Testing My Demand for Alcohol

New Years has some well-defined connotations for most of us: new beginnings, reflections, a chance to better ourselves, and above all, a chance to welcome the new year in a drunken stupor. On that note, here is a slightly belated New Years gift for you all—a statistically insignificant study of the correlation between my savings and the amount I’m willing to spend on alcohol in a given day, based on a year of data from my bank account.

I’ve been told that good writers share themselves with their readership. If that’s the case, I should come out of this looking like Hemmingway. The real gift, of course, is the knowledge that my bank account hasn’t been over $4,000 once in the past year. Next time I get ahead of myself, here or in person, feel free to make me regret my policy of strict honesty.


When I was young, my dad gave me my first lesson in economics—though neither of us saw it that way at the time. I was curious about his job as a wine distributor (to be honest, I still grapple with the finer details of his day-to-day) and he was valiantly trying to provide me with some clarity. After a crude explanation of Maslow’s hierarchy of needs, he settled on a key industry takeaway: “When times are good, people drink. When times are bad, people drink.”

Fast-forward two decades. I have come to appreciate the wisdom of my dad’s words. Indeed, I find that even in periods of unemployment or underemployment, one thing I’m always willing to spend money on is alcohol. Naturally, I thought it might be cool to test out exactly how much bearing my total savings had on my demand for social lubricant. In that case, the null hypothesis will be that my savings have no significant impact on my purchasing amounts. The alternate hypothesis is that my purchases are affected by the amount of money I have in my bank account.


This little “study” is basically an exercise in deficiency. Most glaring is my pathetic sample size (one 25 year old guy). This leaves me open to all sorts of opportunities to draw misleading conclusions, so it’s important to remember that we’re not trying to prove anything about people in general here—this is all relative to me. Also, I could only obtain a year’s worth of data, so it’s only relevant to the last year of my life.

Second, I was only able to track purchases (55 in total) that were made on my debit card. None made in cash or by credit card were included, so there’s probably a significant amount of data missing. Additionally, no purchases by others that I repaid (via Venmo or in cash) were counted.

Third, I was living in Morocco for most of January 2015, which is significant because it’s a Muslim country so alcohol is in short supply (though, as most Moroccans I met were happy to point out, as Muslim countries go it’s “Islam lite”!).

Fourth, there was some lag time on purchase dates due to my bank being closed certain days or general transaction delays.

Last of all, it is possible that even if it turned out that there were an correlation between the dollar amount of alcohol I purchased and my savings, it would still be possible for my physical consumption levels to remain constant if I economized on quality instead of amount. As someone who spent at least four years drinking Keystone Light, there’s a distinct possibility of that being the case.

Without Further Ado: Methods and Findings

I started gathering data by downloading my debit card statements from the last year. I then waded through and pulled out any purchase at a liquor store, bar, or occasionally, gas station. Ironically, I was at times forced to rely on memory to discern which purchases were drinks and which were food or otherwise.

After I was satisfied that I had extracted the best data I could, I created a pivot table. I made x and y values for all days in which I had purchased alcohol by combining all purchases into one sum and taking the maximum value of my bank account during the same day. While I don’t keep explicit track of my balance, I do usually have a good ballpark figure in mind when I go out–a valuable lesson courtesy of a $33 chicken sandwich in 2010.

I then ran the numbers to find the mean of both variables. The mean amount spent on alcohol was $31.65/day and the mean maximum account balance was $2371/day. Respective standard deviations were $21.06 and $878.24. The next step was to plot a graph to get a visual sense of what my buying patterns looked like. The raw coordinates looked like this:

Purchase, Account

Since they were all positive values, it could give the initial impression that there might be some positive correlation at hand. To get a better picture, I subtracted the mean x and y from each data point in order to plot the data points relative to the mean. That graph is a bit more telling.

dev from mean

The origin would have been a day in which I had a maximum of $2371 in my account and spent $31.65 on drinks: the average day during which I purchased alcohol with a debit card. Quadrants I through IV respectively represent days when I had more money and spent more on drinks, had less money and spent more on drinks, had less money and spent less on drinks, and had more money but spent less on drinks.

If you’re questioning the choice to use an average to create a visual representation of this data, you’re in good company. There were a couple of outliers (such as the time I spent $63 out of $422, fully 15% of my account balance, on drinks—what a night!) that distorted the averages of this relatively small data set.

dev from med

Out of curiousity and a desire for a better visual, I took the median of both values of the data and ploted once again. The distribution looks a little nicer, but it doesn’t tell us much more.

The most important piece of information is the r-value, also known as the correlation coefficient. That is, how able are we to predict y based on the value of x? R-values are between -1 and 1; the closer to 0 they are, the less of a correlation exists between the two variables. A value of 1 means that there is a perfect positive correlation between x and y while a value of -1 indicates a perfect negative correlation.

The r-value between my account balance and purchasing values was .1166—a very very weak correlation. In other words, the amount of money in my account is not a reliable predictor of how much money I’d be likely to spend on drinks, at least not with my debit card in the past year.


Based on this admittedly incomplete study, it seems that my dad was correct about demand for alcohol—at least with regard to the amount of money in my checking account. There may be other factors that influence how much I am willing to spend on a night out—such as how many people I go out with, where I am, or what day of the week it is—but that remains to be tested.

As I said earlier, this “study” is too small and problematic to apply to any group without being anecdotal. Therefore, there are no large-scale implications that we can infer from the data of my raucous year.

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