Pre-industrial Inequalities. Branko Milanovic World Bank Training Poverty and Inequality Analysis Course March 5, 2012
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1 Pre-industrial Inequalities Branko Milanovic World Bank Training Poverty and Inequality Analysis Course March 5, 2012
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: 1 G yj yi) p i p j
5 Per capita income of the elite is: yh N sn(1 ) N 1 [ s(1 )] 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 1 1 G* s s(1 ) s ( )
6 If, for simplicity, we express μ as so many (α) subsistence minimums, the Gini becomes 1 1 G* s( 1) (1 ) s limg * 0 1 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 Maximum feasible inequality (G*) The locus of maximum inequalities is inequality possibility frontier B A Average income as multiple of subsistence minimum (alpha) 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 nonoverlapping (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 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 Social Group Population (in 000) Per capita income (livres per annum) Population % Nobles and Clergy Bourgeoisie Shopkeepers and artisans Workers (non agricultural) Servants (non agricultural) Small scale farmers Large scale farmers Agricultural day laborers and servants Mixed workers Total Source: Morrisson and Snyder (2000)
15 Data Sources, Estimated Demographic Indicators and GDI Per Capita (Contd.) Country/territory Source of data Year Number of social classes Population (in 000) Estimated GDI per capita Roman Empire Social tables Byzantium Social tables England Social tables Tuscany South Serbia (w/o foreign) Holland Household survey Census of settlements Tax census dwelling rents , Levant Census of settlements , England and Wales Social tables Holland Tax census dwelling rents Moghul India Social tables Old Castille Income census England and Wales Social tables
16 Data Sources, Estimated Demographic Indicators and GDI Per Capita Country/territory Source of data Year Number of social classes Population (in 000) Estimated GDI per capita France Social tables Nueva España Social tables England and Wales Social tables Bihar (India) Monthly census of expenditures Netherlands Dwelling rents Kingdom of Naples Tax census dwelling rents Chile Brazil Professional census Professional census Peru Social tables China Social tables Java Social tables Japan Tax records Java (w/o foreign) Social tables Siam Social tables British India Social tables 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 (yellow areas included)
22 Inequality Measures Country/territory/ year Gini1 Gini2 Maximum feasible Gini with s=300 Actual Gini as % of the maximum Roman Empire Byzantium England and Wales Tuscany South Serbia (w/o foreign) Holland Levant (w/o foreign) England and Wales Holland Moghul India Old Castille England and Wales France
23 Inequality Measures Country/territory/ year Gini1 Gini2 Maximum feasible Gini with s=300 Extraction ratio: Actual Gini as % of the maximum Nueva España England/Wales Bihar (India) Netherlands Naples Chile Brazil Peru China Java Japan Java Siam British India
24 Gini index Estimated Gini Coefficients and the Inequality Possibility Frontier 90 IPF Nueva España 1790 Chile 1861 Maghreb 1880 Holland 1561 Kenya 1927 India 1947 Old Castille 1752 France 1788 India 1750 Java 1880 Siam 1929 Florence 1427 Peru 1876 Byzant 1000 Brazil 1872 England 1688 Rome 14 Japan 1886 England 1290 Levant 1596 Holland 1732 Netherlands England 1759 England Kenya 1914 Bihar 1807 Java 1924 Naples China 1880 Serbia GDI per capita (in 1990 $PPP) Note: The IPF is constructed on the assumption that s=$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 Gini Ginis and the Inequality Possibility Frontier for the Ancient Society Sample and Selected Modern Societies BRA ZAF MYS CON TZA KEN IND CHN USA SWE GDI per capita 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! KEN PER BRA MEX THA CHL CHN TUR IND IDN SRB ESP ITAENG FRA NDL JPN gdp per capita in 1990 ppp Based on the subsistence minimum = $PPP300.
29 0 Gini Highlight colonies extraction ratio DZA NES KEN IND IND KEN JAV BIH JAV GDI per capita in 1990 PPP dollars
30 Distribution of the extraction ratio across three types of society modern preindustrial non-colonies preindustrial colonies extraction ratio Use Figure25.do file (bottom graph)
31 Relationship between GDI per capita and extraction ratio for ancient societies only Kenya India-Moghul Nueva España Kenya Maghreb India-British Byzantium Bihar South Serbia China Old Castiille Peru Siam Roman Brazil Empire Java1880 Eng1290 Florence Japan Levant Kingdom of Naples Java1924 Hol1561 France Chile Hol1732 Netherlands Eng1688 Eng1759 Eng ln GDI per capita 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 First cut Is Asia different? Drop 2 Javas Ln GDI pc 360.5*** 366.7*** 360.2*** (Ln GDI pc) *** -25.5*** -25.0*** Urbanization 0.349* 0.354* 0.353* Pop. density *** *** * Colony 12.63*** 12.93*** 12,41*** Asia No foreign No. of groups Tax survey Adjusted R 2 (N) 0.75 (28) 0.73 (28) 0.73 (26)
34 And the extraction ratio Parsimonious Add pop density Drop 2 Javas Ln GDI pc ** Urbanization 0.677* Pop. density *** ** Colony 16.12** 25.52*** 25.35*** No foreign ** *** *** Adjusted R 2 (N) 0.34 (28) 0.65 (28) 0.60 (26)
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 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 19 th - 20 th century? W. European mean Gini (1500<year<1810; 8 obs) = LA mean Gini (4 obs) in 19 th 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 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 , 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 , 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 $PPP ,000. Also a few hundred people in England 1759 and the Netherlands (English top income group in is broader.) Incidentally, the rich British in 1947 India had an average per capita income in excess of $PPP 90 50,000 which would place them in the 2 nd 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 Top 1% share in total income (in %) The cut-off point (in terms of mean income) Gini coefficient Byzantium Chile China Nueva España Japan Netherlands France Rome England England Old Castille Siam Average ancient Average modern counterparts Chile UK India
46 Weak correlation (ρ=0.45) between Gini and top 1% income share BYZ CHL CHN JPN PER NES NAP JAVBIH ENG ROM JAV BRA ITA ENG IND IND FRA NLD HOL ENG ENG SYR THA OCA SRB gini2 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 Byzantium Roman E England top percentile 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% cumulative top perc 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 Gini Coefficient Figure 8. Gini versus the y/w Ratio in an Ancient Sample of Twelve Castille 1752 England 1759 England Nueva Espana 1790 England 1688 India Brazil 1872 India 1750 Rome 14 Byz Naples 1811 China Average Economy-wide Income versus Income of Rural Labor (y/w))
51 Five take-away observations Measured annual inequality is not very different in preindustrial societies today than it was in ancient societies. New measure of inequality: maximum inequality compatible with preservation of a society: the inequality possibility frontier. The extraction ratio how much of potential inequality was converted into actual was much bigger in ancient societies. In contrast with modern societies, the top 1% share was not correlated with overall inequality in ancient societies. But the gap between elite or average income and poor people s incomes was correlated with overall inequality. Can we contrast equal societies with a very small and very rich elite (Oriental despotism) vs. those with a more graduated (diversified) income structure?
52 Moving to the present: the use of the IPF and extraction ratio Maximum Gini: a new upper bound on the Gini such a society is sustainable in the long-run. More realistic Gini. Extraction ratio: reflection both of the level of development and rapacity of the elites (or their ability to appropriate the surplus).
53 The extraction ratio and GDI per capita (year 2002) SLE MDG KHM ZAR CPV NER HTI MLI MOZ NICBOL SLV ETHGNB MWI HND JAM BRA COL TCD COG ZMB PRY GTM ZAF TZA GIN PER PAN BFA NGA NPLCIV BEN COM SEN ECU DOM UGA CRIMEX ARG CHL CMR MRT MYS HKG LAO PHL IRN VEN CHN GEO MAR TURURY-U VNM LKA GAB SGP JOR BGD IND-U MKD SYR THA IDN-UCHN-REST ISR MDA LVA RUS USA GBR TJK UZB CHN-U POL ALB EGY GRC AUS BEL ARM BGR BLR BIH ESPITA KGZ PAK ROM CANCHE IRL KAZ LTU IND-R IDN-R HRV KOR NLD UKRYUG SVK DEU AUT CZE SVN SWE FIN HUN JPN NOR FRA DNK LUX ln gdpppp gini/gini_max*100 Fitted values
54 Gini and GDI per capita (year 2002) KHM CPV HTI NICBOL SLV BRA COL JAM ZAF PAN GTM HND PRYPER DOM CRI MEX ECU ARG CHL GIN MYS MDG MOZ SEN CIVCOM CMR IRN VEN NER NPL UGA URY-U TUR COG ZMBNGA PHL CHN ZAR MAR GAB MLI MWI TCD MRT LKA BFA GEO JOR THA SLE BEN SYR CHN-R RUSEST GNB MKD LVA TZA VNM LAO IDN-U POL IND-U CHN-U BGDMDA TJK UZB ROM EGY ALB KAZ BIH ETH KGZ PAK LTU BGR BLR HRV YUG ARM IDN-R SVK UKR CZE IND-R HUN HKG SGP ISR USA GBR GRC ESP ITA AUS CAN BEL CHE IRL KOR NLD SVN DEU AUT SWE FINOR JPN FRA DNK LUX lngdpppp gini Fitted values Using ineq_frontier.do file
55 Probability of civil war ( ) as function of inequality or extraction ratio in the period Mean HBS income (ln) (0.002) (0.000) (0.000) (0.000) Gini (in %) (0.82) (0.49) Extraction ratio (in %) (0.00) Democracy(Polity2) (0.000) Ethnolinguistic fract (0.000) (0.000) (0.000) (0.000) Pseudo R No. of obs Civil war = within-war variable from CoW project; my gdppppreg.dta file; weighted probit probit civil_warcow Giniall lngdpppp if year>1970 & year<1990 [w=hhh]
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