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Current Account Balances’ Divergence in the Euro Area: An Appraisal of the Underlying Forces

  • Emmanuelle Faure , Carl Grekou and Valérie Mignon EMAIL logo
Published/Copyright: October 30, 2023
The B.E. Journal of Macroeconomics
From the journal The B.E. Journal of Macroeconomics Volume 24 Issue 1

Abstract

This paper revisits the crucial issue of current account imbalances and focuses on the determinants of their gaps between eurozone Member States. We conduct robust estimations of the current account balances for a panel of ten founding euro area economies and construct a measure that allows us to diagnose why some countries have started to diverge from the eurozone mean in the last two decades. Our findings show evidence of remaining differences in countries’ economic development, meaning that real macroeconomic convergence has failed in the zone. Price and cost competitiveness, as well as fiscal balances, have also participated in this growing macroeconomic divergence. Overall, while the European authorities cannot influence the part of the current account gaps due to demographic factors, the role of fiscal redistribution and investment at the euro area level could help achieve macroeconomic convergence and thus reduce current accounts’ divergence in the zone.

Keywords: current account; global imbalances; eurozone
JEL Classification: F32; O52; C33
Corresponding author: Valérie Mignon, EconomiX-CNRS, University of Paris Nanterre, 200 Avenue de la République, 92001 Nanterre Cedex, France; and CEPII, Paris, France, E-mail:

Acknowledgments

We would like to thank the editor and two anonymous referees, as well as Thomas Chuffart, Thomas Grjebine, Francisco Serranito, and all the participants at the CEPII and EconomiX-CNRS seminars for their helpful comments and suggestions.

Appendix A: Data

Table A.1:

Data information.

Variables Name Relative to trade partners Construction Data source
Aging growth rate Aging ✓ Difference between the old dependency ratio in t + 20 and the old dependency ratio in t UNCTADstat
Capital control (lagged) l.KAOPEN ✓ Chinn & Ito
Currency misalignments Mis Real effective exchange rate misalignments EQCHANGE (CEPII)
Current account CA Current account balance expressed in percentage of GDP WEO (IMF)
Expected GDP growth rate Expected_GDPg ✓ GDP growth rate in t + 3 WEO (IMF)
Foreign direct investment (lagged) l.FDI ✓ Growth of foreign direct investment (net inflows) expressed in percentage of GDP WDI (World Bank)
Government balance (lagged) l.Gov_Bal ✓ Cyclically adjusted government balance expressed as a percentage of GDP WEO (IMF)
Market performance of exports on exports weighted imports d.MPerf_Exp ✓ AMECO
Net foreign asset position (lagged) l.NFA Net foreign asset position expressed in percentage of GDP Lane & Milesi-Ferretti
Old dependency ratio OADepRatio ✓ Ratio of the population exceeded 64 years old and the population between 15 and 64 UNCTADstat
Output Gap Output_gap ✓ Output gap expressed as a percentage of GDP (GDP per capita in PPP, HP filtered) WEO (IMF)
Population growth rate popg ✓ Population growth rate UNCTADstat
Private sector debt (lagged) l.d.PrvD ✓ Growth of the private sector debt expressed in GDP WDI (World Bank)
Relative income per capita in PPP (lagged) l.GDP_PC ✓ Log of the GDP per capita in PPP WEO (IMF)
Tariff rate, applied, simple mean, all products Tariffs ✓ WDI (World Bank)
Terms of trade (lagged) l.tot ✓ Lagged ratio of export price to import price UNCTADstat
Total factor productivity (lagged) l.CTFP ✓ Total factor productivity at current PPPs Penn World Table
Trade openness (lagged) l.d.Trade ✓ Sum of imports and exports divided by GDP WDI (World Bank)
Unit labor cost (lagged) l.d.ULC ✓ Nominal unit labor cost in log difference AMECO
VIX d.VIX Chicago VIX index CBOE

  1. Time-varying weights have been used to compute variables in effective terms against 186 trading partners (source: EQCHANGE, CEPII).

Table B.1:

Detailed average contributions: deviations from the eurozone average CA (2000–2018).

Austria Belgium Finland France Germany Greece Italy Netherlands Portugal Spain
Competitiveness −0.93 (0.41) −0.41 (0.48) 0.31 (0.48) 0.44 (0.25) 0.76 (0.50) −3.08 (0.56) −1.02 (0.50) 0.84 (0.36) −2.15 (0.39) −0.48 (0.38)
CTFP −0.66 (0.16) −0.21 (0.14) −0.01 (0.22) 0.52 (0.17) 0.34 (0.34) −2.33 (0.56) −0.79 (0.32) 0.93 (0.28) −1.91 (0.20) −0.20 (0.15)
Mis −0.26 (0.30) −0.16 (0.29) 0.31 (0.27) −0.04 (0.07) 0.35 (0.29) −0.80 (0.36) −0.18 (0.20) −0.03 (0.24) −0.21 (0.14) −0.27 (0.30)
ULC −0.01 (0.16) −0.05 (0.22) 0.02 (0.28) −0.03 (0.11) 0.08 (0.25) 0.05 (0.53) −0.05 (0.16) −0.06 (0.19) −0.03 (0.29) −0.01 (0.34)
Conjuncture 0.04 (0.46) −0.01 (0.45) −0.03 (0.73) −0.03 (0.31) 0.04 (0.64) 0.07 (2.47) −0.01 (0.43) 0.04 (0.57) 0.12 (0.55) −0.10 (1.00)
Demographics −1.79 (0.17) −2.10 (0.28) 0.58 (1.79) −0.53 (0.78) 0.16 (0.45) 1.61 (0.61) 0.57 (0.82) 1.17 (0.47) 1.82 (0.39) −0.48 (2.24)
Aging −0.89 (0.28) −1.61 (0.19) 0.79 (1.73) −0.25 (0.90) −0.32 (0.40) 0.33 (0.25) 0.49 (0.58) 1.24 (0.54) 0.83 (0.53) 0.29 (0.89)
Pop growth −0.90 (0.30) −0.49 (0.44) −0.21 (0.31) −0.28 (0.20) 0.48 (0.71) 1.28 (0.76) 0.09 (0.33) −0.08 (0.20) 0.99 (0.42) −0.77 (1.53)
Development 0.22 (0.30) −1.25 (0.64) 0.06 (0.79) −0.82 (0.23) 1.23 (0.53) −3.20 (1.03) 0.03 (0.88) 2.45 (0.40) −5.52 (0.69) −1.33 (0.54)
Financial conditions 0.40 (0.29) 0.80 (0.67) −0.03 (0.31) −0.06 (0.23) 0.07 (0.17) −0.32 (0.91) −0.43 (0.20) 0.64 (0.42) 0.37 (0.24) −0.18 (0.28)
FDI −0.04 (0.17) 0.30 (0.42) −0.01 (0.11) −0.06 (0.03) −0.05 (0.05) −0.10 (0.04) −0.09 (0.03) 0.56 (0.37) 0.00 (0.09) −0.03 (0.05)
KAOPEN 0.47 (0.17) 0.59 (0.34) 0.09 (0.18) 0.09 (0.04) −0.02 (0.09) 0.05 (0.75) −0.30 (0.07) 0.07 (0.12) 0.41 (0.04) −0.12 (0.11)
Private debt −0.03 (0.14) −0.09 (0.33) −0.11 (0.21) −0.09 (0.22) 0.14 (0.17) −0.27 (0.38) −0.04 (0.16) 0.01 (0.21) −0.04 (0.25) −0.03 (0.35)
Fiscal balance 0.10 (0.32) 0.32 (0.40) 0.81 (0.84) −0.28 (0.21) 0.33 (0.53) −1.27 (0.94) −0.14 (0.29) 0.18 (0.34) −0.65 (0.42) −0.16 (1.09)
Welfare state 0.09 (0.36) 0.11 (0.30) −0.03 (0.45) 0.00 (0.26) 0.07 (0.26) −0.18 (0.76) −0.08 (0.21) 0.16 (0.38) −0.24 (0.60) −0.13 (0.59)
Trade policy 0.70 (0.36) 0.38 (0.34) −0.66 (0.64) 0.11 (0.26) 0.27 (0.33) −0.68 (0.73) −0.36 (0.28) 0.07 (0.26) 0.15 (0.49) −0.51 (0.38)
Market perf. 0.01 (0.23) 0.00 (0.37) −0.04 (0.48) 0.01 (0.26) −0.01 (0.21) −0.07 (0.68) 0.03 (0.28) −0.01 (0.33) 0.03 (0.40) −0.02 (0.38)
Tariffs 0.78 (0.18) 0.42 (0.18) −0.49 (0.48) 0.17 (0.11) 0.16 (0.07) −0.68 (0.24) −0.35 (0.12) 0.06 (0.15) 0.15 (0.26) −0.42 (0.14)
Trade −0.09 (0.26) −0.03 (0.28) −0.13 (0.42) −0.07 (0.19) 0.12 (0.18) 0.07 (0.64) −0.05 (0.22) 0.03 (0.29) −0.02 (0.30) −0.06 (0.30)
Others 0.24 (0.20) 1.42 (0.19) 0.41 (0.75) −0.30 (0.28) 0.09 (0.24) −0.60 (0.33) 0.06 (0.13) 0.37 (0.43) −0.47 (0.25) −0.46 (0.51)
NFA −0.01 (0.13) 0.87 (0.18) −0.22 (0.79) −0.02 (0.17) 0.45 (0.23) −1.04 (0.35) −0.21 (0.13) 0.37 (0.49) −1.23 (0.45) −0.91 (0.39)
TOT 0.25 (0.14) 0.55 (0.26) 0.63 (0.17) −0.28 (0.11) −0.35 (0.09) 0.44 (0.55) 0.27 (0.22) 0.00 (0.22) 0.76 (0.27) 0.46 (0.17)
Unexplained 1.76 (1.32) 0.45 (1.10) −0.82 (2.36) 0.29 (0.74) 0.78 (1.98) −0.38 (1.84) −0.33 (1.67) −0.25 (2.22) −0.68 (3.83) −0.56 (2.02)

  1. Entries correspond to the averages of the contributions over the 2000–2018 period. Standard deviations are reported in parentheses. The average euro area CA is computed —on a yearly basis— as the GDP-weighted average of the considered economies’ CA.

Table C.1:

Posterior inclusion probabilities.

Variable Posterior inclusion probability
Model prior
Uniform Fixed Random
Prior 1 Prior 9 Prior 1 Prior 9
Capital_stock 0.1390 0.0064 0.0492 0.0880 0.1194
dChina 0.0294 0.0010 0.0116 0.0084 0.0297
Demographics 0.9594a 0.3927 0.9685a 0.9227a 0.9628a
dlREER 0.0644 0.0018 0.0195 0.0152 0.0581
dMarket_share 0.0370 0.0018 0.0170 0.0176 0.0350
Expected_GDPg 0.7582a 0.3099 0.6341a 0.5808a 0.7458a
Goods_Exp_pgdp 0.2612 0.1694 0.3203 0.3032a 0.2706
Human_Cap 0.2371 0.0308 0.1032 0.1404 0.2058
Insti 0.0579 0.0495 0.0308 0.0296 0.0577
KAOPEN 0.8855a 0.2489 0.8505a 0.8400a 0.8517a
lCTFP 0.9974a 0.2369 0.9933a 0.9874a 0.9972a
ldFDI 0.0446 0.0026 0.0192 0.0131 0.0451
ldPrivDebt 0.9914a 0.9983a 0.9873a 0.9891a 0.9894a
ldULC 0.0786 0.1191a 0.0504 0.0459 0.0743
lGDP_PC 1.0000a 1.0000a 1.0000a 1.0000a 1.0000a
lGDPg 0.0599 0.0064 0.0423 0.0290 0.0612
lGov_Bal 0.9442a 0.9310a 0.9625a 0.9170a 0.9528a
lInflation 0.4564a 0.0088 0.2634 0.2160a 0.4753a
lNFA 0.9494a 0.1526 0.8888a 0.8595a 0.9136a
Manuf 0.0846 0.0336 0.0255 0.0235 0.0828
Mis 0.8175a 0.4070a 0.7595a 0.7360a 0.7664a
MPerf_Exp 0.6946a 0.0340 0.5226a 0.4344a 0.7040a
NIIP 0.1574 0.0137 0.0617 0.0630 0.1514
Output_gap 0.9995a 0.8627a 0.9991a 0.9995a 0.9996a
Quinn 0.0391 0.0076 0.0200 0.0133 0.0369
Remit 0.1493 0.0199 0.1549 0.1581 0.1370
RTFP 0.0566 0.0034 0.0176 0.0210a 0.0571
Tariffs 1.0000a 0.5730a 0.9987a 0.9964a 1.0000a
TOT 0.9797a 0.1405 0.9052a 0.8739a 0.9773a
Trade 0.7766a 0.0472 0.6308a 0.5971a 0.8027a
Welfare_State 0.9981a 0.8951a 0.9899a 0.9850a 0.9953a
Working_Pop 0.0532 0.0010 0.0199 0.0146 0.0513

  1. The dependent variable is the current account (% of GDP). The results are based on 100,000 burns-ins and 200,000 draws. Simulations made using birth-death MCMC sampler. PIPs higher than 0.5 are displayed in bold. “a” over the PIPs indicates that the variable belongs at least to one of the best three models. “d” (resp. “l”) stands for the difference (resp. lag) operator. The variable transformations obey the stationarity and exogeneity exigencies.

Appendix B: Additional Results

Figure B.1: Current accounts: actual and fitted values. The eurozone current account balance is computed as the GDP-weighted average of the countries’ current account balances.
Figure B.1:

Current accounts: actual and fitted values. The eurozone current account balance is computed as the GDP-weighted average of the countries’ current account balances.

Figure B.2: Variables’ evolutions (1997–2018).
Figure B.2:

Variables’ evolutions (1997–2018).

Figure B.2: (continued)
Figure B.2:

(continued)

Figure B.3: Demographic developments.
Figure B.3:

Demographic developments.

Figure B.4: Current accounts: actual values and model-based historical decompositions. The bars indicate the contributions of the different factors (in percent of domestic GDP) to the deviations from the eurozone average CA balances. For a given year, the different contributions sum to the CA balance gap relative to the eurozone average (indicated by the solid black line).
Figure B.4:

Current accounts: actual values and model-based historical decompositions. The bars indicate the contributions of the different factors (in percent of domestic GDP) to the deviations from the eurozone average CA balances. For a given year, the different contributions sum to the CA balance gap relative to the eurozone average (indicated by the solid black line).

Figure B.4: (continued)
Figure B.4:

(continued)

Figure C.1: Model inclusion based on the best 2000 models. The colors reflect the variables’ post mean signs, blue for positive and red for negative.
Figure C.1:

Model inclusion based on the best 2000 models. The colors reflect the variables’ post mean signs, blue for positive and red for negative.

Figure C.2: PIPs’ sensitivity to priors’ choice.
Figure C.2:

PIPs’ sensitivity to priors’ choice.

Figure C.3: Posterior model probabilities.
Figure C.3:

Posterior model probabilities.

Appendix C: Selecting CA Determinants using BMA: Data and Results

This Appendix is devoted to presenting the results obtained from the Bayesian analysis used to select the CA determinants considered in the paper. We begin with a discussion on the data, and present the results.

C.1 The Data

Taking advantage of the vast literature presented in Sections 1 and 2, we consider a set of 32 potential determinants. Accordingly, the set of fundamental variables is relatively trivial.

Regarding the macroeconomic fundamentals, we retain (i) the net foreign asset position (NFA), (ii) the GDP growth (GDPg), (iii) the expected GDP growth (Expected_GDPg), and (iv) the GDP per capita —in PPP terms— relative to trading partners (GDP_PC) —proxying for the Balassa-Samuelson effect.[35] In addition to this standard set of macroeconomic fundamentals, we also select the net international investment position (NIIP). Looking now at structural fundamentals, demographics come first. Rather than considering the usual three variables —i.e., the population growth, the aging rate, and the dependency ratio— we take advantage of the high correlations between the variables and compute a factor —Demographics— summarizing both the structure and the dynamics of the population.[36] To account for the importance and the multidimensionality of trade, we collect different variables: (i) trade openness (Trade), (ii) exports of goods (Goods_Exp_pgdp), (iii) manufacturing value added (Manuf), (iv) market performance of exports of goods and services on export-weighted imports of goods and services (MPerf_Exp), and (v) a measure of global market share (Market_share).

Policy variables are also considered. To account for the effect of fiscal policy, we consider the cyclically adjusted government balances (Gov_Bal). To gain insights into the nature of the spending and the effect in terms of investment-saving behaviors, we collect data on current transfers, tax revenues, and public services expenditures, and summarize those three variables into a factor, Welfare state. The latter is derived relying on factor analysis, drawing on data on current transfers, tax revenues, and public service expenditures. The only factor retained opposes, on the left side, the government tax revenues, and on the right side, the public service expenditures, and current transfers. Hence, the higher the value of the factor, the higher the level of social security/the lower the level of precautionary savings. Further note that considering both the current transfers and public service expenditures —rather than only health expenditures— allows us to cover a broader range of fiscal policies. Along the same lines, the introduction of tax revenues measures the repercussion scale of those policies on households’ disposable income. Overall, our W. state variable accounts for the level of social security or the extent of precautionary savings. To reflect the financial cycles, we consider data on the private debt and foreign direct investment growth rates (dPrivDebt and dFDI, respectively).[37] As is often the case, capital control measures —proxied here by the Quinn and the KAOPEN indices— accompany the aforementioned set of variables.

Turning now to cyclical factors, we retain (i) the output gap (Output_gap), and (ii) the terms of trade (TOT).

Finally, we consider a set of variables that might prove relevant in explaining the differences in the countries’s current account balances: (i) the currency misalignments (Mis), (ii) the institutional quality (Insti), (iii) the applied tariff rates (Tariffs), (iv) the capital stock (Capital_stock), (v) the inflation rate (Inflation), (vi) the remittances outflows (Remit), (vii) the unit labor cost (ULC), (viii) the real effective exchange rate (REER), (ix) a measure of the evolution of China in international trade (China) —to proxy the China shock, and (x) two measures of the total factor productivity (RTFP and CTFP).[38]

C.2 The Results

Table C.1 presents the results of the estimations (the posterior inclusion probabilities) based on a universe of 232 — i.e. 4,294,967,296— possible models. For comparison purposes, we also report results obtained using alternative model priors.[39]

Overall, the BMA analysis identifies 15 robust determinants with posterior inclusion probability (PIP) higher than 0.50. Among these factors, CTFP, Demographics, the GDP per capita, the government balance, the net foreign asset position, the private debt, the output gap, the tariff rates, the terms of trade (TOT), and Welfare_State display very high PIPs. The middle cohort comprises the expected GDP growth, KAOPEN, currency misalignments, market performance (MPerf_Exp), and Trade. However, in addition to these variables, we also retain the inflation rate, which not only appears with a PIP close to the threshold but also generally belongs to the best three models (see Figure C.1).

As a final remark, it is worth recalling that the above exercise does not purport to provide an extensive and up-to-date analysis of the current account determinants for the selected countries. Hence, variables displaying PIPs below our threshold —and so not retained— should not necessarily be interpreted as not having impacts on the current accounts. These low PIPs could, for instance, reflect low discriminating power owing to the low variance between the selected countries.

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Received: 2023-02-22
Accepted: 2023-10-11
Published Online: 2023-10-30

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Advances
  3. Optimal Taxation of Informal Firms: Misreporting Costs and a Tax Reform in Brazil
  4. Initial Beliefs Uncertainty
  5. Credit Resource Misallocation and Macroeconomic Fluctuations in China: From the Perspective of Heterogeneous Financial Frictions
  6. The Bitcoin Premium: A Persistent Puzzle
  7. Contributions
  8. Intermediate Goods–Skill Complementarity
  9. Perfect Competition and Fixed Costs: The Role of the Ownership Structure
  10. Optimal Monetary Policy with Government-Provided Unemployment Benefits
  11. Employment Protection in Dual Labor Markets: Any Amplification of Macroeconomic Shocks?
  12. A Tide that Lifts Some Boats: Assessing the Macroeconomic Effects of EU Enlargement
  13. Current Account Balances’ Divergence in the Euro Area: An Appraisal of the Underlying Forces
  14. Merging Structural and Reduced-Form Models for Forecasting
  15. Trust in Government in a Changing World: Shocks, Tax Evasion, and Economic Growth
  16. The Fiscal Multiplier of Public Investment: The Role of Corporate Balance Sheet
  17. Does Uncertainty Matter for the Fiscal Consolidation and Investment Nexus?
  18. Government Spending Between Active and Passive Monetary Policy: An Invariance Result
  19. A DSGE Model with Government-owned Banks
https://doi.org/10.1515/bejm-2023-0158
Keywords for this article
current account; global imbalances; eurozone

Articles in the same Issue

  1. Frontmatter
  2. Advances
  3. Optimal Taxation of Informal Firms: Misreporting Costs and a Tax Reform in Brazil
  4. Initial Beliefs Uncertainty
  5. Credit Resource Misallocation and Macroeconomic Fluctuations in China: From the Perspective of Heterogeneous Financial Frictions
  6. The Bitcoin Premium: A Persistent Puzzle
  7. Contributions
  8. Intermediate Goods–Skill Complementarity
  9. Perfect Competition and Fixed Costs: The Role of the Ownership Structure
  10. Optimal Monetary Policy with Government-Provided Unemployment Benefits
  11. Employment Protection in Dual Labor Markets: Any Amplification of Macroeconomic Shocks?
  12. A Tide that Lifts Some Boats: Assessing the Macroeconomic Effects of EU Enlargement
  13. Current Account Balances’ Divergence in the Euro Area: An Appraisal of the Underlying Forces
  14. Merging Structural and Reduced-Form Models for Forecasting
  15. Trust in Government in a Changing World: Shocks, Tax Evasion, and Economic Growth
  16. The Fiscal Multiplier of Public Investment: The Role of Corporate Balance Sheet
  17. Does Uncertainty Matter for the Fiscal Consolidation and Investment Nexus?
  18. Government Spending Between Active and Passive Monetary Policy: An Invariance Result
  19. A DSGE Model with Government-owned Banks
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