who rely on it.
However, Indonesia has also been one of the countries hardest hit by the spread of the virus. By November 2021 there were, officially, 4.5 million cases and 144,000 deaths. Data analysts and civil groups say the actual numbers are higher. The pandemic has strained healthcare workers and systems and put people in desperate medical situations.
The dilemma of dealing with dual crises can be seen in the range of policy measures introduced, from minimal economic intervention to large-scale curbs, to more relaxed restrictions to revive the economy.
This report asks the question: What if there was a way for leaders and governments to consider the impacts of different policies on both the economy and health?
The researchers, from the fields of mathematics, data analysis and social sciences, have combined an economic and epidemiological model to create a new model, where the settings can be adjusted in favour of either health or the economy and enable a choice to be made depending on the effects on a population.
Models of both pandemic and economic activity have already been studied separately and extensively. None have modelled economic activity and public health together over a few months. For the purposes of the modelling, two types of government intervention are considered: public health policies and border restrictions.
Testing of the model drew on data from eight areas in Indonesia – Bali, Eastern Indonesia, Jakarta, Java, Kalimantan, Papua, Sulawesi and Sumatra — covering nine economic sectors: agriculture, construction, finance, manufacturing, mining, services, trade and hotel, transport and communication, and utilities. Given the limited data available on the health and economic impacts of the government’s pandemic response, we used hypothetical scenarios to assess the viability of the model.
We included three hypothetical scenarios:
- Do nothing policy – exhibits the highest infection rate but no changes to economic demand or production levels.
- Light policy – places some restrictions on the services, trade and hotel sectors but decreases the transmission rate.
- Medium policy – places restrictions on construction, manufacturing, transportation and communication in addition to increasing the severity of restrictions on services, trade and hotel sectors.
The aim of this project is to create a model to help governments decide how to maximise economic activity while controlling COVID-19.
We want to answer the questions: Which measures can protect the health of the population, and what is the cost for the economy?
In our model we suppose that we can implement two types of governmental measures, either within a single province or between two provinces. Both reduce the spread of the pandemic but hinder economic activity.
The model identifies the best government intervention for all provinces. With the data on the pandemic in Indonesia, we will analyse the solutions provided by our model and compare them with a baseline of no governmental intervention to assess the methodology and model we have created.
The results we present demonstrate the importance of such models, given the magnitude of difference between the various solutions it can produce: we evaluate that heavy governmental intervention can save about 35,000 lives a year, but in the worst case it would trigger a substantial decline in the economy (about 10 percent of GDP in our model).
Our model also reveals that many other solutions involving less costly public health measures, offer different trade-offs which might lead to more lives being saved.
This area of research does require more investigation and there are several ways to potentially improve the solutions that can be found. First, the model itself could be improved to work with the software.
Second, the solving software itself could be better used or parameterised.
Third, the solving software could be extended to better handle problems such as ours. Fourth, different solving techniques could be employed to tackle our models.