Conclusions

The poor countries are not rich because they have failed to adopt the modern tech­niques of production which first emerged in Britain during the Industrial Revolution and then spread to some other nations in Western Europe and elsewhere.

Why would techniques not be adopted even when they are more efficient? Is it not the case that more efficient techniques are more profitable? The main objective of this survey has been to review a large number of studies which show why self-reinforcing traps may prevent the adoption of new technologies. For example, Section 5 showed how increasing returns can generate an incentive structure whereby agents avoid starting modern sector businesses, or invest little in their own training. Section 6 focused on credit market imperfections. Poor individuals lack collateral, which restricts their ability to raise funds. As a result, projects with large fixed costs are beyond the means of the poor, leaving them locked in low return occupations such as subsistence farming.

Recently many economists have highlighted the role of institutions in perpetuating poverty. Section 7 looked at why rent-seeking is both bad for growth and yet strongly self-reinforcing. Essentially similar societies may exhibit very different levels of pre­dation simply as a result of historical accident, or some spontaneous coordination of beliefs. In addition, the role of kinship systems was analyzed as representative of the kinds of social conventions which may potentially harm formation of the modern sec­tor.

Together, these mechanisms add up to a very different picture of development than the convex neoclassical benchmark model on which so much of modern growth theory has been based.

9.1. Lessonsforeconomicpolicy

There is a real sense in which poverty trap models are optimistic. Poverty is not the result of some simple geographic or cultural determinism. The poor are not condemned to poverty by a set of unfavorable exogenous factors, or even a lack of resources. Tem­porary policy shocks will have large and permanent effects if one-off interventions can cause the formation of new and better equilibria.

In practice, however, engineering the emergence of more efficient equilibria seems problematic for a number of reasons. First, we have seen many examples of how bad equilibria can be stable and self-reinforcing. In this case small policy changes are not enough to escape from their grip. Large changes must be made to the environment that people face, and the structure of their incentives. Such changes may be resisted by the forces that have perpetuated the inefficient equilibrium, such as a corrupt state apparatus fighting to preserve the status quo.

Second, coordinating changes in expectations and the status quo is difficult because norms and conventions are highly persistent. While it is possible to change policy and legislation almost instantaneously, it needs to be remembered that informal norms and conventions are often more important in governing behavior than the formal legalistic ones. Informal norms cannot be changed in the manner of interest rates, say, or tariffs. Rather they are determined within the system, and perpetuated by those forces that made them a stable part of the economy’s institutional framework.

Third, policies can create new problems as a result of perverse incentives.^[256] Suc­cessful policies will need to be carefully targeted, and operate more on the level of incentives than compulsion. These kinds of policies require a great deal of informa­tion.

Acknowledgements

Support from the Program of Dynamic Economics at UCLA is acknowledged with thanks, as is research assistance from Athanasios Bolmatis, and discussions with David de la Croix, Cleo Fleming, Oded Galor, Karla Hoff, Kirdan Lees and Yasusada Mu- rata. The second author thanks the Center for Operations Research and Econometrics at Universite Catholique de Louvain for their hospitality during a period when part of this survey was written. All simulations and estimations use the open source programming language R.

Appendix A

Section A.1 gives a general discussion of Markov chains and ergodicity. The proof of Proposition 3.1 is outlined. Section A.2 gives remaining proofs.

A.1. Markov chains and ergodicity

In the survey we repeatedly made use of a simple framework for treating Markov chains and ergodicity. The following is an elementary review. Our end objective is to sketch the proof of Proposition 3.1, but the review is intended to be more generally applicable.

Consider first a discrete time dynamical system evolving in state space S ⊂ R". Just as for deterministic systems on S, which are represented by a transition rule associating each point in S with another point in S - the value of the state next period - a Markov chain is represented by a rule associating each point in S with a probability distribution over S. From this conditional distribution (i.e., distribution conditional on the current state x ∈ S) the next period state is drawn. In what follows the conditional distribution will be denoted by Γ(x, dy), where x ∈ S is the current state.

Because for Markov chains points in S are mapped into probability distributions rather than into individual points, it seems that the analytical methods used to study the evolution of these processes must be fundamentally different to those used to study deterministic discrete time systems.

To see this, note that since the state variable χ_t is now a random variable, it must have some (marginal) distribution on S, which we call ψ_t. Suppose, as is often the case in economics, that ψ_t is a density on S, and that the distribution Γ(x, dy) is in fact a density Γ(x, y) dy for every x ∈ S.In that case the marginal distribution for x_t+1 is a density ψ_t+1, and ψ_t+1 (y) = f_S Γ(x, y)ψ_t(x) dx. This last equality is just a version of the law of total probability: The probability of ending up at y is equal to the probability of going to y via x, weighted by the probability of being at x now, summed over all x ∈ S.

probability mass is mixed across the state space - all areas of S communicate. In the case of (10) it is easy to show that supp Mψ = (0, ∞) = S for every ψ ∈ D. This is clearly sufficient for the condition.

A.2. Remainingproofs

The proof of Proposition 5.1 in Section 5.2 is now given. The first point is that the banks b = 1,..., B — 1 are equal-cost Bertrand competitors, and as a result always offer the interest rate r to all firms in equilibrium. The main issue is the optimal strategy of the last bank B. So consider the following strategy σ⅛ for B, which is illustrated with the help of Figure 30. To firm n the bank offers i* defined by i* = f [(n — 1)/N] — 1 if n ≤ α_cN.To the remaining firms B offers the interest rate r. (Without loss of generality, we suppose that the index of firms from 1 to N and the ranking of the offers made by

Figure 30.

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