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Innovation Cost and the Direction of Learning: Manufacturing Change and Automation
December 31, 2021
Kenneth L. Simons Department of Economics
Rensselaer Polytechnic Institute 110 8th Street
Troy, NY 12180-3590 USA
Tel: (518) 276-3296 Fax: (518) 276-2235
Email: [email protected] Previous titles: Incremental Process Innovation and Competitive Cost Reduction; Innovation Cost and the Nature and Direction of Learning: Lessons in Manufacturing Change and Automation. Acknowledgments: Omar ALShaye assisted in data collection by independently coding innovation characteristics from in-depth reading of articles, then discussing and agreeing appropriate treatment compared to the author’s coding. Helpful comments were provided by Michael Klein, Yu-li Ko, Lucy Xiaolu Wong, and seminar participants at the 2016 International Schumpeter Society Conference, 2016 Industry Studies Conference, 2019 International Industrial Organization Conference, 2021 Mannheim Centre for Competition and Innovation Annual Conference, an NBER Productivity Seminar, and seminars at Colgate University, Rensselaer Polytechnic Institute, and the University at Albany. This work was supported in part by the National Science Foundation under Grant No. SBE-0965310. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
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Innovation Cost and the Direction of Learning:
Manufacturing Change and Automation
Abstract
Manufacturing cost learning curves result, I theorize, from process
innovations whose optimal choice brings mechanization. I test this view with
unprecedented data on 1947-1971 television receiver process innovations. Firm
output drove innovations that propelled automation, multiple-operation
mechanization, and complex mechanization. I confirm causality using prior radio
industry experience. The evidence indicates that larger firms exploited even high
marginal R&D expense innovations to reduce unit cost. The innovations were
typically incremental, duplicative, lacking in strong intellectual property rights, and
routinely manageable. Process innovation drove cost reduction and added a
mechanization dimension to the traditional learning curve.
Keywords:
experience curve, process innovation, R&D cost spreading, incremental
technological change, innovation incentives, manufacturing competitiveness,
television receivers, electronics
JEL codes: O33, O31, D83, D24, L63, N62
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Innovation Cost and the Direction of Learning:
Manufacturing Change and Automation
Directed technological change follows changes in supply of production
inputs (Hanlon 2015) and demand for outputs (Schmookler 1966). Yet
technological change may have a direction without supply or demand shifts. As I
explain through a model of process innovation, firms’ project choices cause firm
size to direct innovations. Innovation choices propel cost reduction in ways that
explain so-called learning curves in unit production cost, bringing mechanization.
Learning curves in firm cost are also known as experience curves or, more
accurately, progressive cost reduction. Firm cost reduction is at the heart of gains
in societal welfare, profitability and firm survival, and national growth miracles
(Arrow 1962, Fudenberg and Tirole 1983, Lucas 1993, Petrakis, Rasmusen, and
Roy 1997, Besanko, Doraszelski, and Kryukov 2014). Learning curves document
productivity growth associated with cumulative output, yielding efficiency, profit,
and social welfare. Learning curves spawned a revolution in business strategy, with
firms recommended to price low and produce a lot to drive unit costs down. Most
industries undergo learning curves (Boston Consulting Group 1972).
A small but growing number of manufacturing industry and firm studies
investigate causes of learning curves (Thompson 2012). Automatic learning by
production workers, despite the term “learning by doing,” does not explain the
typical multi-decade learning curves. Instead, the learning is organizational, and
results not “by doing” the making of goods but by deliberate cost-reducing activity.
Capital investment sometimes explains part of the cost reduction (Thompson 2001).
Innovation, however, most frequently explains the cost reduction, through R&D,
engineering, and process management (Hatch and Mowery 1998, Sinclair, Klepper,
and Cohen 2000, Levitt, List, and Syverson 2013, Hendel and Spiegel 2014).
Engineers and other employees deliberately investigate methods to lower
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manufacturing cost. 1 Despite the central role of innovation, studies have not
systematically documented and analyzed the underlying innovative improvements.
I examine the role of firm innovation choices in cost reduction and
mechanization. I develop unique and detailed data for U.S. television receiver
manufacture in 1947-1971. Television’s postwar excitement and electronics
activity spurred trade articles. The articles provided prestige to firms and engineers,
and Electronics magazine even paid for tips and tricks to aid manufacture. The
disclosure delay and cost of imitation (Mansfield, Schwartz, and Wagner 1981)
apparently made firms willing to allow manufacturing disclosures, so the articles
make television receiver process innovation observable. From 219 trade articles, I
document 303 process innovations. I codify their cost reduction impacts and roles
in mechanization.
Findings reveal sustained innovation that completely reshaped manufacture.
Cost plummeted, partly through mechanization. Mechanization innovations
occurred most frequently in firms with the greatest output, accelerating their
learning curves. Three types of mechanization, automation, multiple operation, and
complexity of operation, all were more frequent among larger firms’ process
innovations. I confirm identification using as instruments prior radio industry entry
time or output.
Additional empirical tests reveal that mechanization came with the cost
reduction curve because of output-driven innovation choices. Process innovation
was dominated by large firms, which therefore had to include expensive marginal
innovations, not just the available inexpensive innovations. The average innovation
of large firms was expensive in additional ways than mechanization, occurring
1 These studies investigate World War II Liberty shipyards, semiconductor
manufacturers, a Fortune 500 batch chemical producer, an automobile assembly
plant, and a steel mini mill.
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often in manufacturing steps with expensive equipment and technologies, and in an
expensive new trajectory of innovations involving printed circuit boards. Also,
large firms’ innovations on average achieved distinctly large cost reduction, a
pattern difficult to explain unless innovation behaviors result from correlates of
innovation expense per saving in unit cost. Process innovation was typically
incremental, duplicative, lacking in strong intellectual property rights, and routinely
manageable.
This study explicitly documents how innovation drove the cost-reduction
learning curve. Although surviving archives lack firms’ manufacturing cost
records so numerical cost curve data are aggregate, nonetheless the innovation data
provide a direct measure of the innovations’ impact on cost. Also, the
organizational cost-reduction learning curve is shown to have a third axis for
mechanization. This three-dimensional learning curve should occur in other
industries with appropriate traits.
This study also advances research on the determinants and direction of
innovation. It reveals the innovations behind continual progress for processes.
Even for products, the underlying innovations behind typical steady improvement
trends such as Moore’s law have almost always gone unmeasured (Sahal 1981, Dosi
and Nelson 2010, 67-69). It derives from first principles the decreasing returns to
scale usually assumed for innovation spending. It expands knowledge of
innovative competition when patents do not drive innovation. Innovations typically
are not strongly protected by patents, and increased patenting after patent rights
were strengthened has been found to be for legal defense without further innovation
(Levin et al. 1987, Cohen, Nelson, and Walsh 2000, Hall and Ziedonis 2001, Moser
2013). And it describes forces underlying the direction of innovation. The rate and
direction of innovation have long been recognized as important (National Bureau
of Economic Research 1962), yet few studies have explored determinants of the
direction of innovation (Furman and Teodoridis 2017).
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Television mechanization foreshadowed concerns of automation-driven job
losses and manufacturing competitiveness. Automation improved labor
productivity, and workforce fell in years of low output growth. Workforce and the
international destination of profits shifted further when American firms’
productivity eventually lagged relative to international competitors. Understanding
mechanization can help understand workforce changes and a nation’s industry
survival. How then might mechanization be part of the learning curve given firm
incentives for cost reduction?
I. Incremental Process Innovation, Cost Reduction, and Mechanization
R&D cost-spreading, hereafter termed R&D expense-spreading, occurs
when an innovation reduces unit cost (or raises willingness to pay) and therefore
brings a financial benefit that is multiplied by the quantity of sales. Schmookler
(1966, 112-115) assumes current output predicts future sales. In his model,
financial returns justify an invention if current sales imply future sales with
expected profit that exceeds the expense of invention. R&D expense-spreading is
embedded in competitive models such as Dasgupta and Stiglitz (1980), Levin and
Reiss (1988), and Klepper (1996).
Implications arise for the innovation rate, as firm size motivates more
intensive innovation. Larger firms carry out, in addition to easy innovations, the
costly innovations needed for high efficiency (Cohen and Klepper 1996b).
Moreover, process innovations are incentivized by current firm size, whereas novel
product innovations that create new markets are not (Cohen and Klepper 1996a).
However, implications have almost never been developed otherwise beyond
aggregate innovation.
Yet theoretical analyses have missed an effect of R&D expense-spreading.
Correlates of high innovation expense coincide with firm size, because larger firms
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pursue a greater proportion of higher-expense innovations. Therefore R&D
incentives drive not just the rate but also the direction of the learning curve.
A. Firm Innovation Decisions
Average production cost can be lowered through process innovation. Each
innovative opportunity k requires an R&D or engineering expense in personnel
and material, and reduces unit production cost by an expected benefit of . The
process innovations cannot be sold, given limitations of patent rights and
differences across firms’ manufacturing methods. Saleable innovations available
through suppliers are not analyzed but are reflected in a base unit cost .
Innovation yields per-unit production cost . Here, is the
average cost absent process innovation, and decision is 1 if the firm implements
innovation k or 0 otherwise. The firm pays for its innovative effort.2
This yields profit
, (1)
where p is price and is a growth cost to produce Q units given prior size S.
S measures resources including managerial capacity, distribution channels, and
production capacity. Growth cost is strictly convex in Q, with a minimum at
and partial derivatives for , , and .3
2 This analysis abstracts from dependencies in which one innovation depends on or
displaces another. 3 A convex expansion cost arises if training workers and installing equipment takes
scarce time from existing employees, who increasingly for higher carry out
fewer duties. It also arises if, in establishing n out of sales and distribution
r k
bk
C
C = C − d kbkk∑ C
d k
r = d kr kk∑
Π = ( p −C + d kbkk∑ )Q − d kr k
k∑ − g(Q;S)
g(Q;S)
Qm(S) ≥ 0 gQ > 0 Q >Qm
gQQ > 0 gQS < 0
Q −Qm
n+1
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Rewriting (1) yields
. (2)
The extra profit from implementing innovation k is , so the firm
profits from innovation k if and only if exceeds .
All or no dollars are spent for each innovation, so the production cost
savings less innovation expense can be expressed as a sum across dollars:
, (3)
where indexes the individual dollars of prospective innovation expenditure,
is the innovative opportunity associated with a prospective dollar, is 1 if
spending occurs or 0 otherwise, and is the average unit cost reduction if
the dollar is spent.
From spending on innovation , the resulting unit cost
reduction is . Optimally, if and only if r is
high enough that is within the first r prospective innovation dollars from highest
to lowest in . This implies that increases monotonically, with
successive dollars spent yielding a non-increasing marginal unit cost reduction.
Approximating the function as twice differentiable yields and .
Further assuming a finite density of yields . Thus innovation
spending choice implies this standard assumption.
An optimal decision requires so the cost ($1) and benefit
( ) balance for the marginal dollar of innovation. An optimal decision
channels, a firm chooses the cheapest n of the channels.
Π = [( p −C)Q − g(Q;S)]+ d kr kQ bk / r k −1/Q( )k∑
r kQ bk / r k −1/Q( )bk / r k 1/Q
d kr kQ bk / r k −1/Q( )k∑ = dℓQ bk (ℓ) / r k (ℓ) −1/Q( )
ℓ∑
ℓ
k(ℓ) dℓ
bk (ℓ) / r k (ℓ)
r = dℓℓ∑ (= d kr k
k∑ )
u(r) = dℓbk (ℓ) / r k (ℓ)
ℓ∑ (= d kbk
k∑ ) dℓ = 1
ℓ
bk (ℓ) / r k (ℓ) u(r)
′u (r) > 0 ′′u (r) ≤ 0
bk / r k ′′u (r) < 0
′u (r) = 1/Q,
′u (r)Q
n+1
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simultaneously satisfies an optimal growth rule, . Both rules
are necessary to maximize .
This implies that r and Q increase with prior firm size S. By the implicit
function theorem,
and ,
with from the second order condition. S increases
r and Q, causing the firm to innovate so much that the marginal benefit falls to .
In fact, prior firm size also affects other innovation characteristics.
B. Innovation Characteristics Compared Between Firms
If innovations’ characteristics are correlated with cost, then prior and
current size drive average characteristics of the innovations firms implement. Some
innovations are far more involved than others, per dollar of unit cost benefit. These
innovations’ R&D-expense-to-benefit ratio is high. Such innovations are
pursued only by firms that innovate enough to obtain high efficiency, the largest
firms, with and near zero.
If firms are willing to pursue innovations with a high innovation-expense-
to-benefit ratio, what types of innovations, , become possible? Mechanization
development imposes a high innovation expense ratio. Changing from hand
work to machine work requires engineering time and material expenses for design,
development, and construction of equipment and electronic controls. Likewise,
manufacturing loci that involve costly equipment and technologies tend to
involve a high innovation expense ratio. A change to hand assembly or simple
equipment can be implemented readily, but a change to large machines requires
specialized equipment, tool design, machining, engineering labor, and material.
∂g ∂Q = p −C + u(r)
Π = ( p −C + u(r))Q − r − g(Q;S)
dr dS = − ′u (r)gQS |H |> 0 dQ dS = ′′u (r)QgQS |H |> 0
|H | = − ′′u (r)gQQQ − [ ′u (r)]2 > 0
1/Q
r k / bk
1/Q ′u (r)
y jk
y1k
y2k
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New technological trajectories impose a high innovation expense ratio because
of their novelty. Novel approaches require engineers to work out ideas and
principles in their infancy and to develop those ideas into process components. So
mechanization, high-cost loci, and new technological trajectories are associated
with disproportionately high ; these innovation characteristics tend to be
pursued for internal use by large firms only.
Radical innovations have high average cost savings relative to prior
manufacturing practice. Were and independently distributed, the low
of these innovations would make most of them worthwhile investments
even for small firms. However, radical innovations require attentive engineering
effort, often large investments in and adaptation of materials and equipment, and
unusual pioneering of ways to think and ways to produce. Hence may rise
with , and radical innovations may be pursued mainly by large firms.
Thus each characteristic j is correlated with a high innovation-expense-to-
benefit ratio: for As increases, for extra
innovations in increasingly large firms, the expected value of tends to increase.
To guarantee a monotonic increase with , I assume that
for all >0. Also, the cumulative probability
distribution is assumed to satisfy .
Then, as proved in the Online Appendix:
Proposition 1: Larger firms disproportionately mechanize.
Proposition 2: Larger firms have a greater rate of process innovation.
Proposition 3: Larger firms disproportionately improve parts of the
manufacturing process that involve costly equipment and technologies.
y3k
r k / bk
y4k = bk
bk r k
r k / bk
r k / bk
bk
corr( y jk ,r k / bk ) > 0 j = 1,2,3,4. r k / bk
y jk
wk = r k / bk
E[y jk |wk = w]> E[y j
k |wk ≤ w] w
F(wk ) ′F (wk ) > 0
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Proposition 4: Larger firms disproportionately pursue new technological
trajectories.
Proposition 5: Larger firms disproportionately pioneer radical (strongly cost-
reducing) innovations only if innovation expense rises more than
proportionally with cost-reducing benefit.
C. Multi-Period Extension
A revised manufacturing process creates innovative opportunities not
previously possible or considered. Exogenous advances in science, engineering,
and tools also enable new innovations. Subsequent time period allows further
innovation within sets of prior innovative opportunities, plus in a set
of new innovative opportunities. Within opportunities with low
have been exhausted, but firms that unexpectedly grow larger pursue
innovations with higher . Within , larger firms exploit the same easy
opportunities as smaller firms plus opportunities with higher . Whether
mechanization and other characteristics increase monotonically with size at t
depends on the multi-period model, however, larger firms strongly tend to innovate
more as long as new innovative opportunities are sufficiently numerous.
D. Cost Reduction and Output
The innovations by firms lower per-unit manufacturing cost, driving a
learning curve. The standard learning curve model depicts average cost as
, where is firm, or industry as a proxy,
cumulative output. The standard model cannot usually be distinguished statistically
from a time-driven model , given problems in estimation
(Thompson 2012, Nordhaus 2014). The learning rate b associated with further
output or time often changes abruptly. While the standard model suggests cost
t >1
Ωt−1,…,Ω1
Ωt Ωt−1,…,Ω1,
r k / bk
r k / bk Ωt
r k / bk
C(t) = C(0)Z(t)−β Z(t) = Q(τ)τ=1
t−1∑
C(t) = C(0)t−β
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reduction based on productive activity, much literature indicates that this story
misrepresents typical multi-decade cost reduction.
The recent industry and firm studies suggest that cost reduction goals spur
innovation. Decisions to reduce unit cost through R&D resulted from expected
future output, and current output well predicted future output, in the learning curve
study that explicitly analyzes process R&D motivation (Sinclair, Klepper, and
Cohen 2000). Current output, the increment behind the standard learning curve,
predicts expected future output and cost-reducing innovation.
Since innovation drives mechanization, mechanization becomes an
additional axis of the learning curve. The logic of how innovation drives
mechanization and cost-reduction beseeches investigation of process innovation in
actual cases. Consider, then, the case of the television receiver.
II. The Development of Television Manufacture and its Process Innovations
In 1894, Guglielmo Marconi excitedly read of Heinrich Hertz’s research on
electromagnetic waves. Marconi began tinkering, and by 1896 he built on work of
Oliver Lodge and others to transmit and detect spark-produced waves across nearly
two miles. He established the British Marconi company in 1897 and an American
subsidiary in 1899, and soon began transatlantic radiotelegraphy. British Marconi
became the dominant firm, and American Marconi became the Radio Corporation
of America (RCA) at the end of World War I. However, a competing approach of
continuous wave radio allowed voice and sound transmission. Despite complex
patent competition and licensing, hundreds of companies began producing diverse
two-way radios and radio receivers (MacLaurin 1949).
Specific radio models made by these companies are documented with
photographs, notes, and diagrams on an enthusiast website, radiomuseum.org.
From the website, I recorded, for all companies that would enter U.S. television
manufacture, the first year each company made radios. RCA’s radios date to 1910
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through its predecessor American Marconi. General Electric, Westinghouse,
Zenith, and other firms followed from the late 1910s. Many manufacturers were
drawn by the advent of radio broadcasting around 1920. Firms’ dates of entry were
dictated largely by when they could enter, that is, by the accident of their birth dates.
While the first year of radio manufacture is one metric of success in radios,
a concern is that RCA and its established corporate partners General Electric and
Westinghouse used patents in a way that may have inhibited entry of firms in the
industry’s earliest years. Radio output in 1940 (MacLaurin 1949) provides an
alternative metric, reflecting which firms developed strong radio manufacturing
before World War II. Upstart firm Philco and its subsidiary Simplex Radio Corp.
together had more radio output than RCA by 1940, General Electric had only a 3%
share, and Westinghouse did not make the list. Otherwise the radio industry was
populated almost entirely by newcomers. The first radio production year and the
1940 radio manufacturing share thus provide alternative metrics of radio success
that are largely orthogonal in their representation of major established firms’ role.
Television manufacture took off after World War II. U.S. television
broadcasting began commercially in July 1941, but only two stations were in
commercial operation and roughly 6,000 receivers installed by early 1942 (Kersta
1942), when a wartime ban halted manufacture on April 22 (Federal Register 7
(42), March 10, 1942, pp. 1793-1794). Radio Manufacturers Association members
produced 6,476 television receivers in 1946 and 178,571 in 1947 (Televisor
Monthly, March 1948). U.S. output grew through 1955 before dipping and leveling
off, rising again only with the growth of color television sales in the 1960s
(Television Factbook 1991, pp. C-329 and C-332-333).
Television output data are available periodically for select firms. However,
the figures depend on noisy estimates and cannot support studies of time variation.
Instead, I use cross-sectional unit output data from 1950, for which I could obtain
the most detailed information on large and small firms (see Online Appendix).
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Market shares of innovating firms remained fairly stable. Although aggregate
output changed somewhat across years, and rescaling 1950 output by an annual
index of industry-wide output generally strengthens fit and increases statistical
significance in univariate regressions, I present results with the simple 1950 output
measure to avoid conflation with time-related trends in data availability. The
statistical analysis thus depends on cross-sectional identification. By contrast,
learning curve researchers to date rarely use plausible exogenous identification
strategies, with one exception being Benkard (2000), who uses instruments that
vary over time instead of this study’s cross-sectional identification.
A. Radio Entry Time or 1940 Production Predict 1950 Television Production
First-stage regressions of the logarithm of 1950 television output on the
above instruments are reported in Table 1. The instruments use no information
beyond 1940. Instead, firms that did not enter by 1940 or that lacked 1940 radio
output data are identified by indicators. These firms’ entry year and log radio
output are replaced by pre-1941 means, and the coefficients of the indicators
include impacts of differences from the means.
In column (1), for each year earlier entry into radio manufacture, 1950
television output is estimated to have been higher by (exp(0.102)–1=) 10.7%. Thus
20 years earlier entry corresponded to 7.7 times more output. Firms that did not
produce radios by 1940 are estimated to have had only 11.5% of the output of firms
that began producing radios at the mean time of 1928.4.
In column (2), a 1% increase in 1940 radio output yielded an estimated
1.12% increase in 1950 television output. Radio manufacturers lacking radio
output data in 1940 had 92.3% lower, than the mean firm with radio output data,
estimated television output in 1950. Firms that were not producing radios by 1940
had 98.2% lower estimated television output.
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Analyses use a sample of innovations. Columns (3) and (4) repeat the first-
stage regressions using observations on the innovations. In this sample, each year
of earlier entry increased expected 1950 television output by 5.8%, and firms that
did not produce radios by 1940 had 59.9% lower television output. Alternatively,
a 1% increase in 1940 radio output yielded a 0.68% increase in 1950 television
output, and the 1950 output was 35.4% lower for firms lacking 1940 radio output
data, or 65.4% lower for firms that did not produce radios by 1940. Column (5)
reports a regression with all the metrics of success in radio manufacture. The
estimates retain the same sign, and all radio metrics except the no-data indicator for
radio output remain highly statistically significant.
Predictive power is strong in all the models. The radio entry year metrics
explain 38% of the variance in 1950 market share in both samples. The radio output
metrics explain 49% and 48% of the variance in the two samples. The model F
statistics of 31.9 to 165.2 consistently indicate a strong relationship for instrumental
variable regressions of innovation and mechanization.
B. Process Innovation
The annual Industrial Arts Index listed 219 trade and technical articles about
television manufacture, and I scrutinized each article. I documented 303 new
manufacturing techniques, when first used for televisions. The innovations were
96% from U.S. firms and 4% from English firms. The articles and innovations span
1947-1971, with no articles immediately preceding or following. Only 5.0% of
innovations were from 1959-1971, when reporting was sparse. Picture tube
innovations are excluded, because picture tubes were made by select manufacturers
only. The innovations pertain mostly to black-and-white television, while color
televisions reached just 0.7% of U.S. homes by 1960 (Boedecker 1974, 44).
I codified mechanization for each innovation, as well as locus in
manufacturing, equipment size, cost impact, pursuit of a new technological
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trajectory, and other metrics. Coding quality was ensured by having an industrial
engineering student double-code the measures, then meeting to agree correct
treatments.4 The measures are described below.
C. Estimation
Did mechanization result because firm output drove increasingly marginal
cost-reducing innovation? Within the sample of innovations, I regress
mechanization on 1950 television output, initially instrumented by year of entry in
radio manufacture or radio output in 1940. Instrumental variables probit (IVP) or
probit maximum likelihood estimation is used for binary outcomes, or generalized
method of moments (GMM) or ordinary least squares for continuous outcomes.
Key variables appear in Figure 1 as a directed acyclic graph (Pearl 2009,
Imbens 2020). Firm television output (1), the model implies, affects the probability
that an innovation contributes to mechanization (2). Omitted variables other than
firm size (3) might affect output (1) and mechanization (2), biasing estimates.
Likewise, causal endogeneity may be suspected from mechanization affecting
output, since some cost-reducing innovations predate the measure of television
output, and unit cost may affect firm growth. The causal endogeneity concern may
be unfounded, given the relative stability of television market share. In any case,
the omitted variables (3) and endogeneity are addressed using radio manufacturing
entry time during 1910-1940 or radio output in 1940 (4) as an instrument.
The output variables (1 and 4) coincide with other aspects of firm size.
Organizational and innovative practices driven by output, such as departmental
innovation roles and culture of innovation, help optimize decisions in response to
output. These facets of firm output are not separated in determining the effects of
firm output on innovation.
4 For cost impacts, only differences of 3 or more were compared and corrected.
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The exclusion restriction that allows instrumented identification is that
radio entry time or output (4) affects television mechanization (2) only through
television output (1). Early entrants frequently became large radio manufacturers,
which after World War II used their production engineering workforces to develop
television production. Thus, manufacturing (and product engineering) capability
carried over from radio to television. Large radio manufacturers had high television
output, growth, and survival (Klepper and Simons 2000).
Might an excluded correlate of radio entry time or output, other than
television output, impact mechanization? Conceivably early radio firms had a
common trait such as mechanical savvy. Any era-specific founding traits, though,
seem unlikely to influence mechanization in a later era, after many founders were
replaced as chief executives and less capable firms and founders were selected
away by survival of the fittest. Conceivably radio output affected a firm outcome
that increased television mechanization but not television output. During 1941-
1949 this likely could have happened only through wartime government contracts.
Wartime contracts to radio manufacturers, though, seem to have been given to firms
roughly in proportion to their commercial output (Peck and Scherer 1962, 602-612),
contracts seem to have been for bulk production mainly when radio firms already
were highly mechanized (Civilian Production Administration 1946), and (log)
radio-related wartime contracts are statistically insignificant in analyses of
mechanization impacted by output (see the Online Appendix).
The most plausible excluded correlate may be simply age. Age might—the
idea has been disputed—make businesses less flexible, hindering innovation. If so,
the hindrance may not persist among firms that exhibit the flexibility to move from
one industry to another and are driven by competitive necessity to innovate.
Moreover, even among typical U.S. manufacturers, no age effect on mechanization
is apparent. Advanced manufacturing technology use among U.S. Census of
Manufactures plants in 1988 increased strongly with plant size but not plant age
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(Dunne 1994). Advanced technologies studied included robots and automated test
and inspection of inputs and final goods, mechanization types used for televisions.
Television output (1) in the theory also drives innovation in costly
production loci and a new technological trajectory, and may be related to other
innovation characteristics (5). Mechanization (2) might be frequent among large
firm innovations because of correlations (6) among innovation characteristics. If
omitted variables (3) or causal endogeneity substantially bias estimates of firm
output’s effect on mechanization, then the bias cannot be removed while controlling
for the related innovation characteristics (5), given the limited number of
instruments (4). However, if omitted variable (3) and endogeneity bias appear to
be minor, statistical analyses can control for the other innovation characteristics (5)
when assessing the effect of television output (1) on mechanization (2). Controlling
for other innovation characteristics (5) that are a deliberate means to
mechanization—notably a new technological trajectory that provides a means to
mechanize—may introduce bad controls that remove the very effect to be observed,
canonically causing attenuation bias toward zero.
Univariate estimates are reported in Table 2. Estimates are shown for both
instrument sets. The estimates are generally similar with the two instrument sets,
except for the alternative outcome Innovations per Year, used when assessing
reasons for the findings on mechanization. Results with the first instrument set,
entry time in radio manufacture by 1940, are a bit closer to zero for most of the
results about mechanization and many of the other outcome variables. Graphs of
the results use the first instrument set, to be conservative. Consider the effect of
television manufacturing output on mechanization.
III. Television Production and Mechanization
Mechanization is defined here in three ways. Automation innovations were
embodied in an object that operated independently for at least multiple seconds
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after activation (33% of innovations). Robots and continuously operating machines
are automated. Multiple operation innovations combined multiple of the same or
different manufacturing steps (29% of innovations). Complex innovations, a
subcase of multiple operation, combined manufacturing activities from different
manufacturing steps (8% of innovations). Pin-making machines so combined
previously divided steps of manufacture (Pratten 1980).
Mechanization rose with 1950 output, substantially and statistically
significantly as Table 2 shows, for all the definitions of mechanization (marginally
for multiple operation instrumented by entry year). Figure 2 plots, using solid lines
and symbols, the implied probability of each definition of mechanization versus
television output. The output values beneath the figure range from the lowest to
the highest output of innovating firms. The probability that an innovation aided
mechanization rises from near zero in the smallest firms to 43% for automation,
39% for multiple operation, and 17% for complex.
A sanity check uses readily available, low-cost mechanical methods. Tool
use indicates whether the innovation involved an object used in physical contact
with or directly operating on a television receiver being made, a component,
another tool, or a shipping container (90% of innovations). No statistically
significant effect of tool use is apparent in Table 2, and no increase in the
probability of tool use with firm output is apparent in Figure 2. As expected with
no barrier to implementation, tool use is not tied to output.
The analyses have used radio manufacturing entry time or 1940 radio
output as instruments to ensure identification of a causal effect of firm output on
mechanization and other innovation characteristics. However, it was a priori
unclear whether in simple probit regressions any bias in estimation might be
expected. In fact the evidence suggests little if any bias. Wald tests of exogeneity
for the mechanization-related regressions of Table 2 all fail to reject the null
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hypothesis of exogeneity (p = 0.2 to 1.0). The lack of strong endogenity suggests
controlling for, despite the inability to instrument for, other aspects of innovation.
Multivariate estimates therefore control, without instrumentation, for
characteristics of the innovative context that might influence mechanization.
Innovations were embodied in a machine of size less than 1.5, 1.5 to less than 7.5,
or 7.5 or more cubic feet, or not embodied in a machine or in a specific machine.
Printed circuit related innovations helped to make or use printed circuit boards,
which simplified wiring and were used to mechanize manufacture. General-
purpose innovations were potentially useful for other electronics mass-manufacture.
Non-specialized innovations could be applied without tailoring the innovation to a
specific television product. Innovations developed in multiple versions
independently and near-simultaneously by competing firms, or developed jointly
by a manufacturer and a supplier, are identified with 0-1 indicators. Year groups
account for change in innovative opportunities.
These controls are added to the models in Table 3, without instruments, for
the three mechanization outcomes. Since printed circuit boards were a route to
mechanization, the control for printed circuit board related innovations is removed
in columns (7), (9), and (11) to avoid controlling for the intention to mechanize.
The predicted probabilities of mechanization by firm size are plotted from columns
(7), (9), and (11) in Figure 2, using dotted lines and hollow symbols. The controls
bring slightly more pronounced estimated effects of firm output on mechanization.
Mechanization was consistently more likely for firms with greater 1950
television output. The estimated role of 1950 output is attenuated, as anticipated,
by the printed circuit control. Larger machines embodying an innovation were
more likely to be mechanized. For automation in column (6), the predicted mean
(average marginal effect) automation probability was 14%, 44%, or 62% for small,
medium, or large machines, or 5% if no machine size pertained. Printed circuit
related innovations had a predicted 36% chance of automation and 50% of multiple
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operation, versus 29% and 22% for other innovations. Innovations that did not
require specialization to the product had a 33% chance of automation but 5% of
complex mechanization, versus 25% and 11% for other innovations. When
competitors simultaneously developed an innovation, its chance of involving
automation was 11% compared to 32% otherwise. Accounting for these
characteristics of the innovative context, firm output increased all the measures of
mechanization. Likewise heteroskedastic probit estimates in the Online Appendix
indicate that firm output increased mechanization.
IV. Causes of the Direction of Innovation
Innovation therefore was directed toward mechanization most in larger
firms, but was this change caused by an innovation expense-spreading mechanism?
If large firms pursue innovations even with low benefit-to-R&D-expense ratios
, as well as with high benefit-to-R&D-expense as in small firms, then the
large firms must innovate more. Innovations per year are analyzed unweighted or
weighted by a measure of cost-reducing impact, a squared seven-point ranking
discussed below. Television output brought dramatic and statistically significant
increases in innovation, as shown in Table 2 and plotted in Panel A of Figure 3.
The smallest television manufacturers with innovations developed 0.15 innovations
per year with a summed squared rank of 0.81, while the largest developed 1.3
innovations per year with a summed squared rank of 7.5. By one way of thinking
this is unsurprising since the rise in process innovation per year was proportionate
to firm size, but recall that every unit manufactured in the smallest firm remained
much more costly than in the largest firm because of the paucity of innovations,
and that the largest firm must have worked hard to carry out even innovations that
were very expensive relative to their benefits—for which was very low—to
create a highly efficient manufacturing process.
bk / r k
bk / r k
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20
Did larger firm innovations span a higher R&D expense per dollar of unit
cost reduction? The R&D expense must generally have been much higher in loci
of the manufacturing process that included large, expensive equipment that would
be costly to change. These parts of the manufacturing process are identified by
having at least 25% of innovations embodied in equipment 7.5 cubic feet or more
in volume, the size of a typical adult male or larger. A 40% or 50% cutoff yields
similar results. Analyzing loci with large equipment, rather than innovations with
large equipment, avoids potential bias from manufacturer scale. Firm output
brought a major and significant increase in applications to high-cost loci as shown
in Table 2 and plotted in Panel B of Figure 3. The probability of innovations
pertaining to high-cost loci rose from 6.9% in the smallest firm with innovations to
59% in the largest firm.
Another high R&D expense was pioneering new technological approaches.
Printed circuits, a predecessor to integrated circuits, were emerging in the early
1950s. Firms developed printed circuit board production, machines to insert
components into boards, and soldering equipment to solder many components to
boards simultaneously. Improvements were particularly challenging for capacitors,
inductors, electron tubes, and transistors, and techniques gradually expanded the
components attached and soldered or even printed on the board. These techniques
were new, required creative technical development, and involved automated
machines. Large firms dominated significantly the printed circuit related
innovations, as shown in Table 2 and Panel C of Figure 3. An innovation’s
probability of printed circuit board use was for innovating firms with a
1950 output share of 0.066%, but 0.57 with an output share of 17.8%. Below 1.4%
share, the probability was less than 0.01.
The other way to examine the R&D expense per unit of cost reduction is
from the cost reduction side. If unit cost reduction were statistically
4.2×10−8
bk
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independent of R&D expense among innovations, then high unit cost reduction
innovations would attract all firms and be frequent especially in small firms. If
instead R&D expense rose sufficiently more than proportionately with unit cost
reduction, then high unit cost reduction innovations could be dominated by large
firms. Firm output was indeed associated with a significantly greater probability
that innovations brought high unit cost reduction, as shown in Table 2 and Panel D
of Figure 3. Cost reductions were ranked on a seven-point scale, with unit cost
decreasing perhaps with the square of the ranking. Firms with less than 1%
television market share developed predominantly innovations of ranks 1, 2, and
occasionally 3, whereas larger firms had an appreciable probability of innovations
ranked 4, 5, and even 6 and 7.
General-purpose and non-specialized innovation have no obvious relation
to benefit or expense. Correspondingly, neither has a clear relation to firm output.
In Table 2, only non-specialized innovations exhibit a marginally statistically
significant relationship, and then only using the radio output instruments.
Thus, larger firms dominated at innovation by pursuing not just easy
innovations but also the low benefit-to-R&D-expense innovations needed for high
efficiency. Although large firms are often accused of resistance to radical
innovation, snubbing of large firms is misplaced for even the more radical
incremental improvements, including new technological trajectories and high unit
cost reductions. Large firms likewise dominated in costly manufacturing loci and
in mechanization. Mechanization was only one aspect of the learning curve’s third
dimension, along with improvement in high-cost manufacturing loci, pursuit of a
new technological trajectory, and radical innovations that dramatically reduced unit
cost.
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V. The Learning Curve
Innovation brought large producers rapidly down the cost-reduction
learning curve. The traditional learning curve for black-and-white television
manufacture is in the bottom plane of Figure 4. The real price axis reflects cost, to
the extent competition keeps price near unit cost. The cost slopes down from $376
in 1947 to $193 in 1953, implying an 8.6% reduction in unit cost for each doubling
of cumulative output. Then the cost slopes down rapidly to $89 in 1968, implying
a 30.7% reduction in unit cost for each doubling of cumulative output. The rapid
decrease in unit cost beginning in 1953 coincides with the application of printed
circuit board methods and the roboticization this made possible in component
insertion, soldering, and testing.
The unit cost reduction variable shows major efficiency improvements from
innovation. Definitions of the cost reduction ranks appear in Table 4. An example
innovation with rank 2 was a foot switch that controlled air flow to an anvil riveter,
pneumatically allowing an operator to locate riveting to a chassis. An example
innovation with rank 6 replaced hand soldering of components, by moving
television chassis or circuit boards with electronic components already inserted,
lifting them above a wave of liquid solder, and creating permanent electrical
connections for many components at once. A similar ranking for automobile
manufacture is used by Abernathy, Clark, and Kantrow (1983).
Automation is the third dimension of the learning curve in Figure 4. The
cumulative squared cost impact ranks of automation innovations rise monotonically
from 0 at the start of 1947 to 801 at the start of 1966. A similar rise occurs in the
other two measures of mechanization, and in all the innovation characteristics
associated with a high ratio of innovation expense to unit-cost-benefit.
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A. Organizational Knowledge Gain, Not Worker Learning
The television manufacturing learning curve was organizational, not
embodied in production line operators. Television hand-wiring operators trained
to full speed in 1-4 weeks if inexperienced, or under 5 days if experienced (Miller
and Rogers 1964, 437). Rapid saturation of production worker learning is typical
and cannot explain most manufacturing cost reduction curves (Thompson 2012).
Expertise was partly embodied in equipment, documents, patents (rarely), and
engineers carrying out R&D.
B. Conditions for Innovation and Learning
Three conditions made television manufacturing ripe for routine internal
process innovation.
Predictable Returns to Innovation.—Innovative risk was hedged across
many innovations, mostly small. As Table 4 indicates, among the 303 innovations,
only 1.7% were ranked 6-7 with widespread benefits, and 9.9% were ranked 4-7
with major benefits local to one part of the manufacturing process. In contrast,
22.4% had rank 3, a large benefit at one production operator’s station or a more
modest benefit across multiple operators’ stations, 48.5% had rank 2, a substantial
benefit at one operator’s station or a little at multiple stations, and 19.1% had rank
1 with its more minor benefit.
Using squared ranks to gauge the total impact of innovations, as in
Abernathy, Clark, and Kantrow (1983), highlights the role of minor innovations.
The sum of the squared rankings is greatest for innovations of rank 3, totaling 612,
versus 256, 225, 72, and 147 respectively for the next four higher ranks, and versus
588 and 58 for innovations of rank 2 and 1. Since small innovations are likely
underreported, the net effect of minor innovations seems predominant.
Independent innovations in many loci also helped spread any risk. The four
most frequent loci were assembly of chassis, testing and alignment, conveyors and
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movers, and work positioning methods, each with 10-13% of innovations. Seven
loci had 4-9% of innovations, and three had 1-3%.
Large firms constructed organizational structures to routinize innovation.
Departments of Philco involved in television development and production included
Design Engineering, Factory Engineering, Field Engineering, Cabinet Design,
Industrial Engineering, Production, and Purchasing (Tele-Tech 1948). Field
Engineering worked with Design Engineering to ensure customer satisfaction.
Factory Engineering and Design Engineering coordinated on design improvements.
Factory Engineering then redesigned and re-tested sets to facilitate the mass
production overseen by Industrial Engineering.
Replenishment of Innovative Opportunities.—Both prior innovations and
new general technologies (like printed circuits, transistors, and integrated circuits)
created new needs and approaches for innovation. As a result, innovation remained
spread across the manufacturing process. Differences over time in the percentage
of innovations in a locus were modest, statistically significant using Fisher’s exact
test for only 5 of the 14 loci. The analysis compares 1947-1950, 1951-1954, 1955-
1958, and (for 15 innovations) 1959-1971. Chassis frame building and cabinet
making innovation mainly occurred early, chassis assembly innovation slowed in
1951-1954, manufacture of wire coils and yokes innovation increased in 1951-1954,
and innovation in making printed circuit boards awaited development of practical
printed circuit board technology in the mid-1950s.
Limited Sale of Innovations and Limited Patentability.—Of the 299 process
innovations from identified firms, only 25 involved suppliers. Only 14 came solely
from suppliers, including 6 that solely affected supplier production lines (for coils
and tuners).
Components and equipment were widely available. Cathode ray picture
tube manufacturers in the U.S. in successive editions of Television Factbook
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increased from 30 in 1949 to 68 in 1956, then fell to 14 in 1971. Pre-assembled
tuner (to tune in broadcast signals) manufacturers in the U.S. in annual editions of
Electronics Buyers Guide increased from 7 in 1949 to 28 in 1950, then fell to 8 in
1967 and rose to 13 in 1971. Picture tubes and tuners were often made by the
television firms. Standardized “Project Tinkertoy” component building blocks and
sets seem to have been doomed by inflexibility to product improvement.
Relevant machinery became widely available in the 1950s. Board
processing equipment to make printed circuit boards, component insertion
machines, and other tools came from firms including United Shoe Machinery
Corporation. Some firms worked with machinery suppliers to lead in equipment
development and adoption, and innovated to change or build on supplier equipment
and to adapt to supplied components.
Limited patentability reduced incentives to sell process innovations to the
industry. I carried out an extensive patent analysis. Only 2.6% of U.S. television
manufacturers’ innovations were wholly patented, and 2.2% were partially patented.
Another 1.9% were described but not claimed in patents. Many innovations were
probably obvious enough that they could not be claimed. A few involved business
processes not subject to patenting. Innovations often built on innovating firms’
production methods, and would rarely apply in other firms without a change to their
manufacturing methods.
C. Duplicative Innovation
Identical manufacturing needs brought duplicate innovation, especially for
high benefit-to-expense innovations. The trade literature revealed that many firms
solved the same problem, with diverse solutions. Conveyors and movers for chassis
being assembled on a production line were remarkably diverse. In some firms,
workers slid the chassis along worktops, while in others wheeled dollies were
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26
placed beneath the chassis, or a continual line of rollers facilitated pushing, or
conveyor belts moved the chassis continually or at timed intervals.
Likewise, diverse solutions were apparent for the placement of machinery
to punch holes in sheet steel to build an empty chassis, the number and layout of
holes punched simultaneously in successive punching operations, the method of
twisting together wires by operators including by hand and with automated air guns,
methods of soldering wires, test procedures and layout and function of test
equipment for television circuitry, methods to hold wooden parts in place when
building cabinets, manufacturing methods for steel and plastic cabinets, burn-in
arrangements to verify receivers’ continued operation, and methods to put
television receivers into packing boxes and close the boxes.
D. Product Innovation and Reliability
Product innovation also occurred steadily. Product innovations necessitated
change to precise manufacturing activity at specific locations in a plant, but almost
never required a change in manufacturing technique. Inserting components and
wires for revised electronic circuits required revised bins of parts and revised
directions for insertion and soldering by a few assembly operators or machines.
Also, an operator’s test procedure might change. A larger television screen might
require a change to the width and spacing of the assembly line, although some lines
accommodated multiple picture tube sizes, or used a single chassis design that
accommodated either of two sizes.
Product reengineering steadily improved manufacturing efficiency, but
product reengineering is almost never captured in the innovations data. The switch
to printed circuits necessitated product reengineering. The switch from wood to
plastic and steel cabinets and to printed veneers, which reduced material and
production cost, required process innovations in cabinet manufacture.
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Reliability of television receivers benefited from manufacturing
improvements. Innovations in integrated circuits, component insertion, and dip
soldering enhanced reliability (Arnold 1985, 113-114) and often had high cost
reduction ranks. Firms likely succeeded in reliability improvement when they
succeeded at cost reduction.
VI. Conclusions
Process innovation for U.S. television receivers drove major cost reductions
and pioneered methods of mechanization. Easy innovations pursued by all firms
were coupled with high-expense innovations in large firms. As a result, large firms
propelled the shift to mechanized production. Three kinds of mechanization,
automation, multiple operation, and complex combination of manufacturing steps,
all were propelled by large firms. These findings conform to a model of firms’
incentive to pursue innovation opportunities, in which leading firms expect to
produce the most and therefore innovate the most intensively. The model extends
the idea of R&D expense-spreading, to show that the direction of innovative
progress must be affected by firm size. Larger television manufacturers
disproportionately pursued innovation in expensive parts of plants where smaller
manufacturers frequently avoided innovating. They made bigger jumps more often,
in projects that must have been disproportionately expensive. Their printed circuit
board and mechanization innovations must have been particularly expensive.
By pursuing higher-cost innovations, larger firms moved faster down the
learning curve of unit cost reduction. Difficult as well as easy innovation projects
meant that large manufacturers refined their manufacturing processes and achieved
efficient production. Cost reduction drove down prices, kept strong innovators
profitable, and helped innovators survive despite the fall in prices. Innovation
continued long beyond the sample studied here. Labor productivity for U.S. radio
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and television receivers improved 75% in 1958-1970, 72% in 1970-1980, and 109%
in 1980-1985 (U.S. Bureau of Labor Statistics 1988, p. 130).
Manufacturers developed hundreds of innovations across the manufacturing
process. Minor innovations greatly outnumbered major innovations, and likely
swamped major innovations in total importance, as Abernathy, Clark, and Kantrow
(1983) observed for automobiles. Some innovations were necessary to allow new
product characteristics, with select incremental product improvements such as set
reliability and new cabinet types effectuated by process change, just as Pisano
(1997) points to new product introductions effectuated by process development.
International competition intensified for television manufacture in the
1970s and 1980s. Eventually, all the original U.S. manufacturers exited or were
acquired by their international competitors. While U.S. firms had been leaders in
applying the new technology of printed circuits, Japanese firms were faster to use
and pursued more intensively a new technological trajectory of integrated circuits
(Wooster 1986). U.S. businesses thereby lost control of the entire consumer
electronics sector, an economically important and profitable sector with close ties
to other electronics industries deemed important to national security. Attention to
the principles of continual process innovation might have prevented this loss, and
still can help nations which once were industrial leaders to become, if not industrial
leaders again, at least substantial producers of important industrial goods.
It remains to be confirmed whether the process innovation patterns for
televisions apply widely in other manufacturing industries and over the industry
life cycle. Historical and trade studies suggest that manufacturing process
innovation typically fits the conditions observed here. In DuPont’s manufacture of
rayon, for example, minor technical changes exceeded major changes in their
aggregate cost reduction, improvements occurred rather steadily across the
manufacturing process, and improvements were sustained (Hollander 1965). How
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widely the marginal cost of innovations drives mechanization and the direction of
cost reduction, however, deserves analysis across industries.
This paper has addressed a central part of the issues of mechanization and
cost reduction considered urgent today. Mechanization is a third dimension of
manufacturers’ cost reduction learning curve, driven by the logic of innovation
decisions. By understanding how innovation expense and benefit incentives drive
production cost reduction, researchers may better understand why and how
automation and other forms of process innovation occur and impact firms and their
workers. These issues are at the core of the efficiency benefits and workforce and
industrial shifts that result from firm innovation.
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Figure 1. Relationships among key variables as a directed acyclic graph. Causal
impacts on each innovation’s unit cost reduction are beyond the scope of analysis.
Figure 2. The estimated probability of an innovation involving mechanization, for
alternative definitions of mechanization, rose with television manufacturer output
in 1950 (logarithmic). Solid symbols and lines denote the fitted equations from
Table 2. Hollow symbols and dotted lines denote average marginal effects after
controls in Table 3 columns (7), (9), and (11).
other innovation characteristics (5)
radio entry time or output (4)
television output (1)
(6)
correlates other than firm size (3)
mechanization (2)
unit cost reduction
0
.2
.4
.6
.8
1
.066 .268 1.08 4.39 17.8Output Share (%) in 1950
automationmultiple operationcomplextool use
after controlsafter controlsafter controls
Probability of Mechanization Innovation
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Figure 3. Other estimated outcomes rose with firm output. A. Number of
innovations and sum of squared cost-impact ranks per year. B. Probability of each
innovation being in a costly-to-modify locus, measured by at least 25% of
equipment being 7.5 cubic feet or larger. C. Probability of each innovation
involving printed circuit board use. D. Probability distribution of innovations’
effect on unit manufacturing cost. Curves are independently estimated probabilities
that cost-impact rank is less than 2, 3, 4, 5, 6, or 7. A-D. Horizontal axes are
television manufacturing output share in 1950 (logarithmic).
number of innovations
summed rank2 ofinnovations
0
2
4
6
8
.066 .268 1.08 4.39 17.8Output Share (%) in 1950
A. Number and Rank2 of Innovations Per Year
0
.2
.4
.6
.8
1
.066 .268 1.08 4.39 17.8Output Share (%) in 1950
B. Probability of Costly (Large Equip.) Locus Innovation
0
.2
.4
.6
.8
1
.066 .268 1.08 4.39 17.8Output Share (%) in 1950
C. Probability Pertinent to Printed Circuit Board Use
Rank 1 Rank 2 Rank 3
Rank 45
67
0
.2
.4
.6
.8
1
.066 .268 1.08 4.39 17.8Output Share (%) in 1950
D. Probability Distribution of Cost-Impact Ranks
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36
Figure 4. Learning curve for televisions. Automation rose as black-and-white
televisions’ real wholesale price (1958 US $) declined with U.S. cumulative
industry output, 1947-1968. Cumulative output and price from Boston Consulting
Group (1972, p. 93). Automation is the sum of squared ranks of automation
innovations by the start of each year, and ranges from 0 to 801.
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Table 1. First-Stage OLS Regressions: Television Manufacturing Log Output Share
in 1950 as Determined by Prior Radio Manufacturing
Observations on Firms Observations on Innovations (1) (2) (3) (4) (5) Radio Entry Time -0.102 -0.0567 -0.0331 (0.0444) (0.00492) (0.00445)
No Radio Entry -2.159 -1.460 -0.913 -0.623 -0.682 by 1940 (0.294) (0.319) (0.116) (0.126) (0.129)
Log Radio Output 1.124 0.682 0.626 Share in 1940 (0.269) (0.0669) (0.0573)
No Data on Output -2.569 -0.437 -0.0983 Share in 1940 (0.402) (0.116) (0.113)
Constant 0.662 -0.923 2.416 0.563 0.970 (0.842) (0.570) (0.0648) (0.172) (0.158) R2 0.375 0.492 0.377 0.480 0.535 F 31.19 133.6 150.6 165.2 164.6
Notes: Columns (1)-(2) use 160 observations, each a firm. Robust standard errors
in parentheses. Columns (3)-(5) use 285 innovation-firm pairs representing 271
innovations. Cluster-robust standard errors in parentheses, clustered by innovation.
Radio Entry Time is years since 1910.
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Table 2. Second-Stage Estimated Effects of Ln(Output Share in 1950) on
Alternative Dependent Variables, Univariate IVP and GMM Analyses
Dependent Instrument Set Dependent Instrument Set Variable Entry Output Variable Entry Output Automation 0.294 0.332 Cost Reduction 0.446 0.366 (0.138) (0.131) Rank≥2 (0.151) (0.152) Multiple 0.269 0.294 Cost Reduction 0.513 0.567 Operation (0.139) (0.129) Rank≥3 (0.130) (0.109) Complex 0.616 0.761 Cost Reduction 0.633 0.553 (0.250) (0.208) Rank≥4 (0.167) (0.152) Tool Use -0.195 -0.148 Cost Reduction 0.510 0.455 (0.181) (0.154) Rank≥5 (0.226) (0.221) Innovations 0.208 0.988 Cost Reduction 0.467 0.316 per Year (0.047) (0.034) Rank≥6 (0.260) (0.277) Cost Reduction 9.493 9.663 Cost Reduction 0.770 0.882 Rank2 per Year (1.116) (0.193) Rank=7 (0.510) (0.381) Loci with Large 0.305 0.290 General 0.197 0.089 Equipment (0.131) (0.122) Purpose (0.198) (0.182) Printed Circuit 0.989 0.737 Non- -0.211 -0.206 Related (0.125) (0.158) Specialized (0.137) (0.124)
Notes: Estimates use IVP conditional maximum likelihood estimation with 285
observations on innovation-firm pairs representing 271 innovations, or for
Innovations per Year and Rank2 per Year, GMM with 160 observations on firms.
Ln(Output Share in 1950) is treated as endogenous, with one of two sets of
instruments: Entry (year in radios) instruments are those in columns (1) and (3) of
Table 1, output (in 1940 radios) instruments are those in columns (2) and (4). GMM
uses asymptotic efficient (under heteroskedasticity) weight matrices. Standard
errors in parentheses are cluster-robust, clustered by innovation, for IVP, or robust
for GMM.
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Table 3. Estimated Effects of Ln(Output Share in 1950) and Controls on
Mechanization Metrics
Automation Multiple Operation
Complex
(6) (7) (8) (9) (10) (11) Ln(Output 0.488 0.555 0.317 0.458 0.656 0.766 Share 1950) (0.118) (0.111) (0.112) (0.104) (0.205) (0.195) In Machine 1.5- 1.207 1.218 0.189 0.223 0.161 0.211 7.5 Cubic Feet (0.252) (0.250) (0.267) (0.252) (0.418) (0.409) In Machine 7.5+ 1.735 1.714 0.768 0.721 0.628 0.705 Cubic Feet (0.238) (0.239) (0.218) (0.216) (0.347) (0.345) Not in Machine, -0.682 -0.681 0.406 0.399 0.451 0.426 Not 1 Machine (0.587) (0.585) (0.374) (0.347) (0.449) (0.420) Printed Circuit 0.483 1.094 0.690 Related (0.283) (0.269) (0.395) General Purpose 0.150 0.209 0.159 0.308 -0.493 -0.346 (0.533) (0.544) (0.372) (0.376) (0.527) (0.520) Non-Specialized 0.404 0.343 -0.174 -0.300 -0.623 -0.687 (0.226) (0.217) (0.219) (0.203) (0.302) (0.277) 2+ Competitors -1.262 -1.193 0.368 0.324 0.273 0.234 Developed (0.612) (0.551) (0.380) (0.365) (0.513) (0.484) Developed with 0.268 0.201 0.280 0.141 0.570 0.383 Supplier (0.459) (0.447) (0.495) (0.485) (0.622) (0.647) Years 0.558 0.631 0.894 1.032 -0.365 -0.0950 1951-1954 (0.306) (0.299) (0.287) (0.288) (0.443) (0.443) Years 0.193 0.426 0.283 0.822 -0.250 0.170 1955-1958 (0.293) (0.271) (0.318) (0.274) (0.482) (0.370) Years 0.951 1.050 1.205 1.347 0.751 0.864 1959-1971 (0.497) (0.452) (0.523) (0.512) (0.548) (0.517) Constant -2.836 -2.980 -2.233 -2.535 -2.454 -2.804 (0.671) (0.685) (0.480) (0.507) (0.576) (0.619) Pseudo 0.346 0.338 0.197 0.149 0.307 0.283
Notes: Probit maximum likelihood estimates with 285 observations on innovation-
firm pairs representing 271 innovations. Standard errors in parentheses are cluster-
robust, clustered by innovation.
R2
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Table 4. Process Innovations’ Cost Impact Ranks: Frequencies and Summed
Squares
Rank Definition N 1 affected one operator with little or no apparent difference, or
multiple operators with no difference, in manufacturing cost
58
2 made a substantial difference at one production operator’s station,
or helped a little at multiple stations
147
3 helped a lot at one station, or fairly significantly at multiple
stations
68
4 helped especially at one station, or quite significantly at multiple
stations
16
5 helped a lot at multiple stations, or requisite to get something done
at one station given how the production line was newly set up
9
6 yielded substantial cost savings in many parts of the plant and
may have opened avenues for yet more improvement
2
7 yielded major cost savings in many parts of the plant and may
have opened avenues for yet more improvement
3
Note: Definitions refer to operators and stations. An operator is a person working
in a production plant. A station is a location where one operator worked, or rarely
two operators simultaneously on the same physical object. More than 105 (in an
early RCA plant) stations, excluding cabinet manufacture, existed (Zeluff 1947).
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FOR ONLINE PUBLICATION
Online Appendix
1. Proof of Main Propositions
The sample space contains innovative opportunities 𝑘 that vary in R&D
expense 𝑟!, benefit to average production cost 𝑏!, and a characteristic 𝑦"! such as
mechanization. See Figure A1. All innovations from the vertical axis to the
boundary ray where 𝑟/𝑏 = 𝑄 are implemented by a firm with output 𝑄. A smaller
firm implements all innovations in the darker shaded region, and a larger firm
implements all innovations in both the darker and lighter shaded regions. Each
ratio 𝑤 = 𝑟/𝑏 corresponds to innovations on a particular ray from the origin.
Let 𝑓"(𝑏#, 𝑟#, 𝑦#) denote the joint probability density. I address the case
where the characteristic is a 0-1 variable; for a continuous characteristic, the proof
is analogous. Let 𝐹(𝑤.) denote the cumulative probability that 𝑟 𝑏⁄ ≤ 𝑤. , i.e., the
probability that an innovation is to the top left of the ray corresponding to 𝑤. , or on
the ray, but not to the bottom right of the ray.
Figure A1. The sample space for 𝑟 and 𝑏, with rays where 𝑏 𝑟⁄ = 1/𝑄 for some 𝑄.
b
r
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Main Propositions: If 𝐹$(𝑤) > 0 exists for all 𝑤 > 0 , and 𝐸5𝑦"!6𝑤! = 𝑤.7 >
𝐸5𝑦"!6𝑤! ≤ 𝑤.7 for all 𝑤. > 0 , then 𝐸5𝑦"!6𝑄7 increases with 𝑄 and 𝐸5𝑦"!6𝑆7
increases with 𝑆, for j=1,2,3,4.
Proof: The text shows that 𝑤. = 𝑄, and that by the implicit function theorem 𝑄
increases with S, so 𝐸5𝑦"!6𝑄7 and 𝐸5𝑦"!6𝑆7 increase with 𝑄 and 𝑆 if %%&'𝐸5𝑦"!6𝑤! ≤ 𝑤.7 > 0 for all 𝑤. > 0. This is shown below.
The expectation of 𝑦"! among the firm’s innovations is
𝐸5𝑦"!6𝑤! ≤ 𝑤.7 = 9 9 : 𝑦#𝑓"(𝑏#, 𝑟#, 𝑦#) ∕ 𝐹(𝑤.)(
)!*#
𝑑𝑏#+∕&'
-!*#𝑑𝑟#
.
+!*#
= 9 9 𝜌 : 𝑦#𝑓"(𝑏! = 𝜌 sin 𝜃 , 𝑟! = 𝜌 cos 𝜃 , 𝑦#)(
)!*#
1𝐹(𝑤.)
𝑑𝜌.
/*#𝑑𝜃
0 1⁄
3*456"#&'
= 9 9𝜌
1 + 𝑤1 : 𝑦#𝑓" E𝑏! =𝜌
√1 + 𝑤1, 𝑟! =
𝜌𝑤√1 + 𝑤1
, 𝑦#G(
)!*#
1𝐹(𝑤.)
𝑑𝑤&'
&*#𝑑𝜌
.
/*#
The second line uses polar coordinates, with 𝜃 the angle in radians from the
horizontal axis to a ray, and 𝜌 the distance from the origin along the ray. The third
line substitutes the 𝜃 coordinate with a 𝑤 coordinate, through the change of
variables 𝑤 = cot 𝜃, so 𝑑𝜃 = −𝑑𝑤 ∕ (1 + 𝑤1), 𝑏# = 𝜌 sin 𝜃 = 𝜌 ∕ √1 + 𝑤1, and
𝑟# = 𝜌 cos 𝜃 = 𝜌𝑤 ∕ √1 + 𝑤1. In the resulting formula, the term /(7&$ corrects for
integration across (𝜌, 𝑤) coordinates, analogous to the necessity to multiply by 𝜌
when integrating across polar coordinates (𝜌, 𝜃).
The partial derivative with respect to 𝑤. is 𝜕𝜕𝑤.
𝐸5𝑦"!6𝑤! ≤ 𝑤.7
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= 9𝜌
1 + 𝑤.1: 𝑦#𝑓" E𝑏! =
𝜌√1 + 𝑤.1
, 𝑟! =𝜌𝑤.
√1 + 𝑤. 1, 𝑦#G
(
)!*#
1𝐹(𝑤.)
𝑑𝜌.
/*#
−9 9𝜌
1 + 𝑤1 : 𝑦#𝑓" E𝑏! =𝜌
√1 + 𝑤1, 𝑟! =
𝜌𝑤√1 + 𝑤1
, 𝑦#G(
)!*#
𝐹$(𝑤.)𝐹1(𝑤.)
𝑑𝑤&'
&*#𝑑𝜌
.
/*#
This is strictly positive for all 𝑤. > 0 if and only if:
9𝜌
1 + 𝑤.1: 𝑦#𝑓" E𝑏! =
𝜌√1 + 𝑤.1
, 𝑟! =𝜌𝑤.
√1 + 𝑤. 1, 𝑦#G
(
)!*#
1𝐹$(𝑤.)
𝑑𝜌.
/*#
> 9 9𝜌
1 + 𝑤1 : 𝑦#𝑓" E𝑏! =𝜌
√1 + 𝑤1, 𝑟! =
𝜌𝑤√1 + 𝑤1
, 𝑦#G(
)!*#
1𝐹(𝑤.)
𝑑𝑤&'
&*#𝑑𝜌
.
/*#
for all 𝑤. > 0. The right hand side of this expression is 𝐸5𝑦"!6𝑤! ≤ 𝑤.7.
The left hand side is next shown to be 𝐸5𝑦"!6𝑤! = 𝑤.7. To see this, first
compute 𝐹$(𝑤.):
𝐹(𝑤$) = ' '𝜌
1 + 𝑤!+ 𝑓" -𝑏# =𝜌
√1 + 𝑤!, 𝑟# =
𝜌𝑤√1 + 𝑤!
, 𝑦$3%
&!'$
(
)'$𝑑𝜌
*+
*'$𝑑𝑤
𝐹′(𝑤.) = 9𝜌
1 + 𝑤.1: 𝑓" E𝑏! =
𝜌√1 + 𝑤.1
, 𝑟! =𝜌𝑤.
√1 + 𝑤. 1, 𝑦#G
(
)!*#
.
/*#𝑑𝜌
Then note that the expectation 𝐸5𝑦"!6𝑤! = 𝑤.7 is
9𝜌
1 + 𝑤. 1: 𝑦#𝑓" E𝑏! =
𝜌√1 + 𝑤. 1
, 𝑟! =𝜌𝑤.
√1 + 𝑤.1, 𝑦#G
(
)!*#
.
/*#
1𝑀𝑑𝜌
where
𝑀 = 9𝜌
1 + 𝑤.1: 𝑓" E𝑏! =
𝜌√1 + 𝑤. 1
, 𝑟! =𝜌𝑤.
√1 + 𝑤.1, 𝑦#G
(
)!*#
.
/*#𝑑𝜌 = 𝐹′(𝑤.)
so that
𝐸5𝑦"!6𝑤! = 𝑤.7
= 9𝜌
1 + 𝑤. 1: 𝑦#𝑓" E𝑏! =
𝜌√1 + 𝑤. 1
, 𝑟! =𝜌𝑤.
√1 + 𝑤. 1, 𝑦#G
(
)!*#
.
/*#
1𝐹′(𝑤.)
𝑑𝜌
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which is the aforementioned left hand side. ∎
Remark: The propositions compare manufacturers of one product, excluding
upstream and downstream firms. The model applies to cost reductions that stem
from adoption of third-party equipment only if there is a substantial expense
equivalent to innovation efforts, such as to identify the right equipment to adopt
and to figure out how to get it working in a plant.
2. Manufacturer Entry Year and Output Data
Manufacturer entry years were determined using current and past names of
each firm, as well as names of acquired radio manufacturers. I use the earliest year
when radio manufacturing entry occurred for merged firms. Name changes,
acquisitions, and mergers were identified using historical and enthusiast books and
websites, intensive searches of New York Times articles, and other newspapers and
magazines.
MacLaurin (1949, 139 and 146) lists 1940 production for 18 radio
manufacturers totaling 87% of the U.S. output of about 11.8 million radio receivers.
Market shares of predecessors are combined for merged firms.
Televisor Monthly’s March 1948 data indicating that Radio Manufacturers
Association members produced 6,476 television receivers in 1946 and 178,571 in
1947 can be viewed at http://earlytelevision.org/us_tv_sets.html. The site is
archived at the Internet Archive.
Datta (1971, 14, 215-216, and 295) reports television receiver market shares,
among 7.4 million units sold, that total 69.6%. Sears’ 0.8% share from Datta is
attributed one-third each to Air King and the two other firms that produced for Sears
around this period (Carbonara 1989) (see also Television Digest and FM Reports
issues from 1948). For remaining firms, projected 1950 outputs from the
Television Shares Management Co. (TSMC) are used if available (Barron's 1950),
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multiplied by a constant of proportionality such that firms in both sources have the
same total share. This leaves 5.83% of sales unaccounted for. Half the remainder
is attributed evenly to 11 second-echelon firms listed by TSMC without output
projections and not listed by Datta. The other half is attributed evenly to the 69
additional firms.
For firm-level analyses, firms not manufacturing in 1950 have zero output,
but when computing log 1950 output share they are assigned one standard deviation
lower log output share than the lowest value for 1950 manufacturers, to
acknowledge their smaller size and avoid undue influence on the results while
retaining the firms in the analysis. The estimates retain the same sign and remain
statistically significant if firms not manufacturing in 1950 are assigned two or zero
standard deviations lower log output share than the lowest value for 1950
manufacturers, or if the firms are dropped.
The value of output for World War II U.S. government contracts comes
from a four-volume report of the Civilian Production Administration (1946), which
disclosed contracts by firm. When totaling the value of radio-related contracts, I
include radios, radio kits, major radio parts, unspecified communication equipment,
unspecified sound signal apparatus, and radio equipment for aircraft. I exclude
components such as tubes and crystals, antennas, and classified products.
Classified products are effectively missing data, but represent only 1.8% of the total
value of wartime contracts filled by the television manufacturers with process
innovations in the data. Television manufacturers are not combined with their
parent firms, to avoid possible different interpretations of the “unspecified”
categories.
3. Innovations Data
Articles relevant to television manufacturing processes come from the
Industrial Arts Index categories “Television Receiving Apparatus: Manufacture”
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and “Television Receivers: Manufacture,” plus manufacturing-related articles from
television testing and other television categories. Articles were found from 1947
to 1971, in the journals Electronics (145 articles), Tele-Tech (12), Iron Age (6),
Journal of the British Institute of Radio Engineers (6), Factory Management and
Maintenance (5), Industrial Finishing (5), Steel (5), and twenty-one other journals
(3 or fewer articles each). Articles in Electronics were often short manufacturing
tips. Other articles, especially in Tele-Tech, were special features with many multi-
page subsections.
Only the first use of a technique is considered an innovation. Later uses of
the same technique by another firm were rarely documented, and here are not
considered innovations. In studying duplicate innovation, close duplicates could
not be measured systematically, given the lack of published information on
additional manufacturers’ implementation of a previously described manufacturing
method.
I developed an earlier version of the data, with cost-impact ranks but absent
other measures of the direction and nature of innovations (Klepper and Simons
1997, 2000, 2005). Beyond the 264 U.S. process innovations in the earlier version,
I added innovations to include 14 non-U.S. innovations and 25 more U.S.
innovations. The U.S. additions include a few innovations missed in earlier
readings of the trade articles, and they disaggregate groups of innovations by a
consistent rule, instead of counting an entire group as one in some cases where
separate improvements were related in purpose or locus. One of the most closely-
related groups, for example, involved (a) rubber pads designed to press against and
thereby clinch tight the wire leads that protruded below a printed circuit board, (b)
an aluminum molding technique to develop master molds to make in quantity the
rubber clinching pads in place of a prior hand-carving method to make the pads,
and (c) development of a foot pedal trigger for an operator to activate hydraulic
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cylinders that sequentially patted down the protruding wire leads and then cut and
clinched them.
Loci of innovation are shown in Figure A2, in which the blocks pertain to
parts of the manufacturing process. The chassis or interior frame of a television
was first built up and riveted together (1), in preparation for the placement of
numerous electronic components inside the chassis. Component parts, usually
purchased from third-party suppliers, were collected, tested where needed for
quality assurance, sometimes worked on further, and sorted into bins (2). Some
components, including coils and yokes (3) and sometimes printed circuit boards (4)
were prepared inside the factory. Design changes in the television itself (5) had
implications for the manufacturing process, and careful design could facilitate
assembly as well as decrease assembly-related faults.
Components were assembled into the chassis, initially by a long line of
employees who plucked parts from bins and placed them into the correct locations
in the chassis or circuit boards (6). Wire leads of the components needed to be
attached to each other or to circuit boards, and this was done by soldering (7), that
is, using a melted metal alloy to attach the component wires. Soldering of sub-
assemblies or parts just assembled might be done before further assembly, so
assembly and soldering were intermixed operations, and together accounted for the
biggest part of the production process. After and sometimes during assembly, the
electronics of a television receiver were tested and aligned (8), in part by attaching
a picture tube and having the assembled television receive a test signal within the
manufacturing plant, then adjusting the circuitry for signal reception.
Picture tubes (9) were occasionally made by the manufacturer of the
television receiver, in a separate plant, but more often were bought from third-party
manufacturers and tested after arrival. Hence picture tube manufacturing
innovations are not analyzed. Cabinets were commonly made within the same
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Figure A2. Categorization of television receiver process innovations.
television manufacturing plant (occasionally) or firm (10). The assembled chassis
and picture tube were together slid into a cabinet (11), then packed into a box and
crated for shipping (12).
Conveyors and movers (13), including rollers, mechanized conveyer belts,
or overhead movers, facilitated transport from one worker’s station or section of a
plant to another. Positioning devices (14) included fixtures and jigs custom-
designed to hold pieces of work and help guide assembly and soldering operations,
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plus chassis trunnions which allowed the chassis to be pivoted during work
operations. Movers and positioning devices also appeared less frequently in other
parts of the process, such as cabinet making, even outside of the dashed lines.
An “Other” category (15) is used for parts of the manufacturing process not
classified elsewhere. This includes plant layout, output count, quality control
scoreboards to motivate employees, use of test results to guide operational
improvements, and pollution controls. Five of the 303 innovations were classified
in the “Other” category.
Equipment sizes were volumes usually estimated from photographs. Air
space was included if the object when not in use would preclude use of the air space.
I matched the U.S. television manufacturers’ process innovations to U.S.
patents by the innovating firms. I used extensive keyword searches of patent full
text to identify patents that might possibly describe an innovation, with sets of
keywords chosen to ensure identification of relevant patents. I read potentially
related patents closely to determine their relevance to an innovation. For the 12
innovating firms that applied for under 3,000 patents during 1935-1975, I checked
all of each firm’s patents from that period to ensure that I did not miss relevant
patents in keyword searches. The studies of all patents of each of the 12 firms
confirmed that the keyword searches correctly identified the relevant patents.
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4. Innovating Firms
Table A1. Innovation Metrics, for Each U.S. Manufacturer with Reported
Innovations in 1948-58
Total in 1948-1958 Per Year Firm Innov. Rank2 Years Innov. Rank2
Philco 38 252 11 3.5 22.9 RCA 31 231 11 2.8 21.0 DuMont(AllenB.)Laboratories 36 173 11 3.3 15.7 GeneralElectric 26 171 11 2.4 15.5 Admiral 9 117 11 0.8 10.6 Westinghouse 28 117 11 2.5 10.6 CBS-Columbia(initiallyAirKingProducts) 16 83 9 1.8 9.2
EmersonRadioandPhonograph 22 100 11 2.0 9.1 SylvaniaTelevision(ColonialRadioCorp.) 7 82 10 0.7 8.2
Crosley(AvcoManufacturing) 14 68 9 1.6 7.6 ZenithRadio 6 46 11 0.5 4.2 OlympicRadioandTelevision 16 40 11 1.5 3.6 Packard-Bell 6 28 11 0.5 2.5 Motorola 4 26 11 0.4 2.4 JacksonIndustries 1 4 6 0.2 0.7 BelmontRadio(subsidiaryofRaytheon) 2 5 9 0.2 0.6
Tele-ToneRadio 1 1 6 0.2 0.2 Notes: Innov. is number of innovations, Rank2 is sum of squared cost-impact ranks
of innovations, and Years is years manufacturing televisions, all during 1948-1958.
Per Year divides by manufacturing years during this period. For 1947, I have been
unable to locate complete data specifying which firms manufactured televisions, so
1947 is excluded in computing these innovation metrics.
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5. Apparent Exogeneity of Log 1950 Television Output, Marginal Effects with
Printed Circuits Control, and Non-Instrumented Univariate Estimates
Table A2. P-Values from Wald Tests of Endogeneity of Log 1950 Television
Output, for Main Regressions in Table 2
Instrument Set Dependent Variable Entry Year Share Automation 0.241 0.346 Multiple Operation 0.198 0.209 Complex 0.217 0.986 Tool Use 0.300 0.352
Note: p-values from Wald tests with distributed test statistics. The null
hypothesis corresponds to log 1950 television market share being exogenous.
Figure A3. Variant of Figure 2, with average marginal effects after controls
computed using the models of Table 3 columns (6), (8), and (10).
χ2(1)
0
.2
.4
.6
.8
1
.066 .268 1.08 4.39 17.8Output Share (%) in 1950
automationmultiple operationcomplextool use
after controlsafter controlsafter controls
Probability of Mechanization Innovation
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Table A3. Extension of Table 2 to Include Non-Instrumented Analyses
Instrument Set Dependent Variable Entry Output None Automation 0.294 0.332 0.415 (0.138) (0.131) (0.094) Multiple Operation 0.269 0.294 0.398 (0.139) (0.129) (0.094) Complex 0.616 0.761 0.760 (0.250) (0.208) (0.204) Tool Use -0.195 -0.148 -0.045 (0.181) (0.154) (0.105) Innovations per Year 0.208 0.192 0.209 (0.047) (0.045) (0.044) Cost Reduction Rank2 per 1.192 1.112 1.267 Year (0.273) (0.255) (0.281) Loci with ≥ 25% of Innov. 0.305 0.290 0.206 Embodied in Large Equip. (0.131) (0.122) (0.086) Loci with ≥ 40% of Innov. 0.310 0.302 0.159 Embodied in Large Equip. (0.130) (0.119) (0.088) Loci with ≥ 50% of Innov. 0.244 0.235 0.106 Embodied in Large Equip. (0.133) (0.121) (0.091) Cost Reduction Rank≥2 0.446 0.366 0.315 (0.151) (0.152) (0.097) Cost Reduction Rank≥3 0.513 0.567 0.358 (0.130) (0.109) (0.098) Cost Reduction Rank≥4 0.633 0.553 0.419 (0.167) (0.152) (0.139) Cost Reduction Rank≥5 0.510 0.455 0.459 (0.226) (0.221) (0.212) Cost Reduction Rank≥6 0.467 0.316 0.324 (0.260) (0.277) (0.265) Cost Reduction Rank=7 0.770 0.882 0.927 (0.510) (0.381) (0.332) Printed Circuit 0.989 0.737 0.746 Related (0.125) (0.158) (0.129) General Purpose 0.197 0.089 0.165 (0.198) (0.182) (0.119) Non-Specialized -0.211 -0.206 -0.123 (0.137) (0.124) (0.087)
Notes: See Table 2 notes. Cluster-robust or robust standard errors in parentheses.
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6. Heteroskedastic Probit Models
Standard probit models assume homoskedasticity. This assumption is
commonly relaxed using heteroskedastic probit models of the form
Pr[𝑦8 = 1|𝑥8 , 𝑧8] = Φ(𝛽$𝑥8 ∕ exp(𝛾$𝑧8)), where 𝑦8 is a dependent variable and 𝑥8
and 𝑧8 are vectors of independent variables for observation 𝑖, 𝛽 and 𝛾 are vectors
of parameters, and Φ(∙) is the standard normal cumulative distribution function.
The heteroskedasticity term 𝛾$𝑧8 may not include a constant term 𝛾# because it
would be jointly indeterminate with 𝛽; dividing 𝛽 by exp(𝛾#) removes 𝛾# from the
model and scales 𝛽, so choosing 𝛾# = 0 defines a scaling for 𝛽.
Heteroskedastic probit models can be unstable and have convergence
problems especially when regressors included in 𝑧8 are also included in 𝑥8 . For
reliable estimation the number of coefficients must remain modest relative to the
number of observations. Complex mechanization occurred for only 21 of 285
innovations, making estimation especially difficult for that outcome. For these
reasons the estimates below maintain reasonably sparse models with a modest
number of variables in 𝑧8 . Distinguishing four time periods often creates
convergence problems, so a single continuous time variable often is used in 𝑧8 and,
for complex mechanization, in 𝑥8 as well.
Tables A4, A5, and A6 present heteroskedastic probit maximum likelihood
estimates for the three mechanization measures. Analyses use 285 observations on
innovation-firm pairs representing 271 innovations. Standard errors in parentheses
are cluster-robust, clustered by innovation. Average estimated probabilities
(marginal effects) of mechanization appear in Table A7.
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Table A4. Estimated Heteroskedastic Probit Effects of Ln(Output in 1950) and Controls on Automation (see notes above) (A1) (A2) (A3) (A4) (A5) Terms in 𝛽$𝑥8 Ln(Output 0.541 0.387 0.540 0.552 0.296 Share 1950) (0.145) (0.163) (0.103) (0.115) (0.104) In Machine 1.5- 1.164 1.181 1.065 1.208 0.646 7.5 Cubic Feet (0.334) (0.165) (0.245) (0.253) (0.215) In Machine 7.5+ 1.644 1.543 1.590 1.716 0.932 Cubic Feet (0.455) (0.304) (0.249) (0.245) (0.300) Not in Machine, -0.622 -1.060 -0.643 -0.678 -0.258 Not 1 Machine (0.579) (2.663) (0.472) (0.583) (0.289) Non-Specialized 0.334 0.222 0.320 0.351 0.191 (0.236) (0.185) (0.184) (0.215) (0.122) 2+ Competitors -1.144 -0.875 -1.231 -1.022 -0.753 Developed (0.633) (0.529) (0.440) (0.578) (0.315) Years 1951-1954 0.593 0.389 0.551 0.575 0.453 (0.337) (0.252) (0.284) (0.352) (0.190) Years 1955-1958 0.405 0.283 0.513 0.388 0.388 (0.268) (0.174) (0.248) (0.293) (0.171) Years 1959-1971 0.988 0.868 0.863 1.013 0.683 (0.535) (0.502) (0.444) (0.438) (0.267) Constant -2.674 -2.213 -2.615 -2.734 -1.617 (0.720) (0.532) (0.428) (0.498) (0.441) Terms in 𝛾$𝑧8 Ln(Output -0.0338 Share 1950) (0.147) In Machine 1.5- -1.172 7.5 Cubic Feet (0.488) In Machine 7.5+ -0.271 Cubic Feet (0.574) Not in Machine, 0.230 Not 1 Machine (1.080) Printed Circuit -0.530 Related (0.289) 2+ Competitors -0.313 Developed (0.455) Year Minus 1947 -0.102 (0.0429) Log Likelihood -115.6 -112.1 -114.3 -115.5 -112.5 Wald 𝛾 = 0, p-val. 0.818 0.0472 0.0668 0.491 0.0169
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Table A5. Estimated Heteroskedastic Probit Effects of Ln(Output in 1950) and
Controls on Multiple Operation (see notes above)
(B1) (B2) (B3) (B4) (B5) Terms in 𝛽$𝑥8 Ln(Output 0.473 0.255 0.427 0.481 0.568 Share 1950) (0.134) (0.123) (0.142) (0.106) (0.317) In Machine 1.5- 0.249 0.717 0.347 0.243 0.233 7.5 Cubic Feet (0.231) (0.145) (0.292) (0.247) (0.324) In Machine 7.5+ 0.729 0.801 0.679 0.760 0.925 Cubic Feet (0.252) (0.142) (0.325) (0.217) (0.727) Not in Machine, 0.466 0.593 0.742 0.519 0.612 Not 1 Machine (0.517) (0.416) (0.416) (0.371) (0.585) Years 0.959 0.461 0.936 1.016 1.101 1951-1954 (0.555) (0.260) (0.336) (0.282) (0.413) Years 0.706 0.241 0.392 0.744 0.733 1955-1958 (0.405) (0.183) (0.334) (0.260) (0.332) Years 1.187 0.478 1.044 1.253 1.665 1959-1971 (0.716) (0.308) (0.571) (0.505) (2.157) Constant -2.357 -1.588 -2.386 -2.438 -2.787 (0.807) (0.393) (0.468) (0.370) (1.270) Terms in 𝛾$𝑧8 Ln(Output -0.0314 Share 1950) (0.243) In Machine 1.5- -2.412 7.5 Cubic Feet (0.613) In Machine 7.5+ -0.995 Cubic Feet (0.638) Not in Machine, -0.348 Not 1 Machine (1.261) Printed Circuit 2.695 Related (5.191) 2+ Competitors -0.0838 Developed (0.503) Year 0.0290 Minus 1947 (0.106) Log Likelihood -145.0 -136.7 -141.5 -145.0 -145.0 Wald 𝛾 = 0, p 0.897 0.000120 0.604 0.868 0.785
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Tabl
e A
6. E
stim
ated
Het
eros
keda
stic
Pro
bit E
ffec
ts o
f Ln(
Out
put i
n 19
50) a
nd C
ontro
ls o
n C
ompl
ex (
see
note
s abo
ve)
(C
1)
(C2)
(C
3)
(C4)
(C
5)
(C6)
Te
rms i
n !!" "
Ln
(Out
put
0.07
23
5.10
8 0.
814
0.72
5 0.
898
0.56
7 S
hare
195
0)
(1.4
88)
(4.6
64)
(0.2
02)
(0.2
05)
(0.1
91)
(0.1
67)
In M
achi
ne 1
.5-
6.12
4 -4
.436
0.
480
0.30
3 0.
399
0.34
5 7
.5 C
ubic
Fee
t (1
0.85
) (8
.071
) (0
.511
) (0
.356
) (0
.422
) (0
.279
) In
Mac
hine
7.5
+ 8.
407
-0.9
53
0.82
2 0.
680
0.78
5 0.
508
Cub
ic F
eet
(11.
42)
(3.0
62)
(0.4
01)
(0.3
28)
(0.3
30)
(0.3
14)
Not
in M
achi
ne,
8.81
3 -1
76.1
0.
927
0.46
1 0.
335
0.42
7 N
ot 1
Mac
hine
(1
3.47
) (3
238.
0)
(0.6
22)
(0.5
79)
(0.4
62)
(0.4
00)
Non
-Spe
cial
ized
-6
.556
-0
.997
-0
.925
-0
.425
-0
.700
-0
.546
(7.5
98)
(0.5
52)
(0.3
70)
(0.7
14)
(0.2
87)
(0.2
65)
Yea
r Min
us 1
947
0.16
5 -0
.003
63
0.01
66
0.02
15
0.03
43
0.04
42
(0
.238
) (0
.099
9)
(0.0
332)
(0
.029
3)
(0.0
299)
(0
.015
6)
Con
stan
t -1
2.95
-1
4.95
-3
.350
-2
.969
-3
.579
-2
.517
(13.
91)
(12.
91)
(0.5
38)
(0.6
27)
(0.5
05)
(0.5
08)
Term
s in #!$ "
Ln(O
utpu
t 0.
922
S
hare
195
0)
(0.4
69)
In
Mac
hine
1.5
-
1.90
7
7
.5 C
ubic
Fee
t
(1.1
33)
In M
achi
ne 7
.5+
1.
648
Cub
ic F
eet
(1
.031
)
N
ot in
Mac
hine
,
5.08
7
N
ot 1
Mac
hine
(17.
90)
Prin
ted
Circ
uit
0.48
1
Rel
ated
(0
.290
)
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OA-17
Tabl
e A
6 co
ntin
ued.
(C1)
(C
2)
(C3)
(C
4)
(C5)
(C
6)
Non
-Spe
cial
ized
-0.2
48
(0.4
93)
2+ C
ompe
titor
s
0.
419
D
evel
oped
(0
.379
)
Yea
r Min
us 1
947
-0
.045
1
(0
.032
2)
Log
Like
lihoo
d -5
3.55
-5
3.74
-5
4.07
-5
5.44
-5
4.97
-5
4.96
W
ald
test
: #=0, p-
val.
0.04
93
0.30
9 0.
0967
0.
614
0.27
0 0.
162
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Table A7. Average Estimated Probabilities of Mechanization from
Heteroskedastic Probit Models
Mechaniz- Model Television Output Share in 1950 ation Type Tab. Col. 0.07% 0.27% 1.08% 4.39% 17.80%
Automation A5 A1 0.0144 0.0475 0.127 0.274 0.482 A5 A2 0.0134 0.0466 0.124 0.280 0.465 A5 A3 0.00828 0.0372 0.120 0.277 0.491 A5 A4 0.00961 0.0422 0.126 0.275 0.480 A5 A5 0.0100 0.0398 0.129 0.280 0.477 Multiple A6 B1 0.0104 0.0348 0.101 0.245 0.476 Operation A6 B2 0.0160 0.0391 0.0964 0.230 0.472 A6 B3 0.0817 0.101 0.150 0.250 0.405 A6 B4 0.00570 0.0280 0.0977 0.248 0.474 A6 B5 0.00993 0.0351 0.105 0.250 0.468 Complex A7 C1 3.18e-24 4.00e-05 0.00197 0.0305 0.204 A7 C2 0.00826 0.00892 0.0101 0.0254 0.224 A7 C3 1.34e-04 0.00108 0.00672 0.0380 0.169 A7 C4 2.16e-06 1.17e-04 0.00269 0.0309 0.193 A7 C5 1.29e-05 2.15e-04 0.00260 0.0290 0.196 A7 C6 5.76e-06 1.53e-04 0.00282 0.0328 0.185
Notes: Marginal effects computed by the delta method, at specified market shares
holding other variables at actual values.
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7. World War II Government Contracts for Radio-Related Production
I compiled the value of World War II radio-related government contracts
fulfilled by firms with innovations in the data, and checked for additional predictive
value after using as a regressor the logarithm of television output. Wartime radio
contracts had an estimated elasticity of 1.1 with 1940 radio output share, were
closely related to radio entry year, and were substantially predictive of 1950
television output—although 1940 radio output if included is most statistically
significant. In Table A8, wartime radio contracts’ added effect on mechanization
is never statistically significant, p = 0.3 to 0.9.
Table A8. Mechanization Predicted by Television Output and Wartime Radio
Contracts
Automation Multiple Operation
Complex Complex
probit probit probit OLS Ln(Output 0.349 0.405 0.813 0.0737 Share 1950) (0.118) (0.113) (0.199) (0.0193) Ln(WWII 0.0667 -0.0129 -0.0470 0.000395 Radio Contracts) (0.0562) (0.0525) (0.0770) (0.00620) Zero WWII 0.292 -0.148 0.0299 Radio Contracts (0.415) (0.451) (0.0320) Constant -1.901 -1.129 -2.514 -0.0513 (0.585) (0.559) (1.007) (0.0735) Joint Wald test p-value 0.469 0.936 0.343 N 285 285 270 285
Notes: Joint Wald test p-value comes from a test of the null hypothesis that the
coefficients of Ln(WWII Radio Contracts) and Zero WWII Radio Contracts are
jointly zero. The probit model for Complex mechanization excludes 15
observations, because Zero WWII Radio Contracts perfectly predicts Complex.
Therefore, a linear probability model is also shown. (An exact logistic regression
is too computationally extensive to estimate). Standard errors in parentheses are
cluster-robust, clustered by innovation.
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8. Alternate Log Output Share Assumptions for Firms Not Producing in 1950
Alternate log 1950 television output assumptions for firms not producing in
1950 affect the estimates for the number and summed squared rank of innovations.
Firms not producing in 1950 did not have innovations in the sample, so other
estimates are unaffected.
Table A9. Univariate Estimates of Effect of Ln(Output in 1950) on Firm-Level
Outcomes
Log Output Share Treatment for Firms Not Producing Televisions in 1950
Outcome Instrument Set 0 SD Below 2 SD Below Excluded Innovations per Entry 0.295 0.161 0.304 Year (0.059) (0.040) (0.062) Output 0.267 0.149 0.274 (0.057) (0.037) (0.060) Cost Reduction Entry 1.732 0.914 1.825 Rank2 per Year (0.353) (0.227) (0.375) Output 1.591 0.855 1.673 (0.333) (0.211) (0.355)
Notes: GMM estimates as in Table 2. The log output share treatment indicates
whether the 58 firms not producing in 1950 were treated as having 0 or 2 standard
deviations lower log output than the log output of the smallest producing firm or
were excluded leaving 102 observations. Coefficient estimates with robust
standard errors in parentheses.
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9. Loci of Innovations
Table A10. Innovations by Locus (%), by Period
Locus All Years
1947-50
1951-54
1955-58
1959-71
N
Chassis frame building 5.3 15.8 5.7 0.0 0.0 16 Preparation of parts 8.3 5.3 13.1 5.5 0.0 25 Manufacture of coils and yokes 7.3 1.8 12.3 3.7 13.3 22 Printed circuit boards 5.6 0.0 2.5 10.1 20.0 17 Design changes for assembly 3.0 3.5 2.5 3.7 0.0 9 Assembly of chassis 10.9 10.5 4.1 19.3 6.7 33 Soldering 6.6 1.8 7.4 8.3 6.7 20 Testing and alignment 12.5 19.3 14.8 7.3 6.7 38 Cabinet making 8.9 19.3 7.4 4.6 13.3 27 Putting chassis into cabinet 4.3 0.0 6.6 4.6 0.0 13 Crating and packing 2.3 1.8 1.6 1.8 13.3 7 Conveyors and movers 12.9 12.3 9.8 16.5 13.3 39 Positioning methods 10.6 7.0 9.8 14.7 0.0 32 Other 1.7 1.8 2.5 0.0 6.7 5 Total 100.0 100.0 100.0 100.0 100.0 303 N by Time Period 303 57 122 109 15
Table A11. Fisher’s Exact Tests of Constant Probabilities of Being in Each Locus
Locus p-value Chassis frame building 0.000 Preparation of parts 0.116 Manufacture of coils and yokes 0.013 Printed circuit boards 0.001 Design changes for assembly 0.891 Assembly of chassis 0.003 Soldering 0.352 Testing and alignment 0.103 Cabinet making 0.014 Putting chassis into cabinet 0.205 Crating and packing 0.109 Conveyors and movers 0.503 Positioning methods 0.273 Other 0.112
Note: Tests constant probability of a locus versus other loci combined, each period.
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10. Production Hours per Unit, Work, and Output for Radios and Televisions
(Indexes)
Figure A4. Production worker hours per radio and television unit fell from 1958 to
1986 as production workforce rose and fell and output rose (indexes, 1977=100).
U.S. Census data for standard industrial classification 3651. Data are unavailable
before 1958, and 1986 data are preliminary. Source: U.S. Bureau of Labor
Statistics (1988, 130).
11. References
Barron's. 1950. "Fund Managers Name TV Output Leaders." Barron's National
Business and Financial Weekly 30 (15: April 10):17.
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OA-23
Carbonara, Corey P. 1989. "A Historical Perspective of Management, Technology
and Innovation in the American Television Industry." Ph.D. Thesis, University of
Texas at Austin.
Civilian Production Administration. 1946. Alphabetic Listing of Major War Supply
Contracts: Cumulative June 1940 through September 1945, Vols. 1-4.
Washington, D.C.: Civilian Production Administration.
Datta, Yudhishter. 1971. "Corporate Strategy and Performance of Firms in the U.S.
Television Set Industry." Ph.D. Thesis, State University of New York at Buffalo.
Klepper, Steven, and Kenneth L. Simons. 1997. "Technological Extinctions of
Industrial Firms: An Inquiry into their Nature and Causes." Industrial and
Corporate Change 6 (2):379-460.
Klepper, Steven, and Kenneth L. Simons. 2000. "Dominance by Birthright: Entry
of Prior Radio Producers and Competitive Ramifications in the US Television
Receiver Industry." Strategic Management Journal 21 (10-11):997-1016.
Klepper, Steven, and Kenneth L. Simons. 2005. "Industry Shakeouts and
Technological Change." International Journal of Industrial Organization 23 (1-
2):23-43.
MacLaurin, W. Rupert. 1949. Invention and Innovation in the Radio Industry. New
York: MacMillan.
U.S. Bureau of Labor Statistics. 1988. Productivity Measures for Selected
Industries and Government Services, Bulletin 2296. Washington, D.C.: U.S.
Government Printing Office.
