Some empirical work

The last post tried to relate the discussion in road traffic and other traffic networks to the current debate in macroeconomics. It has found that the emphasize on the local origins or systematic traffic congestion is matched by the shift of economists' attention from looking at aggregate shocks to considering idiosyncratic sectoral shocks when trying to explain the volatility and sudden starts and stops of the business cycle.

I have argued that some insights that the discussion of road traffic inefficiencies yielded might both support and maybe even enhance the standpoint that academics like Acemoglu or Gabaix take. Local digestions, i.e. idiosyncratic sectoral shocks, are inherent to the system road traffic, due to our imperfect anticipation of other drivers' behaviour. Likewise, most papers that try to argue that sectoral shocks might lead to aggregate fluctuations assume that these idiosyncratic shocks come about with equal probability in every sector. What I want to argue is that these sectoral shocks do only spread through the input-putput supply network if other sectors are not able to substitute away from the sector that experienced the shock (ie change routes as they see a congestion coming up) or if firms that are sufficiently close connected to the sector act sufficiently unccoperative. In other words, countries where sectors are heavily dependent on smooth supply schedules from closely connected sectors should experience higher levels of fluctuations in the business cycle, ie higher GDP volatility.

In order to support this, it might be interesting to see whether one can find some empirical evidence for the argument that the flexibility of a sector's production function across all sectors (ie how volatile the economy as a whole is to sectoral supply shocks)  and intersectoral cooperation are related to GDP volatility.

In order to do so, one will need measures that allow to compare this asymmetry in input-output networks and intersectoral ooperation across countries.

The previous post already hinted at the importance of the notion of network centrality when trying to assess a sector's potential ability to substitute away from certain inputs. In their paper "Vertex centralities in Input-Output Networks reveal the Structure of Modern Economies" (2011) Florian Bloechl, Fabian Theis, Fernando Vega-Redondo and Eric Fisher suggest two possible ways of measuring vertex centralities in input-output networks. I will only focus on Random Walk Centrality, as this seems to be the more relevant measure for the purposes of this project.

Based on Freeman's closeness centrality, which is defined as the inverse of the mean geodesic distance from all nodes to a particular one, the authors define random walk centrality to be a generalization of this measure that allows its application to input-output tables.

The idiosyncratic shocks are assumed to be supply shocks that cloesely connected sectors experience. These supply shocks flow through the network of intermediate inputs. The pattern of this flow is modelled as a random walk. A high random walk centrality of a sector therefore corresponds to the idea that a sector is sensitive to supply conditions anywhere in the economy, ie that he is volatile to idiosyncratic shocks in many other sectors in the economy.

The authors provide codes to obtain the sectoral random walk centralities for a given input-output network. Data on these networks is provded by the OECD and the results are comparable across countries. I would like to argue that the simple mean across a country's sectoral random walk centralities might provide a decent measure for the asymmetry in a country's input output network. The higher the average random walk centrality of a given network, the more sensitive an economy is to idiosyncratic sectoral shocks.

The OECD input-output tables are most reliably available for the year 2000. I therefore suggest to consider the standard deviation from trend for a given country from 1995 to 2005 as a measure of a country's GDP volatility. I obtained this data using the OECD quarterly national accounts and using the standard procedure to obtain the standard deviation from trend (ie the applying  the hp-filter on the logged variables and taking the standard deviation of the cycle).

I yet have some issues finding an appropriate measure that allows to quantify levels of intersectoral cooperation across country's. The World Bank's corruption index might be an option, yet does not fully capture the concept of intersectoral cooperation. It might be possible that the hypothesis needs to be modified in order to allow for an empirical test of the predictions.

For now, I have carried out a simple OLS on the model:
volatility=constant+a*asymmetry+error

The first results are promising , yet far from being ready to be published as more data will need to be obtained in order to allow for a more sophisticated model.