“A philosopher once asked, “Are we human because we gaze at the stars, or do we gaze at them because we are human?” Pointless, really…”Do the stars gaze back?” Now, that’s a question.”
Source: Neil Gaiman
I do not have the words for what is currently happening.
There is so much uncertainty. It is almost crushing, the sheer weight of the unknown. I didn’t realize that it would be so heavy. I didn’t understand how much I relied on knowledge, on the fact that today would at least be similar to tomorrow, that yesterday was like today, and that this would repeat indefinitely.
I didn’t realize how much I was taking for granted.
I remember 2008. I was 11. I remember being worried about gas prices. I remember not going on vacation. I remember things being tense. I could feel the stress that surrounded me.
The ride share market has experienced some changes recently, with the implementation of California AB5 and the subsequent drop of upfront pricing. Lyft and Uber are constantly balancing the dynamics of their business model with increasing regulatory scrutiny and worries of passenger safety. However, both companies continue to state that ride-sharing will one day replace car ownership entirely.
I moved to Los Angeles from Louisville, Kentucky about 6 months ago. I work out of two different offices, one in West LA and one in Downtown. I don’t drive, and have relied on public transportation, my bike, and ridesharing apps. So, my question is: are Uber and Lyft right? Can ride-sharing replace car ownership, especially in one of the most notorious driving cities in the world?
The Uber / Lyft pricing models are difficult to predict due to the large amount of inputs that are consumed in the marketplace algorithm. There are a myriad of variables that are built in, which constantly intake real-time events, such as driver location, number of ride requests, and the time of day, etc
In this paper, I first examine the cost of annual car ownership, for four different vehicle types, building in both direct and indirect costs. Then I combine my Uber and Lyft data from the past several months to examine ridesharing as a broad market, building three regression models to get a rough cost structure for ridesharing to ultimately answer the very important question:
Do we ride or do we drive?
The Cost of Annual Car Ownership
For this analysis, I’ve pulled data from 2019 AAA Driving Costs Report on 4 different types of cars: (1) the medium sedan, (2) the electric sedan, (3) the hybrid sedan, and (4) the medium SUV. Those seem to be the most practical cars for LA usage, and the ones that are most often out on the roads.
The cars all have a similar annual cost, ranging from the $5,700 — $6,800 for baseline mileage. According to AAA, the average cost of owning a mid-size sedan (“ vanilla ice cream of family sedans”) was $8,643 in 2019 (assuming 15k miles driven per year). This included the cost of insurance, license and registration, taxes, depreciation, and a financing charge. The more you drive, the more expensive the vehicle is due to increased depreciation, fuel costs, etc.
I’ve also included costs of cars in California in the analysis. It’s more expensive to own a car here because cost of living is exorbitant. For example, a mid-size sedan is ~$0.3 more per mile, on average.
However, there are more costs that accompany a car than just direct costs. This is where the analysis gets a bit tricky because some of these variables can be subjective, which I will discuss. There are additional “indirect” costs that are incurred over the life of the vehicle, including tickets, parking, and opportunity costs.
In 2018, over $6B in traffic tickets were issued to 41M people, an average traffic ticket cost of $146.34 per person. That doesn’t include the increase cost of insurance premium once you get the ticket, which can be substantial, as outlined below. One ticket in California can raise an insurance premium by 1/3 and a second ticket doubles it. If a driver gets one ticket per year, that would result in a $150 ticket charge + an $850 increase in premiums — a cool $1,000 annual increase in the cost of a car just from one ticket.
On average, Americans spend over $1,000 annually in parking. New York has the highest spend at $2,243, with Seattle in fifth place at $1,205. That doesn’t even account for the opportunity cost of looking for a spot — New Yorkers spent 107 hours looking in 2018. That’s 4.45 days per year, just looking for a place to park the car.
According to INRIX, parking is the largest single expense for vehicle owners. NYC was the most expensive city, as New Yorkers tend to pay for parking more often and pay more every time that they park. Los Angeles was second, with combined search and overpayments totaling $2,139.
Apartment complexes and office buildings usually charge a monthly fee, around ~$50-$150 depending on location. I am going to calculate the combined annual cost of parking and search time (with a buffer) to total ~$4,100 — totaling the total cost of search time and the total cost of parking.
Opportunity Cost ($1,648)
Then, there is total opportunity cost spent driving. I typed some of this article in the back of a ride share— it’s much easier to be productive when you’re not behind the wheel. The average commute time increased to 27.1 minutes in 2018, according to the U.S. Census Bureau. That’s 54.2 minutes a day, 4.51 hours a week, 19.42 hours a month, and 233.06 hours a year.
The average wage in 2019 was $28.29, according to the Bureau of Labor Statistics. Taking the average wage multiplied by the average commute time results in $6,593 in lost productivity and time at work. Even if we were only productive for 25% of that time that we spend driving rather than doing anything else, it would still be a loss of $1,648 annually.
The valuation of time is extremely subjective, and there is a lot room for modification here. Some people don’t place a dollar amount on their time, whereas others value their time more than the average wage, as shown in the calculation above. It’s all relative.
Total Indirect Costs = $6,748
Taking whatever the cost of the vehicle is and adding the indirect annual costs results in an additional annual cost of $6,748. If you drive the vanilla ice cream of family sedans, that puts your estimated annual cost of car ownership at $15,931.
Dynamic Pricing of the Ride Share Market
It’s a hard to find the pricing structure behind ride shares. A lot of the pricing stems from the location, the time of day, and traffic. The companies also appear to modify their pricing models often and there have been multiple articles published on the frequent changes that happen in the ridesharing pricing market. With the changes of California AB5, all of this is uncertain now. According to Uber:
A breakdown from Ride Sharing Driver (which is probably a bit simplistic, but important for illustrative purposes) estimates the cost per mile is $0.80 and the cost per minute at $0.20–$0.28, depending on the type of Uber. The booking fee changes from city to city, but I am going to estimate a $2.30 Booking Fee.
The average commute is 27.1 minutes. The average American travels over 13,476 miles per year in a car, according to the FHWA. According to AAA’s American Driving Survey of 2016, the average American takes 2.13 trips per day, for a total of 777.45 trips per year.
Assuming that the rider just used rideshares, the total cost in booking fees would be $1,788, multiplying the booking fee of $2.30 per ride, with 777 rides per year. The annual cost per mile is $0.8 * 13,476 miles or $10,781. The annual cost per minute total is $5,899, equal to the 777 trips per year, multiplied by the average commute time of 27.1, and the cost of $0.24.
This results in a total annual cost of $17,625 for riding Uber or another ride-sharing platform. I wanted to validate this beyond the basic model, so I scraped my own data.
Kyla’s Ride-Sharing Data
I moved to Los Angeles 6 months ago from Kentucky. I left my beloved Subaru Outback in the hands of my younger brother and decided that I would forge through LA carless — a sin to most Angelenos.
I work out of West LA and I live close to the office. I go to work very early in the morning, and in the beginning, I walked from my house to my office. After being chased by someone, I started biking it. However, biking in LA is a risk, without much reward.
I began to work out of a Downtown office as well, doing split-days three days a week between Downtown and West LA. I usually leave West LA around mid-afternoon to head Downtown, and head back West later in the evening. So, I have three separate commute times, if I choose to rideshare in the early morning, all with varying price pressures and dynamics. It is important to note that most of my transportation is outside of typical rush hour traffic time, so the analysis below might be skewed because of that.
West LA to Downtown is a ~12-mile commute, and I can’t bike it. I used to bike to the Expo Line and park my bike outside, but someone deflated my tires and my head was petted more than once on the line (welcome to Los Angeles Public Transportation), so I’ve been avoiding all public systems for the past few months. I take Uber or Lyft almost every day, usually pulling up both apps to compare the cost between the two.
Because of my work schedule I avoid most price surging that occurs at during rush hour, demarcated in red on the graph below. As shown by the stacked area, I ride most often in late afternoon and the evening. The average prices I receive could be lower because I tend to avoid “surge pricing”.
The below chart details my ride-sharing habits. I take Shared more often than I take a Solo ride, primarily for safety but also to save money and the environment (an attempt at least). If I need to get somewhere quickly, I’ll use UberX or Lyft, which is Lyft’s non-shared ride. If I am indifferent, I’ll use Shared. Shared is usually $5-$7 cheaper. I usually regret taking Uber Pool because it can add up to four additional people, which can be extremely painful.
My average time traveled in Uber is about 4.5 minutes left than Lyft, and my median time is much lower. That could mean that my Uber drivers are more efficient, or perhaps Lyft is cheaper for longer rides, so I choose it more often. 61% of all my rides are from Westside to Downtown, with a larger proportion of my Ubers taking me somewhere other than Downtown. 21% of the time I use Uber to go Downtown, the other 40% of the time, I use Lyft.
I have traveled 580 miles over the past four months. That puts my estimated annual mileage at 1,738, which is extremely low (and a little sad, honestly). If I had my own car, I imagine that this would increase by at least 25%, as I would have freedom to travel on the weekends and escape Los Angeles.
Examining the Data
This is a correlation plot between my three main rideshare variables, fare, seconds, and distance. The goal of this analysis is to predict the fare, or the cost, of the rideshares. I will be extrapolating my data out into the thousands of miles, which means that I assume that the relationship will stay constant.
To illustrate the issue of extrapolation, consider what Robert Chira once said about Apple’s valuation:
“If you extrapolate far enough out into the future, to sustain that growth Apple would have to sell an iPhone to every man, woman, child, animal and rock on the planet.
Model 1: Using Seconds and Distance to Predict Fare
I ran a basic linear regression in R to see the predictive power of seconds and distance in determining fare, since that is what most ride-sharing models are based on, including the aforementioned Ride Sharing Driver’s model, as well as Kyle Hill’s model from 2014.
Taking the data from the regression, the model then becomes:
This implies that for every additional mile and second traveled, the fare will increase by $0.204 and $0.003, respectively. The issue with this model is that the correlation between distance and seconds is 91%, creating a multi-collinearity problem, which I will address later.
Model 2: Including a Dummy Variable for Rush Hour
I modified the equation to add in a dummy variable to determine if we are in rush hour or not. Rush hour (in Los Angeles) is between 6am — 9am and 3pm — 7pm. Any time outside of that window is non-rush hour. I had 40 rides that were outside of rush hour and 18 that were within rush hour specifications.
Taking the data from the regression, this becomes:
This implies that for every additional mile and second traveled, the fare will increase by $0.196 and $0.003, respectively. If you are driving during rush hour, your price increases by $1.015.
Model 3: Dropping Seconds, Generating Interaction Term Between Distance and Rush Hour
By adding in an interaction term between distance and rush hour, I am making the assumption that the effect of distance on fare is different for rush hour vs non-rush hour, which makes sense. You could drive 1 mile in California at 5 pm, and it could take you $30, because several users are demanding the service, whereas the same ride at 4 am would only cost $3— the time of day will impact how much you pay for the ride-share due to the law of supply and demand. The fare will be different depending on if it is rush hour or not. This is basically a proxy for surge pricing.
I dropped seconds out of this model because the correlation between the distance and seconds is 0.913, which is extremely high, as mentioned in the analysis of Model 1. The VIF between the two isn’t as high as ten, but still relatively high. Stripping seconds out of the model didn’t reduce the R-squared dramatically, as I will talk about below.
This model then becomes
Comparing the Models
(Please don’t shun me for text output).
As you can see from the output above, the R-Squared doesn’t vary too much between models despite the change in variables. Unfortunately, the R-Squared isn’t very high. The addition of more variables and more data would improve that. Model 3 is just the relationship between distance and fare.
Model N: Linear vs. Robust Linear Model vs. Quantile Regression
I wanted to keep this analysis as compact and concise as possible, but I also wanted to make sure that I address the fact that there are several other regression methods that I could have used. The linear regression is the most well-known method, and thus was the one that I used throughout this analysis. The Robust linear model simply controls for outliers, which wasn’t a big problem in my dataset.
The quantile regression model, rather than taking the average like a linear model would, estimates using the conditional median function. This also works to control for outliers. Going to in-depth on this is outside the scope of this analysis, especially because the sample size is relatively small, but I wanted to include this as a caveat. Uber actually wrote a piece about quantreg back in 2016 in order to account for the varied pricing dynamics they deal with.
Conclusion: If You Drive Less than 10,000 Miles, Ditch Your Car
I held seconds constant in the analysis for the first two models and included rush hour in the output. I included rush hour because that will give the most robust analysis for the increasingly congested cities and the average person’s commute schedule. For the U.S. analysis, I set indirect costs equal to $3,000 to tease out the cost of California living.
I’ve color-coded each to show the point where you would be better off driving. For most of the models, the cross-over point is between 9,000 – 12,000 miles. If you have a hybrid, electric, or mid-size sedan, you’re getting the most bang out of your buck. The ride share companies only can compete in price up to 9,000 annual miles. SUVs are the most expensive vehicle, and their crossover point is ~12,000 miles.
If you own a car in California, you’re wasting money. At most points, it’s much cheaper to use rideshare. I set indirect costs equal to $6,000 to account for the higher cost of California living.
There are a lot of reasons that the data could be telling this story, including how I priced cars and my personal ride-sharing data. However, ride-sharing is a better option for low-mileage users as compared to driving according to this analysis.
There are a lot of reasons that the data could be telling this story, including how I priced cars and my personal ride-sharing data. However, ride-sharing is a better option for low-mileage users as compared to driving according to this analysis.
There are different ways to value your time, which could impact how you value ride-sharing versus driving. But you have more freedom with a car, and don’t have to worry about other passengers, the driver, or not being in control of the vehicle. Also, this data doesn’t consider the changes that are coming with California AB5, which could radically change all of the models, which is quite sad.
Overall, the driving experience is pretty subjective. From a quantitative viewpoint, it’s sometimes cheaper to get a ride share. Qualitatively, it all depends on what you value.
**Disclaimer: none of this is investment advice, and I have no affiliation with any ride-sharing company
Despite the increase in health consciousness among consumers, chocolate and other confectioneries are still in high demand
The new pricing methods for cocoa could cause candy makers to hike their prices
Some candy products have more room to change their prices because their quantity demanded won’t shift substantially. Other products are completely the opposite.
Candy is one of the most inelastic food categories, across all incomes.
“All you need is love. But a little chocolate now and then doesn’t hurt.” ― Charles M. Schulz
How much are we willing to pay for a candy bar?
The History of Chocolate
Chocolate has long been an indulgent food of choice. With an origin stemming back to Mesoamerica, where it was used for ceremonial purposes, to modern day snacking, candy is currently a $494M industry, and is expected to grow at a 4.3% CAGR over the next 5 years.
That is primarily because of the diversity of candy offerings. There are different sizes, different shapes, and several different flavors. Every consumer has their own candy preference, as detailed by the United States of Candy map below.
However, the confectionery industry faces some headwinds. Developed countries are becoming increasingly health-conscious. With recent trends such as alternative meats, iterations of cannabinoids, and various diets such as the keto diet or Beyonce’s Coachella diet, mainstream consumers haven health at the forefront of their minds more than ever.
What does that mean for the confectionery industry? How can they survive in the face of mushroom coffee and bee pollen?
Candy makers have tried to adapt to the pressures. They are working with Partnership for a Healthier America on various health programs (such as Always a Treat, as well as have committed to 90% label transparency on packaging. They also have a goal to have “instant consumable” treats down to 200 calories or less by 2022.
However, that might not be enough. The chocolate industry is part of the broader “shrinkflation” movement. This is when the price of the product increases but the actual product shrinks, which often results in angry consumers. Hershey hiked wholesale prices by 10% back in July 2019, with Mars announcing a similar hike around the same time.
The shrinkflation problem coincides with the price of cocoa increasing substantially over the past year due to the much needed change in pricing in efforts to boost wages for West African farmers.
The new cocoa method pricing is expected to be implemented in October 2020. All of the candy makers are going to be impacted by the change, but some more than others. Posh chocolate, like Lindt’s truffles, are actually expected to be more insulated from the changes in price due to an evolving consumer preference for high quality (a byproduct of health consciousness).
The questions then becomes — what candies are more elastic than others? Which type will be more insulated from the change in dynamics? This is price elasticity. Consumers are more sensitive to price changes on some products as compared to others, and reduce the amount of product purchased if the price increases beyond a certain point. The exact relationship of elasticity is measured by:
For most items, the relationship between price and purchase is negative — if the price increases, consumers tend to buy less of it. However, for items that are inelastic, like medicine and other necessities, the price can increase substantially without consumer demand shifting. Chocolate bars, on the other hand, are assumed to be a relatively elastic good. We don’t need chocolate. We just like it.
Measuring the Elasticity of Candy
For a basic example, let’s assume that the price of a chocolate bar increased by one-third, from $1.50 to $2.00. 1,000 people bought the candy bar when it was $1.50, but now only 250 people buy it. A 33% increase in price resulted in a 75% drop in quantity demanded, resulting in an elasticity of -2.27. Generally, any elasticity measurement greater than the absolute value of one means that the product is elastic, and thus price greatly impacts demand.
However, if the price dropped by 25%, but the number of consumers only dropped by 20%, that would mean that the candy is an inelastic good with an elasticity of 0.8. The change in quantity demanded is not as great as the change in price.
Posh Candies are Price Inelastic
UBS released this chart back in 2018, detailing the price elasticity of the different confectionery companies.
Lindt has the lowest price elasticity out of all the candy makers, with their main brand, Lindt at an elasticity of 0.7, which means that when prices increase for Lindt chocolates, quantity demanded doesn’t shift that much. However, Ghiradelli has a price elasticity of 1.3, so the quantity demanded of Ghiradelli is sensitive to changes in price. Consumers are more likely to continue buying Lindt after a price hike as compared to Ghiradelli.
Interestingly enough, Hershey company has the most price-sensitive products in KitKats and their Reese Peanut Butter Cup. They don’t have much room to increase prices without consumers responding negatively. Note: the Easter Egg, which is the literally Reese Peanut Butter cup in egg-form, is an inelastic good.
Perhaps companies need to repackage all their products into seasonal forms to avoid the elasticity of demand.
Consumer tastes are shifting, with more consumers preferring non-chocolate candy, which might explain some of the variation that chocolate-dominant Hershey is experiencing. Chocolate candy still leads the way in terms of sales, but non-chocolate candy is catching up. Gum is also a popular item, with 2% year-over-year growth.
Source: Candy Industry
Comparing Price Elasticity Across Industries
Sweets and sugars were actually one of the more inelastic food categories, according to research from Andreyeva, Long, and Brownell. The only good that was less responsive to changes in price were eggs. Soft drinks was one of the most elastic products, and consequently are the most responsive to changes in price.
There’s a reason that chocolate was so inelastic in the study above — it is a grocery cart staple. 69% of all households purchase chocolate once a month, without much variation between income classes, according to research from Smith, Cornelsen, Quirmbach, Jebb, and Marteau. Chocolates represent a small portion of total household expenditure (~2.8%), but it is consistent.
The chocolate industry is relatively inelastic. As with all things, some products are more inelastic than others. Lindt Chocolates and other posh chocolates are more protected from price increases as compared to their Hershey and Mars counterparts.
That’s primarily because a shift in consumer mindset to “healthier” products, which can often be mistaken for more expensive products. The shift benefits the luxury candy makers, but could be impacting the lower-end products. Overall, candy is a relatively inelastic industry, and despite it being a small-part of consumer budgets, it is one of the most consistent items we purchase.
Some variation of Kyle, Kayla (most common), or Kylie will normally replace the two-syllable name that my parents bestowed upon me twenty-two years ago (pronounced kuh/eye-la). Kyla means “triumphant” in Gaelic, a nod to my second-generation Irish heritage.
But all of my life, I’ve been called the wrong name. Often. Almost every single day. I’ve never understood it.
I actually keep track of it, because I live a life of charts and graphs. On average, 75% of people call me the wrong name. 45% do it again. And approximately 10% continue to do it, into eternity (rough estimates).
When I was younger, I made a pact with myself to never be friends with someone who called me the wrong name. A bit harsh, but I wanted to stay true to myself. Also, I was usually too nonconfrontational to correct anyone, so I practiced a strong avoidance technique.
I’ve loosened those regulations a bit, but it still bites whenever someone says “Kayla” – especially if they’ve known me for a while.
It seems as though I am unimportant, not worthy of remembering. I know that (hopefully) is not the intention of the person, but I still feel a sharp pang when it happens. It’s embarrassing.
I did some reading on the subject to try and figure out what this was doing to me psychologically (everything is an experiment in my world, for better or worse).
“A rose by any other name would smell as sweet” according to Romeo, from William Shakespeare’s Romeo and Juliet.
I am here to radically disagree with Romeo.
What’s in a Name?
A name is your identity. It’s what people call you, it’s what you respond to, it’s what you understand about yourself. From the day we are born, we are assigned this identifier. Some people get nicknames or change their name entirely after they are born, but the common thread is a NAME.
Every single thing on planet Earth has a name.
Even if something has no-name, it still has a name, because no-name is a name within itself (how’s that for some philosophy?)
Having an identity is one of the most important things to our human nature.”Personal identity” is tied to our self-worth, how we see ourselves represented on a broad global stage among 7 billion other people.
“Self-identity is not a distinctive trait, or even a collection of traits possessed by the individual. It is the self as reflexively understood by the person in terms of her or his biography. Identity here still presumes continuity across time and space: but self-identity is such continuity as interpreted reflexively by the agent.”
The below diagram illustrates how we view ourselves, and what composes our sense of identity. We pull information from the environment, from the relationships we have, our memories, our thoughts, and how we reflect ourselves to others.
A name is many things, ranging from an “important anchor point of identity” to a “determining factor in personality development“. Names are “Semiotic” or a symbol for a person. How we interpret that name is depicted in the triangle below, also known as the “semiotic prism”.
What does that name mean to you as you search for your sense of self? How do others interpret it? What signal does it send to the world? A name gives you the avenue to answer all of those questions.
Sean Jean Combs is a good example of the power and the identity that a name carries. He went by Diddy, P. Diddy, Puff Daddy, or Bad Boy to segment his work – rapping, producing, or designing. There wasn’t Sean Jean Combs rapping, producing, and designing. That way each aspect of his life got a full allowance of his identity.
Perhaps a bit extreme. But the idea of creating yourself to be present in all parts of your life is interesting. But what happens when our names are taken away from us entirely?
The Westernization of Names
A lot of name research discourse is around the Westernization of names. When immigrants come to the U.S., there is an expectation of acculturation – you will assimilate into this culture, take these names, act the same way we do.
But different cultures have different rules for naming their children. In South Korea, some parents bring in experts to incorporate a child’s saju (a person’s fortune) into their name. In China, some names are comprised of a monosyllabic surname followed by a given name, which sometimes reflects the parent’s future expectations of the child.
You can’t take that away from people. I speak from a pedestal of privilege when I complain about my name being said wrong – it’s just because people look at it quickly or don’t pay attention to the letters – it’s not because they don’t know how to pronounce it.
Yejin Lee wrote a beautiful piece about the power of mispronunciation in 2018, writing to her time as a Korean born in the U.S., and the constant struggle with the idea of “Other-ness” or “foreignness.” People would say things like:
“Don’t you have an American name?” or “It’s too hard, I’m just going to make up a nickname for you” or “You can’t expect me to say your name correctly since it’s not in English.”
Those are all signals that the name difference is unwelcome. That Yejin was unwelcome. That Yejin was different. Social isolation is never a comforting feeling.
We all like to hear our name said. I used to sell cars, and one of the things that we were taught was to repeat the name of the customer back to them. It’s immediately a feeling of comfort and connection.
A Rose By Another Name Would Not Be A Rose
As I mentioned previously, I write from a place of privilege. My biggest complaint is that people say my name wrong. I’ve never been asked to change my name (although someone did ask if they could call me Kayla instead). I’ve never been told that I don’t belong because my name is different.
Our world is becoming increasingly globalized. The barriers to entry to global travel are low. People are still coming to the U.S., and people are still leaving the U.S. Connection across cultures are increasingly common, but that doesn’t mean that our sense of culture just disappears.
Homogeneity is not a place of growth. If things were the same all the time, we would never have a spark that allows for improvements and expansions and advancements. We need diversity of thought and diversity of perspective.
And we can’t have that if we don’t allow people the very root of their human self – their name. Romeo was wrong. A rose is a rose for a reason.
Calling it a daffodil doesn’t diminish the inherent power of the rose, but it does diminish what the rose feels about being a rose. Take great care in pronouncing the rose’s name correctly. No one likes being called the wrong thing.
We all deserve a name, and we deserve it to be said correctly.
Call a rose a rose, or whatever it asks to be called.
This week has been tumultous, to say the least. The geopolitical environment is extremely tense, the discourse is combative, and no one can agree on anything. I spent the last week or so in Kentucky, and came back to Los Angeles last night.
When I was in Kentucky, I set aside almost everyday to spend with friends and family. When you move across the country alone, you realize how important these relationships are. I’ve always been a one-person ship (to my detriment), and often need others to help me steer the right way.
When I was thinking about what I wanted to write about, I wasn’t certain. I was thinking about writing about how women’s voices have become audiply deeper since the 1950’s, dropping 23 Hz due to changing social roles and the need to be taken seriously.
Or I was thinking about writing about battling entropy, how we spend more time trying to give order to a day rather than creating and building.
Or Harland Duman’s training of OpenAI’s GPT2 to write blog posts based on Marginal Revolutions articles from 2010-2016. GPT-2 is incredible, generating content that seems pretty believable. Just give it a prompt, and it will predict the following words and create a decent piece of work.
I decided to write about the most downloaded apps of 2019, because it felt fitting as we move into 2020 to reflect, once again. There’s also a lot of interesting tidbits that can be scraped from this data.
“A man’s bookcase will tell you everything you’ll ever need to know about him,” – Walter Mosley
All the below images are from Apptopia unless otherwise stated
Games
Garena took the lead in 2019, with over 266M downloads. This is the platform that supports games such as Defense of the Ancients and Age of Empires, as well as publishes League of Legends and Black Shot.
A year-over-year comparison doesn’t reveal too much continuity between the games, besides Subway Surfers. I’ve personally never played Subway Surfers, but obviously most of the world has. Most of the games either fall into the Endless Runner or MMPOG (or something similar) category.
Source: Author, Wikipedia
Social / Messaging
WhatsApp once again sweeps the table, with TikTok making big moves in 2019. Apptopia estimates TikTok to have 682.2M downloads for 2019, which is only 10% less than WhatsApp. Facebook owns the majority of the apps in the top 10, including Messenger, Instagram, Facebook Lite, WhatsApp. Helo is an app that is used primarily in India, and Telegram is an “encrypted cloud-based instant messaging service” that is a direct competitor with Messenger.
Entertainment
Netflix sweeps the table here. YouTube Kids, Amazon Prime, Hotstar (owned by Disney) and JioTv , Indian entertainment apps, return in 2019 from 2018. It’s interesting to see Twitch rise in the ranks and Xbox and PlayStation fall – the streaming of content might be outweighing the playing of content. Watermelon and Tencent are Chinese apps. In 6th place is ZEDGE, a wallpaper and ringtone app.
Food and Drink
UberEats is the most popular app in the Food and Entertainment section, followed by McDonalds. DoorDash, Swiggy (serving India), foodpanda (Philippines), Rappi (South America) and iFood (Argentina, Brazil, Colombia, and Mexico), Grubhub, and Postmates are ALL food delivery services. McDonalds and Starbucks are the only true “food apps” of 2019. Zomato and Chick-fil-A got bumped. 2020 is the year of having EVERYTHING delivered.
Dating
Tinder remains number one. According to Apptopia, Tinder had 60% more downloads than Badoo in 2019. Tantan, the Tinder of China, worked its way well into number 3. The Meet Group has representation through MeetMe, LOVOO, and Skout, with Match Group leading the way with Tinder and Plenty of Fish. This space is constantly evolving, and its not uncommon to hear someone’s “dating app pitch” now.
Travel
Uber takes the cake here, followed by Google Maps, in the same fashion as 2018. Booking.com is pretty interesting, with an estimated 11.2M more downloads than Airbnb. Grab is a Singapore-based company offering ride-sharing and food delivery, as well as payment services. Google Earth is an interactive way to explore the world, which is pretty cool. Where is my Train is an app that is primarily used in India, to locate trains (aptly named). Ola Cabs and GOJEK both operate primarily in Southeast Asia.
Shopping
Wish had 128% more downloads than Amazon this year. AliExpress remains extremely dominant as well, outpacing Amazon for the second spot. Pinduoduo is a Chinese group buying platform and Club Factory and SHEIN are Chinese e-commerce stores. Lazada, owned by Alibaba, and Shopee, owned by Sea Group aka Garena, serve Southeast Asia. SHEIN is another Chinese e-commerce company. Flipkart operates primarily in India. MercadoLibre is an online marketplace headquartered in Argentina.
Music & Audio
Spotify and YouTube remain dominant, followed by Shazam. SoundCloud moved its way up the list this year, with Smule (a “social music-making app”) falling back. JioMusic and Gaana Music are apps specializing in Bollywood and other regional Indian music. Deezer is a music streaming app, similar to Spotify and Amazon Music.
Health and Fitness
Apptopia did not have a comparison for 2018 for Health and Fitness. But apparently, 40.5M people need reminders to drink water (myself included). Mi Fit is an app that pairs with an Xiaomi band to track all things health and fitness. Flo Period and Ovulation and Period Tracker are pretty self-explanatory and are a part of a growing women’s health market. Calm is a meditation app. Samsung Health and Fitbit are fitness trackers, and Home Workout and Six Pack in 30 days are both workout apps.
Finance
Apptopia did not have a comparison for 2018 for Finance. Google Pay, PayPal, Cash App, Alipay and Cloud Flash (Chinese Apps), Venmo, and PhonePe (located in India, owned by Walmart) are all mobile payment apps. Sberbank is based in Russia, and Cashier is a financial app in Brazil that allows user to access several different accounts.
Conclusion
I think the biggest takeaway from this analysis is the continued growth of India and Southeast Asia. As those regions continue to gain Internet access, their dominance and influence will only follow.
It’s also interesting to watch the app space because it shows where priorities lie.
I would like to see Finance apps grow in usage, as well as see learning apps such as Duolingo or Grasshopper make these lists one day. Our phones have a lot of power, and I hope that we can use them wisely.
The most downloaded app of 2019 was WhatsApp, far outpacing Garena or Netflix. Social / Messaging apps remain the centerpoint of our attention. We like to check in on each other, we like to people watch, and we like to talk to one another. In an age where face-to-face interaction is increasingly replaced by interfaces such as WhatsApp, I personally think its heartening to know that we are still interacting with one another, despite it being behind a screen. When we lose ourselves to online shopping or endless streaming, perhaps that’s when we should reevaluate our priorities.
It looks like social interaction, gaming, streaming, music, and shopping are the five top things that round out the lives of everyone around the world. It will be interesting to see what our phones say about us in 2021, and if that narrative still remains intact.
“A man’s bookcase will tell you everything you’ll ever need to know about him,” – Walter Mosley
Perhaps now, a person’s phone will tell you everything that you will need to know about them.
Below are all my favorite pictures, ideas, and charts from January – December 2019 – a visual representation of some of the coolest things that I’ve been reading, thinking about, and studying for the past year. Most of them are scraped from the non-financial tweets that I’ve liked over the past 12 months.
