Trade, competitiveness and investment: an empirical assessment

ABSTRACT

The Eurozone crisis has exposed several weaknesses of the European Monetary Union economies. This paper aims to assess the impact on external competitiveness of an expansionary capital stock policy that could contribute to reduce the trade balance asymmetries within the EU and help European exporters to recover their competitive role in international markets. A policy action to increase capital stock accumulation through investment in selected European countries could generate a double dividend: increasing both price and nonprice competitiveness, so stimulating their competitive position as exporters, and consolidating the growth path of EU economy. The analysis employs a bilateral trade model built at INFORUM with several distinguishing characteristics: a comprehensive bilateral data set, econometric estimation of key parameters, and emphasis on sectoral details. Our findings show that a capital stock increase is effective in narrowing trade imbalances within EU. Heterogeneous effects are estimated for commodities in China and the US.

KEYWORDS:

• Bilateral trade
• multi-sectoral modelling
• EU competitiveness
• policy simulation

Acknowledgements

We would like to thank Dr Douglas E. Nyhus for his generous assistance on previous versions of the BTM. His experience has been invaluable for the work presented here. We also benefited from the help and encouragement of Dr Douglas S. Meade who has supported this project since the beginning. The usual disclaimers apply.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Rossella Bardazzi http://orcid.org/0000-0002-4495-8945

Notes

1 The EC is an institution of the European Union that is responsible for proposing legislation, implementing decisions, upholding EU treaties and managing the day-to-day business of the EU. The EU is a union of 28 European member countries (EU-28).

2 Either parameters are drawn from the existing literature or are derived from exogenous hypotheses.

3 The EMU involves the coordination of economic and fiscal policies, a common monetary policy, and a common currency, the euro. For this reason, the EMU is also commonly called the ‘Eurozone’ or the Euro Area (EA). Presently 19 EU member states belong to EMU.

4 ICOR is the ratio of the growth rate of the capital stock relative to the GDP growth rate: the lower this index reading is, the more efficient is the investment.

5 Especially so in manufacturing, where the degree of specialization was observed to be more than double that overall.

6 The original EA-11 (Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg Netherlands, Portugal and Spain) established in January 1999, plus Greece, which was added two years later.

7 We consider the dynamics of equipment investment alone (thus avoiding the problem of investment bubbles, a typical feature of residential investments) in the ten years before the financial crisis (1998–2007). Using this average rate of growth, we estimate a ‘what if’ path of investment in the period 2008–2015; we obtain a more-intense capital accumulation process with respect to the one actually experienced. The cumulative difference between the actual and hypothetical dynamics of investment is then calculated to quantify the ‘lost investment’.

8 The EC has set out an approach based on three pillars: structural reforms, fiscal responsibility and investment (EC, 2015b).

9 This is defined as a moving average of domestic market prices for the last three years. The price is converted from the prevailing national currency to US dollars using annual average exchange rates.

10 The commodity-specific world price is defined as a fixed-weighted average of the effective prices in all exporting countries: $p_{wjt}=\sum _i{s}_{ij0}p$, where the trade shares for the base year are assumed to sum to unity to satisfy the homogeneity condition.

11 The world average capital stock, k_wjt, is defined as a fixed-weighted average of capital stocks in all exporting countries: $k_{wjt}=\sum _i{s}_{ij0}{k}_{eit}$, where the same constraint is applied to the fixed weights s_ij0. In any forecast period each trade share must, of course, be nonnegative, and the sum of shares from all the sources in a given market must add up to 1 (i.e. Σ_is_ij = 1 for all j and t). The non-negativity condition is automatically satisfied in the logarithmic functional form but the adding-up condition is not. Therefore, a procedure is needed to modify the forecasted trade shares so that the condition is met. Estimates of all the n shares are made separately and subsequently adjusted to meet the adding-up condition. In this way, the forecast shares in each market will satisfy both the adding-up and the non-negativity conditions. In scaling the forecast shares to meet the adding-up condition in each import market, those with the better fits will require less adjustment than those with poorer fits. One way to tackle the problem is to use the standard errors of the estimated equations as weights. Thus, the adding-up condition in each import market is imposed by distributing the residuals in proportion to the standard error of each estimated import-share equation.

12 This model has been further developed by Wang (2001) to include bilateral tariff rates in the import-share equations by modifying the relative prices. This feature allows to simulate trade policies with commodity and country-specific tariffs.

13 Considering just the countries explicitly included in the international system of models.

14 We consider the imports of the first 90 reporting countries in UN Comtrade ranked by total imports in 2012, which account for more than 99% of total world imports (Bardazzi and Ghezzi, 2015).

15 Capital stocks by industry are built from investments according to the unit bucket method (Almon, 2014). This method applies distributed lags to compute stocks from flows computing capital replacement as a lagged effect of investment (see the Appendix for more details).

16 A threshold value for defining an import share as significant is set at 0.01%.

17 Pioneering work by Orcutt (1950) criticizes the implications of aggregate, low trade elasticities as these are unreliable for prediction and lead to underestimate the effects of policies. Imbs and Mejean (2015) argue that the discrepancy between macro and micro-based elasticities depends on a heterogeneity bias: macroeconomic data impose a homogeneity constraint on the estimated parameters that does not reflect true sectoral differences.

18 Imbs and Mejean (2010) adapt the methodology to a multicountry framework with alternative calibration choices. Felettigh and Federico (2010) use several data sources and estimation methods, while Corbo and Osbat (2013) use bootstrap methods to obtain more robust measures.

19 We assume that there are no feedback effects from the national models in order to measure only the direct effects of the policy. Therefore, at the national level, we assume that there is no change in wages, in sectoral import rates of growth, and in the exchange rate.

20 Our policy scenario assumes that an increase of investment at the country level does not change the national rate of growth of imports introduced in the baseline scenario.

21 The structural trade balance is calculated by rectifying the actual total imports with the output gap estimated for each country. We assume an elasticity between imports and potential output equal to 1 and we correct the actual imports to take into account the distance between the effective aggregate demand and the long-run demand consistent with the supply side features of the economy. According to the structural trade balance, there are countries in surplus such as Germany, Austria, and Belgium, and countries in deficit such as France, Italy and Spain.

22 See Mourougane et al. (2016).

23 In order to evaluate the overall outcome, the feedback effects from the national models after the first round effect should also be considered.

24 We assume that wages are constant. Indeed, following the increase in productivity there could be an increase in wages and therefore in personal consumption, with a secondary effect on the current account balances.

25 There might be changes also in trade shares of other competitors included in the BTM but not shown in the graphs.

26 Parameters are estimated for each trade-share equation, but to meet the ‘exhaustiveness’ condition, which requires the sum of the shares to be one, a constraint on the evolution of each single share in a specific market for a specific sector is implicitly required. In the case of a sum larger or smaller than one, a ‘spreader’ distributes the difference proportionally among the shares. Therefore, the equations give just the initial estimate of import shares and then a balancing procedure is used to obtain the final estimate.

27 The top positions in this ranking are covered by petroleum, electrical machinery, road vehicles and telecommunications.

28 The world market shares of EU exports in these sectors are 38% (organic chemicals, metalworking machinery, power-generating machinery), 34% (iron and steel) and 42% (specialized machinery).

29 Although we have run equations for each European country in the BTM, here we have estimated import shares for the EU as a whole.

30 Detailed country-specific estimated parameters are available from the authors upon request.