Economics 2333, Topic #3: Manufacturing

Professor Robert A. MargoSpring 2014

Topics

• US ascendancy in manufacturing: role of natural resources (Wright 1990)

• Historical micro-data, 1850-1880• Origins of capital-skill complementarity

(Goldin-Katz; Atack, et. al.) and related.• Economies of Scale: Atack v. Sokoloff.

Wright (1990)• Initially, land-labor ratio is high in the US relative to Europe. Standard

trade theory suggests that country should specialize in goods that exploit its relative factor endowment.

• So: US should export agricultural goods and import manufacturing goods• True ca. 1850: 87% of US exports were agriculture, while about half of

imports were manufacturing• HOWEVER, by late 19th century US is becoming the leading manufacturing

producer and exporter, overtaking UK• Chart #1: Shares of World Industrial Output• Chart #2: Per Capita Industrial Output• Labor Productivity in US Manufacturing MUCH higher than UK or Germany

by late 19th century

Why Did This Happen?• Clue is in next slide: US exports were very capital intensive

compared with US imports• Look for “complementarity” between capital and other inputs• One frequently cited possibility: capital-skill complementarity• Why: (a) more capital per worker raises productivity (b) US

has relatively low skill premium (relatively abundant skilled labor)

• Characteristic of modern manufacturing, began to emerge in early C20 (BUT after US ascendancy)

• Problem: exports relative to imports were NOT especially skill intensive

What Else?• Another possibility: capital/natural resources complementarity• US has abundant natural resources AND (important) institutional

environment that allowed for efficient extraction and allocation to highest productive use

• 19th century manufacturing did not especially economize on use of natural resources

• International markets in natural resources had not yet developed• SO: US mfg benefited• Evidence: Exports/Imports were very natural resources intensive• Regression Analysis: Industries that had high per unit use of natural

resources AND high capital labor ratios tended to export more

What Became of the US Advantage?

• Eventually, it disappeared with the development of world markets in natural resources

• US begins to import particular natural resources, most prominent example being petroleum

US Manufacturing Samples

• Census of manufactures begins in 1810, very poor quality. First legitimate census is 1820. Also 1832 McLane report.

• Sokoloff samples: used to study productivity growth, fixed vs. working capital, relative use of female and child labor (Goldin and Sokoloff)

• Need for a new 1820 sample, also 1832.• 1850-1880: Atack-Bateman samples. Repeated cross

sections. National are self-weighting (1880 has problems). State samples are larger.

• See Atack’s Vanderbilt website.

Capital-Skill

• A central feature of modern economies is that capital and skilled labor are relative complements. Fall in the price of capital goods → increase in the relative demand for skilled labor.

• Has this always been true? Possibly not. “De-skilling” Hypothesis: IR → new machines → decrease in demand for skilled artisans → division of labor and the factory system.

• Some today but more on this later in semester: Katz and Margo (2013). Too simple for manufacturing, not true for entire economy.

Goldin-Katz (1)

• In C20 there is considerable evidence that larger, more capital intensive firms use relatively more skilled labor. Consistent with finding that wages are positively correlated with establishment size.

• This change according to GK, dates from the late C19. Key technical change is electrification + invention of “continuous processing” technology

Goldin-Katz (2)• Simple framework for evaluating the effect of tech change (or falling relative price

of K) on demand for skill• Manufacturing takes place in two steps. Step #1, convert raw capital into usable

capital. This requires skilled labor. Step #2, combine usable capital with unskilled labor.

• Artisan technology uses a lot of usable capital relative to unskilled labor in step #2 but not necessarily a lot of capital per unit of output.

• Factory technology uses relatively more unskilled labor per unit of usable capital → K/L MAY rise (not certain but highly likely), skilled share of labor force falls. Assembly line and CP technology bigger scale but uses much less unskilled labor per unit of usable capital in second stage and more capital per unit of output. Skilled share of labor force rises in move from A to CP.

• Moral: should find positive correlation between capital intensity and percent unskilled in C19 manufacturing but reverse in C20.

Regressions

• Table 3: worker education level is positively correlated with K/L and use of electricity.

• Table 4: average wage is positively correlated with education level, K/L, and electricity use. Note positive coefficient on establishment size.

• Table 6: non-production wage share positively related to capital intensity, wage coefficient indicates elasticity of substitution between production and non-production workers less than one. Capital coefficient higher than in recent decades.

De-Skilling in C19 US Manufacturing?

• Goldin and Sokoloff (1982). Examine variation across establishments in percent women and children. Idea is that women and children were less skilled than adult men. Strong positive correlation between percent female/child and establishment size in 1820-1850.

• Atack, Bateman, and Margo (2004). Examine variation in “establishment wage” (average wage at the establishment level). If de-skilling is present, establishment wage should be a negative function of establishment size (Note: opposite is true in C20). ABM find this to be the case in 1850 and 1880. Also show that the distribution of establishment wages becomes more unequal because mass in left tail increases – more workers at establishments with low average wages.

• Katz and Margo (2013). Update GS using 1850-1880. Show that relative use of female/child labor is positively correlated with capital intensity but this is entirely explained by establishment size. Construct a proxy for overall percent unskilled in 1880 from average establishment wage and daily wages of unskilled and skilled labor. Use IPUMS industry classifications to show that manufacturing occupation distribution “hollowed out” in C19. Interpret in a “task-based” framework.

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Capital “Deepening”

• Key feature of 19th century manufacturing was “capital deepening” → more capital per worker

• Capital deepening positively correlated with establishment size

• More K/L → higher labor productivity

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Steam Power, Part One

• Key new technology of the 19th century was steam power

• Advantages of steam: “footloose”, more reliable BUT variable costs high (coal)

• Diffusion of steam varied geographically → most rapid if water power sites were unavailable

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Steam Power, Part Two• Another key feature of the diffusion of steam → much more likely to

be used by large establishments, where large refers to the number of workers

• Why? Late 19th century evidence that use of inanimate power augmented the division of labor: some steps could be powered, others not

• Large scale establishments shifted strongly away from steam after 1850 and employment shifted towards steam powered factories

• Use of steam raised labor productivity. Some of this is due to greater K/L BUT even if one controls for capital intensity, productivity goes up in larger establishments, consistent with a TFP effect

Economies of Scale• US manufacturing sector “ascends” in C19 and early C20 (Wright

1990). Scale economies seem central to this, because average firm size increases substantially. Chandler (1977) is a classic book on the “rise of big business”. Legal/financial factors facilitate use of the corporate form and rise of a market for “managerial labor”. Corporate governance issues become important (ownership vs. control). Key names working on this: Hilt, Lamoreaxu.

• Not a lot of doubt about scale economies at turn of C20. Chandler has many examples. See also Atack, Rhode, and Margo (in progress).

• What about early in the century? Many economic historians are convinced by Sokoloff (EEH 1984). Uses 1820-1850 samples to estimate standard productions. Finds evidence of scale economies for non-mechanized establishments very early (1820). Strongly suggests that division of labor was a contributor to labor productivity growth and US manufacturing ascendancy.

The entrepreneurial labor input problem

• Sokoloff: entrepreneurial labor input not counted pre-Civil War. Hugely biases upwards labor productivity in very small establishments, which are ubiquitous. Adjusting (ad-hoc) for this, there are substantial economies of scale in US manufacturing prior to widespread mechanization. Inference is that division of labor must have been an important causal factor.

• Atack: initially is on the same wave length as Sokoloff for all censuses up to 1890, but eventually changes his mind. Atack and Sokoloff never resolve their disagreement back in the day. EH profession left in the lurch.

• Margo (2014) resolves the dispute. On the specific measurement issue I side with Atack – however, I agree with Sokoloff that labor input was under-enumerated in small establishments but for a very different reason than he thought. Correction for this is much smaller than advocated by Sokoloff.

• Bottom line: Half-empty: Depressing. C19 manufacturing censuses aren’t very useful in measuring economies of scale at least by the usual production function methods, old or new (hope springs eternal). Half-full: US had highly productive establishments of all sizes, including the very small.

Deep Background, Part One• Atack writes his dissertation (1976) on economies of scale in mid-C19 American

manufacturing. Advised (and influenced) by Bateman (and Weiss). BW believe that pre-1890 censuses generally did not count non-production workers and working capital. Atack implements elaborate adjustment for NP workers using 1890 ratios, subject to the condition that at least one worker is added. Atack finds mixed evidence in favor of economies of scale. See famous JA paper #1 is EEH 1977.

• Sokoloff reads Atack dissertation and writes his own (1982), on pre-1850 manufacturing. Key question is whether there were economies of scale prior to widespread mechanization.

• Laurie and Schmitz (1981) use PHSP data. Make minor adjustment for establishments with zero reported employment but no other adjustment. Reject economies of scale.

• Famous KS paper #1: Goldin and Sokoloff ( JEH 1982). Shows that larger firms were more likely to employ women and children. Suggestive of substitution of unskilled labor/capital for skilled artisan labor. See Katz and Margo (2013) for an extended update.

• Famous KS paper #2: Sokoloff (EEH 1984). Strong evidence of economies of scale IF adjustment is made for “missing” entrepreneurial labor. More in a moment.

Deep Background, Part Two

• Atack (1987) buys into Sokoloff (1984). Others do, too – see Siscic (2004) and Doraszelski (2004) on France. Inwood and Keay (2012) investigate issue for Canada, arguing that 1871 counts entrepreneurial labor input if economically relevant.

• Atack and Bateman (1999b, 2008) examine ROR to capital invested by size of establishment. Re-evaluate Sokoloff’s logic, come to opposite conclusion. Debate (spirited) at NBER DAE conference, but left unresolved.

• Atack (1985) uses MES approach pioneered by Stigler as a test of Chandler. Argues that there was a secular upward drift in MES due to factors (e.g. transportation) in place by 1870.

Sokoloff EEH 1984 (1)• Using 1820 sample Sokoloff compares output in sole proprietorships versus partnerships, for

each level of reported number of workers, up to six. Footnote does not indicate which year but context in paper suggests 1820. Partnerships consistently have higher output, from which he infers that the entrepreneurial labor input was not counted. Otherwise “firms with one worker would have the highest [measured] value added per worker.” Not helpful if you want to show economies of scale.

• Devises an elaborate correction in 1820 based on firm organization, which is derived from the name at the top of the mfg. schedule if it is a proprietorship or partnership. If a joint-stock company, he assumes a manager was hired, and was properly counted.

• Above information on firm type not available for 1850 at the time Sokoloff wrote. For 1850, he simply adds one to the count of workers.

• Results: with adjustment for entrepreneurial input, evidence of economies of scale, either using Cobb-Douglas or trans-log production function in 1820 and 1850. Moreover, this is true even for “non-mechanized” industries, which must be due to division of labor.

Tables 1 and 2• Table 1: shows change in establishment size distribution from 1850-80.

Adjustment for under-reporting of one-worker establishments. Slow movement to larger establishments in terms of numbers, much faster in terms of share of output. Recall mention of Atack (1985) a few slides ago.

• Table 2 presents CD production functions (value added) specification with and without Sokoloff proposed 1850 adjustment (add one to count of workers), for 1850-80. Without Sokoloff adjustment, sum of labor and capital coefficients is significantly less than zero. With the adjustment, the opposite. Why? Adding one to the count of workers drastically reduces measured labor productivity in the smallest establishments, relative to larger ones. Panel C shows for non-mechanized establishments.

• Are there subsets of the data for which scale economies are present WITHOUT the Sokoloff adjustment? Yes, but not many.

Should you believe Sokoloff or Atack?

• Textual evidence suggests that labor input of sole proprietors was supposed to be counted. Why? Census wanted “average wage” to be reported for the “individual labor of a producer, working on own account”. Relevant instruction is given first in 1850, refined in 1870. Bottom line: if a sole proprietorship produced enough to be enumerated ($500 in nominal value added) there should be at least one worker reported in the census form.

• If entrepreneurs were not counted there should be lots of establishments with zero workers. Proportion is negligible in 1850 and 1860 (less than 1 percent). Higher in 1870 and 1880 but still small (4 and 5 percent respectively). Some of these are just missing data.

• Sokoloff compared output in sole proprietorships versus partnerships, controlling for number of workers. Could only do this for 1820 at the time. Subsequently Atack and Bateman added information for 1850-70 samples that allow one to infer organizational form. Table 3 shows coefficients of partnership dummy in regressions of output in establishments with one reported worker. Coefficients are positive but generally insignificant AND fall in magnitude when controls for industry, capital invested, and location in 1850 and 1870. Suggests omitted variable problem, not necessarily uncounted labor.

Was Sokoloff Wrong? Not necessarily

• Textual, distributional, and econometric evidence favors Atack. BUT there might still be need for an adjustment to the labor input for a different reason.

• Different reason: C19 manufacturing censuses asked for “average” labor input. Very unlikely this is literally the case. More likely that data refer to “typical” number of workers. Enumerator instructions permit this explicitly.

1880 Question on Maximum Employment (1)

• 1880 Census of Manufactures asks an unusual question for the time – maximum number of workers employed at the firm during the census year.

• Consider establishments with one reported worker, adult male, producing for 12 FTE months (this is reported in 1880). If one is the literal average, maximum must also be one (because zero is not possible if firm is in business for 12 FTE months). Such firms should NEVER report a maximum greater than one. But Panel A, Table 4 (column 1) shows that 43 percent did! Note also that (Max – average)/average is decreasing in establishment size. Suggests bias is asymmetric, more important for small than large establishments.

1880 Question on Maximum Employment (2)

• Panel B adds maximum number of workers to CD production function. Note that coefficient is positive and significant for reported employment up to 15, but is decreasing in magnitude. Coefficient is small and insignificant for “factories” (16 or more workers).

• If we assume added workers were as productive per day as typical, then for establishments with one reported worker, additional output (0.088 percent) suggests that added workers were employed for about one month (out of twelve). Enough to make a difference in measured output but not around enough to be considered “typical”.

• Suggests that labor input should be increased in small establishments relative to large. If bias term (Max – Average/Average) is added to CD production function for 1880, returns to scale parameter is closer to one in absolute value, but still less. Moral: adjustment is needed but not large enough to make a difference in inferences about economies of scale, at least in 1880.

Interpretation• Half-empty: C19 manufacturing censuses don’t seem up to the task.What

to do? Work harder with existing data OR search for more informative data. Former probably won’t be successful (IMHO), latter.

• Half-Full: we have over-played the “rise of big business” card. US had highly productive small establishments as well as large.

• More informative data. Example: Atack, Margo, and Rhode analysis of late C19 BLS report, “Hand and Machine Labor”. Provides direct (and compelling, subject to cross-section caveat) quantitative evidence that division of labor contributed to economies of scale.

In Progress and Very Preliminary: 1899 BLS Study on Hand vs. Machine Labor (1)

• 13th Annual Report of the Commissioner of Labor, Hand and Machine Labor, Volumes 1 and 2, USGPO, 1899. Study was commissioned by Congress to determine the impact of mechanization on labor productivity.

• Basic unit of observation is matched pairs of “producing units”, one hand labor, one machine labor. Matching is done on a product description and quantity. Descriptions are VERY specific.

• There are 672 matched pairs or 1,344 producing units, in 87 “industries”, including agriculture (N = 27), manufacturing, mining, and transportation. Industries are (fairly) narrowly defined. The bulk of the observations, 600+, are in manufacturing (N = 573 for regression sample).

Hand vs. Machine Labor (2)• Data are reported in two parts. In Part One, for each producing unit:

industry, product, quantity, year in which production occurred, number of separate tasks of production, number of different workers employed, total number of hours of work to produce given quantity, total labor cost, average daily hours.

• Part Two, for each task: written description of task, in order performed; list of capital goods used in the task; motive power; the number of persons assigned to each machine used in the task; number, age, and gender of employees engaged in the task; occupational titles of employees engaged in the task; hours worked by each employee on the task; labor cost of each employee while engaged in the task; number of hours per day of operation; miscellaneous information (e.g. use of animals, etc.).

Hand vs. Machine Labor (3): Example

• Example. Unit 69, Boots and Shoes, 100 pairs of men’s cheap grade, kip, pegged boots, half-double soles.

• Hand Method: 2 workers (1 M, age 35; 1 F, age 32). Male’s occupation is “boot-maker”; female’s occupation is “Stitcher”. Male’s wage is $0.30/hr. Female’s wage is $0.20/hr. There are 83 tasks enumerated, all hand power. Total production time is 1,436.7 hours. Output per hour is 0.069 pairs/hour (more than one ten-hour day is needed to produce a single pair).

• Machine Method: 113 workers, 100+ different occupational titles in 122 tasks. Steam power is used in 49 of the tasks. Total production time is 154.8 hours. Output per hour is 0.645 pairs/hour (in a ten-hour day, 6.45 pairs are produced).

Hand vs. Machine Labor (4): Measuring the Division of Labor

• Average number of tasks per worker: total up the number of workers used at each task and divide by the number of different workers.

• Example #1: in unit 70, hand production (boots and shoes), there is one shoemaker performing 73 tasks. Total number of workers used in all tasks is 73. Example #2: in unit 69 machine method, there are 151 workers used in all tasks and 113 different workers, so average tasks per worker is 1.34.

• Useful to scale by the number of tasks. In example #1, the scaled value is 1 = (73/73). In example #2, the scaled value is 0.011 = (1.34/122 tasks).

• Not ideal but best we can do at the moment. Ideal is to construct a matrix of tasks by individual workers and use this to construct a measure of the division of labor (e.g. Herfindahl index of fraction of tasks performed across workers). Not clear if this is possible.

Preliminary Results: Regression Analysis of BLS Data

• First table following shows sample means, comparing hand and machine firms. Machine units are larger (more workers); greater division of labor (more tasks and average worker performs a smaller fraction of total tasks); and are more productive (less time to produce one unit of output). Labor force younger and slightly more male at machine units.

• Second and third tables show regressions of log (tasks), fraction of tasks performed by average worker, log (hours to produce one unit of output). Regressions include fixed effects for matched hand-machine units, so this is difference-in-difference. Time dummies are also included.

• Second table: Greater division of labor (more tasks, smaller fraction performed by average worker) in larger (more workers) and machine units.

• Third table: (a) larger units are more productive (b) effect of size is substantially reduced when machine dummy is added, see column 2 (c) effect of size is insignificant when division of labor controls are included. Greater division of labor (more tasks overall, fewer performed by average worker) is associated with higher productivity (less time to produce one unit). Substantive results unaffected if demographic controls are added (column 4). Truth in advertising: effects of number of tasks and machine dummy in columns 3 or 4 are robust to specification changes; number of workers is always insignificant; if average worker performs a greater share of total tasks, productivity declines but significance level depends on specification.