Always egalitarian? Australian earnings inequality 1870–1910

CENTRE FOR ECONOMIC HISTORY THE AUSTRALIAN NATIONAL UNIVERSITY DISCUSSION PAPER SERIES ALWAYS EGALITARIAN: AUSTRALIAN EARNINGS INEQUALITY 1870-19101 LAURA PANZA UNIVERSITY MELBOURNE JEFFREY G. WILLIAMSON HARVARD UNIVERSITY & UNIVERSITY WISCONSIM DISCUSSION PAPER NO. 2019-08 JULY 2019 THE AUSTRALIAN NATIONAL UNIVERSITY ACTON ACT 0200 AUSTRALIA T 61 2 6125 3590 F 61 2 6125 5124 E [email protected] http://rse.anu.edu.au/CEH Always Egalitarian: Australian Earnings Inequality 1870-19101 Laura Panza (Melbourne)* Jeffrey G. Williamson (Harvard and Wisconsin)** Abstract Trends in Australian inequality across the twentieth century are now well documented and they closely replicate trends in every other advanced economy: from WWI to the 1970s, inequality fell steeply everywhere, and from the 1970s to the present, it rose just as steeply. Despite following a similar trajectory, Australia remained more egalitarian throughout. Why has it been exceptional and what are its origins? Our previous work has found plenty of evidence documenting a steep fall in Australian income and earnings inequality from 1820 to 1870 (Panza and Williamson 2019a). This paper answers two additional questions. First, what was the level of inequality around 1870 after the fall? While we cannot speak to income inequality in 1870, we do find that earnings inequality was much lower in Australia than in the United States, the United Kingdom, and presumably the rest of Europe. Second, we find that there was no rise in Australian earnings inequality over the half century 1870-1910, but rather a modest fall. These findings rely on the use of an array of primary sources – especially the underutilized government Blue Books reporting annual earnings of an impressive range of white collar occupations – as well as better known secondary sources reporting the earnings of manual workers and farm labor. These occupational (average) earnings data are merged with occupational employment data taken from the censuses to construct social tables for Australia’s 1870 earnings distribution. We do the same for postfederation 1910 Australia. This exercise establishes that the source of modern Australia’s relative egalitarianism is the middle third of the colonial nineteenth century. We also apply Goldin-Katz (2008) analysis to the half century 1870-1910 thus to identify the sources of slow skill demand and fast skill supply growth. Australia missed a rise up some Kuznets Curve before World War I, a rise so common in Europe and most of its offshoots. * [email protected] 1 ** [email protected] We are grateful for the useful comments on a previous draft by Jeff Borland and Tim Hatton. 1 1. Twentieth Century Inequality: Australia versus the Rest Thanks to the impressive work of Anthony Atkinson, Thomas Piketty and many other economists, trends in earnings, income and wealth inequality across the twentieth century are now well established (Atkinson 2008; Atkinson and Bourgingnon 2015; Atkinson and Piketty 2007, 2010; Atkinson, Piketty and Saez 2011; Bengtsson and Waldenstrom 2018; Piketty 2005, 2014). All countries in the OECD experienced a great levelling from World War 1 to the 1970s and almost all experienced a big rise from the 1970s to the present. The timing of the inequality-trend turning point is also very similar across countries. While some underwent more dramatic changes than others, almost all countries for which we have the data exhibit the same trends. Figure 1 illustrates the evolution of the top 1 percent income shares for the US, the UK and Australia between 1913 and 2006.1 With very few exceptions, all advanced economies exhibited much the same decadal trend-related turning-points. This commonality suggests that twentieth century inequality was driven by the same exogenous forces pretty much everywhere: by the switch from Great Depression to post-war prosperity; from two World Wars to relative world peace; from deglobalization to re-globalization; from anti-immigration to pro-immigration; from baby bust to baby boom; from liberal to conservative tax policies; from regulated to deregulated domestic financial markets; and from closed domestic capital markets to an integrated world capital market. All of these big shocks were shared, and all of them had the same predictable impact on inequality, first lowering it, and then raising it.2 The Australian experience followed a very similar path, with income inequality declining over the first three-quarters of the twentieth century before increasing thereafter. Using tax data, Anthony Atkinson and Andrew Leigh show that the income share of the top 1 percent fell from around 11 percent in 1921 to under 5 percent by 1980 and then rose again to around 9 percent in the early 2000s (Atkinson and Leigh 2007b Table 7.1, pp. 315-6; Atkinson and Leigh 2007a, Figure 1, p. 253).3 Earlier studies portray an analogous story of income inequality decline from the 1 Our empirical analysis refers only to non indigenous Australians. While the Aboriginal population became a relatively important source of labor in the settler economy from the mid nineteenth century (Lloyd 2010), there are no available statistics on the Aboriginal labor market to allow us to include them into the analysis. 2 See the summary in Lindert and Williamson (2016: Chapter 8). 3 See also Saunders (1993) on the decline and rise of inequality with the 1970s as turning point. 2 pre-World War 1 decade to the post-World War 2 decades, despite using different data and different inequality measures. For example: Noel Butlin (1983) found a fall in the skilled to unskilled wage ratio between 1901 and 1968, an earnings inequality correlate; Jones (1975) documented an income inequality fall between 1914/15 and 1968/9; and McLean and Richardson (1986) reported a considerable leveling in the distribution of per capita family income between 1933 and 1979. The Australian income inequality rise since the 1970s is even better documented (Travers and Richardson 1993; Boehm 1994; World Bank 2019). Despite the similarities between country-specific inequality trends, it is important to stress that Australia remained much more egalitarian throughout. Table 1 reports top 1 percent income shares for twentieth century Australia, the United States and the United Kingdom. Across the 1920s, when inequality was at a secular peak, the Australian share was only 62 percent of the US and UK average. Following the great leveling, and having reached an inequality trough in the 1970s, Australia again recorded lower figures than the average of the US and the UK, 66 percent. And after the steep rise to the inequality peak in the 2000s, once again Australia recorded much lower figures, only 61 percent of the US and UK average. It is also notable how stable the ratio of Australia to the other two stayed across the twentieth century, while exhibiting the same inequality trends. Why were Australian inequality levels so modest across the twentieth century – at least relatively -- and what are their nineteenth century origins? This paper asks whether this relative egalitarianism is rooted in its colonial past well before the 1901 Federation, and well before a set of policies to reduce perceived inequalities were implemented (McLean and Richardson 1986, p. 67) and during decades recording a growth slow down (McLean 2013) and very modest industrialization. To our knowledge, we are the first to provide an estimate of Australian inequality in the nineteenth century.4 True, our previous work found plenty of evidence documenting a fall in earnings and income inequality from 1820 to 1870, a period of exceptional growth in GDP and living standards (Panza and Williamson 2019b). So much for mid-nineteenth century trends. What about the level of inequality around 1870 and how did it compare with the rest of the world? Was it always more egalitarian than other New World countries and Europe? 4 See Maddock and Olekalns (1984) for a review of the available studies on income inequality in Australia between 1914 and 1980. 3 We overcome the constraints imposed by the lack of income and wealth data before the 1933 census and the tax data reported from the 1920s onwards, by constructing social tables for earnings. Specifically, we build from scratch labor earnings social tables for Victoria, New South Wales and South Australia (the latter using only urban males), which covered about 87 percent of Australian 1870 GDP, in order to estimate its earnings distribution. To this end, we use the colonial censuses to document the occupational structure of the labor force (that is, employment by occupation, gender and location); and two key primary sources for their average earnings by category: the Sessional Papers for working class occupations, and the Blue Books for white collar jobs, since it lists the annual earnings of all public employees by occupation. We find that Australia was exceptional in 1870: the distribution of earnings was far more equal than in the United States in the same year (Lindert and Williamson 2016), and even more so compared with the United Kingdom in 1867 (Baxter 1868) and the rest of Europe. Next, we ask whether Australia maintained that exceptionalism over the four decades between 1870 and 1910 by constructing another social table for the latter year, and the answer is yes. Indeed, earnings inequality actually fell over that half century, while it rose in most advanced economies 2. On Building Australian Social Tables 2.1 What’s a Social Table? Social tables were first used to document national income and its distribution across social classes more than four centuries ago by the English political arithmeticians William Petty and Gregory King, writing in the seventeenth century. They were followed in the eighteenth and nineteenth centuries by Patrick Colquhoun, Dudley Baxter and others (for a summary see Lindert and Williamson 1982). Their idea was to rank income earners by various occupations or social classes from the richest to the poorest with their estimated number of income earners and their estimated average incomes. Petty, King, Colquhoun and Baxter used their social tables to derive national income estimates, but in the absence of modern income surveys and tax records, they can also be used to measure inequality. Social tables are especially useful in evaluating pre-industrial societies where classes were clearly delineated, and the differences in mean incomes between them were clear without any fuzzy edges between classes. As such, the social table is a matrix, sometimes with separate columns by gender and location. Most recent uses dealing with inequality 4 are: Milanovic, Lindert and Williamson (2011) using a large sample of 29 countries from Rome 14 to India 1948 (recently and modestly updated in Milanovic 2018); Lindert and Williamson (2016) for the United States for the years 1774, 1800, 1850, 1860, and 1870; and Burnard, Panza, and Williamson (2018) for 1774 Jamaica. As we suggested above, in the absence of modern survey data or even tax data to construct Atkinson’s top shares, we think social tables offer the best way to estimate earnings or income distributions. While relying on social tables to estimate Australian earnings inequality around 1870 and 1910 has its weaknesses (see section 2.3), it is important to bear in mind that the same weaknesses are shared by our comparators, a US social table for 1870 (Lindert and Williamson 2016: Chapter 6) and a United Kingdom social table for 1867 (our revision of Baxter 1868). While all the above mentioned studies report income distributions (the US and UK document earnings, income and wealth distributions), we are only able (thus far) to estimate annual income distributions for Australia. This is because we have not found data on average property income by occupation. Do earnings distributions typically understate income inequality? Apparently not for one of the three nations being considered here: Lindert and Williamson (2016: Chapter 6) show that for 1870 United States earnings inequality was slightly higher, not lower, than income inequality.5 The observation year 1870 is unlikely to characterize 1867 UK: in both newly-settled economies – compared with Europe – had their wealth and property income distribution still dominated by small owner-operated family farms and firms, implying less income inequality than earnings inequality. In any case, modern evidence shows a very high correlation between income and earnings inequality trends. 2.2 Constructing Social Tables for 1870 and 1910 Australia All six of Australia’s colonies (New South Wales, Queensland, South Australia, Tasmania, Victoria, Western Australia) published occupational employment censuses for a date around 1870, but only two have roughly the same occupational earnings detail in their official documents – New South Wales and Victoria – where aggregation between them is possible. South Australia also has some earnings data, but they are limited mostly to urban males. As was true for all British colonies, 5 One piece of evidence supporting this assumption is the extensive overlap between occupational hires in the two sectors including clerks, lawyers, porters, messengers, teachers, bakers, cooks, printers, lithographers, compositors, clergy, pharmacists, surgeons, physicians, accountants, bankers, store managers common labor and others. 5 the six Australian colonies also published what were called Blue Books which, among other details, report the annual incomes of all public employees by department, occupation, location and gender (e.g. male clerks, matrons, female domestics, porters, police, judges, surgeons, surveyors, engineers, urban common labor, gardeners, stable keepers, carters, cooks, nurses, some artisans, teachers, laundresses, and so on). However, the occupational earnings detail around 1870 is only adequate for the same three colonies – New South Wales, South Australia, and Victoria. According to available GDP estimates (Sinclair 2009), New South Wales and Victoria accounted for 75 percent of Australia’s 1870 GDP (87 percent with the addition of South Australia), thus providing validity to the representativeness of our sample. The social tables for New South Wales and Victoria are reported in the Appendix. As indicated above, while white collar earnings are taken from the Blue Books, some urban unskilled, some artisans, most skilled in the building trades, and all farm labor earnings are missing from the Blue Books; these are derived from other sources, mainly Coghlan (1918: v. ii) and the Sessional Papers from the House of Commons (1868-1873). All of these comments on the 1870 data apply to the 1910 data as well, although the latter are much more detailed by occupation. The occupational earnings and employment for both years are described in much greater detail in the Appendix. 2.3 Potential Weaknesses as Australian Earnings Inequality Measures The colonial and post-federation Blue Books report annual earnings for all public employees, but not for private sector employees. In effect, therefore, we assume that public and private sector labor markets were competitive, and that there was no selectivity regarding employee quality. This assumption seems reasonable to us given the relatively small size of the government sector at that time, implying that private sector labor market conditions drove public sector occupational earnings, not the other way around. In addition, public employees almost always had annual work contracts. The same was true of private sector white collar workers, farm labor and domestics. However, it was not true of skilled, semi-skilled and unskilled manual workers: in the private sector, manual labor was hired by the day or week while all white collar workers and most other 6 public employees were usually paid by the year.6 Thus, we assume that the security of annual contracts were offset by higher daily and weekly wages in the more volatile private sector (with high unemployment risk). In any case, the same assumptions are made for the construction of the 1870 social table for the United States and the 1867 social table for the United Kingdom with which comparisons will be made. In addition, while we can take account of earnings variation within some occupations – e.g. inspectors, clerks and police by grade, domestics by gender – we cannot do so for most unskilled and semi-skilled occupations, and even some artisanal occupations, except when their earnings are reported separately. Thus, each occupation cell in our social tables reports average earnings, calculated from all employees who had their annual earnings listed in that occupation, and that weighted average is then multiplied by the numbers so employed economy-wide as reported in the censuses. In short, while the social tables certainly measure the variance of earnings between detailed occupations, and thus reflect schooling and skills, they do not include all the variance within those detailed occupation. However, we are not convinced that this is a serious shortcoming of the social tables. After all, most of the earnings variance within occupations (controlling for location) is driven by age, health and luck. To the extent that the occupation-specific distributions of age, health and luck vary little across time and location, the social tables should be effective in gauging differences in earnings inequality across time and space. 3. Australian Earnings Distributions in 1870 Table 2 converts our social table for Australia7 -- merging those for Victoria (Table A1) and New South Wales (Table A2) – into a size distribution which yields various inequality summary statistics: top 10 percent earnings share, top 1 percent earnings share, the ratio of the top 10 percent’s average earnings to the bottom 10 percent’s average earnings, the ratio of white collar 6 While the Blue Books report annual earnings by occupation, the other sources typically report only daily or weekly wages that must be converted to annual earnings by days worked per year assumptions. For the latter, we use Panza and Williamson (2018b), which also tells us how in-kind income is added to the earnings estimates for farm labor and domestics. 7 For the 1870 analysis, the label Australia refers to the aggregation of our social tables for New South Wales and Victoria, roughly three quarters of the total colonial economy. 7 average earnings to common labor’s average earnings, and the gini coefficient.8 In the discussion that follows, we will rely on the top 1 percent share as our inequality indicator,9 but note that these five inequality statistics are highly correlated. The subsequent discussion would hardly be changed if we used any of the remaining four inequality indicators. Table 3 reports the same statistics (except for the white collar to common labor earnings ratio) for the United States in 1870 (using Lindert and Williamson 2016), and the United Kingdom in 1867 (using Baxter 1868). We should stress here, however, that some limitations to Baxter’s social table (see Appendix 5, Table A5) suggests that our UK earnings inequality figures are probably biased upwards. Still, we know that income inequality was higher in the United Kingdom than in the United States at that time (Figure 1; Lindert and Williamson 2016: Figure 5-3, p. 119). Indeed, the top 5 percent received an amazing 46 percent of total income in 1867 England and Wales (Lindert and Williamson 1982: p. 96), so there is reason to expect a similar earnings inequality gap between the two. Before considering what Table 2 tells us about Australian earnings inequality across space, note that the data for South Australia are only adequate for estimating urban male earnings inequality (see Appendix 4). Having said so, our estimates show that older, slightly richer (per capita GDP 2.3 percent higher: Sinclair 2009), more industrial (manufacturing value added 9 percent of GDP versus 5.5 percent: Sinclair 2009) and much more urbanized10 New South Wales recorded higher inequality than Victoria (top 1 percent share 7.37 versus 4.17 percent), but still much lower than the United States (top 1 percent share 9.7). While the South Australian data are not available to speak to colony-wide issues, we can see clearly that urban inequality was lower there (top 1 percent share 4.6 percent) than in New South Wales (top 1 percent shares are 6.9 percent versus 4.6 percent). Table 2 also repeats what is almost always found for every country and time: namely, rural earnings inequality was far less than urban inequality in both New South Wales and Victoria but more so in New South Wales (top 1 percent share 7.63 percent versus 5.04 percent) than in Victoria (4.33 versus 3.66 percent). 8 In addition, Appendix Table A4b reports the “Australian” size distribution by decile. 9 Appendix Table A4b reports 1870 earnings shares for all ten deciles. 10 The urban employment share of total employment in New South Wales was double that of Victoria. But note that this figure relates to employees not to the working population. Recall that our earnings social tables document employees and omit self-employed workers on family farms and in family firms. 8 Next, consider the role of gender. Not surprisingly, women were paid considerably less than men, even when doing the same work: in towns, female common labor received only 55 percent of their male co-worker’s pay in both colonies (Tables A1 and A2).11 But female labor participation in paid work was so low that their inclusion in the distribution calculations raises the inequality statistics very little. For Australia as a whole, the top 1 percent received 5.48 percent when females are excluded, but 5.83 percent when they are included. To repeat, the difference is modest simply because female participation in the paid labor market was modest. Finally, consider the central question that motivates this section: Was 1870 Australia a relatively egalitarian place compared with the United States and the United Kingdom? In this paper, the comparison will be limited to the United States and the United Kingdom primarily because very few other countries supply such earnings data. The comparison reported in Table 3 is unambiguous: Australia was a far more egalitarian place in 1870. The top 1 percent share was 5.8 in Australia, 9.7 percent in the United States and 16.8 percent in the United Kingdom. Thus, earnings inequality was only 60 percent of the US, 35 percent of the UK (but an upward bias for UK inequality contributing to a downward bias to the relative Australia estimate), and about 50 percent of the average of the two. This is a remarkable finding: given that the same figure for income inequality was 62 percent in the 1920s and 61 percent in the 2000s. It appears that Australia’s relative egalitarianism has persisted for a century and a half, although that leadership may have eroded a bit between the 1870s and the 1920s. Was the erosion in Australian distribution leadership due to rising inequality there or a fall in the UK, the US or both? Available evidence on income inequality trends suggests that it was likely to have been the latter since Figure 1 points to a fall in British income inequality between 1870 and 1920; and the next section will show that Australian earnings inequality actually fell between 1870 and 1910. 4. Did Australian Earnings Inequality Rise or Fall from 1870 to 1910? 4.1 11 Assessing the 1910 Earnings Distribution As we shall see, the figure was higher in 1910, 66.7 percent, so that there was some reduction in the gender pay gap over those five decades. 9 Tables 4 and 5 summarize our estimates of Australian earnings inequality in 1910.12 Consider gender issues first. Table 4 shows that the distribution of earnings among females was much more equal than among males, simply because women were rarely employed in high-skilled and high-paying jobs. In New South Wales, the gini coefficient was 0.304 for males and 0.249 for females, while the top 10 percent shares were 26.35 and 23.83 percent. The same was true for Australia as a whole, the ginis being 0.378 and 0.282. Male experience clearly dominated the total earnings distributions: the male gini in New South Wales was 0.304 while the gini for the two combined was 0.306. When aggregating across gender, the pay gaps between them certainly raised the gini above that of males, but it did not do so by much, from 0.304 to 0.305. As in 1870, female participation in the labor market was still too modest in 1910 to have a significant impact on total inequality. However, not only did women face job discrimination in high skilled occupations, they also faced wage discrimination within almost every occupation. This was especially true of white collar jobs where women earned only 68 percent of men, but even for lower skilled jobs – like domestic servants, farm labor and urban common labor -- where they earned 71-78 percent of men (Table 5). Next, consider the impact of job distributions across Australian states. Using Australian employment weights, the gini was much higher, 0.373, than in New South Wales. 0.306, simply because there were many more low-paying jobs – farm labor and non-farm unskilled labor – in the more newly settled, less urban and less industrial states like Queensland, Tasmania, South Australia and Western Australia. 4.2 Did Earnings Inequality Rise or Fall? Still Egalitarian in 1910? Did Australia remain egalitarian between 1870 and 1910? Did a relatively equal distribution of earnings persist over the half century? Table 6 supplies the answers. The first fact 12 We simplify by referring to the year 1910, while the social tables and these summary tables actually refer to the 1910 Blue Book and the 1911 Census. In addition, and to repeat table notes and text, 1910 “Australia” refers to the Commonwealth’s total occupational employment figures but to New South Wales for the occupational earnings. Resources permitting, we plan in future versions of this paper first to exploit the earnings data in Victoria’s Blue Book and next, if time permits, that of the other four states. Tables titled by New South Wales use both the earnings and employment data of that state. The 1870 tables use the earnings data from both Victoria and New South Wales (section 3 and Appendix 4), not just the latter. However, as we can see in Appendix 4, the earnings structure was quite similar in the two 1870 colonies, and we expect the same for those two states. 10 to note there is how little average white collar earnings changed over those fifty years, a fall of 17 percent in New South Wales and a rise of 16 percent for Australia as a whole. In contrast, unskilled urban worker’s earnings more than doubled in both New South Wales (up by 2.16 times) and all Australia (up by 2.51 times). Thus, the ratio of white-collar average earnings at the top to that of urban common labor at the bottom fell steeply from 7.11 to 2.74 in New South Wales. However, farm labor’s earnings remained relatively stable. Thus, the rural-urban earning gap rose over the half century. Although farm wage trends must have produced a partial offset, the non-farm earnings trends certainly are consistent with a fall in earnings inequality between 1870 and 1910. And so it did. The gini coefficient fell from 0.399 to 0.306 in New South Wales and from 0.411 to 0.373 in all Australia. To take another example, the top 1 percent share fell from 7.37 to 5.31 percent in New South Wales and from 5.48 to 5.44 percent in all Australia. Appendix Table A6c shows that the earnings compression took place everywhere up and down the decile ladder. While there was a fall in earnings inequality from the beginning to the end of the half century, our evidence does not speak to the possibility that it first rose and then fell. Perhaps it did. A recent paper by Mike Pottenger and Andrew Leigh used executive earnings data from the BHP Billiton company to document the ratio of high executive salaries to average Australian earnings from 1887 to 2012. Although their evidence is only for one company and covers only about half of our period, their results might still be suggestive. The series for directors shows a steep rise 1887-1892, and then an equally steep fall thereafter (Pottenger and Leigh 2018: Figure 1, p. 5) while the one for the CEOs shows volatility 1887-1902 followed by a steep fall (Figure 2, p. 7). This somewhat limited earnings inequality evidence does suggest that our half century may have undergone an early rise and then a later fall, with a modest net decline overall. As far as we know, there is no other published evidence that speaks to these issues in the half century before the Great War. 4.3 Searching for Explanations Trends in earnings inequality can best be understood by explaining the skill premium, or the earnings gap between high-skilled white-collar employees and that of low-skilled manual labor. And as Claudia Goldin and Lawrence Katz (2008) have shown for the United States across the twentieth century, earnings inequality fell when the demand for skills grew slower than the 11 supply, and rose when skill supply grew slower than demand. They dubbed it as a race between schooling and technology. Did schooling and skills grow faster than demand in Australia between 1870 and 1910? Available statistics generally suggest that it was the case: despite sustained GDP growth, the industries relying more heavily on white collar and skilled labor grew a bit slower than GDP. At the same time, the skilled labor supply bottlenecks which characterized Australia during early settlement (Panza and Williamson, 2019a) were overcome by rapidly growing native born skilled and schooled labor augmented by relatively skilled and schooled immigrants from the United Kingdom (Hatton 2019). As a proxy for relative demand growth for skills, we use the performance of manufacturing and modern service sector GDP shares, given that these industries were more white collar-cumskill-intensive than farming, mining or the pastoral sector. Between 1870 and 1910 total output in New South Wales manufacturing and services both grew slower than agriculture: an average yearly growth rate of 4.4 percent for manufacturing, 3.9 percent for services and 4.9 percent for the primary sector (Sinclair 2009). This modest structural change is in sharp contrast with industrialization events in North America and Europe, a performance that came to be called their “second industrial revolution”. More to the point, the Australian output trends lagged behind the increase in Australian skill supply, as documented by available schooling data: indeed, the Australian colonies were among the international leaders in the provision of primary education (Seltzer 2015: p. 93). Free and compulsory primary schooling was introduced in the late 1860s in all colonies. By the late nineteenth and early twentieth century about 98 percent of the New South Wales population could read and write (Pope 1989). School enrolment rates grew fast over time and by 1880, they surpassed all European countries for which data are available (Lindert 2004). Another key contributor to the supply of skilled workers came from immigration, as shown by Glen Withers (1989) some time ago, between 1877 and 1910 most immigrants belonged to skilled and semi-skilled occupations, and more so than the native born. More recently, Timothy Hatton (2019) has shown that this was more true of Australia and New Zealand than Canada and, especially, the United States. Based on the occupations of UK emigrants, the skilled shares between 1877 and 1913 averaged 51.3 percent for Australian immigrants, 37.2 percent for American immigrants, and 30.8 percent for Canadian immigrants (Hatton 2019: p. 14). Furthermore, the share of Australian immigrants that were Irish – the poorest source region of the 12 UK emigrants – was huge in the 1850s and 1860s but then dropped steeply to 10.5 percent between 1877 and 1913 (Hatton 2019: pp. 8 and 17). Late in the period, labor policies also played a role in lowering skill premia by legislation, which served to regulate wages in favor of low skilled manufacturing workers.13 That Australia remained a “workers’ paradise” at the turn of the twentieth century has also been noted by Australian historians: for example, Peter Macarthy (1971) points to the existence of extremely high wages especially for unskilled workers. Finally, while the key driver of the egalitarian earning distribution in the 1870-1910 period was the urban leveling forces listed above, it was partially offset by a big rise in the urban-rural earnings gap. The shift towards a more urban-based economy is illustrated by New South Wales in Table 7. The labor force in the primary sector declined, and the reasons are not hard to find. Figure 2 plots a decline in land acreage per capita, a trend matched by an increase in land values per acre, particularly of rural land (Taylor 1992). Relative export prices showed no fall over the period (data underlying Williamson 2008), so increasing land scarcity must have put downward pressure on labor productivity growth and thus rural wages relative to urban. 5. Road Map for Future Research Australian earnings inequality was already very low in 1870, lower than the United States, and much lower than the United Kingdom. And it was even lower in 1910 than 1870, although similar earnings distribution data are not yet available for the US and the UK thus to know whether Australian relative egalitarianism persisted over the half century (although it was certainly true of income inequality in the 1920s: see Atkinson and Leigh 2007a, 2007b). Our new evidence is also consistent with the Goldin-Katz model: in the Australian case, schooling and skill growth over the half century 1870-1910 exceeded demand growth. What remains to be done? While this paper has been able to use Australian occupational employment data to construct our earnings social tables for 1870 and 1910, our 1910 occupational earnings data are for New South Wales only. It is certainly possible to augment the earnings 13 Australia and New Zealand were the first two countries to pass minimum wage legislation. Furthermore, a set of laws were enacted prohibiting child labor, women’s work at night and setting maximum working hours (Huberman and Meissner 2010). 13 evidence to include the other five colonies/states. The evidence is easy to gather for the manual trades, but it is very hard to extract the white collar occupational earnings evidence from the Blue Books. A still more difficult task would be to uncover property incomes by occupation thus to speak to income inequality. In addition, we need an earnings social table for the 1920s thus to hook up with the Atkinson-Leigh twentieth century income inequality series and also to compare our early twentieth century earnings inequality estimates with already available late twentieth century estimates. And can we identify which was doing most of the work creating such an egalitarian place by 1910? Was it the macro labor market forces elaborated above, or was it the rise in literacy and numeracy generated by public schooling, or was it the influx of skilled and schooled immigrants (perhaps induced by agents’ preferring skilled for steerage subsidies), or was it wage regulations introduced late in the half century? One way to find out is to build another earnings social table for 1890, before that legislation was introduced. So, while the sources of Australian egalitarianism can be found as far back as 1870, we do not yet know which were the main driving forces, a slow conversion from primary product specialization to urban-industrial activities, a big policy commitment to schooling, the attraction of high skilled immigrants, or wage regulation. To the extent that schooling, immigration and wage policies mattered greatly, then perhaps economics should yield to political economy. That is, why were these policies followed so aggressively in Australia, so much more than elsewhere? Persistence matters: the egalitarian distribution of 1870 established a norm which became a powerful driver for the 150 years that followed.14 14 There are other historical cases where this persistence has been stressed, the most important being Latin America where its extensive modern inequality is thought to have its roots in Iberian colonialism five centuries ago (Engerman and Sokoloff 2012; Williamson 2015). 14 References Atkinson, Anthony B. 2008. The Changing Distribution of Earnings in OECD Countries. Oxford: Oxford University Press. Atkinson, Anthony B. and François Bourgingnon (eds.) 2015. Handbook in Income Distribution. Amsterdam: North Holland. Atkinson, Anthony B. and Andrew Leigh 2007a. “The Distribution of Top Incomes in Australia,” The Economic Record 83, 262: 247-61. Atkinson, Anthony B. and Andrew Leigh 2007b.“The Distribution of Top Incomes in Australia”, in Atkinson, Anthony B. and Thomas Piketty (eds.). 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Meissner 2010. “Riding the wave of trade: the rise of labor regulation in the Golden Age of Globalization”, The Journal of Economic History, 10 (3). Jones, F. L. 1975. The Changing Shape of the Australian Income Distribution, 19114/15-1968/69 Lindert, Peter H. 2004., Growing Public: Social Spending and Economic Growth since the Eighteenth Century (New York : Cambridge University Press). Lindert, Peter H. 2015. “Three Centuries of Inequality in Britain and America,” in A. B. Atkinson and F.Bourgingnon (eds). Handbook in Income Distribution. Amsterdam: North Holland, pp. 167-216. Lindert, Peter H. and Jeffrey G. Williamson 1982. “Revising England's Social Tables, 1688-1812,” Explorations in Economic History 19, 4 (October): 385-408. Lindert, Peter H. and Jeffrey G. Williamson 2016. Unequal Gains: American Growth and Inequality since 1700 .Princeton, N.J.: Princeton University Press. Macarthy, P. G. 1971. “Wages in Australia, 1891–1915”, Australian Economic History Review, 10 (1). Maddock, R., Olekalns, N., Ryan, J., and Vickers, M. 1984. The distribution of income and wealth in Australia 1914–80: An introduction and bibliography. Source Papers in Economic History (1). McLean, Ian W. 2013. Why Australia Prospered: The Shifting Economic Sources of Economic Growth. (Princeton, N.J.: Princeton University Press. 16 McLean, Ian W. and Richardson, S. (1986). More or less equal? Australian income distribution in 1933 and 1980. Economic Record, 62(1), 67-81. Milanovic, Branko 2018. “Towards an explanation of inequality in premodern societies: the role of colonies, urbanization, and high population density,” Economic History Review 71, 4: 1029-47. Milanovic, Branko, Peter H. Lindert, and Jeffrey G. Williamson 2011.”Pre-Industrial Inequality,” Economic Journal 121 (March): 255-72. Panza, Laura and Jeffrey G. Williamson 2019a. ”Squatters, Convicts, and Capitalists: : Dividing Up a Fast-Growing Frontier Pie, 1821-1871” Economic History Review 72, 1 (May): 568594. Panza, Laura and Jeffrey G. Williamson 2019b. ”Living Costs and Living Standards: Australian Development 1820s-1870s,” European Review of Economic History (forthcoming). Pottenger, Mike and Andrew Leigh 2018. “Long-Run Trends in Australian Executive Renumeration: BHP, 1887-2012,” Australian Economic History Review 56 (1): 2-19. Piketty, Thomas. 2005. “Top Income Shares in the Long Run: An Overview,” Journal of the European Economic Association 3(2-3): 1-11. Piketty, Thomas. 2014. Capital in the Twenty-First Century. Cambridge, Mass.: Belknap Press. Pope, D. 1989. “The relevance of human capital”, in D. Pope and L. Alston (eds), Australia’s Greatest Asset: Human Resources in the Nineteenth and Twentieth Centuries (Sydney: Federation Press). Saunders, P. 1993. “Longer run changes in the distribution of income in Australia”. Economic Record, 69(4), 353-366. Seltzer, A. (2015). “Labour, skills and migration.” In S. Ville and G. Withers (eds.), The Cambridge Economic History of Australia (Cambridge: Cambridge University Press), pp. 178-201. Sinclair, W. A. 2009. Annual Estimates of Gross Domestic Product: Australian Colonies/States 1861-1976/77, Working Paper, Department of Economics, Monash University (September). 17 Taylor, A. D. (1992), The Value of Land in Australia before 1913, ANU Source Paper in Economic History (Canberra: ANU). Travers, P. and S. Richardson 1993. Living Decently: Material Well-being in Australia, Oxford: Oxford University Press. Vamplew, W. (1987) (ed.), Australians: Historical Statistics (Broadway: Fairfax, Syme and Weldon). Williamson, Jeffrey G. 2015. “Latin American Inequality: Colonial Origins, Commodity Booms, or a Missed 20th Century Leveling? Journal of Human Development and Capabilities (special issue) 16 (August): 324-41. Withers, Glen 1989. “The immigration contribution to human capital formation’, in D. Pope and L. Alston (eds), Australia’s Greatest Asset: Human Resources in the Nineteenth and Twentieth Centuries (Sydney: Federation Press). World Bank 2019. Webpage https://data.worldbank.org/indicator/si.pov.gini accessed May 2019. 18 Figure 1: Top 1% income shares in Australia, UK and US, 1913-2006 20 18 16 14 12 10 8 6 4 2 0 1913 1918 1923 1928 1933 1938 1943 1948 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 Australia US UK Source: Atkinson and Piketty (2007). Figure 2: Acreage per capita in NSW, 1870-1910 400 18 16 14 12 10 8 6 4 2 0 350 300 250 200 150 100 50 0 1870 1875 Acreage per capita 1880 1885 1890 1895 Land value per acre (Crown sales) 1900 1905 1910 Land value per acre (rural land) Source: Acreage per capita, left axis: Vamplew (1987). Land value per acre in £, right axis: Taylor (1992). 19 Table 1. Income Shares of Top 1%, 1920s-2000s, Australia, the UK and the US Australia United Kingdom United States Average (UK &US) Australia/Average 1920s 11.37 19.59 16.98 18.29 0.62 1970s 5.35 8.37 7.9 8.14 0.66 2000s 8.71 12.67 15.69 14.18 0.61 Sources: Australia, the UK and US are from Atkinson and Piketty (2007: Tables 4.1, 5A.1, 7.1). Table 2. Comparative Earnings Inequality across the Australian Colonies 1870 A. Using both Male and Female Total New South Wales Top 10% vs bottom 10% 31.45 Top 10% share (%) 34.40 Top 1% share (%) 7.37 Top 10% mean income (£) 813.79 Gini coefficient 0.399 Victoria Top 10% vs bottom 10% 16.15 Top 10% share (%) 27.46 Top 1% share (%) 4.17 Top 10% mean income (£) 689.38 Gini coefficient 0.353 Australia (VIC + NSW) Top 10% vs bottom 10% 26.90 Top 10% share (%) 33.53 Top 1% share (%) 5.83 Top 10% mean income (£) 706.14 Gini coefficient 0.426 Urban Rural 31.36 39.00 7.63 827.02 0.480 19.01 22.27 5.04 466.43 0.273 16.07 29.38 4.33 685.47 0.377 15.72 21.64 3.66 623.58 0.271 26.17 33.05 5.54 703.36 0.438 23.57 30.89 5.81 621.37 0.369 20 B. Using only male labor force New South Wales Top 10% vs bottom 10% Top 10% share (%) Top 1% share (%) Top 10% mean income (£) Gini coefficient Victoria Top 10% vs bottom 10% Top 10% share (%) Top 1% share (%) Top 10% mean income (£) Gini coefficient South Australia Top 10% vs bottom 10% Top 10% share (%) Top 1% share (%) Top 10% mean income (£) Gini coefficient Australia (VIC + NSW) Top 10% vs bottom 10% Top 10% share (%) Top 1% share (%) Top 10% mean income (£) Gini coefficient Sources: Appendices 1, 2, and 3. Total Urban Rural 22.16 32.72 6.75 813.79 0.377 24.27 36.22 6.90 907.49 0.455 9.80 21.27 4.99 323.38 0.274 11.73 27.35 4.18 689.38 0.343 10.18 28.89 4.22 685.47 0.367 11.79 21.47 3.61 623.58 0.263 13.42 27.26 4.57 624.26 0.361 16.60 32.12 5.48 742.98 0.411 17.49 31.27 5.15 774.50 0.409 15.65 30.09 5.64 651.14 0.356 Table 3. Earnings Inequality around 1870: the US and the UK Top 10% share (%) Top 1% share (%) Gini coefficient United States 44.5 9.7 0.546 United Kingdom 47.5 16.8 0.625 Sources: The US figures are from Lindert and Williamson (2016, Table 6.5, p.156). The UK figures are derived from Baxter (1868) and see Appendix 5. 21 Table 4. Earnings Inequality in Australia, 1910 Top 10% vs bottom 10% Top 10% share (%) Top 1% share (%) Top 10% mean income (£) Gini coefficient Using NSW employment weights Total Male Female 11.05 10.58 8.45 25.93 26.35 23.83 5.31 5.04 3.31 625.67 632.77 409.22 0.306 0.304 0.249 Using AUS employment weights Total Male Female 10.27 10.60 6.04 31.11 31.52 24.91 5.44 5.19 4.74 604.89 617.61 420.34 0.373 0.378 0.282 Table 5: Earning Ratios in New South Wales, 1910 Occupation Mean N. Mean earnings Mean earnings Female/ category earnings (£) occs Male (£) Female (£) Male White collar 316.14 73 330.90 225.11 0.68 Urban common 108.19 10 108.40 79.56 0.73 labor Domestic/servant 80.92 11 103.31 80.92 0.78 Farm 86.43 16 86.7 61.48 0.71 All categories 198.65 197 206.70 144.01 0.696 Notes: This Table is based on NSW labor weights. White collar includes Officials (High, Legal), Officers (Military, Penal, Police, Church, Charity, Health, Scientific Dept., Education Dept., Telephone, Ferry, Harbor), Judges, Clerks, Magistrates, Bailiffs, Barristers/Solicitors, Clergy, Sanitary Inspectors, Medical Practitioners, Medical and Hospital Attendants, Dentists, Pharmacists, Nurses, Midwives, Veterinarians, Chemists, Assayers, Geologists, Biologists/Botanists, Civil Engineers, Engineers, Surveyors, Architects, Draftsmen, University Professors, Teachers (Grammar/High School, State, Church, Private, Music, and Language Schools, Technical Colleges), Tutors, Photographers, Musicians, Bankers/Brokers, Accountants/Auditors, Actuaries, Underwriters, Auctioneers, Storekeepers, Managers (Government Store, Factory, Farm, Station, Store), Undertakers, Postmasters/Sorters, Letter Carriers, Mailmen, Stationmasters (Telegraph, Railway), Electricians/Linemen, Barristers, and Station Agents. Urban common labor includes: Stevedores/Wharf Labor, Watermen, Laborer, Street Cleaners/Chimneysweepers, Deliverymen, Draymen/Carters/Teamsters, Road labor (navvy), Messengers, Railroad Labor, Wood choppers. Farm labor includes: Laborer (Farm, Fruit, Vineyard, Sugar, Pastoral, Dairy, Poultry Farm, Pig farm), Market gardener, Nurseryman, Station Agent, Horsekeeper, Fisherman, Forestry worker, Stablekeeper, Woolwasher. 22 Table 6: Australian Earnings Inequality Trends 1870 - 1910 Using NSW labor weights Ratio 1910 1870 1910/1870 275.60 331.21 0.83 100.47 46.56 2.16 71.85 68.46 1.05 Using AUS labor weights Ratio 1910 1870 1910/1870 348.84 300.24 1.16 111.65 44.54 2.51 61.80 73.51 0.84 Weighted earnings (£) White collar Urban labor Farm labor Earnings ratios White collar/ urban 2.74 7.11 0.38 3.12 6.74 0.46 common labor White collar/ farm 3.84 4.84 0.79 5.64 4.08 1.38 Labor Inequality indicators Gini index 0.306 0.399 0.77 0.336 0.411 0.91 Top 10% share (%) 25.93 34.40 0.75 31.11 32.12 0.97 Top 1% share (%) 5.31 7.37 0.72 5.44 5.48 0.99 Note: Earnings weights are based on occupational’ employment shares within each category. The white collar, urban common and farm labor categories are defined in Table 5. white collar in 1870 include: Assistants, Accountants, Auctioneers, Bankers, Brokers, Architects, Artists, Authors, Editors, Reporters, Civil engineers, Clergy, Dispensing Chemists, Dentists, Chiropractors, Harbor pilots, Lighthouse keepers, High government Officials, Judges, Lawyers, Barristers/Solicitors, Clerks, Physicians, Surgeons, Policemen, Jailers, Professors, Schoolmasters, Teachers. Urban labor refers to labor employed in towns. Farm labor refers to all farm types and pastoral labor. For 1870, Australian labor force weights include employment in New South Wales, South Australia (males only) and Victoria. Table 7: Labor force shares by sector, 1870-1910 Sector 1870 1910 Farm labor 0.25 0.16 Manufacturing (skilled/semi-skilled) 0.16 0.28 Mining 0.10 0.07 Service (non-white collar) 0.21 0.22 Service (white collar) 0.09 0.16 Common labor (unskilled) 0.19 0.11 Source: Authors calculations based on census data: New South Wales Census of 1871 and The First Commonwealth Census (1912). 23 Appendix 1: Victoria 1870 Appendix Table A1 reports our social table for earnings in Victoria around 1870. To fit in the table cells reported here, the occupations listed in the table are selective, but the complete list can be found at the end of Appendix 4. The employment figures are from the Census of Victoria, 1871: General Report and Appendices (John Ferres, Government Printer, Melbourne, 1871-1874). All white collar and some working class and artisan earnings data are taken from the Blue Book of the colony of Victoria for the year 1867 (John Ferres, Government Printer, Melbourne, 1868). Urban working class and farm earnings (e, g. farm labor, building trade workers, blacksmiths, cooks, grooms, stable keepers, gardeners, female domestics, housemaids, nursemaids, laundresses, common labor, and seamen) are all constructed from Timothy A. Coghlan, Labour and Industry in Australia, v. II, chap. 2 (Melbourne: Oxford University Press, 1918) and the House of Commons, Sessional Papers: Statistical Tables relating to the Colonial and other Possessions of the United Kingdom, Parts XIV-XV (1868- 1875). We also constructed and include estimates of in-kind income for farm labor, seamen and domestics as well as converting daily and weekly wages in to annual earnings as explained in Panza and Williamson (2018b). Teacher salaries are from Parliamentary Paper (Victoria. Parliament), no. 66: Education Act .1872.—Regulations (Melbourne : Robt. S. Brain, Government Printer, 1889). All earnings in Table A1 are in Australian an pounds. Table A1. Earnings Social Table for Victoria 1870 Occupation Accountants, auctioneers, brokers Architects, Surveyors, Scientific persons Artist, actor, musician Assistants clerks Authors, editors, reporters Barber, hair dresser Blacksmith, locksmith Butcher, baker, brewer Civil engineers Clergy Commissioner, agent Common labor Dealers, agents, contractors Domestic Dressmaker, seamstress Gender Urban employees Rural employees Urban earnings Rural earnings M 1,293 463 517.93 473.06 M M M M M M M M M M M M M F 2,386 740 31 90 470 2,240 5104 132 508 475 19,346 3,988 18,068 6,186 780 222 6 24 50 1,770 2396 54 287 90 12,725 1,378 10,181 1,396 365.99 225 200 472.50 159.06 126 212.08 341.59 393.26 537 58.66 525.50 67.98 62.50 362.49 210 189.39 472.50 159.06 94.12 212.08 341.59 393.26 537 58.66 386.32 67.98 46.69 24 Farm laborer Farm overseer Fishmonger, milkman Goldsmith, jeweler, engraver Government clerks Harbour pilot, light house keeper Hawkers, peddlers, dealers, shopkeeper Hawkers, peddlers, dealers, shopkeeper High government officials Hotel & lodging operator Hotel & lodging operator Judges, lawyers, solicitors Law clerks Machine and tool maker Manager Midwife Miner Nurse Pharmacists, dentist Physicians, surgeons Police, jailers Private sector clerks Professors, Schoolmasters Seaman, boatman Skilled workers (textiles, paint, tailor, wood) Skilled with leather Skilled with stone and metal M M M M M 2,867 89 825 970 982 19,096 975 868 104 414 90.64 244.79 212.08 297.20 294.95 90.64 244.79 212.08 269.57 286.55 M 154 60 398.85 328.78 F 730 72 40 40 M M F M M M M M F M F M M M M M M 5,599 324 57 2,809 575 425 1,228 721 88 7,596 602 480 516 997 3,216 10 1,176 3,176 189 61 1,540 132 37 167 88 42 24,881 162 95 201 406 442 3 1,187 203.40 820.16 113.57 183.84 732.79 391.51 168 525.50 36 177.80 36 200 399.83 123.78 294.95 971.88 110 203.40 596 113.57 183.84 691.69 369.55 125.50 386.32 36 177.80 36 189.39 376.85 123.78 286.55 820.16 110 M M M 17,479 5,301 3,631 1,372 1,682 1,013 129.5 126 147 96.735 94.12 109.81 25 Appendix 2: New South Wales 1870 Appendix Table A2 reports our social table for earnings in New South Wales around 1870. To fit in the cells here in the paper, the occupations listed in the table are selective, but the complete list can be found at the end of Appendix 3. The employment figures are from the New South Wales Census of 1871, Consisting of Report, Summary Tables, and Appendix, and Detailed Tables (Sydney: Thomas Richards, Government Printer. 1873). All white collar and some working class and artisan earnings data are taken from the New South Wales Blue Book for the year 1867 (Sydney: Government Printer, 1868). Urban working class and farm earnings (e, g. farm labor, building trade workers, blacksmiths, cooks, grooms, stable keepers, gardeners, female domestics, housemaids, nursemaids, laundresses, common labor, and seamen) are all constructed from Timothy A. Coghlan, Labour and Industry in Australia, v. II (Melbourne: Oxford University Press, 1918) and House of Commons, Sessional Papers: Statistical Tables relating to the Colonial and other Possessions of the United Kingdom, Parts XIV-XV (1868- 1875). We constructed estimates of in-kind income for farm labor and domestics as well as converting daily and weekly wages in to annual earnings as explained in Panza and Williamson (2018b). We were not able to find common school teacher salaries for New South Wales, so we assumed that Victorian earnings applied. All earnings figures in Table A2 are in Australian pounds. Table A2. Earnings Social Table for New South Wales 1870 Occupation Butchers, bakers Butchers, bakers Fishmongers, fruiterers Fishmongers, fruiterers Hawkers, peddlers Hawkers, peddlers Hotel, lodging operator Hotel, lodging operator Barber, hairdresser Blacksmith, farrier, Locksmith Dressmaker, seamstress Goldsmith, jeweler, watch maker Printer, compositor, Lithographer Skilled with leather Skilled with stone Skilled worker casual Gender F M F M F M F M M M Urban employees 70 1,495 322 2,862 587 3,,181 385 304 2,488 614 Rural employees 63 461 12 699 43 1,131 101 706 325 205 Urban earnings 26 102.86 26 102.86 41.42 218 26 102.86 49.72 192 Rural earnings 26 102.86 26 102.86 36.04 19 26 102.86 39.6 144 F M 3,815 276 419 30 75.33 186.25 55.75 137.83 M 633 46 150 120 M M M 864 4,142 8,148 218 823 3,267 151 100.19 56.93 120 79.8 45.77 26 Wheelwright, coachmaker Common labor Common labor Domestic Domestic Farm laborer Farm laborer Miners Seaman, boatman Stevedore, lumper, stoker Accountant, banker, Merchant Architect, surveyor Assistants Authors, editors, reporters M F M F M F M M M M M 770 2,369 12,551 10,891 3,901 65 4,038 1,836 1,431 200 1,543 247 263 15,092 5,127 2,810 1,455 36,565 14,530 418 177.50 27.50 56.10 28.00 66.00 65.71 71.20 150.00 120.00 100.31 407.27 154 27.50 56.1 20.16 48 65.71 71.20 150 100.31 M M M 69 65 547 177 6 150 70 505.15 144.99 70 Civil engineers Clergy Dispensing chemists, Dentists Harbor pilot and masterr High government officials M M M 1,998 400 225 415 104 22 582 237.88 270 582 160 270 M M 245 8 70 250 1,050 175 Judges, lawyers, solicitors Law clerks Physicians, surgeons Police, jailers Private sector clerks Private sector clerks Teachers, common school Teachers, common school M M M M F M F M 273 257 252 706 527 3,651 1,342 3,114 37 22 72 132 103 590 499 772 1,472.22 403.13 270.00 225.00 49.86 262.45 75.00 525.74 950 260.13 270 225 43.38 228.33 65 150.9 179 200 27 Appendix 3. South Australia 1870 Appendix Table A3 only reports employment and earnings of urban males since our sources do not document enough female and rural occupational earnings to include inequality statistics for anything other than urban males. Most earnings data come from the Blue Book of South Australia for the year 1867 (Government Printer, Adelaide, 1868) with the exception of the following occupations which are derived from the Sessional Papers: Statistical Tables relating to the Colonial and other Possessions of the United Kingdom, Parts XIV-XV (1868- 1875): butcher; locksmith; tailor; cabinet maker; skilled with leather; mason, bricklayer; wheelwright; domestic, stevedore; carriage maker; engineer; and lithographer. In addition, barber and hairdresser earnings are from Frederick Sinnett, An account of the colony of south Australia (Adelaide, Government Printer, 1863), and teacher earnings are from Kay Whitehead, “Women's life-work: teachers in South Australia, 1836-1906,” Phd thesis, June1996, Department of Education and Women's Studies, University of Adelaide. The employment data come from the 1876 South Australian Census. Table A3. Earnings Social Table for South Australia 1870 Occupation Accountants, bankers, merchants Architects, surveyors, scientific persons Artist, actor, musician Assistants, clerks Authors, editors, reporters Barber, hair dresser Blacksmith, farrier, locksmith Butchers, bakers, brewers Civil engineers Clergy Commissioner, agent Common labor Dispensing chemists, Dentists Domestic Dressmaker, seamstress, tailoress Farm laborer Fishmongers, fruiterers Goldsmith, jeweler, watch maker Government clerks Hawkers, peddlers, pawnbroker High government officials Urban employment Urban Earnings 351 257 113 44 37 30 630 1,073 15 103 274 5,217 99 407 234 1,145 1,144 142 305 358 55 250 408.95 225 76.25 300 52 125 70 187.5 300 300 45.71 124.78 48 125 40.33 115 313.75 165.35 127.5 749.55 28 Hotel & lodging operator Judges, lawyers, barristers Law clerks Machine and tool maker Midwife, nurse Miners Overseer Pharmacists, dentist Physicians, surgeons Police, jailers Printer, compositor, lithographer Private sector clerks Professor Seaman, boatman Skilled with leather (boot, shoe, and saddle maker; tanner) Skilled with stone (mason, bricklayer) and metal Skilled with textiles and wood Stevedore, wharf labor Teachers, common school Tobacconist Wheelwright, coachmaker 20 75 70 313 12 284 35 16 59 133 350 938 3 454 970 150 540.91 224.23 135 93.53 90 310 71.18 450 324.63 313.75 277.67 971.88 120 116.81 2,485 143.33 1,515 22 162 34 535 122.5 92.5 120 150 130 29 Appendix 4: Australia 1870 The Australian earnings social table (Table A4a) simply merges those for Victoria (Table A1) and New South Wales (Table A2). South Australia is excluded since both rural and female occupational earnings data are inadequate for that colony. All earnings data are in Australian pounds. In the text tables, the gini coefficients derived from Tables A1-A5 use the following algorithm: where the W are weights based on employment figures and the X are annual earnings (ordered in ascending order). Following Table A4a, we also report the complete list of occupations which are abbreviated in Tables A1, A2 and A3. Finally, the size distribution underlying Table A4a are reported as decile shares in Table A4b. Table A4a. Earnings Social Table for Australia 1870 Employment Occupation Accountants, auctioneers, brokers Architects, surveyors, scientific persons Artist, actor, musician Assistants clerks Authors, editors, reporters Barber, hair dresser Blacksmith, farrier, locksmith Butcher, baker, brewer, miller Butcher, baker, brewer, miller Civil engineers Clergy Commissioner, agent Common labor Common labor Dealers, agents, contractors Domestic Domestic Earnings Gender Urban Rural Urban Rural M 2,836 642 457.72 396.92 M M M M M M M F M M M M F M M F 2,455 740 96 637 2,958 2,854 6,599 70 2,130 908 475 31,897 2,369 3,988 21,969 10,891 957 222 12 24 375 1,975 2,857 63 469 391 90 27,817 263 1,378 12,991 5,127 359.92 225.00 111.98 500.54 67.09 140.20 187.34 26.00 567.10 324.81 537.00 57.66 27.50 525.50 67.63 28.00 322.26 210.00 129.70 472.50 55.53 99.30 194.46 26.00 554.32 331.22 537.00 30.44 27.5 386.32 63.66 20.16 30 Dressmaker, seamstress, tailoress Farm laborer Farm overseer Fishmonger, milkman Fishmonger, milkman Goldsmith, jeweler, engraver, Government clerks Harbour pilot Hawkers, peddlers, shopkeeper Hawkers, peddlers, shopkeeper High government officials Hotel & lodging operator Hotel & lodging operator Judges, lawyers, solicitors Law clerks Machine and tool maker, millwright Manager Midwife Miner Nurse Pharmacists, dentist Physicians, surgeons Police, jailers Printer, compositor Private sector clerks Private sector clerks Professors, Schoolmasters Seaman, boatman Skiled workers Skilled with leather Skilled with stone and metal Stevedore, wharf labor Teachers, common school Teachers, common school Telegrapher, stenographer Tobacconist Wheelwright, coachmaker F M M M F M M M F M M F M M M 10,001 6,905 89 3,687 322 1,246 982 399 1,317 8,780 332 442 3,113 848 682 1,815 55,661 975 1,567 12 134 414 130 115 4,307 189 162 2,246 169 59 67.39 41.64 244.79 127.30 26.00 272.62 294.95 307.45 40.63 208.68 825.70 37.29 175.94 970.84 395.89 48.78 77.87 244.79 163.36 26.00 240.08 286.55 245.98 38.52 154.98 596.00 58.97 158.39 748.24 328.75 M M F M F M M M M M F M M M M M M M F M M M 1,228 721 88 9,432 602 705 768 1,703 3,064 6,867 527 10 2,607 25,627 6,165 7,773 417 5,514 1,342 160 394 1,994 167 88 42 39,411 162 117 273 538 476 1,032 103 3 1,605 4,639 1,900 1,836 81 2,235 499 70 20 553 168.00 525.50 36.00 172.39 36.00 222.34 357.23 165.74 211.52 277.67 49.86 971.88 115.49 106.43 129.50 122.06 90.33 394.28 75.00 132.00 212.08 145.89 125.50 386.32 36.00 167.55 36.00 204.55 348.67 148.61 202.81 253.27 43.38 820.16 107.48 60.84 97.09 96.36 90.33 198.56 65.00 132.00 212.08 120.87 31 Table A4b: Australian Earnings Distribution by Decile (NSW+VIC) 1870 Decile Total (%) Urban (%) Rural (%) 1st 2.84 2.25 3.04 2nd 3.82 3.89 3.53 3rd 4.42 4.11 4.98 4th 5.46 4.63 7.31 5th 5.94 4.88 8.55 6th 6.12 8.32 8.55 7th 9.04 9.22 8.55 8th 11.51 12.41 8.88 9th 17.32 17.25 15.74 33.05 30.89 top 33.53 32 Appendix 5: The United Kingdom 1867 The social, table for the United Kingdom 1867 comes from R. Dudley Baxter, National Income: The United Kingdom (London: Macmillan, 1868), Appendix IV and V. There are three limitations to Baxter’s 1867 social table, in rising order of seriousness. First, he does not break down the three manual classes -- skilled, semi-skilled and unskilled -- in to occupations, gender, or urban/rural (although the occupations within these three groups are listed). Second, the top income groups are not identified by occupation, but we eliminate them since these high-income recipients were unlikely to have been employed. Third, his three white collar “middle class” groups do not identify occupation or distinguish between labor earnings and property income. We assume that these “middle class” incomes were overwhelmingly labor income, not property income. To the extent that this was not true, our estimates of UK earnings inequality are upward biased. However, the text offers a defense of their use in comparisons with the US and Australia. All income figures in Table A5 are in pounds sterling. Table A5. A Social Table for Earnings in the UK 1867 Total Total Average Recipients Income Income Middle incomes 300-1000 178,300 87,723,000 492.00 Small incomes 100-300 1,026,400 110,950,000 108.10 Lower incomes < 100 1,497,000 81,320,000 54.32 Manual, High Skilled 1,345,000 66,353,000 49.33 Manual, Lower Skilled 5,087,000 160,652,000 31.58 Agriculture and Unskilled 4,529,000 97,640,000 21.56 Total 13,662,700 604,638,000 44.25 33 Appendix 6: Australian Social Tables 1910 Appendix Table A6a reports our social table for earnings in New South Wales around 1910. Table A6b does the same for Australia as a whole using employment figures for all of Australia, but the earnins structure of NSW (also embodied in Table 6A1). In the future, we hope to revise the earenings estimates by including Victoria (data already collected) and perhaps the other four. The employment figures are from the The First Commonwealth Census (Melbourne: Bureau of Census and Statistics, 1912). The earnings data are taken from the following three sources: New South Wales Statistical Returns of the Colony (Blue Book), Sydney: Government Printer, 1911 (BB in Tables A6a and A6b); The Official Yearbook of New South Wales, 1910-11, Sydney: W. A. Gullick, Government Printer, 1911 (YB in Tables A6a and A6b); Peter Macarthy (1967) “The Harvester Judgment: An Historical Assessment”, Unpublished PhD dissertation, ANU (MC in Tables A6a and A6b). We constructed estimates of in-kind income for farm labor, house servants (domestics), farm station clerks and the clergy, based on detailed board and lodging data contained in the Blue Books. Specifically, we augmented annual earning by 18% to account for in-kind income. All earnings are based on annual averages reported in the original sources, with the exception of: a) bricklayer, brick maker, founder, molder, house painter, mason, plasterer and silversmith, which were converted from daily earnings, based on 250 working days.15 b) clothing maker, farm labor (including poultry farm, dairy farm, sugar farm, pig farm) and leathergoods maker, which were converted from weekly earnings. We were not able to find barbers and hairdressers earnings, so we assumed a 16.7% wage premium over servants, inferred from NSW earnings in 1870. Managers earnings are inferred from directors earnings as reported in the Blue Books for the following occupations: Director of Labour, Secretary to Department of Public Instruction and Director of Education; Director of the Botanic Gardens, Director of the Forests Dept.; Director of the Immigration and Tourist Bureau; Director of the Microbiology Institute. Clergy earnings are based on 19 observations of clergymen in gaols reported in the Blue Book, belonging to the following denominations: Church of England, Roman Catholic, Presbyterian, Methodist, Jewish. When female earnings were not reported, we assigned them an average of 66.4% of male earnings, based on data where both wages were available (28 occupations). We did not find earnings data for the following occupations: artists, chemical makers, editor/journalist, irregular clergy, irregular medical practitioner, undertaker, cigar/cigarettes makers, glass maker, confectioner, iceman, lime cutter, miller, paper maker, pottery maker, and stone cutter, all of which representing 10,355 workers. Since these missing observations represent only 1.8% of the total labor force, their omission should not generate a large bias to our inequality estimates. All earnings figures in Table A1 are in Australian pounds. 15 The 250 figure is commonly assumed in living standard calculations for other regions and times, assuring comparability with Australian estimates in this study, (See Panza and Williamson forthcoming). 34 Appendix Table A6b reports Australian employment figures from the The First Commonwealth Census (Melbourne: Bureau of Census and Statistics, 1912) and annual earnings by occupation based on New South Wales experience around 1910 (as in Table A6a, with the same sources). Table A6a: New South Wales Earnings Social Table, 1910 Occupation Accountant, auditor Actuary, underwrite, auctioneer Architect Arms maker Assayer Bailiff Baker Banker, broker Barge master, lighterman Barrister Biologist, botanist Blacksmith Boiler maker Bookbinder Boot maker (craftsmen) Boot maker (factory) Brick layer Brick maker Butcher Meat, fish curer Carpenters Carriage maker Charity officer Charwomen, cleaner Chemist Church officer Civil engineer Clergyman Clothing maker (craftsman) Clothing maker (factory) Coachman, groom Coke burner Compositor Cooper Cordial Maker Dairy farm laborer Deliveryman Dentists Labor force Male Female 827 73 3,939 416 613 22 62 2 113 0 107 0 6,373 2,813 4,637 20 90 0 1,185 0 36 3 5,487 0 1,877 0 628 1,037 2917 0 2,854 1606 4,021 0 3,238 0 7,553 170 2,518 113 12,374 0 5,333 43 93 147 133 703 48 1 27 12 441 1 1,674 162 1,774 14,107 2,890 7,175 1,329 705 358 0 2,933 60 255 0 1,188 87 8,694 167 261 2 1,339 250 Earnings (£) Male Female 346.7 230.2 346.7 230.2 452.6 300.5 210 139.4 260 259.1 143.2 95.1 660 438.2 149.6 560 483.8 321.2 125 210 164.3 136.8 107.7 71.5 137.5 137.5 72 47.8 72 47.8 139.8 210 139.4 187 124.2 79 52.7 483.8 321.2 187 124.2 399.2 265.1 220.7 146.5 159.8 106.1 125.8 83.5 126 83.7 130 164.8 109.4 137 75 50 61.4 40.7 158.2 105 650 431.6 Source BB BB BB BB BB BB BB BB BB BB BB YB BB BB MC MC YB YB YB YB YB BB BB BB BB YB BB YB MC MC BB YB BB MC YB MC BB BB 35 Die/type maker Domestic nurse Draftsman Drayman, carterman, teamster Electrical apparatus maker Electrician Lineman Engine driver (farm) Engine driver (fireman) Mechanical engineer Engineer Stoker Engraver Factory manager Farm laborer Farm manager Ferry officer Fisherman Fishmonger Florist Forestry worker Founder Fruit grower laborer Furniture maker (craftsman) Furniture maker (factory) Gardener Gas supply worker Geologist Grocer, fruiter Guard, signalman Hairdresser, barber Harbour officer Hat, bonnet maker Hatter, milliner Health officer High officials Horse keeper Hospital attendant Hospital nurse Hotel servant House painter House servants Joiner Judges Laborer Laundryman 81 1 408 10,493 7 1,790 983 3,268 1,742 6,971 63 2,196 117 219 26,797 246 577 1,524 561 142 6,358 3,140 3,729 1,411 2,589 1,583 1.808 14 10,323 3,709 2,906 932 451 1,871 113 1,198 159 836 14 7,057 5,340 1,965 1,165 20 23632 546 14 896 2 35 0 0 0 0 0 0 4 0 2 0 4 3 0 1 25 117 6 0 0 0 257 0 0 0 1,371 1 221 3 579 682 7 34 0 664 1,938 7,526 7 34,462 3 3 0 3,158 168.9 122.6 257.8 126 210 130 130 158.2 158.2 210 399.2 183.2 240 680.8 61.36 303.1 225.8 119.6 171.4 171.4 79 125 61.36 132.1 104 101.5 130 483.8 171.4 146.4 122.8 390 75 75 620 906.9 120 122.6 122.6 89.2 125 105.3 156 906.9 86.3 125 112.1 90.4 171.2 83.7 265.1 159.4 40.74 201.3 79.4 113.8 113.8 52.46 69.1 113.8 97.2 81.7 326.1 50 50 411.7 602.2 69.5 90.4 50 83 69.9 103.6 602.2 49.2 YB BB BB BB BB YB YB BB BB BB BB BB BB BB MC BB BB BB BB BB YB YB MC MC MC BB YB BB BB BB BB BB YB YB BB BB BB BB BB BB YB BB BB BB BB BB 36 Law Clerks Leathergoods maker (artisan) Leathergoods maker (factory) Legal officials Letter carrier Lighthouse keeper Lithographer Locksmith Lodging house servant Machinist Magistrates Mailman Maltster, brewer Market gardener Mason Medical attendant Medical practitioner Messenger Midwife Military officers Milkman 1,267 2,223 274 171 718 56 362 53 968 148 20 362 1,403 4,758 1,268 113 1,333 882 0 3,096 2,348 520 76 48 7 0 0 17 0 3,412 0 0 0 6 39 0 0 83 3 1,569 0 125 498.5 136.8 107.7 721.1 202.5 225.7 208.5 137.5 89.2 148.6 432.5 202.5 137 145 137.5 122.6 650 106.4 Millwright Miner Motorman, cab driver Moulder Music teacher Musical instrument maker Nursery man Officer, Scientific Dept. Officer, Education Dept Omnibus driver Optician Ornament makers Pastoral laborer Penal officer (skilled) Penal officer (regular) Pharmacist Photographer Pig farm laborer Pilot Plasterer Plumber Police officers (skilled) Police officers (regular) Porter, gate keeper Postmaster, sorter 2,629 39,551 432 3,140 274 555 1,701 73 141 1,145 224 741 28,189 17 296 1,443 716 109 111 1,681 3,599 73 2,563 556 2,131 0 23 3 0 2,333 29 3 2 7 13 26 718 10 9 40 134 399 0 0 0 0 0 0 5 690 210 131.9 93.7 131.2 252.1 210 101.5 587.5 587.5 154.7 210 210 61.36 304.8 128.6 122.6 250 61.36 275.3 125 130 450 162.5 86.7 364.6 304.8 171.4 202.5 90.8 72.9 481 138.4 50 91 96.3 163.3 70.6 137.5 113.8 87.6 62.2 211.8 139.4 67.4 390.1 305.2 102.7 139.4 139.4 40.74 120 92 90.4 166 60 242.1 BB MC MC BB BB BB BB YB YB BB BB BB MC BB YB BB BB BB BB BB BB YB YB BB YB BB BB BB BB BB BB BB BB MC BB BB BB BB MC BB YB BB BB BB BB BB 37 Poulterer Poultry farm laborer Printer Quarry man Railroad labor Railway station master Restaurant servant Road laborer (navvy) Rope/canvas maker Sail maker Sanitary inspector Sawmill worker Scientific instruments maker Ship master, seaman Shipwright Shirt maker Sick nurse Silversmith Slater, shingler Smelter Sports equipment maker Stable keeper Station agent Station manager Stevedore, wharf labor Steward Store manager Storekeeper Street cleaner, chimney sweeper Sugar laborer Surgical instrument maker Surveyor Tanner, soap maker Tea/coffee seller Teachers Telegraph- Stationmaster Telegraph/telephone maker Telephone officer Textile worker Tinsmith Tobacconist Tramway driver Tutor Typist Umbrella maker Professor 142 1,191 682 952 6,921 3,016 1,478 10,873 78 119 209 1,335 21 4,742 1,809 90 17 977 272 1,261 74 532 1,399 2,860 5,643 1,351 226 6,900 1,967 288 41 1499 8016 698 3,878 697 12 840 525 876 606 4,870 438 1,230 70 93 8 0 116 0 243 12 2,706 1 0 3 2 7 0 0 0 867 1,353 115 0 0 37 8 31 0 0 82 2 2,963 0 7 5 8 154 89 5,430 0 0 0 460 0 69 0 964 161 197 6 171.4 61.36 184.1 94 121 202.5 89.2 87.5 85.4 137 281.1 89 210 114 210 93.7 122.6 137.5 125 125 210 121 202.5 202.5 121 131 171.4 171.4 79 61.4 210 382 81 171.4 257.1 202.5 210 202.5 75 115 171.4 158.2 257.1 150 90 587.5 113.81 122.2 80.3 134.5 50 58.1 91 186.6 59.1 62.2 90.4 91.3 139.4 80.3 134.5 87 113.8 113.8 40.7 139.4 253.6 53.7 113.8 211.8 49.8 113.8 211.8 99.6 59.8 390.1 YB MC BB YB YB BB BB YB YB MC BB YB BB BB BB YB BB YB YB YB BB BB BB BB BB BB BB BB BB MC BB BB YB BB BB BB BB BB YB YB BB BB BB BB YB BB 38 Veterinarian Vineyard laborer Watch maker Waterman Wheelwright Wine seller Wood chopper Wool washer Investment societies clerks Bank clerks Railway, tramway, shipping, telegraph clerks Station clerk 161 383 662 266 595 708 1,284 1,064 47 3,215 5,415 2860 5 5 6 0 0 78 0 29 6 20 72 0 300 61.4 210 119.6 210 171.4 79 52 190 174 209 224.2 199.2 40.7 139.4 113.8 34.5 126.2 115.5 138.78 BB MC BB BB BB BB BB YB BB BB BB BB Table A6b: Australian Earnings Social Table, 1910 Occupation Accountant, auditor Actuary, underwrite, auctioneer Architect Arms maker Assayer Bailiff Baker Banker, broker Barge master, lighterman Barrister Biologist, botanist Blacksmith Boiler maker Bookbinder Boot maker (craftsmen) Boot maker (factory) Brick layer Brick maker Butcher Meat/fish curer Carpenters Carriage maker Charity officer Charwomen, cleaner Chemist Church officer Civil engineer Clergyman Clothing maker (craftsman) Employment Male Female 1,943 178 10,649 904 1,515 53 285 202 451 1 184 0 17,314 3,834 13,770 45 226 0 2,949 0 64 3 17,157 11 3,845 1 1,671 2,689 8,749 0 8,560 4,626 8,031 0 5,976 34 21,252 653 465 147 33,885 3 14,610 272 227 171 259 1,228 5,211 4,373 101 431 2,115 5 61 11 629 44,033 Earnings (£) Male Female 346.7 230.21 346.7 230.21 452.6 300.53 210 139.44 260 259.1 143.2 95.08 660 438.24 149.6 560 483.8 321.24 125 210 164.3 137 108 71.5 137.5 137.5 72 47.81 72 47.81 139.8 92.83 210 139.4 187 124.2 79 52.7 483.8 321.2 187 124.3 399.2 265.1 220.7 146.5 159.8 106.1 39 Clothing maker (factory) Coachman, groom Coke burner Compositor Cooper Cordial Maker Dairy farm laborer Deliveryman Dentists Die/type maker Domestic nurse Draftsman Drayman, carterman, teamster Electrical apparatus maker Electrician Lineman Engine driver (farm) Engine driver (fireman) Engineer Mechanical engineer (stoker) Engraver Factory manager Farm laborer Farm manager Ferry officer Fisherman Fishmonger Florist Forestry worker Founder Fruit grower laborer Furniture maker (craftsman) Furniture maker (factory) Gardener Gas supply worker Geologist Grocer, fruiter Guard, signalman Hairdresser, barber Harbor officer Hat, bonnet maker Hatter, milliner Health officer High officials Hospital attendant Hospital nurse 7,124 3,307 378 7,540 701 3,388 17,216 569 2,979 156 3 1,033 25,493 19 4,645 2,676 9,187 4,535 123 5,730 323 718 92,104 1,206 618 7,795 1,411 326 24,393 10,667 14,185 3,743 6,867 4,162 4,003 30 28,330 10,219 6,807 2,362 1,257 4,387 296 11,771 2,169 17 22,396 2,115 0 140 2 181 523 7 668 15 2,683 8 79 0 100 0 0 0 12 0 14 0 102 7 9 1 68 309 12 62 438 0 559 0 18 0 3,887 22 509 6 1,654 1,526 18 730 1,875 5,001 125.8 126 130 164.8 137 75 52 158.2 650 168.9 122.6 257.8 126 210 130 130 158.2 158.2 399.2 183.2 240 680.83 52 303.12 225.8 119.6 171.4 171.4 79 125 52 132.08 104 101.5 130 483.8 171.4 146.4 122.83 390 75 75 620 906.9 122.6 122.6 83.5 83.7 109.43 50 34.5 105.04 431.6 112.15 90.4 171.18 83.66 265.07 159.36 34.53 201.28 79.41 113.81 113.81 52.46 83 34.53 69.06 113.81 97.21 81.69 326.1 50 50 411.68 602.18 69.5 90.4 40 Hotel servant House painter House servants Joiner Judges Laborer Laundryman Law Clerks Leather goods maker (artisan) Leather goods maker (factory) Legal officials Letter carrier Lighthouse keeper Lithographer Locksmith Lodging house servant Machinist Magistrates Mailman Market gardener Mason Medical attendant Medical practitioner Messenger Midwife Military officers Milkman 28,970 13,912 22,280 21 4,795 97,163 3,270 54 60,634 1,771 3,344 313 7,027 414 1,729 338 976 153 2,583 16,218 155 744 15,488 4,185 204 3,630 2,441 0 5,261 5,611 6 0 0 7,122 1,300 650 432 16 0 0 40 0 9,695 0 0 0 147 0 0 275 4 4,487 0 439 Millwright Miner Motorman, cab driver Music teacher Musical instrument maker Nursery man Officer, Scientific Dept. Officer, Education Dept. Omnibus driver Optician Ornament makers Pastoral laborer Penal officer (skilled) Penal officer (regular) Pharmacist Photographer Pilot Plasterer Plumber 8,526 103,475 0 735 10 6,044 54 48 18 30 30 58 2,032 43 13 105 377 1092 0 0 0 905 735 1,162 6,725 246 368 2,649 525 292 57,843 52 755 3,810 1,923 262 3956 9440 89.2 125 105.26 156 906.9 86.3 125 498.5 136.76 107.68 721.1 202.5 225.7 208.5 137.5 89.2 148.6 432.5 202.5 145 137.5 122.6 650 106.4 50 83 69.89 103.58 49.2 202.5 90.81 72.9 480.97 138.44 50 96.28 163.3 70.65 137.5 304.8 171.4 113.81 210 131.9 93.7 87.58 62.22 252.1 211.8 210 101.5 587.5 587.5 154.7 210 210 52 304.8 128.6 122.6 250 275.3 125 130 139.44 67.4 390.1 305.2 102.72 139.44 139.44 34.5 120 92 90.4 166 41 Police officers (skilled) Police officers (regular) Porter, gate keeper Postmaster, sorter Poulterer Printer Quarry man Railroad labor Railway station master Restaurant servant Road laborer (navvy) Rope/canvas maker Sail maker Sanitary inspector Sawmill worker Scientific instruments maker Ship Master/Seaman Shipwright Shirt maker Sick nurse Silversmith Slater, shingle Smelter Sports equipment maker Stable keeper Station agent Stevedore, wharf labor Steward Store manager Storekeeper Street cleaner, chimney sweeper Sugar laborer Surgical instrument maker Surveyor Tanner, soap maker Tea/coffee seller Teachers Telegraph- Stationmaster Telephone officer Textile worker Tinsmith Tobacconist Tramway driver Tutor Typist Umbrella maker 178 6,369 1,369 5,054 316 1,578 3,253 21594 8,789 3,480 27,026 161 375 367 4,450 57 11,621 2,774 259 23 2,610 486 5,271 169 1,580 3535 13,564 3,701 1,193 15,770 5,107 5,031 102 3,856 3,536 1,668 9,500 1943 2676 1,712 2,669 1,278 8738 998 3,346 182 0 0 17 2016 31 345 0 625 217 7,210 6 0 20 6 18 0 0 0 2,322 4,008 232 0 0 44 25 87 0 243 4 6,759 0 77 16 14 25 231 15,206 0 0 1,882 39 233 55 1,985 606 296 450 162.5 86.7 364.6 171.4 184.1 94 121 202.5 89.2 87.5 85.4 137 281.1 89 210 114 210 93.7 122.6 137.5 125 125 210 121 202.5 121 131 171.4 171.4 79 52 210 382 81 171.4 257.1 202.5 210 75 115 171.4 158.2 257.1 150 90 60 242.09 113.81 122.24 80.34 134.46 50 58.1 90.97 186.65 59.1 62.22 90.4 91.3 139.44 80.34 134.46 86.98 113.81 113.81 34.5 139.44 253.65 53.78 113.81 211.8 49.8 76.36 113.81 105.04 211.8 99.6 59.76 42 Professor Veterinarian Vineyard laborer Watch maker Waterman Wheelwright Wine seller Wood chopper Wool washer Investment societies clerks Bank clerks Railway, tramway, shipping, telegraph clerks Station clerk 233 406 2,121 1,830 479 2026 1,969 4,171 1809 111 8803 15006 1098 34 6 88 17 0 0 209 271 30 2 51 185 0 587.5 300 61.4 210 119.6 210 171.4 79 52 190 174 209 224.2 390.1 199.2 40.7 139.4 113.8 34.5 126.2 115.5 138.78 43 Table A6c: Australian earnings distribution by decile, 1910 (%) Decile NSW LF weights AUS LF weights 1st 3.88 3.03 2nd 4.65 4.58 3rd 5.51 5.40 4th 6.57 6.07 5th 8.30 7.28 6th 9.49 7.65 7th 9.96 8.42 8th 11.26 11.16 9th 14.19 15.29 Top 26.16 31.11 44