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Introduction to Labor Economics. Chapter 1. Labor Economics. Goals: Why did female LFP increase in the 1900s? How does immigration affect wages, labor supply, opportunities, etc of native workers? How do minimum wages affect the unemployment rate?

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Introduction to Labor Economics

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Introduction to labor economics l.jpg

Introduction to Labor Economics

Chapter 1


Labor economics l.jpg

Labor Economics

  • Goals:

    • Why did female LFP increase in the 1900s?

    • How does immigration affect wages, labor supply, opportunities, etc of native workers?

    • How do minimum wages affect the unemployment rate?

    • Do wage and tax subsidies affect the demand for labor?

    • What impact do occupational and health regulations have on hiring, wages, etc.?


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Labor Economics, cont.

  • Do human capital investment subsidies improve the well-being of disadvantaged workers?

  • Why does wage inequality exist?

  • What impact does affirmative action have on earnings, the number of minorities a firm hires, etc?

  • How do unions affect labor markets?

  • How do unemployment benefits affect the incidence of and length of spells?


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Labor Market Participants


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Goal:

Determine:

Whether or not to work

How many hours to work

Which skills to acquire

Whether or not to quit a job

Which occupation to work in

Whether to join a union

How much effort to put forth at work

Labor Market Participants: Workers


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Goal:

Determine:

How many workers to employ

Which workers to hire

How much to pay each worker

Whether to hire additional workers

Whether to fire workers

How much capital to employ

Working conditions

Length of the workweek

Labor Market Participants: Firms


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Labor Market Participants: The Government

  • Regulations determine ground rules in the labor market

    • Taxes on earnings

    • Training subsidies

    • Payroll taxes

    • Affirmative action laws

    • Minimum wages

    • Worker condition regulations

    • Immigration laws


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Appendix: Econometrics

  • Regression analysis:

  • y =

  • x =

  • m =

interpretation:


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Regression Example: GPA

  • Hours = number of hours spent studying

  • Regression analysis attempts to fit the data with a line by minimizing the sum of squared errors


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Regression Analysis, cont.

  • Measuring the quality of the estimated coefficients:

    • Standard Error:

      • Margin of error = 2 x SE

      • Needed because of errors in the data, etc.

    • T-statistic =

      • Measures significance of the independent variable in explaining variation in the dependent variable

      • Coefficient is “significantly” different from zero if t-stat > 1.96


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Multiple Regression

  • GPA is influenced by more than the number of hours someone studies

  • Interpretation of each coefficient:

  • Can only “hold constant” variables included in the regression


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