Beeronometrics: Identifying the effect of concentration level in market efficiency

Professor: Cristoph Hanck
Student: Camilo Santa Cruz
E-mail: camilo.santacruz@tu-dortmund.de
Matrikelnummer: 235633
Msc. Econometrics
Ökonometrisches Seminar
June - Sommer Semester 2023

Abstract

Competition in markets has historically been recognized by economic literature as a crucial factor affecting various aspects of market performance, including product quality, levels of innovation, efficiency, and overall economic welfare. Given this, this study aims to determine whether increased competition in beer markets translates into higher efficiency in the market. Using pooled multiple regression analysis (POLS), as well as Fixed (FE) and Random Effects (RE) in a panel of 96 countries between 2011 and 2020, it is found evidence that lower levels of market concentration lead to higher efficiency in beer market. This evidence comes to support the proposition that more competition leads to a greater quantity produced and higher traded value and therefore greater consumer welfare, as suggested by industrial economics and competition policy theory. In a complementary way, the following research provides a methodological input that helps to demonstrate the effects that competition has on consumption levels in the markets. The study concludes with the main empirical and theoretical inferences drawn, as well as the unknowns and lines of the future that need to be addressed in future research.

Keywords

Beer Economics, Industrial Organization, Competition Policy, Monopoly, Panel Data, Microeconometrics, Consumer Welfare, Market Structure, Economics of Scale, Agriculture Economics, Market Efficiency

Preliminary findings

POLS

	Dependent variable:
	LQ_Capita
	(1)	(2)	(3)
LIHHA1	-1.563***	-0.435**	-0.258**
	(0.224)	(0.171)	(0.114)
LPrice		-0.482***	-0.484***
		(0.065)	(0.064)
LIncome_Capita		0.796***	0.804***
		(0.047)	(0.047)
log(Others)	-0.180***	-0.078
	(0.062)	(0.048)
Constant	16.698***	8.423***	7.505***
	(0.977)	(0.852)	(0.578)
Observations	951	951	959
R2	0.079	0.372	0.368
Adjusted R2	0.077	0.369	0.366
F Statistic	40.424*** (df = 2; 948)	139.977*** (df = 4; 946)	185.115*** (df = 3; 955)
Note:	p<0.1; p<0.05; p<0.01

Consumption = f (Competition)

	Dependent variable:
	LQ_Capita
	(1)	(2)	(3)	(4)	(5)
LIHHA1	-1.563***	-0.230***	-0.249***	-0.125	-1.684***
	(0.177)	(0.066)	(0.066)	(0.085)	(0.593)
log(Others)	-0.180***	-0.018	-0.019*		-0.221
	(0.052)	(0.011)	(0.011)		(0.180)
Others				-0.001
				(0.002)
Constant	16.698***		11.109***	-0.001	17.262***
	(0.775)		(0.307)	(0.003)	(2.607)
Observations	951	951	951	855	96
R2	0.079	0.014	0.017	0.004	0.083
Adjusted R2	0.077	-0.098	0.015	0.002	0.063
F Statistic	40.424*** (df = 2; 948)	6.157*** (df = 2; 853)	14.562***	1.730 (df = 2; 852)	4.216** (df = 2; 93)
Note:	p<0.1; p<0.05; p<0.01

FE and RE

	Dependent variable:
	LQ_Capita
	(1)	(2)	(3)	(4)
LIHHA1	-0.150	-0.116	-0.157**	-0.103
	(0.093)	(0.093)	(0.071)	(0.071)
LPrice	-0.350***	-0.260***	-0.355***	-0.255***
	(0.037)	(0.037)	(0.036)	(0.036)
LIncome_Capita	0.562***	0.409***	0.593***	0.366***
	(0.060)	(0.060)	(0.055)	(0.055)
log(Others)	-0.021*	-0.002	-0.022*	-0.001
	(0.012)	(0.012)	(0.012)	(0.012)
LLitres_Wine		0.315		0.315***
				(0.039)
Constant			8.070***	4.567***
			(0.711)	(0.711)
Observations	951	931	951	931
R2	0.285	0.359	0.292	0.388
Adjusted R2	0.202	0.284	0.289	0.384
F Statistic	84.878*** (df = 4; 851)	93.387*** (df = 5; 832)	388.217***	578.003***
Note:	p<0.1; p<0.05; p<0.01

Residuals in RE models

Residuals in RE models

Residuals in FE models

Residuals in FE models

Economics of scale: Relation of Production and Costs and Monopoly vs Competition

Demand and supply shifts

Heat map of Consumption of beer per inhabitant in 2019 (in liters)

Heat map of Price per liter in 2019

Heat map of Market concentration

Scatter plot