Unveiling the driving gig economy: Key insights from a descriptive study of platform drivers in Kenya

On any given day in Nairobi, the hum of boda bodas cuts through the city’s noise. With just a few taps on a phone, thousands of Kenyans now rely on ride-hailing platforms to beat traffic, get to work, make deliveries, order food, or carry out countless other activities that require moving from point A to B. For commuters, this convenience is now a staple of urban life. But behind this convenience lies a complex workforce navigating the tension between flexibility and precarity.

As part of the Digital Economy Research Impact Initiative (DERII), supported by the Gates Foundation, IDinsight’s Kenya team conducted a descriptive study in collaboration with one of Kenya’s major ride-hailing/delivery platforms. Our goal was to understand the economic realities of motorcycle gig workers and generate insights that can support policymakers and stakeholders in designing more informed and responsive policies for platform workers and the digital gig economy. We examined the realities of two-wheeler drivers(primarily motorcycle drivers) working through the app. We obtained the full population of active drivers from the platform and surveyed 987 drivers who consented to participate in the research. In this blog, we discuss the highlights from the report, which outlines drivers’ demographics, work patterns, earnings, and broader experiences. 

Key Findings

Demographics

The average age of drivers is 32, with 81% married living in households of 3.7 members ( slightly larger than the urban average of 3.1  according to KDHS, 2022). Education levels are similar to those of the average urban male: 50% of the drivers completed high school, and 20% hold a college degree. Notably, 81% of drivers are migrants to the city, most of whom came searching for work. Gender participation is extremely low, with only 1% of drivers identifying as women—a finding consistent with research from Indonesia and South Africa, where cultural norms, safety concerns, and asset constraints significantly limit women’s involvement in ride-hailing (Kabeer et al., 2022, Women in Indonesia’s Gig Economy). We could not reach many female drivers, and their experiences are not represented in this research. 

Entry into  platform work

Financial pressure is the primary motivation for joining platform work. Drivers cited better pay (32%) and the need to support families or find income while job searching (32%) as reasons for joining. Platform work is more accessible to those with initial assets. Before joining the platform 89% of the drivers owned a motorcycle and a smartphone, with smartphone ownership well above the national average of 49% among men aged 15 to 49  (KDHS, 2022). Although ownership rates are high, many drivers acquire these assets through borrowing or renting. A majority of the drivers (64%) still pay off loans or rent their motorcycles, and 40% did the same for their phones. Among these, over 70% report skipping essential expenses like food or rent to meet repayment obligations, a finding aligned with research in Ghana that links asset rentals to heightened financial vulnerability (Woodcock & Graham, 2020). 

Current work

To sustain their livelihoods, many drivers juggle multiple income sources. On average, drivers reported having two income sources.  Nearly half (41%) of the drivers “multi-home” (use multiple apps in real time or switch between them to maximize order volume). Among them, common approaches include switching between apps throughout the day or running all apps simultaneously and accepting whichever order comes first. This mirrors trends observed in South Asia and Latin America, where multi-homing functions as a practical hedge against fluctuating demand (Berg et al., 2018). 

Drivers work long hours to make ends meet. On average, they clock over 66 hours per week, with more than 70% of that time spent on one platform. Nearly half work until they hit a target income, while 45% of full-time drivers tailor hours to demand. While flexibility is a core promise of platform work, drivers’ schedules are shaped more by economic compulsion than by personal autonomy, a dynamic also noted in Indian and Brazilian gig markets (Heeks et al., 2021). 

Gross and net income for full-time consistent drivers 

In this population, typical gross and net earnings are challenging to estimate, given they fluctuate within a day/week/month and differ for different types of drivers. In addition, given the gig nature of this job, some drivers only log in for a few hours a day, and attributing fuel expenses solely to gig work is incorrect. We use administrative data in combination with the survey responses to provide an estimate of gross (pre-expenses income) and net income (after subtracting operating expenses). To estimate these earnings, we use a subsample of “full-time consistent” drivers who worked full-time (over 40 hours/week during the study period) and do not report multi-homing. This choice was made to maximize the accuracy of net income estimation, which is higher for drivers who only use one platform and work full-time.  

We find that during the surveying period (November-December 2024), full-time consistent drivers earned a gross KES 238 and a net KES 102 per hour, which scales to net KES 20,621 per month. According to administrative data, gross earnings during the survey period were about 48% higher than the average between July and November 2024, so they may not reflect typical income across the year. Incorporating a wider measurement period (July-December 2024), we find average gross and net income per hour to be KES 135.5 and KES 46.5, or KES 9,368 per month. 

Using wage data from the 2024 KNBS Economic Survey, we benchmarked platform earnings to other full-time jobs available to this demographic, and we arrive at different conclusions, depending on the measurement period. During the November-December high-earning period, we find that drivers earned more than most similar jobs, which pay between KES 15,201 and 19,668. However, looking at a wider period between July and December, we find that drivers earn less per month. The per-hour equivalent for both periods is lower compared to other full-time jobs. This pattern is consistent with findings from the World Bank (2022), indicating that gig workers often trade time for income without achieving greater security.

Differences in earnings by motorcycle and ownership types

Given the expansion of the electric vehicle (EV) market, we provide differences in earning potential between full-time electric (EV) and petrol bike drivers. Although the EV sample is small, we found suggestive evidence that EV drivers had lower daily expenses (mostly due to lower fuel costs) and earned more in net hourly income. At the same time, using the administrative data on all drivers, we find that EV drivers completed fewer deliveries and earned 27% less in gross hourly income. This may be due to longer charging times or reduced operating range caused by limited charging infrastructure. These findings suggest that while EVs reduce operating costs, productivity losses may offset these gains. High rental costs and weak infrastructure remain barriers to EV adoption (IEA 2024; World Bank 2023). Platform incentives or investment in infrastructure could improve profitability and expand access for more drivers.

We also found that full-time consistent drivers who rent or repay loans for their motorcycles face significantly higher operating costs than owners, leading to a lower hourly income; however, the estimates are not precise. 

Social protection and collectivization

Social protection coverage remains low. Only 47% of drivers are enrolled in the public social health insurance fund, and 45% contribute to the National Social Security Fund (NSSF). While these rates significantly outperform the broader informal sector—where only 10–20% are enrolled in public schemes—they still leave many drivers vulnerable to health and income shocks (Government of Kenya, 2021). Awareness of these schemes is high (>95%), but barriers to enrollment persist, especially for renters and financially insecure drivers. Similar knowledge–enrollment gaps have been observed in Rwanda (Government of Rwanda, 2020). Most drivers are not part of formal unions or collectives, and while many express interest in organizing, few are currently engaged in worker associations that could strengthen their bargaining power. Low levels of collectivization reflect broader trends in LMIC gig economies, where fragmented work structures and restrictive platform policies limit workers’ ability to organize collectively (Atzeni & Cini, 2024; Chambers and Partners, 2025).

Life after [platform] work

We interviewed 193 inactive drivers to understand the reasons for their departure from the platform and their economic activities afterward. 44% left due to app suspension, often tied to algorithmic decisions that lacked clear dispute mechanisms. Many (48%) transitioned to offline taxi services or other ride-hailing platforms (36%), with a majority reporting lower earnings and fewer working hours. While a small subset described increased job stability or career progression, most former drivers faced diminished economic prospects, indicating that platform exit may be welfare-reducing for many. This is consistent with findings from Argentina, where platform exit often leads to downward economic mobility (De Stefano et al., 2021).

Conclusion and recommendations

Motorcycle ride-hailing platforms are a key source of employment in Kenya. They provide income to thousands and offer flexible earning opportunities in a tight labor market. But these jobs also come with risks, especially for drivers without access to social protection, dispute resolution systems, or asset ownership.

To improve driver welfare, we recommend the following:

1. Enable inclusive, flexible social protection: Government agencies (e.g., Social Health Authority, NSSF) and platforms should co-design enrollment systems that allow drivers to opt into social and health insurance products via micro-deductions from earnings. Rwanda’s EjoHeza scheme—built on mobile enrollment and flexible contributions—offers a relevant model for this integration.
2. Expand equitable vehicle access: Key stakeholders (government, financing institutions, manufacturers, and platforms) should develop subsidized credit schemes or guarantee funds to help gig workers acquire motorcycles through favorable, long-term leasing or hire-purchase agreements. This approach can leverage public-private partnerships, drawing lessons from successful models like Gigmile. 
3. Foster gender inclusion: Platforms should integrate safety-enhancing tools and gender-sensitive features to address the barriers limiting women’s participation. Kenya may also consider a national gig economy gender inclusion strategy that promotes safety, fair access, and anti-discrimination protections.

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