Income of Gig Work vs. Previous Job in Pakistan

Richard Heeks, Iftikhar Ahmad, Shanza Sohail, Sidra Nizamuddin, Athar Jameel, Seemab Haider Aziz, Zoya Waheed, Sehrish Irfan, Ayesha Kiran & Shabana Malik

Does the transition to gig work improve incomes in Pakistan?

Many workers join gig work platforms in the belief that their incomes will improve, but is this borne out in practice?  To investigate, the Centre for Labour Research interviewed 94 workers based on six platforms across three sectors: ride-hailing, food delivery, and personal care.

Of these, 51 were able to tell us what their previous monthly income had been in their most-recent employment prior to joining the platform[1].  Stated income varied from the equivalent of US$60 per month up to U$1,200 per month, and averaged US$220 per month[2].

After moving into gig work, average gross income was slightly higher, at US$240 per month but, as the graph below shows, there was a much more differentiated picture behind the average, with around 40% of respondents earning less gross income (red-bordered blue columns) than they had done previously.

However, as the graph also shows, things looked worse when comparing net income (orange columns).  For the great majority of prior jobs, work-related costs were small (only work-to-home transport, which we calculated based on typical commuting journeys in Pakistan to be just under US$18 per month; i.e. less than 10% of average gross income).  But for gig work – much of which relies on journeys by vehicle and continuous internet connectivity – the costs of petrol, maintenance and data eat heavily into gross income.  In addition, for some (only a few in our Pakistan sample) there are costs of renting their vehicle.[3]

These costs represented, on average, 65% of gross income and knocked average net income for gig workers down to just US$85 per month.  When we compare before-and-after for net income, then, we found more than 70% of our sample were earning less than in their previous job, and 45% earned over US$100 per month less.

This was especially an issue for ride-hailing drivers and it does reflect the particular circumstances during our interview period of late 2021 to early 2022: a drop-off in demand for travel due to Covid, and a steep rise in petrol prices.  Indeed, so bad was the problem that just over a fifth – 21 of the 94 – were reporting negative income.  That is, they were effectively paying to go to work as their costs exceeded their gross income; something to which the platforms responded in May 2022 by dropping the commission taken from drivers to 0%.

While recognising the challenging period for gig workers covered by our fieldwork, nonetheless, this does suggest that – by and large – gig work is not delivering the income boost that workers often hope for.  They may, for example, be lured by gross income figures, not realising how much lower net income will be.  Gig work does provide a livelihood – 40% of our sample were unemployed in the immediate period prior to joining – but it is not really fulfilling its promise.  It also falls far from decent work standards: five-sixths of those we interviewed took home less than a living wage.

If you’d like to know more, please refer to the 2022 Fairwork Report on Pakistan’s gig economy.

[1] Those who stated what their prior employment had been gave the following job descriptions: BPO operator, Teacher (2), Housekeeper, Shopkeeper, Gas company worker (2), Safety officer, Business person, Tanker driver, Ride-hailing driver with another platform (3), Traditional taxi driver (3), Farmer, Builder, Computer operator, Cook, Technician, Shop assistant, Domestic worker, Government worker

[2] This average is some way above the overall average earnings of US$140 per month but well below formal sector average monthly salary of US$480.

[3] For further detail, see this discussion of the breakdown of ride-hailing passenger payments.


Digital public goods platforms for development

Nicholson, B. Nielsen, P. Sahay, S. Saebo, J. Digital public goods platforms for development: The challenge of scaling The Information Society available open access at:

Recently there has been an explosion of research into digital platforms.  To provide an indication of the size of the output, a quick search on Google Scholar provided 3270000 “hits”, 39900 in 2022 alone to date with publications across diverse disciplines including management, information systems, economics and more.   In the realm of ICT4D, discourse has focused on how platforms may enable socio-economic development (Nicholson et al 2021) however there is a paucity of examples of empirical research on how this may be realised.  

Digital platforms are defined according to their principal purpose and identifies two broad categories: transaction platforms and innovation platforms. Transaction platforms refer to a two or multi sided marketplace mediated by the platform.  Innovation platforms act as “foundations upon which other firms can build complementary products, services or technologies” (Gawer, 2009, p. 54).

Most prior empirical research on digital platforms involves commercial, for-profit platforms situated in the regulative institutions of the Global North.  Inherent in this prior work is an assumption of “monetisation” and the capitalist market forces, and little is known about platforms that are donor supported and aimed at socio economic development.    

A forthcoming paper attempts to address the knowledge gap by conceptualising innovation platforms as public goods and asking:

How can innovation platforms be public goods?

A goal of the article is to identify the challenges of simultaneously scaling up digital platforms and developing them into public goods.  Empirically, the focus is on health, specifically the empirical example is the District Health Information System (DHIS2). 

The relevance of public goods in development is well-established in the domain of health.  Initiatives driven by global health organisations such as the World Bank and World Health Organization aim to promote digital public goods. Digital Square, a marketplace initiative in digital health, has developed a Global Goods Guidebook and a Global Goods Maturity Model.  Before and during the pandemic, open-source systems have been launched to support outbreak management, such as the Surveillance Outbreak Response Management and Analysis System (SORMAS). SORMAS intuitively displays features of a public good: it is free of charge, open source, independent from tech companies, and interoperable with other platforms such as DHIS2.

Turning to theory of public goods leads us to the economics discipline and centres on two main principles: non-rivalry and non-exclusion. “Goods” such as crime control, flood defences etc. are provided because of failure of the market mechanism.  Government thus intervenes either financially, through such mechanisms as taxation or licensing, or with direct provision.   Public goods are non-rivalrous, implying that one individual’s consumption of the good does not influence what is available for others. They are also non-excludable, in the sense that no one can be excluded from consumption of a public good. 

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Consider a lighthouse where one navigator’s use of the light does not prevent other navigators from doing the same. Many potential public goods exhibit only one of these properties resulting in the tragedy of the commons which can be illustrated with the example of a village pasture. Unrestricted access (non-exclusion) to the commons – pasture belonging to the village as a whole – leads to its degradation (rivalry). However, some scholars question the inevitability of depletion of common pool resources when they are managed in a bottom-up, cooperative way by those most dependant on them.  Under certain conditions, individuals govern themselves collectively, and without market pressures or government regulation, to obtain benefits, even if the temptation to freeride is present.

Global public goods are goods whose benefits cross borders and are global in scope for example eradication of infectious diseases where it is impossible to exclude any country from benefiting and each country will benefit without preventing another.

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The district health information system or DHIS2 supports decentralized routine health management. The architecture is designed with a generic core that enables local innovation and anyone with internet access can at any time download the most recent version of DHIS2, the source code, as well as required libraries and third-party products (such as Chrome or Firefox browsers). DHIS2 also comes with a set of bundled apps, developed by University of Oslo or through its partners in the Global South (such as HISP Tanzania, an independent entity with close collaboration with Oslo) available in an “app store.” It is similar in concept to Apple App Store or Google Play and some DHIS2 apps are also available on these platforms too. The platform architecture allows local innovation as apps, increasing its potential relevance globally.

Due to its openness and flexibility, it is impossible to know the exact number of DHIS2 implementations. It is known that ministries of health and other organizations in more than 100 developing countries use DHIS2, together covering an estimated population of 2.4 billion people.  In November 2020, the ministries of health in 73 countries (primarily developing countries) used DHIS2, out of which 60 were nationwide implementations, and 13 were in the pilot stage. In addition, 22 Indian states used DHIS2. There is also a range of other organizations using DHIS2 independently for reporting in the countries they are operating, including PEPFAR, Médecins Sans Frontières (MSF), International Medical Corps, Population Services International (PSI), and Save the Children.

We can explore the “qualification” of DHIS2 as a public good by considering some of the challenges experienced by developers in Oslo and other implementation sites examined as tensions and paradoxes.  In a seminal paper on paradoxes and theory building, Poole and van de Ven (1989) identify a paradox as “concerned with tensions and oppositions between well-founded, well-reasoned, and well-supported alternative explanations of the same phenomenon” (565). 

Consider the story of the product lead of the DHIS2 analytics team response to the challenge of prioritizing requests by developing a roadmap prioritization matrix. Most use-cases need analytics functionality, and a wide variety of requests are directed to this team. The product lead estimates that the analytics team can only accommodate about half the requests at any stage of the product development cycle. The question facing this individual is: “which requests should be prioritized, coming from whom, and in which release cycle?” The primary implementations of DHIS2 are users from governments in low- and middle-income countries, according to the product lead, who tend to not actively voice their requests for changes in functionality. These groups are constrained by physical separation often across great distance, limiting ability to meet in person and develop social relationships. By contrast, users from donor organizations and other users in the West, tend to have closer proximity and resources to visit Oslo and “make their voices heard,” resulting in greater influence over the DHIS2 functionality development. This mismatch led the product manager to develop this “objective” prioritization methodology.  From the perspective of public goods, the dynamics of donors’ activity affects the rivalry / excludability conditions as their greater influence means that other users are relatively excluded, and access is rivalrous depending on this influence.

There are also paradoxical consequences of scaling at the macro and micro levels.  While the Oslo development team add in their releases of new features for strengthening outputs and analysis towards a generic global platform, the typical user in a district of a developing country requires basic functionalities, and the new features often detract instead of increasing the software’s value for the users.  At the macro-level, the development team are seeking to cater to the universe of users, including district users, researchers, and data analytic experts in multiple country contexts. This requires them to continuously add new features, often for increasingly sophisticated use. This process went counter to the needs at the micro-level of the users in district offices, who want specific and easy to use functionalities for their everyday use.  Thinking again from a theoretical standpoint, the malleability of a digital good compared to the oft cited example of a static lighthouse is clearly evident. The drive towards generic global features at the macro level causes rivalry and excludes some users at the local more micro level.  

Overall, the more macro interests of the donors and drive towards a global generic platform appear incompatible with the smaller players who become increasingly marginalized. Furthermore, their capacity for collective action is limited by structural factors.   This challenges DHIS2’s status as a public good as we can see rivalry and exclusion creeping in.

The problem is not insurmountable, collective action and subsidiarity offer helpful mechanisms of governance. Two main subsidiarity conditions are known to be helpful related to effectiveness and necessity: that action should be taken at the level where it is most effective and that action at the higher level should be taken when lower levels cannot achieve the set goals by themselves. This is in line with ongoing efforts by Oslo to build South-South community-based networks and thereby decentralization into the Health Information System Programme (HISP) network. 


Gawer, A. (2009). Platform dynamics and strategies: from products to services. Platforms, markets and innovation45, 57.

Nicholson, B., Nielsen, P., & Sæbø, J. (2021). Digital platforms for development. Inf. Syst. J.31(6), 863-868.