The findings suggest that many Kenyans associate economic hardship more with domestic governance challenges than with external pressures.
Corruption has been cited as the leading factor driving the high cost of living in Kenya, an Infotrak poll has revealed.
The survey shows that 31 per cent of respondents believe corruption is the main cause of rising living costs.
Taxes followed at 26 per cent, while government policies ranked third at 16 per cent.
The findings suggest that many Kenyans associate economic hardship more with domestic governance challenges than with external pressures.
Only 13 per cent of respondents attributed the high cost of living to global economic factors such as fuel prices and supply chain disruptions.
According to Infotrak, the results point to a significant trust gap between citizens and the state.
Regional data shows that corruption was consistently identified as a top concern across the country.
Nairobi recorded the highest proportion, with 38 per cent of respondents citing corruption as the leading cause of high living costs.
Central Kenya followed at 32 per cent, while the Coast and Eastern regions each stood at 31 per cent. In Nyanza and the Rift Valley, 30 per cent of respondents held the same view.
Taxes featured prominently in several regions, particularly in the Coast at 32 per cent and Western Kenya at 31 per cent.
Government policies were most frequently cited in Nairobi, where 20 per cent of respondents pointed to policy decisions as a key driver of economic strain.
The survey also examined perceptions across gender and age groups. Among men, 34 per cent cited corruption as the main cause of the high cost of living, compared to 28 per cent of women.
Younger respondents aged 18 to 26 were the most likely to blame corruption, with 41 per cent holding this view.
The figure dropped steadily with age, falling to 30 per cent among those over 55.
The poll was conducted on December 19-20, 2025.
Interviews were conducted through Computer-Assisted Telephone Interviews (CATI).
The survey sample achieved was 1000 to represent the universe of adult Kenyans who were 18 years and above at the time of the survey.
The sampling frame was designed using Population Proportionate to size (PPS) guided by the 2019 Census.
Margin of error was ±3.10 per cent at 95 per cent degree of confidence.
Where the achieved interviews differed slightly from the intended sampled proportions per demographic group, the dataset was weighted to correct for over or undersampling, thus ensuring the sample was proportionately representative of the target population.
The survey covered all 47 counties and 8 regions of Kenya.
To ensure national representativeness, the distribution of the survey sample across the regions was proportionately allocated. Data was processed and analysed using SPSS 27 statistical software due to its high accuracy and reliability
