Is cash the most ‘efficient’ Christmas gift?

Some economists think that Christmas gift-giving is a big waste of resources, and that cash is a much more efficient present.

When giving specific gifts, people often get things they don’t want, which is a waste of resources.An estimate by Wharton Professor Joel Waldfogel suggests that 20 percent of gift giving money is wasted this way.

Woldfogel argues that a person who spends $100 on himself or herself will presumably spend that money on something that actually nets them $100 worth of satisfaction. But when another person spends that amount on a gift they may end up getting a painting of a cat for a dog-lover, a sweater in the wrong size, or a coffee maker for a tea drinker, etc.

Woldfogel argues it would be much more efficient to just give cash, so that the recipient can spend something that nets $100 worth of satisfaction.



Are Markets Informationally Efficient?

A key assumption in many mainstream macroeconomic models (both formal and informal) is the Efficient Market Hypothesis. Very simply, this is the belief that markets are informationally efficient — that they reflect information with little (or no) delay, leaving few (or no) arbitrage opportunities.

So the real question here is what information do markets (and by markets, I mean free markets where market participants are free to pay and receive any negotiated value for an asset) really reflect? When we see the price of an asset or asset class fluctuating, what does this movement signify? Is it fluctuation in the fundamental value of the asset? Is it just a fluctuation in the market’s perception of an asset? Is it some combination of these two factors? Or is it just random noise? Fundamentally, markets are composed of a series of transactions, each between a bidder and a seller. Each transaction in itself reflects a discrete set of information — specifically, what the bidder and the seller are willing to pay for, and take for that specific asset. This in turn is typically (although not always!) influenced by a some or all of the following: what others are willing to pay and accept for an asset presently, the use-value of an asset, notions of fundamental value (price-over-earnings, stock-to-flow, EBITDA, cashflow, etc), notions of momentum and what others may be willing to pay and accept for an asset in the future (trendlines, gut feelings, “hot stock tips”, etc).

An intriguing addendum to this is that the automation of trading (high-frequency trading) has created bidders and sellers who are acting on the instructions of algorithms. As these instructions are programmed by humans — usually automating some form of technical analysis — the only real difference is that of (extreme) speed. The beliefs reflected in high-frequency trading reflect the underlying algorithmic instructions programmed by the humans who created the algorithm.

Ultimately, whenever we purchase an asset for the purpose of speculation or investment (and even use-value — prices can change, and the price we paid last week or last year could end up looking very expensive, or very cheap) we are taking a guess as to whether the current bid or sell value is worth it. Each agent makes their guess based on a different set of data and expectations. What the prices in markets signify is the operation of this mechanism — different agents evaluating information and making guesses about the future.

Let’s consider the example of Bitcoin, the price of which is currently soaring. Some choose to buy Bitcoins based on momentum, or their liking of the cryptography, or Bitcoin’s inherent deflationary bias or some other positive belief. This is a speculation that the price may continue to climb. Some may choose to buy bitcoins based on their use-value, as an anonymous, decentralised currency that can be used to buy a wide array of things. Holders of Bitcoins may be motivated to sell by the fact that the price has risen since they bought or mined their coins, or by the belief that bitcoin is “in a bubble”, or some other negative belief.

What the market reflects is the net weight of different opinions and resultant human actions. If those who are motivated to buy outweigh those who are motivated to sell, the price  rises and vice versa. This means that the beliefs of big players in a particular market can have strange and disproportionate effects. Consider the effect of the Hunt Brothers’ attempts to coin the silver market in 1980. The price of silver rose from $11 an ounce in September 1979 to almost $50 an ounce in January 1980, as the Hunt Brothers bought more and more. The market was very efficient at reflecting the fact that the Hunt brothers were willing to buy more than the market could supply at lower prices. And once the Hunt Brothers faced margin calls, the market quickly adjusted to reflect the fact that they were now selling instead of buying, and prices fell.

That’s what (transparent) markets are guaranteed to reflect — bidders and sellers, supply and demand. This information is still useful to firms trying to gauge what, and how much to produce.  Everything else — the information that bidders and sellers are acting upon — is not necessarily reflected in market activity. Very often, bidders and sellers are brought to the market by new information regarding a large number of things — price changes, earnings, business decisions, technologies and inventions, macroeconomic data, etc — but there is no systematic or reliable way to predict what humans will respond to, or how they will respond. Human psychology and human action in this sense is totally unstable and nonlinear — consider the recent contrast in market reaction to earnings data from Apple and Google. This instability is an alternative explanation for why consistently beating the market is indeed very difficult, as the Efficient Market Hypothesis implies.

And prices do not even reflect an aggregation of sentiment toward an asset or asset class — they only reflect the sentiment of those who are involved in the market, in proportion to their level of buying and selling activity. This means that the opinions of big players who buy or sell a lot, are reflected many times more than those of small players who buy or sell a little. And irrationality can create a feedback loop — if stock prices are rising, and macroeconomic fundamentals are weak, many market participants may initially be sceptical. Yet as more participants pile into the stock market purely for reasons of sustained upward momentum, more and more participants may begin to suspend their disbelief, if only to not miss out on a profit opportunity. This is one mechanism (of infinitely many) through which price bubbles can form.

Yet accurately reflecting supply and demand is not the same thing as informational efficiency. Empirical data show that arbitrage opportunities are widely exploitable and exploited even in modern marketsOne of the largest forms of high frequency trading is of course statistical arbitrage. This reality should probably be a final nail in the coffin of the idea that markets reflect anything more than the actions of bidders and sellers. Unfortunately, very many models rest on the assumption of informational efficiency in markets, meaning that this approach is very unlikely to die out any time soon.

What is Profit?

In neoclassical macroeconomic models that assume perfect competition, there can in the long run be no such thing as profit — defined as revenue left over after all costs have been subtracted.

Clearly, in the real world where many businesses have lived and died profitably there is no such thing as perfect competition, and therefore the neoclassical models that treat profit as a short-run anomaly are working from an unrealistic assumption.

My definition of profit is that profit is what happens when a business’s input transactions are priced less than its output transactions. That is, the sum of the cost of a business’s inputs from those it buys is transacted for a lower price than the sum of those it sells its goods and services to. Because transactions are assumed to be voluntary — and when they are not voluntary, any residual gain is theft, not profit — and businesses are assumed to try to negotiate the best price in both inputs and outputs, any profit is due to those who purchase the business’s output valuing the output higher than those who sold the business its inputs. That an output or input transactor would accept a profit-creating price could be for any number of perceived reasons: convenience, or expertise, or prestige, or necessity, or even outright trickery. Their decision to accept the price is subject to their own subjective valuation, and it is the difference between prices that creates the profit.

Marx and Lenin represented this idea as surplus value; that businesses make a profit by extracting uncompensated labour value out of their workers. But why not the other transactors? In my view, profit is derived from the sum of the business’s transactions with all of its transactors: consumers, supplies, labour (etc). Workers (etc) cannot extract a greater share of the firm’s revenue than they can negotiate, and at various points in history (including the present day) the working class seems to have had little real leverage for negotiation.

In my view, any model that attempts to represent real world markets should begin from the historical fact of profit and loss, and the historical fact of a disequilibrium between input transactions and output transactions.