Quantifying Income Earned in the Gig Economy 


The gig economy offers a flexible source of income, and is defined by the Bureau of Labor Statistics as “contingent and alternative employment arrangements.” Contingent workers are people who do not expect their jobs to last or reported that their jobs are temporary. They do not have an implicit or explicit contract for ongoing employment. Contingent and alternative employment arrangements according to the BLS include but are not limited to independent contractors, on-call workers, temporary help agency workers, and workers contracted by firms. 

However, in 2017 the BLS began to measure an emerging line of work, electronically mediated employment (EME). EME is defined by the Bureau of Labor Statistics as short jobs or tasks that workers find through mobile apps that both connect them with customers and arrange payment for the tasks. This includes Uber, Lyft, DoorDash and Instacart, among others. These jobs are often part of the gig economy. 

Because this work is often performed to supplement income from a primary, potentially full-time job, capturing the true size of the gig economy can be a challenge. 

In BLS’s Contingent Worker Supplement (CWS) survey, “questions on contingent work and alternative employment arrangements are only asked about a person's main job.” Therefore, no one IMPLAN Industry uniquely defines gig work. Instead, this is a form of employment that exists in many industries, particularly where temporary or freelance work is common. For example, Instacart shoppers would fall into Industry 520 - Other personal services, and Uber drivers would be classified with other taxi services in Industry 418 - Transit and ground transportation services. Keep in mind that although these jobs exist in such industries, the workers are not broken out separately, that is, employment counts in these industries also include workers who are not a part of the gig economy and all full- and part-time employment.

Because it is not feasible to separate the workers in these industries by type of employment, it is best to quantify the economic impact of gig economy workers through a Household Income Event. Household Income Events are appropriate to model a change in Household Income isolated from Industry production and payroll. They are perfect when we have no idea what Industry paid them.

Household Income represents the income received by people for their participation in production, from government and business transfer payments, and from returns on capital (e.g., interest payments, dividends). Household Income Events allow the analyst to pick which income group(s) will receive the gain or loss of income.

Household Income Event - Supplementary Income

In regards to the gig economy, the majority of workers are utilizing their position as a means of supplementing their main source of income. In the case of a worker supplementing their full-time position, it is likely that said worker will not be in the lowest Household Income bracket.

Take for Example Hubert, who is single and lives in Charlotte, NC. Hubert works as the manager of a local retail store from 9AM-5PM Monday through Friday and makes an annual salary of just over $55k, which would put him in that $50-70K a year group in IMPLAN. However, due to the economic downturn caused by the covid pandemic, Hubert had to take a pay cut of $5,000 a year or risk losing his job altogether. 

To supplement this loss of income, Hubert began to spend his free time working nights and weekends as an Uber/Lyft driver. His role in the gig economy earns him an additional $15K a year. So even though Hubert had to take a pay cut, he will still see an increase in his yearly income in the amount of $10K. Although this calculation does not take into account the fact that Hubert is giving up significant amounts of his personal free time, the net effect is still positive on his yearly income.   

During times of economic uncertainty, Hubert’s situation isn't unique. For exemplification purposes let's say that we know 1,000 people in the city of Charlotte have been forced to make similar decisions to supplement their lifestyle.  This would lead to an additional $10M in labor income for households that earn $50-70K a year in Mecklenburg County. When using a Household Income Event, we want to make sure that the payroll taxes and any income from in-commuters has already been deducted. IMPLAN will further deduct for personal taxes and savings. Learn more in the article Understanding Types of Income

To quantify the economic impact of this $10M Increase in IMPLAN, we could use a Household Income Event. From the Regions screen, we select Mecklenburg County, and create the impact.

Next, we create a Household Income event for the $50-70K bracket with an Event Value of $10M and add it to our Mecklenburg county group, then hit run. Remember that we are using the 2019 Data Year and we want to see the results in 2021 dollars. 

The first thing you may notice from your results is that there are no Direct or Indirect Effects. Household Income Events only affect household spending, so there will only be Induced Effects. Learn more in the article Explaining Event Types.

Navigate to the Economic Indicators by Impact table in the Summary Results tab. From here we see that the total Induced output supported by Hubert’s (and the other 999 people like Hubert) gig economy role is just over $10.1M, and contributes to nearly $6.4M in Value Added within the Mecklenburg County study area. Note that this does not include the 1,000 direct employees working with Hubert or their direct Labor Income, Value Added, or Output as we just modeled what the change in spending for these Households would be.

Household Income Event - Primary Income

Although the majority of gig workers are supplementing their main source of income, this is not the case for all workers in the gig economy. Alexis is a single full-time student at IMPLAN University (Mecklenburg County). Like a lot of students, Alexis spends most weekends either participating in philanthropy or attending University sanctioned events (theatre arts, sports, etc). 

To help pay for her books and tuition, Alexis picked up a part-time role as an Uber/Lyft driver, logging hours after class during the week. This role gains Alexis $10K annually. University experts believe there could be as many as 1,000 IU students who participate in the gig economy in a similar capacity. 

To quantify the impact of Alexis and her fellow student’s part-time gig income, we’ll again use a Household Income Event Type in Mecklenburg County in the 2019 data, and see the results in 2021 dollars for an apples to apples comparison.

However, there are going to be a couple of differences in how we run this analysis. First, we want to choose the Event Specification of Households LT $15K, because this is Alexis’s main source of income and she will be earning less than $15,000. The Event Value is going to be the $10,000 in income that she earns from her part-time role.  

We’re going to leave the Event Value at $10,000 instead of multiplying by 1,000 (for the total number of students with similar financial situations as Alexis’s) because IMPLAN has a built in functionality to accomplish this calculation for us. 

After selecting our Event and dragging it into our Mecklenburg County Group, we have the option to scale the event by whatever figure we see fit. If we wanted to quantify the economic impacts of 50,000 people gaining an additional $10,000 in income for this HH income group, we would scale the event by 50,000. For our example, we want to see the impacts of 1,000 students with additional $10K in income, so we’ll scale our event by 1,000 and hit run. Note that the results would be exactly the same if you just multiplied the $10K by the scaling factor manually and used that for your Event Value (like we did in Hubert’s example).   

Looking at the 'Economic Indicators by Impact' table in the 'Summary Results' tab we see that the total Induced Output supported by Alexis and the 999 Alexis-esque gig economy workers is a little under $11.5M, as well as contributing to over $7.26M in Value Added within the Mecklenburg county economy.

Examining the Results - Why are They Different?mceclip0.png

In our two examples, we quantified the effect of $10M of new gig economy income earned through two different Household Income categories, HH LT$15K and HH $50-70K. The reason we did this was to analyze the difference in resulting effect from people who would use this as a primary source of income, versus those who may only use the gig economy roles to supplement their main source of income. The results consistently suggest that those who use the gig economy as their primary source of income generate a larger economic effect for each indicator. 

There is a simple explanation for why this is the case, savings. Those that earn higher incomes, are more likely to save some of that income than those that earn less. We can confirm this within IMPLAN by navigating to the Region Details screen.

               Region Details
                           > Social Accounts
                                       > IxC Social Accounting Matrix
                                                   > Aggregate IxC SAM

From this IxC Aggregate SAM table we can look at the Household Income groups as columns, and in row 24 we see capital. This figure represents the Household Income group’s payments to capital, which includes net savings.

For households with total income less than $15,000 in Mecklenburg County, this value is missing. Well, this is because they are not net savers. People in this income category spend all of their cash on the necessities and may actually spend more than their income via credit. In contrast, a couple of columns over we can see that for Households earning between $50-70K that there are net savings in Mecklenburg County. We can conclude that in this study area, lower income Households are going to generate a larger economic effect given additional income. This includes, but is not limited to, income earned in the gig economy.      


Explaining Event Types

Household Income Events

Scaling Groups and Events

Understanding Types of Income

Written April 26, 2021