Presentation is loading. Please wait.

Presentation is loading. Please wait.

Uncertainty, inequality, and cheap steel Peter B. Meyer Office of Productivity and Technology U.S. Bureau of Labor Statistics 5 October 2004 Tübingen Outline.

Similar presentations


Presentation on theme: "Uncertainty, inequality, and cheap steel Peter B. Meyer Office of Productivity and Technology U.S. Bureau of Labor Statistics 5 October 2004 Tübingen Outline."— Presentation transcript:

1 Uncertainty, inequality, and cheap steel Peter B. Meyer Office of Productivity and Technology U.S. Bureau of Labor Statistics 5 October 2004 Tübingen Outline 1. Technological change, 1866-1881 2. Weeks report data set 3. Measures of earnings inequality 4. Uncertainty vs. skill bias 5. Occupations and institutions

2 Iron and steel materials Iron ore Pig iron (includes 4% carbon) Blast furnace puddling Bessemer steel process (1860s) wrought (or bar) iron (.1% carbon) Rails for railroads Rolling mill open hearth steel processes (1870s) crucible steel for tools, etc (1.9% carbon) castings Bessemer steel and open hearth steel were new to the U.S. in the 1860s and 1870s. That completed the technology set for 40 years. Wages are available from blast furnaces and rolling mills.

3 Improving technology: output rose and prices fell Source: Historical Statistics of US

4 Weeks report U.S. Census Bureau surveyed manufacturing plants in the 1880s Establishments reported wages by job, usually a retrospective yearly average Most observations are for a job title, not a worker. 1047 establishments in 48 industries 104,413 wage observations – but some are piece rates not day-wages

5 Industries in the data Foundries Ag / food / forestry products processing Wood work industries Construction materials Textiles and clothing Mining and minerals processing Metal work industries Hardware and cutlery Iron blast furnaces Rolling mills Machinery making Tin and sheet iron works

6 Earnings inequality within metalwork industries y-axis has coefficient of variation = Inequality rose within the blast furnace and rolling mills industries starting about 1869, but did not rise in other metalwork industries

7 Earnings inequality within other industries y-axis has coefficient of variation = Inequality rose within the blast furnace and rolling mills industries starting about 1869, but did not rise in the other grouped industries

8 Earnings dispersion in iron and steel (from table 1a) Variance of weighted log-wages after year fixed effects removed 1855-18691870-1881 Blast furnaces and rolling mills.198 (N=2172).284 (N=7251) All other manufacturing wages.292 (N=31662).278 (N=58614) Earnings inequality within iron and steel industries rose.

9 Earnings dispersion in iron and steel (from table 1b) Variance of residuals of men’s weighted log-wages after year and job fixed effects regression 1855-18691870-1881 Blast furnaces and rolling mills.090 (N=1915).112 (N=5731) All other manufacturing wages.100 (N=27276).101 (N=49932) Residual inequality within iron and steel industries rose with job held constant.

10 Earnings dispersion in iron and steel (from table 1c) Variance of residuals weighted log-wages after regression in Table A7 1855-18691870-1881 Blast furnaces and rolling mills.097 (N=2172).145 (N=7251) All other manufacturing wages.120 (N=31662).121 (N=58614) Residual earnings inequality within iron and steel industries rose, perhaps after any regression.

11 Uncertainty and/or skill bias Skill-biased technological change: preexisting skills are more productive and better rewarded by working with the new technology –Katz and Murphy (1992); Juhn, Murphy, and Pierce (1993); Bound and Johnson (1992); Murphy and Welch (1992)) Technological uncertainty: people and firms experiment with the new technology and have divergent results. –Nelson (1961); Malerba (1985); Dosi (1988); Rosenberg (1996); Greenwood and Yorukoglu (1997)

12 Uncertainty model Suppose workers, managers, and firms choose between: doing the work in the standard way quitting But with technological change, more choices are possible: try different technology reorganize the work process invent something new More options  More dispersed outcomes Statistical evidence: Residuals from a substantive wage regression rise for iron and steel after 1869

13 Puddlers, rollers, and inside contracting Puddlers make wrought iron by craft work Rollers send iron or steel through rolls to press impurities out and to shape the material Inside contracting: skilled craftspeople hired and organized their own employees, who were paid as a group by output. Contracting declined after the 1870s.

14 The Rise of Pittsburgh Pittsburgh made no Bessemer steel until 1875, but had advantages: Coal sources are nearby Iron ore travels cheaply over Great Lakes Much iron production already Home of the railroad managers Production near Pittsburgh grew By 1880 it was a fast growing steel producing area Remained dominant for decades In the data, workers receive higher wages in Pittsburgh a location effect

15 New kinds of management Carnegie’s plants were very successful They did cost accounting of inputs The iron and steel industry “learned” from the railroads, which were larger (Pennsylvania Railroad in 1875 had over 50,000 workers. Largest single US plant was Cambria Iron with about 4000.) Managers take charge of tools Chemists hired; formal R&D begins Carnegie’s plants pay high wages and have few strikes during 1870-1881 High wages at Carnegie plant in this data -- a firm effect on wages

16 Managers, cont’d Wages of managers in iron blast furnaces (Figure 5) Articles in technical journals about management first appear in the 1870s Accounting, journals, consultants, and R&D: these are approaches to managing information, replacing craft work and inside contractors Wages of managers of glass-making establishments

17 Conclusion Earnings inequality rose in the iron and steel sector Dimensions of wage differentiation include: –Some jobs faced change and opportunity –Certain locations won out –Managers faced growth, applied cost accounting, and research and development Inequality came from opportunity –novelty –uncertainty –experimentation –information management


Download ppt "Uncertainty, inequality, and cheap steel Peter B. Meyer Office of Productivity and Technology U.S. Bureau of Labor Statistics 5 October 2004 Tübingen Outline."

Similar presentations


Ads by Google