Lecture 2 - Learning by Doing and Scale Economies

Lecture 2 overview: Hilary Term Week 6. Two hour lecture, MSc. Dev. room.

Lecture 2 - Learning by Doing and Scale Economies

Concepts: Learning curves, welfare, and justifications for infant industry policy.

By learning by doing, we mean that the average costs faced by firms or industries decline with experience. Or more specifically, cumulative experience or output. As we will see, there are a number of ways to consider what we mean by experience.

I open the class by revisiting the Melitz(2005) model of infant industry policy with learning by doing dynamics.

The “learning curve” in the LBD model of the world.
How the geometry of this learning curve, initial costs, and other features, interact with welfare-based justifications of industrial policy.
The Mills-Bastable Test: one such tool used to think about the welfare-based justification for infant industry policy.

(Note: You do not need to know this model or derive it. It is merely informative for thinking about a number of conceptual building blocks.)

The Mills-Bastable actual embeds two ideas actually, which we unpack a bit in the lecture. One idea being that a firm or industry ought to be competitive or self-sufficient at some point in time. Second, the idea that the costs incurred by the policy planning period should be justified by the welfare gains.

The crux of this model time. That horizons and time periods matter for learning to occur. Time also matters in the way we justify the policy.

The static versus dynamic dichotomy: welfare, advantage, and scale.

What becomes apparent in this lecture is an important dichotomy in industrial policy thinking: the distinction between static and dynamic. We see this concept make a cameo in the derivation of the lecture.

In the Mills-Bastable test above, there is a concept of the welfare that is realised in a single point of time, versus the dynamic welfare considerations. The latter being the welfare that accumulates through time as a result of learning.

Learning by doing—as shown through the learning curve—also presents a distinction between static and dynamic comparative advantage of an industry. LBD dynamics in the model mean that the cost advantage of an industry can change, from the initial cost advantage of the industry at time zero (static comparative advantage), to one that is realised under Melitz’s infant industry policy. Which is the dynamic comparative advantage.

Learning by doing: internal vs. external scale economies.

The concept of “learning by doing” is intimately connected to the idea of increasing returns to scale. We may already by acquainted with this idea from the models we see in trade or economic geography or growth. Often, however, we are consider static increasing returns to scale. Yet, LBD generates a source of increasing returns to scale that is dynamic in nature.

Like I said, the static versus dynamic dichotomy is always cropping up.

Just as increasing returns to scale can come in different forms (we will see this later with knowledge externalities) “learning by doing” can mean a number of things. They can often get conflated.

In the world of learning by doing, we often think of two forms of learning:

Within firm LBD - A form of internal economies of scale (Lecture 2). Firms experience average costs that are decreasing in cumulative experience or output. Internal economies of scale are inherently linked to imperfect competition.
Within aggregate unit - A form of external economies of scale (Class 2). Here economies of scale occur at industry (or geographic) level. Individual firm costs decline with industry experience output. May not do so at firm level.

The latter form of LBD is key. Why? It implies spillovers between individual firm actions and the aggregate market they are operating in.

Bibliography.

Argote, L., & Epple, D. (1990). Learning Curves in Manufacturing. Science, 247(4945), 920–924. https://doi.org/10.1126/SCIENCE.247.4945.920

Arrow, K. J. (1962). The Economic Implications of Learning by Doing. The Review of Economic Studies, 29(3), 155–173.

David, P. A. (1973). The “Horndal effect” in lowell, 1834–1856: A short-run learning curve for integrated cotton textile mills. Explorations in Economic History, 10(2), 131–150. https://doi.org/https://doi.org/10.1016/0014-4983(73)90006-5

Levitt, S. D., List, J. A., & Syverson, C. (2013). Toward an Understanding of Learning by Doing: Evidence from an Automobile Assembly Plant. Journal of Political Economy, 121(4), 643–681. https://doi.org/10.1086/671137

Lucas, R. E. (1993). Making a Miracle. Econometrica, 61(2), 251–272. https://doi.org/10.2307/2951551

Melitz, M. J. (2005). When and how should infant industries be protected? Journal of International Economics, 66(1), 177–196. https://doi.org/https://doi.org/10.1016/j.jinteco.2004.07.001

Thompson, P. (2001). How Much Did the Liberty Shipbuilders Learn? New Evidence for an Old Case Study. Journal of Political Economy, 109(1), 103–137. https://doi.org/10.1086/318605

Thompson, P. (2010). Chapter 10 - Learning by Doing. In B. H. Hall & N. B. T.-H. of the E. of I. Rosenberg (Eds.), Handbook of The Economics of Innovation, Vol. 1 (Vol. 1, pp. 429–476). North-Holland. https://doi.org/https://doi.org/10.1016/S0169-7218(10)01010-5

Thompson, P. (2012). The Relationship between Unit Cost and Cumulative Quantity and the Evidence for Organizational Learning-by-Doing. Journal of Economic Perspectives, 26(3), 203–224. https://doi.org/10.1257/jep.26.3.203

Thornton, R. A., & Thompson, P. (2001). Learning from Experience and Learning from Others: An Exploration of Learning and Spillovers in Wartime Shipbuilding. American Economic Review, 91(5), 1350–1368. http://www.aeaweb.org/articles?id=10.1257/aer.91.5.1350