What is labor economics?

1. What is labor economics?

2. Wiki’s definition • Labor economics seeks to understand the functioning and dynamics of the markets for labor. Labor markets function through the interaction of workers and employers. Labor economics looks at the suppliers of labor services (workers), the demands of labor services (employers), and attempts to understand the resulting pattern of wages, employment, and income.

3. Approaches Approaches: • Macro vs micro • Theoretical vs empirical • Positive vs normative analyses Issues: • Traditional fields regarding labor supply, demand, and equilibrium. • Newly developed fields relating to health, demographics, education, and public policies.

4. Labor economists in Taiwan • NTU(Jin-Tan Liu, Ming-ChingLuoh, Min-Jen Lin, etc.) • Academia Sinica (Kamhon Ken, Stacey Chen) • NCNU (Yen-Chien Chen) • NCU (Meng-Wen Tsou) There are also not-so-labor applied economists: • NTU(Chun-Fang Jiang, Political Economics) • NCCU(Ming Lien, Health Economics)

5. Theoretical and Empirical • Economic theory helps tell us what happens if a LM parameter (e.g., a policy) changes • To TEST a model, or to tell HOW LARGE a predicted effect is, we have to look to the data • Big Problem: we are not physicists

6. An Example: Earnings and Schooling (graph)

7. Conclusion and challenge: • Conclusion from the graph: schooling seems to have positive effect on earnings. • Challenge: need to control for other factors that may affect schooling and earnings simultaneously, e.g., family background, individual ability, etc.

8. Association versus Causation • Correlation does not imply causation!!! • 3 Possibilities explaining an association 1) Si Yi (causation) 2) Yi Si (reverse causation) 3) Zi Si & Zi Yi (other factors affect both S,Y)

9. Example of random assignment in economics • Marianne Bertrand and Sendhil Mullainathan send out thousands of resumes to different manual and service sector job openings (advertised in Boston and Chicago Newspapers) • They randomize the names of the applicants: e.g. Lakisha versus Emily and Jamal versus Greg

10. What Bertrand and Mullainathan Did:

11. Individuals given white sounding names about twice as likely as getting callbacks!

12. What happens when researchers ignore omitted variables bias? • Under the freedom of information act, the Toronto Star requests Toronto Police data on incidents and whether they led to arrests, and simple demographic information • On March 18, 2003, Star reports that, relative to population size, blacks more likely arrested than whites • Huge uproar, and accusations that results imply racial profiling

13. What Star did in the report:

14. Any Possibility of Omitted Variables Bias? Can we think of other variables that are related both to being a visible minority and being arrested? e.g: on average, lower income, disadvantaged family background Note: Not saying here that racial profiling does not exist, rather that this study is not able to tell

15. We will have to deal with omitted variable bias often in this course • Always be skeptical about research quality (note ‘publication’ bias) • Always question what are potential omitted variables • Directly addressing skepticism with careful research design and analysis makes results so much more convincing

16. Table 1A.1 Average-Wage and Quit-Rate Data for a Set of 10 Hypothetical Firms in a Single Labor Market in 1993.

17. Figure 1A.1 Estimated Relationship between Wages and Quit Rates Using Data from Table 1A.1

18. Figure 1A.2 True Relationships between Wages and Quit Rates (Equation 1A.5)

19. Table 1A.2 Hypothetical Average-Wage and Quit-Rate Data for Three Firms That Employed Older Workers and Three That Employed Younger Workers

20. Figure 1A.3 Estimated Relationships between Wages and Quit Rates Using Data from Table 1A.2

21. OVB formula Consider a model: where Omitted variable bias formula: it says: short equals long plus the effect of omitted times the regression of omitted on included.

22. A sample presentation In you talk, a few components are required: • A well-suited title • Motivations (why the topic is interesting) • One or more clearly defined questions to raised • Answers to the questions • Arguments (theory and evidence) • Conclusions • Your own comments or evaluation

23. A sample presentation A well-suited topic: A discussion on:

24. A sample presentation Motivations of the paper: • Sustainability is a major concern for many countries, including Taiwan. • The 2005 reform was a significant one.

25. A sample presentation Questions: • Does the “equalizing differences theory” predicts the effect of the 2005 reform? (wage and pension contribution are perfect substitutes) • Did the newly introduced program benefit workers in the private sector? If so, how much? • Did the newly introduced program benefit workers in the public sector? If so, how much?

26. A sample presentation Methodology: • Difference-in-difference method: estimate the change in (log) wage for private workers after the policy implementation, relative to the change for the public workers.

27. A sample presentation Results:

28. A sample presentation Conclusions: • The prediction of the “equalizing differences theory” largely holds: Average wage for the private workers dropped by around 5.92%, amazingly close to the contribution rate, 7%, born by the employers. • There was no parallel decline in wage for the public workers.

29. A sample presentation Comments: • Public workers might not be a ideal control group. • DinD method cannot rule out selection bias.