It appears that, in a particul

It appears that, in a particular dataset, women’s average wageis closer to the average wage of men at higher levels of education.So the gap between the average wage of men and the average age ofwomen decreases as education increases. But the average wages don’tconverge because the return to education (that is, the partialeffect of education on wages) for men is constant but there is adiminishing marginal return to education (that is, the partialeffect of education on wages gets smaller as education increases)for women. How would you test this hypothesis using a singleregression? (Hint: Start by drawing a graph to illustrate thehypothesized relationship between wages and education for men andwomen.)

Answer:

The gender pay gap in the United States is theratio of female-to-male median or average (depending on the source)yearly earnings among full-time, year-round workers.

The average woman’s unadjusted annual salary has been cited as78% to 82% of that of the average man’s. However, after adjustingfor choices made by male and female workers in college major,occupation, working hours, and parental leave, multiple studiesfind that pay rates between males and females varied by 5–6.6% or,females earning 94 cents to every dollar earned by their malecounterparts. The remaining 6% of the gap has been speculated tooriginate from gender discrimination and a difference in abilityand/or willingness to negotiate salaries.

The extent to which discrimination plays a role in explaininggender wage disparities is somewhat difficult to quantify, due to anumber of potentially confounding variables. A 2010 research reviewby the majority staff of the United States Congress Joint EconomicCommittee reported that studies have consistently found unexplainedpay differences even after controlling for measurable factors thatare assumed to influence earnings – suggestive ofunknown/unmeasurable contributing factors of which genderdiscrimination may be one. Other studies have found direct evidenceof discrimination – for example, more jobs went to women when theapplicant’s sex was unknown during the hiring process.


 
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