Modeling migration flows: explanations and policy implications (the case of Luxembourg) CMTEA 2008 The future of Europe in a world of uncertainties Romania, Iaşi, September 25-27, 2008

Paris Luxembourg in Europe

The context (1) Migration –migration = change of place of residence and workplace (  residential move) –functional labour market areas (flma)  administrative areas –crossing borders: internal vs international Cross-border commuting –travel daily or weekly from residence to workplace, not necessarily, but generally within flma Luxembourg: commuters  cross-border workers (CBW)

The context (2) Importance of migrations and commuting for Luxembourg –average population growth: 0.9% net migration flows explain 60% of demographic growth today, 40% of the 0.5 million inhabitants are foreigners –average employment growth: 2.6% commuters take 2/3 of net newly created jobs and make up 40% of total employment As a result, 60% of employed workers are foreigners Another illustration: pop. aged 15-64: total employment:

The context (3) This research takes place in the context of the overall modelling of the Luxembourg economy –estimated standard macro-model –endogenize labour supply through modelling of migrations and commuters Stylized facts –net earnings and unemployment differentials with neighbouring countries net earnings are higher, about 40% unemployment is lower, some 5 percentage points –housing prices are higher in (and around) Lux. (>100%) other living costs (food, cothing) are less different

Why this research might be interesting (for others)? –not so many time series studies in the migration context (factors influencing migrations) –few time series studies that apply cointegration testing and error correction techniques –not many studies that compare factors affecting simultaneously migrations and commuting –this work could easily be extended to other regions/countries, experiencing high in/out-flows of labour NB it is ongoing work, paper not finalised… The context (4)

Literature review (1) Causes of migration –gravity models –human capital –income / leisure –job search + matching –equilibrium / disequilibrium Consequences of migration –wages –productivity –demographic trends

Literature review (2) Gravity models –based on the Newtonian law of gravitation –not derived from theoretical modelling of economic behaviour –but widely used, with good results, can be estimated M ij = G * P i  0 * P j  1 * D ij  2 –M ij = migration from i to j –G = constant term –P i = population of origin («weight») –P j = population in destination area –D ij = distance between both destinations

Literature review (3) Modified gravity models –include variables linked to economic behaviour –no formal derivation but taken from other theories M ij = G * P i  0 * P j  1 * D ij  2 * X i  3 * X j  4 –X i, X j = economic and other variables related to regions i and j –Job opportunities, earnings, unemployment, housing prices, risk, geographic characteristics (amenities), political situation, etc...

The model (1) Data , yearly Test / impose restrictions on model coefficients –taking ratios of independent variables: (X i / X j ) –  reduction of the number of parameters to be estimated Other simplifications –drop P i (foreign population) and D ij (distance) foreign population varies much less two “countries”: Luxembourg and “the rest of the world” (ROW, to be defined)  aggregate flows

The model (2) ln(M k /P) =  0 k +  1 k * ln(L/P) +  2 k * ln(Y j /Y i ) +  3 k * ln(U j /U i ) +  4 k * ln(HP j /HP i ) +  k –j = Lux; i = ROW –k = in, out, com: in: in-migration (flow) out: out-migration (flow) com: commuters (stock) L = tot. labour demand in Lux.:  1 in, com > 0;  1 out = 0 Y = relative earnings:  2 in, com >0;  2 out <0 U = rel. unempl. rates:  3 in, com 0 HP = rel. house prices:  4 in 0

The model (3) Some precisions on the variables –Migrations (M in, out, com ): in, out = total (gross) flow com(muters) = stock of foreign workers travelling daily or weekly from B, F, D to L –Labour demand (L) = total domestic employment in Lux. –Per capita earnings (Y): B, F, D (country wise); source = OECD (“Taxing wages”)  after taxes and social transfers –Unemployment rate (U): neighbouring regions (from B, F, D), Nuts3; source =Eurostat –House prices (HP): neighbouring regions (from B, F, D), different sources

Estimation results (1) Order of integration –all variables (ratios) entering the equations are I(1) Estimation of level equations (1st step of Engle- Granger two step procedure) –OLS, stationarity of residuals  cointegration? –Results fail to confirm cointegrating relationship (McKinnon critical values) but residuals “optically” stationary…

Estimation results (2) Error correction models –Dynamic ECM only works for CBW: cointegration clearly confirmed by t-test on error-correction parameter (Banerjee 1998) –Others: retain static LR parameters ↔ Engle-Granger two-step (or Zivot 2000) Endogeneity bias: –to what extent the immigration rate does it cause (some of) the independent variables (for example house prices)?  to be studied –other non-tackled issues: small sample bias, outliers…

Estimation results (3)

Estimation results (4) Final long-run specifications: log(M in ) = log(L) *log(Y j /Y i ) – 0.25*log(U j /U i ) log(M out ) = log(P) *log(U j /U i ) *log(HP j /HP i ) log(M com ) = log(L) *log(Y j /Y i ) – 1.33*log(U j /U i ) +1.67*log(HP j /HP i )

Simulations (1) Set up a model linking the labour market with population dynamics: –3 migration equations –population dynamics (linked to migrations) –unemployment 2 simultanous feedback variables: population + unemployment But: partial model –no feedback from unemployment to prices/wages –total domestic employment (L) = exogenous

Simulations (2)

Simulations (3) Integrate the “new” migration equations into a complete macro-model: –wage equation (WS-PS), depending i.a. on UE –wage-price spiral –price-competitiveness –employment is endogenous –capacity constraints –etc… Simulate the same shocks in both set-ups (partial and complete)

Simulations (4) Simulations: generate shocks to main RHS variables: –domestic labour demand and unemployment –foreign unemployment, house prices and labour earnings Rationality of the shocks: –test impact of national policies acting on the labour market: higher employment, lower unemployment –reproduce stylized facts: higher unemployment in bordering regions, lower net wages and house prices

Simulations (5) 10% increase in labour demand (in Lux.) –increases resident employment and commuters (CBW) impact on CBW stronger (except for the two first years in partial model) for a transition period, but, in the LR, convergence towards increase of 10% –part in newly created jobs: 2/3 commuters; 1/3 resident –resident unemployment only decreases initially decrease in resident unemployment attracts new foreign workers  unsustainable –Full model: multiplier effects  impact on total employment > 10%  decrease in resident UE a little stronger

Complete modelPartial model

Complete modelPartial model

Complete model

Simulations (6) 1 ppt decrease in domestic unemployment (UE) –the initial decrease in domestic UE increases foreign labour supply… –…which pushes up UE in L there is a 1:1 substitution between resident workers and CBW –as a result, the decrease in UE is almost completely reversed only in the complete model is there a sligthly bigger decrease in resident UE, because migrations increase less… …due to lower net wages (overall negative demand shock)

Complete modelPartial model

Complete modelPartial model

Simulations (7) Modifiy (foreign, exogenous) variables that act on foreign labour supply: –unemployment –earnings –house prices Modifiy these variables in a way to emphasize stylized facts: –higher UE, lower earnings and lower house prices in the neighbouring regions

Simulations (8) Results: –in all cases, increased foreign labour supply depresses resident employment and increases res. UE –the initial negative impact on GDP reverses after some periods, due to the favorable evolution of price competitiveness (fall in domestic prices) –in case of a fall in foreign house prices, the negative demand shock lasts longer (although the amplitude of the results of the shocks on the national variables can generally not be compared)

Conclusions (1) Econometric evidence confirms the importance of earnings, unemployment and house prices for explaining cross-border worker’s (commuters) movements Estimations of migration equations are less robust (econometrically), but the obtained coefficients are sensible

Conclusions (2) A positive demand shock on the national economy, having an impact on employment and/or unemployment, increases foreign labour supply, possibly as much as to reverse, partially or totally, the positive initial impact of the favourable shock Increased foreign labour supply, due to unfavourable exogenous causes (negative shocks on foreign economies), is generally positive for the domestic economy, after some lags, with the exception of unemployment, that increases

Thank you very much for your attention Questions?