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Pakistan Deaths
Pakistan Cases

Over the past year, Covid-19 has metamorphosed from a devil we knew little to nothing about to a devil we now, ostensibly, know quite painstakingly. There are still gaps that need to be filled but there is a mountain of information being collected (and in turn analysed) on virus intensity, associated risks, impact on different demographics, strategies to combat the virus itself and mitigating measures to protect financial and economic interests of the people. In Pakistan’s context, enter PBS.

The Pakistan Bureau of Statistics’ (PBS) latest data collection exercise to evaluate the socio-economic impact of covid-19 during the first wave has invaluable insights. The data that was collected from 6000 households with urban to rural ratio of 70:30 covers crucial impact areas such as employment, incomes, food security, coping strategies and government/NGO assistance. But before summarizing some of its disturbing findings—specially for the share of the population not impacted by the virus and hence, suffering from the joys of ignorance—it is important to understand why this data is so crucial.

After small bursts of downtime, the virus keeps coming back with a vengeance. The country, much like the rest of the world, is in its third wave of the virus and it seems, this latest wave is proving to be more deadly than its predecessors. Naturally, the government has to curate strategies in a way that on the one hand, shrinks the pace of the virus spread, but on the other hand, does not cause the economy to destabilize again, which is arguably still on shaky ground.

How did the first wave fare then? The PBS found that due to the shut-down of economic activities after lockdown, nearly 27 million people either faced unemployment, could not work or suffered from a reduction in their income, resulting in the working population falling from 35 percent to 22 percent. That is a massive number. The most affected region remained Sindh that saw its working population reduce by 15 percent.

Of that 27 million, the largest share i.e., 20 million had lost their jobs due to the lockdown while 6.7 million experienced reduced income scenarios. Perhaps an even more important finding here is that amongst the affected population, people employed in the informal sector such as daily wagers, casual workers and own-account workers (shop-keepers, street vendors, and taxi drivers) were the most affected and vulnerable to the covid-19 lockdowns—constituting of 75 percent of the total affected population.

Further reinforcing the earlier finding, based on household wealth quintiles, it was found that the highest percentage of households in the richest quintile (56%) did not perceive any shock to their financial well-being due to the lockdowns compared to 25 percent of the poorest households. Another way to look at it is that, 75 percent of the poorest households did in fact experience moderate to severe financial impact during the first wave. To cope with their shrinking wallets, 56 percent of the population reduced non-food expenditure, 50 percent switched to low quality and low quantity of foods, 47 percent sold their property or spent their savings, and 30 percent ended up taking out loans from friends and family.

Post-first wave scenario shows working population improved once again (during Aug-Oct 2020) up to 33 percent—less than the pre-covid era but still a substantial improvement. However, soon after this lull, the second wave hit the economy and now the third wave. Though the country has not gone into a complete lockdown situation, several restrictive measures (in terms of mobility, keeping business doors opens etc.) have been taken over the subsequent waves which naturally means that the recovery in the working population and incomes may have been reset to an extent.

While there is a lot of learning here, unfortunately, there is a paucity of data on the socio-economic profiles of the population that actually contracted the virus, ended up in the hospital, needed immediate healthcare, and/or succumbed to the virus. This data should have been collected because it is vital to draw a comparison here on the impact of the virus itself on different socio-economic segments versus the so-called indirect consequences of the virus on these very socio-economic segments, including the mitigating strategies (such as lockdown) adopted by the government.

There is a growing belief that the poorer segments were more immune to virus contraction, though there is no certain way to validate that. If this assertion is contrasted with the confirmed fact that poorer segments were more heavily impacted by lockdowns, it would be a rather disconcerting piece of information. But should also ultimately inform better policies for the government as the third wave becomes stronger.

What’s certain is that the government must move toward country-wide prevention policies (while running side-by-side vaccination awareness campaigns)—which would entail free vaccinations across the population to achieve herd immunity. Inoculating 70-75 percent of the population is the only sure-fire way to ensure that a) the virus stops spreading and re-emerging b) the government no longer has to curb economic activity and enforce restrictive measures which has evidently adversely affected the larger population, and almost disproportionately hurt the most poor and vulnerable segments, irrespective of whether these people actually contracted the virus or not.

But vaccinating 75 percent of the population is a massive undertaking which could take years. The good news is, Covid-19 is the devil we now know and in theory, can tackle. The government needs to rally together large funds for mass vaccinations—jabbing as many as and as fast as possible—while mobilizing efforts to arrange steady supply of vaccinations to private sector players so more people can opt to get the jab in case the government is unable to reach them, which is the likeliest scenario at the moment.