I recently spent 11 weeks from April through July taking a new online course from Harvard Business School.
Called HBX CORe (Credential of Readiness), it’s a “pre-MBA” course that tackles the basics of statistics, economics, and accounting using HBS’s case study method and a proprietary user experience.
Its target demographic includes professionals who are looking to make up for their lack of a formal business education in hopes of advancing their careers. Business Insider covered the $US1,500 price (which has since increased to $US1,800).
After spending approximately 150 hours with it, I can say that I believe it is worth the financial and time investment, and is superior to free online courses in the same subjects.
HBS professors Jan Hammond, Bharat Anand, and V.G. Narayanan take students through the mechanics of their respective classes, but the case studies help teach practical lessons that can add another layer of understanding about how companies operate every day.
Here are some of the most useful lessons I learned:
The Winner’s Curse means in many auctions, the winner may actually be the loser.
Unless everyone in an auction has perfect information about the value of the item up for grabs, the winner is just whoever happens to be the most aggressive or optimistic.
Someone who bid lower than the item’s worth has a surplus of zero and doesn’t win, and someone who bids exactly what the item is worth also has a surplus of zero and doesn’t win. That means the person who wins overestimated the value and has a negative surplus.
Product bundles maximise value rather than creating waste.
You’ve probably bought several product bundles in your life where you felt like you were forced to buy something you really didn’t want: You really needed Microsoft Word, could maybe use PowerPoint, and figured you’d maybe use Excel once or twice, but the bundle was a better deal than just buying Word and wondering if you should also pay full price for PowerPoint.
But the beauty of this classic Office bundle is that it maximizes revenue for Microsoft not because products are forced on you, but because there is a negative correlation among preferences of customers. There was someone whose preferences were similar to yours, except they really wanted Excel, moderately wanted Word, and barely wanted PowerPoint.
In the end, Microsoft wins by creating price discrimination, a strategy where similar goods are offered at different prices in different markets, like someone paying full price for paper towels and another purchasing paper towels only because they have a coupon.
Price ceilings and price floors can end up further complicating the problems they’re meant to solve.
When the state of New York decided to enact an anti-price gouging law on gasoline in the wake of Hurricane Sandy, it was with the intention of keeping prices fair for New Yorkers of all walks of life.
Because the price was below the equilibrium price, in which supply equals demand, there was a shortage of gasoline and people had to spend hours in line for a shot at filling up. And because the price could not be raised, companies from other states had no incentive to ship gasoline to New York customers, resulting in some struggling citizens without gas.
A similar situation occurs when a minimum wage, an example of a price floor, is used in an elastic labour market — one in which workers would be willing to work more hours if paid a higher wage — and increases unemployment due to a surplus of labour.
A company’s fixed costs are irrelevant to pricing decisions.
Fixed costs like rent and equipment expenses don’t vary as the quantity produced of a product changes. Variable costs like raw materials and packaging do vary with quantity changes.
Fixed costs are relevant to whether or not a company should enter an industry or stay in business, but are irrelevant to pricing. If a coffee shop owner, for example, spent $US300,000 setting up her business and produces a standard cup of coffee at a cost of $US2, any value from a price above $US2 is her profit, and she can lower the price all the way down to $US2 if a price war with a competitor breaks out; the $US300,000 is not a factor in determining how much the coffee should go for.
The Central Limit Theorem explains why we can confidently use random samples to learn about a population.
If you’re measuring the impact of a variable on a population — say, how distance from downtown affects the price of a suburban home — the distribution of the averages of those samples will be normally distributed (i.e. it will be in the shape of a bell curve). The average of this resulting bell curve will be equal to the true average of the population.
In the real world, no one actually uses the huge amount of time and resources it would take to collect a bunch of samples and map their averages. Instead, we know that there is a 95% chance that the average of the sample is within two standard deviations of the population’s average. Without getting into the maths behind it, this is how we confidently determine ranges of values from a sample.
A company has a degree of freedom over how it does its accounting, like how it chooses to determine depreciation of assets and the value of inventory.
When a soda bottler purchases a new bottling machine, it has the option to decide how it will measure its depreciation, the way its cost is spread over time. Two main ways of doing so are straight-line depreciation, it which the expense is equally spread over the amount of time it’s expected to be useful, and double declining balance depreciation, in which more expense is recognised in its early years and less as time passes.
Similarly, when a juice company changes the price of one of its products, it can value its inventory by either the first in first out (FIFO) method, in which the juice containers with the lower price would be marked as being sold first, or the last in first out (LIFO) method, in which the juice with the higher prices would be marked as being sold first, both regardless of the reality of the situation.
The way cash flows in and out of a company can reveal the health of its business.
The statement of cash flows records the cash that flows in and out of a company from its operating (what it takes to produce product), investing (cash primarily related to long-term assets like property and equipment), and financing activities (cash related to raising money from investors and paying them back). Here’s how to interpret them:
Startups typically have cash outflows in operating and investing activities with large fluctuations in financing activities.
Profitable/growing stage companies typically have cash inflows from operating activities, outflows from investing activities, and fluctuations in financing activities.
Mature/steady stage companies typically have inflow from operating activities, slight outflow from investing activities (e.g. replacing equipment), and general outflow from financing activities (paying down loans or giving money to shareholders through dividends).
Decline stage companies typically have outflow from operating activities, inflow from investing activities (e.g. selling off assets not in use), and outflow or inflow from financing activities (will likely be unable to find new loans but could also struggle pay off existing ones).
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