Blue Collar Cost

It’s been a while since I added an entry to the Quantitative Philosophy section.  And in light of the recent glass door replacement debacle, as well as my growing experience with home-ownership in general, I have enough information now to present a new calculator: The Blue Collar Cost Estimator!

What is this calculator?  Well, ever notice how what would seem like an affordable project immediately becomes cost-prohibitive when requiring hired help?  So here’s how it works: for any home renovation/repair, input what you think would be the conservative estimate for the raw materials.  The calculator will then add the contractor’s up-charge and account for the cost of labor (which is substantial).  Here’s the formula:

Estimated Materials Cost * 1.45 * 4 = Final Cost

Here’s the logic.  The 1.45x multiplier seems, at least anecdotally, to be the materials’ up-charge.  The 4x multiplier seems to be the labor charge, which inexplicably scales directly with the initial cost of the materials.  I guess they figure the risk of damage warrants greater skill/care?  Dunno.

But that’s it.  Nice and simple.  For calibration, I tested two expenses.  The latest was the door replacement, which I estimated would have a materials cost of $1000.   1000*1.45*4=$5800, the exact amount of the final cost.  We also had a garage door spring replaced, which I estimated at $120.  120*1.45*4=$696, which is pretty close to the $700-ish final cost we paid.

There you have it: the scaling cost of blue collar labor.  Glad I figured out how to install laminate flooring.  The last room I did would have cost us almost $2500.  So try to be handy–your wallet depends on it.


Football Conversations

As a non-football watcher, I’ve spent many a conversation pretending to have watched something I didn’t, or to care about something I don’t, and to use grammatically unsound complex sentences of negation.

At first, I would maintain the charade as football fans, when discussing football, are complete conversational narcissists, and would never notice that I wasn’t adding anything meaningful to the conversation.  These one-sided discussions would invariably crescendo to an emotionally-charged climax, upon which I would just agree with whatever was said last and laugh, which in turn led to some mutual conclusion that escaped me because I don’t watch football.

Now, I just don’t care enough about garnering favor with random people at the coffee station, so I don’t humor the smalltalk anymore, or so was my intent.  Unfortunately, a surprising majority of people take the dismissive comment to be a joke (for what kind of American doesn’t watch football?), and interpret it as encouragement–thus putting me into the conversation anyway.

So I decided that, as it’s been said: If you can’t beat ’em–kill everyone.  Or rather, inwardly sigh sadly and pretend to follow along.  But I need assistance.  I need information…obtained through any other means than reading, watching TV, or conversing with my fell Man.

I needed an aggregator and summarizer.  I needed the absolute bare minimum content required to form a cohesive thought.  I needed the equivalent of a Twitter feed of sports commentary, but without the racism/sexism/homophobia (the entire social aspect, basically).  I needed a means by which to trawl football articles and identify the most-used words, negating general sentence structure such as definite articles and conjunctions.

Fortunately I found this site:  Probably not its intended use–I began pasting the top football news articles into its form and analyzing their content.  I checked 5 such posts, and compiled their keywords:

The first two articles didn’t have enough meaningful content for a full 10 words

Okay, I could work with this.  This Bryant fellow seems to be a highlight.  I’m sure I could muddle through the rest.

I decided to test my theory on Liz, and texted her the following message:

“I heard that in Bryant’s week one, he scored enough points that it’ll be his big season.  He’ll make a good five-star Fantasy Football pick.  Despite the initial loss, Arkansas will recover with enough victories to stay in the running.”

Liz responded:

“What are you reading?”

She was intrigued!  Had I pulled it off?!  I replied, ambiguously:

“Just the highlights.”

She validated my success by sending me an unrelated photo of a dog that was up for adoption.

…Okay, maybe my method needs a little refinement.  Maybe I can pull a larger sampling of articles and write a formula to analyze the character strings.

Or maybe, just maybe…when I tell you I don’t watch football you could stop talking to me about football and I wouldn’t have to design a logic-based analysis of textual media to formulate responses to your banal and pointless rambling.  Now quit hogging the coffee machine.


Query Quotient

Working for a large company, I often find myself in the scenario of needing information.  I therefore seek to resolve this knowledge deficit by sending a simple email to an individual who holds said knowledge.  Yet all too often my queries go ignored.  Why is that?  What deep underlying motivations have possessed this individual to turn a deaf ear to the needs of others?  What cruel, sociopathic inclinations govern this person’s actions?

I debated at length these social dynamics, but the answer wasn’t nearly so disturbing as my overly-dramatic introduction might have implied.  Rather, I conclude there are a few and very simple factors: Does the person feel they have time (an extension of job title and pay grade), does the person feel the inquirer is worthy of their time (also an extension of job title and pay grade), can the person benefit from the inquirer, has the inquirer committed some social slight against them, and does the person like the inquirer?

To distill this even further, from the contactee’s perspective:

  • Are you at my level?
  • Has there or will there be a quid pro quo?
  • Do I like you?

Yet all reasons are not created equal, so based upon entirely subjective reasoning, I have developed a formula to weight them properly:

  1. Each party’s pay grade.  The first thing an email recipient looks at when receiving an email from an unknown party is that person’s job title.  A lot of information can be instantly determined from the hierarchy.  If you’re higher than me, I’d better listen, for my future promotion could depend on it.  If you’re lower than me, well…(dismissive wave of the hand).  If we’re the same level, I should at least consider you a peer, and there’s the possibility that I might work for you one day.
  2. Subjectives.  How well do I know this person, do we work together, do we have a good working relationship, and do I like you?  So much is difficult to determine from an email, but in short, if you’ve pissed me off, then you’re probably not going to get an answer.  Fair?  No.  True?  Always.  From failed experiences, I know to always humble myself accordingly when initiating contact.
  3. Positive Empiricals.  Have you done good work for me before and are you a potential cardinal to my promotion?  Obviously I would want to maintain a relationship with someone who’s benefiting me directly.
  4. Negative Empiricals.  Have you done lousy work for me before and have you beat me out for a job or opportunity?  Obviously I’d want to distance myself from a poor worker, but the last point does seem petty.  However, people take ego blows very seriously, and it’s no coincidence that former colleagues have severed contact when I became competition, and especially if I won.

As for probability, I’ve determined from experience that I will always get a response from a peer if every positive category is satisfied.  I will generally always get a response from someone lower with almost all of these conditions satisfied.  And I will usually get a response from someone higher with every condition satisfied.  However, if any negative conditions are satisfied, then the response rate very quickly drops.  As I stated, it’s weighted, and formerly positive relationships are always easy to sabotage since the human mind tends to remember the bad and not the good.  Here’s the formula for reference:

=IF(Their pay grade>Your pay grade,100*((1/(0.5*(Their pay grade-Your pay grade))/4)+(Do you know them?+Do you work with this person currently?+Is person within your department?+Do you have a positive working relationship?+Do they like you?)/25)+(Have you done good work for them before?+Can you get this person a job/opportunity?)/10)-(Have you done bad work for them before?+Have you beat that person out for job/opportunity?)/5)),IF(Your pay grade>Their pay grade,70+(100*(Do you know them?+Do you work with this person currently?+Is person within your department?+Do you have a positive working relationship?+Do they like you?)/25)+(Have you done good work for them before?+Can you get this person a job/opportunity?)*10)),60+(100*(Do you know them?+Do you work with this person currently?+Is person within your department?+Do you have a positive working relationship?+Do they like you?)/25)+(Have you done good work for them before?+Can you get this person a job/opportunity?)*10)-(Have you done bad work for them before?+Have you beat that person out for job/opportunity?)*10))))

Of course, that nightmarish formula is more readily understood in its natural format: a spreadsheet, so naturally I’ve provided it along with instructions:

Out of curiosity, I tested it with a recent scenario involving someone from our Legal department.  The calculator suggested a 33% chance of receiving a response, and seeing as it took 3 weeks to get any answer, this figure seems pretty accurate.  Hopefully this tool will allow you to adjust your project timelines accordingly.


Ironic Inverse Ratio

Years ago, before my employer started its regular “Great Places to Work” program, it maintained a less grandiose practice of occasionally but regularly asking employees for feedback on how it could improve.  At the time I figured this was pointless lip-service, but I dutifully responded with reasonable requests.  One of these requests was for free coffee.

I didn’t expect them to hire a barista, serving Arabica blends.  Of course, I didn’t expect them to seriously consider the request at all.  But after several years, respond they did, and by popular demand installed coffee machines.  And for a good solid month I enjoyed free coffee–nothing great, but a drinkable instant coffee blend.  Quick and effective.

This is how the work coffee comes in

Then, someone cut costs and changed the blend.  Now, I can drink some pretty awful coffee, but overnight, the coffee had turned into toxic waste.  And toxic waste is probably less bitter–you know, the glowing green kind?  Sadly, I returned to making my own.  But the years passed and the machines remained, so someone had to of been drinking it.  Upon this realization, I started more closely observing who was still getting cups of the sludge.  They all fell into a certain demographic: from Sales, tall, men, middle-aged.  I wondered why successful businessmen were less picky about the quality of their coffee.  Then, I considered my father-in-law.  He is a retired defense-contractor engineer.  He also drinks Folgers.

I wondered: is coffee quality preference inversely proportionate to income level?  To answer this question, I decided to waste time and put off auditing the emails I needed to send out.

To quantify this correlation, I needed figures.  I felt it was safe to assume that the cost of the coffee blend increases with its quality.  What I needed then, were some salary figures.  To graph the slope, I only needed two points.  The first point was easy: take the most expensive coffee I see regularly in grocery stores: $15 a bag; and the lowest income bracket, minimum wage: $15,080.  For the second point, I needed the cost of the cheapest instant coffee available (what I presumed was being used in the machines at work).  Courtesy of Amazon, I found it at $3.33 a bag.  Then, consulting the various online utilities designed to inform the masses that everyone’s underpaid, I found the average salary for an experienced Sales manager to be around $115,000.  Now I had two points.  It was time to calculate the equation.

First, I calculated the cost per ounce of each coffee.  Going off a 12-ounce bag, the expensive coffee was $1.25 and the cheap coffee was $0.28.  But, to make these number more manageable for a formula, I multiplied by 100 to use cents, creating nice whole numbers to work with: 125 and 28.

With standard algebra, we can calculate the slope with (Y2-Y1)/(X2-X1):

(28-125)/(115000-15080)=~-0.000970777, or if you want to follow significant figures, -0.00097.

Following Y=MX+B, we need B to be X0 (in this case, the baseline of minimum wage) to equal the $15 coffee mark.  But first we divide by 100 to bring the scale back down.  After doing so, B is simply calculated to be 140.  Final formula:


Peet'sSadly, I could not find an online calculator that provides coffee products by cost per ounce.  Searching for one only yielded a number of self-righteous articles criticizing how much coffee costs and how stupid people are for buying Keurigs or going to coffee shops.  But I did plug some numbers into the calculator, and my own coffee preference: Peet’s, ranks approximately by cost the type of coffee I should be buying.  So once again, the math doesn’t lie:

Aqua Vitae


Effort Quotient

There comes a time in every man’s life when he thinks: “Huh, this job sucks.  Why am I here?”  And it is indeed a very good question.  Blessed not are the proletariat masses who punctually arrive at work, only to question the work they do, and by extension, the meaning of life.  But rather than pass a joint and write poetry, I will continue my series on Quantitative Philosophy and instead enlist the field of mathematics to answer these questions.

Actually, I’m just going to calculate whether or not your job’s compensation is sufficient for its level of stress, and subtly suggest whether or not you should seek alternate employment.  How will I do this?  Why, the same way I compile data for all subjective forms of human existence: polls.

And don’t you judge me, all you MBAs out there.  In my experience, employers have carefully calculated just how little they can pay for a given job, and they do this well, otherwise we wouldn’t have minimum wage laws.  This is merely an extension of that philosophy, only calculated from the employee’s side instead.


We will start with a job’s variables that make it less desirable, or as I will translate: things which cause stress (Stress Factors).  Through discussion, the common complaints and therefore sources of job stress are:

  1. Superiors
  2. Suboordinates
  3. Customers/Clients
  4. Coworkers
  5. Self (Internal stress, related to self-actualization)
This albatross is experiencing job-related stress (‘BRING ME THE EPIDERMAL TISSUE DISRUPTOR!’)

Financial compensation is obviously the primary negator, but a broader perspective of that is what we do with the paycheck that negates the stress.  Therefore, we start with the weekly net income, and from this figure subtract the negators (or, negators from the negator–double negatives).  Again, using poll data, I have narrowed these variables to:

  1. Weekly gross income of minimum wage (because you have to be making more than minimum wage to have disposable income, and this is a base figure for which we all weigh our financial success)
  2. Weekly estimated cost of alcohol consumption (substitute your drug of choice)
  3. Weekly estimated cost of luxury edibles (fine dining–a universal constant)

The assumption being made is that the minimum required amount of excess finances to achieve happiness with an average stress level of 65% at a weekly net income of $450.  This sets the baseline…or at least it did.  Minimum wage has gone up considerably since I made this calculator, so the explanation is no longer consistent with the math.  Now it appears that for the given salary, a job caps at about 25% the maximum level of stress a job could offer.  This is a pretty low level of stress.  In any case, here is the formula:

(0.14(net income – (sum of negators)))/0.5(sum of stress factors +1))

And as before, the formula is scaled, this time to range from 0-5 (5 being the ideal job), with each stress factor receiving a rating of 0-10, 10 being the most stress.  Inputting my own figures, I receive a 4.33.  Hmmm, I’m not so certain that this has scaled well with time, or if it’s entirely linear.  A job with a maximum stress level appears to only require a weekly net income of ~$585, and I would not be a stock broker for less than $60K a year.  But it does appear that I’m on version 6 of this calculator as of 1/7/16, so it may be due for an update.  Also, it doesn’t account for an area’s cost of living, so adjust the minimum wage accordingly.

In any case, give it a try and find out where you rank.  I can tell you with certainty that if you rank below a 1, scaling issues aside, your job sucks and you need to find a new one (I told you I would give a subtle suggestion).  Now stop reading this and get back to work!