Author Archive: Conrad Schiff

This post is a continuation of the previous one where we look at the basic idea of how to optimize the fractions $f_A$ and $f_B$ of the assets A and B (with corresponding returns $r_A$ and $r_B$) from the toy portfolio used by van de Wijst in his Chapter 3.

Most of the statistical tools used were developed in the previous blog but it is worthwhile summarizing and generalizing them here. Note that some of the notation is tweaked from what was used in the last post to make it more generalizable. The basic primitive terms (or building blocks) are:

• An asset $I_i$, where the I stands for financial instrument and the index $i$ (or $j$ or some other appropriate index) runs over the various assets available. For the toy model, $I_1 = A$ and $I_2 = B$.
• A possible future scenario ${\mathcal S}_m$ where the index $m$ (or $n$ or some other appropriate index) runs over the various possible futures we consider, which are usually a subset of the infinite possibilities that may come about.
• The rate of return of the asset in a given scenario $r_{i,m}$ where in the subscript notation, the first index (regardless of symbol used) refers to the asset and the second index (again, regardless of symbol used) refers to the scenario.
• The probability ${\mathcal P}({\mathcal S}_m)$ that a given scenario will occur. For convenience, this will almost always be denoted by the slightly shorter form ${\mathcal P}_m$.

In terms of these primitives, we define:

• Expected return of an asset: $E_i = \sum_{m} {\mathcal P}_m r_{i,m}$,
• The variance of an asset $I_i: \sigma^2_i = \sum_m {\mathcal P}_m (r_{i,m} – E_i)^2$,
• The covariance of an asset $I_i$ with asset $I_j$ : $\sigma_{ij} = \sum_m {\mathcal P}_m (r_{i,m} – E_i)(r_{j,m} – E_j)$,
• A portfolio $P = \sum_i f_i I_i$, where each asset is assigned a fraction of portfolio $f_i \in [0,1]$ subject to $\sum_i f_i = 1$,
• A portfolio return for a given scenario $R_m = \sum_i f_i r_{i,m}$.

Then the key parameters we will be optimizing are the expected return of the portfolio, given by
\[ E[P] = \sum_m {\mathcal P}_m R_m = \sum_m {\mathcal P}_m \sum_i f_i r_{i,m} = \sum_i f_i \sum_m {\mathcal P}_m r_{i,m} = \sum_m f_i E_i \]

and the portfolio variance
\[ \sigma^2[P] = \sum_m {\mathcal P}_m (R_m – E[P])^2 = \sum_{i,j} f_i f_j \sigma_{ij} \; , \]

which we will use directly, or indirectly in the form of the standard deviation, as a measure of risk.

A Brief Aside

Since the final form of the variance shown above will be one of the two central formulas we use below, I’ll supply the proof that was omitted in the last post (for reasons of clarity) in this post (for reasons of necessity). Start by defining the residual portfolio return as $\delta R_m = R_m – E[P]$ and then substitute in the definitions given above to express the residual portfolio return as

\[ \delta R_m = \sum_i f_i r_{i,m} – \sum_m {\mathcal P}_m \sum_i f_i r_{i,m} \; . \]

Now exchange the order of the sums in the second term and combine the terms to get

\[ \delta R_m = \sum_i f_i \left( r_{i,m} – \sum_m {\mathcal P}_m r_{i,m} \right) = \sum_i f_i \left( r_{i,m} – E_i \right) \; .\]

Defining $\delta r_{i,m} = r_{i,m} – E_i$, we get
\[ \delta R_m = \sum_i f_i \delta r_{i,m} \; ,\]

which we can immediately substitute back into the variance formula to get
\[ \sigma^2[P] = \sum_m {\mathcal P}_m \sum_{i,j} f_i f_j \delta r_{i,m} \delta r_{j,m} \; .\]

Exchange the sum over the m scenarios with the sums over assets to get
\[ \sigma^2[P] = \sum_{i,j} f_i f_j \sum_{m} \delta r_{i,m} \delta r_{j,m} = \sum_{i,j} f_i f_j \sum_m {\mathcal P}_m (r_{i,m} – E_i) (r_{j,m} – E_j) \; .\]

 Recognizing that the last term is the definition of the covariance completes the proof.

Optimizing the Fractions

The key, remaining question is then: what values of the fractions $f_i$ should we pick? This question doesn’t have an unambiguous answer. Should we favor higher returns over lower risk? Lower risk over higher returns? Some blended measure? We’ll defer this question for a later post and we will content ourselves with ‘getting the lay of the land’ using the toy portfolio since we only have one free parameter $f_1$ (since $f_2 = 1-f_1$)

The simple, one-dimensional nature of this problem makes it easy to evaluate the expected portfolio return

\[ E[P] = \sum_m f_i E_i \]

and the portfolio standard deviation

\[ \sigma[P] = \sqrt{ \sum_{i,j} f_i f_j \sigma_{ij} } \; \]

parametrically as $f_1$ ranges over 0 to 1.

This is a simple matter to perform in a spreadsheet, which seems to be the primary tool in the world of finance. In the case that van der Wijst provides as a toy model a plot of expected return (which must, necessarily be between 8% and 9% as $f_1$ varies between 0 and 1) looks like

Note how the volatility is equal to either 2.82% when $f_1 = 0$, corresponding to the variance of 0.0008 for asset $I_2 = B$ or 4.90% when $f_1 = 1$, corresponding to the variance of 0.0024 for asset $I_1 = A$. What is desirable is that a blend of the two lowers the variance and hence the volatility (or standard deviation) due to their negatively correlations. The optimal point is approximately $f_1 = 0.4$. At this point, the expected return is 8.4%, down from the maximum possible value of 9% if we had committed to $I_1 = A$ completely. However, as this next plot shows the $\pm 1 \sigma$ envelope around the expected return shows

a total commitment to $I_1 = A$ can, very probably, give a return as low as 4.1% (even though it might be as high as 13.9%). The ‘throat’ of the envelope is narrowest at approximately $f_1 = 0.4$ meaning that if we pick this value we are willing to give up something on the upside return (9.4% v. 13.9%) in order to hedge against the ‘bottom dropping out’ (5.5% v. 4.1%). Indeed, if we were to look at the $\pm 3 \sigma$ envelope we see that blending the two assets in some degree is required to avoid negative returns in the event of a downturn on either asset. In this way, diversification puts up a stabilizer that prevents us from losing too much for the cost of giving up the ‘get rich’ position of a raw bet.

We pick up in this post the theme from the last – namely that one needs to look beyond a simple expectation value in order to assess the goodness of a ‘bet’.  van der Wijst makes this point at the beginning of Chapter 3 by saying:

There is an interesting nuance here in that the variance of a statistically varying measurement is always expressed in units that are the square of the underlying units of the thing that is fluctuating.  This can be a bit confusing when price is being considered because it isn’t obvious how to think about ($dollars)².  The alternative is to the standard deviation since it has the same units as the underlying entity but the downside (as we’ll see in some detail below) is that variances add linearly whereas standard deviations don’t.  The compromise that seems work best (at least as suggested by van der Wijst) is the use of a percentage return rather than the price itself, where the return is defined as

\[ r_n = 100 \frac{p(t_n + 1) – p(t_n)}{p(t_n)} \; , \]

where the index $n$ tracks the particular element in a time series (e.g., $n = 7$ may mean the seventh business day of the time series which starts on Jul 31, 2025).

Defined in this way $r_n$ is dimensionless and so we sidestep the whole issue associated with the underlying units.

van der Wijst then gives a toy example to start off the connection between risk, return, and variance.  In this toy example, there is a portfolio of two assets, $A$ and $B$, each with an possible return of $r_A$ and $r_B$, subject to some outcomes to be determined in the future.  Note that I am deliberately avoiding using $r_1$ and $r_2$ as does van der Wijst to avoid clashing with the definition of $r_n$ given above; when I need to point to either ‘A’ or ‘B’ without directly specifying which I will use the index ‘i’.

Regarding the future outcomes, there are envisioned three possible scenarios with corresponding different returns for each asset.  For clarity, the Scenarios will be labeled with the index ‘m’ when need arises to point to any (or all) of them without specifying which.  Each scenario is equally likely to occur; in other words the probability for each scenario is exactly 1/3, which is denoted as ${\mathcal P}(S_m) = 1/3$.

The final ingredient is the fraction of the portfolio that is comprised of Asset A, $f_A$, and Asset B, $f_B$.  For this example, the portfolio is made up of a 50-50 split between the two (i.e., $f_A = f_B = 0.5$).

 With this information we can calculate the expected return for each asset across the various scenarios, given by

\[ E[r_i]  = \sum_{m=1}^3 {\mathcal P}(S_m) \cdot r(S_m) \; \]

as well as the portfolio return for each scenario, given by

\[ r(S_m) = \sum_{i=A}^B f_i r_i(S_m) \; .\]

The table below summarizes both the individual returns per scenario for each asset, $r_i(S_m)$ (note it bears two indices – the ‘i’ to specify asset and the ‘m’ to specify scenario) along with $E[r_i]$ and $r[S_m]$ (in the margins).

Regardless of which sum is performed first, by asset as

\[ E[\text{portfolio}] = \sum_{i=A}^B f_i E[r_i] \; \]

or by scenario as

\[ E[\text{portfolio}] = \sum_{m=1}^3 {\mathcal P}(S_m) r(S_m) \; , \]

the expected return of the portfolio is 8.5%

The picture isn’t complete without having a risk assessment and that means we want to calculate the variance of the portfolio.  However, our task will be far simpler if we introduce some intermediate results.

First let’s calculate the variance of each asset across the scenarios, using

\[ \sigma^2[r_i] = \sum_{m=1}^3 {\mathcal P}(S_m) \left[ r_i(S_m) – E[r_i] \right] ^2 \; \] to arrive at

\[ \sigma^2[r_A] = \frac{1}{3} (15-9)^2 + \frac{1}{3}[9-9]^2 + \frac{1}{3}[3-9]^2 = 0.0024 \; \]

and

\[ \sigma^2[r_B] = \frac{1}{3} (6-8)^2 + \frac{1}{3}[6-8]^2 + \frac{1}{3}[12-8]^2 = 0.0008 \; .\]

Because variance involved squaring the underlying quantities we have reverted from percentages to floating point numbers since the only other alternative is to write these values as 24%² and 8%², with all the foreign-looking notation and mental gymnastics required to switch to dimensionless numbers.

The variance of the portfolio is given by

\[ \sigma^2[\text{portfolio}] = \sum_{m=1}^3 {\mathcal P}(S_m) \cdot \left[ r(S_m) – E[\text{portfolio}] \right]^2 \; . \]

Inserting the numbers from this toy problem gives

\[ \sigma^2[\text{portfolio}] = \frac{1}{3}(0.105-0.085)^2 + \frac{1}{3}(0.075-0.085)^2 \\ + \frac{1}{3}(0.075-0.085)^2 = 0.0002 \; .\]

Note that the variance of the portfolio is actually far lower than either the variance of asset A (by a factor of 12) or the variance of asset B (by a factor of 4).  The question, of course, is why. 

The answer lies in returning to the formula for calculating the portfolio variance and expanding the sums over the individual assets that make up $r(S_m)$.  The calculation is a bit long and not terribly instructive and, moreover, it can be found in so many places, that I’ll just cite the result:

\[ \sigma^2(\text{portfolio}) = f_A^2 \sigma^2[r_A] + f_B^2 \sigma^2[r_B] + 2 f_A f_B \sigma[r_A,r_B] \; , \]

where the cross-correlation between assets A and B is given by

\[ \sigma[r_A,r_B] = \sum_{m=1}^3 {\mathcal P}(S_m) \left[ r_A(S_m) – E[r_A] \right] \left[ r_B(S_m) – E[r_B] \right] \; .\]

This cross-correlation term can be positive (increases the variance), zero (does nothing), or it can be negative (decreases the variance).  Only in the latter case is the risk mitigated and the portfolio said to be diversified.  In other words, diversification came about because Asset A was high in scenarios where Asset B was low and vice-versa (although not perfectly so).  The cross-correlation term $\sigma[r_A,r_B] = – 0.0012$. 

Next month, we’ll look at how to optimize the weights $f$ to produce a portfolio that balances risk versus return.

Well, it’s March and most people have abandoned their resolutions.  Here, we are not abandoning it but rather expanding it a bit and redirecting some of the effort.  At the core is still the idea of working through the basic concepts of quantitative finance during the year of 2026 but we are going to supplement the content of van der Wijst’s treatment with other texts.  The obvious question is why?  The obvious answer is that van der Wijst’s chapters (beyond the easy Chapter 2) are both too long and too wandering and obscure for an easy consumption. 

The central idea of Chapter 3 is a statistical analysis of risk and how diversification mitigates that risk and how these ideas propagate and mature in various asset pricing models such as the capital asset pricing model (CAPM) and the arbitrage pricing theory (APT) for determining the return (and therefore the price) of a given asset such as a stock, a corporate bond, a hedge fund, etc., and also the famous Black-Scholes model for pricing options.

Nonetheless, van der Wijst drops the student right into discussions about correlation matrices and weighted outcomes that result from various possible scenarios.  This treatment, while mathematically rigorous and interesting, I believe misses the intuition building that can only come from solving simple-appearing problems that challenge the student to go deeper and have a strong working knowledge of risk beyond the statistical measures that can be deployed.

After looking around at a variety of texts, I landed on supplementing this exploration using the book entitled Quantitative Finance: A Simulation-Based Introduction using Excel, by Matt Davison. 

Davison’s Chapters 2 and 3 make an excellent precursor to the more abstract treatment of van der Wijst’s Chapter 3.  There are three questions that are particularly thought-provoking that illustrate what Davison calls key concepts.

One of the first of the key concepts is that in analyzing risk one must first look past the expected return.  This idea was discussed in an earlier blog (Economics and Ergodicity) but the circumstances are rather complicated and dense.  Davison does a much better job by stressing that “variance is a good measure for distinguishing good bets from bad bets (provided their expected values are equal)”. 

To illustrate this he gives a number of simple ‘what-if’ questions in Chapter 2. 

For example, in Question 1, Davison asks us if we would prefer a \$10-payout with certainty or a 50-50 shot of either getting \$0 or \$20 from a bet.  The expectation value for each trial is \$10 but one would have to be deeply confused to go for the latter case and, indeed, when one examines the standard deviation (STD) one arrives at:

• STD[\$10-sure thing] = \$0
• STD[50-50 bet] = \$10

Here I have chosen to explicitly use standard deviation instead of variance to avoid giving explicit answers in dollars-squared (I believe Davison uses variance because of its more convenient properties and for the fact that if figures into CAPM).

He expands this idea that the expectation value is not sufficient on its own in the following question when he asks the reader to consider the following family of possible ‘bets’:

The game involves paying \$1 to pull a single card from a standard 52-card deck.  If the player gets an ace-of-spades, he wins \$50, otherwise he gets nothing.  Of course, the player would be stupid to take this bet as his expected winnings are negative (\$1/26 or about \$0.04 to be precise and the standard deviation of this game is approximately \$6.87, making it a very stupid bet, indeed) but the real set of questions center on the banker.  Davison points out, that with the stakes amplified by a factor of 100, that:

The point he is making is that despite the fact that a naïve expectation calculation would tell us that we should, on average, expect to make \$4 each time the game is played we intuitively (or quantitatively) sense (or understand) that the standard deviation also scales to \$687 and that that value is too high to blindly accept the expected value as a good guide.

To better illustrate this idea, I constructed a simple simulation of the bank where I ran a succession of the same bet (1,000 trials in total) and I repeated this scenario 16 separate times.  The cumulative sum for each scenario looks like:

In 5 of the scenarios, the bank ended with a loss with the first scenario (upper left) being a dismal failure that went south quickly and never recovered.  In that scenario, the final bank balance was -\$70k.  Despite the fact that several of the scenarios ended with gains, none these demonstrated a ‘steady’ profit and many hovered around zero with often prolonged dips into negative territory.

There is one silver lining, which is a point that Davison makes in Questions 5a and 5b (although he doesn’t use this bet in those questions): the division into many smaller pieces helps to dilute the risk.  Suppose instead of a 1000 separate trials, the player and the bank decided to scale the bet upwards by a factor of 1000, with the cost to play now at \$100,000 and the payout being either \$0 (for 51 out of 52 possibilities) or \$5,000,000 (for the elusive ace of spades).  Again, the maximum expected loss for the bank is \$4,900,000 but the odds of this occurring are (51/52)¹⁰⁰⁰, which is approximately 4 chances in 1 billion trials.  As evidenced by the plot above, none of the repeated bets got anywhere near those limits (recall the worst case scenario for the bank ended with a \$70k shortfall – a far cry from nearly \$5 billion).

So, the fundamental point is clear that both the expected value and the variance, taken as a pair, will be critical for quantitative finance.  With these basic concepts better in hand, we will return to van der Wijst’s analysis of correlation and covariance in the next post.

In Chapter 2, van der Wijst covers what he terms are the basic, fundamental concepts and techniques for finance.  His aim is to provide mechanisms by which even somewhat qualitative aspects of decision making can be made quantitative so that a rational decision can be made in such a fashion that another person, consuming the same facts and following the same algorithms would arrive at.  For example, if we want to be able to understand whether or not we should be investing profits from a business in one way or another, we first need to have a sense of how much a particular investment is going to cost and how to weigh pursuing that investment versus other investments (i.e., opportunity cost). 

To these ends he provides three basic suites or types of tools:

Time Value of Money

van der Wijst starts by discussing the time value of money defined as the notion that a dollar today has a greater value than it will tomorrow and less than it had yesterday.  He cites two reasons for this effect.  The first reason is the observation that immediate enjoyment is worth more to almost all people than the delay of their gratification.  An individual can only be enticed into foregoing consumption by the payment of a premium that compensates for both the loss of the immediate gratification but also hedges against the risk that the opportunity may not be there in the future.  The second reason is what he calls productive investments in which the original amount of money is put to use to generate a greater amount in the future.  As an example, van der Wijst gives the concept of grain or livestock that can be consumed for a given amount in the present or cultivated to bring a larger harvest are some passage of time.  Nowhere in these two reasons is reckoned inflation since inflation reflects the expansion of the money supply within an economy in ways that are independent of the other two. 

The premium to be paid for deferring immediate consumption is summarized entirely in what is called the risk-free interest rate, denoted by r.  To compare amounts of money in two distinct times one can either ‘move’ the current money forward (called compounding) or ‘move’ the future money backwards (call discounting).  In both cases, the future value (FV) is related to the present value (PV) by the well-known formula

where T is the elapsed time (in years, months, etc.) between the two epochs.

Since it’s more common to speak of compounding when investing or saving in a bank, most people think of it that way and call it compound interest.  Note that the risk-free rate r is difficult to pin down quantitatively in practice since actual interest rates offered by financial institutions take into account inflation but the general rule is that U.S. government bonds (typically 3-month T-bills) set the value of r.

van der Wijst spends some time showing that it, while the formulas are mathematically simple, the thinking associated with using them isn’t trivial.  He does this by examining annuities, which are defined as specific fixed payments that are done on an annual or periodic basis and he offers the following question to ponder:

Suppose you just inherited €1 million from a distant relative. The accountancy firm that handles the estate suggests to make the money available over a longer period of time, so that you are not tempted to spend it all at once. They offer to pay out in fifteen amounts of €100,000, one now and one after each of the following fourteen years. If the interest rate is 10 per cent, is this a fair offer?

Even though the absolutely value of the money is €1.5 million, by properly accounting for the time value of money (with r=0.1) the actual present value is PV = 836,670 and so the offer isn’t fair.  Fairness (or perhaps the correct term should be equity) is achieved when the annuity payments are €119,520 or about 20% larger than what has been proposed by the accountancy firm.

Assigning Monetary Value

Once it is clear how to move quantities of money forward in time by compounding or backwards in time by discounting, the next step is to use a standard process by which a firm or business can be assigned a monetary value.  At the heart of this approach, van der Wijst says that a firm’s accounting system is meant to record two things. The first is the flows of goods and money through the firm and the effect those flows have on the firm’s assets. The second is the claims by various parties against the firm.  In essence, this is the standard accounting formula that says

with one variation, only a subset of what an accountant cares about is considered when assigning a monetary value for the purposes of finance.  Things like amortization and depreciation are largely ignored because these things impact questions of taxes and so only indirectly impact the money available.  In addition, intangible assets (such as reputation) and illiquid assets (such as buildings) are left out of the financial representation because they lack a monetary value (reputation) or have an accounting book value that is different from the market value and that the market value is hard to find and/or achieve (buildings).   Monetary value is assigned by mining the balance sheet, the income statement, the statement of cash flows and the statement of stockholders/business equity (alternatively names the statement of retained earnings) to produce a financial (as opposed to accounting) representation of the firm.  Typically, this financial representation is summarized as:

van der Wijst gives a tangible example in this style of investment analysis by posing the question as to whether a fictitious company, that he calls ZX Co., should contemplate a potential upgrade in certain purification installations.  He goes through a simplified accounting representation (balance sheet, income statement, cash flows, and statement of equity) and a set of costing assumptions associated with the potential upgrade:

At the end of a straightforward but lengthy analysis, van der Wijst shows that the net present value (NPV) is €15.7 million and therefore the financial investment is sound in so far as it will earn money, not lose it.  To decide whether this is the best investment opportunity, we have to turn to the third and final consideration.

Picking Between Alternatives

van der Wijst closes by examining the role of utility and risk aversion should play in making a quantitative financial decision.  Of the two of these, he considers utility as less useful in making financial decisions but, despite that initial statement, he starts there and he spends a fair amount of space talking about the mathematical ways to represent utility.  Using three basic assumptions:

van der Wijst then shows how either the natural logarithm U(W) = ln(W) or a negative curvature quadratic U(W) = a + b W – c W² can serve as mathematical tools provided certain cautions are observed if we can assign a quantitative value W (called wealth) to the preference.  He then uses these tools to express risk aversion in terms of two distinct ratios between the curvature (second derivative) and slope (first derivative), called the Arrow-Pratt absolute risk aversion coefficient

or the relative risk aversion coefficient

He further notes that each of these suffers from the fact that they increase with W in contrast to the fact that most people become less risk averse as their wealth increases.  However, he only touches upon these points and doesn’t elaborate how to assign utility and, subsequently, risk to a financial situation – perhaps deferring it to later chapters.

The final tool leveraged on the idea of utility is the notion of an indifference curve, which is essentially a contour or level curve in utility space where U(W) takes on a constant value.  The curve tells us which combinations of good or services (say apples and bananas) provides us with the same utility.  Since marginal utility is always decreasing (adding 1 apple to a stack of 2 may be fine but adding it to a stack of 100 is useless), the indifference curve helps to pick between combinations all with the same utility. 

In Closing

In closing, I found that Chapter 2 of his book lacked the clear narrative focus of Chapter 1 and that the treatment was uneven with some areas explored deeply while others were barely touched.  I was hoping for a focused case study for ZX Co. where utility and risk would help select between all investment opportunities with positive NPV.  I assume that this will come further in the book.

As we all know, people tend to make resolutions for the New Year.  Whether they keep them or not is a different matter, but the rage is to make resolutions.  For a change, I thought I would join the trend and make a resolution concerning this column but making it a resolution that is relatively easy to keep, although it marks a significant departure from years past.  I’ve decided that it might be both fun and profitable to focus on a single book associated with money and related matters for the foreseeable future.

The book I’ve chosen is Finance: A Quantitative Introduction by Nico van der Wijst

mostly because he promises a course offering a “quantitative approach that is particularly well suited to students with backgrounds in engineering and the natural sciences”, which is encouraging for someone like me with a background in physics.

My plan is to cover a chapter a month for the entirety of 2026 (perhaps this resolution will crumble as so many others do but I’ll try) starting this month with his overview in the Preface and Chapter 1.  Along the way, I might also throw in a dash of ideas and concepts from other works but how much, how often, and if at all remains to be seen.

van der Wijst starts his ‘hook’ with two enticing statements.  The first, aimed at anyone in the world with any sort of economic sense is the most surprising (and perhaps alarming):

Although his book was written over a decade ago, it is a matter of a few well engineered prompts to get a rough estimate (probably good to 10-20%) of the dollar values involved in 2025.  The gross world product is estimated at $117 trillion dollars, with about 25% of that total coming from the United States and another 25% coming from Europe.  In contrast, the total nominal or face value of all derivative contracts is estimated at about $850 trillion dollars or about a factor of 7 larger.  Of course, various sources go out of their way to point out that this value is a measure of all contracts, both positive and negative, and doesn’t represent the actual capital at risk.  A more ‘modest’ value comes from the gross market value of $22 trillion, which is a value just 20% lower than the GDP production of goods and services from the US or Europe.  So, even if van der Wijst is not quite right about the economic size, $22 trillion dollars is nothing to sneeze at.

Second, van der Wijst promises that the STEM professional will gain a better understanding of how project managers and business professionals arrive at funding decisions for all sorts of activities such as engineering design, manufacturing, and deployment.  His stated main emphasis is on “investments in real assets and the real options attached to them.

The main ingredients for this enhanced understanding of decision making for investments are:

Each of these ingredients builds a coherent picture aimed at one goal: how people choose between uncertain future values.  The ideas centering around how allocation of resources is made in the face of scarcity and various possible alternative uses of those scarce resources.

van der Wijst lists some typical problems:

Each of these, so the promise goes, should be amenable to rational decision making assuming a correct valuation of the choices and the probabilities for future outcomes.  van der Wijst argues that these are just the ingredients that make finance a science on par with physics or chemistry as its practitioners make theoretical models and test their predictions against observed outcomes but he makes some allowances for it being different in certain details.  For example, since we can’t run the world economy as a controlled experiment with hundreds or thousands of copies, each differing in only one precise way he argues that finance is different from the natural sciences that “usually show little dispersion around their theoretically predicted values.”  Here I believe he is indulging in a hasty generalization drawn from some well-publicized successes in physics and chemistry but his point is still valid: finance as a science is built on looser foundations than the natural sciences but, being quantitative, it is tighter than social sciences.

Since the central theme of finance involves choices between uncertain future values the two main problems then become: 1) valuation of each choice and 2) determining the probability that a given choice may be realized.

Valuation is particularly tricky because the value of a good or service is not the amount of money exchanged at the time of the latest transaction.  As has been pointed out in this column on a number of occasions, value is in the eye of the beholder.  However, for the sake of argument, let’s assume that the value of the asset is amount of money used in the last transaction for it.  That value is attached to a time in the past and the notion of choice implies a decision to be made in the future.  So, we are faced with the problem of moving a valuation forward in time (for an asset already delivered) or backward in time (for a promised asset).  The process for doing this time-movement of money is compounding (forward in time) or discounting (backward in time) and it assumes we have some idea about interest rates. 

Finally, as Yogi Berra is attributed as saying “It’s tough to make predictions, especially about the future”.  Any idea of a choice must be informed, either qualitatively or quantitatively, with a notion about how likely it is that that choice will turn out favorably.  Thus, both quantitative tools are need to price an asset in the future and to assign a probability to its realization.

As a prime example, van der Wijst points to techniques such as the Black-Scholes options pricing model that, while not perfect by any means, gives a way to value a given choice based on three ingredient:  1) ‘greedy’ investors, 2) a constant interest rate and level of stock market volatility, and 3) so-called frictionless markets.  Using these assumptions, markets worldwide price options contracts (for good or bad) and, ostensibly, this powers the modern, globalized economy.  There are, of course, other pricing models, such as CAPM and APT but his overall point remains that finance is a science.

In the months to come, I’ll use van der Wijst’s text as a guide to this science and we’ll see where we land.

Many years ago (over a decade, in fact), in the space of this very blog, I spoke about why Kodak lost is way (along with its market share) by focusing on ‘the how’ versus ‘the what’.  Insisting that they were a company that made photographic emulsion films rather than a company that embraced their founder’s goal of capturing memories, Kodak allowed other companies to market their ideas, take their customers, and, eventually, almost completely marginalize a once titan of industry.

In the years that have followed that post, I have become more and more convinced of the brutal truth behind the Kodak lesson.  Keeping ‘the what’ separate from the ‘the how’ and then treating both of them appropriately (without mixing ideas and methods designed for one with the other) is vital to the success of any endeavor as the next two stories show.  In the first vignette, we’ll see a cautionary tale about how slavish devotion to a ‘how’ undermined ‘what’ a software project was trying to do.  The second vignette, plucked from the most recent political headlines, weaves the story of how a quasi-government agency, with grandiose dreams of achieving a ‘what’, failed to focus on the ‘how’ which ultimately doomed it to failure.

Story #1 – Too Much ‘How’

A few days ago. I sat down to eat lunch. And, as is my wont, I fired up YouTube on my phone and decided to watch a short video.  One particular thumbnail (mysteriously suggested by the algorithm) caught my eye:

The title of the video, Why Spaghetti Code Beat Clean Architecture, was nearly as provocative and the whole story, told by one Eric Roby, was riveting for the entire 15 minutes run time.  Eric’s story is about a colossal failure he was forced to be a party of when he was a junior developer working for an online retailer with an annual revenue of roughly $50 million. 

Eric was assigned to a team that was going to clean up the old, ‘horrible’, ‘spaghetti’, legacy code into some brilliant, shiny new ‘clean code’ example.  Steven, the team lead, was a subject matter expert brought in to set the vision (based on his “why code quality is your competitive advantage” message), motivate the team, and supervise the development and deployment of a new system.  Central to Steven’s approach was the ‘clean code’ idea in which the level of abstraction and esthetic look-and-feel of the code were of paramount importance.  The code needed to have “good patterns” and “solid test coverage”.

Eric says that he was initially impressed by Steven’s approach but the lead’s way of presenting seemed to be “confidence that borders on dismissiveness” – a foreshadowing of things to come. 

Eric began looking at the legacy code as directed by Steven.  He found that while it is true that the old system’s code was nearly impossible to read and maintain without a huge investment in a learning curve it was also true that it worked really, really well.  Over the previous Black Friday, the legacy code handled 85,000 concurrent users, fulfilled over 77,000 orders, all with a latency of only about a quarter of a second, and an uptime of 99.4%. In addition to this sterling performance, the legacy system provided the development team a huge number of metrics to monitor and diagnose system health.  When he shared these observations, Steven brushed them aside saying that all that infrastructure was the legacy system’s way of compensating for the bad way it was written and that clean code didn’t need that.

As development and testing proceeded, Eric again tried to sway Steven on two subsequent occasions.  First, Eric meticulously documented many side-by-side comparisons between the clean and legacy codes which showed that the legacy code had more metrics and more observability, indicating that the legacy code was more resilient. Steven’s response was that Eric was over-engineering. Later Eric pointed out that factory testing used a way too small and far too sanitized group of users to be a valid test.  He was told the “don’t let perfect be the enemy of good-enough” line.

As is often said, pride foes before the fall, and the fall came in Autumn as the firm was beta testing in advance of Black Friday.  The beta test consisted of running the clean code for 5% of the users (maximum of maybe 1,000) and monitoring its performance.  For a very brief time, the system looked like it might be working but soon reports came in from the help desk of long checkout times and other errors. Shortly after it became clear that the clean code version was a bust when it consumed all available resources and effectively crashed.  This embarrassing fail was further compounded by the fact that without ample metrics there was no clear way of knowing where the problems was.  The firm reverted back to a sole dependence on the legacy code, essentially admitting that their clean code experiment was a colossal failure.

Eric explains this failure on the fact that Steven emphasized one and only one ‘pillar’ (clean code) of the four he believes need to be addressed – the others being infrastructure & scaling, observability, operational culture.  Of these, Eric emphasizes the failures in the last pillar, the lack of an operational culture.  The firms culture “treated [Steven’s] confidence as expertise”, “rewarded elegance over resilience”, and “treated production readiness as a nice-to-have”.  In other words, the firm focused on ‘the how’ and not on ‘the what’ and paid the price.

Story #2 – Too Much ‘What

Our second story comes from one of the most controversial political situations to emerge from the Trump 2.0 administration: The Department of Government Efficiency, aka DOGE.  Just after President Trump’s election in November of 2024, there was this incredible enthusiasm and energy associated with having the Department of Government Efficiency being formed, being led by Elon Musk and Vivek Ramaswamy, and that this agency was going to go and clean house across the government.

Having worked around the federal government for over 30 years and being frustrated by the sclerotic pace of the bureaucracy, I actually considered the possibility of volunteering for DOGE.  After all, I knew where all the bodies were buried and I had very strong ideas about how to make government efficient, having learned how to navigate through various useless processes and procedures.  I ultimately demurred because of the possible conflict of interest, but I was enthusiastic that DOGE would be able to produce the kind of fiscal responsibility I believe the government needs, or at least some fiscal responsibility.

The country’s initial reaction to DOGE just after President Trump’s inauguration can be best described as ‘shock and awe’, borrowing a phrase from Desert Storm.  DOGE claimed to be routing out billions of dollars of waste, well on its way to the ultimate goal of 2 trillion dollars.

However, if one were to query current headlines a mere 8 months later using the search string “Musk DOGE”, one comes up with a host of articles with headlines that essentially say quote Elon Musk saying “DOGE somewhat efficient, but I wouldn’t do it again”.

Skimming any of the articles, looking past all the bloviating and padding the standard reporter does in order to get their word count up, one sees that each article contains two basic points:  1) DOGE was nowhere near as efficient or as sweeping in its changes as Musk had hoped it to be and 2) Musk had anticipated it would have worked properly because it had worked in all his companies in the past.

What is the reason for this even more colossal failure than the one in Story #1 above?  Put simply, DOGE focused on ‘the what’ of reducing governmental bloat without ever stopping to ask themselves how they would recognize the bloat when they see it.  When Musk took over Twitter, as big as that company might have been, as many computers and employees and mocha lattes and yoga rooms that it might have had under Jack Dorsey, it was still fundamentally a simple company with a simple single vision.  A user types something stupid or wise or fun or mean or whatever and then tweets it to the universe. That was it. That’s all there was to it. SpaceX also has only one simple, solitary vision. Put some high explosives underneath a payload made of metal and plastic and silicon and ignite them to push the payload off the Earth (and hopefully not blow it up).  Simple, clean, straightforward. In other words, even if Musk had a million people working for him, he only had to navigate a few corporate visions.

The federal government, in contrast, has not only a million people literally working for it, but it has millions of visions, many of them often competing with or in contradiction to other visions by the very nature of the democratic process.  Musk’s team failed to take into account the nature of what they were trying to change. They failed to take into account ‘how’ they would implement those changes.  Being charitable, all they wanted to do was make government smaller and more efficient, a laudable goal, but they failed miserably because they were unable to understand how to make the changes they claimed they wanted. 

To close, what is the lesson we can draw from these tales of woe and incompetence?  The answer is it’s important in any endeavor to sit and think clearly about ‘what’ is being done and ‘how’ it should be accomplished.  The more complicated the task is, the greater amount of care that is needed in making these distinctions.  Focusing too much on a particular ‘how’ ends up with the project falling into the trap best described by the old adage that when one only has a hammer every problem tends to look like a nail.  Likewise, overly focusing on ‘what’ runs an equally dangerous risk best thought of as reaching into the toolbox indiscriminately for any heavy object, say a wrench, when one needs to pound a nail.  Neither of these two approaches is a good idea.

Picture, if you will: the date is April 21, 1961, the network is CBS, the show: The Twilight Zone.  For last 59 episodes, Rod Serling, the creative mastermind of one of the most influential and important of American TV shows had been serving up anthology stories with one part wonder blended one part terror blended with a strong helping of morality that served as its ethical compass.

On the particular evening in question, Serling presented an episode entitled The Rip Van Winkle Caper about a quartet of thieves who conceive a daring and unusual plan to steal a truck carrying gold, hide it in a Death Valley case, and outwait the authorities, thus allowing them to evade the consequences of their larceny and live large on the proceeds.

But, perhaps, the term outwait is inadequate to convey what they have in mind.  The head of this little band of bandits, Mr. Farwell, is a scientific genius with a flair for the absurd.  He invents a suspended animation device that sends the foursome a hundred years into the future under the assumption that by then the furor will have totally died down.

As most of the Twilight Zone tales go, there is a moral and a moral cost to this group’s bad decisions.  One of them never lives to awaken a century later due to a malfunction in his unit and another one kills a third over mistrust and suspicion but, in the process, deprives the now-halved set of survivors of the ability to transport all that gold (too say nothing of themselves) through the desert and back to civilization.

Greed being what it is, the remaining two, the scientist and the weapons man, start off on foot with far too much gold and far too little of the water.   Eventually, they dwindle down to just Farwell dragging an ever-dwindling supply of gold bars as he struggles more and more against the blazing sun and the deadly threat of dehydration.

In the final moments a traveler finds Farwell, who, being on the brink of death, offers his last bar of gold for just enough water to survive.  Sadly, before the traveler can do anything to save him Farwell dies.  Then the surprise of the tale comes:

This tale vividly illustrates the seemingly irreconcilable contrast between the practical value of water and the perceived value of gold.  This well-known effect is called the Paradox of Value, the solution of which eluded many of the most profound thinkers of human history.  Afterall, how can a substance so useless to the health and preservation of life (e.g., diamonds or gold) be so highly sought after while the essential ingredients of human existence (air and water) are so cheap that everyone can afford them?

Economists argue that the resolution of the paradox comes by first recognizing the role that marginal utility plays in determining value.  Marginal utility is defined as the additional joy, fulfillment, enjoyment, or satisfaction (i.e., utility) that comes from using one additional unit of a good or service.

One of the earliest illustrations of marginal utility comes from the economics work entitled The Positive Theory of Capital by Eugen V. Bohm-Bawerk.  In this work, Bohm-Bawerk asks us to imagine a farmer who has just harvested 5 sacks of corn and must decide what to do with them.  The first sack he deems essential for keeping himself just as subsistence while the second he uses for food to keep himself healthy enough to plow, plant, and harvest the following year.  The next two sacks he designates for the more comfortable purposes of growing livestock so that he can have meat, cheese, butter and so on (third sack) and corn mash liquor (fourth sack).  Finding no additional needs or burning wants, he uses the fifth sack to feed the parrots that flock near his farm “whose antics amuse him”.  Bohm-Bawerk then asks us to further imagine the farmer now having lost a sack of corn.  Will he take a fifth of the remaining four sacks so that he might again feed the parrots?  Bohm-Bawerk’s answer is that the farmer will not as parrot-feeding is the least of his worries.

Returning to the water/gold question, the resolution is simply that in most cases a person can always find water and so the marginal utility of getting more water is low whereas gold, being quite rarer, has a higher marginal utility.  Serling’s morality play being set in the Death Valley makes water rarer still and thus raising its marginal utility above gold’s.  The final surprise, which is still enjoyable, merely underscores the notion that most things don’t have an intrinsic value but rather derive their value in context relative to everything else.

And this theory is all well and good as far as it goes, but it does raise an interesting follow on as to whether this theory helps us understand the modern-day phenomenon of the collector.  To sharpen this idea, consider the commonly-asked question concerning a fascination that certain women have for fashion:  just how many pairs of shoes does she need?

We can try to follow the example of Bohm-Bawerk by imagining a woman who collects shoes – an ordinary shoe collector and not the extreme as exemplified by Imelda Marcos.

Supposing that her collection is made up of, as is usually the case, distinct pairs of shoes.  Does marginal utility really help us understand her economic decision making?  Does it help us understand why a new pair of shoes is a priority when so many others are taking up space in a closet?   On one hand, the styles differ one might well ask if a new pair of shoes doesn’t have the same marginal utility.  After all, pumps and sandals hardly send the same fashion statement.  On the other hand, just how many slightly different black shoes with kitten heels can one wear?

Judging by lack of scholarly articles and general website links this seems to be wide-open territory for the economic researcher.  Perhaps the next Nobel prize in economics will go to the person who figures out the theory of value of the slingback.

There’s an old saying that says “third time’s the charm”.  In the case of California, perhaps the better slogan should read “fourth time’s the curse” as once again somebody out in the Golden State has made a real mess of public policy through their ignorance of the simply laws of economics.

To be clear, it wasn’t my intention to go back to Cali for a fourth time in a row (with all due respect to LL Cool J) but when the ABC 7 Bay Area report entitled SF re-examines procurement process after business ban on 30 states backfires came across my YouTube feed I just couldn’t resist.  The video, which is almost 6 minutes long, reviews the ‘unintended consequences’ of the City of San Francisco’s ‘progressive views’ on public policy and the economy and its eventual repeal of some of the laws under the forces of economics.  Sadly, the larger philosophical issues posed by their ‘best of intentions’ probably remains unrecognized.

In 2016, San Francisco put into law specific bans on the city government doing business with or even visiting companies in 30 different states because, in the eyes of San Francisco officials, these states didn’t uphold the same values as the city, specifically in relation to LGBTQ rights, voting rights, and access to abortion.

The most obvious consequence of excluding so many suppliers for goods and services is that it severely limits the competition and thereby raises the price.  The most famous example of this being the Noe Valley Public Toilet that city was willing to pay 1.7 million dollars for when much cheaper alternatives existed but were forbidden by the ban.  As Aaron Peskin, a San Francisco supervisor, notes, the remaining vendors were savvy enough to know that they could raise prices with impunity.

Of course, Peskin may have also noted that even if the valid vendors had no intention of gouging the city they would, no doubt, been compelled to raise prices at least enough to cover the compliance burden associated with whatever additional paperwork they needed to push through to demonstrate that they were allowed under city ordinance to be on the approved list.

This compliance burden, which continues even after the softening of the ban, is still formidable as is evidenced by the very long portion of an even longer regulation that an ABC 7 Bay Area reporter displays at one point in this report.

Neither was the ban as absolute as one might think during the 7 years it was in effect.  As the report clearly discloses, the city recognized that it rarely worked since they not only granted hundreds of waivers, it expanded its work force to handle all of these requests.

The whole process was bloated and inefficient, wasting not only valuable taxpayer resources but forcing the people to wait excruciatingly long times for the resolution to whatever it was they were requesting.  These long waits disproportionately impacted the most vulnerable among the city’s population who waited for homeless services or similar support.

So, to summarize, the San Francisco ban on ‘people not like us’ wasted time and money, increased the bureaucracy, lowered response time and quality of service all in the name of …. Well what exactly was it in the name of?  This is the major question going unasked.

On the surface, the city’s intention in the ban was to either punish or, at least, not reward, those vendors who didn’t share the enlightened values of the country’s most progressive municipality.  But let’s examine what such a policy might actually entail.

Unscrupulous vendors who set up shop in California were automatically given a green light by the ban even if they didn’t support any of the progressive policies San Francisco holds so dear.  For example, an ultra-bigoted but eminently pragmatic business may happily submerge its own political agenda in short term while it fleeced the very people who held beliefs it despised.  It could do this knowing that once the transfer of wealth was complete it could use its acquired wealth and influence to exercise greater political control.

Likewise, people who align with the politics of the City by the Bay but who live in one of the 30 banned states have their ability to influence local politics in a way that would be more to San Francisco’s liking because of the blanket ban that automatically excluded them from competing for the city’s business.

Ultimately, like price ceilings and floors, such an artificial constraint on the market is self-defeating in enacting the very change desired.  In other words, San Francisco’s own economic ignorance in enacting the ban did more to harm their precious cause than it did to help it.

But that is only one consequence of the economic ignorance running rampant in that ‘progressive gem of a city’.  As discussed in an earlier blog (Division of Labor), any economy is a webwork within which no man really knows how to make anything, even with something as humble as a pencil.  Against this interconnectedness, what did San Francisco think it was banning?  Even if direct goods and services didn’t come from, say Nevada, which is California’s biggest neighbor, in terms of border, indirect goods and services did.  Food, water, equipment, etc. from Nevada surely found its way into vendors in California, some of who then resold it to San Francisco at a markup.  The city punished nobody but itself.

And that is the biggest tragedy here – that San Francisco’s economic ignorance made the city its own worst enemy.

California and fast food.  Fast food and California.  There seems to be no way to keep the two of them apart – at least in the news.  No sooner had last month’s column on the mess that the California legislature was making in the fast food delivery sector of the economy (Another California Mess) published than yet another fast food-related story bounds into the news.  And this one is making national headlines.

Simply put, the In-N-Out Burger chain is closing the only store they have ever closed and they say they are doing it not because of profit but because of crime while other voices say that crime is simply an excuse for not turning a profit.

(video clip taken from NBC news story For the first time ever, In-N-Out closes one of its stores authored by Mirna Alsharif)

The various reactions to this announcement say a lot about how business and the economy are regarded in today’s public sphere by both those outside the enterprise and those within.  But before getting to that analysis, let’s dive a bit deeper into the bare facts.

Alsharif provides some data to support the claim that this In-N-Out location saw way more than its share of criminal behavior.  She notes that there were “60 incidents of theft and armed robbery” in that region Oakland the week prior to the article’s publication.  She links these statistics to the proximity of the Oakland International Airport.  Alsharif also provides anecdotes from various business owners, including ‘Khalil’ who relates his standard greeting to the customers who visit his establishment:

Other authors provide additional crime statistics which serve to set the context.

Amanda Bartlett, in her article Oakland’s only In-N-Out to permanently close, cites the rental car companies serving the Oakland International Airport strongly cautioning their customers from using the nearby gas stations to fill up the rentals before returning them.  For example, the Oakland Police stated that a single Shell station, located within a mile of the airport, was the scene of 271 auto burglaries, 15 robberies, and 5 instances of vehicle theft in 2023.  Finally, she notes that security guards had taken more theft reports at that In-N-Out than they had at any other of the locations they patrol.

Jordan Valinsky, author of In-N-Out has never closed a location, until now. It cites crime as the problem, cites additional statistics noting that Oakland saw increases of 21%, 23%, and 44% in violent crime, burglaries, and motor vehicle thefts, respectively, in 2023.

Finally, the AP notes that car-related crime has particularly grown (In-N-Out to close first location in its 75-year history due to a wave of car break-ins and robberies) with police logging 1,174 car break-ins since 2019 and that In-N-Out makes a logical choice for the would-be perpetrator since the restaurant “attracts travelers headed to the airport and baseball fans who attend A’s games at the Coliseum.”

That the restaurant found itself in a high crime rate location seems well-established.  But what about the claim that this was the explanation for the restaurant’s closing on March 24 of this year.  Was this branch of In-N-Out popular and did it, as Valinsky put it, attract customers?

Bartlett provides some useful background on the company as a whole, writing that In-N-Out was

She also noted that this particular location, which has been operating there for the past 18 years, had drive-thru lines during the dinner rush often stretched all the way out into neighboring parking lots.

In her article, Alsharif simply cites the In-N-Out statement that states:

But Alsharif notes that, nonetheless, there are contrary voices who challenge the “true reason major companies are choosing to shutdown stores… saying the decision has more to do with financial performance than safety concerns.”

It is this tension: between the noblesse oblige and laissez-faire currents and attitudes towards business, that makes this story so interesting and worth pondering.

On the noblesse oblige front, seemingly, In-N-Out anticipated that they would have to answer for what some may perceive as and assert to be an abandonment of their responsibility.  They end their statement by saying:

No doubt they are trying to be good corporate citizens but also trying to get ahead of the very complaint that Alsharif ends her report with.  Supporters of the noblesse oblige attitude want businesses to justify each and every closure beyond simply pursuing a profit, an attitude that harkens back to a well-known position of Michael Moore, who has famously said that a business should not allowed layoffs if it is making a profit.

Supporters of the laissez-faire attitude respond by asking why an owner can’t simply stop doing business whenever he pleases.  They would argue that the owner took/takes the risk of running a business, that his first and only obligation is to provide a return on investment to the stakeholders who risked their capital on the business.  They would ultimately argue that nobody owns or can demand the labor of another and that, therefore, any business can close based on ‘financial performance’ versus ‘safety concerns’.

At the risk of sounding Aristotelian, it seems that a middle-of-the-road balance between these two oppositely directed views of an enterprise’s relationship to the greater community, is the best course of action.

That said, it seems obvious that crime is out of control in and around the In-N-Out location in question.  Whether In-N-Out could financially endure it by staying open is not the question – even from the noblesse oblige perspective – since those in political power in Oakland also have an obligation.  The political arm should also operate under noblesse oblige by fulfilling their duty to keep their community safe while also looking to improve the economic and social opportunities for those most marginalized.  No clearer economic can be made that these political obligations are abrogated than the economic anecdote given by Valinsky who writes

We may applaud the savvy diner for having the wisdom for putting a ‘kill switch’ on his vehicle while simultaneously lamenting the fact that he lives in a community that requires him to spend valuable resources doing so. And he is not alone is wasting valuable resources: consider how much was spent on the following sign that sits outside a local business

That is the real tragedy of the In-N-Out leaving Oakland: the laissez-faire attitude toward governments noblesse oblige.

There is never any shortage of policy ideas that, on the surface, seem to be good but that lead to stupid consequences.  These are the types of policies that inevitably end up causing unintended consequences.  At the bottom of this perverse cause-and-effect relationship are a set of twisted incentives.  Sometimes these incentives are accidentally put in place by people who feel more than they reason and, so, don’t think about how people will respond to the new policy all the way through.  Sometimes the policy enactors know exactly what they are doing and use a veneer of ‘caring emotion’ to cover what is really a coldly calculated set of incentives that are meant to benefit nobody but them.  Regardless of which type of motive the policy maker may have (and, yes, there can be both types supporting) society suffers.  It’s no wonder that economists focus on incentives.

Reason TV has made a set of delightful videos documenting what they call Great Moments in Unintended Consequences that are worth watching.  One case that stood out is the Oakland, California gun buyback program of 2008.

One hopes that this ridiculous escapade, which is also documented by Alexander T. Tabarrok in his article Oakland’s Gun Buyback Misfires, was really done with good intentions but it is a close call.  According to the City of Oakland’s own website, the stated intent of June 2022’s Guns to Gardens event is to provide “a unique and innovative gun buyback”.  A poster accompanying the website list the biblical verse “And they shall beat their swords into plowshares” Isaiah 2:24.  These types of evidence suggests that the organizers are sincerely trying to help stem gun violence in Oakland but, if so, they clearly understand biblical ethics better than economics.  Although they seem to have learned a few things from the 2008 fiasco.

The original 2008 buyback incentivized a variety of actions that did nothing to stem gun violence in Oakland.

First, with a fixed price of $250, gun owners with property valued far less than that had ample reason to participate.  Anyone owning say 4 guns, each valued at $10, would make nearly a thousand dollars for simply dumping their junk onto the city’s lap.  This was, no doubt, particularly attractive for gun dealers with useless, unmoving stock.  However, for criminals who use guns for various enterprises $250 was likely too low.  For example, a Glock 9 purchase, at the time, would have been closer to $350-$400.  In addition, a firearm is working capital for the criminal robber.  Why would he trade in for an one-time payment an instrument that allows him net hundreds or thousands per stick up or mugging.

Second, with no restriction to local residents (either Oakland proper or the city plus delineated surrounding locales) there was ample incentive for people to come far and wide for the buyback, thereby crowding out, either literally or by exhausting the fund, locals who wished to participate.  Oakland basically transferred wealth from their already impoverished neighborhoods to what are likely affluent municipalities elsewhere.

Third and finally, by bringing a host of guns into close proximity, Oakland had organized a large, unregulated, open-air gun show.  Just the territory where an enterprising criminal might go to buy a new weapon, with no questions asked and no background check.

Thankfully, the city has seemed to learn from some of its mistakes.  The Guns to Gardens event adjusts the buyback amount from a fixed price to gift cards in the range $100-$300 “depending on type of gun” (their words).  This fig-leaf of a correction only partially addresses the first issues.  They may think that providing gift cards addresses all the other issues but gift cards, even if they are only for local merchants, have market value that makes them as good as cash.  No doubt secondary markets will arise to move/launder them.  Whether the was any thought given to bringing guns and gun buyers together in one setting is unknown.  The website mentions no restrictions along these lines but maybe Oakland had something in mind.

But maybe all these objections about twisted incentives really don’t matter.  The City of Oakland has what seems like a good idea.  They seem to have good intentions.  What could possibly go wrong?