Altomonte, C. & Aguilante, T. (2014) “The ‘dos and don’ts’ of a growth-friendly policy mix for the Euro area – combining monetary, fiscal and structural measures at the union level and at the national level“, Bruegel Think Tank, 01 October.
When looking at possible ways out of the euro area crisis, there is a growing consensus that it will require “a policy mix that combines monetary, fiscal and structural measures at the union level and at the national level”, as Mr. Draghi recently put it. However, stipulating the details of this policy mix is far more controversial. On the monetary side, there is a debate on the extent to which the TLTRO operations of the ECB would achieve the desired goal of reactivating the credit flow in the euro area (Claeys, 2014; Merler, 2014). Similarly, it is not clear whether the ECB should push its expansionary monetary policy into the realm of ‘non-conventional measures’ to fight the risks of deflation (Claeys et al., 2014; Altomonte and Bussoli, 2014). On the fiscal / structural side, the status of the debate is in even more dire straits. No clear consensus exists on the direction of the fiscal stance, often summarized through the ‘flexibility vs. austerity’ controversy; at the same time the implementation of the national reforms’ agenda keeps facing many internal political obstacles, especially at a time of stagnation and high unemployment.
And yet, leaving aside the (relatively more mature) monetary debate outlined above, recent results from a research line on competitiveness, based on previously unavailable micro-level data (e.g. EFIGE, available here, and the ECB CompNet), allow us to point to some clear ‘dos’ and ‘don’ts’ when looking at the fiscal/structural side of a growth-friendly policy mix for the euro area.
To grasp this idea, think at the 80-20 rule, in which 80 per cent of a given phenomenon (e.g. the total number of people living in cities in the world, or the total exports of a country) is explained by just 20 per cent of the concerned observational units (e.g. the top 20% largest cities or exporting firms). In a ‘normal’ distribution these figures would be 50-50.
The key starting point of this line of research is the recognition that competitiveness is essentially a firm-level phenomenon. Often, competitiveness policies are designed at sector or country level, but targeting the sector or country means targeting the “average firm” of a given sector/country. The idea is that what is good for the average firm is good for all the firms. Nevertheless, this might reveal itself to be highly problematic as the “average firm” does not exist; rather, firms are very heterogeneous in their performance. Indeed, like the length of rivers in the world, or the size of cities, where it is possible to observe a large number of relatively short rivers (or small cities), and a few very long rivers (or very large cities), firm performance across countries and industries is typically characterized by many relatively ‘bad’ firms performing below the average, but also a certain number (although less numerous) of particularly good firms. Technically, we can thus claim that firm level data on a given performance index (e.g. productivity) are typically characterized by a distribution ‘skewed to the right’, i.e. what is known as a ‘Pareto’ distribution, versus an assumed symmetric normal distribution (see Altomonte et al., 2011 for a detailed discussion, and Altomonte et al., 2012 for some evidence from EFIGE data).
Relevant posts:
- Boughton, J. (2014) “Restoring financial stability with economic growth“, VoxEU Organisation, 15 September.
- De Grauwe, P. (2014) “The wrong medicine, The Eurozone’s remedies against sluggish growth rates aren’t working. The problem: a misdiagnosis of the illness“, The European Magazine, 26 August.
- Petersen, Τ. (2014) “20 years of the European single market: growth effects of EU integration“, Policy Brief 2014/02, Future Social Market Economy, Bertelsmann Stiftung, 29 July.