Related Experiment Video
Updated: Jun 21, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
The nonlinear link between height and wages in Germany, 1985-2004
1Leibniz Universität Hannover, Institut für Empirische Wirtschaftsforschung, Königsworther Platz 1, 30167 Hannover, Germany. huebler@ewifo.uni-hannover.de
Economics and Human Biology
|July 21, 2009
Summary
Height impacts wages differently for men and women, with shorter women and average-to-tall men seeing advantages. Discrimination plays a role, especially for tall men in the private sector.
Area of Science:
- Economics
- Sociology
- Labor Market Studies
Background:
- Previous research on height and wages primarily focused on linear relationships.
- Understanding non-linear effects of anthropometric traits on economic outcomes is crucial.
Purpose of the Study:
- To investigate the curvilinear relationship between height and wages across genders.
- To analyze the role of sector (public vs. private) and gender in wage determination.
- To decompose wage differences into endowment and unexplained (discrimination) components.
Main Methods:
- Utilized data from the German Socio-Economic Panel.
- Employed Blinder's decomposition to analyze wage differentials.
- Examined linear and non-linear height effects on wages.
Main Results:
- Height has a curvilinear effect on wages, more pronounced in men.
- Shorter women and men of average to above-average height (not tallest) experience wage premiums.
- Wage discrimination is sector- and gender-dependent, with short and very tall men in the private sector most disadvantaged.
Conclusions:
- Height is a significant, non-linear predictor of wages.
- Labor market outcomes related to height differ by gender and sector.
- Evidence suggests height-based discrimination, particularly for certain male subgroups in the private sector.
Related Concept Videos
01:17Two-Way ANOVA
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
01:29Application of Nonlinear Inequalities
A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality: can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the key values are 3...
01:18Polygenic Traits
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
01:12Coefficient of Correlation
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
01:10Nature and Nurture
Many human characteristics, like height, are shaped by both nature—in other words, by our genes—and by nurture, or our environment. For example, chronic stress during childhood inhibits the production of growth hormones and consequently reduces bone growth and height. Scientists estimate that 70-90% of variation in height is due to genetic differences among individuals, and 10-30% of variation in height is due to differences in the environments that individuals experience, such as differences...
01:22Nonlinear Pharmacokinetics: Causes of Nonlinearity
Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...