Firstly, welcome to the new reports format. Now, instead of posting when earnings are made, we’re posting a monthly summary of earnings. We hope this makes the reading experience more informative, convenient and most of all, enjoyable for our readers.
Secondly, please welcome our new contributor, Steve the Driver. Steve is a GPT driver in a medium US city. In order to keep with the spirit of this project, we’re only counting earnings from up to two hours of his work per day – what we estimate would be the max free time a normal person would be able to regularly devote to a side-gig while not taking on a second job.
At this point, money earned by Steve the Driver is included in the earnings reports and totals, but is not added to the investment accounts. Should that money be invested, a separate item will be made to report the investment return on that money.
With those announcements done, let us now proceed to this month’s report!
Gross earnings in September totaled $1139.56. It should be noted that September was a quarter-end for some accounts, so some of the earnings credited this month are aggregate earnings over an entire quarter.
This month saw passive earnings of $533.53 which includes money earned from equity appreciation, dividend payments, loan repayments, loyalty and rebate programs, and passive phone-farm income (i.e. Perk and Swagbucks, the genesis of this project!).
In terms of the passive category, investment accounts outperformed all other passive income categories, with Betterment being the clear star performer and Prosper actually underperforming when measured against Swagbucks (not counting future earnings from reinvested principle repayments, of course).
While in September the total earnings from passive sources was slightly lower than earnings from GPT sources, the hourly rate returned for the passive category was, unsurprisingly, superlative (see below) – thus maintaining passive earning’s place at the top of the earning’s pantheon.
This month the non-passive income category slightly outperformed the passive income category in terms of total money returned. This month all non-passive sources returned $573.02.
The two stand-out sources of GPT income were User Testing (not a surprise for regular readers) and a new source: Caviar food delivery (thanks Steve!). For the uninitiated, Caviar is a food delivery service which contracts drivers to pick up and delivery food from member restaurants to member customers. Caviar offers various bonuses which can increase earnings quite nicely (more on that below).
While activity on Caviar brought in $393.57, and User Testing only $105.00, the higher time cost for Caviar (not to mention gas, insurance, maintenance, etc.) means User Testing is still the clear winner when it comes to opportunity cost, if not total earnings.
Steve does Easy Shift and Mobee while doing Caviar, so one could reasonably add money from those programs to Caviar non-bonus earnings without distorting the GPT earning’s picture too much.
Note: I’ve asked Steve to tally his miles driven while doing on-the-road GPT, and to calculate his per-mile cost, which will be included in future reports to give a better idea of the efficacy of on-the-road GPT earnings.
Total Income by Source and Hourly Rate Breakdown
As noted previously, this month the GPT category slightly outperformed the passive category. Though trades on /r/giftcardexchange made up only a small fraction of the total monthly income at $33.01, the relative ease of trading on that platform meant that per hour of actual work, trading brought in $180.05.
Naturally, the passive income category remained untouchable in terms of earnings per hour since the only real work needed to be done is ordering gift cards from e.g. Perk or Swagbucks, and physically buying more debt on Prosper (though we will use their automated investment feature from now on).
The passive income category saw an hourly rate of $1103.86!
The GPT category, which is naturally work-heavy still came in at a respectable general hourly rate of $45.60. In fact, interestingly enough, even when calculating the hourly earnings rate for the individual GPT sources, each one was above even the highest US minimum wage – which is currently $11.50 per hour. The lowest hourly rate was Mobee at $14/hr while the highest was User Testing at $91.30/hr.
Interestingly, looking at the data for Caviar earnings, specifically, highlights the importance of taking advantage of free money when it is available. With respect to Caviar, I refer to the various bonuses the service offers it drivers for hitting certain benchmarks, but the lesson is portable to all income and even savings streams.
This month total income from Caviar was $393.57 of which $54.00 was earned as bonuses – or, ~14% of total income was free money. While $0.14 more for each $1.00 might not seem like much difference, it is enough, in the aggregate, to raise the hourly earnings of Caviar from $38.81 to $44.98. That is, in effect, a raise of $6.17 an hour.
A Note on Surveys
Except for focus groups and that sort of thing, doing online surveys for extra money is a waste of time. While no one here does surveys, this month I did took a sampling of the surveys on offer at three major GPT sites: Swagbucks, Vindale, and InstaGC. If one were to do all the surveys I looked at, and the estimated time for completion would be correct (hint: it wouldn’t be, and it wouldn’t also account for qualifying time), the return would have been $26.84 from 465 minutes of work. As a comparison the income from the GPT category was $573.02 from 754 minutes of work. Whereas the non-survey GPT hourly rate was $45.60, the survey hourly rate was a measly $3.46 – before taxes.