The Learning Curve--Some Controversial Issues

Baloff, Nicholas

doi:10.2307/2097555

Cited by 67 publications

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“…(Arrow, 1962) argued that that learning is subject to "sharply diminishing returns." Looking at studies within single plants, (Hall and Howell, 1985) and (Baloff, 1966) these studies. Some have suggested, that as a result, a cubic or logistic function offers a more realistic functional form than a power function (Carlson, 1973).…”

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confidence: 99%

How Well Does Learning-By-Doing Explain Cost Reductions in a Carbon-Free Energy Technology?

Nemet

2006

SSRN Journal

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SummaryThe incorporation of experience curves has enhanced the treatment of technological change in models used to evaluate the cost of climate and energy policies. However, the set of activities that experience curves are assumed to capture is much broader than the set that can be characterized by learning-by-doing, the primary connection between experience curves and economic theory. How accurately do experience curves describe observed technological change? This study examines the case of photovoltaics (PV), a potentially important climate stabilization technology with robust technology dynamics. Empirical data are assembled to populate a simple engineering-based model identifying the most important factors affecting the cost of PV over the past three decades. The results indicate that learning from experience only weakly explains change in the most important cost-reducing factors-plant size, module efficiency, and the cost of silicon. They point to other explanatory variables to include in future models. Future work might also evaluate the potential for efficiency gains from policies that rely less on 'riding down the learning curve' and more on creating incentives for firms to make investments in the types of cost-reducing activities quantified in this study. Keywords: Learning-by-doing, Experience Curves, Learning Curves, Climate Policy JEL Classification: O31, Q42, Q48, Q55 Discussions with Arnulf Grübler at the International Institute for Applied SystemsAnalysis (IIASA) were extremely helpful in the early stages of this project. I also gratefully acknowledge Brian Arthur, Severin Borenstein, Martin Green, Bronwyn Hall, Daniel Kammen, Robert Margolis, Paul Maycock, David Mowery, and Chihiro Watanabe for providing valuable comments at various stages. I thank the U.S. National Academies for financial support while I was at IIASA. This paper was presented at the EAERE-FEEM-VIU Summer School on "Computable General Equilibrium Modeling in Environmental and Resource Economics", held in Venice from June 25th to July 1st, 2006 and supported by the Marie Curie Series of Conferences "European Summer School in Resource and Environmental Economics". Address for correspondence:Gregory F. Nemet Energy and Resources Group University of California 310 Barrows Hall 3050 Berkeley CA 94720-3050 USA E-mail: gnemet@berkeley.edu Technological change and learning curvesThe rate and direction of future technological change in energy technologies are important sources of uncertainty in models that assess the costs of stabilizing the climate (Edenhofer et al., 2006). 1 Treatment of technology dynamics in integrated assessment models has become increasingly sophisticated (Grubb et al., 2002) as models have incorporated lessons from the economics of innovation and as increased processing power and improved algorithms have enabled optimization of phenomena, such as increasing returns, which in the past had made computation unwieldy (Messner, 1997). Yet the representation of technological change in large energy-economic model rema...

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confidence: 99%

How Well Does Learning-By-Doing Explain Cost Reductions in a Carbon-Free Energy Technology?

Nemet

2006

SSRN Journal

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“…Figure (4) shows that this is not the case. (4a), with a simulation where each unit was capable of two elementary operations, all else being equal.…”

Section: Learning Curves: Routingmentioning

confidence: 94%

“…Point (i) is exemplified by the case of technical innovations, that quite often take place at the same time organizational units are striving to develop better routines [4]. A few cases where innovation was totally absent [6] [13] [33] highlighted that without continuous stimulation and injection of novelties the learning curve reaches a plateau [21] [29] [7] [1].…”

Section: Learning Curves: Sequencingmentioning

confidence: 99%

From Men and Machines to the Organizational Learning Curve

Fioretti

2011

Industrial Innovation

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“…Even in his seminal work on learning-by-doing Arrow (1962) argued that that learning is subject to "sharply diminishing returns." Looking at studies within single manufacturing facilities, Baloff (1966) and Hall and Howell (1985) find that learning rates become essentially flat after a relatively short amount of time-approximately 2 years in these studies. As a result, some have suggested that a cubic or logistic function offers a more realistic functional form than a power function (Carlson, 1973).…”

Section: Characterizing Unacknowledged Uncertaintymentioning

confidence: 99%

Interpreting Interim Deviations from Cost Projections for Publicly Supported Energy Technologies

Robert

Nemet

2008

SSRN Journal

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Widespread public funding of nascent energy technologies, combined with recent increases in the costs of those that have been most heavily supported, has raised the stakes of a policy dilemma: should policy makers sustain these programs anticipating that recent cost increases are temporary disturbances or should they eliminate them to avoid risking billions of dollars of public funds on technological dead ends? This paper uses experience curves for photovoltaics (PV) and wind to (1) estimate the range of possible policy outcomes and (2) introduce new ways of assessing near term cost dynamics. For both technology cases, the costs of the subsidies required to reach cost targets are highly sensitive to the choice of the historical time series used. The variation in the discounted social cost of subsidies exceeds an order of magnitude. Vigilance is required to avoid the very expensive outcomes contained within these distributions of social costs. Two measures of the significance of recent deviations are introduced. Both indicate that wind costs are within the expected range of prior forecasts but that PV costs are not. The magnitude of the public funds involved in these programs heightens the need for better analytical tools with which to monitor and evaluate cost dynamics.

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The Learning Curve--Some Controversial Issues

Cited by 67 publications

References 0 publications

How Well Does Learning-By-Doing Explain Cost Reductions in a Carbon-Free Energy Technology?

How Well Does Learning-By-Doing Explain Cost Reductions in a Carbon-Free Energy Technology?

From Men and Machines to the Organizational Learning Curve

Interpreting Interim Deviations from Cost Projections for Publicly Supported Energy Technologies

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