immigration and unemployment

1985 IMMIGRATION AND UNEMPLOYMENT 555

direct migrants toward ‘bottleneck’ areas could offset this on the supply side. In particular, government admission of migrants on economic criteria seems to be strongly procyclical. Thus the outcome is difficult to determine in apriori terms. It becomes a n empirical matter. The task of this paper is to help to resolve empirically the relationship between migration and unemployment.

II Tests of Statistical Causality’ In recent times the deficiencies of conventional

structural estimation using econometric methods have become increasingly well known. In consequence non-structural estimation methods have developed afresh, though often to supplement or precede rather than to necessarily replace structural estimation. In particular, one view has arisen that a two-stage method is required whereby ‘facts’ are determined by non-structural tests as a preliminary to subsequent structural estimation. In this way untested ‘priors’ on the endogenous or exogenous status of variables need not be relied upon (Sargent, 1981).

The most widely used technique of non-structural estimation is that of ‘statistical causality’, a technique which is an advanced correlation analysis that avoids some of the problems of simple correlation. It is based upon Granger’s (1969) definition of causality which states that if there are two variables, X and then X i s said to cause Y if, with respect to a given data set, present Y can be better predicted using past values of X than by not doing so. If X causes Y and also Y causes X, feedback is said to exist.

Now these definitions only pertain to stationary non-deterministic time series. A (weakly or covariance) stationary series in turn is defined as trendless in mean and covariance, while the deterministic component of a time series is that part which can be predicted with certainty from its own past history. Hence the non-deterministic component is that which cannot. Accordingly, to test for causality between immigration and unemployment we follow the general causality s t ructure embodying these properties as implemented by Sargent (1976). and we posit the following two basic estimating equations:

I More details on causality methods and the resulu in this application are given in Withers and Pope (1984).

where A=change operator, U=unemployment, LF=labour force, T=linear time trend, d=quarterly dummy variable, M = migration, and P=population.

These equations allow the examination of causality from migration to unemployment and vice versa. In the former case the motivating hypothesis is that migration has effects upon both demand and supply which are difficult t o assess apriori. In the latter case there is also the less politically significant but much debated question in the migration literature as t o whether migrant supply responds significantly to unemployment, both as a matter of individual migrant choice and as a consequence of government restrictions on inflow, i.e. reduced government ‘demand’ for migrants. Our focus is on the former case, but we will provide some results also for the latter.

The specified equations examine incremental predictability. Having produced effectively ‘white noise’ data series by the structure of the equations imposed, the method of explanation adopted is to ask whether a vector representation of a particular exogenous variable improves explanation of the dependent variable. If immigration is important as a contributor to unemployment, its effect should be detectable. Nevertheless there is an inconclusive debate in the statistical literature as to whether causality methods can suffer an omitted variable problem. We d o not attempt to resolve that issue but instead prefer to examine for robust results via our later Sections IV and V which also test the hypothesis of migration causing unemployment using conventional quat ions specified by economic theory.

The specific estimating form of the causality equations and the measurement of variables deserves some mention prior to reporting the results. A change transform is used on the unemplayment rate so that a flow (migration) is compared to a flow (change in unemployment rate-since the unemployment rate is a stock measure). This also helps avoid the problem that

556 THE ECONOMIC RECORD J U N E

TABLE 1

Tests for Statistical Causality between Migration and Unemployment

Dependent Causal Data Causal Significant Variable Variableperiod Lag Test Statistics Individual

F Wald Lagrange Likelihood Causal Lags Statistic Statistic Multiplier Ratio

a) URC

b) NMR

c) URC

d) NMR

e) URC

f) NMR

g) URC

h) NMR

NMR

URC

NMR

URC

NMR

URC

NMR

UR C

1948( 1)-

1948(1)- 1982( 3)

1982(3) 1948( I ) 1982(3) 1948( 1)- 1982(3)

1982(3) l950( 1) 1982(3) 195q1)- 1982(3)

1982(3)

1950( 1)-

I95q 1)-

0- 6

0- 6

0-12

0-12

0- 6

0- 6

0-12

0-12

1.82

1 1.16"

I .09

6.12''

2.65

12.92"

1.64

7.20"

15.78'

96.50' *

18.73

104.44"

23.25'

113.52"

28.65'

125.45'

14.02

54.65''

16.31

57.10''

19.42'

57.85'*

23.05'

60.80' *

14.86'

71.65*'

17.43

76.07''

21.22'

79.53**

25.65'

8 5.46' *

-

- 1, - 2 , - 4 , + 5 , - 6

-

- 1 , - 4 , + 5

-

- 1 , - 2 , - 4 , + 5

-

- 1 , - 4 , + 5

Notes: U R C = Unemployment rate change NMR =Net migration rate

significant at .05 level ** significant at .01 level

migrant flows are small relative to the workforce stock.

It might be argued that use of the unemployment rate does not capture adequately the state of the labour market during a period in which labour shortages were common. To examine whether this is so, a supplementary measure using the ratio of unemployed to job vacancies is also tested. This means that both 'short-sides' of a market in disequilibrium can be examined.

The migration rate is defined as total annual arrivals less total departures (i.e. net movements) as a proportion of the total Australian population. No distinction is made between Australian born and overseas born since the intention is to measure supply and demand impacts of cross-border movement for the country. Nevertheless, migration can also be examined in terms of its components. In particular, it might be thought that a stricter definition of migrants such as only permanent and long-term movements should be used and perhaps, within that, only those entering the labour force. However i t can be argued that expenditure and supply effects ensue for all classes of migrants, and testing for the overall impact is what is required.

Therefore, each definition is examined empirically, as are the separate arrival and departure components underlying the net movements. (Further details on data sources and treatment are given in the Appendix.)

Finally, note that examination of the data indicated substantial seasonality, both for migration and for unemployment. The quarterly dummy variable method is used to control for this. Prior seasonal adjustment of data would be quite inappropriate because pre-adjustment uses moving average techniques that would remove part of the lag sensitivity that is of direct concern in causality analysis. In distributed lag models we need actual data to avoid bias (Sims, 1974).

111 Statistical Causality Results Table 1 outlines the results for estimation of

equations (1) and (2). A twelve-quarter lagged dependent variable is adopted as standard in the table, since this 12th-order autoregression is quite 'profligate'. The results were insensitive to other lesser autogression specificiations, so that these others are not reported here.

1985 IMMIGRATION A N D UNEMPLOYMENT 557

The primary criteria for evaluation are the following test statistics. The F Statistic for the group of causal coefficients p in (1) is the most common statistic used to determine incremental predictability. But the Wald (W) Statistic, Lagrange Multiplier (LM) and Likelihood Ratio (LR) also are useful statistics for testing the null hypothesis that the coefficients on the causal lags are jointly zero. In this case, the separate null hypotheses are that migration rates add nothing to the prediction of unemployment rates, and vice versa. The W, LM and LR statistics are tested as X I statistics. Individual coefficients and coefficient subsets can also be examined for significance as well as the completed vectors of coefficients. But, in fact, individual coefficients here turn out to be significant only where group significance is established, so no further subset detail is presented since it does not alter the basic pattern of results.

From Table 1 the test statistics indicate that there is a highly significant relationship to be found for unemployment causing migration. However, causality from migration to unemployment is uniformly rejected. The contrast is an important one for those who might be inclined to regard the test as statistically 'weak'.

This result applies whether the total net migration or the permanent and long-term definition of net migration is used and whether the unemployment rate or unemployment-vacancies ratio definition of labour market balance is used. In the latter case it is true that the significance levels are not as high for all F tests, but this is likely to be explained by the well-known deficiencies in the available vacancies series (CES registrations) as a measure of true vacancies. This explanation is supported by the fact that the F statistic is smaller for both directions of causality, compared to the outcome when only the unemployment rate measure is used instead of the unemployment- vacancies ratio measure.

In terms of total net migration compared to permanent and long-term net migration, the contrast between insignificant migration to unemployment causality and significant unemployment to migration causality is heightened by the use of the permanent and long-term measure. Since it is the permanent and long-term movements that are also of greater policy interest, the range of X I test statistics is also reported for this specification and is found to be quite consistent with the F statistic results. Accordingly, only F statistics are further reported for the other specifications and for results given in Table 2.

TABLE 2

Tests for Statisticd Causalily ber ween Arrivals/ Departures and Australian Unemployment , I948{I)- I982{3)

Dependent Causal Causal F Variable Variab.e Lag Statistic

Total ArrivaWDepartures URC TAR 0-12 URC TDR 0-12 TA R URC 0-12 TDR URC 0-12 URC TAR 1-12 URC TDR 1-12 TA R URC 1-12 TDR URC 1-12

1.83 I .74 3.67" 0.93 1.90 1.90 3.89'' 0.92

Permanent and Long-Term Arrivals/Departures URC PLTAR 0-12 1.98 URC PLTDK 0-12 I .98 PLTAR URC 0-12 6.54" PLTDR URC 0-12 2.53' URC PLTAK 1-12 1.90 URC PLTDH 1-12 1.90 PLTAR URC 1-12 6.87" PLTDR URC 1-12 2.08

Notes: URC =Unemployment rate change TARITDR =Total migrant arrivaVdeparture

rate PLTARIPLTDR = Permanent and long-term arrival/

departure rate significant at .05 level

** significant at .01 level

Finally, with res2ect to Table 1, if the current period observation is excluded from the causal lag specification the test statistics increase slightly in value, and they also increase more for a longer lag specification. But the sensitivity in both cases in no way challenges the basic structure of the results so that it is clear that. on this evidence, we must accept the null hypothesis of 'no effect' for migration 'causing' unemployment and reject the null hypothesis of 'no effect' for unemployment 'causing' migration.

In Table 2 further information is provided in terms of separately distinguishing arrivals and departures rather than treating only net migration, as in Table 1. But the same conclusion is affirmed as for Table 1 , with the extra information that the sensitivity of permanent and long-term migration to unemployment operates substantially through arrivals and not departures.

558 THE ECONOMIC RECORD JUNE

Next, the matter of stability might be addressed. To examine for changes in the relationship being considered the standard statistical tests for stability were considered. These are the CUSUM and CUSUMSQ statistics of Brown, Durbin and Evans (1975). The stability tests produced no evidence of instability at the conventional levels of significance. It might be argued that specific stability tests would be preferable since there are good reasons to expect a stability break in 1974 due to both economic and migration policy changes at this time. But a Chow test centred on this period also supports the stability hypothesis.

IV Structural Testing It was stressed above that the causality analysis

of the preceding sections could be interpreted as an exercise designed to clarify the statistical ‘facts’ to which theoretical specification should conform. It is tempting to say that the result obtained of no significant causal relation operating from post-war immigration to Australian unemployment shows that structural specifications of unemployment can ignore immigration as an explanatory variable.

However, given the relative novelty of the causality method and its seeming ‘black box’ approach, it is useful to also affirm and demonstrate this result by direct structural estimation. The conventional empirical method in economics is to use theory to specify the relevant form and content of equations which are then estimated by regression techniques to test the hypotheses or to provide numerical estimates of magnitude within accepted qualitiative hypotheses. There are two extant studies which do claim to provide evidence in this way on the issue of immigration and unemployment for post-war Australia data.

First, in Harper (1980) a basic unemployment- vacancies relationship that exhibits instability is augmented by adding real unemployment benefits and assisted migrant arrivals as additional explanators of unemployment because of their likelihood as sources of structural change in the unemployment-vacancies relationship and hence of the observed instability. Harper concludes:

Some significant contribution could be attached to change in the level of assisted migration . . . the period 1972-73 saw substantial cutbacks by the then Labor government in Australia’s traditional migrant intake programs, and this can hardly have failed to exert some influence on the

degree of mis-matchedness evident within the Australian labour market [p 2421. However Harper did not actually report full

regression results including immigration. Indeed, he says that absence of published data on assisted arrivals prior to 1%1 inhibited his analysis and that regression estimates for the period 1961 to 1978 found migrant regressors with the a priori correct sign but that these regressors did not actually attain statistical significance.

Whatever its other merits, Harper’s work is an insufficient guide to the migration question in relation to structural unemployment. The reliance upon assisted migrant arrivals misses the changing pattern of arrivals and departures. At the same time a longer data series is required to meet Harper’s own concerns about adequate degrees of freedom.

In fact, these problems are readily remediable. The requisite migration data can be obtained unpublished before 1%1 from the Australian Bureau of Statistics and the data base can be extended back to 1948 and forward to 1982. Also there are important recording errors which require correction in Harper’s data for unemployment benefits. If Harper’s model and his approach are otherwise fully conformed to, including data sources, equation specification and estimation techniques, then for immigration defined as net migration rate we obtain for the period 1948((3) to 1982(3) the following re-estimated equation (with t statistics in parentheses): log (U/LF)t =

-0.0070- 0.6901 log (V/Ll;)r ( - 0.0879)( - 8.0377) + 0.3849 log ( V / L F ) f - 1

(4.5976) + 0.7577 log(CJ/LF)t - 1 - 0.1849

(8.8 147) ( - 2.7866) log(U/LF)t-2+ 0.0158 RUB[

(3.7600) - 0.0020 RCJEt.lOg(V/LF)~- O.oo00 Mt ( - 0.6920) (-0.0081) - o.oo00 Mt.log (V/LF)[ ( - 0.0609)

- 0.4298dlt - O.2357d2t - 0.0683d31 (-6.5116) (-3.9445) ( - 1.1224) - 0.5283~1-4 ( - 7.0076)

The adjusted R’ is .98, and U,LF,M,D are as


defined for equations (1) and (2), V is job vacancies, RUB is real unemployment benefit, and E is the error term.

It is observed that the equation overall has a high degree of statistical explanatory power. But the coefficients on the migration variables are not significantly different from zero. This result holds if alternative but less preferred measures of migration are used, viz. net permanent and long- term migration, settler arrivals. Migration does not seem to contribute to the explanation of the unemployment-vacancies locus, as represented by the Harper model, and Harper’s speculations were misplaced.

The second study is Warren (1982). Warren also presents an unemployment-vacancies model, but in his case oriented toward testing changes in ‘equilibrium’ or ‘natural’ unemployment. Hughes (1975) suggested that immigration changes affect such unemployment. But in Warren’s model the coefficients associated with the immigration variable are found not to be significantly different from zero. Warren concludes: ‘there is no evidence, therefore, that the reduction in migration during the 1959-65 period was associated with either an increase in labour turnover or a decrease in job search efficiency’ (p. 455).

This conclusion confirms our own findings for the Harper model, but it must be recognized that there are problems with the Warren analysis. The first is his use of a blunt instrument: a binary variable to represent migration levels. Certainly Warren’s dichotomous representation of the timing of ‘low immigration’ versus ‘high immigration’ via dummy variables can be questioned as somewhat arbitrary. A continuous variable representation of migration would be preferred. A second problem is that the data period ends in 1973, despite the later publication date of the study. This excludes a period of major interest, with the post 1974 changes in unemployment and immigration being much larger than in the preceding years.

Both of these concerns can be readily met and Warren’s analysis can be replicated with a continuous measure of migration replacing the dummy variable and with the data extended to reflect the post 1974 experience.

If this is done, it is found that Warren’s earlier conclusion regarding migration is confirmed (though his model now has considerably reduced overall explanatory significance). This conclusion also holds for alternative measures of migration, viz., permanent and long-term net migration, settler arrivals. The result for the period 1955(1) to 1982(3)

is (t statistics in parentheses):

( E f + l -Ei)/Ef=O.24286- 73.141 XI- 0.009 Mi (2.9894)

(-2.3615) (-0.3421)

+0.1081 M i . X i (1.0567)

(0.0924)

+0.0286 qt + 0.04581 S& (0.2994) (0.4549)

+0.0122 s&

+ 0.0962 t t - 1 (1.0085)

Adjusted R’=O.IS, and E is employment, X is Warren’s composite measure of unemployment and vacancies, and S; is Warren’s seasonal dummy variable which nets out the effect on the constant term.

Further details on replication and extension of Harper and Warren’s models are given in Chapman, Pope and Withers (1985). But a further point that goes beyond replication detail should now be emphasized. We have simply accepted the model specifications given in Warren and Harper and then examined if improved measurement alters their conclusions regarding immigration. However the question of the overall adequacy of the unemployment-vacancies approach must also be raised.

A hint that the approach is in fact inadequate is gained from the stability characteristics of the estimated models. The equations reported above are actually quite sensitive to the data period specified and to the variables included and standard stability tests such as CUSUM and CUSUMSQ confirm such problems formally. The obvious concern is specification error and the likely source of the problem is the focus of the models only on the fr ic t ional-s t ructural dimension of unemployment. This focus ignores cyclical unemployment , the major component of unemployment since 1974. Even models concerned only with frictional-structural unemployment need to control for the factors influencing cyclical unemployment, since measured unemployment will reflect both phenomena. Equally, any interest in the effects of immigration on unemployment needs to consider migration effects on cyclical as well as structural unemployment.

For these reasons we must go beyond the existing

5 6 0 THE ECONOMIC RECORD JUNE

statistically structural analysis of immigration and unemployment and examine a model that incorporates both frictional-structural and cyclical unemployment considerations.

V Cyclical a n d Frictional-Structural Unemployment

Trivedi and Baker (1982) give aggregate unemployment equat ions which explicitly incorporate a core of frictional-structural factors and then add alternative specifications of cyclical factors, according t o alternative rationing and natural rate approaches.

The natural ra te approach encompasses monetarist and rational expectations interpretations of cyclical unemployment. The rationing approach incorporates both classical (real wage) and Keynesian (demand deficiency) explanations of cyclical unemployment , without fur ther differentiating between them. Trivedi and Baker find the rationing model better explains empirically the behaviour of unemployment in Australia: ‘all formulations in which a non-clearing labour market is postulated and the effects of an exogenous real- wage or job-availability are incorporated explain the behaviour of unemployment in Australia much better’ (p. ii). They also include that ‘in view of the relative insignificance of unanticipated inflation variables, it seems that there is nothing in the natural rate model which is not acounted for by the rationing equilibrium model. The latter does encompass the former’ (p. 66).

We accept these. findings and here re-estimate the Trivedi-Baker preferred model for explaining Australian unemployment, extended to allow for immigration. The existing rationing model incorporates terms for both frictional-structural and cyclical unemployment. But Trivedi-Baker did not include a migration variable in their analysis. We simply adopt their preferred model and augment it by adding migration. The model is statistically structural in that it derives from specific theoretical arguments regarding the determination of frictional-structural and cyclical unemployment. The equation is re-estimated for the same specification, definition of variables, data sources and data period (1964.2 to 1982.1) as Trivedi- Baker, with the addition of a migration variable. Adherence to the same period is dictated by the use of the complex constructed variable for demand dispersion used by Trivedi-Baker. The estimation method is ordinary least squares for a semi- logarithmic functional form and without correction

for serial correlation, as was determined by Trivedi- Baker after various diagnostic analyses and confirmed in the re-analysis. The unemployment measure is CES registered unemployed and capacity utilization is the G U T variable from the Treasury national income forecasting model. Because cyclical unemployment is specifically controlled for (through the inclusion of real wage and capacity utilization variables) this equation removes the excluded variable concern that could apply to the Warren and Harper analyses. Indeed, both of those models can be seen as being broadly encompassed in the frictional-structural component of the equation. The equation is:

log U t = a o + a l R W t - l + a 2 ( R W t - 2 -RWt-3) / RW!-3+a3 log W t - 1 + aqCUTt + a5RUBt + a5RUBt t agM1

+a7 STI +ag S R + a 9 Dl t+a l@2t

+ a 1 l D 3 t + t t

where U = unemployment rate, R W= real wage, G U T = c a p a c i t y utilization, R U B = r e a l unemployment benefi t , M = migrat ion, S T l =demand dispersion index to 1972(4), S R =demand dispersion index from 1973(1), and Di = seasonal dummy for the ith quarter.

The results are presented in Table 3. They broadly conform to the Trivedi-Baker findings for the common variables, though the (Stoikov) index measures of demand dispersion now become insignificant and one of these alters in sign in this extended analysis, and the seasonalable coefficients are altered. But the size and significance of the coefficients for the major explanatory variables remain unaltered. It is noteworthy that the real unemployment benefit variable which was significant in the Harper unemployment analysis becomes insignificant in this better specified equation.

The main finding for our purposes is that in the Trivedi-Baker preferred model, immigration appears of marginal statistical significance in explaining unemployment. But the effect is negative indicating that, i f anything, immigration reduces unemployment. And the effect is very small. For the preferred net migration rate measure, the mean elasticity is - .02, indicating that a doubling of migration would reduce unemployment by 2 per cent.

Further, the results are highly sensitive to the measurement of the capacity utilization variable.


TABLE 3

Immigration and Unemployment: A Rationing Model Approach, I964(2) to 1982(1)

Migrant Measure Net Migration Permanent and Permanent and Rate Long-Term Net Long-Term

Migration Rate Arrival Rate

Constant 3.7004 4.3006 4.2363 (4.6368) (4.83 14) (5.0297)

Real wages (- 1)

Wage change ( - 1)

Unemployment rate ( - 1)

Capacity utilization (Gun

Real unemployment benefit

Migration

Demand dispersion to 1972(4)

Demand dispersion from 1973(1)

2.6100 (5.4538) 1.1308

(2.6109) 0.61 10

(10.628) -4,7152

( - 5.4972) - 0.0046

( - 0.8452) -0.ooo1

( - 1.2592) 0.0333

(1.1390) - 0.0047

( -0.1577)

2.5448 (5.3635) 1.221 1

(2.8233) 0.5819

(9.8 160) -5.2331

( - 5.7205) - 0.005 1

( - 0.%12)

2.8424 (6.0305) 1.2474

(2.9062) 0.5621

(9.1682) -5.1520

( - 5.8775) - 0.0072

(-1.3143) - 0.0003 - O.Oo04 - 1.8302) (-2.1039)

0.02% 0.0344 (1.0236) (1.2121)

-0.2157) ( - 0.1044) - 0.0064 - 0.0030

First quarter dummy - 0.3025 - 0.2582 -0.2634 ( - 6.5150) ( - 8.6729) ( - 8.8900)

Second quarter dummy - 0.2422 - 0.2479 -0.2633 ( - 8.3595) ( - 8.8327) ( - 8.9525)

Third quarter dummy - 0.0744 -0.0641 - 0.0694 (-2.5523) ( - 2.1623) ( - 2.3964)

Adjusted R’ .99 .99 .99

If the job vacancy rate is used instead of the Treasury’s GUTvariable, the migration coefficients are of the same sign but of even smaller magnitude and n o statistical significance. The impact of migration o n unemployment is then not significantly different from zero and this result too is not altered by alternative measures of immigration.

Finally. stability tests using conventional CUSUM and CUSUMSQ measures and also specific Chow tests directed at the possibility of structural break in 1974, now d o not detect any significant instability, as was also found for the original Trivedi-Baker work without migration.

Overall it is clear that the cyclical factors of real wage and demanddeficiency dominate explanation of Australian unemployment movements over this period, and immigration adds little or nothing further to that explanation, just as was concluded for the causality analysis and for the Harper and

Warren analyses. The robustness of the conclusion that immigration has not increased unemployment is striking, as it applies across a range of alternative empirical analyses.

VI Conclusion It seems that concern over immigration

contributing to aggregate unemployment is not well founded historically. The statistical causality analysis developed in this paper was unable to find any association from migration t o unemployment (though there was strong evidence of a significant effect of unemployment on subsequent migration).’

’ This inverse causalii y result also finds support from existing structural evidence. Unemployment in the country of destination is typically found to be a significant explanator of international population flows both through individual migrant supply behaviour (Layton, 1983) and government policy (Kelley and Schmidt, 1979)

562 THE ECONOMIC RECORD JUNE

Moreover, this finding was strongly supported by evidence from more conventional economic analyses, including the revision and extension of the earlier relevant empirical work on frictional- structural unemployment by Harper and by Warren, and the extension of the work by Trivedi and Baker on cyclical and frictional-structural unemployment. In all cases n o strong statistically significant relationships from immigration to unemployment could be discerned. Certainly there was no evidence to indicate that immigration increased unemployment . Since the same conclusion is also reached by a process of qualitative reasoning and casual empirical evidence in the recent work on South Australia by Harrison (1984), the robustness of this result to alternative models and methods is impressive.

The implication of our findings is that, overall, immigration did not significantly affect structural unemployment and that, as regards cyclical unemployment, migrants created at least as many jobs as they filled.’ On balance immigration seems to have neither enhanced nor substantially reduced unemployment in the post-war period. This includes the recessionary period since 1974 and applies despite the changing composition of migration over the period.

It follows that immigration policy need not be dominated by fear of immigration causing unemployment. Certainly the previous rates of immigration experienced during recession are sus- tainable, and this includes inflows of up to 119 OOO permanent settlers per annum and 128 OOO net migration (permanent a n d long term) as experienced in 1981-82. What would happen for substantially larger intakes or for dramatically different composition within this range cannot be ascertained from past experience. But in the past migrants have created at least as many jobs as they have occupied.

APPENDIX Data Sources and Treatment Data were gathered on a quarterly basis, 1948(1) to 1982(3). The unemployment rate is measured as those persons registered a s unemployed with the Commonwealth Employment Service (CES) as a proportion of the civilian labour force, all persons. Pre-1953 unemployment data are unpublished CES

’ There may nevertheless be important compositional effects of immigration not considered here. See Harrison (1983) and Chapman, Pope and Withers (1985).

registered unemployed as provided by Dr P.K. Trivrdi of the Economics Faculty, ANU, and documented in Trivedi and Baker (1982). Unemployment data 1953(1) to 198q1) are from the Commonwealth Department of Employment, Monthly ’ Review of the Employment Situation. Post 198q1) registered unemployment data are estimated from ABS, The Labour Force, Australia (6003.0). survey unemployed persons. The 198q1) quarter overlap between CES registrations and ABS survey unemployed was used to render the survey measure commensurate with the registration measure which was not collected in that period. Recommencement of the CES statistics in 1983 indicated that this interpolation was reasonable. In relation to unemployment, it would have been useful to distinguish migrant from Australian-born unemployment, but data in this form were not available for the time period examined. The denominator for the unemployment rate was the labour force, all persons. Data for the period 1948(1) to 1963(4) were unpublished estimates by the then Commonwealth Department of Employment, and these were updated to 1982(4) from ABS survey estimates of the labour force as provided to 1981(4) by Trivedi and thereafter direct from ABS, The Labour Force, Ausfralia (6003 .O).

Where sensitivity of the analysis to alternative measures of labour market pressures was tested, the measure used was the ratio of registered unemployed to registered vacancies. Vacancies were all persons registered for employment with the CES and were derived in the same way and from the same sources as for registered unemployed, with the exception that the interpolation 1980(1) to 1982(3) is from ABS, Job Vacancies(6231.0).

The sources for the net migration data are ABS, Quarterly Summary of Australian Statbfics (1.3) for the period to 1976(1), ABS, Monthly Review of Business Sfafbficr (1.4) for the period to 1979(2) and its successor, ABS, Monthly Summary of Sfofistics (1304.0) for 1979 (3) to 1982(4). The denominator for net migration was estimated Australian population as at the end of the period, as provided in the same sources. Migration components for disaggregate analysis were from the same sources a s for net migrat ion for the total arrivals/departures disaggregation and for permanent and long-term arrivaWdepartures, except for I948( 1) to 1961(1) which came from unpublished data provided to the authors by the ABS. No further disaggregation into permanent and long term separately was possible from existing sources.

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Withers, G.A. and Pope, D. (1984), ‘Post-war Immigration and Australian Unemployment’, Working Papers in Economic History, No. 24, ANU.

immigration and unemployment

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