Abstract
We attempt to uncover the relationship between the real exchange rates and exports shares of manufacturing firms in Turkey by taking into account FX exposures and various firm characteristics. We use a large panel of manufacturing firms to carry out an empirical analysis for the period 2002–2010. Contrary to macro-evidence, firm-level empirical evidence suggests that a depreciation of the Turkish lira seems to favor the external competitiveness of firms in general. We document that a real depreciation of the Turkish lira has a positive impact on export shares and its impact is muted to some extent for firms operating in sectors that use imported inputs intensively. In addition, we estimate that export shares increase as a result of real depreciation for firms having low (naturally hedged) and moderate FX debt-to-export ratios. We do not confirm a strong balance sheet channel where a depreciation of the currency may harm firms’ export performance due to currency mismatch. On the contrary, FX borrowing is estimated to support export performance probably due to undermining finance constraints.
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Notes
Following the anonymous referee’s comments concerning selection bias, we use export share instead of export volumes to incorporate also non-exporter firms into the empirical analysis.
Micro-evidence on the impact of exchange rate variations on firm-level export shares is almost non-existent for the Turkish case. The issue has been studied extensively by using macro- or industry-level data (Aydın et al. 2007; Bozok et al. 2015; Culha et al. 2014; Berument et al. 2014; Neyaptı et al. 2007).
The percentage of firms agreeing to provide this data to the CBRT is approximately 75%.
The denominator of the liability dollarization ratio is made up of cash and non-cash FX-denominated debt of a non-financial firm. Trade credits are not considered within the FX debt as we cannot identify trade credits with respect to currencies. The numerator of the ratio is the sum of the denominator and cash and non-cash Turkish lira-denominated debt. Non-cash debt is often made up of letters of guarantee and credit.
Similar classification is done by Echeverry et al. (2003) that set the upper and lower boundaries of the hedge area as liability dollarization (FX debt to total debt ratio) = (3/2) export to sales ratio (EXPS) and FX debt to total debt = (2/3) EXPS, respectively. For the period of 1992 to 2003, using Turkish sectoral data on non-financial sectors, in Kesriyeli et al. (2011), the lower and upper boundaries of the hedge area were set as FX debt to total debt = (1/2)EXPS and FX debt to total debt = EXPS, respectively.
See “Appendix” for the construction of the index.
Alternatively, we calculate t values by taking the ratio between the difference of the coefficient estimates of the exchange rates obtained from the two samples that we compare (columns 3&4 and 5&6) and the square root of the summation of the respective variances. The results are in line with the dummy variable interaction approach. Calculated t values are more than the critical values at the 95% confidence level, for the level specification, suggesting that the difference of the coefficients is statistically significantly different from 0. In this set up, GMM estimators are assumed to be asymptotically distributed as normal and the two samples are assumed to be independent from each other.
Calculated t-ratios based on the coefficients of exchange rates between firms belonging to young–mature, young–moderate, moderate–mature age groups are greater than 1.96 (in absolute value) suggest that difference of the coefficients are statistically significantly different from 0 at the 95% confidence level except for the young–mature comparison for the level and moderate-mature comparison for the log difference specifications.
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Appendix
Appendix
We followed Goldberg (2004) to calculate industry-specific real exchange rate measures based on pre-period weights (1996–1999). In this framework, we constructed industry-specific exchange rates by using the trade weights of each industry’s trading partners in exports and corresponding bilateral real exchange rates normalized using 2005 as the base year. The export-weighted industry-specific real exchange rate (based on pre-period, \(t_{1}-t_{2}\), weights) was calculated as:
where
j represents industry, k represents trading partner, and t represents time. For pre-period weights \(t_{1}\) stands for the year 1996 and \(t_{2}\) stands for the year 1999. \(rer_{kt}\) is the bilateral real exchange rate of trading partner k at the time t. \(w_{jkt}\) is the corresponding weight based on the export value, \(X_{jkt}\) of the trading partner k in sector j. Due to the lack of consistency in the data available, we dropped several countries. Consequently, across the two-digit manufacturing industries, the number of trading partners varied between 27 and 30, constituting 75–90% of total exports depending on the industry.
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Karamollaoğlu, N., Yalçin, C. Exports, real exchange rates and dollarization: empirical evidence from Turkish manufacturing firms. Empir Econ 59, 2527–2557 (2020). https://doi.org/10.1007/s00181-019-01733-1
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DOI: https://doi.org/10.1007/s00181-019-01733-1