How valid is the claim that the twentieth century experience of economic development was ‘Divergence: Big Time’?

John Stuart Mill wrote of late nineteenth century capitalism that “[h]itherto it is questionable if all the mechanical inventions yet made have lightened the day’s toil of any human being.” As Brad DeLong (2008) explains, as late as 1871 (the last edition of Political Economy issued in Mill’s lifetime) Mill still did not think it accurate to substitute “hitherto” for “formerly.” The same cannot be said of the economic development of the twentieth century; the toil of the poorest human beings has been lightened considerably and, although there remains ambiguities, by many measures the poor world has converged on, not diverged from, the rich world.

Economic development is a term which can be defined in many ways. The most common, and straightforward, of which is a simple measurement of Gross Domestic Product GDP per capita. The historical GDP data used by Lant Pritcett (1997) led him to conclude that the growth rates of poor countries have not kept up with, nor converged on, the growth rates of rich countries over the last century leading to “Divergence, Big Time” in per capita income. Using Pritchett’s figures it is hard to argue that the world did not seen divergence in economic development in the twentieth century. With a theoretical lower bound on income at around $250 at purchasing power parity and the distribution of per capita income today, income and growth rates must have diverged across the last 150 years. From 1870 to 1990 the average absolute gap in incomes of all countries from the leader had grown from $1,286 to $12,662, an order of magnitude (Pritchett, 1997, pp 9-12).

However, the data used by Pritchett is not definitive; although both inequality within countries and inequalities between countries have increased it does not logically follow that inequality between all individuals has increased, because the first claim refers to individuals and the second refers to the per capita income of countries (Sala-I-Martin, 2006, pp 382-383). Even if the wealthiest have increased their income most quickly in India and China, by taking into account the increases in income at the bottom of the scale in these countries the “Divergence, Big Time” identified by Pritchett in the latter half of the twentieth century disappears, rather we have “Convergence, period!” (Sala-I-Martin, 2006, pg 392). Over the whole of the twentieth century, income inequality, as measured by the Gini coefficient, remained somewhat higher in 2000, at 0.637 (Sala-I-Martin 2006, pg 384), than it was at the end of the nineteenth century, between 0.588 and 0.610 (Bourguignon and Morrisson 2002, pg 731). There are problems comparing Gini coefficients constructed from two different data sets but the rough agreement between Pritchett and the data from Bourguignon and Morrisson (2002) and Sala-I-Martin (2006) reinforce the finding.

Although there is evidence that the world has seen some convergence in per capita income in the latter half of the twentieth century, the results necessarily remain ambiguous because large amounts of data are of uncertain quality. However, other measures of economic development show distinct and unequivocal signs of convergence. The most important of these is not per capita income but the poverty rates of the rich and poor world. By this measure the 20th Century has been a massive success, particularly in China and India, the worlds’ two most populous countries. Bourguignon and Morrisson offer an estimate for global poverty rates in 1890 of 71.7% and for 1910 of 65.6% (2002, pg 731).[1] Although development has been unequal throughout the twentieth century the reduction in poverty in the last century has been truly transformative for billions of people. Even in the later part of this century the decline in absolute poverty continues, by Chen and Ravaillon’s calculations from 1981 to 2001 “[e]xpressed as a proportion of world population the decline is from 33% to 18%” (2004, pg 151).

In fact, this may be an underestimate for the progress made in eliminating poverty as Sala-I-Martin argues that by properly aggregating the data by taking into account the population size of poor countries the “poverty rate [of $1 a day] in 2000 was 7 percent.” In fact, despite a near quadrupling of world population in the twentieth century, extreme poverty fell in by both relative and absolute measures from 1,127.7 million people (Bourguignon and Morrisson 2002, pg 731) to 1089 million people (Chen and Ravaillon 2004 pg 153) or 322 million people (Sala-I-Martin 2006, pg 374). Even if you find Sala-I-Martin’s data somewhat overcooked, the trend is undeniable, economic development in the twentieth century has seen massive convergence, not divergence, for one of the most important measures.

Other measures also lend credence to the idea that the twentieth century was one of convergence in economic development, not divergence. The most high profile of these complementary measures is the Human Development Index which measures income, life expectancy and educational standards. HDI has shown significant convergence since 1950 (Crafts, 2004, pg 6) between all regions, even those which have experienced strong divergence in per capita income, like sub Saharan Africa. HDI is not the only non-monetary measure of wellbeing which can be quantified and compared. A range of other indices such as health, mortality and even Beer production (Kenny, 2005, pg 8) strongly suggest a convergence in wellbeing across the world. The most basic measures of wellbeing such as life expectancy and child mortality (which combine with other measures for the composite HDI measure) show convergence. In the middle of the 20th Century infant mortality began to decline in the developing world with this change was a concomitant increase in life expectancy (Deaton 2004, pg 28).

It is safe to argue that we have seen a convergence of many non-GDP measures of economic development. However, this aggregate convergence clouds a lot of regional differences. Even within one continent we see massive disparities in performance. Sub Saharan Africa has been ravaged by the AIDS epidemic and some have seen their life expectancy reduced to levels last seen in the 1950s (Deaton, 2004, pp 30-31). In contrast North Africa has seen rapid increases in life expectancy. The latest World Development Report (2010) highlights that Algeria, Tunisia and Morocco were some of the most successful states for improving their HDI scores, despite both relatively low GDP growth and the health disaster to their immediate south. Were “Divergence, Big Time” to be really true, it could perhaps better be used as a description for differences between poor countries than for differences between rich and poor countries.

In conclusion, while the penalties for getting institutions and policies wrong has been very high in the twentieth century in terms of accelerating GDP growth (Crafts, 2004, pg 7), other indicators have been broadly positive for economic convergence throughout the last century. The divergent average growth rates developing economies enjoyed (or suffered) between 1960 and 1990 highlights the high stakes of getting economic policy right or wrong; the worst states shrunk by an average of 2.7% per annum, while the best grew by 6.9% per annum (1997, pg 14).

However, by the end of the century, very fast growth in two very large and very poor countries, India and China, had gone some way to reversing this divergence in growth (Sala-I-Martin 2006). Although it is unclear to what extent total per capita income had diverged by the end of the twentieth century, it is fair to conclude that a claim of “Divergence, Big Time” is difficult to substantiate for anything but a very narrow reading of the term “economic development.” In fact, the latest figures from the IMF’s World Economic Outlook support this view. GDP growth at the end of the last century was roughly similar in both developing and developed worlds, 2.8% and 3.8% respectively. The last decade has seen significantly stronger growth in the developing world and this trend is predicted to continue, with growth of 2.7% predicted for 2011 for the developed world and 7.1% for the developing (IMF, 2010, pg 177).

From the outset GDP was never intended as the sole criterion of economic development, even Simon Kuznets said of his measure that “the welfare of a nation can scarcely be inferred from a measurement of national income”. The data on life expectancy, infant mortality and educational achievement all corroborate Kuznets’ 70 year old caveat. As well as the above indicators, the most important measure of economic convergence we have, extreme poverty, has been converging for most of the twentieth century. Whether we use Sala-I-Martin’s (2006) optimistic data or Chen and Ravaillon’s more modest calculations (2004), it is clear a smaller proportion of the earth’s population than ever before is living in extreme poverty. Although economic development still remains patchy and uneven across the different regions of the globe it is fairer than ever to conclude that the world has seen anything but “Divergence, Big Time.”
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A. Gerschenkron, ‘The Approach to European Industrialization: A Postscript’, in Economic Backwardness in Historical Perspective (USA: Harvard University Press, 1962) pp 353-364

[page 353] Europe in the 19thC was a continent in which many different states were at many different states of backwardness. The degree of backwardness had a distinct impact on the way in which the state developed economically. The variations in the course and character of their industrialisation can be surmised in six propositions.

  1. “The more backwards a country’s economy, the more likely was its industrial8isation to start discontinuously as a sudden great spurt proceeding at a relatively high rate of growth of manufacturing output.
  2. [354] The more backwards a country’s economy, the more pronounced was the stress in its industrialisation on bigness of both plant and enterprise.
  3. The more backwards a country’s economy, the greater was the stress upon a country’s stress on producers’ goods as against consumers’ goods.
  4. The more backwards a country’s economy, the heavier was the pressure upon the level of consumption of the population.
  5. The more backwards a country’s economy, the greater was the part played by special institutional  factors designed to increase supply of capital to the nascent industries and, in addition, to provide them with less decentralised and better informed entrepreneurial guidance; the more backwards the country, the more pronounced was the coerciveness and comprehensiveness of these factors.
  6. The more backwards a country, the less likely was its agriculture to play any active role by offering to the growing industries the advantages of an expanding industrial market based in turn on the rising productivity of agricultural labour.”

The countries of Europe could roughly be split up into three groups, advanced, moderately backwards and very backwards. Although not a discrete scale, the effects of number 5 can be seen in qualitative differences in industrialisation.

[355] In moderately backwards countries Factories were directed with banks towards capital and entrepreneurial guidance; in very backwards countries they were directed by banks and the state.

[356] There are historical similarities across all successful industrialisations. England is like Germany is like Russia. But there are big differences which are important to examine. In moderately backwards Germany capital was directed by banks whereas in very backwards Russia the state played a large role in directing capital.

Certain things are held to be essential for industrialisation; the abolition of archaic modes of agricultural production with a concomitant increase in productivity; the creation of an influential elite materially or ideologically interested in economic change; the necessary social capital in an area’s residents; a value system favouring entrepreneurial endeavour. [357] However, there are considerable conceptual and empirical problems with a simplistic modernisation story of industrialisation.

It is difficult to say whether certain preconditions are in fact necessary for industrialisation to occur. Industrialisation may have begun in Russia even without the abolition of serfdom, for example, yet it is commonly held that the end of serfdom was a necessary condition for the onset of industrialisation.

Just because something was necessary for industrialisation in one country, for example England, it does not mean it is necessary for industrialisation in all countries. Empirically this is true, either the preconditions for English industrialisations were not present in late industrialisers or were present to a very small extent.

[358] Therefore, to some extent countries substituted for these missing preconditions, as described in point 5 above. Capital for enterprise in advanced countries could have been provided by previously accumulated wealth, in backwards countries banks and states created similar conditions in the course of industrialisation which were not present because of their backwardness. [359] This substitution was not necessarily a conscious substitution; people groped for effective methods and substitutions were created as needed.

Gerschenkron’s approach allows him to “predict” what he expects to find, and give him the ability to test his theory. He looked Italian industrialisation, where as a moderately backwards country he expected to find banks playing a central role in the process, he did indeed find banks playing a central process in Italy’s industrialisation. This method of industrialisation was imported from Germany where it had already proved successful.

[360] Gerschenkron’s work is an attempt to explain the deviations from England’s method of industrialisation. It uses the degree of backwardness as an organising concept. Russia’s industrial structure was shaped by its backwardsness, it focussed on capital goods because it had to import much productive technology and because technological development in the immediately preceding period had been more focussed on capital goods. [361] However, as well as this economic factor, capital goods were favoured for political reasons, for improving the Russian states backwards war making apparatus.

[362] The Bulgarian experience, which saw no industrial take off, shows us that lacking the correct institutions it is possible to miss out on industrialisation. [353]There are advantages to backwardness because the stock of knowledge on which can be drawn increases, but there are also disadvantages and it is easy to miss the opportune moment for industrialisation as Italy did.

[364] The degree of backwardness may not be the defining characteristic of how a country will industrialise, but it remains a very useful  conceptual tool.

How valid is the claim that the twentieth century experience of economic development was ‘Divergence: Big Time’?

 Before we answer the above question we have to define our terms. Convergence means the closing of the gap between two variables, Divergence means the widening of the gap between the two.

If we are going to evaluate whether or not the world has seen “Divergence, Big Time” we are going to have to work out which variables are worth examining.

The most common one used is GDP per capita, so we can start there. GDP per capita is the final value of the goods and services produced in a country divided by the number of people in that country. This is a rough and ready metric on income within a predefined area.

  • Advantages
    • It is easy to measure.
    • We have good data for nearly all now developed countries going back to around 1870.
    • We have reasonably good data for most developing countries today going back to the immediate post war period.
    • It is the best measure we have of how good a society is at producing things which fulfil people’s material needs.
  • Limitations
    • It does not describe the distribution of income. Equatorial Guinea has vast oil wealth, but most of it has not reached the average citizen, many of whom still live in absolute poverty.
    • It does not capture other measurements of wellbeing. This brings us to…

There are other metrics worth using. For example, the UN has developed the Human Development Index, which looks at a broader range of metrics than just income. It combines three measures.

  • Life expectancy at birth.
  • Adult literacy rate (given a 2/3 weighting in this measure) & a combined primary, secondary, and tertiary gross enrolment ratio (given a 1/3 weighting).
  • Standard of living, as indicated by the natural logarithm of gross domestic product per capita at purchasing power parity.
    • Advantages – More things matter than just income. Very often income is used as a proxy for improvi2ng outcomes like life expectancy and literacy (richer people live longer, and read more) therefore a measure like HDI shows gets directly to those important metrics.
    • Limitations – It can lead to downplaying the importance of income. HDI is Subject to diminishing returns. Literacy can only reach 100%, life expectancy 70/80 whereas there is a less well defined upper limit on income. HDI ignores the power which citizens of wealthy nations enjoy by virtue of being citizens of wealthy nations.

There are also other metrics which can be used equally legitimately.

  • War deaths per capita – dying in war is unpleasant. Has the tendency to die in war become more egalitarian or less? War deaths per capita is the least encouraging measure. The world remained a warlike place in the developing world throughout the end of the 20th C.
  • Free press – No substantial famine has ever occurred in a country with a relatively free press. I haven’t found anything conclusive but there remain significant differences in various part of the world on press freedom. India has a rigorous free press whereas China does not. According to so far this year:
    • 25 Journalists killed
    • 2 media assistants killed
    • 157 journalists imprisoned
    • 9 media assistants imprisoned
    • 112 netizens imprisoned
  • Extreme poverty – have the number of people in extreme poverty in poor countries converged on the rate for rich countries (0%). Yes – lots of the movement has been from China and India. The Millennium Development goal to half 1990 level is on target to be reached.

What are poor countries being asked to converge on? This table from Lant Pritchett shows the bunching up of wealthy countries at the top of the income scale. There has been Convergence, Big Time, for the countries that have made it.


Pritchett also gives us figures to suggest that we have seen divergence, big time, since the begninning of the industrial revolution. Since the 1950s it is argued we have at best seen stagnation if not outright regression.

  1910 1950 1992
Gini coefficient 0.61 0.64 0.66
Mean world income (PPP at 1992 prices, $) 1450 1806 2801
Extreme poverty (headcount %) 66 55 24
Number of extreme poor (million) 1128 1376 1294
% of world inequality explained by between-country inequality (based on Theil index) 37 60 60

Maddison shows that the long term and continuing pattern in income is one of divergence. In 1000 AD Western Europe and Africa had roughly equal GDP per capita ($400), but by 1998 Africa had reached the income Europe had in the early 19th C whereas Europe’s income was now 13 times that. Since 1950 inequality has not so much continued to diverge as stagnate.

However, there have been other works which suggest that the world’s fortunes have not been massively divergent.

Xavier Sala-i-Martin (2005) Rather than the “divergence, big time” famously described by Pritchett [1997], we find that individual incomes have followed a process of “convergence, period!”

Large poor countries became somewhat less poor, having a massive effect in terms of convergence. Inequality between countries has increased and has inequality within countries. However, if a large country becomes more unequal while growing fast enough world inequality can indeed fall. This, Sala-i-Martin argues, is what has happened in China (and to a lesser extent India) and is why we have seen “Convergence, Period.”

So the picture for income is unclear, what about non-income related measures of welfare?

In almost everything that matters we have seen some degree of convergence. Health, education, rights and infrastructure have been converging and have been converging for some time.

Looking at the data they show us that it takes one tenth of the income that it did in 1870 to live the same amount of time. Life expectancy for countries with a GDP per capita today of $300 have the same life expectancy as countries of 1870 with a GDP per capita income of $3000. Life expectancy has also become far more egalitarian than it was in 1870.

Although Sala-I-Martin argues we have seen income convergence world wide, it has been very uneven. The convergence of HDI is much more broad based.

We have to ask then, if it is not income that has driven convergence in HDI, what is? There could be large returns to small increases in income for the very poorest people. A little extra food can hugely improve the immune system for example.

But that is not all… Mozambique saw it’s per capita income decline over the period 1950-99 but its life expectancy, literacy and primary enrolment all increased.

Public Health… Better immunisation plays a role. Some of these reforms are self-reinforcing, Increase in primary enrollment and literacy have helped the efficacy of other public health. Urban mortality is lower than rural mortality.

Economic policy is hard. Public Health theory is much easier. ORT saves over a million children’s lives a year and it is just a sugar/salt solution. Washing hands. Globalisation of knowledge appears more benign than globalisation of production.

Has the world seen Divergence, Big Time?

It depends both on your datasets, on what weighting you give to different countries and whether you are interested in income or other indicators.

Divergence, Big Time

  • World wide trends versus trends in China and India. Martin Wolf argues that World Trends without China is like Hamlet without the Prince but China’s history and progress is very different to that of the world in general.
  • China’s size means that when it does something stupid or clever it has global ramifications. But it is not necessarily accurate to say it represents global patterns.

The twentieth century experience of economic development has been wildly divergent, regardless of what these aggregate figures tell you.

  • The iron curtain in NE Asia and E Europe
  • Africa has been attempting to build states for a lot of the 20th C, the prologue to economic development
  • Poverty was an Asian phenomenon, but it has now become and African phenomenon. While parts of Asia have begun to converge on the wealthy world, Africa’s prospects continue to diverge (although there has been recent progress riding on surging demand for raw material from Asia).

Kenny, Charles (2005) “Why Are We Worried About Income? Nearly Everything that Matters is Converging” in World Development Vol. 33, No. 1, pp. 1–19

[1] Summary. — Convergence of national GDP/capita numbers is a common, but narrow, measure of global success or failure in development. This paper takes a broader range of quality of life variables covering health, education, rights and infrastructure and examines if they are converging across countries. It finds that these measures are converging as a rule and (where we have data) that they have been converging for some time. The paper turns to a discussion of what might be driving convergence in quality of life even as incomes diverge, and what this might mean for the donor community.

Everyone is interested in economic convergence; it would represent the catching up of the poor world with its rich contemporary. Basically, everywhere started poor, but now some places are wealthy. Income has been the sole, or at least overriding criteria, for some time and this stems from a humanitarian impulse to see improvements in the global standard of living. Kenny quotes Lucas:

[I]s there some action a government could take that would lead the Indian economy to grow like Indonesia s or Egypts? If so, what exactly? If not, what is it about the ‘‘nature of India’’ that makes it so? The consequences for human welfare involved in questions like this are simply staggering: once one starts to think about them, it is hard to think about anything else (Lucas, 1988).

[2] This interest in income comes from the linking of income to most if not all quality of life measures. Even if income isn’t your main focus in a humanitarian sense, an increasing income in a poor country will probably help you achieve your aim. As income has diverged many have inferred that quality of life was diverged as well. However, while income has diverged, quality of life measures are converging almost across the board.

The link between the quality of life an income

GDP per capita is an incomplete measure of wellbeing, this is one of the reasons HDI was introduced by the UN. In fact, from infant mortality to life expectancy to war deaths per capita, there is very little correlation between income, income growth and other quality of life indicators. If you have been seeking to increase someone’s life, then increasing their income may not be the easiest way to do so.

Evidence for the convergence in measures of the quality of life

This paper builds on the work of Crafts, Ram and Ingram.

HDI of poor and rich countries are converging, despite relative inequality and massive poverty remaining common. [2-3] Calorific intake, primary enrolment and urbanisation are all converging.

Methods of measuring convergence and methodological issues

 There is a discussion of different measures of convergence and divergence here which kinda goes over my head. I will revisit if necessary.

Data quality is an issue, with income and all other quality of life data.

[4] Weighting is also problematic, should China count the same as Sierra Leone, or should measures be weighted to reflect China’s massive population? Using individual data rather than national per capita data can massively change our analysis.

Which quality of life measures to use is also a  question which is important. There seems little scientific way to select which measure to use and how much importance to give it. However, effort has been made to use measures for which there is good coverage in area and time.


The results presented in Appendix Tables 11–13 suggest almost every potential quality of life variable shows significant variation across countries. In turn, this suggests that, either throughout history some quality of life indicators have been higher in some parts of the world than others, or that, in some point in the past, there must have been divergence. The available evidence suggests elements of both stories, although with a predominance of the second. The evidence also suggests that more recently (for most of the 20th century) the story is reversed—it is one of convergence [my emphasis].

Maddison shows that the long term and continuing pattern in income is one of divergence. In 1000 AD Western Europe and Africa had roughly equal GDP per capita ($400), but by 1998 Africa had reached the income Europe had in the early 19th C whereas Europe’s income was now 13 times that. Since 1950 inequality has not so much continued to diverge as stagnate.

[5] However, apart income GDP, and depending on which measure of convergence you use, all or nearly all other measures have converged since the industrial revolution worldwide.


There is a historical minimum life expectancy of around 24 (younger than this and presumably societies just collapse). Divergence occurred from the early modern period until the end of the 19th C and then convergence in life expectancy began.  This convergence in life expectancy has been driven by a convergence in infant mortality. Calorific intake improved in many places and this has in part driven the convergence, although this factor is unlikely to have been enough to enough to explain all the convergence.


Divergence in literacy levels can be traced back before the 18th C. By 1913 literacy in India was 13% and in the UK around 96%. Global literacy in the rich world has reached ~100% and the poor world’s literacy has improved too, closing the gap. Between 1950-99 global literacy rose from 52% to 81%, driven largely by improvements in the poor world.

A driving factor in this is the increased availability of primary education. Tertiary education has also become more common in the developing world.

Social Indicators

Female literacy as a percentage of male literacy (an important measure of economic potential and gender equality), has converged since the 1970s from 59% to 80%. The percentage of children not in the global labour force has also decreased from 76% to 90%.

War deaths per capita is the least encouraging measure. The world remained a war like place in the developing world throughout the end of the 20th C.

Other, more lighthearted but important, measures have also converged. Beer production per capita has nearly doubled since 1950, representing an increase in “non-necessary” production.

What is going on?

Why have we seen convergence in quality of life measures accompany divergence in income?

There  could be large returns to small increases in income for the very poorest people. A little extra food can hugely improve the immune system for example.

How do we explain the performance of Africa over the last 50 years? GDP per capita has increased from $477 to just $561 over the 40 years (1960–99), falling from 4.8% to 1.9% of the average for a high-income country. Compare this to an under-five survival rate which has risen from 746 to 839 per 1,000 live births over the same period—or 77% of the high income survival rate to 84% of that rate. In terms of infant survival (86–91%), life expectancy (57–60%, despite the impact of the AIDS crisis reducing life expectancy by three years 1992– 99) and gross primary enrollment (35–70%), the trend is also one of convergence (all figures from World Bank, 2000). While Africa remains far behind, it is catching up on these measures, which is more than can be said for its performance on income.

Ingram argues that income tends to have a declining marginal impact on quality of life. Small increases in income reap massive rewards.

However, there appears to be more going on (even if the above argument appears very important). Mozambique saw it’s per capita income decline over the period 1950-99 but its life expectancy, literacy and primary enrollment all increased.

Looking at the data they show us that it takes one tenth of the income that it did in 1870 to live the same amount of time. Life expectancy for countries with a GDP per capita today of $300 have the same life expectancy as countries of 1870 with a GDP per capita income of $3000. Life expectancy has also become far more egalitarian than it was in 1870.

Better immunisation plays a role. Increase in primary enrollment and literacy have helped the efficacy of other public health campaigns, who can now use posters, where they before had to rely on word of mouth. 

To back up this “public health policy not just income helps improve quality of life” argument it has been observed that urban mortality is lower than rural mortality – something only achieved in Europe and the US after extensive public health campaigns.


Different income measures yield different degrees of divergence. For example, the proportion of people living in extreme poverty (less than $1 a day) has decreased tremendously; a massive convergence with the rich world. However, income has diverged in more general terms.

While aid, the Washington Consensus or globalisation have had mixed outcomes with respect to income, by other measures they should surely deserve some degree of credit for the almost across the board improvement in quality of life measures.

The debate today rests on the assumption that there has been a failure in the developing world, because there has been so little improvement in income, perhaps this pessimism is misplaced.

Protected: EH483 Notes for Students (Rather a lot of LSE information here so I’m password protecting this one)

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Falling behind and catching up

There are two contrary things which have happened since the instigation of industrialisation two centuries ago. The first is a divergence in income and the second is a convergence in nearly all other measures of human wellbeing.

One of the questions we have to address is “will globalisation lead to convergence or divergence for the world’s citizens?”

The case for convergence.

As markets integrate, some maintain, material inequality will diminish.

  • There is a modernisation argument. Once poor countries get started on industrial development and the insitutions of a modern state they will continue to develop. This view was popular in the 1950s, but has fallen out of favour today.
  • Late-development. As popularised by Rostow and Gerschenkron, they argue that there are certain advantages of backwardness which can be exploited. This allows for faster growth for poorer countries, leading to them converging on the technological limit of wealth which the richest countries have hit.
  • Neoclassical growth theory argues that market integration will lead to price equalisations of land, labour and capital and thus increased demand for the poor world’s labour and resources which will cause convergence in income and prices.
  • New Growth Theory argues that a similar process as described above occurs for the diffusion of knowledge – a non-rivalous, non-excludable public good – which allows countries to catch up.

This leads to closing inequalities.

The case for divergence.

  • Marxist argue that capitalist production involves the extraction of surplus value from labour. Thus it creates and reproduces inequality because a capitalist income is in effect an economic rent, extracted from labour for by owning capital. Market integration leads to this process merely becoming international.
  • Dependency – Labour is not fully marketable and there will always be some degree of coercion between core and peripheral economies which keep the rich rich and poor poor.
  • Geography – (from Krugman) – Different economic processes have different economies of scale, therefore different developmental paths will lead to a differing economic mix. Because some economies enjoy larger returns to scale, and there are not intrinsic reasons certain industries are in certain places, inequality will naturally occur in a global environment.
  • In contrast to the case for convergence, increasing amounts of knowledge are protected and do not diffuse easily. Those with this knowledge will be wealthier than those without it.
  • Complementary factors play a role. Certain industries, skills and workers cluster in certain places. This produces network effects and positive externalities to which the returns are higher.

This leads to increasing inequalities.

What question does this lead us to ask?

  • How has the world distribution of income changed since the beginning of modern economic growth? 
  • What are the reasons for increasing or decreasing inequality in incomes?
  • Did all benchmarks of growth follow similar pathway? Did quality of life follow the same path as income?Ineq

Measuring inequality.

There are two ways of measuring inequality. World inequality and international inequality.International inequality shows us the inequality between the per capita income of average members of different nations. It offers a good measure because we have the relevant data to make this measure useful for a large number of countries across a long timescale. World inequality treats each individual as a unit and takes into account intra and well as international inequality. It gives a better picture but we only have data from the middle of the 20th C.

There are different measures which we can use to describe world and international inequality.

We can sue the Gini Coeffiecent, the Theil Index or a simple ratio of the richest 10% to the poorest 10%. Each tells us something different and has different methodological flaws and benefits.

There are also different Indices which we can measure. Income is one measure of wellbeing, but there are more. For example, literacy, life expectancy at birth and child mortality.

We also have to think about the level of aggregation which we use. We can aggregate the income/life expectancy of the world, down to the aggregate income/health of London, each is useful in certain ways, but each also obscures certain things.

What data do we have on the past?

Next we turn to the data which we have on international and world inequality back into the 19th C and before. Much of this data come from Angus Maddison, good obit here. He trailblazed the study of income of those living in the distant past. Without his work we would have a lot less to work with in Global History and would have to rely a great deal more on conjecture.

There are limits to his data, administration and statistical records are sketchy for much of the past. For example, large land empires like the Muhgal and Ottoman decentralised much of their administration – local administrators had less need for complex statistics and could rely on local knowledge, hence a dearth of records. On top of this locational problem there are difficulties in certain economic sectors too. As most wealth came from land for much of the world’s history, income information from land is well recorded. Handicrafts and transport are both more mobile and harder to tax, so records are scarcer. Both of these lead to a bias in the data, where we have no information it is safest to assume nothing has happened, this biases our view of the poor past towards it being undynamic.

However, the data sets are still magnificent and all we have to work with. 

  1820 1910 1950 1992
Gini coefficient 0.50 0.61 0.64 0.66
Mean world income (PPP at 1992 prices, $) 659 1450 1806 2801
Extreme poverty (headcount %) 84 66 55     24
Number of extreme poor (million) 887 1128 1376 1294
% of world inequality explained by between-country inequality (based on Theil index) 12 37 60 60

A few things stand out. World inequality was high in 1800 but got higher through to the 1950s where it has held steady since. The proportion of the extremely poor as sunk massively. Much of this increase inequality came from international not world changes, in some countries everyone got rich, in some very few or none did.

There is a slow down in 1950. The lead in growth rates between Europe & its offshoots and the rest of the world shrinks, a relative slowdown occurs. Catchup growth begins in Japan, and then later Southeast Asia as a whole. Different clusters of countries drive these changes.

The dominance disequalising force in the 19th C was the relatively slow growth of Asia. Income per capita in India between 1820 and 1950 rose 10% in total, in China over the same period the total was 17%, the US economy expanded 800% over the same period.

This leads us to some otehr questions which we will address.

  • Origins of inequality. When did it begin?
  • Cultural exceptionalism: Did regions have distinct features? Politics (colonialism, despotism)? Institutions? Resources? Scale of trade?
  • European convergence
  • Catching-up does work, or does it?

The period 1950-1992 (from when Maddison’s data ends) shows the effects of catch up in Asia. Three groups of countries; poor countries with low and variable growth, middle income countries with high and variable growth, and the wealthy world with low but steady growth.

The story of the modern era has been one of income divergence, but a subplot (or the main story, depending on your position) has been the convergence of various quality of life indicators.

What is driving this?

  • High-return-to-small-change hypothesis – altering behaviour slightly can massively increase survival, better personal hygeine and widespread, but cheap, vaccinations can massively boost quality of life.
  • Public health hypothesis – public health policy has been directed far better than economic policy in these contries, fewer differing national interests and fewer differences of opinion on what to do.
  • Urbanization hypothesis – as the poor world urbanises, more people more closer to doctors and they benefit from economies of scale.
  • Triumph of globalization hypothesis – the globalisation of knowledge on health and nutrition allow people to live better lives.

Is this divergence between income and other quality of life indicators important or mere trivia? Three quotes 

The income measure has always overplayed the difference between India and the United States.

Kenny, World Development 

Technologies, which appeared to have done little in increasing Third World income, have improved other measures of the quality of life … globalization has been too quickly dismissed by some as a driver of development.

Kenny, World Development 

HDI convergence is more a logical than an empirical result, arising from the index’s definition, and so is of little interest in the debate about world inequality.

Bob Sutcliffe, World Inequality and Globalization Oxford Review of Economic Policy, 20(1), 2004, 15-37.

HDI converges on well known limits, whereas maximum potential income continues to increase at 2/3% a year. HDIs are bound to converge by definition. However, the improvements in wellbeing are not trivial.

What has driven the improvement in people’s quality of life?

  1. Globalisation > Income > Health?
  2. Globalisation > Knowledge > Health?

Each of the above two mechanisms, increased income leading households to better care for themselves, or increased knowledge of best practice leading states and households to alter behaviour at relatively little cost are both viable explanations. 

Above is the Preston curve of life expectancy at birth S.H. Preston, ‘The changing relation between mortality and level of economic development’, Population Studies, 29(2), 1975.

Preston aruges that int data shows that the association between income and health is strong in poorer countries because of public policy failures. People can only afford to increase public policy if income increases. Therefore any improvement income-dependent.


  • International inequality in income increased 1820 – 1950, remained stable 1950-92.
  • 1820-1950: ‘dominant disequalizing force’ is the stagnation in India and China
  • 1950-1992: Selective catching up.
  • 1992 onward: a faster and broader catch-up?
  • Catching up faster in health and education.
  • Did globalization play a differential role in income (technology of production) and HDI (technology of health)?

EH483: The Development and Integration of the World Economy in the 19th and 20th Centuries: Introductory Lecture

Lecturers = Dudly Baines – Albrecht Ritschl – Tirthankar Ray – Kerry Hickson = Monday 1300-1400 in the New Academic Building. Seminars are on Tuesdays.

The coursework for this unit will include two formative essays of 1500 words (pah, my undergrad essays were 4000 words) and one assessed essay of 2500 words. These will be due towards the end of the course. There will also be a three hour exam in May 2011.

All the details and deadlines are in the Notes for Students on Moodle.

Enough admin, to the meat!

There is an element of comparative development of the world economy. The unit is economics focussed and 19th century focussed. The course will be very long run to begin with and we wil then drill down into more detail later.

Objectives of the course

Explain the origins and evolution of  international inequality in the “modern world.”

Comparative elements – Demography, geography, trade, urbanisation, culture, institutions, technology and policy.

Case Study elements – Industrialisation, catch-up, fall behind, factor price integration.

GDP per capita (1990 international $)

  1870 1913 1950 1973 1998
UK 3191 4921 6907 12022 18714
W.Europe 1974 3473 4594 11534 17921
US 2445 5301 9561 16689 27331
Latin America 698 1511 2554 4531 5795
Japan 737 1387 1926 11439 20413
Other Asia 543 640 635 1231 2936
Africa 444 585 852 1365 1368

Shares of World GDP (%)

  1870 1913 1950 1973 1998
UK 9.1 8.3 6.5 4.2 3.3
W.Europe 33.6 33.5 26.3 25.7 20.6
US 8.9 19.1 27.3 22.0 21.9
Latin America 2.5 4.5 7.9 8.7 8.7
Japan 2.3 2.6 3.0 7.7 7.7
Other Asia 36.0 21.9 15.5 16.4 29.5
Africa 3.6 2.7 3.6 3.3 3.1

Every region is richer now then it was in 1870 (the time when a truly modern economy began to appear), but some are regions are richer than others. Two things stand out.

Some areas expereinced rapid growth and some did not. We need to look to the genesis of this rapid growth. It was not an increase in the use of land or other inputs which started this growth, but an increase in productivity.

The world became more unequal and we see a great deal of divergence between economies that industrialised and those that did not.


Classical economics looked at incereasing inputs to creat more wealth. Growth rested on using inputs more efficiently.

Growth was assumed to run out at some opoint, however, this has evidently not happened.

Marx helped introduce the idea that industry could grow even when land and other inputs became scarce.

Innovation has been treated as exogenous in many growth models driving progress forward. This is of course not good enough. Endogenous growth theories are essential.


Economics helps us describe interpersonal inequality, this can be scaled up with some modification to help us look at international inequality.

Key concepts

  • Factor Endowments
  • Factor Rewards

Classical economics looked at three factors, each controlled by a different class: Landlords controlling land, Workers controlling labour, and capitalists controlling finance and physical capital. The distribution of income is dictated by different factor endowments and factor rewards. Labour’s share is dictated by what is required to fulfil its own subsistence and reproduction; the Landlord’s share is dictated by the fertility and scarcity of land; and the capitalist’s share by the demand for loanable funds (However, Marx argued that a capialist’s income was a form of economic rent on the labour of the workers).

Neoclassical economics looks at the world differently. My lecturers argues that the shape of the world changed somewhere around 1850 and the borders between the different classes break down.  Individuals and households are now better described as possessing a portfolio of skills and capital. The market then sets their rewards on the basis of the demand for the productive factors they can supply.

Two issues complicate this

Factor Ownership – Risk and uncertainty – transaction costs – institutions. These shape both the supply and rewards of certain productive facotrs. For example;

  • Gender plays a role in reducing the rewards which women receive in return for their skills.
  • Slaves versus free labour
  • Spain – their two tier labour market

Technology – change alters the composition of skills and factors in demand. This leads to wage inequality. Some factors are complimentary; for example you couldn’t print the Gutenberg Bible without the creation of the wine press. Network externalities – in high density areas the same skills can receive larger rewards because there is a greater interaction with complimentary factors.

You can map this discussion of interpersonal inequality onto international inequality.

Nations have different endowments. Different rewards persist in the presence of trade cost, transaction cost, formal institutions, culture, political system, geography, demography  etc.

Market integration should lead to a convergence of costs, prices and incomes. This however is far from inevitable.

What made the The 19th Century special? The period encompassing the industrial revolution also saw a transport revolution which helped increase trade, Smithian specialisation and productivity advancements:

  • The cost of freight fall

  • Industrialisation
  • The introduction of more uniform legal codes
  • Communications costs plummeted
  • This was the 1st Globalisation
  • trade and mobility of factors
  • population growth
  • urbanization
  • technological and institutional change

Convergence seen in the North Atlantic (and Dominions), but the rest of the world sees Divergence, Big Time.

The course will address the question of why did some areas experience this revolution and why did some not?

  • Marx – markets did not determine rewards – political economy – colonialism
  • Rostow argued that there was one process – modernization – and that this proceeded at different speeds. Parts of the world were merely not yet ready.
  • Some argue that Geography played the most important role. Tropical countries didn’t develop, so it must be harder to develop in the tropics, natural
  • resources like coal helped the UK develop so countries without these natural endowments were not able to develop.
  • Some areas did not develop because of Markets and risks – risks of commodity market fluctuations – risks of financial integration.

Other reasons to be examined include:

  • Some countries had better institutions (property rights, commercial culture, civic mindedness), which allowed them to utilise their endowments.
  • Some areas developed effective contract law and contract design so that the risks of economic activity were reduced.
  • Culture may have played a role. For example there may have been a bias against innovation in some places.

A further part of the course will examine “catch-up” development. What policies, factors and contingent events aided countries in catching up with the developed world.

  • Gerschenkron examined late industrialisers like the US, Germany and Russia and saw how the later the industrialisation, the more the state had to intervene to shape markets.
  • Alice Amsden has argued that Southeast Asia Industrialised by ‘getting prices wrong’. They forced surpluses out of productive agriculture into unproductive activities to prompt industrialisation.
  • “Death of distance.” As space becomes smaller it is easier and easier to trade knowledge, goods and services. This process has accelerated over time, particularly since the 1970s.

The course will also examine on whether growth counts as development. Is a measure like the Human Development Index better? Does equality or equity have an important role to play?

All will be revealed over the next year.

EH483: The Development and Integration of the World Economy in the 19th and 20th Centuries – Indicative reading


  • B Arthur (Ed), Increasing Returns and Path Dependence in the Economy (1994)
  • M Bordo, A Taylor, J Williamson (2003), Globalization in Historical Perspective
  • J Diamond, Guns, Germs and Steel (1997)
  • S Engerman & K Sokoloff, Factor Endowments, Institutions and Differential Paths of Growth among New World Economies (1994)
  • R Findlay, K O’Rourke (2009), Power and Plenty
  • B Foster, The Vulnerable Planet: A Short Economic History of the Environment (1993)
  • J Goody, The East in the West (1996)
  • Wang Gungwu (Ed), Global History and Migrations (1997)
  • I Inkster, Science and Technology in History (1981)
  • E L Jones, Growth Recurring (1988)
  • M Livi-Bacci, A Concise History of World Population (1997)
  • P Mathias & J Davis (Eds), Agriculture and Industrialization from the 18th Century to the Present Day (1996)
  • M Obstfeld, A Taylor (2004), Global Capital Markets
  • D Puga, ‘Urbanization Patterns: European vs. Less Developed Countries’, Journal of Regional Science (1998) Here
  • A van der Woude, A Hayami & J de Vries (Eds), Urbanisation in History (1990)
  • World Bank, Global Integration and Decentralization in an Urbanizing World (1999)

Pritchett, L “Divergence, Big Time” in The Journal of Economic Perspectives, Vol. 11, No. 3. (Summer, 1997), pp. 3-17.

From here.

The topic of this paper is the massive divergence which has been observed between currently rich countries (European Countries and their offshoots plus Japan) and the other countries. No grouping is really all that accurate, but the theme of divergence in growth rates, productivity and wealth split the world into two fairly distinct groups.

The period discussed is that since 1870s. This if often chosen as a start date for “modern” economic history. First of all, for rich countries decent economic information is available more or less uninterrupted since this date. Also 1870 follows on from a series of major events, US Civil War, Franco-Prussian War, and Japan’s Meiji Restoration.

The above table is used to illustrate a number of things. First of all, there was some sort of Golden Age for capitalism between the end of the war and the end of the 1970s. There is a strong convergence within this subset of countries; the poorest six countries in 1870 had five of the six fastest national growth rates for the time Period. 1870 to 1960. The five richest in 1870 had the five slowest growth rates.

Secondly, Even with the catch up of the poorest countries growth rates are relatively uniform: the standard deviation of the growth rates is only .33. Evans (1994) formally tested the hypothesis that the growth rates of European countries and their offshoots (not Japan) were equal in the period and could not reject it.

Thirdly, although there has been substantial variation over time there has been no substantial acceleration of growth rates over time. Growth rates have been remarkably stable.

Unfortunately all these observations are drawn from a self selecting group of countries which are now rich; the observation they have grown strongly and consistently over the last 100+ years and that they are now rich is almost tautological. Countries like Japan which did converge are included, but countries which didn’t like Argentina are not.

A Lower Bound for GDP

There is a paucity of data for historically poor for a variety of obvious reasons. However, there is a physical limit on how poor a country can be, “even deprivation has its limits.” Pritchett argues that $250 expressed in 1985 purchasing power equivalents is the lowest GDP per capita could have been in 1870.

No one has ever observed lower living standards in the modern poor world; this level is set well below modern levels of “absolute poverty” and is at the limit of viable nutritional intake; a lower standard of living and the population could not expand.

PPP is very important in measuring living standards in poor countries (see disclaimer here), tradeable goods cost more or less the same everywhere, but haircuts etc are much cheaper in poor countries. $70 in 1985 US market exchange rate dollars = our P$250 minimum GDP per capita level.

Divergence, Big Time

If you accept: a) the current estimates of relative incomes across nations; b) the estimates of the historical growth rates of the now-rich nations; and c) that even in the poorest economies incomes were not below P$250 at any point-then you cannot escape the conclusion that the last 150 years have seen divergence, big time.

If we assume that all countries have grown at roughly the same rate and backcast from now then we come to the conclusion that soem countries had incomes lower than P$100 in 1870, since this is impossible then we must have seen massive divergence.

The magnitude of the divergence is staggering. From 1870 to 1990 the average absolute gap in incomes of all countries from the leader had grown from $1,286 to $12,662, an order of magnitude.(pp 9-12 are well illustrated and should be read in full). Bairoch (1993) argues that developed countries and developing countries were largely economically equal as late as 1800, this implies and even more startling era of divergence since.

Divergence is not a thing of the past

There are a number of countries catching up and growing at historically unprecedented rates (Korea, Taiwan, etc), but manuy continue to stagnate and some have even regressed (i.e. negative growth rates since 1960). Many countries have seen slowdowns and some have seen “meltdowns.”Annual growth rates amongst developing countries from 1960-1990 range from -2.7 percent to 6.9 percent.

There has been no obvious acceleration of growth in most developing countries, either relatively or absolutely, and no reversal in divergence. Almost nothing that is true about the growth rates of developed countries is true of that for developing countries.

North, D.C, “Epilogue: Economic Performances Through Time” in Empirical Studies in Institutional Change edited by L. Alston, T. Eggerston and D. North, pp 342-356 (USA: University of Cambridge Press, 1996)

Taken from here.

North is attempting to delineate what can and what cannot be learned about the way societies change over time. Section I is on the process of economic change, section II is on what questions we should ask and section III speculates on what questions can be answered and which cannot.


North explains that “a theory of economic performance through time would entail an integration of institutional change, demographic change and the change in the stock of knowledge.” In this essay he chooses to focus on the change of institutions because economics is the study of a process and processes are embodied in the institutions of a society.

Institutions are created by humans to structure human institutions in order to reduce uncertainty in pursuit of their goals (or those making the rules) in social, political and economic exchange.

Institutions are defined as the formal rules (constitutions, statute and common law, regulations etc.) , informal constraints (norms of behaviour, conventions, internally imposed codes of conduct etc.) and the enforcement characteristics of each.

Institutions are not Organisations. Think of Institutions as the rules of the game and Organisations as the players. “Organizations and their entrepreneurs are the actors; they will introduce new institutions or technology when they perceive that they can improve their competitive position by such innovation.”

North now goes into more detail on the processof institutional change.

The institutions of a period define what organisations will exist. Organisations exist under a condition of scarcity and therefore competition. The rate of innovation and change in the institutions which govern our organisations is governed by the incentive structure constructed by this competition.

This is not the competitive conditions of the economic theory of perfect competition but rather the institutional environment of the organizations–the framework of rules and norms–which determines the incentives for innovative activities. An improvement in an organisation is not necessarily an improvement in productivity but could be the creation of a monopoly or the redistribution of wealth in some advantageous way.

Sometimes the innovation involves a change in formal rules and structures [like the repeal of the corn laws] so a study of institutional change must take embody a theory of political change. Sometimes changes involve a gradual change in informal norms of exchange [like the end of religious prohibitions on usury].

Whether organisations change institutions in ways which boost productivity or by redistributing wealth depends on the incentive structure of currently existing institutions. If institutions and the way they evolve are the key to economic performance through time what determines the way they evolve?

The immediate answer is that individual entrepreneurs who are in the position of modifying the rules of the game in political or economic markets and have implicit or explicit theories about the consequences of policies act upon those theories to modify the rules to improve their competitive position; the perceptions of entrepreneurs shape their policies and over time it is the way these perceptions evolve that determines the way institutions evolve. There is no implication that results of the choices that are made will coincide with intentions; indeed more often than not the belief systems that underlie perceptions produce unintended and unanticipated results.


This approach to understanding economic performance through time is at odds with the mainstream literature.

Arnold Toynbee popularised the term “Industrial Revolution” in a series of lectures in 1880-81 and historians have ever since usually onsidered technological change to be the most important determinant for economic performance. For example “take off” theories of development. This is wrong, technologies give us our upper bound, but do not tell us about why this upper bound is so often missed. In contrast, Malthusian pressures have determined the gloomy lower bound.

The economic growth literature has seldom posed the questions which North wants answered. Growth accounting can explain the proportions with which human capital, physical capital, increasing returns, or technology contribute to economic growth but it cannot explain why poor countries don’t invest more in human or physical capital.

Studies of the Third World have often implicitly assumed incentives are aligned correctly, when they are almost always provide incentives for redistributive institutions are larger than those for productive activity. The failure of humans to organise themselves the provide the right incentives must be the centre of inquiry.

Too much study in economics looks at static equilibria, the study of economic performance through time needs a dynamic approach. Given the above, North now reveals his questions.

  1. Are institutions really the carriers of the process of economic change and if so what are institutions?
  2. If institutions play such a role how do they interact with the other key actors in the process of economic change, demographic change, and changes in the stock of knowledge?
  3. What are the sources of institutional change?
  4. What is the process of economic change? How useful are models drawn from evolutionary biology?
  5. How can we explain the diversity of economic performance through time?
  6. How far can we go in constructing a framework that can provide guidelines to improving the performance of third world and transition economies? Can we construct a dynamic theory of change?
  7. Where are we going?


In this section North  approaches each question in term.

Are institutions really the carriers of the process of economic change and if so what are institutions?

Mainstream economics neglects institutions. The evidence of the myriad levels of economic performance even amongst countries facing similar technological and demographic limits should illustrate how important institutions actually are.

However, while mainstream economics do not incorporate institutions into their theory, when they concern themselves with policy they do so in discussing changes in laws and regulations – formal institutions. But Informal institutions are also very important.

If institutions play such a role how do they interact with the other key actors in the process of economic change, demographic change, and changes in the stock of knowledge?

They interact in many ways, most of which we are yet to fully understand. Modern economic growth has its source in the stock of knowledge which is rooted in the scientific revolution of the sixteenth and seventeenth century, yet the roots of this revolution are far from clear.

The formal and informal institutions which led to this scientific revolution need to be understood to explain how it led to the massive changes in demographics and technology which followed.

What are the sources of institutional change?

The source of change reflects the perceptions of economic and political entrepreneurs who perceive ways in which they can improve their position.

In this context, actors do not act as neoclassical theory would suggest. This approach assumes pervasive scarcity and individuals making choices reflecting their preferences. The aggregate of this preference is then constructed  in the context of fixed resources, private goods and given technology. This model is good for illustration the benefits of a decentralised market system but is less useful in understanding institutional change.

When solving economic problems frictions arise [transaction costs] is the context of imperfect information and imperfect enforcement of agreements, and markets are the creatures of political forces. In the real world beliefs define the actions of actors.

We must model beliefs if we are to understand any social science.

Risk versus uncertainty comes into play when discussing someone’s beliefs. Risks can be described as a probability distribution and can be insured against. This is impossible for uncertainty and economics has not created a body of work on this subject. However, all human endeavour is conducted on the basis of some sort of uncertainty – religions, myths, taboos and half baked ideas all serve as the basis of (sometimes life and death) decision making.

The study of economic performance through time must entail the study of how humans learn and meld beliefs and preferences; why they develop these choices in the face of such uncertainty; and why some ideas die out and others prosper.

What is the process of economic change? How useful are models drawn from evolutionary biology?

There are parallels between Darwinian Evolution and the process of economic change. However, Institutional change is largely Lamarkian, change is intentional and is intended to improve the position of those making the changes.

Part of our the scaffolding on which society is built is genetic, part of being human, and part of it is cultural, also I guess part of being human, but in a different way.

Experimental Economists have found evidence which lends support to the position of evolutionary psychologist, human beings cooperate in small groups when transaction costs are small but noncooperative outcomes are favoured in large groups or under conditions of private information. Stephen J Gould and others have suggested that cultural evolution still has a large role to play in the architecture of how humans act. A look to the variety of human societies which share the same genetic material, suggests that cultural evolution play a large role.

Initially the architecture of the  structure is genetic, but interaction with the physical environment and those from the socio-cultural linguistic environment affect this. Categories of experiences are formed which become models on which we decide actions and predict outcomes, as we use these models we update them through confirmation and falsification.

No one individual can understand the whole world but each develops their own models. These models would diverge were it not for ongoing communications from those of a similar cultural background. The interaction of these models creates a culture, provides a method for intergenerational transfer, a means for internal communications, and a method to explain experiences outside the experience of our specific individual. This last function explains the helpes existence of irrational beliefs, some explanation is better than no explanation.

All this implies that individuals are constantly somewhat irrational, unknowledgeable and subject to experiences which may reinforce rather than dismiss incorrect beliefs. North proposes some more questions on what this means for the study of economic performance through time.

  1. What difference does it make that the agents fall far short of substantively rational behavior (full knowledge of all possible contingencies, exhaustive exploration of the decision tree, and a correct mapping between actions, events,and outcomes)?
  2. Is it possible that the agents simply get it wrong in terms of modeling the process of change or representations of the environment?
  3. It may be that the past experiences of the actors lock them in to a belief system and institutional framework that however well they have solved previous problems are strikingly bad at solving new problems.

How can we explain the diversity of economic performance through time?

History is a record of unanticipated consequences and outcomes of decisions made in the face of uncertainty. It could hardly be otherwise.

Economic History is the study of the huge increase in wealth and well-being of humanity, but is is also the study of actions which have produced death, war and famine on a colossal scale. Even what look today like the best laid plans (the US Constitutions) have in fact been aided hugely by chance.

One of the biggest challenges to economic growth for a society is the move from personal to impersonal exchange – a necessary component for greater specialisation and enlarged trade. For example, Genoese traders outperformed Islamic traders in the 11th and 12th century because the Islamic traders relied on in-group social communication networks to enforce collective action while the Genoese formed formal and legal enforcement mechanisms. Thus, due to path dependency, the Genoese could gain more from specialisation and enlarged trading networks.

Institutions change through time and impact one another in unpredictable ways. Because Islamic traders relied on personal exchange, they couldn’t specialise as much as the Genoese, which impaired change in the stock of knowledge which altered the path of demographic change which impacted the change of institutions etc.

How far can we go in constructing a framework that can provide guidelines to improving the performance of third world and transition economies? Can we construct a dynamic theory of change?

A theory of dynamic change is not possible in the same way that a theory of general equilibrium is. We make changes based on new information all the time. To predict those changes would require us to predict that future knowledge, at which point it ceases to be future knowledge and simply becomes knowledge and then becomes now. The past can inform the present and the future, but it cannot predict it.

We know something about the evolution of formal institutions but we know very little about how informal rules change, and these can be just as important.

We need to understand path dependency because it plays a major role in constraining change, if institutions exist which retard economic growth then we need to know hoe easy it is to change them.

Where are we going?

The foregoing discussion suggests that our potential foresight is relatively limited. Forecasting what humans will learn and how the human environmental landscape will change in consequence of that learning and non-human alterations is beyond our capacity and in consequence imposes severe temporal limits on our understanding of economic performance in the future.