The skewed world GDP distribution and the interdependence of ...

The skewed world GDP distribution and the interdependence of national institutions Alex Coad SPRU, University of Sussex, Falmer, UK VERY PRELIMINARY PAPER FOR THE DIME CONFERENCE DEADLINE. DO NOT CITE, DO NOT QUOTE, DO NOT DISTRIBUTE.

Paper presented at the DIME Final Conference, 6-8 April 2011, Maastricht.

Keywords: Institutions, institutional asymmetries, technological development, entrepreneurship, technological frontier.

The skewed world GDP distribution and the interdependence of national institutions Alex Coad

a ∗

a SPRU, University of Sussex, Falmer, UK.

Paper presented at the DIME Final Conference, 6-8 April 2011, Maastricht. VERY PRELIMINARY PAPER FOR THE DIME CONFERENCE DEADLINE. DO NOT CITE, DO NOT QUOTE, DO NOT DISTRIBUTE. Keywords: Institutions, institutional asymmetries, technological development, entrepreneurship, technological frontier

“a period of rapid growth does not materialize overnight simply because an institutional barrier to industrialization has disappeared. Such a period requires a simultaneous development of complementary efforts in many directions. The component elements of growth in the individual industrial branches must be adjusted to each other.” Gerschenkron (1962, p. 125)

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Introduction

Institutions are a major determinant of economic development (North, 1990; Acemoglu and Robinson, 2005). The list of supporting institutions is not short, however. Instead, there are many factors that are important for economic growth ∗

Corresponding Author : Alex Coad, Freeman Centre, SPRU, University of Sussex, Falmer, Brighton, BN1 9QE, UK.

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(Lipsey, 2009). Some authors have emphasized the importance of political stability and property rights (Kormendi and Meguire, 1985), or levels of skill and human capital (Kremer, 1993). Others have emphasized the importance of factors as diverse as the role of law, industrial innovation, human rights, trust, efficient financial markets, effective labour markets, and so on (see e.g. Easterly and Levine (2001) for a survey). The list is indeed long. In this paper, we focus on two characteristics of institutions – that they are numerous, and that they are interdependent.1 An illustrative example: Bill Gates wants to start his business in a developing country. The attractiveness of such an endeavour depends on a long list of factors including: • Efficient public authorities that don’t make entry costs too high, don’t ask for bribes, and won’t expropriate successful businesses • Good transport networks allowing employees to get to work, and distribution of final output • Relatively efficient capital markets • Low crime (so one won’t be robbed on the way to work) • Macroeconomic stability (e.g. no hyperinflation) • Efficient fluid labour market (which in turn requires an adequate education system) • Property rights that are respected • Good police and legal systems 1

Our paper thus has many similarities with the ‘O-ring’ production function introduced in Kremer (1993). However, many differences between our approach, and Kremer’s, can be mentioned. Kremer has a production function based approach (see his equation (1)). Here we just have a sum of random terms. Kremer insists on skill, here we take a wider view and focus on institutions. Kremer shows no empirical evidence. Kremer says that n can be increased (pp. 561-562), but in fact if we make n a random variable this will have an effect on the resulting distribution, taking it away from lognormal. For example, if n is exponentially distributed the distribution tends to a Pareto (Reed, 2001; Coad, 2010).

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• Consumer confidence in the ability to obtain after-sales service after the purchase of faulty products and so on. Failure in any one of these dimensions could seriously hold up the venture. A low value for any one of these reduces the overall desirability of setting up a business. In order to be successful, the entrepreneur requires broad-based support on a large number of dimensions. In this perspective, the success of an economy cannot be attributed to just one actor (the entrepreneur) but it is very much a collective effort. Therefore, it is the strength of societies, rather than the strength of any one individual, that determines the level of economic development.

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Institutions as multifaceted and having multiplicative interactions

We now formalize the main ideas of the paper. Proposition 1 Institutions are multidimensional. We denote a particular institution by xi , where i ∈ 1 . . . N . We posit that N is large. Furthermore, we introduce a multiplicative nature for the interactions of institutions, instead of an additive nature, because failure in any one dimension could have serious consequences for the entire system. (Interestingly enough, this implies that while success is a collective action, failure is more easily seen to be the responsibility of individual factors.) Proposition 2 Since each institution has a proportional influence on the overall outcome, we adopt a multiplicative (rather than additive) specification to account for the high degree of interdependence. Table 1 helps to further illustrate the multiplicative specification. If all relevant dimensions score highly, the business will succeed. If all but one relative dimension scores highly, but one dimension performs very poorly, the business will have great

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Table 1: Multiplicative interdependent institutions. A numerical illustration is provided, where a high score is 0.9, a medium score is 0.6 and a low score is 0.1 Ease of entry (entry costs) HI 0.9 LO 0.1 LO 0.1 HI 0.9 HI 0.9 HI 0.9 MID 0.6

Absence of corruption HI 0.9 LO 0.1 HI 0.9 LO 0.1 HI 0.9 HI 0.9 MID 0.6

Efficiency of credit markets HI 0.9 LO 0.1 HI 0.9 HI 0.9 LO 0.1 HI 0.9 MID 0.6

Efficiency of labour market HI 0.9 LO 0.1 HI 0.9 HI 0.9 HI 0.9 LO 0.1 MID 0.6

Entry trivial 0.6561 very difficult 0.0001 difficult 0.0729 difficult 0.0729 difficult 0.0729 difficult 0.0729 feasible 0.1296

difficulties. If, however, each dimension has an intermediate score, the business will do better than in the previous scenario. Consider the case where an economy has three high-performance institutions (scoring 0.9) and one low-performing institution (scoring 0.1). In this model, this economy will perform worse than an economy with medium-performance institutions (scoring 0.6 in each dimension) even if the sum of resources in the first economy (0.9 + 0.9 + 0.9 + 0.1 = 2.8) is higher than the sum of resources available in the second economy (0.6 + 0.6 + 0.6 + 0.6 = 2.4). The overall economic output Y , measured in terms of GDP per capita, can therefore be represented by the product of each individual institutional dimension, and can be written as follows:

Y = x1 × x2 × . . . × xN =

N Y

xi

(1)

i=1

Hence we obtain a lognormal distribution of GDP per capita:

N N Y X Log(Y ) = log( xi ) = (log(x1 )) + (log(x2 )) + . . . + (log(xN )) = (log(xi )) (2) i=1

i=1

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Figure 1: The world income distribution. Left: kernel density based on our elaboration on IMF data for the year 2009 (Epanenchnikov kernel, bandwidths shown). Taking a finer-grained bandwidth does not reveal any striking bimodality. Right: GDP per worker, taken from Jones (1997). Jones emphasizes the shift towards a bimodal income distribution. Since the (log(xi )) are i.i.d. distributed, Central Limit Theorem implies that Log(Y ) is normally distributed, and hence that GDP per capita is lognormally distributed.

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Model verification: The lognormal distribution of GDP per capita

Is the lognormal distribution a good approximation for the distribution of world GDP per capita? In the left panel of Figure 1, we see a kernel density of the GDP per capita distribution, based on IMF data for 181 countries in 2006.2 Figure 1 shows the distribution of GDP per capita in 2009. At first glance, the distribution appears to be roughly approximated by the unimodal Gaussian. Taking a finer-grained kernel bandwidth shows some underlying noise in the distribution, although it is not clear that we should suspect that the true distribution is bimodal (as emphasized by Jones (1997), see Figure 1, right). The empirical density has a skewness of 0.04386 which is close to the Gaussian 2

See http://www.imf.org/external/pubs/ft/weo/2010/01/weodata.

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Table 2: Normality tests Shapiro-Wilk Shapiro-Francia SK test

W 0.97457 W’ 0.97819 Pr(skewness) 0.803

V 3.478 V’ 3.226 Pr(kurtosis) 0.000

Z 2.854 Z 2.427 adj Chi2(2) 19.10

p-value 0.00216

Obs 181

0.00761

181

0.0001

181

value of 0, but the kurtosis (2.0537) is lower than the Gaussian value of 3. Formal normality tests of this distribution can be found in Table 2. The p-values are sufficiently low for us to reject the hypothesis of normality. To conclude, we find a significant degree of dispersion in the distribution GDP per capita, with the distribution appearing to be roughly unimodal and symmetric once logarithms are taken, which indicates some degree of accord with the predicted lognormal distribution. The empirical data do not fit the lognormal density perfectly, however, as shown by formal statistical tests. Further refinements to the basic model would be helpful in providing a closer fit to the empirical density.

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Implications of the model

A number of implications can be drawn from this framework: 1. Economic growth is very complex and thus are very hard to influence. Improvements in one particular institution may not have any detectable impact in aggregate economic statistics (as in Solow’s famous paradox that “We see computers everywhere except in the productivity statistics”). In this paper we give one explanation for why macroeconomic growth regressions typically cannot account for the majority of variance (i.e. the associated R2 statistics are not particularly high). On a more practical level, we also give a justification for using log transformations for variables in regressions. (This is because a log transformation converts a multiplicative specification into an additive specification, and multivariate regressions are based on an additive specification framework.)

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2. Due to the multiplicative nature, policies such as those associated with the ‘Washington Consensus’ that focus on the development of a narrow range of institutions (such as financial deregulation) without paying due attention to the other institutions will probably not succeed. Instead, the representation here suggests that governments would do better to aim for a coherent set of institutions, where each institution is at a comparable level of development, and then to identify their weakest institutions and try to improve these. 3. This framework suggests that economic success is a collective outcome, it is a team effort, rather than due to one rational individual acting alone. Our approach differs from the conventional economic view in that it doesn’t suggest that non-cooperative behaviour is the ‘winning’ strategy to be chosen by rational agents (Ghoshal, 2005). Instead it emphasizes collective action, in the context of broad-based institutional support. Economic development depends on the strength of societies, rather than the strength of individuals. The success of the individual depends on a much wider collection of institutions on which the individual is dependent. Therefore, our model suggests that inequality in the levels of development of institutions should be kept low. We all depend on each other. No one wants a butcher with no idea of hygiene, a careless brewer or a thieving baker. 4. Our model implies that the world technological frontier should be defined in terms of ability to produce complex technologies (e.g. high-speed trains, hybrid cars), and not in terms of narrowly-focused technologies such as ‘facebook’ and ‘google’. In our view, the technological frontier should not necessarily be defined in terms of GDP per capita. Some western countries have high levels of GDP per capita, combined with low scores in terms of provision of basic goods such as electricity and water; poor public transport; low average education; low life expectancy; high rates of obesity (which is a form of malnutrition); high crime; ill-equipped houses (i.e. a low proportion of houses with wellfunctioning showers, double-glazed windows, reliable plumbing) etc. High 7

GDP per capita is often a feature of ‘pay-as-you-go’ societies where individuals have to pay for goods that would otherwise be public goods, and must frequently repair and replace their low-quality goods. High GDP (in the short run) is also compatible with environmental degradation (such as air, water and noise pollution), inefficient government and healthcare sectors, and growing indebtedness (Stiglitz, 2009). 5. Our framework has implications for entrepreneurship policy. Entrepreneurship ‘zealots’ often assume that entrepreneurship makes a positive contribution to economic growth, although a closer investigation of the matter finds no such causal effect (Beck et al., 2005). We suggest that entrepreneurial success does not rely on individual efforts, but depends mainly upon the degree of development of the entire society. Entrepreneurship may therefore be easier in more developed countries. Indeed, this may explain observations that entrepreneurship rates are higher in developed countries, even though entrepreneurship makes no causal influence on economic development (Beck et al., 2005). In developing countries, which are farther from the world technological frontier (and hence rely less on innovation and more on scale and investment), the appropriate industrial policy would be reliance on large firms rather than entrepreneurship (Acemoglu et al., 2006). Individuals in developing countries with exceptional entrepreneurial talent may well do better to migrate and start their business in a developed country (to the benefit of the developed country) rather than remaining in their home country. 6. There are also implications for a theory of value. Economic performance depends on many individuals at all levels of the economy, and to reflect this we suggest that profits should be shared among stakeholders rather than extracted at the top.

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Extensions to the model

I was in a rush to finish this paper for the DIME conference deadline. As a result, there are certainly several directions in which I can take the ideas further. One possible avenue of further work could be in moving from a unimodal lognormal distribution of GDP per capita to a bimodal distribution (as emphasized by Quah (1996), Jones (1997), and other authors commenting on the ‘twin peaks’ in the world income distribution). To this end, it might be worthwhile introducing a distinction between primary goods (such as food, and basic goods) and more complicated high-tech goods that are dependent on public infrastructure. Some countries may have aspirations towards high complexity, while others try to struggle through with low complexity in institutions. Another extension could be to distinguish between complicated high-tech goods in terms of their requirements in high-tech infrastructure. For example, many African countries are relatively developed in terms of mobile phone penetration and air transport, in comparison to fixed-phone equipment and road transport. It has even been suggested that mobile telephony works better in Africa than the US (Friedman, 2010)! This is presumably because mobile telephony has relatively low requirements in public infrastructure compared to fixed telephony. Another possible extension would be to explicitly model interdependence by considering the performance of one institution to be a direct function of the productivity of its ‘neighbouring’ institutions.

References Acemoglu, D., Aghion, P., and Zilibotti, F. (2006). Distance to frontier, selection, and economic growth. Journal of the European Economic Association, 4(1):37– 74. Acemoglu, D. and Robinson, J. (2005). Unbundling institutions. Journal of Political Economy, 113(5):949–995.

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Beck, T., Demirguc-Kunt, A., and Levine, R. (2005). SMEs, growth, and poverty: Cross-country evidence. Journal of Economic Growth, 10(3):199–229. Coad, A. (2010). The exponential age distribution and the Pareto firm size distribution. Journal of Industry Competition and Trade, 10(3):389–395. Easterly, W. and Levine, R. (2001). What have we learned from a decade of empirical research on growth? it’s not factor accumulation: Stylized facts and growth models. World Bank Economic Review. Friedman,

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