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This salary is divided by the number of pay periods in the year, as set by your company, to determine the salary for each pay period. If salaried employees are paid monthly, this employee would receive $1666.67 a month ($20,000 divided by 12). Example: An hourly employee is paid $9.62 an hour.
The interpretation of using the logarithm of wages is that you are looking at wage gaps of a particular percentage, rather than of a particular dollar value. Previous empirical work in this area suggests that wage equations are most appropriately specified in logarithms.
0:08 6:37 Suggested clip Log-Level Regression & Interpretation (What do the Regression YouTubeStart of suggested client of suggested clip Log-Level Regression & Interpretation (What do the Regression
A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease.
When they are positively skewed (long right tail) taking logs can sometimes help. Sometimes logs are taken of the dependent variable, sometimes of one or more independent variables. Substantively, sometimes the meaning of a change in a variable is more multiplicative than additive. For example, income.
There are two main reasons to use logarithmic scales in charts and graphs. The first is to respond to skewness towards large values. i.e., cases in which one or a few points are much larger than the bulk of the data. The second is to show percent change or multiplicative factors.
The interpretation of using the logarithm of wages is that you are looking at wage gaps of a particular percentage, rather than of a particular dollar value. Previous empirical work in this area suggests that wage equations are most appropriately specified in logarithms.
Your variable has a right skew (mean > median). Taking the log would make the distribution of your transformed variable appear more symmetric (more normal). However, if you have outliers in your dependent or independent variables, a log transformation could reduce the influence of those observations.
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