A new measure of openness

A new way of measuring openness

Jean Imbs, Laurent Pauwels

Contact with foreign shocks is often regarded as highly reliant on foreign trade and measures of openness usually build exclusively on measures of direct trade. This column argues that in an environment of global value chains, concentrating on direct trade provides distorted view of the contact with foreign shocks. It proposes a fresh way of measuring openness which computes the fraction of gross output sold to downstream customers located abroad. This measure finds most sectors to become more open which increased openness is estimated to cause rises in productivity and contagion, without observable effects on growth.

Conventional measures of openness usually build exclusively on measures of direct trade. Either, the full total value of trade is measured by its value added or adequately normalized exports or imports are accustomed to approximate trade costs. And raw exports tend to be filtered to isolate the worthiness added they contain. 1 However in an environment of global value chains, focusing exclusively on direct trade provides distorted view of the contact with foreign shocks (‘openness’). What counts isn’t whether a sector is available to trade, but instead whether its customers are down the worthiness chain. We introduce a way of measuring high order trade, ‘HOT’ for short, that abstracts from direct trade altogether. This presents two advantages: first, we are able to compute precise contact with foreign shocks for activities that trade none of their output directly, most prominently services. Second, we are able to introduce instruments for openness at a rate of aggregation and coverage that’s unprecedented.

For every sector, HOT computes the fraction of gross output sold to downstream customers located across a border. Generally, downstream customers may buy a sector’s output directly, or indirectly from its (direct or indirect) customers. Our innovation is to consider the domestic/foreign status not merely of the direct purchasers of a sector’s output, but also of its indirect purchasers, at second and higher orders. We consider this as an intuitive generalization of the typical method of measuring openness, and a timely one as high-order linkages increasingly cross borders with the advent of global supply chains.

Computing HOT for all sectors in 43 countries reveals a country ranking that’s like the one obtained by other measures: small countries like Luxembourg or Ireland have become open, and large ones like Japan or the united states are closed. 2 Figure 1 depicts the values of HOT for five large economies as time passes, and confirms that Germany is quite open as the US and Japan are closed. The figure also exhibits the dip in world trade experienced soon after the crisis of 2008. Across sectors, however, the conclusions have become different. According to conventional measures, the distribution of openness across sectors is highly skewed: open sectors are usually the exception, even in open countries. That is illustrated in Figure 2 which plots conventional measures of openness across sectors for every country. For example, the median ratio of export to value added across sectors is 0.15 in holland, suggesting that a lot of sectors are actually closed even though holland is an extremely open country. Germany is a good example, with hardly any, very open sectors. Hence, according to find 2, foreign shocks should affect only a minority of sectors, even in very open economies. The world according to HOT is a lot more open typically. This is intuitive: although some sectors usually do not trade directly over the border, supply chains that never cross a border are rare. The distribution of HOT across sectors is more symmetric compared to the alternatives predicated on direct trade. Some sectors are open even in countries that are relatively closed typically. Open countries generally have open sectors over the board, including the ones that are customarily labelled “non-traded.” Overall, foreign exposure is widespread on the globe economy according to HOT. It really is, too, in line with the violent, and almost universal effects COVID-19 had around the world economy.

Figure 1 High Order Trade (HOT) values

Notes: High Order Trade (HOT) is depicted as time passes for five countries and the world. Country values are value added weighted averages of sector level HOT. World HOT is a GDP weighted average of country HOT. Value added is converted in USD at PPP exchange rate.

Figure 2 Dispersion of High Order Trade (HOT), exports in accordance with PPP GDP and trade in value added in accordance with PPP GDP across sectors for every country in 2014.

Note: The mid-point may be the median, the thick segment may be the interquartile range, and the whiskers are extreme values.

Trade in services is hard to measure. One approach is to compute service trade using intermediate trade as reported in input-output tables. 3 Another approach is to compute value added trade for services, but which can be difficult when direct trade is near zero. 4 Figure 3 plots for every sector the distribution of HOT across countries. Normally, services rank at the center of the distribution of sectors: less open than most manufacturing, but a lot more open than numerous others, like construction, property or food. Services are consistently more open according to HOT than measures predicated on direct trade. Actually, some services are being among the most open sectors in a few countries – e.g. IT in India. That is intuitive and plausible in a globalized world where services tend to be sold to domestic exporters.

Figure 3 Dispersion of High Order Trade (HOT) across countries for every sector in 2014.

Note: The mid-point may be the median, the thick segment may be the interquartile range, and the whiskers are extreme values.

Clearly, there are large differences between HOT and its own predecessors, especially across sectors. The question is whether HOT does a more satisfactory job than other measures at capturing the propagation of shocks across borders, which we realize to occur via the supply chain (see Acemoglu et al. 2015). To answer this question, we implement three estimations that are generally found in firm-level data and in country panels (Imbs and Pauwels 2020). We first ask whether a sector’s openness correlates systematically using its productivity. 5 Second, we study whether openness correlates with growth. 6 Third and lastly, we introduce a bilateral version of HOT and have whether it correlates with the synchronization of business cycles at sector level. Once more, that question is rampant in the aggregate and at the firm level. 7

We document systematic positive and significant correlations between HOT, labor productivity, growth, and synchronization at sector level, which is evidence for shocks propagating via the global value chain. Running the same estimation with conventional measures of openness leads to unstable coefficients exhibiting the incorrect sign. Thus, correlates of openness at sector level are in keeping with firm-level (plus some aggregate) evidence when openness is measured by HOT, however, not when it’s measured by some of its predecessors.

However, as trade will not happen in vacuum pressure and as exporters have a tendency to operate in high productivity, high growth environments, this may also explain the correlation between productivity and growth, and HOT. However in this case, causality would run from growth and productivity to openness. Of course, establishing the putative consequences of openness to trade can be an important area of research. To take action, we introduce a musical instrument for HOT at the sector level, as time passes, and for just about any country with input-output data. That is another important improvement of our HOT measure over existing measures of openness, as those are often virtually impossible to instrument at such an even of generality.

Our instrument uses the network structure of HOT: for every sector, we separate the first- from the higher-order links, as first-order links are clearly endogenous to the circumstances of the considered sector. There is little question a sector’s first-order, direct openness could be due to its productivity: a sector trades more over the border if it’s has more high-performing firms. However the fact that downstream sectors themselves are more open is less inclined to be due to upstream productivity: downstream openness is mainly due to downstream productivity. 8

Using these instruments, we set up a significant aftereffect of HOT on productivity and synchronization. But there is absolutely no significant aftereffect of HOT on growth, in keeping with a Ricardian view of trade where openness triggers reallocation, with level effects but no permanent growth consequences.

Our results show that people need a new way of measuring foreign exposure that’s in keeping with the emergence of global value chains and our recent experience with the propagation of COVID-19 shocks. HOT provides such a measure.

Acemoglu, D, U Akcigit and W Kerr (2015), Networks and the Macroeconomy: An Empirical Exploration, Chicago: University of Chicago Press, p 273-335.

Alcalá, F and A Ciccone (2004), “Trade and Productivity”, The Quarterly Journal of Economics 119(2): 613-646.

Antràs, P and D Chor (2013), “Organizing the global value chain”, Econometrica 81(6): 2127-2204.

Antràs, P, D Chor, T Fally and R Hillberry (2012), “Measuring the upstreamness of production and trade flows”, American Economic Review: Papers & Proceedings 102(3): 412-16.

Baldwin, R, R Forslid, P Martin and F Robert-Nicoud (2003), “The core-periphery model: Key features and effects”, in The Monopolistic Competition Revolution in Retrospect, Cambridge, MA: Cambridge University Press.

Bernard, A B and J B Jensen (1995), “Exporters, jobs, and wages in U.S. manufacturing: 1976-1987”, Brookings Papers on Economic Activity 26 (1995 Microeconomics): 67- 119.

Bernard, A B and J B Jensen (1999), “Exceptional exporter performance: cause, effect, or both?” Journal of International Economics 47(1): 1-25.

Bernard, A B and J B Jensen (2004), “Why some firms export”, The Overview of Economics and Statistics 86(2): 561-569.

Bernard, A B, A Moxnes and Y U Saito (2019), “Production networks, geography, and firm performance”, Journal of Political Economy 127(2): 639-688.

Bernard, A B, A Moxnes and K H Ulltveit-Moe (2018), “Two-sided heterogeneity and trade”, The Overview of Economics and Statistics 100(3): 424-439.

Bøler, E A, A Moxnes and K H Ulltveit-Moe (2015), “R&D, International Sourcing, and the Joint Effect on Firm Performance”, American Economic Review 105(12): 3704-3739.

De Loecker, J and J Van Biesebroeck (2018), “Aftereffect of international competition on firm productivity and market power”, in E Grifell-Tatje, C Lovell and R Sickles (eds.), The Oxford Handbook of Productivity Analysis, Oxford: Oxford University Press.

di Giovanni, J, A A Levchenko and I Mejean (2017), “Large firms and international business cycle comovement”, American Economic Review 107(5): 598-602.

di Giovanni, J, A A Levchenko and I Mejean (2018), “The micro origins of international business cycle comovement”, American Economic Review 108(1): 82-108.

Dietzenbacher, E, B Los, R Stehrer, M Timmer and G de Vries (2013), “The construction of World Input-Output Tables in the WIOD project”, Economic Systems Research 25: 71-98.

Dragusanu, R, D Giovannucci and N Nunn (2014), “The economics of fair trade”, Journal of Economic Perspectives 28(3): 217-36.

Eaton, J and S Kortum (2018), “Trade in goods and trade in services”, in L Y Ing and M Yu, (eds.), World Trade Evolution: Growth, Productivity and Employment, chapter 4, pages 82-125. London: Routledge.

Halpern, L, M Koren and A Szeidl (2015), “Imported inputs and productivity”, American Economic Review 105(12): 3660-3703.

Head, K and T Mayer (2004), “The empirics of agglomeration and trade”, in Handbook of Regional and Urban Economics, chapter 59, North-Holland.

Imbs, J and L Pauwels (2020), “High Order Openness”, CEPR DP 14653.

Johnson, R C (2014), “Trade in intermediate inputs and business cycle comovement”, American Economic Journal: Macroeconomics 6(4).

Johnson, R C and G Noguera (2012), “Accounting for intermediates: Production sharing and trade in value added”, Journal of International Economics 86(2): 224 – 236.

Kalemli-ozcan, S, E Papaioannou and J L Peydró (2013), “Financial regulation, financial globalization, and the synchronization of economic activity”, The Journal of Finance 68(3): 1179-1228.

Timmer, M, B Los, R Stehrer and G de Vries (2016), “An anatomy of the global trade slowdown predicated on the wiod 2016 release”, GGDC Research Memorandum GD- 162, Groningen Growth and Development Centre, University of Groningen.

Topalova, P and A Khandelwal (2011), “Trade liberalization and firm productivity: The case of India”, The Overview of Economics and Statistics 93(3): 995-1009.

1 See Alcalá and Ciccone (2004), Baldwin et al. (2003), Head and Mayer (2004), Johnson and Noguera (2012).

2 The computations are performed using the 2016 release of the World Input Output Tables. The united states coverage represents about 85 percent of world GDP. For information regarding WIOT, see Dietzenbacher et al. (2013).

3 See for example Eaton and Kortum (2018).

4 See for instance Johnson (2014).

5 See among numerous others the seminal studies of Bernard and Jensen (1995, 1999, 2004) at firm level, or productivity enhancing reallocation effects in Amiti and Konings (2007), Topalova and Khandelwal (2011), Bernard et al. (2018), or DeLoecker and Van Biesebroeck (2018).

6 See for example the survey by Baldwin et al. (2003) across countries, or Amiti and Konings (2007), Halpern et al. (2015) or Bøler et al. (2015) at firm level.

7 See Frankel and Rose (1998), or Kalemli-ozcan et al. (2013). At firm level, see di Giovanni et al. (2017, 2018).