1. Labor Income Profiles Sang-Hyop Lee November 5, 2007 Prepared for NTA 5 th Workshop SKKU, Seoul, Korea

2. Outline of Panel Discussion I.Development of New Methodology (self-employment income) II.Analysis 1.Cross-Section Comparison 2.Time-Series Analysis (X) III. More In-Depth Analysis (from Ogawa) (X) IV. Remittances (from Salas) V. Other Issues (smoothing, etc)

3. Issues in estimating self-employment income –Labor markets in low-income countries (Rosenzweig 1988) Large proportion of agricultural sector Low proportion of wage earners and large proportion of family enterprises or unpaid family workers Empirical issues; especially estimating labor income for unpaid family workers I. Development of New Methodology (self-employment income)

4. Unpaid Family Workers Old Method –Don’t impute. –It may underestimate/overestimate the share of earnings for age x New Method –Estimate using the age profile of earnings of employees as a share to allocate household self-employed income to self- employed workers including unpaid family workers. Ex) A household (2/3 of household self-employed income = 30) AgeEarnings per employeeImputed 18 (unpaid)20010 44 (self emp.)40020

9. Philippines (2002) Indonesia (1996) Thailand (1996) Taiwan (2001) Imputed?YesNoYesNoYesNoYesNo Mean age45.246.743.044.341.042.141.441.6 Share of life time earnings Under 258.86.49.17.913.110.28.07.9 Over 659.911.65.67.73.43.72.22.4 Under 202.81.32.62.24.62.91.21.1

10. II. Analysis-Comparative Summary statistics for 18 economies Age earnings profile for 20 economies –Suggestions for outliers (explain, estimate another year, etc) Wages vs. self-employment income

11. Labor IncomeMeanPeakMedian25th75thInterqrtile % 65 Austria39.843382947184.814.69.80.4 Brazil42.846413349162.88.55.73.9 Chile44.944433353202.38.25.97.4 China41.241393049193.913.39.33.0 Costa Rica42.239403150192.210.27.93.4 France42.449413250181.37.26.00.7 India46.049443454202.67.65.08.2 Indonesia43.544413251193.410.16.7 Japan45.348443553181.16.04.93.5 Mexico46.941443456222.97.64.711.6 Philippines46.743443455211.47.15.610.5 S.Korea42.136403149182.39.97.53.3 Slovenia40.834393147161.37.15.80.8 Sweden45.449443454201.87.65.85.3 Taiwan42.141403249171.07.26.12.4 Thailand43.140413251192.28.76.44.1 Uruguay42.138403150192.69.97.22.4 US45.847443553181.14.93.95.3 Average43.542.941.532.451.118.72.38.66.44.6

12. Outliers?

13. Thick Flat Tails

14. Cliffhanging (at a certain old age)

15. Start late, exit late

16. Steep in early ages

17. The winner and the runner-up

18. Why do they differ? Mechanical decomposition 1.(Y/N) =(Y/E) * (E/N) 2.Per capita labor income = Earnings per employee * (effective) labor force participation rate 3.(Y/N) =w*(Y/N)employee+(1-w)(Y/N)self-employed Thus per capita labor income profile depends on –Share of self-employed in the economy –Composition: Labor force participation rates (LFPRs) by age (inverse U), working hours by age (inverse U), unemployment rate by age –Productivity: Age specific productivity (concave/inverse U) (health, technological change, OJT), selection effect (hazard rate may increase over time) –Institution (minimum wage, seniority-based wage system) Decisions made by three demographic groups (women, children, and elderly) are most important

27. Relationship with Macro Variables Level of development (per capita GDP) Share of sector (e.g. agricultural sector, service sector, etc) Enrollment of secondary schooling Old age dependency Pension / Tax enforcement (not done)

34. II-2. Time Series Analysis Has an advantage –Consistent data sets & definitions –Decomposition across years –Policy change analysis

37. Source: provided by Ron Lee

40. Decomposition of the Change in Per-Capita Labor Income, Chile, 1987-1997

41. Source: provided by Ron Lee

42. Summary The share of self-employed income is an important factor affecting profiles for developing countries. Decisions made by women, children, and elderly might be important in shaping the labor income profiles across countries and over time. These decisions may be somewhat related with the level of development, but there are other factors affecting the relationship.

43. III. More In-Depth Analysis IV. Remittances Age earnings profile also reflects a host of vital economic and social conditions. Regular vs. Non-regular or Part-time vs. Full-time distinction (share of full-time, regular workers decrease in Japan) Demand side or macro economic condition (lack of job opportunities) Women’s labor force participation Other sectoral allocation of the labor force Age profile of compensation from/to ROW may be also different from those of residents.

44. V. Other issues Smoothing –Use SUPSMU in the R statistical package. Smoothing spans are determined on an ad hoc basis. –Any ages with a profile value of zero are left out of the calculation and added to the series after smoothing. For example when a survey only covers ages 14 and above, all values below 14 were set identically to zero.

45. Remaining Issues Refining estimation Other analysis –How does labor income interact with private consumption and private transfer? –How policy matters? Public pension programs Education (e.g. mandatory schooling) –How does labor income profile differ by education/gender/place of residence/living arrangement?