notes: Standard error in parenthesis; .01 - ***; .05 - **; .1 - *; Dependent variable: 1=Severely overpaid, 2= Somewhat overpaid, 3= Fair, 4=Somewhat underpaid, 5=Severely underpaid; Controls for survey round not shown. Base category for gender is female, Base category for age is 25, Base category for pay is the lowest pay. Columns 1-3 report results of ordinary least squares regression, while columns 4-6 report results for ordered logit regression. Blue collar occupations include store clerk, farm worker, receptionist, and taxi driver. White collar occupations include software engineer, manager, medical doctor, and schoolteacher.

Nonetheless, there were systematic differences with respect to blue- and white-collar occupations and the randomized age and wage rate of the subject of the vignette. The results reported in table 6 show that the gradient of responses vary by age-cohorts in the vignettes. Comparing the coefficients for the age assignment between columns 1-3 and 4-6 reveals striking differences. The age of vignette subjects in bluecollar occupations are uncorrelated with the perceived appropriateness of the wages they are paid. While in white-collar jobs, there is a strong tendency to say that the vignette subject was underpaid when randomly assigned higher ages (columns 4-6). This pattern suggests that wage increases (and correspondingly perceptions of being underpaid) are much flatter in blue-collar compared to white-collar occupations. This assessment of experience returns in the labor market for occupations that have a career promotion pathway vs. those that tend to have flat income expectations over time tracks real world patterns.

**Table 8: Within blue- and white-collar occupations**

|||**Across all co**|**untries, by blu**|**e/white collar**|**occupations**||
|---|---|---|---|---|---|---|
|||_Blue collar_|||_White collar_||
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**_Random assignment sex_**|||||||
|Male|0.098***|0.098***|0.093***|0.090***|0.090***|0.095***|
||(0.010)|(0.010)|(0.012)|(0.009)|(0.009)|(0.012)|
|**_Random assignment age_**|||||||
|Age 35|0.005|0.005|-0.009|0.070***|0.070***|0.058***|
||(0.014)|(0.014)|(0.017)|(0.013)|(0.013)|(0.016)|
|Age 45|0.014|0.014|0.017|0.099***|0.099***|0.098***|
||(0.014)|(0.014)|(0.017)|(0.013)|(0.013)|(0.016)|
|Age 55|0.008|0.008|0.001|0.096***|0.096***|0.097***|
||(0.014)|(0.014)|(0.018)|(0.013)|(0.013)|(0.016)|
|**_Random assignment pay_**|||||||
|Pay Group 2|-0.571***|-0.570***|-0.571***|-0.557***|-0.557***|-0.565***|
||(0.016)|(0.016)|(0.020)|(0.016)|(0.016)|(0.020)|
|Pay Group 3|-0.937***|-0.936***|-0.948***|-1.024***|-1.024***|-1.046***|
||(0.017)|(0.017)|(0.021)|(0.016)|(0.016)|(0.020)|
|Pay Group 4|-1.181***|-1.181***|-1.201***|-1.322***|-1.322***|-1.354***|
||(0.017)|(0.017)|(0.022)|(0.016)|(0.016)|(0.020)|
|Pay Group 5|<br>-1.387***|<br>-1.387***|<br>-1.417***|<br>-1.547***|<br>-1.547***|<br>-1.586***|
||(0.019)|(0.019)|(0.023)|(0.017)|(0.017)|(0.022)|
|Female Respondent||-0.007|||0.010||
|||(0.015)|||(0.015)||
|Constant|4.131***|4.135***|4.141***|4.218***|4.211***|4.252***|
||(0.022)|(0.024)|(0.027)|(0.021)|(0.023)|(0.025)|
|Sample|All|All|Female|All|All|Female|
|Observations|35,112|35,112|22,980|35,112|35,112|22,980|

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