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Minimum wages in monopsonistic labor markets

Luis F. Munguía Corella
Luis F. Munguía Corella
   | 12 nov 2020
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Pubblicato online: 12 nov 2020
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DOI: https://doi.org/10.2478/izajole-2020-0007
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© 2020 Luis F. Munguía Corella, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Creation of clusters of industries or labor markets.Notes: The red link indicates that the two industries have more relative flows than any other pair (top pair). Green links indicate a strong relationship (the top three pairs or more than 90th of relative flows between industries); the sum of red and green links defines the preferred classification. Yellow links are weak connections, and the sum of yellow, green, and red links defines the flexible method.
Creation of clusters of industries or labor markets.Notes: The red link indicates that the two industries have more relative flows than any other pair (top pair). Green links indicate a strong relationship (the top three pairs or more than 90th of relative flows between industries); the sum of red and green links defines the preferred classification. Yellow links are weak connections, and the sum of yellow, green, and red links defines the flexible method.

Figure 2

Evolution of the HHI in the United States: 2000–2016.Note: The HHI is estimated by averaging industries and counties by year (weighted by population).
Evolution of the HHI in the United States: 2000–2016.Note: The HHI is estimated by averaging industries and counties by year (weighted by population).

Figure 3

HHI in the United States across counties: 2000–2016.Notes: The HHI is estimated by averaging industries and year by county (weighted by population). I use the hybrid method for the estimation of the HHI.
HHI in the United States across counties: 2000–2016.Notes: The HHI is estimated by averaging industries and year by county (weighted by population). I use the hybrid method for the estimation of the HHI.

Figure 4

Effect of the minimum wage under monopsony by deciles.Notes: I calculate MW/average wage and split the estimation in deciles. The higher the decile, the more binding is the minimum wage. All the estimations are evaluated with HHI or mobility equal to 1. HHI = 1 indicates full concentration. Mobility = 1 implies that the worker remains in the same industry for all the periods.
Effect of the minimum wage under monopsony by deciles.Notes: I calculate MW/average wage and split the estimation in deciles. The higher the decile, the more binding is the minimum wage. All the estimations are evaluated with HHI or mobility equal to 1. HHI = 1 indicates full concentration. Mobility = 1 implies that the worker remains in the same industry for all the periods.

Figure A1

Effect of the minimum wage under monopsony by quintiles.Notes: I calculate MW/average wage and split the estimation in quintiles. The higher the quintile, the more binding the minimum wage is. All the estimations are evaluated with HHI or mobility equal to 1. HHI = 1 indicates full concentration. Mobility = 1 implies that the worker remains in the same industry for all the periods.
Effect of the minimum wage under monopsony by quintiles.Notes: I calculate MW/average wage and split the estimation in quintiles. The higher the quintile, the more binding the minimum wage is. All the estimations are evaluated with HHI or mobility equal to 1. HHI = 1 indicates full concentration. Mobility = 1 implies that the worker remains in the same industry for all the periods.

Average number of establishments by HHI

HHILow mobility
MeanMedianMeanMedian
Monopsony = 15.383.18581.35361.81
90th10.967.75847.61564.40
10th1,815.18611.202,135.22826.06
5th2,261.91708.591,689.81708.81

Effects of the log of the MW interacted with the HHI and low mobility on the log of teenage employment

(1)(2)
Dependent variable: Ln (teen employment)HHILow mobility
Monopsony variable (HHI or LM)−0.833

p < 0.05

 (0.324)		−0.915

p < 0.01

 (0.235)
Monopsony × Ln (MW)0.459

p < 0.05

 (0.180)		0.476

p < 0.01

 (0.124)
Elasticity of the MW depending on monopsony
Monopsony = 0−0.418

p < 0.01

 (0.112)		−0.183 (0.146)
Monopsony = 0.2−0.326

p < 0.01

 (0.0931)		−0.0876 (0.148)
Monopsony = 0.4−0.234

p < 0.01

 (0.0858)		0.00755 (0.155)
Monopsony = 0.6−0.142 (0.0930)0.103 (0.165)
Monopsony = 0.8−0.0507 (0.112)0.198 (0.179)
Monopsony = 10.0411 (0.138)0.293 (0.194)
Constant−0.193 (0.741)−1.786 (1.553)
Observations199,23118,126
R-squared0.9880.989

Effects of the log of the MW interacted with the HHI and low mobility on the log of teenage employment, allowing different effects of MW by industry

(1)(2)(3)(4)(5)
Dependent variable: Ln (teen employment)HHILow mobilityNAICSFlexibleTop pairs
Ln (MW)−0.233 (0.130)0.0907 (0.261)−0.160 (0.129)−0.190 (0.133)−0.142 (0.140)
HHI or low mobility−0.714

p < 0.01

 (0.120)		−0.119

p < 0.1

 (0.0691) −		0.585

p < 0.01

 (0.117)		−0.712

p < 0.01

 (0.126)		−0.335

p < 0.01

 (0.121)
HHI or low mobility × Ln (MW)0.389

p < 0.01

 (0.0615)		0.0685

p < 0.1

 (0.0357)		0.314

p < 0.01

 (0.0602)		0.365

p < 0.01

 (0.0625)		0.257

p < 0.01

 (0.0654)
Constant−0.496 (0.333)−7.681

p < 0.05

 (3.136)		0.384 (0.371)1.147

p < 0.01

 (0.350)		−1.400

p < 0.01

 (0.370)
Observations2,201,02118,0011,954,2521,921,1382,603,089
R-squared0.8180.9700.8310.8310.793

Effects of the log of the MW interacted with the HHI and low mobility on the log of prime age

(1)(2)(3)(4)(5)
Dependent variable: Ln (prime age employment)HHILow mobilityNAICSFlexibleTop pairs
Ln (MW)−0.0272 (0.0866)0.139 (0.113)0.0345 (0.0797) −0.00485 (0.0830)0.0400 (0.0992)
HHI0.149 (0.296)0.317 (0.286)0.199 (0.290)0.389 (0.322)
HHI × Ln (MW)0.0514 (0.151)−0.0572 (0.141)0.0136 (0.148)−0.0601 (0.160)
Low mobility0.452 (0.368)
Low mobility × Ln (MW)−0.220 (0.186)
Constant0.960 (0.672)2.732

p < 0.01

 (0.602)		0.897 (0.678)0.930 (0.683)0.828 (0.665)
Observations204,98418,130204,984204,984204,984
R-squared0.9990.9990.9990.9990.999

Percentage of the teenage employment by the significance of the minimum wage effects depending on the monopsony variable

Share of the total teenage employment (%)
Negative significant55.81
Negative44.06
Positive0.12
Positive significant0.00

Effects of the log of the MW interacted with the low mobility on the log of teenage employment (low mobility using workers who moved to other county)

(1)
Dependent variable: Ln (teen employment)Low mobility
Monopsony variable (HHI or LM)−0.968

p < 0.01

 (0.275)
Monopsony × Ln (MW)0.503

p < 0.01

 (0.145)
Elasticity of the MW depending on monopsony
Monopsony = 0−0.184 (0.158)
Monopsony = 0.2−0.0843 (0.161)
Monopsony = 0.40.0158 (0.169)
Monopsony = 0.60.116 (0.181)
Monopsony = 0.80.216 (0.197)
Monopsony = 10.316 (0.215)
Constant−1.691 (1.761)
Observations18,127
R-squared0.988

Effects of the log of the MW interacted with the HHI and low mobility on the log of teenage employment (HHI calculated only for teenage workers)

(1)(2)(3)(4)(5)
Dependent variable: Ln (teen employment)HybridLow mobilityNAICSAll nodesTop pairs
Ln (MW)−0.223

p < 0.05

 (0.0938)		−0.167 (0.138)−0.188

p < 0.1

 (0.0944)		−0.165

p < 0.1

 (0.0928)		−0.543

p < 0.01

 (0.124)
Monopsony variable (HHI or LM)−0.381

p < 0.01

 (0.138) −		0.859

p < 0.01

 (0.242)		−0.241

p < 0.05

 (0.106)		−0.349 (0.228)−1.173

p < 0.01

 (0.342)
Monopsony × Ln (MW)0.223

p < 0.01

 (0.0757)		0.447

p < 0.01

 (0.127)		0.126

p < 0.05

 (0.0558)		0.196 (0.122)0.631

p < 0.01

 (0.184)
Constant−0.494 (0.723)−1.806 (1.539)−0.416 (0.750)−0.577 (0.740)−0.0139 (0.727)
Observations195,20518,121195,205195,205199,123
R-squared0.9880.9890.9880.9880.988

Robustness check: effects of the log of the MW interacted with all the classifications of clusters for the HHI on the log of teenage employment

(1)(2)(3)
Dependent variable: Ln (teen employment)NAICSFlexibleTop pairs
Monopsony variable (HHI or LM)−0.972

p < 0.01

 (0.341)		−0.920

p < 0.01

 (0.336)		−0.998

p < 0.05

 (0.377)
Monopsony × Ln (MW)0.527

p < 0.01

 (0.189)		0.504

p < 0.05

 (0.191)		0.523

p < 0.05

 (0.204)
Elasticity of the MW depending on monopsony
Monopsony = 0−0.458

p < 0.01

 (0.133)		−0.438

p < 0.01

 (0.125)		−0.461

p < 0.01

 (0.145)
Monopsony = 0.2−0.353

p < 0.01

 (0.109)		−0.338

p < 0.01

 (0.102)		−0.357

p < 0.01

 (0.117)
Monopsony = 0.4−0.247

p < 0.01

 (0.0958)		−0.237

p < 0.01

 (0.0912)		−0.252

p < 0.05

 (0.0990)
Monopsony = 0.6−0.142 (0.0961)−0.136 (0.0952)−0.147 (0.0959)
Monopsony = 0.8−0.0365 (0.110)−0.0354 (0.113)−0.0427 (0.109)
Monopsony = 10.0690 (0.134)0.0653 (0.139)0.0619 (0.134)
Constant−0.119 (0.708)−0.140 (0.711)−0.0320 (0.717)
Observations199,231199,231199,231
R-squared0.9880.9880.988

Statistics of the HHI and low mobility by method of estimation

MeanMedianMinMaxSD
HHI0.5950.5750.0771.0000.070
Low mobility0.6080.6080.0001.0000.071
NAICS0.5870.5700.1691.0000.069
Flexible0.5780.5600.1251.0000.069
Top pairs0.6120.5980.3311.0000.060

Effects of the HHI and low mobility on the log of teenage wages

(1)(2)
Dependent variable: Ln (wage)HHILow mobility
HHI−0.0993

p < 0.01

 (0.0254)
Low mobility−0.127 (0.161)
Constant8.128

p < 0.01

 (0.716)		6.784

p < 0.01

 (1.391)
Observations199,16818,121
R-squared0.7180.888

Effects of the log of the MW interacted with the HHI and low mobility on the log of teenage employment, average of the HHI in different periods

(1)(2)
Dependent Variable: Ln (Emp)HHILow mobility
Panel A: Using the period average
Ln (MW)−0.501

p < 0.01

 (0.142)		−1.123

p < 0.01

 (0.192)
HHI or Mobility (average) × Ln (MW)0.575

p < 0.05

 (0.238)		1.905

p < 0.01

 (0.376)
Constant−0.846 (0.780)−2.918

p < 0.05

 (1.098)
Observations200,05226,657
R-squared0.9880.988
Panel B: Using the average from 2000 to 2001
Ln (MW)−0.306

p < 0.05

 (0.131)		−0.999

p < 0.01

 (0.294)
HHI or mobility (average) × Ln (MW)0.330 (0.200)1.711

p < 0.01

 (0.514)
Constant−0.967 (0.930)−2.906 (1.896)
Observations168,21914,036
R-squared0.9880.988

Effects of the log of the minimum wage on HHI and low mobility

(1)(2)(3)(4)(5)
Dependent variable: HHI or low mobilityHybridLow mobilityNAICSFlexibleTop pairs
Ln (MW)−0.00382 (0.00514)−0.00325 (0.0165)0.00301 (0.00510)−0.00729 (0.00636)−0.00347 (0.00307)
Constant1.083

p < 0.01

 (0.0914)		1.174

p < 0.01

 (0.419)		1.123

p < 0.01

 (0.0666)		1.090

p < 0.01

 (0.125)		1.082

p < 0.01

 (0.0681)
Observations199,42118,126199,421199,421199,421
R-squared0.9090.3650.9740.8560.981
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