Living gig to gig and payslip to payslip

by Johanna Peetz & Jennifer Robson
Posted on April 06, 2020

When income is changeable and uncontrollable: a laboratory study of saving decisions

The global COVID-19 pandemic brought and still brings about major ripples of economic uncertainty. Governments and companies are all attempting to find ways to help their citizens or customers handle shock after shock. In many cases, household incomes are plummeting. People stay away from work to care for family members, or to prevent getting infected. In many countries, gig workers are hit hard, whilst not always qualifying for traditional public income support programs.

Gig work is an important part of labour markets today. Approximately one in six workers is self-employed, and one in eight is on a temporary contract (OECD, 2018). A key difference between gig workers and traditional salaried workers is that their month-to-month incomes may be more unpredictable. When hit by an unexpected shock, can these workers fall back on a saved buffer? Or do swings in monthly income make it less likely that they manage to save up for emergencies like the coronavirus?

Swings in monthly income, whether coming from gig work or not, have been linked to lower overall savings (Fisher, 2010; Barr, 2012; Mullainathan & Shafir, 2013), and to more frequent missed payments for bills and mortgages (Farrell & Greig, 2016; 2017; Diaz-Serrano, 2005). Our own previous studies showed that workers reporting more month-to-month income volatility also reported more trouble at making ends meet, and also at planning ahead and saving money.

Off to the lab

Does income volatility actually cause financial stress and change financial decisions? Or do the type of people who make more short-sighted financial decisions tend to go for gig work and self-employment? We examined this question in a controlled lab experiment.

Our participants were 149 community members from Ottawa, the capital city of Canada.1 We randomly assigned them to complete a simulated work task of about 40 minutes, designed to simulate 3 work types:

  • gig work, where income and the amount and pace of work varied. Participants received very different pay-out across three ‘work periods’. Tasks appeared in random order; participants could choose to skip a task but could not choose from a task list;
  • Self-employment, where income varied but participants could choose how much work to take on. Participants received very different pay-out across three ‘work periods’. Participants could choose the tasks they wanted to complete from a task list;
  • or salary-style work, where income and work were steady. Participants received the same pay-out across three ‘work periods’. Participants were given all tasks and told to work through them at their own pace.

After finishing this simulated workday, participants were paid $15 for their work. After being told their payment, participants were given a choice:

  • to take the $15 now, or
  • to “save” their earnings by waiting 2 weeks and receiving $17 ($15 earnings plus $2 in “interest”).

This decision-making process mimics real-life saving ones about how to manage their earned income, and similar approaches have been widely used in experiments on saving. Participants who were randomly assigned to work in a way that resembled gig work for just 40 minutes made different saving decisions than participants who were randomly assigned to work in a way that resembled salaried work:

84% of participants in the salary condition chose to save their money, whereas only 68% of participants in the gig-work condition chose to do so. We were surprised to see that participants in the self-employed condition were also relatively less willing to save their earnings (68%). We had expected that people who felt more control over their (variable) income might also be more willing to save. Instead, our results suggest that any variation in pay encouraged participants to prefer immediate rewards. So even when gig workers have some say over their work, that may not be enough to overcome the negative effect of income volatility.

Other influences on the decision to save

We also examined whether our participants' saving decisions changed if some demographic variables changed. Age and gender did not affect saving decisions, but more educated participants were more likely to save. Most importantly, participants were still less likely to save their earnings if they had been assigned to the gig work condition - even after controlling for age, gender, education, income, income volatility, and income type.

Consider two people. First there's Jane, a freelance writer who works as often as possible, on various platforms. Her income goes up and down from one month to the next. She finds it hard to predict which project pitches she'll win. Then there's John, a salaried writer at a large newspaper. He makes as much money as Jane, with one major difference: his income is predictably the same each month. Who saves more?

According to our results, John would. Jane will save less, not because she is a less frugal person than John, or values her savings less, but simply because of the nature of her income. Swings in her income will mean that Jane prefers to use her money now, instead of waiting and saving to have more money later on. If a sudden emergency were to happen, Jane could be at risk in two ways: 1) when she doesn’t qualify for government programs, often meant for people in traditional employment, and 2) when she realizes she hasn't got much of a savings buffer, because she is used to living from payslip to payslip.

In sum, our research suggests that financial decisions might be a result not only of the amount of money someone makes, but also of the predictability or volatility of that income. For stakeholders, these findings raise important questions: practitioners in financial services should consider whether their products and services are set up to work for clients whose incomes rise and fall unpredictably. Policy-makers in government should consider whether income support programs assume that people save up some of their money for emergencies, even if the nature of their work means they may be living payslip to payslip.

Johanna Peetz is Associate Professor of Psychology at Carleton University. Jennifer Robson is Associate Professor of Political Management at Carleton University.


  1. Most of our participants had some university education (62%), were single (65%) and females (60%). Their average age was 34 years (range 18 – 79 years) and they had an average self-reported income of CAN$40,000 per year. The majority (63%) worked salaried jobs.


  • Barr, M. S. (2012). No Slack. Washington D.C.: The Brookings Institute.
  • Cobb-Clark, D. A., Kassenboehmer, S. C., & Sinning, M. G. (2016). Locus of control and savings. Journal of Banking and Finance, 73, 113-130.
  • Diaz-Serrano, L. (2005). Income volatility and residential mortgage delinquency across the EU. Journal of Housing Economics, 14(3), 153-177.
  • Farrell, D., & Greig, F. (2016). Paychecks, paydays, and the online platform economy: Big data on income volatility. JP Morgan Chase Institute. Washington.
  • Farrell, D., & Greig, F. (2017). Coping with Costs: Big data on expense volatility and medical payments. JP Morgan Chase Institute. Washington.
  • Fisher, P. J. (2010). Income uncertainty and household saving in the United States. Family and Consumer Sciences Research Journal, 39(1), 57-74.
  • Mullainathan, S., & Shafir, E. (2013). Scarcity: Why Having Too Little Means So Much. Macmillan. Organization for Economic Cooperation and Development (2018). “The Future of Social Protection: What works for non-standard workers”, Policy Brief, May 2018, OECD, Paris. Retrieved from