Inter-industry wage inequality: persistent differences and turbulent equalisation

2020 ◽  
Vol 44 (4) ◽  
pp. 919-942
Author(s):  
Patrick Mokre ◽  
Miriam Rehm
Keyword(s):  
Economic Theory ◽  
Bayesian Inference ◽  
Wage Inequality ◽  
Fixed Effects ◽  
Wage Differentials ◽  
Wage Determination ◽  
Inference Model ◽  
Hierarchical Bayesian Inference ◽  
Industrial Wage ◽  
Profit Rates

Abstract The empirical stylised fact of persistent inter-industry wage differentials is an enduring challenge to economic theory. This paper applies the classical theory of ‘real competition’ to the turbulent dynamics of these inter-industrial wage differentials. Theoretically, we argue that competitive wage determination can be decomposed into equalising, dispersing and turbulently equalising factors. Empirically, we show graphically and econometrically for 31 US industries in 1987–2016 that wage differentials, like regulating profit rates, are governed by turbulent equalisation. Furthermore, we apply a fixed-effects OLS as well as a hierarchical Bayesian inference model and find that the link between regulating profit rates and wage differentials is positive, significant and robust.

scholarly journals Leave‐Out Estimation of Variance Components

Econometrica ◽  
2020 ◽  
Vol 88 (5) ◽  
pp. 1859-1898 ◽  
Author(s):  
Patrick Kline ◽  
Raffaele Saggio ◽  
Mikkel Sølvsten
Keyword(s):  
Wage Inequality ◽  
Quadratic Forms ◽  
Fixed Effects ◽  
Linear Models ◽  
Large Data ◽  
Wage Determination ◽  
Fixed Effects Model ◽  
Relative Contribution ◽  
Estimation Of Variance ◽  
Linear Restrictions

We propose leave‐out estimators of quadratic forms designed for the study of linear models with unrestricted heteroscedasticity. Applications include analysis of variance and tests of linear restrictions in models with many regressors. An approximation algorithm is provided that enables accurate computation of the estimator in very large data sets. We study the large sample properties of our estimator allowing the number of regressors to grow in proportion to the number of observations. Consistency is established in a variety of settings where plug‐in methods and estimators predicated on homoscedasticity exhibit first‐order biases. For quadratic forms of increasing rank, the limiting distribution can be represented by a linear combination of normal and non‐central χ 2 random variables, with normality ensuing under strong identification. Standard error estimators are proposed that enable tests of linear restrictions and the construction of uniformly valid confidence intervals for quadratic forms of interest. We find in Italian social security records that leave‐out estimates of a variance decomposition in a two‐way fixed effects model of wage determination yield substantially different conclusions regarding the relative contribution of workers, firms, and worker‐firm sorting to wage inequality than conventional methods. Monte Carlo exercises corroborate the accuracy of our asymptotic approximations, with clear evidence of non‐normality emerging when worker mobility between blocks of firms is limited.

scholarly journals Bayesian inference of genetic parameters for reproductive and performance traits in White Leghorn hens

2018 ◽  
Vol 63 (No. 6) ◽  
pp. 230-236 ◽  
Author(s):  
J.O. Rosa ◽  
G.C. Venturini ◽  
T.C.S. Chud ◽  
B.C. Pires ◽  
M.E. Buzanskas ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Fixed Effects ◽  
Egg Production ◽  
Genetic Parameters ◽  
Laying Hens ◽  
Reproductive Traits ◽  
A Posteriori ◽  
White Leghorn ◽  
Performance Traits ◽  
And Performance

This study estimated the genetic parameters for reproductive and performance traits and determined which ones can be used as selection criteria for egg production in laying hens using the Bayesian inference. The data of 1894 animals from three generations of White Leghorn laying hens were analyzed for fertility (FERT), hatchability (HATC), and birth rate measurements at 60 weeks of age (BIRTH), body weight at 16 and 60 weeks of age (BW16 and BW60), age at sexual maturity (ASM), egg height/width ratio, weight, and density at 28, 36, and 40 weeks of age (RHW28, RHW36, RHW40, WEGG28, WEGG36, WEGG40, DENS28, DENS36, and DENS40, respectively) traits. The genetic parameters were estimated by the Bayesian inference method of multi-trait animal model. The model included the additive and residual genetic random effects and the fixed effects of generation. The a posteriori mean distributions of the heritability estimates for reproductive traits ranged from 0.14 ± 0.003 (HATC) to 0.22 ± 0.005 (FERT) and performance from 0.07 ± 0.001 (RHW28) to 0.42 ± 0.001 (WEGG40). The a posteriori mean distributions of the genetic correlation between reproductive traits ranged from 0.18 ± 0.026 (FERT and HACT) to 0.79 ± 0.007 (FERT and BIRTH) and those related to performance ranged from –0.49 ± 0.001 (WEGG36 and DENS36) to 0.75 ± 0.003 (DENS28 and DENS36). Reproductive and performance traits showed enough additive genetic variability to respond to selection, except for RHW28. This trait alone would have little impact on the genetic gain because environmental factors would have a higher impact compared to those from the additive genetic factors. Based on the results of this study, the selection applied on the BIRTH trait can be indicated to improve FERT and HATC of eggs. Furthermore, the use of the WEGG40 could improve egg quality in this population.

Wage-differentials and Their Determinants Across Industries in the Organized Manufacturing Sector of India

2020 ◽  
pp. 097215092091256
Author(s):  
Chandrima Ganguly ◽  
Joydeb Sasmal
Keyword(s):  
Wage Differentials ◽  
Manufacturing Sector ◽  
Wage Bargaining ◽  
Wage Determination ◽  
Farm Size ◽  
Total Employment ◽  
Annual Survey ◽  
Productivity Differentials ◽  
Specific Factors ◽  
Using Data

This article calculates the magnitude of wage differentials across industries in the organized manufacturing sector of India and identifies the major determinants of wage differentiation among the industries. Using data from Annual Survey of Industries in India for the period from 2000–2001 to 2015–2016, this study shows that mean wage is less in labour-intensive industries compared to the capital-intensive industries. The results of panel regression of annual average wage on various industry-specific factors show that productivity of labour is the most important factor in wage determination, and productivity largely depends on capital–labour ratio. The other significant factors in this regard are farm size, amount of profit and proportion of casual and female workers in total employment. Important policy implication of this study is that regulatory wage fixation and wage bargaining outcomes are not as significant as productivity differentials in explaining wage gaps across industries.

Sign in / Sign up

Export Citation Format

Share Document