Construction of Net Exposure

The theoretical considerations in Section 3.1 imply that the effect of the apprecia-tion in 2015 is likely to be larger, the larger the share of products or services that the firm sells abroad. The effect decreases, the more a firm is naturally hedged against the appreciation through relatively lower costs of imported intermediate inputs. Following Ekholm et al. (2012), we thus define firm 𝑖’s net exposure to currency movements as 𝑠_𝑖= 𝑋_𝑖− 𝐶_𝑖, where 𝑋_𝑖 is a firm 𝑖’s initial export share in sales and 𝐶_𝑖 is its initial share of imported intermediate inputs in total cost. Be-cause the export and imported inputs share lie in the interval [0,1], net exposure is a variable ranging from -1 to 1. Note that firms’ net exposure is time-invariant: we use firms’ initial (pre-shock) net exposure in the analysis. Note also that this defi-nition of net exposure differs from the defidefi-nition used in the other sections of the report, where net exposure is defined in terms of the share of imported inputs in sales (rather than costs).¹³

13 In general, the definition of 𝐶𝑖 in terms of firms’ sales is preferable, as we do not expect an effect of an appreciation on firms with a net exposure of 0 using a definition with a common denomina-tor. Conversely, if 𝐶_𝑖 is defined in terms of costs, as in this section and in Ekholm et al. (2012), firms’ profits are affected by exchange rate movements even for a firm with zero net exposure.

This is because sales is usually higher than costs. A one percent appreciation will thus have a larg-er effect on sales than on costs. We cannot adapt the definition of net exposure in tlarg-erms of sales as the KOF investment surveys do not provide information on the relationship between costs and sales of firms.

The regular KOF investment surveys provide firms’ export share in sales but only in four relatively broad categories: 0-5%, 5-33%, 33-66%, and 66-100%. In the KOF survey in 2012, however, firms were given 11 different options to report their export share (i.e. 0%, 10%, 20%, and so on). For firms participating in the 2012 survey (about 1/5 of all firms), we thus use the detailed information from the 2012 survey. Moreover, for these firms, we also observe both, the broad and the detailed export variable, i.e. we see whether the export share of a firm that falls into the 5%-33% category is closer to 5% or 33%. Using this information, we refine the measurement of the export share for the rest of the firms (for which we only ob-serve the broad variable) using a simple regression procedure.¹⁴ We follow a simi-lar approach to impute missing data on the share of imported intermediate inputs in total costs. As with the export share, this variable was directly levied in the KOF investment survey in spring 2012. For firms that did not participate in this specific survey, we predict the imported inputs share using a regression approach.¹⁵

Figure 10 shows the distribution of initial net exposure for our sample of firms in the survey of spring 2012. To illustrate the measure, we estimate kernel densities separately for manufacturing and service sector firms. As is illustrated by the fig-ure, most firms’ net exposure is close to zero or slightly below zero, reflecting that many firms have no exports but import at least some intermediates. The median net exposure of the manufacturing firms in the sample is -0.01. In the service sector, the median is -0.06%.¹⁶ In contrast to the service sector, in which only 9% of firms have positive net exposure, 43% of firms in the manufacturing sector have positive net exposure. The 90^th percentile of net exposure in the manufacturing sector is 0.54. These firms are strongly exposed to currency fluctuations.

14 For firms participating in the 2012 survey, we regress the detailed export share variable on the less detailed variable and a set of dummy variables (i.e. industry, canton and size dummies). Using the results from this regression, we predict the detailed variable for the firms which did not participate in the 2012 survey. In this regression we use a generalized linear model with a logit link, which deals with the fact that the outcome is a fractional response. Obviously, this imputation proves to be very accurate, as the less detailed export variable is an extremely good predictor of the detailed variable.

15 The regression model contains the same covariates as the model for the export share (i.e. industry, size, and region dummies). We also include the broad export share variable as a further covariate, as heavy importers are often heavy exporters (Amiti et al., 2014). The imputation of the import share variable leads to some measurement error, which may lead to a classical measurement error problem, biasing our estimates towards zero. However, our analysis below is based on a simple DiD comparison of firms with positive and non-positive net exposure. Since we categorize firms into the two bins, measurement error is arguably only a concern for firms close to the net exposure threshold of 0. Our results are almost unchanged if we omit firms that are within close range to 0.

16 Average and median net exposure is lower in this section compared to the previous chapter because we define net exposure as the difference between the export share in sales and the imported inputs share in total costs. In the other chapters, net exposure is defined as the difference between the ex-port share in sales and imex-ported inputs share in sales.

Figure 10: Distribution of Net Exposure in the KOF Investment Survey, by Sector

Notes: The figure shows the distribution of firms’ net exposure as observed in the period in the spring survey 2012. Net exposure is firms’ initial export share in sales minus its initial import share in total costs. The distributional plots are constructed using an Epanechnikov kernel function. A small num-ber of firms with net exposure above 0.75 and below -0.75 are discarded.

Figure 11 illustrates the relevance of the net exposure measure in predicting the impacts of exchange rate appreciations on firms’ revenues. The KOF investment survey in spring 2012 contained special questions in which KOF levied infor-mation on the hypothetical effects if the SNB were to change the ceiling from 1.20–––which was the exchange rate peg defended by the SNB at the time–––to 1.10. In this special survey, firms were asked about the expected consequences of such an appreciation on their nominal sales. Figure 11 provides a binned scatter-plot, relating firms’ answers to this question to firms’ net exposure, averaging firms’ responses in steps of 0.05. The size of the dots indicates the number of firms in the respective bin. The figure additionally provides the regression line of a weighted linear regression of the expected change in sales on firms’ net exposure.

We observe a negative relationship between the two variables. Firms with a large negative exposure (low export share but large imported input share) expect close to zero consequences of the appreciation on their sales. Firms with very high expo-sure, on the other hand, expect sales to decline by about 6%. The figure suggests that the exchange rate elasticity of nominal sales is about -0.2 for firms with aver-age net exposure.

Figure 11: Illustration of the Relevance of the Exposure Measure

Notes: The figure shows a binned scatterplot of firms’ net exposure as reported in the KOF invest-ment survey in spring 2012 against the expected effect of an appreciation of the Euro/CHF exchange rate from 1.20 to 1.10 on firms’ (nominal) sales. These effects were reported by firms in a special questionnaire of the KOF investment survey in spring 2012 about the effects of exchange rate appre-ciations. The size of the dot indicates the number of firms in the respective bin of net exposure. Net exposure is firms’ initial export share in sales minus its initial import share in total costs.