Pre-industrial Inequalities

1. Pre-industrial Inequalities Branko Milanovic World Bank Training Poverty and Inequality Analysis Course March 3, 2011

2. Questions ● Is inequality caused by the Industrial Revolution? Or, has inequality been pretty much the same before and after? ● Is inequality in poor pre-industrial economies today pretty much the same as in ancient pre-industrial economies? ● Was inequality augmented by colonization? ● Have some parts of the world always had different levels of inequality than others?

3. Constraints on the Elite in Ancient Pre-Industrial Societies ● Fact: Ancient pre-industrial societies had average income levels usually twice, but sometimes 4-5 times, the subsistence level. ● Fact: Low average income, combined with the requirement that few fall below subsistence, meant that the elite’s surplus (and thus inequality) could not be very large. ● Query: What happened when average income and the potential surplus rose? Did the poor subsistence workers get any the added surplus or did the elite grab it all?

4. A New Measure: the Inequality Possibility Frontier • Divide society into 2 groups: people with subsistence income and elite (fraction ε of total population) that shares the surplus equally among themselves. • There is no overlap between the two classes, and no inequality within each. • Then, the Gini simplifies to:

5. Per capita income of the elite is: where N=total population, μ=overall mean income, s=subsistence. • Per capita income of people is s; and respective population shares are ε and (1-ε). • Substituting all of this into Gini gives

6. If, for simplicity, we express μ as so many (α) subsistence minimums, the Gini becomes IMPORTANT: The expression gives the maximum Gini compatible with mean income of αs; ε fraction of the elite, and no inequality among either elite or people. When ε tends to 0 (one Mobutu), G* = (α-1)/α. With α=1, G*=0; α=2, G*=0.5; if α=100 (like in the US today), G*=0.99. Introduction of inequality among the elite does not affect the maximum Gini.

7. Other interpretations • This is the maximum inequality which may exist at a given income level when the entire surplus income is appropriated by (at the extreme) one individual. • The size of the overall income (the pie) limits the level of measured inequality (measured by the synthetic measures like the Gini where all incomes matter). • It is a new and realistic generalization of the Gini index since it requires that the society be sustainable.

8. New Measurement of Inequality • The ratio between the actual Gini and the maximum Gini (a point on the IPF) is the inequality extraction ratio. • The inequality extraction ratio shows what percentage of maximum feasible inequality an elite is able (or wishes) to extract = ratio A/B (next slide).

9. The locus of maximum inequalities is “inequality possibility frontier” B A Note: Vertical axis shows maximum possible Gini attainable with a given α.

10. How are we going to study “ancient inequalities” • There are no household survey data, but.. • There are social tables akin to King’s 1688 table. • We shall use mostly the social tables that have already been produced or the data that can allow us to produce such tables (in some cases from professional censuses). Plus Ottoman censuses of settlements (2 cases) • Inequality (Gini) calculated from such tables assumes that (i) all members of a group have the same income, and (ii) groups are non-overlapping (i.e, all members of an upper group have higher incomes than all members of a lower group). This is our lower-bound Gini1.

11. We relax assumptions (i) by calculating maximum feasible inequality within the income ranges of the groups (thus allowing for an estimate of within-group inequality). But we have to keep (ii) although we know that there are members of (say) nobility who may have lower income than some merchants. This is our upper-bound Gini2. • The ratio between Gini2/G* estimates inequality extraction ratio for a given country.

12. What countries do we include? • Wherever we could find a social (class) table with estimated mean class income and population shares. • We set time limits: for the developed world, 1810; for the rest, 1929 (with India 1947 as an exception). • Difficult decision to decide what is a country: an officially distinct territory with autonomous or foreign government (the latter is a colony). • We do not include cities (Jerez, Paris, Amsterdam for which data exist).

13. This leaves us with 30 data points, ranging from Rome 14 to India 1947. • Four data points from England (1230, 1688, 1759, 1801) and three from Holland though (1561, 1732, 1808) • Number of social classes mostly in double digits except in Nueva España and China (3 classes only), Moghul India (4) and England 1290 (7). Median number of classes = 20, but Tuscany (1427) almost 10,000 households, Levant (1596) 1415 settlements. • Does number of classes matter? Sensitivity analysis suggests Not (see below). • Estimated per capita incomes in 1990 $PPP almost all from Maddison; if not, use the ratio between the estimated mean LC income and estimated subsistence (α) and price the latter at $PPP 300 (Byzantium paper) • In the sample, α ranges from 1.6 to 6.7 (based on a subsistence minimum of $PPP 300).

14. An example of a social table: France 1788 Source: Morrisson and Snyder (2000)

15. Data Sources, Estimated Demographic Indicators and GDI Per Capita…(Contd.)

16. …Data Sources, Estimated Demographic Indicators and GDI Per Capita Notes: GDI per capita is expressed in 1990 Geary-Khamis PPP dollars (equivalent to those used by Maddison 2003 and 2004).

17. 18th century included countries

18. 19th century included countries 12 countries before the French revolution, 18 countries after… No social tables for the United States (!), Russia, Africa (except Kenya and Maghreb)

19. … but more may be coming American colonies 1776/1800 (Lindert and Williamson working on it) Czarist Russia (Mironov) Poland Mehmet Ali’s Egypt More Ottoman defters Madagascar Audiencia de Quito

20. Kingdom of Naples around 1810

21. Map of Levant 1596-97 (yellow areas included)

22. Inequality Measures

23. Inequality Measures

24. Estimated Gini Coefficients and the Inequality Possibility Frontier Note: The IPF is constructed on the assumption that s=$PPP300. Estimated Ginis are Ginis2 unless only Gini1 is available

25. At α<3, Ginis range from 25 to low 60s and are clustered around the IPF. These countries “extract” quite a large share (on average ~ 80% of maximum inequality). • With higher mean income, as the IPF becomes higher, Gini does not rise to the same extent, and the extraction ratio goes down. • This is true when we compare ancient and modern societies, but true within ancient as well as within modern (application of IPF methodology to the contemporary societies; see below) • All countries with the extraction ratio around 100% were colonies: Moghul India 1750 (112%), Nueva España 1790 (105%), Maghreb 1880 and Kenya 1927 (100%), Kenya 1914 (96%). 4 different colonizers.

26. For the ancient, if α<3, the median Gini is 42 and median extraction 78% (n=18). If α>3, the median Gini is 49 and median extraction 64% (n=12). Ho of ↓ extraction accepted (p=0.999), Ho of ↑Gini accepted (p=0.972; Kuznets). • Thus, Gini alone is not a sufficient measure of inequality. • A Gini of (say) 40 in Rome and in the US does not mean the same thing. In Rome, that Gini extracts 75 percent of maximum inequality, in the US less than 40 percent.

27. Ginis and the Inequality Possibility Frontier for the Ancient Society Sample and Selected Modern Societies Note: Modern societies are drawn with hollow circles. IPF drawn on the assumption of s=$PPP 300 per capita per year. Horizontal axis in logs.

28. Inequality extraction ratio for the ancient and the “same” modern societies All but one, colonies! Based on the subsistence minimum = $PPP300.

29. Highlight colonies’ extraction ratio

30. Distribution of the extraction ratio across three types of society Use Figure25.do file (bottom graph)

31. Relationship between GDI per capita and extraction ratio for ancient societies only Note: 95 percent confidence interval

32. Can we try to explain determinants of ancient inequalities and extraction ratio? • Paucity of data points (30 in total) and possible explanatory variables • However, we have some: income per capita (Kuznetsian relationship), urbanization rate, population density, dummy for being a colony

33. Gini determinants

34. And the extraction ratio…

35. Drawing together Gini and the extraction ratio • Kuznets quadratic relationship relatively strong for Gini, but income negatively associated with the extraction ratio (as we saw before) • Asynchronism in the behavior of the Gini and extraction ratio as societies get richer: Gini at first ↑, but the extraction ratio ↓ throughout • Population density puts downward pressure on both Gini and the extraction ratio. The effect on the latter particularly strong—so much that both urbanization and income lose significance • Colony very significant: adds 12-13 Gini points, and twice as many extraction points throughout • Controls for different types of surveys and number of social groups not significant

36. Other implications • Asia (absence of economies of scale in the cultivation of rice) does not appear to have been more equal in Gini terms; population density more important (although high population might have been made possible by the absence of extreme inequality) • No causality can be proven. • 2 possibilities: (i) less extractive regimes –however they might have arisen-- allow population to increase; (ii) greater population density ---however it happened-- threatens the rulers more so the extraction ratio goes down (Campante and Do). Think why Louis XIV moved from Louvre to Versailles. • Most likely both effects operate and impossible to disentangle them • IMP: Why and how population density limits elite’s predatory power?

37. Other implications (cont.) • Re. Engelman-Sokoloff Ho: If Western Europe was as unequal as Latin America, why were the trajectories of the two so very different in 19th-20th century? • W. European mean Gini (1500<year<1810; 8 obs) = LA mean Gini (4 obs) in 19th century = 53. But Europe’s extraction ratio 70% vs. LA 85%. • Their Ho should be recouched in extraction, not Gini terms

38. Two propositions • Proposition 1. While the estimated Ginis for pre-industrial societies fall in the same range as inequality levels observed today, ancient inequality was much greater when expressed in terms of the maximum feasible inequality. • Proposition 2. Under conditions of economic growth, particularly in poor or middle-income societies, constant inequality reflects great restraints on exploitation because the inequality extraction ratio is falling. The reverse is true during periods of economic decline (e.g., Russia under Yeltsin).

39. Global inequality and poverty • If we take all 12 countries within years 1750 and 1880, we have 583 income groups representing incomes of almost 650 million people. • Over that period, average world population was around 900 billion. • These LC incomes are converted into $PPP (Geary-Khamis, 1990) • What is inequality among world citizens, and poverty rate?

40. Gini for these individuals is 38.2. This is only about a half of global Gini today (70 with the new $PPP data; 65 with the old $PPP data). • The poverty headcount (with the PL=$PPP410) is 85 percent. Crucially depends on China.

41. Global inequality then and now MLW data 1820, 1870 from Bourguignon and Morrisson, 2005 from Milanovic

42. Global poverty then and now(much more dependent on the assumption re. income of the poor in China than inequality calculations) MLW data 1820, 1870 from Bourguignon and Morrisson, 2005 from Chen and Ravallion

43. Who were the people with the highest incomes then? • European colonizers in Java: about 2,500 people had per capita incomes in excess of $PPP90 100,000. • Also a few hundred people in England 1759 and the Netherlands 1808. (English top income group in 1801-3 is broader.) • Incidentally, the rich British in 1947 India had an average per capita income in excess of $PPP90 50,000 which would place them in the 2nd richest percentile in the US today. • Little wonder colonies were good for colonizers!

44. An added dimension: the share of top 1% • Recent work (Piketty etc) implies that there is a strong correlation between the top 1% (and fewer) income share and inequality. • Is it true in ancient societies? • Caveat: these are not true distributions of people or families but of social classes. • Estimate the top share using Pareto interpolation (assumes Pareto distribution at the top).

45. Estimated top of income distribution: ancient and modern counterparts

46. Weak correlation (ρ=0.45) between Gini and top 1% income share twoway scatter top_percent gini if sample==1, msize(vlarge) mlabel( country)

47. Top five percentiles of income distribution in Rome 14,Byzantium 1000, and England 1688 Note: All data points except for the top 1 percent are empirical. The top 1 percent share is derived using Pareto interpolation.

48. Embourgeoisement of England: increasing share of top 5% and declining share of top 1% Based on per capita transformation of King, Massie and Colquhoun social tables

49. Third proposition (re: the top shares) ● Fact: The share of the top percentile in ancient societies is not tightly connected with overall inequality in contrast with modern societies. ● Proposition 3. What drove ancient inequality was not the top share, but rather the size of the income gap between average income (y) and the average income of poor (w) = y/w.

50. Figure 8. Gini versus the y/w Ratio in an Ancient Sample of Twelve 70 Nueva Espana 1790 60 England 1801-3 England 1688 Castille 1752 England 1759 50 India 1947 India 1750 Byz 1000 40 Brazil 1872 Rome 14 Gini Coefficient 30 Naples 1811 China 1880 20 10 0 0.00 1.00 2.00 3.00 4.00 5.00 6.00 Average Economy-wide Income versus Income of Rural Labor (y/w))