Abstract
We study the impact of import intensity in production of exporters and their suppliers on exchange rate pass-through to export prices. For identification, we use rich micro-level databases – domestic firm-to-firm sales and firm-product-level customs – from a large emerging market, Turkey. We find that ignoring suppliers’ import reliance misses nearly half of the picture: while exporters’ degree of reliance on own imported goods is 24%, this number reaches nearly 40% once their suppliers are taken into account. A higher degree of import reliance by exporters’ suppliers significantly increases pass-through to export prices by inducing higher imports-driven marginal costs passing over to downstream exporters. Moreover, exporters with a higher concentration in their domestic supply networks have a higher pass-through.
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Notes
Indeed, Amiti et al. (2014) acknowledge for Belgian exporters that some of imports are likely to be made not directly by exporters but through other firms, and they note that they are unable to control for suppliers’ imports without more detailed data. Our use of firm-to-firm sales database suggests that a significant portion of exporters’ reliance on imports may be due to their suppliers.
The database covers firm-to-firm transactions above a modest threshold of 5000 TL (which, on average, corresponds to about 2500 US dollars).
The Turkish customs database follows EUROSTAT and uses the Combined Nomenclature (CN) classification for classifying exported or imported goods. The CN coincides with the Harmonized System (HS) classification up to the sixth digit.
Summing up the direct and indirect import intensities, one can also obtain an aggregate measure of import intensity for exporters. When we do so, we find that exporters with higher aggregate import intensity raise their export prices by 6.2% more.
Amiti et al. (2014) use destination currency pricing and therefore report lower pass-through as a result of higher import intensity. In producer currency pricing, which we use, their results would point to higher pass-through.
Moreover, while the mechanism in our paper operates through import-intensive domestic suppliers, similar spillover effects can further be prevalent in an international setting. For instance, a shock to a supplier abroad may affect their downstream across-the-border firms. For instance, di Giovanni et al. (2018) and Auer et al. (2019) show evidence for how global supply linkages may render comovement of business cycles or prices across countries. Based on French micro-level data, Giovanni et al. (2020) show that firms that import intermediate inputs react significantly more to foreign shocks, and lay out the quantitative importance of large ‘granular’ firms in transmitting foreign shocks to the French economy.
As we show in Section 4, change in import intensity of suppliers (change in indirect import intensity) is on average close to zero, implying that exporters cannot easily switch to suppliers with lower import intensities.
To include all imported inputs in a firm’s supply network, the Leontief inverse of import intensity could be calculated as \(\varphi _{f,t}= [I - \Omega ]^-1 M = \sum _{k=0}^{\infty }\Omega ^k M\), where \(w_{i,j,t} \in \Omega\) is the share of inputs from firm i in j, and M is a vector of own import intensities of direct importers. The Leontief inverse of import intensity takes into account all imports within a firm’s supply chain and does not differentiate between different orders of suppliers. As we show later, the majority of supplier import intensity is from first order suppliers.
As we show in the Robustness section, we find virtually the same results if we were to exploit variation within the CN 4-digit good level.
A potential reason for why exporters have their import intensities largely unchanged following changes in the exchange rate might be due to costly adjustment in changing the production structure or buyer-supplier linkages (e.g., exporters may not easily switch to suppliers with low import use after a domestic currency depreciation). For evidence that inter-firm linkages are in general costly to adjust, see Huneeus (2018).
Alternatively, as a proxy for an exporter’s market power within its suppliers, we also calculate the share of the largest supplier in an exporter’s total supplier purchases. When we use this measure in our regressions – to be presented below, our results remain strongly robust.
Classification of goods at a CN 8-digit level follows an international standard, and we use this level of disaggregation in our estimations. More disaggregated classifications, e.g., CN 12-digit level as reported by the Turkish Ministry of Customs, may be used, but would be too restrictive since our dependent variable is in terms of changes (i.e., we would then include only those that export the same CN 12-digit level good for two consecutive years). The results are strongly robust, though, to using CN 12-digit classification (available upon request).
Firms with annual gross sales above a relatively modest threshold of around 200,000 Turkish liras (c.a. 100,000 US dollars) report their balance sheets. Since exporters are on average larger than the rest of the firms, we have balance sheet and income statements for almost all the exporters.
See https://www.ticaret.gov.tr/istatistikler/dis-ticaret-istatistikleri for details.
An increase in the cross-sectional variation in exporters’ import intensity also helps for better identification.
In Ozcan Tok and Sevinc (2019), the level of aggregation is close to the 2-digit NACE level, but some sectors are aggregated by Turkish Statistical Institute to the 1-digit level.
Turkish Statistical Institute reports that 61% of Turkish imports (of exporters and non-exporters) are invoiced in US dollars, and 33% in Euros (on average over our sample period).
Our results are also strongly robust to exploiting the cross-sectional variation in import intensities of exporters that export the same – but wider – product category to the same destination country at the same year. When we replaced good\(\times\)destination\(\times\)year fixed effects, \(\mu _{i,k,t}\), in our baseline regression (equation 8) with sector\(\times\)destination\(\times\)year fixed effects, where sectors are defined more broadly than goods (CN 4-digit-level sectors vs. CN 8-digit-level goods), our results remained similar.
As evident from the number of observations, 10,000 USD is close to the sample median.
The precise average number of unique CN-2-level varieties per exporter is 3.089.
When we construct the distribution of export shares by product category for each firm, the 10% cutoff coincides with the 25th percentile of the product share in exports distribution. That is, by focusing on main products, we loose about 25% of observations.
To give an example, an exporter importing "17021100.Lactose or lactose syrup containing by weight 99% or more lactose" may switch to importing "17023010.Glucose and glucose syrup, not containing fructose or containing in the dry state less than 20% by weight of fructose". By having a wider definition of goods – for the sake of this example, using “1702.Other sugars, including chemically pure lactose, maltose, glucose, and fructose, in solid form”– accounts for this possibility.
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Appendix A1 – Exporters’ Dynamic Adjustments of Imports
Appendix A1 – Exporters’ Dynamic Adjustments of Imports
We test exporters’ adjustments in imported goods on two dimensions: a shift away from source countries or from goods when the domestic currency depreciates against the source country currency. In doing so, we first aggregate the firm-product-source-year-level imports database at the firm, CN 4-digit-level product category, source country and year level. We use a wider definition of goods (CN 4-digit instead of CN 8-digit) to account for the possibility that exporters may switch within CN 4-digit products from a given source country.Footnote 24 In particular, we estimate
where the dependent variable is (i) the change in the share of a source country s in total imports of a product category j of exporter f from \(t-1\) to t (\(\Delta Y_{f,j,s,t}\)), or (ii) the change in the value of total imports of j by exporter f from \(t-1\) to t (\(\Delta Y_{f,j,t}\)), where \(\Delta Y\) is defined as (\(Y_{t}-Y_{t-1}) / ((1/2)*(Y_{t}+Y_{t-1}))\), to account for the extensive margin. \({\mathcal {N}}_{f,j,t-1}\) is the total number of source countries for imports of j in \(t-1\) by exporter f. \(\mu _{f}\), \(\mu _{s}\), and \(\mu _{j,t}\) stand for firm, source country, and CN-4 product category\(\times\)year fixed effects, respectively.
Table 10 presents the results. Column (1) indicates that an exporter facing an increase in import bill due to importing from a particular source country reduces the share of that source country in its imports of CN-4 products. Numerically, a one percent ex-ante increase in production costs due to domestic currency losing its value against the source country currency is estimated to reduce the share of imports from that country by 13%. Column (2) further shows when a product becomes more expensive to import due to borrowing from a source country for which the domestic currency gets less valued against, the exporter reduces its total imports of that product. A 1% increase in costs is now associated with a 5% reduction in imports of that good.
In columns (3) and (4), we further shed light on whether exporters’ shifting away from source countries or goods entail frictions. This essentially serves as a verification test for the data since shifting sources of imports or goods should be costly as it requires looking for alternative foreign/domestic suppliers or changing the production structure, a friction one could expect to hold in the data. We look into a particular aspect of such frictions: whether having higher number of source countries for a particular imported product ameliorates such frictions. If shifting entails frictions, than exporters that work with a wider range of source countries should be better positioned. Indeed, as columns (3) and (4) show, exporters that ex-ante import the same product category from a higher number of countries reduce the source country share more strongly and decrease their reliance on importing such product category less mildly.
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Akgündüz, Y.E., Fendoğlu, S. Export Prices, Imported Inputs, and Domestic Supply Networks. IMF Econ Rev 70, 383–419 (2022). https://doi.org/10.1057/s41308-022-00159-7
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DOI: https://doi.org/10.1057/s41308-022-00159-7