Making a Maradona: Meat Consumption

and Soccer Prowess

Martín A. Rossi (Universidad de San Andrés)

Christian A. Ruzzier (Universidad de San Andrés)

DOCUMENTO DE TRABAJO N° 161

Julio de 2022
Los documentos de trabajo de la RedNIE se difunden con el propósito de
generar comentarios y debate, no habiendo estado sujetos a revisión de pares.
Las opiniones expresadas en este trabajo son de los autores y no
necesariamente representan las opiniones de la RedNIE o su Comisión
Directiva.

The RedNIE working papers are disseminated for the purpose of generating
comments and debate, and have not been subjected to peer review. The
opinions expressed in this paper are exclusively those of the authors and do
not necessarily represent the opinions of the RedNIE or its Board of Directors.

Citar como:
Rossi, Martín A. y Christian A. Ruzzier (2022). Making a Maradona: Meat
Consumption and Soccer Prowess. Documento de trabajo RedNIE N°161.
 Making a Maradona: Meat Consumption
and Soccer Prowess

Martín A. Rossi Christian A. Ruzzier

Universidad de San Andres Universidad de San Andres

July 2022

Abstract

We provide novel evidence that poor individuals born in countries with high
consumption of meat are more likely to show soccer prowess. Our findings are robust
to controlling for population, per capita income, and interest in soccer. We posit that
the combination of relatively cheap meat and low opportunity costs of engaging in a
career in professional soccer can explain this association between meat consumption,
low socioeconomic status, and soccer prowess. Access to cheap meat implies a higher
quality diet, which allows for the development of the cognitive functions required to
excel in soccer. Poverty implies a low opportunity cost of putting those improved
cognitive functions to use in soccer.

Keywords: Sports; Meat consumption; Soccer; Nutrition; Poverty; Cognitive functions.

JEL classification: Z20; Z22; J22; J24.

Declarations of interest: none.


Martín A. Rossi (email: mrossi@udesa.edu.ar) and Christian A. Ruzzier (corresponding author; email:
cruzzier@udesa.edu.ar; address: Vito Dumas 284, B1644BID, Victoria, Buenos Aires, Argentina; phone:
+54 11 7078 0400 ext. 4570) are at the Department of Economics, Universidad de San Andres. Sebastián
Einstoss Mastracchio and Pascuel Plotkin provided excellent research assistance.

1
1. Introduction

What do Maradona, Pelé, Messi, Cristiano Ronaldo, and Cruyff have in common,

other than being consistently ranked amongst the best soccer (association football)

players of all times? They were all born and raised in countries of high per capita

consumption of meat, within low-income, working-class households. The central tenet

of this paper is that this is not a coincidence.

We posit that the combination of relatively cheap meat and low opportunity costs

of engaging in a career in professional soccer can explain the association between meat

consumption, low socioeconomic status (SES), and soccer prowess. Being raised in a

country where meat is relatively cheap implies that, even if poor, individuals have

easier access to a high-quality diet, crucial for the development of the brain, in general,

and of the cognitive skills required to excel in soccer, in particular. Being relatively

poor, on the other hand, means that the opportunity cost of pursuing a career in

professional soccer is relatively low (see Rossi and Ruzzier, 2018, for related evidence

and a model of career choices).

Soccer is indisputably the most popular sport in the world. According to the 2006

Big Count, a FIFA survey of its 207 member associations, 265 million players

(professional, registered, and occasional) were actively involved in this sport (Rossi

and Ruzzier, 2018). Soccer is also the number one sport for global audience, TV

viewers, internet popularity, number of professionals, market value (Marchiori and De

Vecchi, 2020), and research (Reilly et al., 2000). As in many other domains, the key

resource in soccer is talent (Kuper and Szymanski, 2014), and identifying talent is of

crucial importance to the multi-million-dollar business soccer has become (Morris,

2000; Williams and Reilly, 2000).

2
 We begin by unpacking the main ingredients of the meat-brain-talent argument

above. Next, we produce aggregate (i.e., country-level) quantitative evidence of a

robust, positive correlation between meat consumption and soccer talent (measured by

the number of players, born in the country, nominated to the Ballon d'Or in 2016-19),

conditional on wealth, size, and interest in soccer. Last, we go down to the individual

level of the most talented players, and look at their personal life stories to classify

players along the two dimensions of interest: SES of the family and meat consumption.

We find that a disproportionate fraction of the most talented players does come indeed

from low-SES households in high-meat-consumption countries. In the concluding

section, we provide some final thoughts.

2. You play how you eat: the underappreciated link between meat, mind, and

soccer prowess

While it is undisputed that performance at the elite level in professional soccer

requires superior physical abilities and motor coordination, there is also a recent surge

of interest in the cognitive (and creativity-related) processes that are important for

soccer, as physical skills and coordination alone have been shown to have a low

predictive value (Vestberg et al., 2012; 2020). Executive functions – a family of top-

down mental processes needed for the cognitive control of behavior (Diamond, 2013)

– have been associated with talented soccer players, even after controlling for training

hours and competitive level (see Voss, 2010; Vestberg et al., 2012, 2017, 2020; Wright

et al., 2013; Verburgh et al., 2014; Huijgen et al., 2015; Memmert, 2017; Fink et al.,

2018; Scharfen and Memmert, 2019). Moreover, superior executive functioning

predicts success in the sport (Vestberg et al., 2012; 2017; 2020; Sakamoto et al., 2018;

Scharfen and Memmert, 2021).

3
 Executive functions include core functions – such as working memory, cognitive

flexibility, and inhibitory control – and higher-order functions – like reasoning,

problem solving, and planning (Diamond, 2013). All of these cognitive abilities are

required in soccer; actually, executive functions are regularly called “game

intelligence” in that context (Vestberg et al., 2017; Sakamoto et al., 2018). In an open

skill sport like soccer, successful players are required to react in a dynamically

changing, unpredictable, and externally-paced environment (Huijgen et al., 2015), by

making fast decisions that need to be constantly reevaluated depending on the

circumstances (Vestberg et al., 2017). Inhibitory control and cognitive flexibility are

key to this ability to adapt quickly to new demands in the face of rapidly changing

situations (Huijgen et al., 2015). Working memory, on the other hand, may be useful

for choosing positions and mentalizing possible options in the game (Verburgh et al.,

2014).

Executive functions are also closely linked to creativity (Diamond, 2013).

Successful solutions in soccer are “often original and surprising, characterized by the

flexible production of novel, unexpected passes, and moves” (Fink et al., 2018: 1), and

the evidence shows that creativity is a key factor for success in soccer (Kempe and

Memmert, 2018; Roca et al., 2018; Rominger et al., 2021).

While adequate nutrition is important for normal brain development (Prado and

Dewey, 2014) in general, the role of nutrition in the development of these cognitive

functions, in particular, has been the focus of much behavioral work recently

(Wainwright and Colombo, 2006). The cumulating evidence points in the direction of

a positive association between food quality and executive functioning (see the review

by Cohen et al., 2016). Growing evidence also associates early-life undernutrition (like

protein-energy malnutrition, or micronutrients deficits such as iron deficiency anemia)

4
with permanent negative effects on cognitive skills, likely supporting a causal effect

(Bryan, 2004; Engle et al., 2007; 2011; Grantham-McGregor et al., 2007; Walker et al.,

2007; Hoddinott et al., 2008; 2013; Isaacs et al., 2008; Victora et al., 2008; Maluccio

et al., 2009; Macours et al., 2012; Puentes et al., 2016; Cheatham, 2019). Early

childhood is a crucial period for the development of cognitive functions, since the brain

develops most rapidly at this stage (Huttenlocher, 1979; Georgieff, 2007; Gertler et al.,

2014; Bolbocean et al., 2018; Li et al, 2021).

Meat is a high-quality food, as it is an excellent source of readily digested protein,

energy, and micronutrients like iron, zinc, and vitamins of the B group. Improving

access to and utilization of meat and other animal source foods has been advocated as

a sensible way of promoting social and economic development (Neumann et al., 2002).

Increased consumption of meat early in life has been shown to improve cognitive

performance, both in childhood and later in life (Sigman et al., 1989a; 1989b; 1991;

Neumann et al., 1992; 2003; 2007; Whaley et al., 2003, Gewa et al., 2009; Hulett et al.,

2014) – even after controlling for SES factors and schooling.

Low SES correlates with low-quality diets because, among other things, poor

households are priced out from high-quality foods like meat (Neumann et al., 2002;

Woldemichael et al., 2022), which are regarded as too expensive. Poverty, actually, is

the top reason given for the absence of meat in the diet (Neumann et al., 2002). Access

to relatively cheap meat can mitigate the negative impact of low SES on cognition, and

this is more likely when poor households are located in a country where meat is

relatively inexpensive.

Finally, cognitive functions are necessary for soccer talent, but talent must also

be found, developed, and nurtured (Kuper and Szymanski, 2014). Many factors mediate

this process. Money, know-how, infrastructure (all arguably related to a country’s

5
wealth), country size (since more populous countries tend to have a larger supply of

talented people), and interest in soccer in the country are natural candidates (see, e.g.,

Kuper and Szymanski, 2014; The Economist, 2018). We wish to emphasize here a

previous step: talent must be applied to soccer to be developed and eventually

manifested. Put differently, professional soccer is a career choice.

In choosing careers, opportunity costs are a paramount consideration (Rossi and

Ruzzier, 2018). Individuals from low-SES households arguably have lower opportunity

costs, and actually most of the world’s best soccer players started life poor (see, e.g.,

Kuper and Szymanski, 2014; and section 4.2 below). Boys from poor households are

less likely to go to college (Perna, 2006; Fack and Grenet, 2015). Living in crowded

homes, they tend to spend more time outdoors, playing. Being poor, they have less

money to spend in other leisure activities (Kuper and Szymanski, 2014).

The main prediction stemming from this line of reasoning is that countries with

a high per-capita consumption of meat should produce more soccer talent on average,

even after controlling for things like country wealth, size, and interest in soccer. We do

not observe meat consumption by SES, but we can look at the personal backgrounds of

the most talented players to assess their families’ SES when growing up, and assign

them the per-capita meat consumption of their country of origin at the time of their

childhood. If our argument is correct, we predict that most players will come from low-

SES households in high per-capita-consumption countries. The next two sections

describe our data and present evidence consistent with these predictions.

6
3. Data

Our outcome of interest is Soccer Prowess, which counts the number of players,

born in a given country, nominated to the Ballon d’Or in the period 2016 to 2019.1 59

players (from 25 different countries) were nominated to the Ballon d’Or in this period.

The countries that contribute more players are France (11 players), Brazil, Netherlands,

Spain, Portugal (4 players each), Argentina, Belgium, England, Germany, and Uruguay

(3 players each).

We obtained data on meat and protein consumption by country from the Food

and Agriculture Organization (FAO) of the United Nations.2 Meat Consumption is the

per capita consumption of meat, in kilograms, in 2000. Protein consumption is the per

capita consumption of proteins (in grams, per day) in 2000. We choose the year 2000

to measure meat consumption because it is within our 2016-19 Ballon d’Or nominees’

early childhoods. As discussed in the previous sections, the preschool years (i.e. 0–5

years of age) is a time of rapid and dramatic brain development, and of fundamental

acquisition of cognitive development (i.e., working memory, attention and inhibitory

control) – see, e.g., Victora et al. (2008), Rosales et al. (2009), and Jackson (2015).

Following Kuper and Szymanski (2014), we control for population and wealth in

our regressions. A larger population means a larger supply of talented people in the

country, and richer countries are better at finding, training, and developing talent. The

data on countries’ population and income comes from the World Bank.3 Population is

the total population of the country in 2000, in millions. It counts all residents regardless

of legal status or citizenship. GNI per capita, our measure of wealth, is the gross

1
The Ballon d’Or is the most prestigious soccer award, presented annually (since 1956) by France
Football (https://www.francefootball.fr/ballon-d-or/).
2
https://www.fao.org/faostat/en/#data/.
3
https://data.worldbank.org/indicator/.

7
national income in 2000, converted to thousands of U.S. dollars using the World Bank

Atlas method, divided by the midyear population.

To proxy for a country’s interest in soccer (see, e.g., The Economist, 2018), we

rely on the 2006 FIFA Big Count, a survey conducted by FIFA (Fédération

Internationale de Football Association, the world governing body of soccer) in 2006 to

its 207 member associations. The 2006 FIFA Big Count reports the number of people

actively involved in soccer, by country. We divide these figures by the country’s

population (in millions) to construct the variable Interest in Soccer.

Table 1 presents summary statistics of the main variables used in our analysis.

[Table 1 about here]

4. Making a Maradona: meat and soccer

4.1. Econometric methods and results

We begin by examining the effect of meat consumption on soccer prowess in a

regression framework. Formally, we want to estimate the following equation:

𝑆𝑜𝑐𝑐𝑒𝑟 𝑃𝑟𝑜𝑤𝑒𝑠𝑠𝑖 = 𝛼 + 𝛽 ∗ 𝑀𝑒𝑎𝑡 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖 + 𝛾 ∗ 𝑋𝑖 + 𝜀𝑖 (1)

where Soccer Prowess is the number of players born in country 𝑖 that were nominated

to the Ballon d'Or in the period 2016 to 2019, Meat consumption is the per capita

consumption of meat in country 𝑖 in 2000, and 𝜀 is an error term. The vector of control

variables, 𝑋, includes Population and, depending on the specification, GNI per Capita,

Interest in Soccer, and a set of continent fixed effects. The coefficient of interest is 𝛽,

which we expect to be positive.

Table 2 reports Ordinary Least Squares (OLS) estimates of equation (1). The

estimated coefficient of Meat Consumption in column (1) is positive and statistically

significant at the 1 percent level, indicating that countries with a higher per capita

8
consumption of meat are more likely to breed soccer talent. The magnitude of the

estimated coefficient is important: a one-standard-deviation increase in the

consumption of meat is associated with an increase of 1/3 of a standard deviation in

Soccer Prowess.

As shown in columns (2) to (4) of Table 2, the result is robust to progressively

controlling for GNI per Capita, Interest in Soccer, and continent dummies. The

estimated coefficient on Meat Consumption is smaller when we include the full set of

control variables, but remains positive and statistically significant. In all cases, the

control variables are statistically non-significant in explaining Soccer Prowess.

[Table 2 about here]

Table 3 shows our findings are robust to using Protein Consumption instead of

Meat Consumption as the main explanatory variable in equation (1). In all cases, the

coefficient on Protein Consumption is positive and statistically significant. Again, all

control variables are statistically non-significant in explaining Soccer Prowess.

[Table 3 about here]

4.2. Socioeconomic status, meat, and talent

Having stablished a positive association between the consumption of meat and

soccer prowess, we now move to explore the role of the opportunity cost of time. To

do so, we study the early lives of each of the 59 players nominated to the Ballon d’Or

in the period 2016 to 2019, and classify them according to their SES during childhood.

We use three socioeconomic categories: high (including middle-high) class, middle

class, and low (including middle-low) class. Among the 59 players, 33 were raised in

9
a low- or middle-low-income family, 16 in a middle-income family, and 10 in a high-

or middle-high-income family.4

We also divide countries according to terciles of per-capita meat consumption.

We call the first tercile “countries of high meat consumption”, the second tercile

“countries of middle meat consumption”, and the third tercile “countries of low meat

consumption”.

For example, Lionel Messi (Argentina) and Cristiano Ronaldo (Portugal) were

raised in middle-low-income families in countries with high meat consumption;

Neymar Júnior (Brazil), Luis Suarez (Uruguay), and Kylian Mbappé (France) in low-

income families from countries with high meat consumption; Gianluigi Buffon (Italy)

and Kevin De Bruyne (Belgium) in high-income families from countries with high meat

consumption; and Sadio Mané (Senegal) and Edin Džeko (Bosnia and Herzegovina) in

low-income families from countries with low meat consumption.

In Table 4, we report a classification of players according to childhood SES and

meat consumption in their country of birth. As predicted, 28 of the 59 players

nominated to the Ballon d’Or in the period 2016 to 2019 were raised in a low or middle-

low income family, in countries with a high consumption of meat – a 47.46 percent,

where pure chance would imply a figure of just 11.11 percent.

[Table 4 about here]

5. Concluding remarks

This paper provides novel evidence that poor individuals born in countries with

high consumption of meat are more likely to have soccer prowess. More meat

4
To obtain the information we conducted extensive Google searches in pages such as
https://lifebogger.com/. Two research assistants independently classified all players, with 100%
coincidence in their choices.

10
consumption implies a higher quality diet, which allows for the development of the

cognitive functions required to excel in soccer. Poverty implies a low opportunity cost

of putting those improved cognitive functions to use in soccer.

Meat consumption may explain part of the unexplained variability in soccer

potential among national teams. The Economist (2018) builds a model that attempts to

predict soccer goal differences through a country’s wealth, size, interest in soccer, and

home advantage. As it turns out, the top overachievers (countries performing above the

predictions of the model) are countries of high per capita consumption in our database

(Uruguay, Brazil, Argentina, Portugal, and Spain).

How to improve a country’s performance in soccer is of paramount importance

to many countries. Chinese president Xi Jingpin, for instance, is committed to

transforming his country into a soccer superpower by 2050 (The Economist, 2018). To

that end, 50,000 schools will be teaching soccer by 2025 in China and 20,000 new

training centers are being built. The funds committed to the program are impressive.

The largest training center alone, located in Guangzhou, will cost $185 million. Yet,

China does not have much to show for all this spending, failing to qualify for the 2018

and 2022 World Cups (The Economist, 2018). Our results suggest that perhaps some

of that money would be better spent in improving the nutrition of Chinese toddlers to

support Mr. Xi’s goals. More generally, the children most behind on executive

functions would benefit the most from any intervention that improves these functions

(Diamond, 2013).

11
 References

Bolbocean, Corneliu, Frances A. Tylavsky, and James E. West (2018). U.S.

safety net programs and early life skills formation: results from a prospective

longitudinal cohort study. NBER Working Paper 24832, July.

http://www.nber.org/papers/w24832.

Bryan, Janet, Saskia Osendarp, Donna Hughes, Eva Calvaresi, Katrine Baghurst,

and Jan-Willem van Klinken (2004). Nutrients for cognitive development in school-

aged children. Nutrition Reviews 62(8): 295–306.

https://doi.org/10.1111/j.1753-4887.2004.tb00055.x

Cheatham, Carol L. (2019). Nutritional factors in fetal and infant brain

development. Annals of Nutrition and Metabolism 75(Suppl. 1): 20–32.

https://doi.org/10.1159/000508052

Cohen, Julianna F., Mary T. Gorski, Staci A. Gruber, Laura Kurdziel, and Eric

B. Rimm (2016). The effect of healthy dietary consumption on executive cognitive

functioning in children and adolescents: a systematic review. British Journal of

Nutrition 116(6): 989–1000.

https://doi.org/10.1017/S0007114516002877

Diamond, Adele (2013). Executive functions. Annual Review of Psychology 64:

135–168.

https://doi.org/10.1146/annurev-psych-113011-143750

Engle, Patrice L., Maureen M. Black, Jere R. Behrman, Meena Cabral de Mello,

Paul J. Gertler, Lydia Kapiriri, Reynaldo Martorell, Mary Eming Young, and the

International Child Development Steering Group (2007). Strategies to avoid the loss of

developmental potential in more than 200 million children in the developing world. The

Lancet 369 (9557): 229–242.

12
https://doi.org/10.1016/S0140-6736(07)60112-3

Engle, Patrice L., Lia C. H. Fernald, Harold Alderman, Jere Behrman, Chloe

O’Gara, Aisha Yousafzai, Meena Cabral de Mello, Melissa Hidrobo, Nurper Ulkuer,

Ilgi Ertem, Selim Iltus, and the Global Child Development Steering Group (2011).

Strategies for reducing inequalities and improving developmental outcomes for young

children in low-income and middle-income countries. The Lancet 378 (9799): 1339–

1353.

https://doi.org/10.1016/S0140-6736(11)60889-1

Fack, Gabrielle, and Julien Grenet (2015). Improving college access and success

for low-income students: evidence from a large need-based grant program. American

Economic Journal: Applied Economics 7(2): 1–34.

https://doi.org/10.1257/app.20130423

Fink, Andreas, Jürgen U. Bay, Karl Koschutnig, Katharina Prettenthaler,

Christian Rominger, Mathias Benedek, Ilona Papousek, Elisabeth M. Weiss, Anna

Seidel, and Daniel Memmert (2018). Brain and soccer: functional patterns of brain

activity during the generation of creative moves in real soccer decision-making

situations. Human Brain Mapping 40(3): 755–764.

https://doi.org/10.1002/hbm.24408

Georgieff, Michael K. (2007). Nutrition and the developing brain: nutrient

priorities and measurement. The American Journal of Clinical Nutrition 85(2): 614S–

620S.

https://doi.org/10.1093/ajcn/85.2.614S

Gertler, Paul, James Heckman, Rodrigo Pinto, Arianna Zanolini, Christel

Vermeerch, Susan Walker, Susan M. Chang, and Sally Grantham-McGregor (2014).

13
Labor market returns to an early childhood stimulation intervention in Jamaica. Science

344(6187): 998–1001.

https://doi.org/10.1126/science.1251178

Gewa, Constance A., Robert E. Weiss, Nimrod O. Bwibo, Shannon Whaley,

Marian Sigman, Suzanne P. Murphy, Gail Harrison, and Charlotte G. Neumann (2009).

Dietary micronutrients are associated with higher cognitive function gains among

primary school children in rural Kenya. British Journal of Nutrition 101(9): 1378–

1387.

https://doi.org/10.1017/S0007114508066804

Grantham-McGregor, Sally, Yin Bun Cheung, Santiago Cueto, Paul Glewwe,

Linda Richter, Barbara Strupp, and the International Child Development Steering

Group (2007). Developmental potential in the first 5 years for children in developing

countries. The Lancet 369(9555): 60–70.

https://doi.org/10.1016/S0140-6736(07)60032-4

Hoddinott, John, John A. Maluccio, Jere R. Behrman, Rafael Flores, and

Reynaldo Martorell (2008). Effect of a nutrition intervention during early childhood on

economic productivity in Guatemalan adults. The Lancet 371(9610): 411–416.

https://doi.org/10.1016/S0140-6736(08)60205-6

Hoddinott, John, Jere R. Behrman, John A. Maluccio, Paul Melgar, Agnes R.

Quisumbing, Manuel Ramirez-Zea, Aryeh D. Stein, Kathryn M. Yount, and Reynaldo

Martorell (2013). Adult consequences of growth failure in early childhood. The

American Journal of Clinical Nutrition 98(5): 1170–1178.

https://doi.org/10.3945/ajcn.113.064584

Huijgen, Barbara C. H., Sander Leemhuis, Niels M. Kok, Lot Verburgh, Jaap

Oosterlaan, Marije T. Elferink-Gemser, and Chris Visscher (2015). Cognitive functions

14
in elite and sub-elite youth soccer players aged 13 to 17 years. PloS ONE 10(12):

e0144580.

https://doi.org/10.1371/journal.pone.0144580

Hulett, Judie L., Robert E. Weiss, Nimrod O. Bwibo, Osman M. Galal, Natalie

Drorbaugh, and Charlotte G. Neumann (2014). Animal source foods have a positive

impact on the primary school test scores of Kenyan schoolchildren in a cluster-

randomised, controlled feeding intervention trial. British Journal of Nutrition 111(5):

875–886.

https://doi.org/10.1017/S0007114513003310

Huttenlocher, Peter R. (1979). Synaptic density in human frontal cortex –

Developmental changes and effects of aging. Brain Research 163(2): 195–205.

https://doi.org/10.1016/0006-8993(79)90349-4

Isaacs, Elizabeth B., David G. Gadian, Stuart Sabatini, Wui K. Chong, Brian T.

Quinn, Bruce R. Fischl, and Alan Lucas (2008). The effect of early human diet on

caudate volumes and IQ. Pediatric Research 63(3): 308-314.

https://doi.org/10.1203/PDR.0b013e318163a271

Jackson, Margot I. (2015). Early childhood WIC participation, cognitive

development and academic achievement. Social Science & Medicine 126: 145–153.

https://doi.org/10.1016/j.socscimed.2014.12.018

Kempe, Matthias, and Daniel Memmert (2018). “Good, better, creative”: the

influence of creativity on goal scoring in elite soccer. Journal of Sports Sciences

36(21): 2419–2423.

https://doi.org/10.1080/02640414.2018.1459153

Kuper, Simon, and Stefan Szymanski (2014). Soccernomics. New York, NY:

Nation Books.

15
 Li, Shaoping, Kevin Chen, Chengfang Liu, Jieying Bi, Zhenya He, Renfu Luo,

Yanying Yu, and Zimeiyi Wang (2021). Association of dietary diversity and cognition

in preschoolers in rural China. Nutrition 91-92: 111470.

https://doi.org/10.1016/j.nut.2021.111470

Macours, Karen, Norbert Schady, and Renos Vakis (2012). Cash transfers,

behavioral changes, and cognitive development in early childhood: evidence from a

randomized experiment. American Economic Journal: Applied Economics 4(2): 247–

273.

http://dx.doi.org/10.1257/app.4.2.247

Maluccio, John A., John Hoddinott, Jere R. Behrman, Reynaldo Martorell, Agnes

R. Quisumbing, and Aryeh D. Stein (2009). The impact of improving nutrition during

early childhood on education among Guatemalan adults. The Economic Journal

119(537): 734–763.

https://doi.org/10.1111/j.1468-0297.2009.02220.x

Marchiori, Massimo, and Marco de Vecchi (2020). Secrets of soccer: neural

network flows and game performance. Computers and Electrical Engineering 81:

106505.

https://doi.org/10.1016/j.compeleceng.2019.106505

Memmert, Daniel (2017). Tactical creativity in sport. In: Kaufman, J.C.,

Glaveanu, V.P., Baer, J., editors. Cambridge Handbook of Creativity across Domains,

Cambridge: Cambridge University Press, 479–491.

https://doi.org/10.1017/9781316274385

Morris, Tony (2000). Psychological characteristics and talent identification in

soccer. Journal of Sports Sciences 18(9): 715–726.

https://doi.org/10.1080/02640410050120096

16
 Neumann, Charlotte G., Nimrod O. Bwibo, Marian Sigman (1992). Final Report

Phase II: Functional implications of malnutrition, Kenya Project. Nutrition CRSP. Los

Angeles, CA: University of California, Los Angeles.

Neumann, Charlotte G., Diane M. Harris, and Lisa M. Rogers (2002).

Contribution of animal source foods in improving diet quality and function in children

in the developing world. Nutrition Research 22 (1-2): 193–220.

https://doi.org/10.1016/S0271-5317(01)00374-8

Neumann, Charlotte G., Nimrod O. Bwibo, Suzanne P. Murphy, Marian Sigman,

Shannon Whaley, Lindsay Allen, Donald Guthrie, Robert E. Weiss, and Montague

Demment (2003). Animal source foods improve dietary quality, micronutrient status,

growth and cognitive function in Kenyan school children: background, study design

and baseline findings. The Journal of Nutrition 133(11): 3941S–3949S.

https://doi.org/10.1093/jn/133.11.3941S

Neumann, Charlotte G., Suzanne P. Murphy, Constance A. Gewa, Monika

Grillenberger, and Nimrod O. Bwibo (2007). Meat supplementation improves growth,

cognitive, and behavioral outcomes in Kenyan children. The Journal of Nutrition

137(4): 1119–1123.

https://doi.org/10.1093/jn/137.4.1119

Perna, Laura W. (2006). Studying college access and choice: a proposed

conceptual model. In: Smart, J.C., editor, Higher Education: Handbook of Theory and

Research, vol. 21. Dordrecht: Springer.

https://doi.org/10.1007/1-4020-4512-3_3

Prado, Elizabeth L., and Kathryn G. Dewey (2014). Nutrition and brain

development in early life. Nutrition Reviews 72(4): 267–284.

https://doi.org/10.1111/nure.12102

17
 Puentes, Esteban, Fan Wang, Jere R. Behrman, Flavio Cunha, John Hoddinott,

John A. Maluccio, Linda S. Adair, Judith B. Borja, Reynaldo Martorell, and Aryeh D.

Stein (2016). Early life height and weight production functions with endogenous energy

and protein inputs. Economics and Human Biology 22: 65–81.

https://doi.org/10.1016/j.ehb.2016.03.002

Reilly, Triston, Jens Bangsbo, and A. Franks (2000). Anthropometric and

physiological predispositions for elite soccer. Journal of Sports Sciences 18(9): 669–

683.

https://doi.org/10.1080/02640410050120050

Roca, André, Paul R. Ford, and Daniel Memmert (2018). Creative decision

making and visual search behavior in skilled soccer players. PLoS ONE 13(7):

e0199381.

https://doi.org/10.1371/journal.pone.0199381

Rominger, Christian, Karl Koschutnig, Daniel Memmert, Ilona Papousek,

Corinna M. Perchtold-Stefan, Mathias Benedek, Andreas R. Schwerdtfeger, and

Andreas Fink (2021). Brain activation during the observation of real soccer game

situations predicts creative goal scoring. Social Cognitive and Affective Neuroscience

16(7): 707–715.

https://doi.org/10.1093/scan/nsab035

Rosales, Francisco J., J. Steven Reznick, and Steven H. Zeisel (2009).

Understanding the role of nutrition in the brain and behavioral development of toddlers

and preschool children: identifying and addressing methodological barriers. Nutritional

Neuroscience 12(5): 190–202.

https://doi.org/10.1179/147683009X423454

18
 Rossi, Martín A., and Christian A. Ruzzier (2018). Career choices and the

evolution of the college gender gap. The World Bank Economic Review 32(2): 307–

333.

https://doi.org/10.1093/wber/lhw038

Sakamoto, Shota, Haruki Takeuchi, Naoki Ihara, Bao Ligao, and Kazuhiro

Suzukawa (2018). Possible requirement of executive functions for high performance in

soccer. PLoS ONE 13(8): e0201871.

https://doi.org/10.1371/journal.pone.0201871

Scharfen, Hans‐Erik, and Daniel Memmert (2019). Measurement of cognitive

functions in experts and elite athletes: a meta‐analytic review. Applied Cognitive

Psychology 33(5): 843–860.

https://doi.org/10.1002/acp.3526

Scharfen, Hans‐Erik, and Daniel Memmert (2021). Fundamental relationships of

executive functions and physiological abilities with game intelligence, game time and

injuries in elite soccer players. Applied Cognitive Psychology 35(6): 1535–1546.

https://doi.org/10.1002/acp.3886

Sigman, Marian, Charlotte Neumann, Michael Baksh, Nimrod Bwibo, and Mary

Alice McDonald (1989a). Relationship between nutrition and development in Kenyan

toddlers. The Journal of Pediatrics 115(3): 357–364.

https://doi.org/10.1016/S0022-3476(89)80832-7

Sigman, Marian, Charlotte Neumann, Ake A. Jansen, and Nimrod Bwibo

(1989b). Cognitive abilities of Kenyan children in relation to nutrition, family

characteristics, and education. Child Development 60(6): 1463–1474.

https://doi.org/10.2307/1130935

19
 Sigman, Marian, Mary Alice McDonald, Charlotte Neumann, and Nimrod Bwibo

(1991). Prediction of cognitive competence in Kenyan children from toddler nutrition,

family characteristics and abilities. The Journal of Child Psychology and Psychiatry

32(2): 307–320.

https://doi.org/10.1111/j.1469-7610.1991.tb00309.x

The Economist (2018). What makes a country good at football?

https://www.economist.com/international/2018/06/09/what-makes-a-country-good-at-

football

Verburgh, Lot, Erik J. A. Scherder, Paul A.M. van Lange, and Jaap Oosterlaan

(2014). Executive functioning in highly talented soccer players. PLoS ONE 9(3):

e91254.

https://doi.org/10.1371/journal.pone.0091254

Vestberg, Torbjörn, Roland Gustafson, Liselotte Maurex, Martin Ingvar, and

Predrag Petrovic (2012). Executive functions predict the success of top-soccer players.

PLoS ONE 7(4): e34731.

https://doi.org/10.1371/journal.pone.0034731

Vestberg, Torbjörn, Gustaf Reinebo, Liselotte Maurex, Martin Ingvar, and

Predrag Petrovic (2017). Core executive functions are associated with success in young

elite soccer players. PLoS ONE 12(2): e0170845.

https://doi.org/10.1371/journal.pone.0170845

Vestberg, Torbjörn, Reyhaneh Jafari, Rita Almeida, Liselotte Maurex, Martin

Ingvar, and Predrag Petrovic (2020). Scientific Reports 10: 9852.

https://doi.org/10.1038/s41598-020-66180-w

Victora, Cesar G., Linda Adair, Caroline Fall, Pedro C. Hallal, Reynaldo

Martorell, Linda Richter, Harshpal Singh Sachdev, for the Maternal and Child

20
Undernutrition Study Group (2008). Maternal and child undernutrition: consequences

for adult health and human capital. The Lancet 371(9609): 340–357.

https://doi.org/10.1016/S0140-6736(07)61692-4

Voss, Michelle W., Arthur F. Kramer, Chandramallika Basak, Ruchika Shaurya

Prakash, and Brent Roberts (2010). Are expert athletes ‘expert’ in the cognitive

laboratory? A meta-analytic review of cognition and sport expertise. Applied Cognitive

Psychology 24: 812–826.

https://doi.org/10.1002/acp.1588

Wainwright, Patricia E., and John Colombo (2006). Nutrition and the

development of cognitive functions: interpretation of behavioral studies in animals and

human infants. The American Journal of Clinical Nutrition 84(5): 961–970.

https://doi.org/10.1093/ajcn/84.5.961

Walker, Susan P., Theodore D. Wachs, Julie Meeks Gardner, Betsy Lozoff, Gail

A. Wasserman, Ernesto Pollitt, Julie A. Carter, and the International Child

Development Steering Group (2007). Child development: risk factors for adverse

outcomes in developing countries. The Lancet 369(9556): 145–157.

https://doi.org/10.1016/S0140-6736(07)60076-2

Whaley, Shannnon, Marian Sigman, Charlotte Neumann, Nimrod Bwibo, Donald

Guthrie, Robert Weiss, Susan Alber, and Suzanne Murphy (2003). The impact of

dietary intervention on the cognitive development of Kenyan school children. The

Journal of Nutrition 133(11):3965S–3971S.

https://doi.org/10.1093/jn/133.11.3965S

Williams, A.M., and Tony Reilly (2000). Talent identification and development

in soccer. Journal of Sports Sciences 18(9): 657–667.

https://doi.org/10.1080/02640410050120041

21
 Woldemichael, Andinet, Daniel Kidane, and Abebe Shimeles (2022). Food

inflation and child health. The World Bank Economic Review, forthcoming.

https://doi.org/10.1093/wber/lhac009

Wright, Michael J., Daniel T. Bishop, Robin C. Jackson, and Bruce Abernethy

(2013). Brain regions concerned with the identification of deceptive soccer moves by

higher-skilled and lower-skilled players. Frontiers in Human Neuroscience 7: 851.

https://doi.org/10.3389/fnhum.2013.00851

22
 Table 1. Summary statistics
N Mean Standard Minimum Maximum
Deviation
Soccer Prowess 221 0.335 1.166 0.000 11.000
Meat Consumption 170 43.732 31.366 3.525 125.131
Protein Consumption 171 74.516 20.706 36.460 123.650
Population 217 34.948 129.650 0.044 1262.645
GNI per Capita 184 6.921 10.253 0.130 45.650
Interest in Soccer 198 0.059 0.036 0.006 0.266
Notes: Soccer Prowess is the number of players nominated to the Ballon d’Or in the
period 2016 to 2019. Meat Consumption is the per capita consumption of meat, in
kilograms, in 2000. Protein consumption is the per capita consumption of proteins (in
grams, per day) in 2000. Population is in millions. GNI per capita is in thousands of
US dollars. Interest in Soccer is the ratio between the number of people in the country
that are actively involved in soccer and the country’s population (in millions).

23
 Table 2. Meat consumption and soccer prowess
Dependent variable: Soccer Prowess
(1) (2) (3) (4)

Meat Consumption 0.0126*** 0.0104** 0.0099** 0.0097**

(0.0042) (0.0044) (0.0046) (0.0047)
Population 0.0004 0.0003 0.0004 0.0006
(0.0005) (0.0004) (0.0004) (0.0005)
GNI per Capita 0.0134 0.0124 0.0055
(0.0144) (0.0144) (0.0130)
Interest in Soccer 1.9593 0.6179
(2.0420) (2.4209)

R-Squared 0.115 0.130 0.133 0.181

Continent fixed effects No No No Yes
Observations 170 161 160 160
Notes: Robust standard errors are in parentheses. All regressions are estimated using
Ordinary Least Squares and include a constant. **Significant at the 5% level.
***Significant at the 1% level.

24
 Table 3. Robustness check: protein consumption and soccer prowess
Dependent variable: Soccer Prowess
(1) (2) (3) (4)

Protein Consumption 0.0195*** 0.0164** 0.0162** 0.0114*

(0.0070) (0.0070) (0.0070) (0.0068)
Population 0.0002 0.0002 0.0003 0.0005
(0.0004) (0.0004) (0.0004) (0.0004)
GNI per Capita 0.0129 0.0101 0.0107
(0.0125) (0.0124) (0.0123)
Interest in Soccer 2.8705 0.4283
(2.0021) (2.5541)

R-Squared 0.121 0.133 0.140 0.174

Continent fixed effects No No No Yes
Observations 170 161 160 160
Notes: Robust standard errors are in parentheses. All regressions are estimated using
Ordinary Least Squares and include a constant. *Significant at the 10% level.
**Significant at the 5% level. ***Significant at the 1% level.

25
 Table 4. Players’ socioeconomic status and meat consumption
Consumption of meat
High Middle Low
Socioeconomic High &
status Middle High 15,25% 1,69% 0,00% 16,95%
Middle 22,03% 5,08% 0,00% 27,12%
Middle low
& Low 47,46% 5,08% 3,39% 55,93%
84,75% 11,86% 3,39% 100,00%
Note: Figures in each cell correspond to the percentage of players nominated to the
Ballon d’Or in the specific combination of SES and meat consumption.

26