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Wages and Labor Markets in the US 1820-1860
1. By Robert A. Margo
Presentation: Michael Kirmes
Feb. 1st 2013
2. PREVIOUS DATASETS
• Retrospective data from payrolls:
– Weeks (1886) and Aldrich (1893)
– Wright (1885): Massachusetts
• Archival records
– Smith (1963): Erie canal in northern New York
– Zabler (1972): Iron firms in eastern Pennsylvania
– Adams (1968, 1970): Philadelphia
– Adams (1982): Manufacturing in south-eastern Pennsylvania
– Adams (1986): Agriculture in Maryland
– Adams (1992): Western Virginia
1820
3. PREVIOUS DATASETS
• Retrospective data from payrolls:
– Weeks (1886) and Aldrich (1893)
– Wright (1885): Massachusetts
• Archival records
– Smith (1963): Erie canal in northern New York
– Zabler (1972): Iron firms in eastern Pennsylvania
– Adams (1968, 1970): Philadelphia
– Adams (1982): Manufacturing in south-eastern Pennsylvania
– Adams (1986): Agriculture in Maryland
– Adams (1992): Western Virginia
1860
5. REPORT OF PERSONS AND ARTICLES HIRED
standardized data collected by forts since 1818:
–date and place of hire
–money wage (daily/monthly)
–days worked per month
–army rations received
–occupation
–slave/free (South only)
8. ESTIMATING A DATASET
• 3x4 „Hedonic Regressions“:
–Adjust for changes in the composition over time
–Regress bundle price (wage)
on characteristics of (bundled) commodity (the worker)
ln 𝑤𝑖𝑡 = 𝑋𝑖𝑡 𝛽 + ∑𝛿𝑡 𝐷𝑡 + 𝜖𝑖𝑡
–Hedonic component price vector 𝛽 is assumed to be constant
over the sample period
9. CONSTRUCTING THE
NOMINAL WAGE TIME SERIES
𝑤 𝑡 = 𝑤 1850 ∙ exp( 𝛿𝑡 − 𝛿1850)
• What are the 1850 wages?
– Unskilled workers and artisans: 1850 census (needs slight corrections)
– White collar workers: hedonic regression with appropriate weights 𝑋∗
Laborer (daily) Artisan (daily) White Collar (monthly)
Northeast 0.94 1.42 42.17
Midwest 0.80 1.35 47.12
South Atlantic 0.68 1.44 42.95
South Central 0.85 1.81 60.84
10. REAL WAGE GROWTH
• Price deflator:
– Goldin & Margo (1992): region-specific, fixed-weight
– Wholesale price data from Cole (1938)
– Does not include housing
• Real growth rates (1821-1860, % per year)
Common Laborer Artisan White Collar
Northeast 1.28 1.18 1.57
Midwest 0.71 -0.07 0.87
South Atlantic 0.97 0.24 1.12
South Central 0.85 0.66 1.44
12. INTERSECTORAL EFFICIENCY:
FARM VS. NON-FARM WAGES
• Testing the efficiency of labor markets
• Census of Social Statistics manuscripts, 1850 and 1860
–At the county level
–Common laborers
• Calculation of wage gap:
𝑔 = ∑𝛼𝑖 𝑓𝑖 − ∑𝛽𝑖 𝑛𝑖 = ∑𝛽𝑖 𝑔𝑖 + ∑ 𝛼𝑖 − 𝛽𝑖 𝑓𝑖
within-county
wage gap
wage gap due to sector
distribution across counties
13. WAGE GAPS
0
4
8
12
16
20
24
28
32
MA PA MI IA NC VA KY TN North South
Non-farm (adj., 1850) Farm (1850)
Non-farm (adj., 1860) Farm (1860)
.97 .99 .91 .94 .94 .90 .96 1.08 1850
1.01 .96 1.01 .97 .96 1.04 1.03 1.00 1860 }Real Wage Ratios
(NOMINAL & REAL)(NOMINAL)
14. GEOGRAPHICAL ASPECTS OF
LABOR MARKET INTEGRATION
• But why for lower income?
–Self-selection? (“safety valve”, Turner 1920, Ferrie 1997)
–Capital gains? (Galenson & Pope 1992)
–Wage gap?(Coelho & Shepherd 1976)
1800 1810 1820 1830 1840 1850 1860
South Central
South Atlantic
Midwest
NortheastGo West!
%oftotallaborforce
19. REAL WAGES IN CALIFORNIA
0
20
40
60
80
100
120
140
160
180
200
Feb
1848
May
1848
Dec
1848
1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860
Common Laborer
Artisan
White Collar
20. CONCLUSION
• annual time series of nominal & real wages 1820-1860
• short-run volatility, long-run growth
• distribution of income
• effectiveness of labor markets
–between sectors
–geographically
–around shocks
Summary