Organizational experience and cost-cutting agreements in the Formula One industry: a dynamic capabilities approach

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Organizational experience and cost-cutting agreements in the Formula One
industry: a dynamic capabilities approach
Luis Carlos Sancheza and Carlos Varela-Quintanab
a Department of Economics, University of Oviedo, Spain
b Department of Applied Economics, University of Oviedo, Spain
This is an Original Manuscript of an article published by Taylor & Francis
in “European Sport Management Quarterly” on 17 Jan 2025, available at
https://www.tandfonline.com/doi/full/10.1080/16184742.2024.2445547 and
https://doi.org/10.1080/16184742.2024.2445547

Abstract
Research Questions: The Dynamic Capabilities theory posits organizational experience
as a key source of sustained competitive advantage. However, empirical evidence remains
limited in predictable environments, particularly when distinguishing between continuous
and discontinuous changes. This study is the first to address this gap by examining the
Formula 1 industry under resource constraints.
Research Methods: We used a dataset of 4,479 observations, including economic and
performance data from the 2009-2019 Formula 1 seasons. Through a multilevel model,
we estimated the output elasticities of driver, car, and team experience, and evaluated
their relationship with the Resource Restriction Agreement (RRA), implemented between
2010 and 2013.
Results and Findings: We found that team experience contributed 18% to sporting
success, but turned negative during the RRA, indicating it functions as a dynamic
capability under continuous, predictable changes, but is less effective with discontinuous
changes. Despite the RRA disadvantaged older teams, the Gini coefficient remained
stable, likely due to the uneven distribution of driver talent across teams.
Implications: Cost reduction agreements can promote competitive balance but require
complementary strategies. Recommendations include ensuring team survival, prioritizing
team acquisition over creating new ones, addressing driver skill disparities, and designing
flexible and enforceable cost-cutting agreements.
Keywords: resource restriction agreement, competitive balance, economies of
learning, Formula One, output elasticities

Is Organizational Experience a Resilience Dynamic Capability? An Analysis with
Resource Restriction Agreements in Formula One
Introduction
Recognized as a non-tradable asset, organizational experience has been identified
as a critical source of sustainable competitive advantage by the Dynamic Capabilities
(DC) theory (Teece & Pisano, 2003). However, despite solid theoretical foundations—
learning curve and diseconomies of time compression—the relationship between
organizational experience and performance remains inconclusive, with studies yielding
mixed results (Maula et al., 2023). Moreover, traditional DC theory has focused on
environments characterized by predictable, continuous change. Although recent research
has shifted attention to discontinuous and disruptive transformations, much of this work
centers on human capital constraints (Kim & Makadok, 2023) and supply chain resilience
(Stadtfeld & Gruchmann, 2024). To date, the performance of organizational experience
in predictable environments, distinguishing between continuous and discontinuous
changes, remains underexplored.
Formula One (F1) seems particularly well-suited for such an empirical test.
Scholars have typically explored the constraints on talent accumulation in the sports
industry to derive insights applicable to broader contexts (e.g., Ahtiainen & Jarva, 2022;
Fort et al., 2016; Mulholland & Jensen, 2019). However, the real world is rarely that
simple. Outside sports, managers must coordinate multiple production factors, typically
capital and labor, while facing innovation challenges. Motorsports, particularly F1,
provides a unique opportunity to analyze this environment. In most sports, the use of
advanced materials by athletes is controversial (Dyer, 2015; Potts & Thomas, 2018),
while in F1 there is a direct relationship between positive perception of technological
innovation and satisfaction with F1 broadcast (Schneiders & Rocha, 2022).

Season-to-season changes in technical standards confer a dynamic character to the
F1 industry, creating a compelling context for scholars to analyze the role of
organizational experience through the lens of Dynamic Capabilities (DC) theory.
However, despite the increasing use of the F1 environment as a laboratory (see Baecker
et al., 2022), analyses of resource constraints remain limited. A handful of empirical
studies have analyzed how rule changes relate to competitive balance (Judde et al., 2013;
Mastromarco & Runke, 2009) and team exploration levels (Marino et al., 2015), without
examining whether there are variations in the contribution of production factors.
Moreover, existing analyses of resource contributions to sporting success have focused
on the driver and the car, neglecting the role of organizational experience, even though
some studies have highlighted the experimental learning processes in teams’
technological trajectories (e.g., Jenkins & Floyd, 2001). For example, Bell et al. (2016)
emphasized the importance of the car over the driver, while Rockerbie and Easton (2022),
stressed their complementarity nature. Furthermore, these analyses have limitations, such
as the use of analysis of variance (intraclass correlation and commonality analysis), which
prevents quantifying the magnitude and direction of the relationship between each
predictor and the outcome variable. In particular, the decomposition method outlined by
Rockerbie and Easton (2022) could be influenced by the set of explanatory variables
chosen for driver and team performance.
We aim to contribute to the management literature in three specific ways. First, in
response to the call by Maula et al. (2023), we examine whether organizational experience
qualifies as a dynamic capability, specifically, whether it provides a sustainable
competitive advantage in fast-changing environments with predictable and continuous
changes. This topic is relevant for potential investors who must choose between acquiring
an established firm or starting a new venture (Fowler & Schmidt, 1989; Pennings et al.,

1994). Empirical literature has examined this issue through the lens of firm age (e.g.,
Brouwer et al., 2005; Coad, 2018; Coad et al., 2013). However, this variable is susceptible
to survival bias in longitudinal data, which can lead to inaccurate variance estimates,
deflating/inflating errors, and obscured causal mechanisms (Delmar et al., 2022). Because
the F1 industry is free of this bias, it may shed light on the subject. Another advantage is
that the industry’s output (points) is homogeneous and constant from race to race, so the
risk that firm age reflects variables unrelated to experience, such as product lifecycle or
reputation, is better controlled in F1 than in real life.
Second, following the recent literature’s interest in capabilities that foster
resilience in turbulent times, we seek to examine whether organizational experience
contributes to maintaining competitive advantage under predictable but discontinuous
changes, introducing the novel construct of resilient dynamic capability (RDC). To this
end, we leverage the F1’s Resource Restriction Agreement (RRA) implemented between
2010 and 2013. Although the RRA was an internally agreed change among teams, making
it an endogenous shift, it represented a discontinuous change due to the magnitude of the
impact on the industry. This restriction was a precursor to the cost cap introduced in the
2021 season, aimed at promoting competitive balance, sporting fairness, and long-term
financial sustainability of teams (FIA, 2021). Unfortunately, limited access to financial
data makes it difficult to conduct a thorough analysis of the cost cap after 2021.
Third, the peculiarity of F1 as a “multi-input sport” makes it an adequate
framework to use an output elasticity approach as a tractable way to estimate the
contribution of production factors expressed in different units of measure (Dhyne et al.,
2020). Through elasticities, we can discern the relationship between the budget cap and
investment in capital, labor, and organizational expertise, which may have implications
for the institutional design of sports leagues and the allocation of resources by managers.

Similar to Rockerbie and Easton (2022), we use financial data to measure driver and car
quality. An advantage of using financial data is that we can measure the expected quality
of inputs with a single variable.
Below, we proceed as follows. First, we provide the theoretical background of our
research, and an overview of recent literature on the F1 industry. Next, we introduce our
model, variables, and data, followed by the results. Finally, we conclude with a discussion
of the results, including implications, and suggestions for future research.
Theoretical Background
Our research is grounded in the Dynamic Capabilities (DC) and the organizational
resilience (OR) approaches. The DC is usually seen as an extension of the Resource Based
View (RBV). The RBV suggests that a firm can achieve sustained competitive advantage
by possessing valuable, rare, inimitable, and non-substitutable resources, which can be
classified in three categories: physical capital, human capital, and organizational capital
(Barney, 1991). According to Barney, having a unique and valuable organizational capital
(e.g., organizational culture) developed in the early stages of the firm’s history may
provide an advantage that is difficult for competitors to replicate.
The DC approach extends the RBV towards dynamic environments. Teece et al.
(1997) defined dynamic capabilities as “the firms’s ability to integrate, build, and
reconfigure internal and external competences to address rapidly changing environments”
(p. 516). The concept of dynamic capability is a source of debate not existing total
consensuses in the literature regarding both in their nature and the impact on their
competitivity advantage (Collis & Anand, 2021). Particularly important for our work is
the type of changes that the DC approach covers. While Teece et al. (XXXX) consider
that a dynamic environment refers to

The current study focuses on organizational capital resources, and particularly on
organizational experience. Organizational experience can be defined as “the cumulative
production history of the organization”, provides each member of the organization with
the opportunity to master established routines and practice, learn how to work together
and benefit from knowledge accumulated by others (Reagans et al., 2005, p. 870).
In doing so, our research also draws on the organizational learning literature,
which emphasizes the pivotal role of organizational experience (Levitt & March, 1988;
Levinthal & March, 1993) and the challenges faced by younger firms, commonly referred
to as the “liabilities of newness” (Stinchcombe, 2000) and the “liabilities of adolescence”
(Fichman & Levinthal, 1991). Particularly relevant in this context are the cumulative
implications of the concepts of “absorptive capacity” (Cohen & Levinthal, 1990; 1994)
and “combinative capability” (Kogut & Zander, 1992). Absorptive capacity posits that a
firm’s ability to recognize, assimilate, and apply new, external information depends on
its prior related knowledge. The concept of combinative capability, in turn, suggests that
firms generate new skills by recombining their current capabilities, building on the social
relationships that currently exist in a firm.
Based on this, a growing number of studies (e.g., Brouwer et al., 2005; Coad et
al., 2013; Jensen et al., 2001; Macher & Boerner, 2006) have provided evidence that firm
age (i.e., the number of years the company has been in operation) has a positive
relationship with productivity levels. However, the relationship between firm age and
performance is non-linear (Coad, 2018). Companies can improve their performance by
learning from experience, but they can also become increasingly inert and inflexible over
time (Coad et al., 2013). Moreover, the relationship also depends on the environment,
being more important in industries where the learning period is long enough (Geylani &
Stefanou, 2013) and the changes are moderate enough (Sørensen & Stuart, 2000).

Literature Review
The question of what is more important for winning (the driver or the car) has not
been explored until recently. Eichenberger and Stadelmann (2009) and Phillips (2014)
addressed this issue comparing the performance of drivers from different teams and eras
to identify the best drivers. Bell et al. (2016), for their part, studied the influence of teams
and drivers on race results using a multilevel approach. Their work showed that the team
effect significantly outweighed the driver effect, accounting for 86% of the driver
variation and increasing over time. Finally, Rockerbie and Easton (2022) found strong
complementarity between car and driver by decomposing the total variation in rank finish
into four components: driver, team, shared between driver and team, and unexplained.
Despite extensive research on the importance of car and driver, other critical
inputs, such as team experience, have remained relatively unexplored. To our knowledge,
the paper by Malagila et al. (2021) is the only one that has analyzed the role of clubs’ age
on sport performance. Their work found no significant influence of this variable on points
earned in the top UK football leagues. F1 could be different. Lapré and Cravey (2022)
found that win probability follows an inverted U-shaped function of cumulative races and
that drivers learn from their own success, the success of their teammates, and the failures
of their own cars. A limitation of their work is that they used the cumulative races of
drivers and their teammates to measure experience over their careers, which does not
capture the organization’s knowledge accumulation over time in the way that constructor
age might. A second difference between their paper and ours is that we quantify the
contribution of experience relative to other inputs, allowing us to know their relative
importance.
In contrast to other sports, where extensive literature exists on salary caps (e.g.,
Larsen et al., 2006; Mulholland & Jensen, 2019; Totty & Owens, 2011), the relationship

between cost-cutting and F1 performance has received limited study. One exception is
Mastromarco and Runkel (2009), who provided theoretical support and empirical
evidence that low competitive balance increases the number of rule changes in the
following season and that these changes improve competitive balance. Similarly, using
data from 1950 to 2010, Judde et al. (2013) showed that regulation change has a positive
impact on F1 championship uncertainty but not on race uncertainty or long-term
dominance. Analyzing the 2010 season and part of the 2011 season, they concluded that
the RRA had the potential to promote more balanced competition, but that only the
passage of time would reveal its ultimate implications. Finally, using a DEA model,
Gutiérrez and Lozano (2014) found that inefficient teams must make a substantial
reduction in their budget to become efficient.
Methodology
Conceptual Model
Following Judde et al. (2013), we assume that the main objective of F1 teams is
to maximize points. Teams are incentivized by bonus money and sponsorship depending
on their final position at the end of the season, making point maximization for both drivers
a credible goal, although Ferrari, Red Bull, Mercedes, McLaren, and Williams also
receives a heritage bonus (for a detailed description of revenue allocation see Budzinski
& Müller‐Kock, 2018). This assumption is supported by the data, as the teams that won
the Constructors’ Championship in the eleven seasons analyzed also placed their two
drivers in the top three of the Drivers’ Championship. According to the literature
(XXXX), we assume that teams with higher performance have a competitive advantage
over rivals.
This maximization process depends on the relative resources of the teams, the
characteristics of the circuits, and, of course, random factors. The amount of the budget

and its allocation between car and driver are the variables used by teams to achieve their
goal. Since constructors1 can substitute drivers with different skills for cars with different
technologies (and vice versa), we assume they are heterogeneous and must be expressed
in units of quality. This allows us, in line with Szymanski (2003), to replace “talent” with
“cash”. Implicit in this statement is that factors of production charge an amount related
to their marginal revenue product. Here it follows that better drivers and better cars, which
produce more success, cost more. Furthermore, we consider that the cumulative
experience of the organization can play a role through learning-by-doing.
To analyze how these factors contribute to generating competitive advantage, we
exploit the extended Solow model with human capital proposed by Mankiw et al. (1992),
where the production function is a Cobb-Douglas function with three inputs: labor,
physical capital, and human capital (or, in our case, organizational experience).2
In addition, we control for variables such as weather conditions, order of the race,
number of drivers, and home advantage. As indicated in Figure 1, our aim is to analyze
the contribution of labor, capital, and organizational experience to points earned, as well
as how these factors behave under the RRA.
[Figure 1 here]
Econometric Specification
The robust Hausman specification test supports the utilization of a random effects
model. However, in F1, sporting performance is subject to grouping influences, such that
observations within the same group are positively correlated. For example, points earned
by drivers from the same team are likely to be more similar to each other than to drivers
from other teams. This lack of independence produces excessive Type I errors and biased
estimates under traditional analysis (Peugh, 2010). To address this problem, we followed
1 We use the terms “constructor” and “team” interchangeably.
2 Please refer to Appendix A for details.

the empirical strategy by Bell et al. (2016) who used a linear multilevel model with four
levels of nesting, where each race result (e.g., 25 points) is nested within a driver (e.g.,
Fernando Alonso), a constructor-season (e.g., Ferrari in 2010), and a constructor (e.g.,
Ferrari). As pointed out by Bell et al. (2016), the levels are not strictly hierarchical, as
drivers can change constructors from season to season. One advantage of this approach
is that it attributes variability to the appropriate level, which allows obtaining more
accurate and precise estimates of the effects of the variables included in the model
(Monsalves et al., 2020).3
Variable Description
Number of Points
The number of points is the dependent variable of the model. More than half of
the participants obtained zero points. This prevents us from expressing this variable in
natural logarithms to estimate the output elasticities of capital and labor. To solve this
issue, we use the inverse hyperbolic sine (asinh) of the points, a close approximation to
the natural logarithm commonly used in literature to retain zero values (e.g., Aksoy et al.,
2021; Card & DellaVigna, 2020). The use of the inverse hyperbolic sine in the dependent
and independent variables allows us to interpret the coefficients as output-elasticities, just
as if they were expressed in logarithms. In the robustness-check section, we use
ln(1+points) and a Tobit model (e.g., Bernard & Busse, 2004; Forrest et al., 2010) as
alternatives to the asinh specification.
Although the FIA has modified the scoring system several times since 1950, only
three changes affected our sample. In 2010, the distribution of points 10-8-6-5-4-3-2-1
was replaced by a new scheme (25-18-15-12-10-8-6-4-2-1). Later, in 2014, the last race
of the season awarded double points; and, since 2019, the driver who completes the fastest
3 See Appendix B for details.

lap receives a bonus point if he finishes in the top ten. In our estimates, we use the original
point distribution and, in the robustness check, we apply the 2010 scoring system to all
races.
Quality of Cars
The racing car is the most expensive element in F1, reflecting the high
manufacturing costs of sophisticated parts, the high salaries of the engineers involved in
its development, and the extensive testing required to obtain a competitive car. Building
on Szymanski (2003) and expenditure-performance studies (e.g., Hogan & Norton, 2000;
Szymanski & Kuypers, 1999), we assume that the higher these costs, the higher the car’s
quality. Implicit in this assertion is that the car’s cost is the sum of the expected marginal
revenue product of all the inputs used in its development, and that the greater the quantity
or quality of these resources, the more successful the team will be. This assertion is
supported by the high positive value observed for the correlation coefficient between the
annual points won by teams and their expenditure excluding salaries, both variables
expressed in inverse hyperbolic sine (r=0.7247).
To estimate the cost of cars, we take the team’s total budget and subtract the
salaries of its two full-time drivers; the cost of one car would be half that value. This
strategy defines the value of the car broadly, including not only the development and
manufacture of the car, but also spare parts, fuel, and staff (e.g., mechanics, engineers and
test drivers). Given the difficulty of having more disaggregated data, this is the best
approach we can take. As with the dependent variable, we apply the inverse hyperbolic
sine transformation to this variable instead of logarithms.
Quality of Drivers
F1 drivers’ exceptional skills, including superior reflexes, heightened physical
fitness, and engineering knowledge, are essential for racing success (Beloussov, 2018).

According to human capital theory (Becker, 1962; Mincer, 1974), extensively tested in
sports economics (see Castellucci et al., 2011; Celik, 2020; Forrest & Simmons, 2002;
Simmons, 2022), we assume that salaries reflect the driver’s expected marginal revenue
product based on characteristics observed at the time of contract signing (e.g., past
performance, experience, age, talent, or motivation). The correlation coefficient between
annual salary and past performance of the drivers, measured by the average points won
in the previous five seasons, supports the idea that salaries reflect the past quality of the
drivers corrected by expectations of their future performance (r=0.8979).
The salaries of 171 observations belonging to reserve drivers are unknown and,
therefore, excluded from the analysis (reserve drivers replace full-time drivers when they
are unable to race). As before, we applied the asinh transformation.
Economies of Learning
According to Levitt and March (1988), lessons of experience persist and
accumulate within the organization’s routines despite personnel turnover and the passage
of time. Rules, procedures, technologies, beliefs, and cultures are preserved through
systems of socialization and control. In this way, historic teams like Ferrari and McLaren
have an advantage over new ones that must start from scratch (e.g. Toyota, HRT or Haas).
However, newcomers are not always inexperienced. Sometimes, they can buy the
organizational structure of a team that has just left the competition; for example,
Mercedes bought the structure of Brawn GP (which had bought that of Honda).
To test this, we use team age, a variable generally considered a valid proxy in the
literature (e.g. Fan & Wang, 2021; Geylani & Stefanou, 2013; Sørensen & Stuart, 2000)
to measure routinization and the firm’s position on the learning curve (Coad, 2018). Team
age is defined as the number of consecutive years the constructor has participated in F1,

including years of inherited teams where applicable. The asinh transformation allows us
to account for the non-linear relationship of team age.
It is worth noting that firm age could reflect extraneous variables such as product
lifecycle or reputation (Coad, 2018). This problem is limited in F1, where (except for rule
changes) the output produced in each race is homogeneous and constant.
Cost Reduction in the Period 2010-2013
In response to the 2008 global financial crisis and Honda’s sudden exit, F1’s
governing body proposed a £40 ($65) million budget cap from 2010 (Spurgeon, 2009).
The measure was rejected by major constructors, organized under the Formula One
Teams Association (FOTA), and replaced by the Resource Restriction Agreement (RRA),
which outlined a progressive cost-cutting package from 2010 to 2017. The cost-cutting
measures were negotiated by teams and incorporated by the FIA into the F1 technical
regulations for the season (Noble, 2012). Thus, in 2010, teams reduced staff, limited
engines, and banned testing and refueling. In 2011, gearbox changes were restricted, and
certain technical elements (double diffuser, F-duct system, and adjustable front wings)
were prohibited. In 2012, in-season testing returned, but reactive ride systems, exotic
engine maps and helium use in air guns were banned. In 2013, mid-season testing, and
double DRS (Drag Reduction System) were banned, DRS was restricted, and the
minimum weight was increased to discourage over-development (see “History of
Formula One regulations,” 2023).
To our knowledge, the RRA did not impose penalties during the period,
effectively operating as a voluntary arrangement. After years of disagreement, the RRA
collapsed in 2014 with the disbanded of FOTA. The introduction of the V6 turbo-hybrid
power unit and relaxed engine development rules caused costs to skyrocket again

(Collantine, 2015). In line with the literature (Ahtiainen & Jarva, 2022; Larsen et al.,
2006), our model includes a dummy variable identifying the period 2010-2013.
Home Advantage of Teams and Drivers
One of the best-documented regularities in sport is the increased chance of
winning when teams or athletes compete in their own stadium or country. Main
explanations include travel fatigue, familiarity with facilities, referee bias, and
psychological reasons such as athletes’ increased confidence and sense of territoriality
(Courneya et al., 1992; Nevill et al., 1999). We control for this including two dummies
indicating whether racers and teams compete in their home country.
Influence of Weather Conditions
Drivers face even more pressure in a wet race. Tire strategy, reflexes or luck can
make all the difference. The red flag may signal that the race must stop due to poor track
conditions. When this happens, cars must slowly move to the pit lane and stop,
maintaining their positions. As Bell et al. (2016) suggested, we control for weather
conditions using a dummy variable for rainy races.
Influence of Street Circuits
From 2009 to 2019, races were held at 27 tracks in 25 countries. Among them,
urban circuits pose a serious challenge due to their bumps and lack of grip. Like Bell et
al. (2016), we control for this using a dummy variable identifying street circuits (Baku,
Melbourne, Monaco, Montreal, Singapore, and Valencia).
Order of the Race
The literature shows that not all sporting events are equally relevant. For example,
the final soccer matches are usually seen as more decisive (Page & Page, 2007; Varela-
Quintana et al., 2018). Something similar could also occur in F1. Drivers may be under
more pressure or tempted to adopt more aggressive (or conservative) strategies in the final

races. We factor this in by including a variable with the number of races remaining to
finish the championship.
Number of Drivers
As suggested by Bell et al. (2016), estimates may be affected by changes in the
number of drivers, as average points tend to be lower when there are more participants.
We address this by including the number of drivers competing.
Technical and Sporting Changes Over Time
Regulatory modifications are frequent in F1, covering numerous sporting and
technical aspects such as scoring, chassis, engines, tires, and penalties. In 2010, for
example, the points system was changed. In 2011, team orders were banned and Pirelli
replaced Bridgestone as the tire supplier. In 2014, naturally-aspirated 2.4-liter V8 engines
were replaced with turbocharged 1.6-liter V6 engines. In 2018, the Halo safety device
became mandatory. These changes, which may not be reflected in the budget, can affect
performance (Jenkins & Floyd, 2001; Judde et al., 2013; Marino et al., 2015;
Mastromarco & Runkel, 2009). We control for these using year dummy variables.
Sample Size and Data Collection
Sample
To conduct the empirical study, we used information from 63 drivers and 21
constructors who participated in 215 races from 2009 to 2019 (11 seasons), resulting in
an unbalanced panel of 4,479 observations. The 2020 season was discarded to avoid the
influence of the absence of spectators during the COVID-19 pandemic on variables
related to team and driver home advantage. Additionally, the final 2020 budgets could
have been affected by the cancellation of six Grands Prix and the rescheduling of races.
Team financial data is not available for the 2021 and 2022 seasons.

Information on driver performance, circuits, and weather conditions is taken from
the Stats F1 website. Economic and financial information—driver’s salaries and team
budgets—is based on estimates from renowned experts, compiled from specialized F1
media. Financial data and salary contracts are not publicly available. Therefore, like
Rockerbie and Easton (2022) and in other sports—see e.g. Transfermarkt—we rely on
estimates. The sources used are listed in Appendix C. The dataset and Stata code are
available upon request.
Subsample With Permanent Teams
Team entries and exits are common in F1 and are useful for analyzing how team
experience may relate to performance. However, they pose a challenge when assessing
the relationship between cost-cutting and team performance, as we could be capturing
these entries and exits rather than cost reduction per se. For example, in 2010 Toyota, a
big-budget team, withdrew from the competition and was replaced by three smaller
constructors (HRT, Lotus, and Virgin). To address this, we used a subsample with only
permanent teams, i.e., constructors that participated throughout the period employing a
permanent basis, either their own or inherited from a previous team. This subsample,
shown in Appendix D (Table D1), consists of nine permanent teams: (1) BMW
Sauber/Sauber/Alfa Romeo, (2) Brawn GP/Mercedes, (3) Ferrari, (4) Force India/Racing
Point, (5) McLaren, (6) Red Bull, (7) Renault/Lotus, (8) Toro Rosso, and (9) Williams.
Empirical Results
Descriptive Statistics
Table 1 summarizes the variables—annual car budget and annual driver salary are
deflated to 2009 US dollars.4 There are huge salary differences among drivers. While the
average annual salary was $7.27 million, the standard deviation was $10.52 million.
4 More detailed statistics can be found in Appendix D.

Sebastian Vettel was the highest-paid driver of the period ($52.34 million in 2018),
followed by Kimi Raikkonen ($45 million in 2009) and Lewis Hamilton ($43.79 in 2019).
At the other extreme, Roberto Merhi ($0.05 million in 2015), Charles Leclerc ($0.131
million in 2018) and Pascal Wehrlein ($0.133 million in 2017) were the lowest paid.
Although the money spent on car development was higher (14.4 times the cost of
the driver), the dispersion was smaller. From 2009 to 2019, the average car value was
$104.9 million, with a standard deviation of 62.4. The Red Bull RB10, designed by
Adrian Newey for 2014, was the most expensive car ($246.18 million), while the 2011
HRT car was the cheapest ($16.21 million).
The average team experience was 29.91 years, with a standard deviation of 18.07.
The most experienced constructors were Ferrari, McLaren, and Mercedes (69, 53 and 49
years, respectively, in 2019), while the most inexperienced, with zero years, were HRT,
Lotus and Virgin in 2010 and Haas in 2016. Rain affected 1.8% of races and 23.9% of
Grands Prix were held on street circuits.
[Table 1 here]
Figure 2 illustrates the team’s annual budget progression and how it was allocated
between full-time driver salaries and car development. The left bars show the average of
all teams, and the right bars show the average of teams that participated uninterrupted
during the eleven years (i.e., permanent teams). As can be seen, expenses were not stable
throughout the period. In 2010, the average total team budget fell by 41.3%, from $271.5
to $159.3 million. This drop was due to two reasons: the RRA agreed by FOTA, and
changes in grid composition. The withdrawal of Toyota, one of the richest teams, and the
inclusion of HRT, Lotus, and Virgin pushed the average down. After removing this
statistical effect (see right bars), the drop would have been 23.6%, from $255.9 to $195.5
million. Permanent teams’ spending spiked again in 2014 (averaging $299.1 million),

slowly declining to $241.8 million in 2019, a figure comparable to the pre-cost-cutting
era.
The evolution of payroll for regular drivers also seems to be affected by team
entries and exits. Although wages fell by 11.1% in 2010 with the RRA, they increased by
7.4% when the effect of team entries and exits is eliminated, suggesting that spending
cuts particularly affected car development.
[Figure 2 here]
To analyze the cost reduction, we conducted a Student’s t-test on Table 2
comparing the 2011-2013 seasons (RRA period) and the 2009, 2014-2019 seasons (non-
RRA period). To have comparable figures, we only consider permanent teams. As can be
seen, the annual budget fell from $272.9 to $202.3 million (25.9% reduction), a
significant difference at the 5% level (p=0.0106). This cost-cutting especially affected the
annual budget of the two cars (p=0.0058), with no significant variation in the payroll of
the two drivers (p=0.5742).
[Table 2 here]
The cost-cutting was not evenly distributed but affected richer teams more,
possibly because they used more personnel and engines per season, conducted more tests,
and developed lighter cars. As a result, the inequality of budget distribution, as measured
by the Gini index, decreased from 0.276 to 0.197, with the difference being significant at
the 1% level (p=0.0078). This phenomenon concentrated in 2012 and 2013. Spending by
the three richest teams (Ferrari, Mercedes, and Red Bull) fell by nearly eight percentage
points in 2012 compared to the previous year, while spending by the poorest teams
(Sauber, Toro Rosso, and Force India) increased by nine percentage points (see Appendix
D for details).5
5 We thank our anonymous referees for this suggestion.

The Role of Labor, Capital and Team Experience in the F1 Industry
Table 3 shows maximum likelihood estimates of the mixed model. For each
specification, we report the coefficients (fixed part) and the variance of the random
intercepts (random part). The dependent variable is the inverse hyperbolic sine
transformation of points obtained by each car in each race. All tests are two-tailed, except
for the variance components, which are one-tailed. The coefficients of the independent
variables expressed in asihn form are interpreted as output elasticities (the same as if
expressed in logs). As a robustness check, we also estimate these elasticities using the
procedure suggested by Bellemare and Wichman (2020), obtaining fairly close results.
The null model (Model 1) shows how the total residual variance is distributed
across teams (30.9%), team-years (14.7%), drivers (3.2%), and residuals (51.2%) when
no explanatory variables are included. The likelihood ratio test indicates that the variances
of random intercepts are significantly greater than zero, confirming the suitability of the
four-level partitioning for modeling unexplained variability. These results validate the
preference for a multilevel model over conventional single-level regression.6
[Table 3 here]
Model 2 estimates the production function using labor and capital inputs. As
expected, the quality of both factors, measured by wages and car budgets, is positively
related to performance (significant at the 1% level). Output elasticities for labor and
capital are 0.184 and 0.796, respectively. These results, in line with literature, underscore
the greater importance of investing in the car compared to the driver. The sum of labor
and capital elasticities (0.979) is not significantly different from one (p=0.544),
suggesting constant returns to scale.
6 As usual, we perform the likelihood ratio test for variances.

The Mankiw-Romer-Weil specification (Model 3) incorporates team experience.
Our estimates indicate that a 1% increase in team age results in a 0.182% increase in
points, significant at the 1% level. This variable reduces capital contribution to 0.539
while labor elasticity remains unchanged. These results suggest capital was partially
mediated by experience. The sum of elasticities (0.904) is significantly different from one
(p=0.011), leading us to reject the hypothesis of constant returns to scale.
In Model 4, we include control variables to verify that results are not affected by
omitted variable bias. As expected, the "number of competitors" coefficient is negative,
but far from significant. We also find no evidence that racing at home benefits
constructors or drivers. Regarding difficult race situations, such as rainy conditions, street
circuits and races near the championship end, none of these variables is significant. Model
3 is the best according to the Akaike Information Criterion.
Performance With Cost-Cutting
In this section, we analyze the 2010-2013 cost-cutting measures, interacting the
dummy identifying this period (RRA) with labor, capital, and team experience. Table 4
presents two types of analysis: models 5 and 6 study the whole sample, and models 7 and
8 examine the permanent teams. In these last two models, we recalculate points, adjusting
them to the 18 drivers (nine teams) competing in each race.
[Table 4 here]
The estimates provide evidence that driver quality became more important when
cost cutting was applied. As can be seen in Model 5, the driver contribution was 12.6
percentage points higher in the cost-cutting years (0.267=0.141+0.126) than in the rest of
the period (0.141), while the capital contribution remained unchanged at around 0.79.
Similar results are obtained in Model 6 when we include team age: the interaction of RRA
with capital and team experience is negative but not significant.

The RRA further increased the importance of drivers among permanent teams. As
shown in Model 7, their contribution was three times higher during the cost-cutting period
(0.398=0.136+0.262) than in the rest of the years (0.136). Capital contribution suffered a
significant reduction, from 0.804 to 0.379 (0.804−0.425). Model 8 shows that the RRA
particularly hurt the most experienced teams.
What was RRA’s implication for competitive balance? Applying the 2010 scoring
system to the sample of permanent teams, we calculate the Gini coefficient for drivers
and teams in RRA and non-RRA years. As shown in Table 5, the difference between
periods is not significant.
[Table 5 here]
Robustness Checks
To ensure our results are not conditioned by the applied methodology, we
introduced several changes. First, we use two alternatives to the inverse hyperbolic sine:
a ln(variable+1) transformation and a Tobit model with the share of race points as the
dependent variable. Second, we apply the 2010-point system for the entire period. Third,
since monetary measures can be controversial, we use drivers’ historical performance
(average points earned in the previous five seasons) as an alternative measure of labor
quality. Fourth, to verify that the results are not determined by the endpoint of the series,
we re-estimate the models including the 2020 season. As shown in Appendix E, our
results remain valid.
Discussion and Conclusion
Summary of key findings and contribution to literature
F1 offers a unique opportunity to study how labor (driver), capital (car), and
organizational experience contribute to production at the micro level. We contribute to
the literature by providing evidence that the inclusion of the organization’s age reduces

the capital contribution, suggesting that it is mediated by the team’s experience. Thus,
investing in capital is crucial, not only for securing a more competitive car but also for
retaining the knowledge accumulated by the organization. Over the period 2009-2019,
the estimated output elasticities for driver, car, and team experience were 18%, 54% and
18%, respectively. These estimates show that new teams do not compete under the same
conditions as old ones, even if they have the same financial capacity. This finding
supports the idea that organizational experience acts as a dynamic capability, providing a
competitive advantage under predictable continuous changes. This is consistent with the
literature on the liabilities of newness (Stinchcombe, 2000) and adolescence (Fichman &
Levinthal, 1991) as well as the concepts of “absorptive capacity” (Cohen & Levinthal,
1990; 1994) and “combinative capability” (Kogut & Zander, 1992).
The cost-cutting measures implemented between 2010 and 2013 were associated
with a reduction in both the level and dispersion of vehicle costs. We also found a
diminished contribution of team age to racing success, suggesting that organizational
experience does not create a competitive advantage when changes, though predictable
and endogenous, are discontinuous. While this seems to generate a more favorable
environment for smaller teams, we did not observe an improvement in competitive
balance (as measured by the Gini coefficient), possibly due to shifts in the contribution
of other factors. Estimates provide evidence that, under the RRA, the contribution of
drivers increased at the expense of cars. This aligns with production theory, as the RRA
focused on reducing capital needs, a capital-saving technical change known to increase
the Marginal Rate of Technical Substitution (MRTS=MPL/MPK).
Managerial and Policy Implications
Our results are relevant to addressing the issue of “overshooting” described by
Thomas and Potts (2016) in equipment-based sports, such as windsurfing and paddle-

boarding. According to their hypothesis, participants in these sports can become trapped
in a technological race that raises entry costs, thereby threatening the sport’s long-term
viability. This issue could apply to F1, as teams’ struggles to keep up with innovation
might contribute to the decline in competitive balance over time (Budzinski & Feddersen,
2020). Given the potentially positive relationship between F1 audiences and outcome
uncertainty, this could help explain the decrease in TV audiences (Mourão, 2017; Sylt,
2018). However, it is important to recognize the complexity of the relationship between
outcome uncertainty and F1 spectatorship/attendance. While some studies have found a
positive correlation (Schreyer & Torgler, 2018), others have reported mixed results
(Baecker et al., 2022; Garcia-del-Barrio & Reade, 2022; Gasparetto et al., 2022;
Krauskopf et al., 2010) or no significant relationship (Fahy et al., 2023).
Our work opens several possibilities for the sport’s governing bodies to improve
competitive balance. The fact that organizational experience is a dynamic capability
underscores the importance of promoting the continuity of existing teams by allowing
new entrants to adopt the structures of departing teams, or by implementing compensatory
measures to support new entrants. However, these objectives often conflict with current
rules and practices in F1 and other sports, such as the imposition of entry fees on new
entrants and the provision of “historic payments” to long-established teams (see
Budzinski & Müller‐Kock, 2018). The value of experience has broader implications,
highlighting the need to implement measures to mitigate bankruptcy and preserve
organizational knowledge during economic downturns while protecting emerging firms.
The fact that organizational experience does not provide an advantage under
discontinuous changes allows governing bodies to reduce the expertise gap by
introducing discontinuous changes in the competition. Our results provide evidence that
the RRA disadvantaged older teams, possibly by preventing the efficient allocation of

resources and the use of their organizational knowledge. This would explain the notorious
disagreements over which resources to restrict and the subsequent collapse. The unstable
nature of such arrangements suggests that a cost cap must be both flexible and mandatory
to ensure compliance. Table D4 shows that initially, top teams reduced their budgets in
anticipation of enforcement, but later reverted to growth as enforcement proved weak. By
contrast, smaller teams, initially increasing their budgets to compete, also returned to pre-
RRA strategies once they realized enforcement was lacking.
Although older teams were unable to exploit the competitive advantage based on
their experience, the RRA did not promote more balanced competition, contrary to Judde
et al.'s (2013) and our own expectations. One possible reason is that the cost-cutting
measures gave more weight to the driver. In principle, this could improve competitive
balance, as the driver is theoretically a more unpredictable resource than the car.
However, it is important to stress that increasing the human factor alone does not
necessarily lead to an improvement in competitive balance and could even exacerbate it
if the dispersion of driver quality is greater than that between cars. Therefore, cost-cutting
measures should be complemented by actions to reduce driver quality dispersion. For
example, attracting more talented drivers to the championship.
Our findings also have implications for how managers allocate resources. Our
work shows that in a highly R&D-intensive industry such as F1, team managers should
prioritize the acquisition of an established structure over the creation of a new one. This
finding is consistent with research showing that firms lacking technological knowledge
or R&D intensity in the industry choose to acquire incumbents because such knowledge
is time-consuming and costly to develop internally (Chen & Zeng, 2004; Cho &
Padmanabhan, 1995; Harzing, 2002). This option is particularly convenient in highly
concentrated industries to avoid the market power of incumbents (Caves & Mehra, 1986).

Our work also shows that a team can sustain its competitive advantage by
maintaining superiority in various resources. Consequently, managers aiming to preserve
this advantage under a cost-cutting environment should actively seek out talented drivers,
as their skills can significantly impact performance. Additionally, in the context of a
salary cap, investing in capital assets becomes crucial, allowing teams to enhance their
overall capabilities and ensure long-term competitiveness.
Limitations and Future directions
It should be noted that our study is not without limitations. A consequence of the
cost-cutting measures was the narrowing of the gap between car budgets, as evidenced
by our results for the 2012 and 2013 seasons. This could have improved the survival
prospects of the less affluent and newer teams in the competition. However, the
experiences of teams such as HRT, Virgin, and Caterham/Team Lotus do not support this
notion. This may have been influenced by the fact that the financial circumstances in
which they entered the competition were not as previously promised.
A second limitation is that the conclusions we draw about the relevance of
organizational experience should not be applied across the board. For example, club age
does not seem to be relevant for success in football (Malagila et al., 2021), possibly due
to the shorter learning period required. It is therefore advisable to conduct a rigorous
sport-by-sport analysis of the role of organizational experience.
The 2021 budget cap shares the same philosophy as the RRA (limiting costs and
not salaries) so it is reasonable to expect similar results. However, there are some
differences that suggest the need for further research. First, the 2021 budget cap is
enforced by the Cost Cap Administration (among others) rather than by teams themselves,
potentially making sanctions more credible. Second, the new budget cap gives teams
more freedom to manage their own costs, which reduces incentives for noncompliance

and allows teams to manage their budgets more efficiently. Certainly, more research
should be done when data becomes available.

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Table 1
Descriptive Statistics
Points per driver and race
4,479
4.623
6.977
0
50
Driver’s salary (mill. $)
4,479
7.267
10.523
0.050
52.343
Budget per car (mill. $)
4,479
104.868
62.431
16.209
246.180
Team experience (years)
4,479
29.909
18.074
0
69
No. of drivers
4,479
21.521
1.753
15
24
No. of races
4,479
9.388
5.683
0
20
Home circuit (driver)
4,479
0.036
0.186
0
1
Home circuit (constructor)
4,479
0.039
0.194
0
1
Rainy weather
4,479
0.018
0.134
0
1
Street circuit
4,479
0.239
0.427
0
1
Subsample (permanent teams)
4,479
0.837
0.370
0
1
RRA (2010-2013)
4,479
0.395
0.489
0
1
Table 2
Mean-Comparison Tests of Team Budgets
Non-RRA
(n=63)
RRA
(n=36)
Diff. p-value
Total annual budget (mill $)
272.94
202.33
-70.61**
0.0106
Budget for two cars (mill $)
255.84
187.41
-68.43***
0.0058
Salaries of two drivers (mill $)
17.10
14.92
-2.18
0.5742
*** p<0.01, ** p<0.05. RRA=Resource Restriction Agreement

Table 3
Contribution of Labor, Capital and Organizational Experience on Point Production
Model 1 Model 2 Model 3 Model 4
Fixed part: coefficients
asinh(wage of the driver)
0.184***
(0.018)
0.183***
(0.018)
0.183***
(0.018)
asinh(budget per car)
0.796***
(0.041)
0.539***
(0.058)
0.540***
(0.059)
asinh(team age)
0.182***
(0.021)
0.182***
(0.021)
Number of drivers in the race
-0.006
(0.025)
Home circuit (for driver)
0.001
(0.083)
Home circuit (for constructor)
-0.065
(0.076)
Rainy weather
-0.016
(0.125)
Street circuit
-0.005
(0.038)
Number of races to the end
-0.001
(0.003)
Constant
Random part: variances
0.938***
(0.016)
-17.354***
(0.666)
-13.067***
(0.993)
-12.934***
(1.118)
Team intercept
0.624***
(0.022)
0.100***
(0.016)
0.124***
(0.017)
0.124***
(0.017)
Team-year intercept
0.298***
(0.025)
0.252***
(0.024)
0.233***
(0.023)
0.233***
(0.023)
Driver intercept
0.065***
(0.011)
0.044***
(0.008)
0.044***
(0.008)
0.044***
(0.008)
Residuals
1.035
(0.027)
1.035
(0.027)
1.035
(0.027)
1.035
(0.027)
Observations
Year dummies
AIC
4479
No
13317.3
4479
Yes
13273.1
4479
Yes
13270.7
4479
Yes
13281.7
Note. Mixed regressions. The dependent variable is the inverse hyperbolic sine transformation of the number of points obtained by each car in each race.
Bootstrapped standard errors in parentheses (from 10,000 replications). AIC=Akaike information criterion.
*** p<0.01, ** p<0.05.

Table 4
Influence of the RRA in the 2010-2013 Period
Model 1 Model 2 Model 3 Model 4
Fixed part: coefficients
asinh(wage of the driver)
0.141***
(0.022)
0.139***
(0.022)
0.136***
(0.023)
0.130***
(0.023)
asinh(budget per car)
0.793***
(0.058)
0.624***
(0.066)
0.804***
(0.067)
0.776***
(0.070)
asinh(team age)
0.177***
(0.028)
0.138
(0.088)
RRA
-2.251
(1.235)
-0.079
(1.817)
4.602
(2.486)
2.174
(2.474)
asinh(wage of the driver)×RRA
0.126***
(0.031)
0.131***
(0.032)
0.262***
(0.043)
0.282***
(0.044)
asinh(budget per car)×RRA
0.052
(0.082)
-0.061
(0.116)
-0.425***
(0.157)
-0.189
(0.158)
asinh(team age)×RRA
-0.034
(0.038)
-0.591***
(0.117)
Constant
Random part: variances
-16.469*** (1.038)
-13.845*** (1.173)
-16.408*** (1.265)
-16.316*** (1.258)
Team intercept
0.097***
(0.014)
0.115***
(0.016)
0.123***
(0.019)
0.111***
(0.022)
Team-year intercept
0.247***
(0.023)
0.234***
(0.023)
0.264***
(0.027)
0.255***
(0.027)
Driver intercept
0.039***
(0.007)
0.039***
(0.007)
0.033***
(0.007)
0.033***
(0.007)
Residuals
1.035
(0.027)
1.035
(0.027)
1.197
(0.030)
1.197
(0.030)
Observations
Year dummies
Control variables
AIC
4479
Yes Yes
13280.1
4479
Yes Yes
13281.2
3748
Yes Yes
11643.1
3748
Yes Yes
11643.5
Note. Mixed regressions. The dependent variable is the inverse hyperbolic sine transformation of the number of points obtained by each car in each race.
Bootstrapped standard errors in parentheses (from 10,000 replications). AIC=Akaike information criterion. RRA=Resource Restriction Agreement.
*** p<0.01, ** p<0.05.

Table 5
Mean-Comparison Tests for the Gini Index of Points
Non-RRA
RRA
Diff.
p-value
Gini index for drivers
0.518 (n=7)
0.506 (n=4)
-0.012
0.692
Gini index for teams
0.740 (n=7)
0.737 (n=4)
-0.002
0.984
Note. The Gini coefficient is estimated per season. To ensure comparability, we used the
permanent-team sample, applied the 2010 scoring system, and normalized the annual points
by dividing them by the number of races in each season. RRA=Resource
Restriction Agreement.

Figure 1
Conceptual Model
Figure 2
Average Spending per Team on Drivers and Car Development
Note. Left bar=all teams, right bar=permanent teams.