policymakers demonstrate

Winners and Losers of Globalization 241
individual firms, but also to better quantify the aggregate effects of policy. To
understand aggregate effects, we need to rely on industry and macroeconomic
models (discussed in Section 6.3).
Challenge 1 Harmonize firm-level trade and balance sheet data across countries.
Multidimensional Trade Data
Recent empirical work has used customs transactions data to analyse the patterns
of trade. The availability of such data has opened up the possibility to ask
questions beyond the volume of trade and its broad sectoral composition. A typical
customs declaration (which serves as the primary unit of observation for
most trade statistics) records the exporting and the importing firm, the precise
classification of the product being shipped, the precise date of shipments, the
mode of transport and many other logistical details about shipment. This has
made it possible, for example, to study the distribution of trade across products,
destination markets and firms.
Bernard et al. (2007) survey the empirical evidence on multi-product and
multi-country traders. They find that although most exporters (40% of the total)
sell only one product to one destination, most exports are done by large multiproduct,
multi-destination exporters. The number of products and firms shipping
to a particular market increases with market size and decreases with distance.
Similar patterns emerge for imports.
Armenter and Koren (2014) caution that patterns in multidimensional trade
data may be difficult to interpret because such data is sparse. That is, there are
few observations relative to the number of product, firm and country categories.
What is the quantitative relevance of the sparsity of trade data? Armenter and
Koren (2014) build a statistical benchmark (which can be thought of as a special
case of a wide class of economic models), in which trade shipments are ‘randomly’
assigned to trade categories. The randomness is conditional on the size
distribution of firms, countries, and products, so it does not imply that exporters
behave erratically. Such a ‘balls-and-bins’ model can quantitatively fit many of
the statistics reported about the number of exported products, exporting firms,
and export destinations. Given that many models are consistent with the ballsand-bins
framework, we cannot distinguish among them on the basis of such
simple statistics.
We hence need new statistical methods to deal with large multidimensional
trade datasets. Armenter and Koren (2014) do not offer a universal tool, but
their reliance on the statistical properties of the multinomial distribution may
be a useful starting point for further analysis. A more structural approach is
followed by Eaton et al. (2012) and Armenter and Koren (2013), who build
trade models with infrequent purchases.