Key takeaways

 

 

 

In a Consumer Morsel last year, we discussed the rise of the gig economy during the pandemic. Interestingly, the latest Bank of America internal data suggests that this upward trend might be reversing. As Exhibit 1 shows, after rising threefold during the pandemic, the percentage of Bank of America customers who received income from gig platforms through direct deposits or debit cards slipped to around 2.7% in February 2023, from 3.3% in March 2022. What is driving the decline and is the peak of the gig economy behind us?

In our view, the recent decline likely reflects both lower demand and a lower supply of gig workers.

Less help wanted in deliveries and social commerce marketplaces

Let's start with the demand side of the story. Looking at the composition of gig jobs, we find that the decline in overall gig engagement has primarily been driven by fewer people working for delivery and social commerce marketplaces, although a small increase in ridesharing partially offset this (Exhibit 2). Note that social commerce marketplaces include online platforms where users can buy and sell goods.

  Exhibit 1:  Percentage of Bank of America customers receiving gig income (monthly, %)

The share of Bank of America customers receiving gig income is down

Exhibit 1:  

Source: Bank of America internal data

 

 

  Exhibit 2:  Percentage of Bank of America customers receiving select types of gig income (monthly, %)

Fewer gig workers are participating in deliveries and social commerce

Exhibit 2:  

Source: Bank of America internal data

 

 

Specifically, the share of customers engaging in delivery-type gig jobs peaked in early 2022 and has trended down consistently ever since. This is likely due to slowing demand for restaurant takeout or grocery deliveries as the economy has reopened. That said, the levels of delivery gig jobs have remained well above 2019.

Exhibit 3 shows the level of inflation-adjusted Bank of America online (card not present) credit and debit card spending per household (i.e., "real" spending) on groceries and at restaurants, which we use as a proxy for delivery purchases. We find that online spending in these categories has moderated steadily since mid-2021, broadly in line with the share of customers receiving gig income from delivery platforms.

Moreover, those who have continued to work in delivery gig jobs are also doing so less frequently - the average number of months per year that a gig worker received income from delivery platforms is down from 2.7 in 2021 to 2.5 in 2022. Similarly, the decline in the share of customers engaging in gig jobs at social commerce marketplaces was likely driven by the general decrease in goods demand.

  Exhibit 3:  Bank of America customers receiving delivery gig income (%) vs. Bank of America online (card not present) credit and debit card spending per household on groceries and at restaurants (index, Jan 2019=100, inflation adjusted)

Both the share of customers receiving delivery gig income and our proxy for delivery purchases are moderating

Exhibit 3:  

Source: Bank of America internal data. Note: Card not present is largely online but could include purchases made over the phone.

 

 

  Exhibit 4:  Average number of months per year that workers received gig income, based on Bank of America internal data

Gig workers in deliveries are working less frequently than in 2021

Exhibit 4:  

Source: Bank of America internal data

 

 

Less supply of gig workers as traditional jobs see stronger wage growth

On the supply side, some gig workers might have rotated back into the traditional jobs market as wage growth has increased.

Keep in mind that gig workers may or may not also have other traditional employment. We find that for those gig workers who also have traditional employment (i.e. they also receive paychecks in their Bank of America accounts), the most common industries they work in are retail and restaurants (Exhibit 5). This makes sense, as these two sectors generally provide the lowest hourly earnings and workers are likely to seek gig jobs to complement their income.

Notably, these two sectors have seen the biggest increase in wages and salaries over the past two years. The Employment Cost Index from the Bureau of Labor Statistics (BLS), which provides a more holistic view of compensation and includes wages, salaries as well as employee benefits, has increased at a much faster pace on a year-over-year (YoY) basis for retail trade and accommodation & food services than the average for sectors (Exhibit 6).

By contrast, we saw no meaningful increase in the average monthly income from gig platforms, based on Bank of America internal data - in February 2023, average monthly gig income deposited to consumer accounts was actually slightly lower than the 2019 average (Exhibit 8).

  Exhibit 5:  Percentage of customers who receive paychecks through direct deposits that also receive gig income, by industry of their paycheck

 The most common industries from which gig workers receive their primary income are retail and restaurants

Exhibit 5:  

Source: Bank of America internal data

 

 

  Exhibit 6:  Employment Cost Index: total vs. select sectors (% growth YoY, quarterly data through 4Q 2022)

Restaurant employees' average hourly earnings have experienced higher YoY% growth as the economy has reopened

Exhibit 6:  

Source: Bureau of Labor Statistics

 

There are two implications. First, for those gig workers who also have a traditional job, large wage increases in their primary jobs could mean less need to make up any shortfall in the gig economy. As a result, they may do gig work less frequently or even exit from the gig economy.

Second, for those gig workers who do not have a traditional job, they might start to enter the traditional labor market due to higher wages. In our view, this dynamic might be playing out among Gen Z workers and could at least partly explain the increase in labor force participation for this group.

As explained by a Boston Fed report, some gig workers might not be considered participants in the labor force either because their work is irregular (and they happen to not work, nor look for work during survey period), or because they do not believe gig work counts as labor. If this is the case, then if these workers were to rotate from gig economy to traditional jobs, we would expect to see an increase in the labor force participation rate.

Bank of America internal data and BLS data seem to be aligned with this view. Specifically, we find that among Bank of America customers, Gen Z customers have seen the biggest pullback in gig activities (as measured by income received through gig platforms). The percentage of Gen Z customers receiving gig income slid by 1.2 percentage point lower between February 2023 and the peak a year ago. As a comparison, the drop was just 0.5 percentage point for all age groups. At the same time, according to the Bureau of Labor Statistics, labor force participation rate for those aged between 16-24 has increased notably over the last year, exhibiting negative correlation to gig participation (Exhibit 9).

  Exhibit 8:  Average monthly gig income, according to Bank of America deposit data (index, 2019 average =100)

In 2023, average monthly gig income deposited to consumer accounts was below the 2019 indexed average

Exhibit 8:  

Source: Bank of America internal data

 

 

  Exhibit 9:  Percentage of Bank of America Gen Z customers receiving gig income vs. labor force participation rate for 16-24 years old (monthly, %)

Gig engagement for younger workers has shown negative correlation with labor force participation

Exhibit 9:  

Source: Bank of America internal data, Bureau of Labor Statistics. Note: part of the decline in Gen Z gig activity might be related to natural rotation into traditional labor markets as this group of workers enter prime working age.

 

 

Methodology

Selected Bank of America transaction data is used to inform the macroeconomic views expressed in this report and should be considered in the context of other economic indicators and publicly available information. In certain instances, the data may provide directional and/or predictive value. The data used is not comprehensive; it is based on aggregated and anonymized selections of Bank of America data and may reflect a degree of selection bias and limitations on the data available.

Bank of America credit/debit card spending per household includes spending from active US households only. Only consumer card holders making a minimum of five transactions a month are included in the dataset. Spending from corporate cards is excluded. Data regarding merchants who receive payments are identified and classified by the Merchant Categorization Code (MCC) defined by financial services companies. The data are mapped using proprietary methods from the MCCs to the North American Industry Classification System (NAICS), which is also used by the Census Bureau, in order to classify spending data by subsector. Spending data may also be classified by other proprietary methods not using MCCs.

If applicable, the consumer deposit data based on Bank of America internal data is derived by anonymizing and aggregating data from Bank of America consumer deposit accounts in the US and analyzing that data at a highly aggregated level.

Gig type of income referenced in this report is derived from aggregated inflows to consumer direct deposits or debit cards from gig platforms.

Generations, if discussed, are defined as follows:

1. Gen Z, born after 1996

2. Younger Millennials: born between 1989-1995

3. Older Millennials: born between 1978-1988

4. Gen Xers: born between 1965-1977

5. Baby Boomer: 1946-1964

If applicable, any payments data represents aggregated spend from US Retail, Preferred, Small Business and Wealth Management clients with a deposit account or credit card. Any reference to aggregated spend include total credit card, debit card, ACH, wires, bill pay, business/peer-to-peer, cash and checks.

Unless otherwise stated, data is not adjusted for seasonality, processing days or portfolio changes, and may be subject to periodic revisions.

Additional information about the methodology used to aggregate the data is available upon request.

 

 

 

Contributors

Anna Zhou

Economist, Bank of America Institute

Taylor Bowley

Economist, Bank of America Institute

Sources

Dale Lin

Senior Quantitative Finance Manager

Elaine Li

Quantitative Finance Analyst

 

 

Disclosures

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