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Sectoral allocation and macroeconomic imbalances in EMU

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Abstract

We document how, over 1996–2008, large capital inflows in Southern Europe coincided with broad-based growth of the nontradable sector, extending beyond the construction and real estate sectors. We then present a tractable two-sector, two-region (‘North’ and ‘South’) model of a monetary union, in which we show how the sharp, permanent, fall in Southern real interest rates that occurred in the run-up to EMU can explain the Southern consumption boom, wage growth, growth of the nontradable sector, and deteriorating external position. Upward pressure on the EMU-wide interest rate induces an opposite process in North. Consequently, both real exchange rates and external positions of the two regions diverge. Including a third country with a flexible exchange rate vis-à-vis the euro amplifies the effects of monetary integration in South, while dampening them in North. We confirm the key model predictions using a panel-BVAR for the euro area and investigates various policy reforms to facilitate the ongoing rebalancing process in the eurozone.

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Notes

  1. See for instance Feldstein (2012) who describes the large intra-EMU capital flows and the seminal paper of Blanchard and Giavazzi (2002) for a—at the time—common interpretation of these capital flows.

  2. Comunale and Hessel (2014) describe how the surge in domestic demand was the root cause behind the emergence of current account deficits. Fagan and Gaspar (2007) show that capital inflows fueled a consumption boom while Eichengreen (2010) and Holinski et al. (2012) show that the Southern countries became relatively less productive after monetary integration.

  3. Reis (2013) focuses on financial frictions to show why relatively unproductive firms in the nontradable sector grow at the expense of the tradable sector. Gopinath et al. (2017) and Cecchetti and Kharroubi (2015) show that financial frictions can contribute to the misallocation of capital within sectors, as capital is allocated to firms that have higher net worth but are not necessarily more productive. Sy (2016) emphasizes how the interaction of a common monetary policy and heterogeneous inflation rates implies real rates that are lower in the South than in the North, contributing to growth of the Southern nontrable sector. To rationalize the boom-bust cycle experienced by much of the Eurozone, Ozhan (2017) shows how bank balance sheets can amplify fluctuations that are driven by news on the valuation of non-traded sector capital. Coimbra (2010) presents a small open economy model in which falling interest rates lead to an increase in the collateral value of housing, inducing growth of the housing sector and a deterioration of the trade balance.

  4. Over 1999–2007, the former high interest rate countries’ represented 32–36% of euro area GDP and 40–41% of the euro area population, rendering the assumption that these countries can be represented as small open economies within the euro area counterfactual. See also Fagan and Gaspar (2007).

  5. Relatedly, Teimouri and Zietz (2018) document that in middle-income countries, capital surges contribute to deindustrialization.

  6. Consistent with this pattern, Berger and Nitsch (2014) provide evidence of a significant widening of bilateral intra-euro area trade imbalances.

  7. The financial sector, another sector typically mentioned as a fast growing (closed) ‘services’ sector, is too open to be part of our nontradable sector and thus not driving the growth thereof.

  8. i.e. minimizing \(P^{j}_{t}C^{j}_{t} = \sum _{j'}^{j} \{P_{t}^{j',T} C_{t}^{jj',T}\} + C_{t}^{j,N}P_{t}^{j,N} \) subject to the constraint \(C_{t}^{j} = (C_{t}^{j,N})^{\eta }(C_{t}^{j,T})^{1-\eta }\).

  9. During the first decade of EMU risk premia were mostly absent while they suddenly spiked when the solvency of the Southern states became questionable. Section 5.4 describes the consequences of such a sudden increase in the interest rate premium.

  10. We assume that, within regions, actuarially fair priced state-contingent securities exist that insure each household against idiosyncratic variations in labor and dividend income. Consequently, at the regional level, individual household income will correspond to aggregate household income.

  11. In the pre-integration steady state the cost of intermediation theoretically also contains \(-\omega *NFA^{s}_{t-1}\). As however \(\omega \) is calibrated to achieve net foreign asset positions of zero, this term always equals zero.

  12. If one were to look two digits behind the comma, the growth rate of the nontradable sector would be very slightly larger when the degree of competition is low.

  13. We attempted to estimate the model using a simple panel-VAR approach. While the estimation results are qualitatively similar, the impulse response functions are less precise and show oscillating behavior, suggesting over-parameterization. The main efficiency gain of the Bayesian approach, despite an agnostic prior, comes from the assumptions that all parameters are drawn from the same distribution and that the impact of the lags is decaying with each further lag.

  14. We also estimated the model using nominal interest rates, optionally including inflation expectations as a separate variable. The results, which are available upon request, are qualitatively the same.

  15. Our preferred specification is a VAR using demeaned growth rates. First, demeaning the variables ensures that the impulse responses are not overly affected by the large fluctuation around the financial and euro area crisis. Second, growth rates ensure stationarity of our macro-economic variables [confirmed by the Levin et al. (2002) panel unit root test]. However, as (non)tradable output and the price level appear to be integrated of order 1, we also estimated the model in demeaned log-levels (Sims et al. 1990). In Fig. 15 in the “Appendix 1” we show that both approaches give rise to broadly similar results.

  16. To verify the robustness of the identification strategy, the model is also estimated using different identification schemes, for instance, by assuming that nontradable and tradable growth can respond contemporaneously to innovations in the interest rate. The main results presented in the paper are robust to these different identification schemes.

  17. The various information criteria available suggest different lag lengths. Results are qualitatively robust to including one up to six lags.

  18. We have tested the sensitivity of our results to a range of hyperparameter values: a prior coefficient value in the range of 0.5–1, an overall tightness parameter in the range of 0.05–0.2 and a lag decay parameter in the range of 0.5–4. Results are qualitatively unaffected.

  19. To check whether our results are driven by individual countries, we re-estimated all regressions while excluding one country at the time and found similar results. Ireland was the only exception, likely due to tradable growth rates that varied from − 20 to 58%.

  20. The Eurostat classification is slightly different from the WIOD classification used in Fig. 2. Specifically, the WIOD contains more detailed information about the openness of sectors, but data is only available until 2011. We therefore match the WIOD classification with the Eurostat classification to categorize the Eurostat sectors in a tradable and nontradable sector, see Table 3 in the “Appendix 1”.

  21. For some sectors this classification is rather arbitrary as the sector contains both tradable and nontradable industries. We re-estimated our BVAR while switching our classifying and find qualitatively similar results.

  22. For robustness we experiment with 10-year government bond yields as those are also available for Greece before 1999. The nominal rates are transformed in ex-ante expected real rates using the 1-year inflation expectations. This assumes that inflation expectations remain constant over the 10-year period. Results, which are not presented here, are similar to the results presented below.

  23. Dynamics may differ between the build-up phase and the period following the sudden bust. In a related analysis, Bobeica et al. (2016) for instance finds a negative relation between domestic demand pressure and exports during busts, but not during booms. We therefore estimated our model over the sub-period 1996Q3–2008Q3. However, in this case, both the response of the tradable and nontradable sector following an interest rate shock is insignificant at the 90% credibility level, most likely due to the reduction in sample size.

  24. The panel-BVAR is estimated using the ECB BEAR-toolbox developed by Dieppe et al. (2016), which builds on the methodology surveyed by Canova and Ciccarelli (2013).

  25. In the paper, to test our theoretical predictions, we focus on the response to interest rate shocks. The full set of responses—which do stretch our identification assumptions—can be found in the Online Appendix. Most impulse response functions have the expected sign. A sudden increase in inflation, for instance, is followed by a deterioration of tradable output, while nontradable output increases. A shock to a country’s current account is associated with a decline in its inflation rate—making the country’s export goods more attractive—and an increase in its tradable output and a decrease in its nontradable output. Vice versa, positive innovations in tradable (nontradable) output are associated with an increase (decrease) in inflation and an improvement (worsening) of current account positions.

  26. The FOC w.r.t. \(K_{t}^{j,Z}(i)\) also contains the second order term \(- \frac{\phi }{2} \left( \frac{I_{t+1}^{j,Z}(i)}{K_{t}^{j,Z}(i)} -\delta \right) ^{2} + \phi \left( \frac{I_{t+1}^{j,Z}(i)}{K_{t}^{j,Z}(i)} -\delta \right) \frac{I_{t+1}^{j,Z}(i)}{K_{t}^{j,Z}(i)}\) which we omit to keep the model tractable.

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Acknowledgements

We would like to thank Jan Marc Berk, Job Boerma, Peter van Els, Paulo Esteves (discussant), Jorien Freriks, Jeroen Hessel, Harry Garretsen, Jakob de Haan, Jan Jacobs, Mark Mink, Ahn Nguyen, Volker Nitsch (discussant), Christiaan Pattipeilohy, Robert Vermeulen, Sweder van Wijnbergen, Zhiwen Zhang (discussant), the anonymous referee, the editor, Mathias Hoffmann, seminar participants at the Dutch central bank, the University of Groningen, the 2016 SOM PhD Conference, the 2017 Royal Economic Society Annual Conference, the ADEMU session at the 2017 Barcelona GSE Summer Forum, the 92nd Annual Conference of the Western Economic Association International and the 2017 CEUS Workshop at the Otto Bisheim School of Management, for valuable comments and suggestions.

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    Niels Gilbert & Sebastiaan Pool

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  1. Niels Gilbert
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Correspondence to Niels Gilbert.

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Appendix 1

Appendix 1

1.1 1.1. Sectoral dependence on domestic demand

See Fig. 12 and Table 3.

Fig. 12
figure 12

Share of value added from domestic demand in the euro area. The red-bar sectors sum to a nontradable sector that produces 33% of total euro area output and the red- and yellow-bar sectors sum to a nontradable sector that produces 50% of total euro area output. Source: own calculations using WIOD, release 2013 (Timmer et al. 2015)

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Table 3 WIOD, EUROSTAT and KLEMS classification and mapping
Full size table

1.2 1.2. Households problem

Household maximization problem:

$$\begin{aligned}&\max { \sum _{v=0}^{\infty } (\beta ^{j})^{v}\left[ \log {(C_{t}^{j,N})^{\eta }(C_{t}^{j,T})^{1-\eta }} - \frac{\theta ( L_{t}^{j})^{1+\sigma _{l}}}{{1+\sigma _{l}}} \right] } \quad {\text {s.t.}} \nonumber \\&\quad \sum _{j}^{j'}B_{t}^{j}+ C_{t}^{j,T} + P_{t}^{j,N}C_{t}^{j,N} = \sum _{j}^{j'}(1+r_{t-1}^{j})B_{t-1}^{j}+ \pi _{t}^{j,N}+\pi _{t}^{j,T}+L_{t}^{j}W_{t}^{j}. \end{aligned}$$
(16)

Households maximize their utility by choosing both consumption goods, labor supply, money holding and bond holdings, subject to the budget constraint and a no-Ponzi condition. The FOCs are:

$$\begin{aligned} \frac{1-\eta }{C_{t}^{j,T}}&= \lambda ^{h}_{t}, \end{aligned}$$
(17)
$$\begin{aligned} \frac{\eta }{C_{t}^{j,N}}&= P_{t}^{j,N}\lambda ^{h}_{t}, \end{aligned}$$
(18)
$$\begin{aligned} \lambda ^{h}_{t}&= \lambda ^{h}_{t+1}(1+r_{t}^{j})\beta ^{j}, \end{aligned}$$
(19)
$$\begin{aligned} \theta ( L_{t}^{j})^{\sigma _{l}}&= W_{t}^{j} \lambda ^{h}_{t}, \end{aligned}$$
(20)

where \(\lambda ^{h}_{t}\) denotes the households’ Lagrangian multiplier. Using the FOC for the tradable consumption good (17), \(C_{t}^{j,T}\), to substitute the Lagrangian multiplier out gives:

$$\begin{aligned} C_{t}^{j,T}&= \frac{C_{t+1}^{j,T}}{(1+r_{t}^{j})\beta ^{j}} , \end{aligned}$$
(21)
$$\begin{aligned} \frac{1-\eta }{C_{t}^{j,T}}&= \frac{\eta }{C_{t}^{j,N}P_{t}^{j,N}}, \end{aligned}$$
(22)
$$\begin{aligned} C_{t}^{j,T}&= \frac{1-\eta }{\theta } \frac{W_{t}^{j}}{( L_{t}^{j})^{\sigma _{l}}}. \end{aligned}$$
(23)

1.3 1.3. Firms

Retailers are perfectly competitive. We therefore consider a representative retailer which buys input \(y_{t}^{j,Z}(i)\) from intermediate firm i and produces output \(Y_{t}^{j,Z}\) according the following aggregator function:

$$\begin{aligned} Y_{t}^{j,Z}=\left[ \int _{0}^{1}y_{t}^{j,Z}(i)^{(\mu ^{j,Z}-1)/\mu ^{j,Z}}di\right] ^{\mu ^{j,Z}/(\mu ^{j,Z}-1)}. \end{aligned}$$
(24)

The retailer has a budget constraint which is denoted by: \(P_{t}^{j,Z}Y_{t}^{j,Z}=\int _{0}^{1}p_{t}^{j,Z}(i)y_{t}^{j,Z}(i)di\). Retailers minimize their cost subject to their production function:

$$\begin{aligned} {\mathcal {L}}_{t}^{j}=\int _{0}^{1}p_{t}^{j,Z}(i)y_{t}^{j,Z}(i)di-\lambda _{t}^{r}\Bigg ( Y_{t}^{j,Z}-\left[ \int _{0}^{1}y_{t}^{j,Z}(i)^{(\mu ^{j,Z}-1)/\mu ^{j,Z}}di\right] ^{\mu ^{j,Z}/(\mu ^{j,Z}-1)}\Bigg ), \end{aligned}$$
(25)

where \(\lambda _{t}^{r}\) is the retailer’s marginal cost of producing an extra unit of final output. Dividing the FOC w.r.t. to production input \(y_{t}^{j,Z}(i)\) of firm i and production input \(y_{t}^{j,Z}(i')\) of firm \(i'\) gives the relative pricing equation:

$$\begin{aligned} y_{t}^{j,Z}(i)=y_{t}^{j,Z}(i')\Bigg (\frac{p_{t}^{j,Z}(i)}{p_{t}^{j,Z}(i')}\Bigg )^{-\mu ^{j,Z}}. \end{aligned}$$
(26)

If we combine the budget identity \(P_{t}^{j,Z}Y_{t}^{j,Z}=\int _{0}^{1}p_{t}^{j,Z}(i)y_{t}^{j,Z}(i)di\) and aggregator function (24) and substitute subsequently the relative pricing equation (26) to solve for \(P_{t}^{j,Z}\), we obtain:

$$\begin{aligned} P_{t}^{j,Z}=&\left[ \int _{0}^{1}p_{t}^{j,Z}(i)^{1-\mu ^{j,Z}}di\right] ^{1/1-\mu ^{j,Z}} . \end{aligned}$$
(27)

We can substitute (27), together with the relative pricing equation (26) for \(y_{t}^{j,Z}(i)\), back in the budget identity to obtain the retailer’s demand for intermediate product \(y_{t}^{j,Z}(i)\):

$$\begin{aligned} y_{t}^{j,Z}(i)=\left[ \frac{P_{t}^{j,Z}}{p_{t}^{j,Z}(i)}\right] ^{\mu ^{j,Z}}Y_{t}^{j,Z} . \end{aligned}$$
(28)

Intermediary firms maximize the discounted value of future cash flows:

$$\begin{aligned}&\max _{I_{t}(i),K_{t}(i),L_{t}(i)}{V_{0}^{j,Z}(i) = E_{0} \sum ^{\infty }_{t=0}}\left( \frac{1}{1+r_{t}}\right) \left( y_{t}^{j,Z}(i) - W_{t}^{j}L_{t}^{j,Z}(i) -I_{t}(i) - \frac{\phi }{2} \left( \frac{I_{t}^{j,Z}(i)}{K_{t-1}^{j,Z}(i)} -\delta \right) ^{2}K_{t-1}^{j,Z}(i)\right) \end{aligned}$$
(29)

subject to the capital accumulation identity:

$$\begin{aligned} K_{t}^{j,Z}(i) = I_{t}^{j,Z}(i) + (1-\delta ) K_{t-1}^{j,Z}(i) \end{aligned}$$
(30)

and subject to the production function:

$$\begin{aligned} y_{t}^{j,Z}(i) = A_{t}^{j,Z}(K_{t-1}^{j,Z}(i))^{1-\alpha ^{j}}(L_{t}^{j,Z}(i))^{\alpha ^{j}} \end{aligned}$$
(31)

We denote the Lagrangian multipliers by \(q_{t}^{j,Z}\) for the capital accumulation identity and \(\lambda _{t}^{j,Z}\) for the production function, respectively. The FOC w.r.t. \(L_{t}^{j,Z}(i)\) is represented by:

$$\begin{aligned} W_{t}^{j}=\lambda _{t}^{j,Z}\frac{\alpha ^{j}y_{t}^{j,Z}(i)}{L_{t}^{j,Z}(i)}, \end{aligned}$$
(32)

The FOC w.r.t. investment \(I_{t}^{j,Z}(i)\) is represented by:

$$\begin{aligned} q_{t}^{j,Z} = 1+\phi \left( \frac{I_{t}^{j,Z}(i)}{K_{t-1}^{j,Z}(i)}-\delta \right) , \end{aligned}$$
(33)

where \(\lambda _{t}^{j,Z}\) is the Lagrangian multiplier representing the intermediate firms’ marginal costs of producing an additional unit of output. The FOC w.r.t. \(K_{t}^{j,Z}(i)\) is represented by:

$$\begin{aligned} q_{t} = E_{t} \left( \frac{1}{1+r_{t+1}}\right) \left( \lambda _{t+1}^{j,Z} \frac{(1-\alpha ^{j})y_{t+1}^{j,Z}(i)}{K_{t}^{j,Z}(i)} + (1-\delta )q_{t+1} \right) , \end{aligned}$$
(34)

where \(q_{t}^{j,Z}\) is the Lagrangian multiplier of intermediate firms with respect to accumulating an additional unit of capital.Footnote 26 Using the FOCs in the production function gives the expression for marginal costs, \(\lambda _{t}^{j,Z}\), which is the same for all intermediate firms:

$$\begin{aligned} \lambda _{t}^{j,Z}=\frac{1}{A_{t}^{j,Z}}\Bigg (\frac{(1+r_{t}^{j})q_{t}^{j,Z}+(1-\delta )q_{t-1}^{j,Z}}{(1-\alpha ^{j})}\Bigg )^{(1-\alpha ^{j})}\Bigg (\frac{W_{t}^{j}}{\alpha ^{j}}\Bigg )^{\alpha ^{j}}. \end{aligned}$$
(35)

As retailers face imperfect substitutability between intermediate inputs, intermediate firms have some market power and can set their prices as a markup over their marginal costs \(\lambda _{t}^{j,Z}\). Intermediary firms maximize their profits w.r.t. prices:

$$\begin{aligned} \pi _{t}^{j,Z}(i)=[p_{t}^{j,Z}(i)-\lambda _{t}^{j,Z}(i)]y_{t}^{j,Z}(i). \end{aligned}$$
(36)

The FOCs w.r.t. \(p_{t}^{j,Z}(i)\) after we have substituted demand for \(y_{t}^{j,Z}(i)\) (28) and solving for \(p_{t}^{j,Z}(i)\) gives:

$$\begin{aligned} p_{t}^{j,Z}(i)=\Bigg (\frac{\mu ^{j,Z}}{\mu ^{j,Z}-1}\Bigg )\lambda _{t}^{j,Z}. \end{aligned}$$
(37)

Hence, intermediate firms set prices as a markup over their marginal costs. We can subsequently use (24) and (27) to aggregate over all firms and rewrite (6), (32) and (34) in terms of aggregate output \(Y_{t}^{j,Z}\) and aggregate prices \(P_{t}^{j,Z}\).

1.4 1.4. Including the rest of the world

The RoW economy is set up the same way as the Northern and Southern region, but has its own (floating) exchange rate. As before, the various regions are denoted by superscript j, with \(j \in \{n, s, r\}\).

Prior to monetary integration, we assume the Rest of the World to be connected to the Northern part of the euro area via an UIP:

$$\begin{aligned} 1+ r_{f,t}^{n} = (1+ r_{f,t}^{r})\frac{E_{t+1}^{r, n}}{E_{t}^{r, n}}, \end{aligned}$$
(38)

where \(E_{t}^{r, n}\) is the nominal exchange rate between the rest of the world and the Northern part of the euro area (expressed as the price of one unit of RoW currency in units of region n currency). As in the 2-region version of the model, Northern and Southern currencies are pegged, with the UIP between North and South given by:

$$\begin{aligned} r_{f,t}^{n} + \omega = r_{f,t}^{s}. \end{aligned}$$
(39)

As such, in the above setup Southern Europe pays a risk premium vis-à-vis both the Northern part of Europe and the rest of the world that can be easiest thought of as an exchange rate risk premium. Following monetary integration, as the peg is exchanged for a more-difficult-to-reverse common currency, this premium disappears.

The Law of One Price is assumed to hold both within Europe, as between Europe and the rest of the world:

$$\begin{aligned} p_{t}^{n}= E_{t}^{r, n}p_{t}^{r}. \end{aligned}$$
(40)

World equilibrium in the market for financial assets is now given by:

$$\begin{aligned} NFA_{t}^{n} + NFA_{t}^{s}+ E_{t}^{r,n}NFA_{t}^{r} =0, \end{aligned}$$
(41)

where \(NFA_{t}^{j}\) represents the net financial assets held by region j denominated in domestic currency.

We set the weight of leisure in RoW to 0.009, implying that the union is responsible for 23% of global output, and RoW for 77%, in line with Gomes et al. (2012). In terms of other parameters, such as the degree of competition in the nontradable sector, the Rest of the World mimics the Northern part of Europe (see Table 4).

Table 4 Calibrated parameters—RoW
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1.5 1.5. Sensitivity to degree of competition in southern NT sector

See Fig. 13.

Fig. 13
figure 13

Impact of monetary integration on relative sectoral sizes in South, for different values of the NT markup in South. This figure illustrates the effects of monetary integration on the relative sectoral size in South, \(\frac{Y_{t}^{s,N}}{Y_{t}^{s,T}}\), for different values of \(\mu ^{s,N}\), in the 2-region version of the model. See Sect. 5.1

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1.6 1.6. Reaction to a sudden increase in the elasticity of interest rates to debt levels

See Fig. 14.

Fig. 14
figure 14

Reaction to a sudden increase in the elasticity of interest rates to debt levels. Figure illustrates the effects of a permanent increase in the debt-elasticity of interest rates in the 3-region version of the model. Starting point of the simulations is the post-monetary integration steady state

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1.7 1.7. Empirics

See Tables 5, 6 and Figs. 15, 16.

Table 5 Descriptive statistics
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Table 6 Quarterly data tradable and nontradable definition
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Fig. 15
figure 15

Log-level impulse response functions following a shock in the real interest rate in EMU countries. The black lines represent the median response to a real interest rate shock estimated in log-levels over the time period 1996Q3–2017Q3 using a Bayesian panel-VAR with 4 lags and a Cholesky decomposition (ordering as in Eq. 15). Shaded areas denote 90% credibility intervals which are generated by drawing 50,000 draws from the posterior distribution of which 40,000 draws are discarded as burn-in iterations. Horizontal axes specify quarters. Vertical axes denote percent point deviations from average euro area growth, ratio or rate

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Fig. 16
figure 16

Log-level and cumulative growth rate impulse response functions following a shock in the real interest rate. The black (dotted) lines represent the median cumulative (log-level) response to a real interest rate shock estimated over the time period 1996Q3–2017Q3 using a Bayesian panel-VAR with 4 lags and a Cholesky decomposition (ordering as in Eq. 15). Horizontal axes specify quarters. Vertical axes denote percent point deviations from average euro area growth, ratio or rate

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Gilbert, N., Pool, S. Sectoral allocation and macroeconomic imbalances in EMU. Rev World Econ 156, 945–984 (2020). https://doi.org/10.1007/s10290-020-00388-w

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