Notes on Drive

Drive

I told myself that I’d read Drive after finishing The Mythical Man-Month, but I lost interest in the latter pretty quickly. Drive’s “surprising” truth isn’t all that surprising to those of us who are fortunate enough to be creative workers in a frothy tech industry: after a certain point, material compensation isn’t as important to us as the joy we get from doing our work.

I discovered when I reached the end of the book that taking notes on Drive was superfluous; the end of the book is packed with helpful reference material, including an executive summary. Nevertheless, these may come in useful:

  • Motivation has changed through the ages
    • Food, and not being killed
    • Basic needs are met, seek reward and avoid punishment more broadly
    • Motivation 2.1: Loosened dress codes, more flexible hours
    • Motivation 3.0: autonomy, mastery, purpose
  • Problems with carrots and sticks
    • They can extinguish intrinsic motivation
    • They can diminish performance
    • They can crush creativity
    • They can crowd out good behavior
    • They can encourage cheating, shortcuts, and unethical behavior
    • They can become addictive
    • They can foster short-term thinking
  • Carrots work if the task is dull, but you have to acknowledge that it’s boring and necessary, then let people complete the task in their own way.
  • Friedman’s two types of people
    • Type A: Excessive competition drive, aggressiveness, impatience, and a harrying sense of time urgency. Significantly more likely to develop heart disease.
    • Type B: Just as intelligent as ambitious as Type A, but their ambition steadies them, and gives them confidence and security.
  • McGregor’s two outlooks on employees
    • Theory X: People dislike work, fear responsibility, crave security, and badly need direction.
    • Theory Y: Work is as natural as play or rest. People are creative and ingenious, and they will seek responsibility under proper conditions.
  • Pink’s two types of people
    • Type X: fueled by extrinsic desires, concerned about the rewards that activities lead to, rather than the inherent satisfaction of the activity itself.
    • Type I: fueled by intrinsic desires, concerned about the inherent satisfaction of the activity, rather than the rewards that it leads to.
    • Type I’s almost always outperform Type X’s in the long run.
    • Type I’s don’t disdain money or recognition. Employee compensation must hit a baseline or their motivation will crater regardless of their type.
    • Pay a Type I enough and you’ll take money off the table, allowing them to focus on their work. For a Type X, money is the table.
    • Type I’s are physically and psychologically healthier.
    • Humans are, by default, Type I.
  • Autonomy
    • Autonomy is the most important of the three basic human needs.
    • Empowerment is not autonomy. It is simply a more civilized form of control.
    • Type I behavior emerges when people have autonomy over the four T’s:
      • Task. The “20% time” that companies like Google, Atlassian, and DOW give their employees results in many new products.
      • Time. Lawyers are universally sullen because they bill by the hour, and law is a zero-sum game.
      • Technique
      • Team
  • Mastery
    • A mental state of Flow is how people achieve mastery.
    • In Flow, the relationship between what someone has to do and what they can do is perfect.
    • There are two things companies can do to help employees achieve mastery:
      • Give them “Goldilocks Tasks” — challenges that are not too challenging but not too simple.
      • Trigger the positive side of the Sawyer Effect, and turn work into play.
    • Achieving mastery is painful. Therefore, grit is essential to mastery. In fact, it is as essential as talent.

Algebraic Data Types in Swift

An algebraic data type is a type that’s the union of other data types. That is, it’s a type that may be one of several other types. Here’s how we would implement a linked list as an algebraic data type in Swift:

enum LinkedList<Element> {  
    case empty
    indirect case node(data: Element, next: LinkedList)
}

This defines an enum called LinkedList that might either be .empty or a .node that points to another LinkedList. There are three interesting things to note. The first is that we’ve created a generic data type, so the type of Element is declared by the consumer of LinkedList. The second is that the .node case uses LinkedList recursively, and must therefore be marked with indirect. The third is that since the .empty case has no parameters, the parenthesis may be omitted.

Here’s how we define instances of LinkedList:

let a: LinkedList<Int> = .empty  
let b: LinkedList<Int> = .node(data: 1, next: .node(data: 2, next: .empty))  

To work with an algebraic data type, we deconstruct it using pattern matching. Here’s how we would print a LinkedList:

enum LinkedList<Element>: CustomStringConvertible {  
    '' // cases omitted for brevity
    var description: String {
        switch self {
        case .empty:
        return "(end)"
        case let .node(data, next):
        return "(data), (next.description)"
        }
    }
}

let b: LinkedList<Int> = .node(data: 1, next: .node(data: 2, next: .empty))

print("b: (b)") // => b: 1, 2, (end)  

We’ve implemented the CustomStringConvertible protocol so that we can use string interpolation to print LinkedList instances. While it is possible in Swift to pattern match using an if case statement, the switch is preferable because the compiler will warn us if we’ve forgotten to handle a case. This safety is one big advantage that algebraic data types have over their classical counterparts. In a traditionally implemented linked list, you would have to remember to check if the next pointer was null to know if you were at the end. This problem gets worse as the number of cases increase in more complex data structures, such as full binary range trees with sentinels.

Note that since the description instance variable only has a getter, we do not need to use the more verbose syntax:

var description = {  
    get {
        // etc
    }
    set {
        // etc
    }
}

Our print function was useful, but in order to do interesting things we need to be able to modify algebraic data types. Rather than mutate the existing data structure, we’ll return a new data structure that represents the result after the requested operation. Since we’re not going to mutate the original data structure, we’ll follow the Swift 3 naming convention of using a gerund for our methods. Here’s how we would add an .inserting method to LinkedList:

enum LinkedList<Element>: CustomStringConvertible {  
    // cases and "description" omitted for brevity
    func inserting(e: Element) -> LinkedList<Element> {
        switch self {
            case .empty:
            return .node(data: e, next: .empty)
            case .node:
            return .node(data: e, next: self)
        }
    }
}

let c = b.inserting(e: 0)  
print("c: (c)") // => c: 0, 1, 2, (end)  

The key is that we’re returning a new LinkedList that represents the result after the insertion. Notice how in the .node case, we do not need to pattern match on .node(data, next) because data and next are not needed in order to construct the new .node; we can simply use self as the next: node.

Finally, let’s implement the classic “reverse a linked list” interview question using our algebraic data type:

enum LinkedList<Element>: CustomStringConvertible {  
    // cases, "description", and "insert" omitted for brevity
    func appending(_ e: Element) -> LinkedList<Element> {
        switch self {
            case .empty:
            return .node(data: e, next: .empty)
            case let .node(oldData, next):
            return .node(data: oldData, next: next.appending(e))
        }
    }

    func reversed() -> LinkedList<Element> {
        switch self {
            case .empty:
            return self
            case let .node(data, next):
            return next.reversed().appending(data)
        }
    }
}

print("reversed c: (c.reversed())") // => reversed c: 2, 1, 0, (end)  

I’ll leave it as an exercise to the reader to figure out what the running time for this algorithm is.

Here’s the Playground on GitHub

Notes on Higher Education and the New Society

higher-education-and-the-new-society-cover

I picked this little book up at a secondhand shop in Adams Morgan a couple of weeks ago. Keller was a professor at Johns Hopkins University, where he specialized in higher education. Higher Education and the New Society was published in 2008, a year after his death.

Keller thinks that many critics of higher education are exaggerating the stubbornness of his beloved institution. He backs this up by summarizing the last hundred and fifty years of American history, and pointing out how higher education has responded to the dramatic changes in society. Keller concedes that while higher education has been changing, those changes have been incremental. The major systems and structures haven’t changed since the 1910s, when the first and only revolution in American higher education happened. He concludes with three proposals for change.

My Notes
  • Higher education is not an institution that exists in isolation. Educational systems change in response to society. In order to critique the current state of higher education, one must also understand what changes in society have happened.
  • Major transformations in society:
    • Younger generations are influenced by friends and the media. Older generations are influenced by community leaders, and familial elders.
    • Changing Demographics
      • A plummeting fertility rate means that the population is aging
      • The elderly are wealthier than ever.
      • Immigration has rapidly increased, and 90% of immigrants are coming from Latin America, Asia, and Africa. Prior to 1965, 70% of immigrants were from Europe.
      • The nuclear family is crumbling. The divorce rate has doubled, 36% of children are born out of wedlock, and many 60% of children will spend part of their childhood in a single parent home.
      • “We find it easier to love others if we ourselves have been loved. We learn self-sacrifice as we learn so many other things — in small, managable steps, starting close to home.”
      • Good parenting is a better predictor of a child’s success than affluence.
      • The first six years of a child’s life, to a large extent, determines academic achievement later.
      • Two-parent families serve as a bank and venture capital fund for their children.
      • Illegitimacy and divorce are responsible for essentially all the growth in poverty since 1970.
      • Interracial relationships are increasing, which means that the current system of classifying students by race will break down as racially ambiguous students become the norm.
      • The internet might fundamentally change education, but that remains to be seen — the same was said about television in 1957.
    • Changing Economics
      • The US was relatively unscathed after WWII, and siezed the opportunity to build the mightiest economy in the world. The 1960s were a golden age. The US government was flush with money, and compassionate liberals poured money into federal aid and research universities.
      • In the 1970s, the economic boom eroded. It was a decade of deterioration and disruptive change.
        • High quality goods from Japan and Germany led to the US becoming a net importer.
        • The outbreak of global terrorism resulted in the formation of a costly department of homeland security.
        • Inflation rose to 17% at one point, before drastic measures and a recession cut it back down to 3.8%.
        • US leaders could not rein in the spending that started in the 1960s and doubled taxes instead.
        • Watergate happened, and the news media became more hostile and adversarial. Polls revealed that the public had a widespread new contempt for authority.
        • The globalization of trade caused the US to transition into a knowledge economy. American companies farmed lower-skill manufacturing out to other nations and refocused their efforts on inventing new technologies, making scientific advances, and innovating in marketing. Scientifically educated and internationally attuned people were now in high demand, causing disciplines like History to fall into decline.
      • The US has performed admirably in the new global economy. More wealth has been created since 1983 than in the previous 150 years.
        • The demand for “skill” is the root cause of income inequality.
        • Colleges were the primary producers of high level skills.
        • More students hoped to go to college and study a professional field rather than the liberal arts.
        • The new economy lifted the best professors to new heights of affluence, influence, and importance.
        • “Intellectuals rose to the status of a privileged class” – Christopher Lasch
        • The top universities now admit based on academic achievement, and downplay the importance of family, fame, and alumni connections.
    • There are four major types of universities
      • Research Universities. These prestigious universities are a copious new source of new ideas, scientific findings, and discoveries.
      • Small Liberal Arts Colleges. The most sentimentally revered segment, they train students holistically for leadership and public service.
      • State colleges, polytechnics, proprietary schools, and small private colleges. These institutions provide the country with essential middle-range workers.
      • Two-year community and private colleges that enroll 40 percent of all students in higher education. They prepare people for vocational tasks.
    • The United States has made much progress towards equal opportunity for all. This has led to sometimes ironic results.
      • The already high degree of individualism in the US has increased.
      • Those who take advantage of the new opportunities have been richly rewarded. However, those who have not embraced the new openness earn much less and experience greater difficulty.
      • The rich and successful become more arrogant and feel they are richly deserving, but the poor become more sulky, angry, violent, and self-destructive and wonder whom to blame. “Every year, it becomes more difficult to use ant external barrier as an excuse” – Michael Young
  • Contrary to popular belief, colleges have adapted
    • Admissions offices are now grander than ever, and are frequently colocated with the financial aid office, as colleges make an effort to woo students of every race, from every corner of the country.
    • There are more classes for working adults and the elderly. Harvard makes $150 million a year from adults — 10% of its budget.
    • Colleges have also made changes to accommodate the torrent of immigrants, such as English as a Second Language classes.
    • In response to the dissolving nuclear family, colleges have increased financial aid packages and increased student affairs staff to handle the growing volume of date rape, harassment, and plagiarism cases.
    • Regarding computer technology…
      • Universities have heavily invested in computer technology, and often have CIOs in their cabinet.
      • “People don’t become physicists by learning formulas… Learning involves inhabiting the streets of a community’s culture.”
      • IT has not lowered the cost of higher education. It has increased it.
    • The significant increase in cost of higher education is not unique. It is happening in other service fields like healthcare, legal services, fine dining, and theatrical performances.
      • Slow growth in productivity compared to other activities. You can’t make a symphony orchestra that much more productive.
      • Labor intensive personal attention is required. You can’t reduce the labor cost of a chef or surgeon without serious loss of quality.
      • Highly trained expert personnel are required. These people are very expensive.
      • Costly equipment is needed, like medical devices or stage props and sets.
      • Expanding demand for a scarce number of extraordinary people.
    • Colleges have adapted to rising costs by investing their endowments in hedge funds instead of historically safe stocks, and reducing their annual withdrawal from endowment.
    • Colleges became less effective as they lowered costs.
      • Princeton closed its department of Statistics, and Columbia ended its department of Geography.
      • Colleges are employing many more part time professors, and are using graduate students to teach lower-class undergraduates. In 1980 one in four professors were part time. In 2001 only one in four new professors were on a tenure track position.
      • America is using higher education to accelerate social change. This means classifying students as a member of some group that merits special treatment, instead of treating each student as a thinking individual. The aim of a liberal education should be to “liberate our students from the contingencies of their backgrounds” (Searle). However, many professors have lifted political transformation above the disinterested pursuit of the truth.
  • The critics saying that higher education needs a massive overhaul have a point.
    • The changes in higher education since the 1970s have been incremental, and were accomplished in the same century-old structures.
    • The only academic revolution to happen in the US was between 1870 and 1910. This was when colleges got academic departments and majors, deans and presidents, electives, research and graduate programs, numbered courses and a credit system, academics relevant to industry, tenure, alumni associations, and organized fund raising. It was propelled by the desire to scrap the heavy emphasis on Latin, Greek, and Christian pedagogy and connect higher education to the actual conditions of the emerging American economy. It was kicked off by the Morrill Act of 1863, that established land-grant colleges.
    • There is tension between the three aims of higher education: preparation for work, well-rounded and deeply grounded learning, research and scholarship. To quote Aristotle, “It throws no light on the problem whether the proper studies to be followed are those which are useful in life, or those which make for goodness, or those which advance the bounds of knowledge. Each sort of study receives some votes in its favor.”
    • The semester system is a holdover from America’s agricultural past.
    • Graduate programs are devoted to research, not to the craft of teaching
    • Higher education used to be for the elite 15% that completed secondary education. Today, more than 60% of high school graduates go to college. This means that there are many more less academically prepared and less motivated students in college. Colleges are designed to educate a small elite for research, not masses of people.
    • “A multi billion dollar industry has developed outside established education institutions, responding in more direct, and usually more effective ways to the needs of industry and the labor market” – Michael Gibbons
    • Universities used to train high-minded people like von Humboldt and Newman, who pursued knowledge for knowledge’s sake. Today, they’re expected to train qualified manpower and producing knowledge for society’s benefit.
    • Only fifty or sixty of the 3800 accredited colleges in the US are premier research universities. At the rest of them, teaching is most important.
  • Proposals for change
    • Replace many four year colleges with three year programs. More students are taking AP tests, so many students are entering with sophomore status. They’re also older, and need less grooming. Many students are graduating in seven semesters. At Johns Hopkins, 20% of students complete at least one semester early.
    • Abolish the semester system. Students are no longer needed to plant crops in the spring and harvest in the fall. This would allow colleges to operate for an additional three months a year. Students would be able to complete their degrees more quickly, and facilities wouldn’t be wasted lying dormant for months.
    • Reform the sports programs at Division I universities. Big time sports have nothing to do with education, and are run as businesses that drain millions of dollars of the university’s money, while enjoying tax-exempt status. The students in these sport programs are not there for the education — 40% of basketball players do not graduate — they’re there because the only way to become a highly paid professional is to go through an education institution.

Fees and Credits In Shared Upside Transactions

Marketplace startups are challenging to run because sellers, buyers, and operators of a marketplace have conflicting goals. Sellers want to sell their goods for as much as possible. Buyers want to pay as little as possible. The operators of the marketplace want to reach liquidity as quickly as possible. Each group uses the platform for their own benefit.

Sharing the upside of a transaction is one way to build trust between two parties. For example, realtors typically get a percentage of the final sale price of a house. This incentivizes them to sell the house for a high price, and incentivizes the owner of the home to flexible with open house schedules. To increase profitability, marketplaces use fees and credits to incentivize or punish behaviors.

Managerial accounting of fees and credits in shared upside transactions isn’t as straightforward as I expected. The goal of managerial accounting is to interpret the financials of a company in a way that supports strategic decision making. Here’s an example based on my experience.


GizmoCorp is a hot new startup that buys gently used Gizmos from people in America and ships them overseas to China, where used Gizmos are in high demand because new Gizmos cost an exorbitant amount of money.

Jane, the founder of GizmoCorp, came up with the original idea in the shower and decided to try out the business model. That weekend, Jane convinced her childhood friend Dana to give her Gizmo-for-cash idea a try. When Jane arrived at Dana’s house in Oakland, she noticed that Dana’s Gizmo was rather dirty, and needed professional cleaning before it could be sold. Jane guaranteed Dana at least $15,000 for her Gizmo, and promised to split the difference if the Gizmo sold for anything more than that. Dana agreed, and Jane trucked the Gizmo to the port of Oakland, where it was cleaned, loaded onto a container ship, and sent off to China. Jane has a business partner in China that takes care of selling the Gizmos once they arrive in China. Her business partner charges a flat $1,000 fee per Gizmo sold. A few weeks later, the Gizmo sold for $20,000, and Jane received a wire transfer from her partner in China for $19,000. Thrilled, Jane sent Dana a cheque for $17,500, a thank you card for being GizmoCorp’s first customer, and this receipt:

Dana
Guaranteed Price $15,000
Actual Sale Price $20,000
GizmoCorp Commission -$2,500 ($20,000-$15,000) x 50%
Final Payment $17,500

Jane Goes To Sand Hill Road

Fast forward a year, and Jane is selling tens of Gizmos a day. Jane goes to Capital Partners, a top venture capital firm, and pitches GizmoCorp to them. They decide to invest, but set very aggressive goals for Jane. Jane is only making an average of $2,000 per Gizmo sold right now. The investors are pushing her to make at least $3,000 per Gizmo. Jane is confident that she’ll hit that goal, and takes the money. Jane heads back to GizmoCorp with the good news, explains the new goal for the business, and takes the team out for a happy hour. As the bar is closing, Jane surprises the team by giving them the rest of the week off, and tells everyone to come back on Monday refreshed and ready to go.

The $500 Cleaning Fee

The GizmoCorp executive team gets together on Monday morning to figure out how to make more money per Gizmo. They analyze their past transactions and figure out that they’ve been spending about $500 per Gizmo on cleaning costs. Jane proposes that they pass the cleaning cost on to their customers: they’ll deduct $500 from the usual guaranteed price if a Gizmo needs cleaning. Everyone nods their heads in agreement. They call this the $500 Cleaning Fee.

That day, two customers with identical Gizmos contact GizmoCorp. Ariel’s Gizmo is in pristine condition, and doesn’t need any cleaning. Becky’s Gizmo has been sitting outside for years and needs to be professionally cleaned. Both Gizmos are sold for $20,000. Here are the receipts that were sent out.

Ariel (Without Cleaning Fee)
Guaranteed Price $15,000
Actual Sale Price $20,000
GizmoCorp Commission -$2,500 ($20,000-$15,000) x 50%
Final Payment $17,500
Becky (With Cleaning Fee)
Guaranteed Price $14,500 $15,000-$500
Actual Sale Price $20,000
GizmoCorp Commission -$2,750 ($20,000-$14,500) x 50%
Final Payment $17,250

Problems With The $500 Cleaning Fee

A month later, the executive team meets to discuss the results of the Cleaning Fee. Things are off to a good start! 60% of customers are opting to clean their Gizmos prior to pick-up themselves, up from 30% the previous month. However, there are two unexpected problems.

Firstly, GizmoCorp notices that they’ve had fewer customers than usual this month. The executive team theorizes that it’s because the Cleaning Fee sounds really bad in a pitch. People don’t like fees, because that sounds like money is being taken away from them.

Secondly, Jane pointed out that even though the Cleaning Fee is $500, GizmoCorp is only saving $250 whenever someone cleans their Gizmo themselves. The executives compare Ariel and Becky’s receipts and convince themselves that this is indeed true: their final payments only differ by $250.

The $500 Cleaning Credit

The executive team comes up with a brilliant solution: they’ll implement the Cleaning Fee as a credit instead. They’ll give everyone a guaranteed price that’s $500 lower than usual, but offer a $500 credit if they clean their Gizmos themselves.

That day, two customers with identical Gizmos contact GizmoCorp. Abby’s Gizmo is in pristine condition, and doesn’t need any cleaning. Bernie’s Gizmo has been sitting outside for years and needs to be professionally cleaned. Both Gizmos are sold for $20,000. Here are the receipts that were sent to Abby and Bernie:

Abby (With Cleaning Credit)
Guaranteed Price $14,500
Actual Sale Price $20,000
GizmoCorp Commission -$2,750 ($20,000-$14,500) x 50%
Cleaning Credit $500
Final Payment $17,750
Bernie (Without Cleaning Credit)
Guaranteed Price $14,500
Actual Sale Price $20,000
GizmoCorp Commission -$2,750 ($20,000-$14,500) x 50%
Cleaning Credit $0
Final Payment $17,250

Problems With The $500 Cleaning Credit

A month later, the executives meet up again to discuss the results of the Cleaning Credit. GizmoCorp’s monthly customer count is back to normal. It seems like positioning the fee as a credit has helped. 80% of customers are opting to clean their Gizmos themselves, up from 60% last month. The executives laid out all five receipts next to each other, and added new “breakdown” rows to figure out how much more money GizmoCorp made as a result of the Cleaning Fee and the Cleaning Credit. Here are the formulas they used:

Commission Revenue = (Actual Sale Price - Guaranteed Price) / 2  
Cleaning Fee Revenue = IF clean THEN $0 ELSE $250  
Cleaning Credit Revenue = IF clean THEN $0 ELSE $500  

Comparing Abby’s and Bernie’s final payments, the executives are convinced that they’re now saving $500 whenever a customer cleans their Gizmos themselves.

Jane
(The Original Customer)
Ariel
(Without Cleaning Fee)
Becky
(With Cleaning Fee)
Abby
(With Cleaning Credit)
Bernie
(Without Cleaning Credit)
Guaranteed Price $15,000 $15,000 $14,500 $14,500 $14,500
Actual Sale Price $20,000 $20,000 $20,000 $20,000 $20,000
GizmoCorp Commission -$2,500 -$2,500 -$2,750 -$2,750 -$2,750
Cleaning Credit N/A N/A N/A $500 $0
Final Payment $17,500 $17,500 $17,250 $17,750 $17,250
Commission Revenue $2,500 $2,500 $2,750 $2,750 $2,750
Cleaning Fee Revenue N/A $0 $250 N/A N/A
Cleaning Credit Revenue N/A N/A N/A $0 $500

Jane notices that something is wrong. She adds two more rows.

Dana
(The Original Customer)
Ariel
(Without Cleaning Fee)
Becky
(With Cleaning Fee)
Abby
(With Cleaning Credit)
Bernie
(Without Cleaning Credit)
Guaranteed Price $15,000 $15,000 $14,500 $14,500 $14,500
Actual Sale Price $20,000 $20,000 $20,000 $20,000 $20,000
GizmoCorp Commission -$2,500 -$2,500 -$2,750 -$2,750 -$2,750
Cleaning Credit N/A N/A N/A $500 $0
Final Payment $17,500 $17,500 $17,250 $17,750 $17,250
Commission Revenue $2,500 $2,500 $2,750 $2,750 $2,750
Cleaning Fee Revenue N/A $0 $250 N/A N/A
Cleaning Credit Revenue N/A N/A N/A $0 $500
Revenue Breakdown Total $2,500 $2,500 $3,000 $2,750 $3,250
Actual Revenue $2,500 $2,500 $2,750 $2,250 $2,750

The breakdown rows can’t be correct because they don’t always sum up to the actual revenue for each deal! Jane thinks hard and realizes what the problem is: When a fee is implemented as a credit in a shared upside deal, the commission revenue is increased by a constant amount equal to half the value of the credit, regardless of whether it was applied or not. This constant amount will be double counted unless it is removed from the commission revenue.

Therefore, these are the correct formulas

For Cleaning Fee deals:

Commission Revenue =  
  IF clean
  THEN (Actual Sale Price - Guaranteed Price) / 2
  ELSE (Actual Sale Price - Guaranteed Price) / 2 - $250
Cleaning Fee Revenue = IF clean THEN $0 ELSE $250

For Cleaning Credit deals:

Commission Revenue = (Actual Sale Price - Guaranteed Price) / 2 - $250  
Cleaning Credit Revenue = IF clean THEN -$250 ELSE $250  

And they result in this table:

Dana
(The Original Customer)
Ariel
(Without Cleaning Fee)
Becky
(With Cleaning Fee)
Abby
(With Cleaning Credit)
Bernie
(Without Cleaning Credit)
Guaranteed Price $15,000 $15,000 $14,500 $14,500 $14,500
Actual Sale Price $20,000 $20,000 $20,000 $20,000 $20,000
GizmoCorp Commission -$2,500 -$2,500 -$2,750 -$2,750 -$2,750
Cleaning Credit N/A N/A N/A $500 $0
Final Payment $17,500 $17,500 $17,250 $17,750 $17,250
Commission Revenue $2,500 $2,500 $2,500 $2,500 $2,500
Cleaning Fee Revenue N/A $0 $250 N/A N/A
Cleaning Credit Revenue N/A N/A N/A -$250 $250
Revenue Breakdown Total $2,500 $2,500 $2,750 $2,250 $2,750
Actual Revenue $2,500 $2,500 $2,750 $2,250 $2,750

Much better! Now the revenue breakdown matches the actual revenue. The last thing to do is replace the now redundant “Revenue Breakdown Total” row with something more useful: Profit.

Profit = Revenue - Cleaning Cost - Chinese Partner's $1000 Fee  
Dana
(The Original Customer)
Ariel
(Without Cleaning Fee)
Becky
(With Cleaning Fee)
Abby
(With Cleaning Credit)
Bernie
(Without Cleaning Credit)
Guaranteed Price $15,000 $15,000 $14,500 $14,500 $14,500
Actual Sale Price $20,000 $20,000 $20,000 $20,000 $20,000
GizmoCorp Commission -$2,500 -$2,500 -$2,750 -$2,750 -$2,750
Cleaning Credit N/A N/A N/A $500 $0
Final Payment $17,500 $17,500 $17,250 $17,750 $17,250
Commission Revenue $2,500 $2,500 $2,500 $2,500 $2,500
Cleaning Fee Revenue N/A $0 $250 N/A N/A
Cleaning Credit Revenue N/A N/A N/A -$250 $250
Revenue $2,500 $2,500 $2,750 $2,250 $2,750
Cleaning Cost -$500 $0 -$500 $0 -$500
Chinese Partner’s Fee -$1000 -$1000 -$1000 -$1000 -$1000
Profit $1,000 $1,500 $1,250 $1,250 $1,250

From this table, we immediately notice two things:

  1. GizmoCorp is making a constant amount of profit, equal to half the value of the Cleaning Credit, regardless of whether the customer decided to clean their Gizmo themselves or not.
  2. The Cleaning Fee, in the best case, actually led to more profit than the Cleaning Credit. In the worst case, it made just as much. The executives had intuitively expected to make more from the credit than the fee.

Going Negative

How about the case where the Gizmo unexpectedly sells for less than the guaranteed price, and there is no upside? Here’s the same table, except that the Actual Sale Price has been lowered to $10,000.

Dana
(The Original Customer)
Ariel
(Without Cleaning Fee)
Becky
(With Cleaning Fee)
Abby
(With Cleaning Credit)
Bernie
(Without Cleaning Credit)
Guaranteed Price $15,000 $15,000 $14,500 $14,500 $14,500
Actual Sale Price $10,000 $10,000 $10,000 $10,000 $10,000
GizmoCorp Commission $0 $0 $0 $0 $0
Cleaning Credit N/A N/A N/A $500 $0
Final Payment $15,000 $15,000 $14,500 $15,000 $14,500
Commission Revenue -$5000 -$5000 -$5000 -$5000 -$5000
Cleaning Fee Revenue N/A $0 $500 N/A N/A
Cleaning Credit Revenue N/A N/A N/A $0 $500
Revenue -$5000 -$5000 -$4500 -$5000 -$4500
Cleaning Cost -$500 $0 -$500 $0 -$500
Chinese Partner’s Fee -$1000 -$1000 -$1000 -$1000 -$1000
Profit -$6,500 -$6,000 -$6,000 -$6,000 -$6,000

Like the earlier examples we hold Commission Revenue constant in order to make apples-to-apples comparisons. Here we see that Cleaning Fee Revenue and Cleaning Credit Revenue are identical, and when not zero, are equal to the full amount of the credit or fee: $500. Not only that, but Profit is identical across both the Cleaning Fee and Cleaning Credit. The conclusion: in the case where a Gizmo sells for less than the guaranteed price, the Cleaning Fee or Cleaning Credit will reduce losses by $500 regardless of whether the Gizmo was clean.

The Twilight Zone

How about the case where the Gizmo sells for somewhere between the highest and lowest guaranteed price (between $15,000 and $14,500)? Here’s the same table with the actual sale price set to $14,750.

Dana
(The Original Customer)
Ariel
(Without Cleaning Fee)
Becky
(With Cleaning Fee)
Abby
(With Cleaning Credit)
Bernie
(Without Cleaning Credit)
Guaranteed Price $15,000 $15,000 $14,500 $14,500 $14,500
Actual Sale Price $14,750 $14,750 $14,750 $14,750 $14,750
GizmoCorp Commission $0 $0 $125 $125 $125
Cleaning Credit N/A N/A N/A $500 $0
Final Payment $15,000 $15,000 $14,625 $15,125 $14,625
Commission Revenue -$250 -$250 -$250 -$250 -$250
Cleaning Fee Revenue N/A $0 $375 N/A N/A
Cleaning Credit Revenue N/A N/A N/A -$125 $375
Revenue -$250 -$250 $125 -$375 $125
Cleaning Cost -$500 $0 -$500 $0 -$500
Chinese Partner’s Fee -$1000 -$1000 -$1000 -$1000 -$1000
Profit -$1,750 -$1,250 -$1,375 -$1,375 -$1,375

Like the earlier examples we hold Commission Revenue constant in order to make apples-to-apples comparisons, but it’s definitely harder to intuitively explain what’s going on here. Here is what seems to be happening:

Cleaning Fee Revenue = IF clean THEN $0 ELSE $250 + GizmoCorp Commission

Cleaning Credit Revenue = IF clean THEN GizmoCorp Commission - $250 ELSE GizmoCorp Commission + $250  

A Counterintuitive Result

GizmoCorp leadership made these changes because they wanted to increase profit by encouraging customers to clean their Gizmos themselves. The executives were celebrating increases in the percentage of customers who cleaned their Gizmos, because they intuitively thought it was correlated with cost savings. After performing this analysis, it was clear that GizmoCorp was getting extra profit from every customer regardless of whether they clean their Gizmos themselves or not. They didn’t have to wait to know how much extra profit they were going to make from the credit: if we ignored the rare case where a Gizmo sells at a “twilight zone” price, it is simply:

$250 x expected Gizmos sold above guaranteed price + $500 x expected Gizmos sold below guaranteed price

Cleaning Credit 2

This issue can be avoided by lowering the guaranteed price by $1000 instead of $500. We’ll call this “Cleaning Credit 2”. Cleaning Credit 2 Revenue is indeed $500 when cleaning is required, and $0 when cleaning is not required. Here’s how Cleaning Credit 2 compares to Cleaning Credit 1:

Abby
(With Cleaning Credit)
Bernie
(Without Cleaning Credit)
Angela
(With Cleaning Credit 2)
Beatrice
(Without Cleaning Credit 2)
Guaranteed Price $14,500 $14,500 $14,000 $14,000
Actual Sale Price $20,000 $20,000 $20,000 $20,000
GizmoCorp Commission -$2,750 -$2,750 -$3,000 -$3,000
Cleaning Credit $500 $0 $500 $0
Final Payment $17,750 $17,250 $17,500 $17,000
Commission Revenue $2,500 $2,500 $2,500 $2,500
Cleaning Fee Revenue N/A N/A N/A N/A
Cleaning Credit Revenue -$250 $250 N/A N/A
Cleaning Credit 2 Revenue N/A N/A $0 $500
Revenue $2,250 $2,750 $2,500 $3,000
Cleaning Cost $0 -$500 $0 -$500
Chinese Partner’s Fee -$1000 -$1000 -$1000 -$1000
Profit $1,250 $1,250 $1,500 $1,500

Now, GizmoCorp is making $1,500 in profit in either scenario, and the amount of revenue that can be attributed to Cleaning Credit 2 is either $0 or $500. The “Twilight Zone” problem still persists, but it should happen with low enough frequency that we can safely ignore it.

Notes on Gender Equality by Design

What Works Gender Equality By Design Cover.jpg

Iris Bohnet is a Professor of Public Policy at Harvard. Her new book, “What Works: Gender Equality by Design”, is amazing. It’s full of research-backed recommendations for moving the needle on diversity. Here are my notes, but you should really just buy her book.

Notes

  • When cues about a position’s typical wage range is clear, women are as good as negotiating as men.
  • When employees are prohibited from discussing their salaries with each other, pay discrimination by sex and race is more likely to persist
  • 93% of women do not negotiate their initial offer. 43% of men do this
  • Those who are willing to negotiate advance quicker; performance is not necessarily what gets you promoted
  • Managers have a negative perception of women who ask for a pay increase. The same does not apply to men.
  • Leaning in can backfire. To “lean in safely”, invoke someone else like a supervisor when negotiating. “My team lead suggested that I talk to you about my comp because its not clear to us if its at the top of the pay range”
  • Mentors of women tend to be less senior, have less organizational clout, and they don’t advocate for them as much as mentors of men do for their proteges.
  • Emphasizing meritocracy and merit-based reward practices leads to greater male favoritism
  • Evaluating people sequentially leads to biased conclusions. Comparing people solves this problem.
  • Job postings for male-dominated jobs contain more words like “competence” that signal to women that they don’t belong. This happens in letters of recommendation too.
  • 40% of the gender gap in SAT Math scores can be explained by the unwillingness of women to guess
  • When Asian girls were reminded of their ethnicity, they performed better on Math tests than when they were reminded of their gender
  • Women underestimate themselves, men overestimate themselves
  • When there are only women in the room, women do better at competitions
  • Before 1975, some states still declared men and women adults at different ages
  • Seeing women in leadership positions increases women’s self-confidence and changes both men and women’s beliefs about what an effective leader looks like
  • When there are significantly fewer women than men in senior positions, the senior women are less likely to mentor other women because they see them as competition
  • Men are more likely to support women’s causes when they have daughters
  • Male CEOs who have daughters, especially firstborn ones, are associated with a difference in female employees’ wages
  • The more daughters a male Danish CEO has, the better his employees are paid
  • Start-ups with highly paid women among their first hires were more successful and stayed longer in the market than all-male start-ups
  • People are more willing to accept an unfavorable outcome if they believe the process was fair, but some people don’t believe that quotas are fair
  • The “pipeline problem” is real in some fields like STEM, so quotas might not be realistic
  • Assigning responsibility for diversity to people or groups of people is strongly associated with an increase in workforce diversity

Action Items

  • Invite people to negotiate
  • Be transparent with what is negotiable
  • Have people negotiate on behalf of others
  • Hire & promote in batches, comparing candidates against others in the batch
  • Remove clues that might trigger performance-inhibiting stereotypes
  • Adjust risk when gender differences may bias outcomes
  • Managers should give their reports feedback about their performance, instead of asking them to self-assess
  • Diversify the portraits on your office walls
  • Increase diversity in leadership roles through quotas or other means
  • If you cannot include more than one woman in a team, keep it homogenous so that nobody is a token member
  • Use rankings to motivate people to compete on gender equality
  • Use rules, laws, and codes of conduct to express norms
  • Make specific people responsible for diversity and hold them accountable