With all the coronavirus stuff, I’ve started to use Instacart more to shop at my local grocery store Marianos which is owned and run by Kroger.

With my latest cart, I was curious how much Instacart was charging versus the store. There’s a little vague note that I’m getting the Everyday Store Price, but I’m not sure what that actually means in practice. Instacart defines it as:

Standard store pricing. Loyalty cards not accepted. Most in-store sales, promotions, offers, coupons and discounts do not apply. Instacart specific coupons, promotions and discounts available. Price as displayed. 

So let’s get started, this is what my final Instacart receipt looked like with Everyday Store Pricing. $201.65 on products and tax, and then the remainder $44.26 on service, tip, and delivery fees.

Compare this to my Kroger pick up total of $194.45 for products and taxes. I added every single thing–including replacements–from my finalized Instacart order to the Kroger Clicklist order. Kroger pickup will give you the sale prices which is the cause of the difference between the Kroger subtotal and the Instacart subtotal. Because of this, I missed out on some BOGO bread, which I’m a little sad about now that I know about it.

There’s something odd about the tax total that I don’t understand how the Instacart value has lower taxes with a higher subtotal compared to the Kroger tax total on a lower subtotal. I’d imagine that there’s probably something wrong going on with Instacart’s algorithm that they’ll get in trouble over in the future.

So because Instacart charges about $10 more for the same products, I am in essence paying an even bigger premium for the grocery delivery than I originally expected.

In total adding up everything I basically paid $51.46 or a premium of almost 20% for someone else to deliver for me. versus if I had a Kroger employee shop for me and I picked up. Granted I could have shorted the tip a little bit (from 15%), but that seemed rude to short change some when I personally didn’t want to risk getting infected by going shopping myself. But you know, there’s definitely a lot of cost savings to be had if you’re willing to do the pick up.

Edit 4/26: We just got an Instacart from Aldi, and the shopper left the receipt. The total from Aldi was $188 with tax, and Instacart charged $207 for the products + tax, so the hidden Instacart price inflation ray was about 10%.

Months ago, I requested a copy of my Intuit data to see what’s been tracked through my Mint account the last 12 years I’ve had it open. Well they weren’t kidding that it’d take up to 45 days to process the request. I requested it in early Feb and I finally got the download link in mid-March! amazing over a month to make a data dump!

What was inside?

After over a month of waiting, I ripped open the download to find that it’s a bunch of text files saved in JSON format. There wasn’t anything useful inside of the files. The budget data was unlabeled, and transaction data was unlabeled. Here’s an example of it looks like after I formatted the extracts in R so I could actually read them.

There’s a lot data in the files, but a lot of it is actually 100% unlabeled. I’m not sure what Intuit was thinking, but the data is completely unusable and doesn’t even match the “Export All Transactions” button that’s on Mint.com on the transaction list button.

How to read your Data.

If you’re familiar with R, I basically used the step below to read thru most of the data.

The main file that looked like it had some meat was 10+mb in my extract and located in the Mint sub-folder and had a name like mind_data_78438432894932482339jdjfd.txt. Here’s the code I used to read the data in R:

library(rjson)

#update the working directory and mint_data_
setwd("Your working directory")
con <- file("mint_data_YOUR FILE NAME.txt",open="r")
#read in the file, there might be an error
line <- readLines(con) 
#the main file has a bunch of JSON objects, mine had 22, So this step splits them up 
split_vars <- strsplit(line, "\\]")
#22 sub objects, I basically just went thru each one by updating X and re-running to see what was in it
x <- 1
mint_data <- as.data.frame(do.call(rbind, fromJSON(paste0(split_vars[[1]][x],"]"))))

For my data extract, these were what I think I saw in the extract:

• #1 Personal Info: Most of it was wrong though
• #2 Opt ins
• #3 User actions?
• #4 IDK what this is
• #5 Something about banks mostly empty
• #6 Something about stocks has my old stocks
• #7 Stocks
• #8 Budget, but unlabeled
• #9 Goals, I don’t use these but the data looked clean
• #10 Transaction types
• #11 Transactions
• #12 Account information
• #13 Loans
• #14 Something about credit card balances
• #15 ?
• #16 Currency
• #17 Account ids
• #18 Currency?
• #19 Currency?
• #20 Credit cards?
• #21 Currency?
• #22 Accounts and status

How to get a copy of your Data

If you’re still interested to see if maybe you’ll have better luck than me, here’s how you request a copy of the data they have. You can navigate to this page from Mint by clicking on Settings > Intuit Account > Data & Privacy > Request an extract of your data

I included the Federal Funds Rate (source: St Louis Fed, Federal Funds Rate) as a comparison as this is the rate that banks lend to each other. You can see the spike in Ally Bank rates generally following increases and decreases with changes to the Federal Funds Rate. So if you’re on the fence about figuring out when is a good time to lock in a rate with Ally Bank on a CD, just look at how the Federal Funds rate has been trending.

In addition, I included the national average for the 12 month CD rates (source: St Louis Fed Avg 12 Month CD). This average seems to be less responsive to the Federal Funds rate, but still shows some similar movement.

Table of Ally 12 Month CD Rates 2010-2020

Note for anyone else, I had some trouble with the Web Archive providing inconsistent results on the pages I was using to get the interest rates from. This is why for some of the more recent recordings, I used blog posts to get the rates. I think there is some kind of issue with how the data is saved since it looks like the data is loaded by javascript I think my browser is caching the javascript file with information from a previous snapshot.

Current Ally Rates

Recorded 2/1/2020

• 9 Month: 1.25%
• 12 Month: 2%
• 18 Month:
  • Less than $5k Deposit : 1.9%
  • $5k – $25k Deposit : 2.0%
  • Greater than $25k Deposit: 2.05%
• 3 Year:
  • Less than $5k Deposit : 1.95%
  • $5k – $25k Deposit : 2.05%
  • Greater than $25k Deposit: 2.10%
• 5 Year: 2.15%

Dang the stock market did really well this year. As a result for the first time ever, my net worth increased by more than my total gross income for the year (just barely though). That was a shock.

You don’t have to be particularly skilled at investing when VTI which is Vanguards ETF for the entire stock market, goes up 27.7% (plus 2.12% dividends) with out you doing anything.

Bring on 2020

Have a Happy New Year, and good luck next year! Who knows what it will bring.

I’ve been getting a lot of advertisements touting the salary increases from getting an MBA or a Masters in something else business related. But with any adverisment, is it really worth it?

I decided to look at this from two angles:

1. You want to stay an individual contributor
2. You want to move into management

Is it worth it if you don’t want to be a manager?

I don’t doubt that a lot of people get a bump from it, but I think that some of the people getting a bump from an MBA are transitioning from being individual contributors like analysts or data scientists to becoming people managers. As a result for people who don’t want to do that, I decided to investigate if it was still worth by using data from the American Communities Survey (2013-2017) and comparing the income distributions for individuals in the same occupation and age group (30-40 years old) by their education either a bachelors degree (any field) or a masters degree (ex MA, MS, MEng, MEd, MSW, MBA).

For analysts of different types, you can see that there are median increases to wages ranging from 10% to 47%. Compare this to the nominal value of this translates to between $7k to $26k per year depending on the occupation.

Compare this to the cost of a MBA’s or other related masters programs which comes to $50k to $150k to complete a full course depending on the quality of the program. Since we’re comparing the median graduates before and after, you’ll have to use your own judgement into your calculations. These increases aren’t huge if the program has a list price of $50k, but you’re going to only expect to bump up $15k in the case of an Operations Research analyst. Some quick math says it will take at least 3.3 years to make back that amount without taking into account any tax implications or opportunity cost. In fact, one important thing to note though is that the 75th percentiles for masters degree holders is much further out than for BS degree holders so your ceiling will probably increase as well.

Is it worth it if you want to move into management?

It’s hard to say if people get MBA’s and become managers or if the people who get MBA’s want to be managers and already have the qualities needed to do that but just need some extra qualifications to make it.

Using the same data table we can see that the portion of managers in these areas who have masters degrees is substantial at 19% compared to 11% for the working population aged 30-40 years old, suggesting that it helps if you get a masters degree but it isn’t a requirement in all management areas.

Most noteworthy, 3 fields stood out with very elevated levels of masters and doctoral degree holders in management and those were:

• Architectural and Engineering Managers (36% Masters, 5% Other Advanced Degree) : Supervise engineers and architects.
• Natural Science Managers (39% Masters, 17% Other Advanced Degree) : Supervise the work of researchers, scientists, chemists, physicists, and biologists.
• Social and Community Service Managers (35% Masters, 5% Other Advanced Degree) : Many social workers get more advanced degrees and coursework that overlaps with health.

Therefore if you work in one of those areas, you may find more benefit than the median person.

Data Table

Analyst Occupations by Salary and Education Level

Proportion of Workers By Education Level

Methodology

I pulled the data from the raw American Communities Survey data for 2013-2017 and restricted to individuals between the ages of 30-40. The average MBA student is 28, so I decided to focus on the age range after where most of the people would have graduated. After extracting the data, I ran some summaries to calculate the income percentiles and educational distributions.