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Related Concept Videos

Ranks01:02

Ranks

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Spearman's Rank Correlation Test01:20

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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The Mantel-Cox Log-Rank Test01:19

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The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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A Rapid Food-Preference Assay in Drosophila
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RANKING FIRMS USING REVEALED PREFERENCE.

Isaac Sorkin1

  • 1Stanford University and NBER.

The Quarterly Journal of Economics
|August 7, 2018
PubMed
Summary

Workers

Area of Science:

  • Labor Economics
  • Econometrics
  • Sociology of Work

Background:

  • Understanding worker preferences is key to analyzing labor markets.
  • Employer-to-employer transitions offer insights into firm rankings and worker mobility.
  • Previous research has explored wage determinants but often overlooks firm-specific preferences.

Purpose of the Study:

  • To estimate workers' preferences for specific firms using employer-to-employer transition data.
  • To quantify the role of compensating differentials in explaining worker mobility and earnings variation.
  • To develop a method for ranking firms based on worker flow data.

Main Methods:

  • Utilized U.S. administrative data on employer-to-employer transitions.
  • Applied numerical linear algebra techniques to measure the central tendency of worker flows.

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  • Analyzed patterns of worker movement to identify preferences beyond layoffs or recruitment intensity.

    • Main Results:

    • Identified a discernible ranking of firms based on worker transition patterns.
    • Found significant evidence of compensating differentials, where workers move to lower-paying firms.
    • Quantified that compensating differentials explain over half of the firm-specific variance in earnings.

    Conclusions:

    • Worker preferences for firms are measurable and influence labor market dynamics.
    • Compensating differentials play a substantial role in explaining earnings inequality across firms.
    • The methodology provides a novel approach to ranking firms based on worker choices.