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Thus, I also examine the effects on employment and the likelihood of having a valid wage.
Thus, to examine this issue more directly, Table 8 reports the effect on the likelihood of having a valid wage.
As shown later, the unemployment rate at entry does not have a statistically significant negative effect on the likelihood of having a valid wage.
If the likelihood of having a valid wage were affected by the unemployment rate at entry, it would cause a sample selection bias.
The MSAs/PMSAs beginning in columns (3 -(4) are the MSAs and PMSAs that constitute the 39 CMSAs that we can track for the entire sample period and that meet the data sufficiency requirement (50 valid wage observations per MSA/PMSA and month).
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I find that 2.86% of the observations with valid wages and within 12 years since entry are enrolled in college.
The proportion with missing wages due to non-employment varies across gender and race: women and blacks are less likely to have valid wages.
This leads me to believe that, even if there are biases from non-random selection of those lacking valid wages, they will not alter the qualitative conclusions.
Table 2 Number of observations with and without valid wages (% of total number of observations in the corresponding race-sex-potential experience category) Exp.
Including only population with valid wages or restricting the age to only population between 20 and 34 years old do not affect the main results.
Table 2 shows the number of observations with valid (positive) hourly wages, missing wages due to non-employment, and missing wages for other reasons for each race-gender-potential experience cell.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com