The Food Truck Industry: Cheap Talk to Limit Competition
Brian A. McNamara
January 9, 2014
This paper investigates the location choices made by food trucks in the Washington, DC
metropolitan area. In the food truck industry, firms have the ability to change their location with
minimal cost, thereby adding another dynamic to competition. Each truck within a cuisine type
has a common goal, to set up shop at different locations, thereby creating a coordination
problem. Food trucks post schedules as a form of cheap talk to improve coordination. My
findings show that trucks are less likely to go to a location where there are other trucks that serve
a similar cuisine, so as to avoid direct competition with competitors that are closer substitutes.
Consistent with improved coordination, my findings also show trucks are less likely to go to a
location where a truck posts a schedule and serves a similar cuisine than to a location with a
truck that does not posts a schedule and serves a similar cuisine. The data used in this analysis
are daily location choices for DC area food trucks over a nine month timeframe. The DC food
truck industry supports over 150 food trucks serving approximately 24 common locations.
D22 - Firm Behavior: Empirical Analysis
L89 – Other
L1 - Market Structure, Firm Strategy, and Market Performance
1
1. Introduction
This paper investigates the choice of location made by food trucks in the Washington,
DC metropolitan area. In this industry, firms have the ability to change their location with
minimal cost, thereby adding another dynamic to competition. The data used in this analysis are
daily location choices for DC area food trucks over a nine month timeframe. The data indicates
that trucks are less likely to go to a location with other trucks that serve similar food. The
location choice enables trucks to avoid direct competition with competitors that are closer
substitutes, i.e. trucks that serve the same cuisine type. In the DC area food truck industry, truck
location is a coordination problem because all trucks with the same cuisine type have a common
goal; to set up shop at different locations.
Farrell (1987) showed that in an environment with symmetric mixed-strategy
equilibrium with limited conflict such as the battle of the sexes, costless non-binding
communication or cheap talk can achieve asymmetric coordination. Food trucks use posting a
schedule as a form of cheap talk to improve coordination. There is minimal cost associated with
posting or adhering to a truck’s schedule, but this communication lets other competitors know
they will be at that location and gives competitors the choice of direct competition or moving to
a different location. Consistent with improved coordination, trucks are less likely to go to a
location where a truck serves a similar cuisine and posts a schedule with a larger negative effect
when compared to competitors that do not post a schedule. Trucks use their choice of location
and posting a schedule to manipulate the level of competition they face.
There is a limited amount of empirical data on coordination and cheap talk. Most
evidence related to how cheap talk improves coordination comes from experiments. Cooper et al.
(1989) showed that non-binding messages improved coordination in the battle-of-the-sexes
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game, in which subjects stayed with their communicated choice 80 percent of the time. Crawford
(1998) provides a survey of experimental evidence of cheap talk. There continues to be
experimental work done with cheap talk, including Qu (2013) “How Do Market Prices and
Cheap Talk Affect Coordination”. There has been one empirical study from the information
systems literature that finds evidence of cheap talk in the crowdsourcing contests at
Topcoder.com. Archak (2010) argues that talented computer programmers sign up for contests
early, as a form of cheap talk, to limit the number and quality of competitors faced in the contest.
Food trucks use their location decision to differentiate themselves from competitors.
Since Hotelling (1929), there have been numerous studies on how product differentiation affects
competition in oligopolies. Firms use product differentiation to gain market power and increase
their profits. In the Hotelling framework, firms choose a location first and then compete in
prices. This is consistent for the vast majority of the firm location decisions. Simon et al (1992)
compare the equilibria from the two-stage location-then-price game to the case where location
and price can change each period. They find that agglomeration is only possible for the
simultaneous game. They also discuss the extension where one firm is mobile, while the other
firm is fixed. In this setting, the firm tends to do better by choosing to be immobile.
Seim (2006) studies firm location choice with product differentiation by investigating
location decisions of video retailers, without the use of pricing or quantity data. She finds that
firms use spatial differentiation to increase a firm’s market power. Mazzeo (2002) also examines
how product differentiation and market structure affect competition among firms. He uses data
from motels, where firms are differentiated based on the quality of service, and finds that
competitors are less “harmful” when they produce a more differentiated product. 1
1
There are other papers that look at how product differentiation and competition affects a firm’s choice of
location, such as: Chisholm et al (2010), Yang (2012), and Ellickson et al (2013)
3
The remainder of the paper proceeds as follows: Section 2 provides background on the
food truck industry in DC; Section 3 presents details about the data and Section 4 discusses the
variables in the analysis; Section 5 presents the empirical results; Section 6 concludes with a
discussion of possible extensions.
2. DC Food Truck Industry
In January 2009, Fojol Brothers launched their mobile food truck service during President
Obama’s first inauguration, thereby establishing themselves as the first Twitter-based food truck
in DC. From that point on, the food truck industry grew rapidly, expanding to over 150 trucks in
2013. Most trucks serve the office worker lunch crowds by parking near large concentrations of
office buildings between the hours of 11:30 am to 2:00 pm weekdays. Every weekday each food
truck must decide what location they will serve and then broadcast that location on Twitter to let
their customers know the day’s location. Trucks tweet their daily location when they arrive at a
parking space, or when they head out to a location or the night before. In the DC market, there
are a small percentage of trucks that do not use Twitter. 2 Various websites, such as Food Truck
Fiesta and Washingtonian Magazine, collect all the location tweets and compile a daily list of
trucks for each location. Hungry customers may follow trucks on Twitter, consult the web
postings, or simply go to their nearest location to see which of their favorite trucks have set up
shop nearby.
Within the DC market there is considerable variety in the type of foods served by the
food trucks. Trucks usually focus on specific cuisine such as Mexican, Korean, or Middle
2
In March of 2013 DC food truck fiesta (http://foodtruckfiesta.com/dc-food-trucks/) listed in the DC area, 171 food
trucks that tweet locations and only 8 trucks that didn’t tweet.
4
Eastern. Due to demand, many trucks offer similar cuisines. For example, there may be multiple
trucks that only serve cheese steaks, while others try to differentiate themselves from the other
trucks by serving specialized cuisine such as Mac and Cheese. Many trucks also seek to
differentiate themselves from other trucks by their appearance, and opt for loud, memorable
paint jobs or specialized music. The names of the trucks are also used as targeted marketing
tools, for example, Ball or Nothing, What the Pho, and Jamaican Mi Crazy.
Although food trucks have the option of parking on any street in DC, specific areas have
become favorite food truck destinations. Ingredients for a popular location include a large office
population, limited number of brick and mortar restaurants, and ample street parking. These
areas tend to have high demand for food during the weekday lunch hour but little demand other
times, thereby making it difficult for brick and mortar restaurants to thrive given high rents.
There may also be a network effect with food trucks, where patrons may go to popular food truck
destinations because they know there will be a large selection of food trucks. Due to a limited
amount of time available for lunch, many patrons are only willing to walk a couple of blocks to
find their lunch. Food trucks parked at different locations are not as close of a substitute as if
they were at the same location, therefore, there is less competition between two trucks parked at
different locations. Because of the uncertainty of which trucks will be at the trucks choice of
location during the lunch hour, trucks are unsure of the mix of cuisine types at a location.
Therefore, with this uncertainty and the mutual benefit for avoiding trucks that serve a similar
cuisine, a coordination problem is created.
At the time of this sample there are no specific areas that are reserved for food trucks.
Food trucks park in legal parking spaces and are required to pay all parking fines. Based on
antiquated ice cream truck regulations, a food truck can only stop if hailed by a customer, and is
5
required to move to a new location if there is no line of customers waiting. The truck may face a
fine if found at a parking space with no customers. Over the course of this sample, the
enforcement of these regulations was lax, enabling food trucks to stay in a location for the
duration of the lunch rush. Since the inception of the food truck industry in 2009, the DC
government has struggled to update regulations designed to nurture this nascent industry while
also appeasing the established brick and mortar restaurants. On October 1, 2012, a law was put in
place that required food trucks to collect a 10 percent sales tax. In the spring of 2013, the DC
government looked into changing food truck regulations, possibly by limiting a food truck’s
location choice, however, no regulations were changed over the course of my sample. 3
Regulations define a food truck as a mobile kitchen no longer than 18 feet and 5 inches.
With limited kitchen space on the truck, they are able to offer a narrow range of menu items
compared to a more traditional restaurant. Each food truck is also required to operate a brick and
mortar kitchen where food is prepared and ingredients are stored. The truck is inspected at least
twice a year and the brick and mortar location at least once a year by the Department of Health.
Food trucks are required to have a vending license, and the owner of the license is required to be
on the truck at all times. Despite these regulations, the startup costs for a food truck are much
smaller than the cost of a more traditional brick and mortar restaurant. Owning a food truck has
been used numerous times by future restaurateurs as a way of gaining restaurant experience,
building a client base, and building capital prior to opening up a brick and mortar restaurant in
DC.
3
After my sample, in the end of June of 2013, DC government passed regulations limiting the locations for food
trucks.
6
3. Data
There are 124 food trucks in the sample. Trucks that only serve desserts or snacks such
as ice-cream, cupcakes, and popcorn were removed from the sample, as these trucks are likely to
move to multiple locations during the lunch hour. The sample needs to only include trucks that
stayed during the entire lunch period. It also seems more likely that a person can have ice-cream
everyday during a week versus a lunch option like Mexican. From these concerns, there is the
expectation that competition between snack trucks is different than the competition between
lunch trucks. I also only focus on food trucks that operate in DC. Northern Virginia and
Maryland locations were dropped from the sample due to concerns regarding larger location
areas, which may affect competition differently within cuisine types when compared to DC. Due
to the size differences in Northern Virginia and Maryland, trucks at the same location may not be
as close of a substitute as trucks at a DC location. DC locations were cut down to 24 of the most
popular locations. 4 The remaining locations have had at least 10 total visits by any truck, and
were identified from DC Food Truck Fiesta. The sample begins on September 1, 2012 and ends
on May 24, 2013, and contains food truck locations for Non-Holiday weekdays. The data were
obtained by downloading all tweets from each food truck or by downloading the truck’s daily
locations from DC Food Truck Fiesta. The sample consists of 11,685 food truck location days.
Locations differ in the number of food trucks that visit them on an average weekday. The
average number of trucks by location on a weekday ranges from 0.06 trucks for the least popular
location to 10.9 trucks for the most popular location. The most trucks at a location in one day is
21. There is also fluctuation in the average total number of trucks on the road in DC. Figure 2
4
This is more of an estimation issue. I am using a conditional logit and by expanding the location choice set for
only a couple of observations would cause an issue in estimating.
7
presents the average daily number of trucks by week. There is a severe drop in the number of
trucks at week 52. This corresponds to the week of Christmas, when many trucks were not
operating.
Food trucks in the sample have multiple observable characteristics. Out of the 124 trucks,
only 28 post schedules. There are several ways a truck may post future locations. At the start of
the week they can tweet out the expected locations for the coming week, post a schedule on their
Facebook page, or post a schedule on their website. For this analysis, the definition of a truck
posting a schedule is that the truck consistently let their customers know their expected locations
for multiple weekdays in advance. This also ensures that customers and competitors will know
the expected future locations of the truck.
Some food trucks have expanded to operate multiple trucks. Twenty two operate two
trucks, four operate three trucks and one operates five trucks. By employing multiple trucks,
food truck purveyors are able to serve multiple locations at the same time. Since tweets are used
to advertise locations, the number of Twitter followers may be used to gauge the popularity of a
truck. The average number of Twitter followers per truck is about 1740, with the maximum
being Red Hook Lobster with over 25,000 followers. The last characteristic of food trucks is
when their Twitter profile was created, which establishes the amount of experience gained by
each truck. The Food truck market is a relatively new industry. In the my sample, six trucks
created their Twitter profile in 2009, 19 in 2010, 42 in 2011, 49 in 2012 and 8 in 2013. The time
between the creation of their Twitter profile and the first day on the road varies, ranging from
immediately to seven months. Table 1 lists the cuisine types, number of firms, average number
Twitter followers, and the number of trucks that post schedules.
8
To give a measure of how often a truck parks at different locations in the same week, the
percentage of unique locations by the total number of times in a week was calculated. The range
of this calculation is 0.02 to one, where 0.20 represents that the truck went out 5 times with each
time parking at the same location and one represents that the truck went to a new location each
time out. In the sample, the average for this measure is 0.869 with a standard deviation of 0.177.
From this average, it appears that most trucks in the sample move to different locations in a
week.
The number of competitors at a location and the number of trucks with the same cuisine
type varies by location and cuisine type. For all locations and cuisine types the average number
of competitors at a location is 0.0987 with a standard deviation of 0.346. However, the average
increases when we look at a popular location, such as L’Enfant Plaza, where the average
increases to 0.379 with a standard deviation of 0.646. Averages also vary by cuisine type. The
average if the cuisine type is Korean is 0.161, and increases when we look at the average of
Korean trucks at L’Enfant Plaza to 0.69. The average for Middle Eastern trucks at L’Enfant
Plaza is 1.42, with the minimum 0 trucks and maximum 4 trucks.
A very naïve exercise to indicate if trucks with the same cuisine type avoid each other is
to calculate the probability of two or more trucks serving the same cuisine park at a location
when the location is completely random and compare that to the actual percentage of more than
one truck in the sample. First, calculate the probability that two or more Middle Eastern trucks
arrive at Metro Center on the same day when truck location is completely random. On average
across the sample, there were 63 trucks out per day, 7 trucks visited Metro Center and 8 trucks
were Middle Eastern. Using these averages, the probability is:
9
𝑃𝑟𝑜𝑏(𝑀𝑜𝑟𝑒 𝑇ℎ𝑎𝑛 1 𝑀𝑖𝑑𝑑𝑙𝑒 𝐸𝑎𝑠𝑡𝑒𝑟𝑛 𝑇𝑟𝑢𝑐𝑘 𝑎𝑡 𝑀𝑒𝑡𝑟𝑜 𝐶𝑒𝑛𝑡𝑒𝑟)
= 1 − (𝑃𝑟𝑜𝑏(𝑧𝑒𝑟𝑜 𝑡𝑟𝑢𝑐𝑘𝑠) + 𝑃𝑟𝑜𝑏(1 𝑡𝑟𝑢𝑐𝑘))
8 55
8 55
� �� �
� �� �
0
7
= 1 − ⎛�
� + � 1 6 �⎞
63
63
� �
� �
7
7
⎝
⎠
= 0.214 or about 21 percent of the time.
In the sample, there were 26 days when there were more than one Middle Eastern truck at
Metro Center, with 173 total days for Metro Center, this equated to 15 percent of the time. In
this sample, Middle Eastern trucks were less likely to have more than one truck located at the
same location on if locations were random. This calculation was also used to sample the number
of Korean trucks at Navy Yard. The probability of there being more than one Korean truck at
Navy Yard, if truck location is random, is 2 percent, and in the sample there were zero days
where there was more than one Korean truck at Navy Yard. At Franklin Square, the percentage
of more than one Korean truck when truck location is random was calculated to be 5.7 percent,
but the sample had 4.8 percent of the days with more than one Korean truck at Franklin Square.
This exercise provides some evidence that trucks with the same cuisine type avoid each other as
a way to achieve market power.
4. Model
In the food truck industry, prices tend to be stickier as compared to the trucks location.
For the most part, the only daily decision a food truck makes is which location they will visit.
Because of this, food trucks compete through their choice of location. Food trucks choose the
location that will give them the most profit. The profit for location l is
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𝜋𝑙 = ℎ(𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑜𝑟𝑠 𝑎𝑡 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛) + 𝜑𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛 + 𝜖𝑙
where 𝜑𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛 are the location characteristics that make a location desirable compared to
other locations. This may include the number of office workers, the number of brick and mortar
restaurants and number of parking spaces. ℎ(𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑜𝑟𝑠 𝑎𝑡 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛) denotes the
level of direct competition faced by that food truck. ℎ(𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑜𝑟𝑠 𝑎𝑡 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛)
is the number of competitors that serve the same cuisine type at that location. After controlling
for all of location characteristics, a truck will want to go to a location with fewer direct
competitors. Although there may be many food trucks at a popular location, trucks try to avoid
going to locations with trucks that serve similar food because they are closer substitutes. To
increase product differentiation, trucks will try to park at locations with fewer food trucks that
serve the same cuisine. In fact, other trucks with the same cuisine type have the same incentive.
This creates a coordination problem, as trucks try to vary their locations in a given week in order
to get maximum exposure to customers, while also choosing to locate to locations where there is
less competition with trucks with the same cuisine type.
The model used to parse out how posting a schedule affects competition is:
𝑦𝑖𝑗𝑡 = 𝛽1 𝑁𝑢𝐶𝑜𝑚𝑝𝐴𝑡𝐿𝑜𝑐𝑁𝑜𝑆𝑐ℎ𝑖𝑗𝑡 + 𝛽2 𝑁𝑢𝐶𝑜𝑚𝑝𝐴𝑡𝐿𝑜𝑐𝑆𝑐ℎ𝑖𝑗𝑡 + 𝜑𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛 + 𝜖𝑖𝑗𝑡
In the model, there are a total of 24 location choices for a firm. For each day 𝑡 firm 𝑖 chooses
location j if the location gives the most profit. The model predicts the likelihood that a truck
chooses a particular location on a particular day given the truck goes out for business. The unit of
observation is firm/day/location, so for each firm/day there are 24 observations. The dependant
variable 𝑦𝑖𝑗𝑡 is equal to 1 if firm 𝑖 has chosen location j at time 𝑡 and equals zero otherwise.
The level of competition between two firms depends on the similarity of their products.
Firms with similar products would directly compete for customers, but in the food truck case,
11
firms can mitigate direct competition by moving to a location with fewer direct competitors.
NuCompAtLocNoSch is the number of non-schedule posting competitors at location 𝑖 at time 𝑡.
If this was a complete coordination problem, like in the battle of the sexes where trucks make
simultaneous location decisions, then trucks would not know where their competitors will be
located. If this is the case, the number of competitors at a location would have no predictive
power. However it is expected that trucks do have some idea where their competitors will be.
Anyone truck can see the competitors that have already parked at a location when they arrive,
but waiting too long for competitors to move to a location is risky due to the risk of being unable
to park. If the number of competitors at a location that doesn’t post a schedule is negative and
significant than this is evidence that it is not a complete coordination problem.
NuCompAtLocSch is the number of schedule posting competitors at location 𝑖 at time 𝑡.
A truck that posts a schedule does so to not only let their customers know where they will be but
also to inform competitors as to future locations. The schedule helps coordinate competition
away from the truck’s location, as we expect that trucks will want to stay away from trucks with
similar cuisine types. Posting a schedule improves coordination, therefore the expected sign for
this variable is negative.
Firms want to stay away from direct competitors, so if there is no coordination problem,
there would be no difference between NuCompAtLocSch and NuCompAtLocNoSch with both
being negative. If there was a complete coordination problem, then NuCompAtLocNoSch would
be zero, and NuCompAtLocSch would be negative. The difference between NuCompAtLocSch
and NuCompAtLocNoSch is the effect of improved coordination or the effect of cheap talk
resulting from posting a schedule.
12
Location dummy variables are used to capture the characteristics of a surrounding area, such
as the number of office workers, number of brick and mortar restaurants, and the number of
parking spaces, which differs by location and causes a different level of attraction for the food
truck. Location dummy variables help capture the average effect on profit of all location-specific
factors that are not included in the model. 𝜖𝑖𝑗𝑡 is the error term which is unobserved and
considered independently, identically distributed extreme value. Because of the inclusion of the
location constants, 𝜖𝑖𝑗𝑡 has zero mean. For the conditional logit model, variables that do not
change the profit of one location with respect to another location should not be included in the
model. This estimation is not estimating whether the truck goes out today, but given that the
truck went out, what location do they choose. Potential interesting variables, such as day of week
and week dummies, were excluded in the conditional logit estimations.
5. Results
This discrete choice model has multiple alternatives, and the covariates of interest vary
across these locations. Therefore, the pooled conditional logit model was estimated with firm/day
as the id. Column 1 in Table 2 estimates the model with the number of direct competitors at a
location. The coefficient has the expected sign of negative, and is significant at the one percent
level. A firm is less likely to go to a location when there are other firms serving similar food.
This provides support that food trucks avoid locations where their competitors are located.
Therefore, the truck’s choice of location is used as way to increase product differentiation which
is a means of gaining market power. From this result, it is unclear if food trucks face a
coordination problem or if firms are able to coordinate through some type of communication.
13
The results indicate that a firm parks at a location with fewer direct competitors, and the
next step is to determine if posting a schedule affects the amount of competition faced by the
firm. Column 2 in Table 2 shows results where the number of competitors at a location is split
apart to include separate variables for the number of competitors that post schedules and the
number of competitors that do not post schedules.
The number of competitors that do not post a schedule at that location has a coefficient of
-0.202. This indicates that trucks avoid competitors, even ones that do not post a schedule, and
also indicates that the food truck industry is not an environment with a complete coordination
problem. This is to be expected, as trucks are not simultaneously moving to locations. Some
trucks will be at a location when a truck makes a location decision. A truck may use this partial
information to avoid competitors, however, posting a schedule has a larger effect. The number of
competitors that post a schedule at that location has coefficient of -0.453. This estimate indicates
that trucks avoid competitors that post a schedule, and the effect is much larger than competitors
without a schedule. The estimate gives evidence that trucks are more likely to avoid other trucks
that serve a similar cuisine, especially if those other trucks post a schedule.
The large difference between posting and not posting a schedule gives potential evidence
that there is a coordination problem. The conditional logit provides an easy way to compare two
coefficients. In this case, posting a schedule has over twice the negative effect as not posting a
schedule. Posting a schedule helps inform competitors of that trucks intention and helps alleviate
the coordination problem. The coordination literature calls this type of pre-play signaling “cheap
talk”, because there is no cost to communicate this information and there is no penalty if the
information is not correct. For food trucks, there is a minimal cost to not going to the scheduled
location. In this case, the food truck sends out a new tweet apologizing to their customers for
14
relocating to a new location. The reason given for the change is usually that the truck was not
able to find a parking spot.
A concern could be that the difference between posting a schedule and not posting a
schedule is not from posting a schedule serving as a form of cheap talk. May be more
experienced or more popular food trucks are better at avoiding competitors or that competitors
are more likely to avoid more popular food trucks and these more popular trucks are more likely
to post a schedule. Because all of the trucks in this sample use Twitter to broadcast their location
to their customers, the log of the number of Twitter followers is used as a proxy for truck
popularity. Table 3 Column 1 provides estimates with the log of competitor Twitter followers
broken apart by competitors that post and do not post a schedule. The coefficient for the log of
the number of Twitter followers for competitors that do not post a schedule at that location is 0.031. As expected, this is negative and significant at the one percent level. The coefficient for
the log of the number of Twitter followers for competitors that do post a schedule at that location
is -0.051. This is also significant at the one percent level. Although, the effect is no longer over
twice as strong, there is still a significant difference between posting a schedule and not posting a
schedule. Therefore, after controlling for truck popularity, posting a schedule faces less direct
competition compared to not posting a schedule. This difference comes about as a result of
improved coordination. Posting a schedule could be seen as a form of cheap talk.
Other specification tests were performed in Columns 2 and 3 in Table 3. Food trucks are
grouped by their number of Twitter followers. There are four groups: Group 1: the bottom 25
percent (fewer than 174 followers), Group 2: the middle bottom (greater than 173 but less than
464 followers), Group 3: the upper middle (greater than 463 but less than 1279 followers), and
Group 4: the top 25 percent (greater than 1278 followers). The number of competitors was
15
multiplied by the competitor’s group number, so as to give more weight to more popular trucks.
The results are shown in Column 2 in Table 3, and are consistent with previous estimates. The
coefficient for the number of competitors that do not post a schedule multiplied by their Twitter
group number at that location is -0.07, and is significant at the one percent level. The coefficient
for the number of competitors that does post a schedule multiplied by their Twitter group number
at that location is -0.114 and is significant at the one percent level. This different specification
still produces the result that posting a schedule faces less competition than not posting a
schedule.
A final specification was estimated to look at each group and compare within a group
how posting a schedule affects the amount of competition faced. Column 3 in Table 3 presents
the results. For trucks with fewer than 174 followers there were no trucks that posted a schedule,
so there were no estimates to see how that affects the location choice. For the other three groups
there are differences in the probability of going to a location between posting a schedule and not
posting a schedule. For the bottom middle group, the coefficient is negative but not significant.
The two upper groups are negative and significant at the one percent level. The coefficient for a
truck that posts a schedule and has more than 1278 followers is -0.622, and if they do not post a
schedule the coefficient increases to -0.13. A truck that has more than 1278 followers is less
likely to face a competitor if they post a schedule as opposed to not posting. This is also true for
the upper middle group, where the coefficient is -0.610 if they post a schedule and -0.117 if they
do not post a schedule. For those trucks with a similar number of Twitter followers, posting a
schedule still has a larger effect in avoiding competitors. Controlling for firm popularity, posting
a schedule faces less direct competition than not posting a schedule.
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6. Conclusion
This paper investigated competition in the food truck industry by examining the location
decision. Trucks are less likely to go to a location where a truck serves a similar cuisine. This
provides evidence that trucks use location choices as a way to gain market power. Trucks are
also less likely to go to a location where a truck posts a schedule and serves a similar cuisine
than going to a location where the truck does not post a schedule and serves a similar cuisine.
Posting a schedule can be seen as a form of cheap talk, thereby improving coordination between
trucks with similar cuisine types.
This being the first study of the food truck industry, there are other avenues of study that
could still be explored. Future work on the food truck industry could include investigations of
how product differentiation affects varying locations or are firms will less direct competitors
more likely to vary locations? Future work may also investigate why certain firms post a
schedule. It appears that more established firms post a schedule, but could it also be that the
higher quality firms post a schedule? Exploration of a network effect at popular food truck
locations could also provide interesting results.
17
Table 1 Food Truck Cuisine Types
Cuisine Type
American
Asian fusion
BBQ
on stick
Brazilian
cheese steaks
Chivito Sandwiches
Crepes
dessert BBQ
Doners
Dumplings
empanadas
Ethiopian
fried chicken and
seafood
global cuisine
Greek food
grill cheese
Hawaiian
hot dogs
Indian
Indonesian
Italian
Japanese mexican
Korean
Mac and Cheese
Meatballs
Mexican
Middle Eastern
Panini
Peruvian
Pizza
Salad & sandwich
sandwiches
Seafood
Southern
Number of
firms
7
4
4
1
1
6
1
3
1
1
1
3
4
Average
Followers
Count
953
904
3090
1775
75
702
3296
409
3111
63
236
2682
3113
Trucks
that
post a
schedule
1
0
1
0
0
1
1
0
0
0
0
1
0
2
1
2
1
1
4
2
1
1
1
9
1
2
8
16
3
2
1
1
6
2
6
392
1411
207
9088
3442
507
743
299
2962
114
2262
9151
345
2580
1057
416
322
8798
767
1359
13932
787
1
0
0
1
1
1
0
0
0
0
3
0
1
1
2
0
0
1
1
1
2
2
18
Spanish
Sushi
Turkish platters
Vietnamese
West Indian Cuisine
2
2
1
6
3
4363
287
97
774
865
0
1
0
3
1
19
Table 2
Conditional Logit. Dependant variable is truck choose that location.
VARIABLES
Number of competitors at location
Number of competitors at location that
does NOT post a schedule
Number of competitors at location that
does post a schedule
Location Dummies
Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
20
(1)
-0.253***
(0. 0232)
Yes
239,390
(2)
-0.202***
(0.025)
-0.453***
(0. 050)
Yes
239,390
Table 3
Conditional Logit. Dependant variable is truck choose that location.
VARIABLES
Log of the number of Twitter followers for
competitors at location that does NOT
post a schedule
Log of the number of Twitter followers for
competitors at location that does post a
schedule
Number of competitors times their
Twitter group at location that does NOT
post a schedule
Number of competitors times their
Twitter group at location that does post a
schedule
Number of competitors at location when
the truck does NOT post a schedule and
has less than 174 followers
Number of competitors at location when
the truck does NOT post a schedule and
has between 173 and 464 followers
Number of competitors at location when
the truck does post a schedule and has
between 173 and 464 followers
Number of competitors at location when
the truck does NOT post a schedule and
has between 463 and 1279 followers
Number of competitors at location when
the truck does post a schedule and has
between 463 and 1279 followers
Number of competitors at location when
the truck does NOT post a schedule and
has greater than 1278 followers
Number of competitors at location when
the truck does post a schedule and has
greater than 1278 followers
Location Dummies
(1)
- 0.031***
Yes
Yes
Yes
Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
239,390
239,390
239,390
21
(2)
(3)
(0.0040)
-0.051***
(0.0064)
-0.070***
(0.009)
-0.114***
(0.014)
-0.090*
(0.052)
-0.285
(0.054)
-0.764
(0.708)
-0.117***
(0.039)
-0 .610***
(0.082)
-0.134**
(0.060)
-0.622***
(0.071)
Figure 1 Average daily trucks on road by week.
22
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