How Uber economists create value

Marketing in China is different [updated]

Article documenting US/Chinese differences in marketing:

• China merchants have access to consumers over many different media:

... most notably, Baidu, Alibaba, and Tencent, which together are known by the acronym BAT. To put this in a Western context, imagine if Amazon, Bank of America, Google, Facebook, Activision Blizzard, CNN, and ESPN were all owned by one company. That’s essentially how the big conglomerates work in China, with the BAT companies controlling most of the digital content across industries. 

[This kind of consolidation allows for data collection that most westerners would find objectionable; but consolidation makes it possible for cross-platform programs to engage consumers seamlessly on a variety of media.]

• Chinese companies have low discount rates

“Chinese companies have a growth mindset,” explained Hai Ye, a partner at McKinsey & Company. “Many of them can tolerate relatively low profitability in order to scale up quickly. This difference—driving market share versus bottom-line profit—gives the Chinese companies an advantage because they are willing to make big investments and take short-term losses to achieve long-term dominance.”



• Speed-to-market is a huge priority:

Danielle Jin of Visa recalled how she approached her job when she worked as a marketer in the U.S. consumer packaged goods industry. “We would sit down with Walmart one to two years in advance and think about what seasonal promotion we would want to have,” she said. “We would have a thought-out calendar that centered primarily on price promotions—a system and process created over decades that was based on a somewhat rigid planning process.” By contrast, she explained, “a marketer with a Chinese mindset would talk about creating seamless content that cut across multiple platforms and was temporally relevant" [weeks, not years].



• Bottom Line: Certainly US marketers could learn a lot from China.  But it is not clear that these techniques would work in the US, for at least three reasons
1. Privacy concerns would prevent much of the data collection and sharing that is prevalent in China.  
2. Antitrust Laws would likely prevent the kind of media consolidation that makes much of this possible 
3. Older (mature) firms in the US where the struggle is to steal share rather than grow the market, mean that speed and growth may not be as important as profit [think Prisoners' Pricing Dilemma] 

UPDATE FROM JUSTIN:

• Up until the last few years, the cost of capital/ loans was low.  Competitors that couldn’t beat you on quality and profit margins would go for scale and top line numbers. This results in everyone going opting for the top line. Contributing to this are state owned enterprises who are in every industry and tasked with top line growth, not profit margins.

• China is also really new at marketing. Most of the big sales companies are distributors who have only sold existing, western brands so they don’t have experience building brands themselves. If you think about the biggest Chinese brands, they are known for their size, not their crafty branding. 

• There is also a thing in China about “face.”  You can call it honor, prestige, or ego. Whatever it is, the biggest is seen as the strongest. I am working on launching a brand in China right now and I just had dinner two nights ago with my friend / distributor. We both agree that we would prefer to launch in tier 2 & 3 cities and get a bigger margin than fight it out in the more competitive tier 1 cities and lose money. As a Chinese guy once told my dad, “Sometimes we save face, but lose ass.”

What do jet skis have in common with financial derivatives?

They are both safety devices:  just as jet ski's make it possible for surfers to ride 80-foot waves in relative safety, so too can financial derivatives make it possible for investors to make big investments by offloading some of the downside risk.

This analogy was taken from An Economist Walks into a Brothel: And Other Unexpected Places to Understand Risk. The best part about the book are the interviews with people like prostitutes, big wave surfers and professional photographers who discuss ways they manage risk:

In An Economist Walks into a Brothel, Schrager equips readers with five principles for dealing with risk, principles used by some of the world's most interesting risk takers. For instance, she interviews a professional poker player about how to stay rational when the stakes are high, a paparazzo in Manhattan about how to spot different kinds of risk, horse breeders in Kentucky about how to diversify risk and minimize losses, and a war general who led troops in Iraq about how to prepare for what we don't see coming.

It starts off slowly, but stick with it.  Highly recommended, and not just for finance students.

Reason podcast discussing book

Newsflash: Washers and Dryers are Strong Complements

A new NBER paper by Aaron B. Flaaen, Ali Hortaçsu, Felix Tintelnot estimates the effects of the 2018 50% tariff on washing machines. As expected, they found that domestic producers increased washer prices by about 12%. Interestingly though:

the price of dryers—a complementary good not subject to tariffs—increased by an equivalent amount.

Dryers were not directly affected by the tariffs. Indirectly though, domestic white goods producers were also able to increase the price of dryers due to their complementarity in demand. Evidently, customers are not willing to break up a set even for a 12% lower price on dryers

Amazon fulfillment center tours

Went on a tour of an older sixth generation (no robots) Amazon fulfillment center in Chattanooga.   The infrastructure necessary to support Fulfillment by Amazon and Prime (two-day shipping, or two-hour in select areas) is impressive.  Here is what you see:

1. Where products enter the warehouse 

At the inbound dock, products get taken off trailers by forklift or manually built into pallets. Freight is separated between that coming from another Amazon facility and directly from a vendor, such as a seller using Fulfillment by Amazon (FBA). With FBA, small businesses store their products at fulfillment centers, and Amazon picks, packs, ships, and provides customer service, helping these businesses reach more customers. Half the items sold on Amazon.com are from small businesses and entrepreneurs. 

2. The stow process 

Instead of storing items as a retail store would—electronics on one aisle, books on another—all of the inventory at Amazon fulfillment centers is stowed randomly. Yellow, tiered "pods" stack bins full of unrelated items, all of them tracked by computers. This counterintuitive method actually makes it easier for associates to quickly pick and pack a wide variety of products. 

Robots ferry these pods to associates at stow stations based on product size, navigating 2D barcodes on the floor and yielding way to one another depending on which has more pressing business. The stower looks for suitable space for each item and stows it into the pod, making it available for purchase on Amazon.com. 

3. Picking orders 

Pickers are like personal shoppers, plucking from hundreds of items a day to fulfill customer orders. When the order comes in, a robot brings pods full of items to associates working at pick stations. The picker reads the screen, retrieves the correct item from the bin, and places it into a yellow plastic box called a tote. 

The robots are incredibly smart, but they aren't competing for jobs—they're creating them at Amazon fulfillment centers. Transporting thousands of pods per floor with millions of products stowed inside, the robots enable more inventory to pass through a fulfillment center, which means more associates are needed for handling that inventory. Since 2012, Amazon has added tens of thousands of robots to its fulfillment centers, while also adding more than 300,000 full-time jobs globally. 

4. Quality assurance 

Different teams along the way ensure the fulfillment process runs smoothly. The Inventory Control and Quality Assurance team makes sure an item's physical location actually matches what's in the computer, tracking millions of units of inventory. The robots need support too, so Amnesty Floor Monitors make sure the floors are clear and reset the units when needed. Many other checks along the way verify the right product goes to the right place. 

Touring an Amazon fulfillment center, you witness firsthand a process that is constantly being fine-tuned. While associates once needed to hand-scan a bin location after stowing each item, for example, machine learning now enables the system to know automatically the location where the associate has placed the item. It's impossible to predict today what technological innovation you might witness in six months. 

5. Packing orders 

First, items that belong to different shipments are organized and scanned for accuracy. Then they're sent to the pack station, where the computer system recommends box sizes to associates, and a machine measures out the exact amount of tape needed. Many items are shipped in their original boxes, and Amazon works with vendors to reduce packaging. At this stage, there's no shipping label—machines handle that down the line, protecting the customer's privacy and keeping the process efficient. 

6. Shipping orders out 

Packed envelopes and boxes then race underneath the SLAM (Scan, Label, Apply, Manifest) machines, which deposit shipping labels with astonishing speed and, contrary to the name, a light touch. For quality control, the package is weighed to make sure the contents match the order. A shipping sorter reads package labels to determine where and how fast customer orders should be sent, serving as a kind of traffic conductor. 

Ready to roll, the packages are nudged from the conveyor down slides into the correct trailer based on shipping method, speed of delivery, and location. Each door at the shipping dock accommodates trailers from a variety of different carriers and locations.

What doesn't kill you makes you stronger

New research on the benefits of early career setbacks, from evidence on scientists whose grant applications were rejected:

 On one hand, it significantly increases attrition, ... Yet, despite an early setback, individuals with near misses systematically outperformed those with near wins in the longer run, as their publications in the next ten years garnered substantially higher impact. 

HT:  MarginalRevolution.com

Don't Panic: A Guide to Claims of Increasing Concentration

Don't Panic: A Guide to Claims of Increasing Concentration 

Antitrust Magazine, Forthcoming

Vanderbilt Owen Graduate School of Management Research Paper No. 3156912

Vanderbilt Owen Graduate School of Management Research Paper No. 3156912

 Gregory J. Werden U.S. Department of Justice - Antitrust Division

 Luke M. Froeb Vanderbilt University - Owen Graduate School of Management

 Abstract: The Obama Administration’s Council of Economic Advisers expressed concern that competition was threatened by increasing industry concentration. Academics, commentators, and journalists have joined the chorus. But none demonstrated increasing concentration of meaningful markets, as are used in antitrust to assess the impact of mergers and trade restraints. The claims of increasing concentration are based on data that are far too aggregated. Market concentration can remain the same or decline despite increasing concentration for broad aggregates. Mergers have not increased concentration in airline and banking markets. Moreover, where market concentration has increased, that does not demonstrate a failure of antitrust law or its enforcement; market concentration naturally increases when the most innovative and efficient firms grow.

 Keywords: concentration, competition, antitrust, mergers

Inferring causality with double machine learning

Interview of Preston McAfee, former DOJ colleague, about how economists are helping Microsoft.  I particularly liked the description of the "double machine learning" they use to estimate the causal effect of price on quantity demanded, (the "price elasticity of demand").

In general, it is very hard to identify causal effects from non-experimental data because correlation is not causality.  Unless you run an experiment by randomly varying price, you cannot identify causality. 

Preston solves this problem by "creating" an experiment:

What we do is first we build a model of ourselves, of how we set our prices. So our first model is going to not predict demand; it's just going to predict what decision-makers were doing in the past. It incorporates everything we know: prices of competing products, news stories, and lots of other data. That's the first ML [machine learning]. We're not predicting what demand or sales will look like, we're just modeling how we behaved in the past. Then we look at deviations between what happened in the market and what the model says we would have done. For instance, if it predicted we would charge $1,110, but we actually charged $1,000, that $110 difference is an experiment. Those instances are like controlled experiments, and we use them in the second process of machine learning to predict the actual demand. In practice, this has worked astoundingly well.

In other words, by predicting how price was normally set in the past, the Microsoft economists create a "control group" to which they compare current prices.  The difference is the "experiment" that they use to identify causality.

BOTTOM LINE:  study econometrics!

How does Walmart compete with Amazon? (II)

Earlier we blogged that Walmart was acquiring firms that would speed development of:

1. an "artificial intelligence system that could someday power an automated personal-shopping service;" and 

2. autonomous cargo vans for home grocery delivery.

Now we learn that Walmart announced the first fruits of their partnership with Google:

Beginning this month, customers can say, 'Hey Google, talk to Walmart' and the Google Assistant will add items directly to their Walmart Grocery cart. Best of all, customers can be extra confident that we can quickly and accurately identify the items they are asking for with the help of information from their prior purchases with us. The more you use it, the better we’ll get.

This seems like a good example of Hal Varian's thesis (Hal is chief economist at Google) that the Tech Giants are being drawn into competition with one another:

Consider today’s leading tech companies: Google, Amazon, Facebook, Microsoft and Apple. They are not stuck in silos or hemmed in by a single business model. Instead, they compete intensely among themselves. For consumers this boils down to tangible benefits: products and services are better, faster and cheaper than ever before. 

...Entering a new market in the online world is far easier than in the offline world, since the necessary assets — hardware, software, and motivated employees — are readily available. But if companies can switch to new products quickly, so can their customers. You do not even have to walk across the street to switch between Lyft and Uber, or Google and Bing. Competition is a click away, so competitive advantage can erode quickly.

In this case, Google's search expertise allows Walmart to better compete with Amazon's in online retail and distribution.

Price discrimination Saves Lives!

Great post from Marginal Revolution on how drug companies enforce price discrimination against richer countries:

[Patient IDs] will be used to put an identifying barcode on the bottles they receive with their name and other info. Not only can the code be used to guarantee only residents of the country get the drugs…the provisions require that patients then return a bottle to get a new bottle and allows them to get only one bottle of their prescription at a time, even though allowing them to get multiple bottles could “ease the burden on patients and health providers,”

Although groups like Médecins Sans Frontières are outraged by these restrictions, what they don't realize is that the alternative is one world price, and it would be a lot closer to the $85,000 US price than to the $1,000 price in Egypt and India.  In other words, Price Discrimination Saves Lives!