Supply Chain Risk and the Pattern of Trade

Supply Chain Risk and the Pattern of Trade Maximilian Eber and Hannes Malmberg∗ November 25, 2015 Abstract We analyze the interaction of supply chain risk and trade patterns. Our model yields a novel deter- minant of comparative advantage. In the model, countries with low supply chain risk specialize in risk- sensitive goods. We also show that risk-sensitivity is determined by the number of customized components used in production. Based on our theory, we construct a novel empirical measure of risk-sensitivity from input-output tables and measures of customization (Rauch, 1999). Using industry-level trade data and a variety of risk proxies, we show that countries with low supply chain risk indeed export risk-sensitive goods disproportionately. The model has policy implications: Countries that strive to attract a risk- sensi- tive industry such as car manufacturing can do so by improving supply chain reliability. 1 Introduction This paper is motivated by a growing concern in the policy and business community over supply chain risk. The 2011 tsunami in Japan illustrates how important reliability is for modern production. For example, General Motors had to close a factory in Louisiana due to a lack of Japanese-made parts.1 The Inter- American Development Bank notes that “firms fragmenting production internationally are likely to look for locations with adequate transport and logistics infrastructure to reduce disruptions in the supply chain” (Blyde, 2014). Similarly, the US Department of Commerce argues that “Expected gains from offshoring can often be erased by [...] unexpected delays.”2 In this paper, we study the trade pattern consequences of country differences in supply chain risk. ∗The project was supported by the Private Enterprise Development for Low-Income Countries (PEDL) exploratory grant. 1Source: http://www.nytimes.com/2011/03/20/business/20supply.html (last accessed Nov 17th, 2015) 2http://www.esa.doc.gov/economic-briefings/assess-costs-everywhere-shipping (last accessed Nov 17th, 2015) 1 It is clear that countries vary in the degree of supply chain risk. Some countries offer high-quality infrastructure and predictable, quick bureaucratic services. In other countries, poor logistics systems and low government effectiveness mean that supply chain risk is high: components get stuck in the port; roads rain away; land rights are not transparent; import permits are delayed; and foreign currency availability is uncertain. Variation in country-level supply chain risk induces comparative advantage as goods vary in their sensitivity to supply chain risk. Consider two industries, plain t-shirt production and cars: For t-shirts, there are very few separate intermediate inputs used in production. In this case, a risky supply chain is not too problematic. On the other hand, a modern car factory based on lean production principles requires a con- tinuous flow of hundreds if not thousands of customized components. Supply chain reliability becomes key. If a country’s infrastructure and institutions create severe supply chain risk, the country can be expected to have a comparative advantage in t-shirt production, and a comparative disadvantage in modern car production. We formalize this intuition by constructing a model in which each sector produces a good using intermediate inputs. Intermediate input production is subject to disruption risk, which means that production (including delivery) fails with some probability. Proximate causes of production failures are delays in ports, infrastructure failures, strikes, political instability, and unpredictable bureaucratic procedures. A key fea- ture of the model is that the effect of disruption risk depends on whether intermediate goods are standardized or customized. Standardized intermediates are homogenous and traded on centralized exchanges whereas customized intermediates are delivered directly from intermediate goods producers to final goods producers. For standardized goods, the centralized exchange insulates producers from disruption risk through a law of large numbers. There will be a steady supply of goods even if some suppliers fail for idiosyncratic reasons. This is not the case for customized components, which are often produced by only one or two suppliers. In this situation, idiosyncratic risk matters for production. We derive a novel aggregation result that makes the model highly tractable. We show that aggregated supply and demand of a sector can be characterized by a representative firm with deterministic production, even though the underlying firms experience stochastic shocks. Supply chain risk enters the sectoral production function as productivity penalty. As all customized intermediate inputs are essential for production, this productivity penalty grows exponentially with the number of customized intermediate inputs used in production.3 As a by-product of the theory, we derive a new measure of different industries’ risk- 3For simplicity, we are assuming independent shocks to input suppliers. 2 sensitivity. Because each customized intermediate good represents an independent source of error, the risk-sensitivity of a product depends on the number of customized components used in production. We embed this sectoral production structure in a simple trade model. In the model, we let goods vary in their number of customized inputs m, and countries vary in the disruption risk p. We show that productivity is log-submodular in p and m. Thus, we can use the insight from Costinot(2009a) to conclude that there will be negative sorting between p and m. In other words, risky countries (high p) will produce goods with few customized intermediate inputs (low m). In the empirical part of this paper, we test this hypothesis in trade data using the methodology in Romalis (2004). In a first step, we construct a measure of how many customized intermediate inputs each industry uses from Input-Output tables and the definition of customized goods by Rauch(1999). To proxy for disruption risk, we use the World Governance Indicator (WGI) of government effectiveness and the World Bank’s Logistics Performance Index (LPI). We then test whether countries with more effective government and logistics systems export relatively more goods with highly customized components. We show that this effect exists and is statistically and economically meaningful. The effect is present in a wide range of specifications, even when we (over-) control for country income levels. The effects we find are of similar magnitude to other institutional determinants of trade patterns, for example contracting quality Nunn(2007). However, our theory builds on a different mechanism. Nunn empasizes that a bad contracting environment leads to a higher cost of customized intermediate inputs via lower levels of relation-specific investments. Therefore, he measures the proportion in value terms of inputs that comes from customized intermediate inputs. Our paper focuses on disruption risk and therefore considers the number of customized intermediate inputs. Different perspectives on the sources of comparative advantage also imply a different policy levers. Many countries are actively trying to attract “sophisticated” industries such as advanced electronics or machinery equipment manufacturing. If relationship-specific investments are key—as implied by models such as Antràs(2003) and measured by Nunn(2007)—then the main task for governments is to improve the quality of the contracting environment and the rule of law. If supply chain risk also matters, as suggested by our results, then it is important for governments to improve the reliability of the business environment through, for example, more efficient bureaucracy, infrastructure, and promotion of third party business facilitators. Our theory also has implications for the measurement of the quality of the business environment. Very few indicators focus on uncertainty and risk. For example, the World Bank’s Doing Business Indicators have 3 been used widely to illustrate the challenges for businesses in poor countries. It measures de jure time and cost to complete a wide range of functions such as time to export, import, receive electricity, and open a business. However, it only provides a single estimate per country of the time required for a given task, and it contains little information about the variability of its implementation. We stress the importance of risk and uncertainty in the business environment. Our findings suggest that characterizations of the business environment should include measures of risk. For example, surveys would benefit from reporting not only the average time to obtain a permit, but also the variance associated with the time to obtain such a permit and the chances of not obtaining a permit at all. The paper proceeds as follows: We discuss related literature in section2. Section3 provides the model. We bring the model to the data in section4. Finally, section5 concludes. 2 Literature The paper connects to a number of different literatures. As it analyzes the role of disruption risk, it connects to the literature on macroeconomics and uncertainty. The production structure in which all components are vital for production relates the paper to O-Ring theory. Furthermore, we analyze how institutional features interact with risk to shape countries’ trade patterns. Therefore, the paper also adds to the literature on institutional sources of comparative advantage. Risk and Uncertainty in economics First, the role of risk in shaping macroeconomic

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