Labor Income Profiles

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Labor Income Profiles

• Sang-Hyop Lee
• November 5, 2007
• Prepared for NTA 5th Workshop
• SKKU, Seoul, Korea

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Outline of Panel Discussion

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

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I. Development of New Methodology (self-employmen
t income)

• 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

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Unpaid Family Workers

• Old Method
• Dont 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)

Age Earnings per employee Imputed
18 (unpaid) 200 10
44 (self emp.) 400 20
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Philippines (2002) Philippines (2002) Indonesia (1996) Indonesia (1996) Thailand (1996) Thailand (1996) Taiwan (2001) Taiwan (2001)
Imputed? Yes No Yes No Yes No Yes No
Mean age 45.2 46.7 43.0 44.3 41.0 42.1 41.4 41.6
Share of life time earnings Under 25 8.8 6.4 9.1 7.9 13.1 10.2 8.0 7.9
Over 65 9.9 11.6 5.6 7.7 3.4 3.7 2.2 2.4
Under 20 2.8 1.3 2.6 2.2 4.6 2.9 1.2 1.1
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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

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Labor Income Mean Peak Median 25th 75th Interqrtile lt20 lt25 20-24 gt65 lt20 lt25 20-24 gt65 lt20 lt25 20-24 gt65 lt20 lt25 20-24 gt65
Austria 39.8 43 38 29 47 18 4.8 14.6 9.8 0.4
Brazil 42.8 46 41 33 49 16 2.8 8.5 5.7 3.9
Chile 44.9 44 43 33 53 20 2.3 8.2 5.9 7.4
China 41.2 41 39 30 49 19 3.9 13.3 9.3 3.0
Costa Rica 42.2 39 40 31 50 19 2.2 10.2 7.9 3.4
France 42.4 49 41 32 50 18 1.3 7.2 6.0 0.7
India 46.0 49 44 34 54 20 2.6 7.6 5.0 8.2
Indonesia 43.5 44 41 32 51 19 3.4 10.1 6.7 6.7
Japan 45.3 48 44 35 53 18 1.1 6.0 4.9 3.5
Mexico 46.9 41 44 34 56 22 2.9 7.6 4.7 11.6
Philippines 46.7 43 44 34 55 21 1.4 7.1 5.6 10.5
S.Korea 42.1 36 40 31 49 18 2.3 9.9 7.5 3.3
Slovenia 40.8 34 39 31 47 16 1.3 7.1 5.8 0.8
Sweden 45.4 49 44 34 54 20 1.8 7.6 5.8 5.3
Taiwan 42.1 41 40 32 49 17 1.0 7.2 6.1 2.4
Thailand 43.1 40 41 32 51 19 2.2 8.7 6.4 4.1
Uruguay 42.1 38 40 31 50 19 2.6 9.9 7.2 2.4
US 45.8 47 44 35 53 18 1.1 4.9 3.9 5.3
Average 43.5 42.9 41.5 32.4 51.1 18.7 2.3 8.6 6.4 4.6
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Outliers?
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Thick Flat Tails
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Cliffhanging(at a certain old age)
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Start late, exit late
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Steep in early ages
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The winner and the runner-up
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Why do they differ?

• Mechanical decomposition
• (Y/N) (Y/E) (E/N)
• Per capita labor income Earnings per employee
  (effective) labor force participation rate
• (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

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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)

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II-2. Time Series Analysis

• Has an advantage
• Consistent data sets definitions
• Decomposition across years
• Policy change analysis

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Source provided by Ron Lee
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Decomposition of the Change in Per-Capita Labor
Income, Chile, 1987-1997
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Source provided by Ron Lee
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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.

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III. More In-Depth AnalysisIV. 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)
• Womens 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.

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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.

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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?