The regional impact of cultural diversity on wages: evidence from Australia

2.1 The HILDA Survey

This paper uses the un-confidentialised version of the HILDA Survey (Release 11) which has postcode data that allows for area-level analysis (see Watson 2012, for detailed description of the HILDA Survey). This postal area data is exploited to measure an index of diversity as the key variable in measuring the distribution of foreign nationals and to merge with an instrument and an alternative index of diversity computed from census data. The initial sample interviewed in the first wave (wave 1) consisted of 7682 households while the corresponding sample of enumerated persons was 19,914 people. Out of these, 24 % are children aged below 15 years. Of those who are eligible, the response rate was 92.3 % (n = 13,969). In subsequent waves, new household members and children increased while overseas emigration and deaths decreased the sample size. In addition, attrition rates of 3.7–13.2 % across waves contributed to the decline of the sample size in subsequent waves.² On average, 13,438 respondents were interviewed every year of which 7228 individuals continued to participate in the survey each year without missing any wave. However, the combined sample size for the 11 waves (waves 1–11) including those who were added in subsequent waves is 26,028. Out of this, a long panel was constructed for the 11 waves amassing an overall sample of 286,308 observations.

Since the sample of interest in this paper is the labour market performance of those who are potentially active in the labour force, the sample was restricted to the working age group. The total sample size for waves 1–11 in the long panel of those aged 16–45 years is 79,636 (27.8 %). Of these, those who are employed and earning wages account for 64.7 % (n = 51,538). Finally, in the multivariate analyses, the sample was further restricted to allow for a longer time period (3-year lag), with the final sample of 44,634 (56 %).

2.2 Country of birth data in ABS census 2001 and 2011

Census data was used to generate an index of diversity at the local government area (LGA) level as well as an instrument based on the projected population. This data, which includes the regional distribution of Australians by country of birth, was obtained from the ABS via TableBuilder using the 2001 and 2011 census data (ABS 2012a). A total of 293 countries classified under the four-digit Standard Australian Classification of Countries (SACC) are available in the census (ABS 2011a), along with a total of 2516 postcodes.³ These data are also available at the LGA level.

The LGA is a geographically contiguous classification which divides Australia into 676 regional categories. Each LGA, also known as a local council, handles community-related tasks and town planning within its jurisdiction. Diverse local entities including cities, towns, suburbs, shires, and villages make up these local councils. We originally obtained the census data at the postcode level. To make the analysis relevant to regional labour market and community characteristics, we decided to broaden the classification to the LGA level. The LGA-level data also accounts for socio-demographic and regional policy differences across Australia. Using the 2006 ABS Postal Area Concordances that map LGAs and postcodes, the postcode data was merged and then collapsed into 676 LGAs across Australia. The LGA-level data was used to generate the regional distribution of diversity and the main instrument. Then the LGA and postal area data were used to merge these census-based data with the HILDA data. Although the merging variable is the postcode, the diversity index used for analysis is at the LGA level. The diversity measure from the census is used for spatial analysis, visually portraying changes in diversity over the 2001–2011 period.

2.3 Annual population estimates 2001–2011

Australia’s annual population estimates for the period 2001 to 2011 were obtained from the ABS website. These datasets include an aggregate distribution of the population estimates by country of birth at the national level.⁴ A total of 255 countries of origin were represented in these datasets. From this distribution, the annual growth rate of the population was estimated and was utilised along with the 2001 census data in the calculation of time-variant shift-share instruments for each wave (see Section 3.7 for a detailed discussion).

3.1 Diversity index

The cultural diversity literature uses the fractionalisation index in measuring the impact of diversity on economic outcomes. Ethno-linguistic fractionalisation (ELF) is defined as the “probability that two randomly chosen citizens in a country belong to a different ethnic group where(in) group belonging is attributed by language” (Neumann and Graeff 2013). Vigdor (2008) uses a slightly different approach, the probability that a randomly selected individual is an immigrant, to estimate an assimilation index in the USA. Others have used the country of birth data to measure cultural diversity (Alesina et al. 2013; Bellini et al. 2013; Damelang and Haas 2012; Longhi 2013; Ottaviano and Peri 2006). Given the availability of data in the Australian context, this study uses country of birth/nationality instead of ethnicity/language.⁵ Specifically, the proportion of the nationals of each country of origin (birth) in each LGA in Australia is used to compute a fractionalisation index. This index (hereafter diversity index) has a similar theoretical interpretation to the Herfindahl Index which is widely used in marketing research to measure the market/monopoly power of firms located in specific areas (Gomez-Mejia and Palich 1997) and is given by

$$ D{I}_{rt}=1-{\displaystyle \sum_{i=1}^I}{C}_{irt}^2\kern3em \forall\ i=1,2, \dots N,\ t=1,2,\dots T $$

(1)

where C _irt represents the proportion of the nationals of country i in region (LGA) r in a given year t. The values fall in the range [0, 1] with “zero” indicating perfect homogeneity and “1” indicating perfect heterogeneity. We use the HILDA panel to construct the index of diversity. For the sake of visual comparison, we also estimated the indices for the 2001 and 2011 censuses (see Fig. 1). However, given the annual time series nature of our data, our main analyses is based on the indices constructed from HILDA.

These maps are constructed based on ABS census data. Although we had diversity data available for 676 LGAs, the 2011 Australian Standard Geographical Classification (ASGC, ABS 2011b) digital boundaries allow for only 560 LGAs upon which the maps reported in Fig. 1 are based. The first figure, Fig. 1a, is the distribution of the diversity index based on the 2001 census while Fig. 1b is based on the 2011 census. A comparison of the two figures indicates that cultural diversity increased in several regions over the decade. This is particularly visible in the metropolitan areas including Sydney and Melbourne (see Figs. 2 and 3, respectively). Both interregional mobility and international migration have contributed to this demographic change (Hugo and Harris 2011). Therefore, the analysis of diversity in this study accounts for both factors, by using a predicted instead of actual diversity index.

3.2 Share of migrants

The “share of migrants” is an alternative measure to assess whether the proportion of immigrants in a region per se has any effect on weekly wages. In addition, a diversity index is estimated for migrants excluding the Australian-born population. This is then included to see whether diversity among migrants (as opposed to diversity in general) contributes to labour market outcomes.

3.3 Weekly wages

The main dependent variable in this analysis is the log of weekly wages. Originally, HILDA respondents were asked a series of questions such as “For your [job/main job] what was the total gross amount of your most recent gross pay before tax or anything else was taken out?” Responses were recorded as “gross weekly wages and salaries” for the responding persons. For the complete panel, the mean weekly wage was $651.6 (SE = $254.1).

3.4 Other control variables

In addition to diversity (fractionalisation) and the share of migrants, standard demographic variables (age, age squared, gender, marital status) are included as control variables. English language fluency is also included, as the ability to speak English well is usually associated with labour market outcomes for migrants (Dustmann and Fabbri 2003). Foreign-born HILDA respondents were asked how well they spoke English with four response options ranging from “very well” to “not at all”. The third and fourth options (“not well” and “not at all”) are collapsed because those who responded with the fourth option were negligible (0.02 %).

3.5 Analytic framework

Apart from the observable characteristics, there can be individual heterogeneity that can affect the relationship between diversity and labour market outcomes. Longhi (2013) shows that the positive wage effects of diversity reported in cross-sectional studies (Nathan 2011) can be explained by individual differences. A fixed effect (FE) model is therefore estimated in this study capturing the unobserved individual characteristics among HILDA respondents. All the explanatory time-variant variables included in OLS are also included in the FE models.

3.6 Endogeneity of cultural diversity

The impact of cultural diversity on an economy is confounded due to the possibility of reverse causality, whereby Eq. (2) results in a spurious correlation (Friedberg and Hunt 1995). Our purpose is to determine the effect of diversity on wages, but a two-way causality between diversity and wages is possible. While diversity can directly affect economic performance, it is also possible that people from diverse backgrounds can self-select to live in places with economic opportunities.

The impact of diversity on economic outcome can be positive or negative. On the positive side, diversity can augment economic performance as it can stimulate creativity and problem solving. Diversity can also boost economic growth by drawing labour from a pool of immigrants. On the negative side, it can deplete trust and social capital due to ethnic/racial fragmentation. This can in turn weaken economic performance. Whether the positive effects of diversity on economic performance outweigh the negative ones, at one level, is a simple empirical question. However, when economic outcomes directly or indirectly affect diversity rather than the reverse, there arises an econometric issue.

In this study, the issue of reverse causality arises when variations in regional weekly wages resulted in the concentration of people from diverse cultural backgrounds in specific regions. For example, in Australia, there is no restriction in the mobility of immigrants within the country, and potentially, immigrants can move to places with more perceived economic opportunities (see Hugo and Harris 2011). The HILDA data, for example, shows substantial internal migration across waves among the respondents. Therefore, reverse causality cannot be ruled out from a regression of economic performance on cultural diversity. Instead of diversity causing variation in regional labour market outcomes, the economic conditions such as prospects of employment may be driving the regional distribution of diversity. This poses an econometric issue, endogeneity, in estimating the causal effect of cultural diversity on employment outcomes. The effect of the explanatory variable, diversity, as measured by the share of foreign country citizens in a region (LGA) is confounded by the possibility of migrants’ concentration in response to economic incentives. Therefore, the coefficient of diversity cannot be consistently estimated due to correlation with the error term in the wage regression where the share of migrants is endogenous. This entails the violation of the Gauss-Markov (zero conditional mean) assumption in OLS (Wooldridge 2010).

A suitable procedure to correct the endogeneity problem is to apply instrumental variable (IV) estimation (see Baltagi 2008; Wooldridge 2010). The main challenge in applying this procedure is the identification of a valid instrument. If such an instrument can be found, the confounding, for example, between diversity and economic performance can be disentangled and causal relationship between these two variables established. In this study, the shift-share method is used to instrument for the index of cultural diversity and the share of migrants. Following Bellini et al. (2013), Ottaviano and Peri (2006), Longhi (2013), and, recently, Alesina et al. (2013), two-stage least squares (2SLS) estimation is applied to OLS and FE models.

3.7 Identification strategy

For an instrumental variable estimation to be specified for Eq. (2), two assumptions should be satisfied. First, the instrument chosen should be correlated with cultural diversity, the key explanatory variable, and second, it should not be correlated with economic performance. In addition, a correctly specified model should not omit relevant variables. Several instruments have been developed in the literature to solve the endogeneity issue in relation to cultural diversity. Altonji and Card (1991) use the 1970 immigrant stock in the USA while Hunt (1992) uses regional temperature and French repatriates of 1962 in a French-Algerian migration study. Ottaviano and Peri (2006) use distance from gateway cities in the USA while Longhi (2013) uses “the proportion of minorities joining the ‘New Deal Program’” in the UK. As detailed in the introductory section, mixed (positive and negative) results were obtained by these studies regarding the impact of diversity on economic outcomes.

An instrument suitable for the data used in this paper is the shift-share variable first utilised by Card (2001) in assessing the local labour market impact of immigrant flow in the USA. This instrument was later used in modelling the causal effect of cultural diversity on wages and rental prices for US cities (Ottaviano and Peri 2006) and European regions (Bellini et al. 2013). The shift-share analysis assumes that the regional migrant distribution can be used to generate an exogenous variable using two-time-period data. For example, the 2001 Australian census datasets have regional distribution of Australians based on their country of birth which along with ABS annual population estimates can be used to construct a measure of diversity.⁶ The latter is composed of nationally aggregated distribution and has annual estimates by country of birth for the period 1992–2014. In this study, we use the period 2001–2011. These datasets offer two variables that are relevant here. One is the total number of population in each region by country of birth, and the other is the total number of population in each region. From these variables, it is possible to calculate the annual population growth in Australia by country of origin. Then these annualised estimates can be used along with the baseline (2001) regional population data to estimate the predicted population for each year up to 2011. Since these predicted figures are based on historical (year 2001) regional distribution rather than actual regional distribution, they are not confounded by population growth that could have resulted from economically driven mobility. Therefore, they are assumed to be exogenous to regional economic shocks.

Generally, the indices of diversity and the instruments generated using the 2001 census data and population estimates are correlated, satisfying the relevance criteria. However, the correlation coefficients are larger for diversity measure based on annual population estimates with r = 0.40 compared to diversity based on a 3-year lag where r = 0.30. On the other hand, the exogeneity criteria are also met, as the instruments are not correlated with the error term. The correlation coefficients between the residual and the two instruments (predicted diversity index and predicted share of migrants) are r = 0.06 and r = 0.06, respectively.