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Getting a Foot in the Door: “Learning,” State Dependence, and the Racial Integration of Firms

Published online by Cambridge University Press:  03 March 2009

Warren C. Whatley
Warren C. Whatley
Affiliation:
The author is Associate Professor of Economics at the University of Michigan, Ann Arbor, MI 48109.
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Abstract

Economists have emphasized supply-side learning when explaining long-term trends in racial income differences. This article demonstrates that learning also occurred on the demand side. Estimation of a state-dependence model of the sequence of racial employment outcomes of firms in Cincinnati, Ohio, during World War I shows that the introduction of black workers into a previously all-white firm generated new experiences within the firm, altering its future racial employment decisions.This suggests that more research should be done on how firms and labor markets processed information about workers and how that influenced worker opportunities.

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Articles
Information
The Journal of Economic History , Volume 50 , Issue 1 , March 1990 , pp. 43 - 66
Copyright
Copyright © The Economic History Association 1990

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References

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15 Firms were then placed into two-digit Standard Industrial Classification categories. All are two-digit level except Fertilizer and Cottonseed Oil. These two are singled out because they are such large employers of blacks in the South.Google Scholar

16 Firms' addresses were used to locate them on ward maps for 1920, and ward-level data on the racial composition of population were gathered from the Bureau, U.S. of the Census, 14th Census of the United States, 1920, vol. 3: Population-Composition and Characteristics of the Population By States (Washington, DC, 1920), pp. 226, 447.Google Scholar

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20 See Higgs, Robert, “The Boll Weevil, the Cotton Economy, and Black Migration, 1910–1930,” Agricultural History, 50 (04 1976), p. 491.Google Scholar

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23 Flora Gill found that migration was a response to economic incentives, but the financial constraint caused by poverty and imperfect capital markets bore heavily on the decision to move. See Gill, Flora, “The Economics of the Black Exodus” (Ph.D. diss., Stanford University, 1974).Google Scholar

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27 Detroit Bureau of Government Research, The Negro in Detroit; Chicago Race Commission, The Negro in Chicago; and U.S. Dept. of Labor, The Negro at Work during the World War, p. 10.Google Scholar

28 A lower cost of living in the South may explain the lower entry-level wage in Atlanta, but it would only exacerbate any wage premium skilled white workers in Atlanta might have enjoyed over skilled whites in Cincinnati. This finding becomes more puzzling when we note that whites in Atlanta were not more educated than whites in Cincinnati. In 1920 the illiteracy rate in Atlanta was 1.2 percent for whites over the age of 10. In Cincinnati it was 0.4 percent. In 1910, 79.3 percent of whites in Atlanta between the ages of 6 and 14 were in school. In Cincinnati the figure was 90.9.Google Scholar See U.S. Bureau of the Census Thirteenth Census of the United States, Population (Washington, DC, 1913), vol. 3, part 2, pp. 418–19, and vol. 2, p. 400Google Scholar; and U.S. Bureau of the Census, Fourteenth Census of the United States, Population (Washington, DC, 1914), vol. 3, pp. 222, 784.Google Scholar

29 In Atlanta a similar pattern prevailed: black workers found jobs in industries that employed large percentages of common, unskilled labor.Google Scholar

30 Baruch, Bernard M., American Industry During the War (New York, 1941), chaps. 4, 6, and appendices 5–10Google Scholar; and Rockoff, Hugh, Drastic Measures: A History of Wage and Price Controls in the United States (Cambridge, 1984), chap. 3.CrossRefGoogle Scholar

31 This regional wage gap is one way to interpret the split labor market hypothesis. See Bonicich, Edna, “A Theory of Ethnic Antagonism: The Split Labor Market,” American Sociological Review, 37 (10 1973)Google Scholar; and Bonicich, Edna, “Advanced Capitalism and Black/White Race Relations in the United States: A Split Labor Market Interpretation,” American Sociological Review, 41 (02 1976).Google Scholar

32 The comparison, by skill category, shows the following: The figures are from U.S. Department of Labor, The Negro at Work during the World War, pp. 40–49.Google Scholar

33 See Perlman, Jacob and Frazier, Edward K., “Entrance Rates of Common Laborers in 20 Industries, July 1937,” Monthly Labor Review, 45 (12 1937), pp. 1494, 1497. In the North the average white wage for entry-level workers in 20 industries was 55.8 cents, that for blacks was 56.6 cents. In the South the figures were 43.4 and 34.5 cents.Google Scholar

34 See Bailer, Lloyd C., “The Negro Automobile Worker,” Journal of Political Economy, 51 (10. 1943), pp. 415–29CrossRefGoogle Scholar; and Northrup, The Negro in the Rubber Tire Industry, pp. 30–39.Google Scholar

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39 The probability is equal to Wn. Robert Higgs uses this formula to explain the variation in integration by scale in 1909 Virginia. See Higgs, Robert, “Firm Specific Evidence on Racial Wage Differentials and Workforce Segregation,” American Economic Review, 67 (03. 1977).Google Scholar

40 Both cities had racially segregated residential patterns. In Cincinnati blacks were concentrated along the waterfront, and in Atlanta they were concentrated in the western-middle section of the city. As one study of workers in Cincinnati found, “less than one-third were in walking distance of their jobs,” and “when going to their jobs and returning home…the street cars are crowded, jammed, loaded-down, and decorated like bequilled porcupines with tools.” Some paid up to 10 percent of their wages on transportation.Google Scholar See Miller, Zane, Boss Cox's Cincinnati (New York, 1968), p. 32.Google Scholar For Atlanta, see Preston, Howard, Automobile Age Atlanta (Athens, 1981).Google ScholarThe Housing Corporation data show statistically significant cross-sectional variation in racial wage ratios for firms in both cities. Of the 12 wards in Atlanta with firms that employed blacks, four had average firm-level racial wage ratios that were significantly different from the city average and represented 46 percent of the firms in the sample that employed blacks. In Cincinnati, 9 of the 20 wards had average racial wage ratios significantly different from the city average and covered 35 percent of the firms that employed blacks.Google Scholar

41 Myrdal, An American Dilemma, pp. 392–93.Google Scholar Others had similarly optimistic views. In 1906 W. L. Bulkley described the situation in New York City: “The northern employer is too prone to turn off the colored applicant with the bland assurance that he himself would have no objection, but his white workmen would disrupt the business if a black competitor were forced upon them…. This intolerant attitude against the Negro workman is largely a matter of fad and fancy. Upon the show of firmness on the part of the employer it would soon vanish away.” Bulkley, W. L., “The Industrial Conditions of the Negro in New York City,” Annals of the American Academy, 27 (1906), p. 87.CrossRefGoogle Scholar

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44 Milgrom and Oster generate an equilibrium where workers who are undervalued initially are discriminated against in promotion because promotion reveals their true abilities to potential “raider” firms. The resulting allocation of labor in the firm is inefficient but profitable because firms are able to reap rents from their invisible workers. See Milgrom and Oster, “Job Discrimination, Market Forces, and the Invisibility Hypothesis.” One way to reap those rents without revealing this to the workers responsible is to segregate high-quality invisible workers and low-quality visible workers into different low-level jobs so that comparisons between them are never made.Google Scholar

45 Economists working in both the Marxian and Coasian traditions have long emphasized how worker management influences labor performance, making a priori and independent measure of worker ability problematic. See Gintis, Herbert, “The Nature of Labor Exchange and the Theory of Capitalist Production,” Review of Radical Political Economics, 8 (Summer 1976), pp. 36–54CrossRefGoogle Scholar; Bowles, Samuel, “The Production Process in a Competitive Economy: Walrasian, Neo-Hobbesian, and Marxian Models,” American Economic Review, 75 (03 1985), pp. 16–36Google Scholar; Coase, Ronald, “The Nature of the Firm,” Economica, 4 (11 1937), pp. 387–405CrossRefGoogle Scholar; and Williamson, Oliver, “The Organization of Work,” Journal of Economic Behavior and Organization, 1 (03 1980).Google Scholar

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49 To demonstrate the probability structure of state dependence in outcomes, think of all firms having identical urns of balls at time t, each ball having a value yt(say, the number of black workers hired at time t). A firm randomly chooses a ball from the urn which happens to have the value yt*. It then adds to the urn more balls with values greater than yt*. In the next period the firm randomly chooses a ball which happens to have the value y t + 1**. The outcome in period t + 1 is state dependent because the outcome in period t increased the probability that y t + 1** is greater than yt*.Google Scholar

50 Myrdal, An American Dilemma, p. 392, fn. 6.Google Scholar

51 It is best to think of BLACKit, as the result of the previous period's employment activity. BLACK 18 is the result of decisions made between Jan. 1, 1916, and Oct. 1918. BLACK 16 is the result for the period Jan. 1, 1914, to Dec. 31, 1916, and so forth. Ideally we would want continuous-state, continuous-time data so we could estimate instantaneous state dependence. Given our data limitations, the best we can do is measure the average state dependence emanating from the average experience of the previous period. The dates are chosen to make the periods approximately equal in length.Google Scholar

52 Cross-sectional data cannot estimate this equation because each firm has its own intercept, producing more coefficients than observations. On the other hand, estimation of the equation without the inclusion of δit will create an upward bias in the estimated state-dependence coefficient unless that part of δit that is correlated with BLACKit - 1 is fully captured by a linear combination of the other independent variables. One way to handle this is to fix the individual component and coefficients over time and assume Cov(nit, nit - 1) = 0. Now simple differencing eliminates δi. Time-invariant variables drop out of the equation, but here we are interested in θ as a measure of state dependence and θ is still identified.Google Scholar

53 Moreover, the LF variable now measures the black percentage of population in the city, not the firm's ward, so as to pick up the effect wartime changes in the supply of black workers may have had on changes in black employment.Google Scholar

54 LF(16 – 18) captures this. Since it is the same for all firms in the city it enters the regression as a constant, and the constant is never significant statistically.

55 See Scott, Negro Migration during the War, chap. 8.Google Scholar

56 Fogel, The Negro in the Meat Packing Industry, p. 105.Google Scholar

57 Northrup, The Negro in the Tobacco Industry, pp. 73–74.Google Scholar

58 Northrup, The Negro in the Rubber Tire Industry, p. 30.Google Scholar