Some data on Australian income inequality and globalisation

Some Measures of Inequality.

Figure 1 plots income-inequality data computed by Leigh (2005) using Australian taxation statistics for the period 1970 to 2001.[11]

Figure 1: Inequality Among Adult Males in Australia, 1970–2001
Figure 1: Inequality Among Adult Males in Australia, 1970–2001

This paper focuses on three measures — GiniPre is the Gini coefficient for pre-tax income, GiniPost is the Gini coefficient for post-tax income, and P9050 is the ratio of the income of an individual at the ninetieth percentile divided by the income of an individual at the fiftieth percentile.[12] The reason for using both of the first two measures is to distinguish the impact of the progressivity of the taxation system in possibly neutralising the effects of globalisation. The third measure focuses on the issue of whether individuals at the top of the income distribution have fared particularly well during the latest wave of globalisation (as argued by Atkinson, 2003, for example).

Notwithstanding their differences, the three measures are highly correlated. Observe that all three measures increased over the 32-year time span — with pre-tax income inequality rising by almost 22 per cent.

A Measure of Globalisation

To assess the extent to which any country is more (or less) globalised at any particular point requires much more than employing data on flows of trade, migration or FDI. When a phenomenon like globalisation encompasses several aspects that, taken together, may have an effect greater than the sum of their constituent parts, it appears logical to assess these effects together. Composite indices provide an excellent way to accomplish this since they provide a single statistic on which comparisons can be based, without the confounding effects of variation at lower levels of aggregation.

The KOF index (Dreher et al., 2007) fits the bill; here it is simply labelled ‘KOF’. The index is derived from 25 variables grouped into six ‘sub-indices’: actual flows of trade and investment; restrictions; variables measuring the degree of political integration; data quantifying the extent of personal contact with people living in foreign countries; data measuring trans-border flows of information; and a proxy for cultural integration.

The sub-index on actual economic flows includes data on trade, FDI and portfolio investment. Trade is measured as the sum of a country’s exports and imports and portfolio investment is the sum of a country’s assets and liabilities (all standardised by GDP). The KOF index includes the sum of gross inflows and outflows of FDI and the stocks of FDI (again, both standardised by GDP). While these variables are standard measures of globalisation, income payments to foreign nationals and capital are included to proxy the extent to which a country employs foreign labour and capital in its production processes.

The second sub-index includes restrictions on trade and capital using hidden import barriers, mean tariff rates, taxes on international trade (as a share of current revenue) and an index of capital controls. Given a certain level of trade, a country with higher revenues from tariffs is less globalised. To proxy restrictions on the capital account, data on 13 different types of capital controls are used.

The KOF has a sub-index on ‘political globalisation’, drawn from the number of embassies and high commissions in each country, the number of international organisations in which a country has membership and the number of United Nations peace missions participated in.

The remaining three sub-indices of the KOF index concern ‘social’ globalisation; one on ‘personal contacts’, another on ‘information flows’, and a final one on ‘cultural proximity’. The index on personal contacts includes international telecom traffic and the extent of tourism. Government and workers’ transfers received (and paid) measures the extent to which countries interact, while the stock of foreign population is included to capture existing interactions with people from other countries. Finally, the average cost of a phone call to the United States measures the cost of international interaction.

While personal contact data are meant to capture interactions among people from different countries, the sub-index on ‘information flows’ measures the potential flow of ideas and images. It includes the number of internet hosts and users, telephone mainlines, cable television subscribers, the number of radios and sales of daily newspapers. ‘Cultural proximity’ is arguably the dimension of globalisation most difficult to grasp. One indicator is the number of McDonald’s restaurants located in a country. For many people, the global reach of McDonald’s is symbolic of globalisation itself.

These dimensions are then combined into an overall index of globalisation with an objective statistical method.[13] Table 1 reports the weights of the individual components.[14] As can be seen, economic, political and social integration obtained roughly equal weights. Table 1 shows that globalisation has increased dramatically.

A Measure of Unionisation and the Terms of Trade

The ‘terms of trade’ (ToT) is measured in this paper as the ratio of the implicit price deflator of exports of goods and services to the implicit price deflator of imports of goods and services.[15] In the case of Australia, rising terms of trade are predicted to result in a movement of labour and capital from manufacturing to primary industries. Depending on these structural changes and any impediments to labour mobility, this could adversely affect the distribution of earnings via a straightforward interpretation of the Stolper-Samuelson theorem (Henry, 2006). All other data are from the OECD.[16]

Table 1: Components of the KOF index of globalisation

Indices and Variables




Economic Globalisation




i) Actual Flows




Trade (per cent of GDP)



FDI, flows (per cent of GDP)



FDI, stocks (per cent of GDP)



Portfolio investment (per cent of GDP)



Income payments to foreign nationals (per cent of GDP)



ii) Restrictions




Hidden import barriers



Mean tariff rate



Taxes on international trade (per cent of current revenue)



Capital account restrictions




Social Globalisation




i) Data on Personal Contact




Outgoing telephone traffic



Transfers (per cent of GDP)



International tourism



Foreign population (per cent of total population)



International letters (per capita)



ii) Data on Information Flows




Internet hosts (per 1000 people)



Internet users (per 1000 people)



Cable television (per 1000 people)



Trade in newspapers (per cent of GDP)



Radios (per 1000 people)



iii) Data on Cultural Proximity




Number of McDonald's restaurants (per capita)



Number of Ikea outlets (per capita)



Trade in books (per cent of GDP)




Political Globalisation




Embassies in country



Membership in international organisations



Participation in U.N. Security Council missions


Union’ is union membership standardised by the total labour force (that is, expressed as a percentage). As discussed in the previous section, the usual prediction is that de-unionisation worsens earnings and income inequality. From Table 2 below, note that the rate of unionisation fell by an astounding 42 per cent. The minimum wage is converted to real terms using the CPI. This measure is included to capture the response of the welfare state to increased global uncertainty. Interestingly, this measure rose strongly over the latter part of the 32-year period.[17] On the face of things, this rise is consistent with the argument made by Rodrik (1998) discussed in the previous section. Finally, ‘Open’ is the usual openness measure; that is, the ratio of total trade (imports plus exports) divided by GDP. It displays extraordinary growth from 1970 to 2001. It is included separately from the broad index of globalisation to focus on growing economic integration in particular.[18] Based on previous research, small effects on the income distribution are anticipated.

Table 2 and Figure 2 present several dimensions of the data used in our econometric analysis.

Table 2: Descriptive Statistics
Table 2: Descriptive Statistics

The decline of union membership during the 1990s is quite stark. The real minimum wage (converted to an index in the figure) displays fluctuations, but is considerably higher at the end of the period compared to the beginning. In contrast, ToT is lower in 2001 compared to 1970 (and predates the most recent minerals-driven increases in the terms of trade). Both trade openness and KOF trend sharply upwards over the entire period.

Time Series Analysis

The formal statistical analysis of each of the inequality measures (KOF, ToT, Open, Union and LRMW) indicates that they are co-integrated with one co-integrating vector. This suggests estimation of a vector error-correction model to establish the relationship between the variables[19] All the standardised beta coefficients for the long-run relationships are summarised in Table 3.

Figure 2: Time Series of Covariates, 1970-2001
Figure 2: Time Series of Covariates, 1970-2001
Table 3: Summary of the long-run relationships: standardised beta coefficients
























Note: *, ** and *** denote rejection of the null at 10 per cent, 5 per cent and 1 per cent significance levels, respectively.


The results of Table 3 indicate that globalisation, as measured by KOF, unambiguously increases income inequality. (A one standard deviation increase in KOF leads to 0.52 standard deviation increase in GiniPre (pre-tax income inequality), ceteris paribus.[20] This is in line with the findings of Dreher and Gaston (2007b), who found that globalisation increases income inequality in a panel of OECD countries.[21] The finding mirrors the unease with which non-economists and the public generally view globalisation. While the academic literature fails to find consistent evidence that traditional measures of economic openness and integration — such as international trade flows and immigration — adversely impact the labour market, this may be attributable to an overly narrow view of globalisation generally adopted by most economists.[22]

Perhaps a more surprising result of Table 3 is that improving terms of trade and greater trade openness are equity-enhancing for Australia. Of course, Australia is somewhat ‘peculiar’ for a developed economy in that it mainly exports primary commodities and imports manufactured goods. Pope and Selten (2002) have noted the importance of improved terms of trade for Australia’s manufacturing sector. Perhaps for this reason, not only do improved terms of trade boost Australian welfare and income, they also have a beneficial impact on equity.

The results for ‘Union’ are large and significantly negative as well as straightforward to interpret. It’s quite clear that de-unionisation has exacerbated income inequality. The result for the minimum wage varies across the different measures of income inequality. A higher real minimum wage lowers pre-tax income inequality. The impact on post-tax inequality is positive and significant, albeit at just the 10 per cent level. This may indicate that the progressivity of taxes is relatively more important for generating a more equitable income distribution than are increases in the minimum wage, at least for Australia. Unsurprisingly, the minimum wage has no impact on the income distribution for the more wealthy.