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Exporter’s Productivity and the Cash-In-Advance Payment: Transaction-Level Analysis of Turkish Textile and Clothing Exports

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Abstract

This study examines how the productivity of an exporter influences the choice of cash-in-advance (CIA) payment. Using the transaction-level data of Turkish textile and clothing exports from 2009 to 2017 merged with the firm-level information, we find that the relationship between the exporter’s productivity and the likelihood that the exporter uses the CIA is nonlinear. An exporter with higher productivity is more likely to choose the CIA; however, this tendency is mitigated among the highest-productivity exporters. We build a parsimonious theoretical model considering firm productivity heterogeneity and provide a rationale for those empirical findings. Furthermore, the CIA is more commonly used for exports in small transactions and to countries with a weak rule of law. We also show that the CIA is more likely to be used for destination countries with low gross domestic products per capita. In addition, the CIA is used more commonly when the value of the Lira is low against the destination country’s currency.

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Fig. 1

Source: Foreign Trade Statistics (FTS)

Fig. 2
Fig. 3

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Data Availability

FTS and AISS data supporting this study's findings are obtained from TUIK. Restrictions apply to the availability of these data, which were used under license within the TUIK premises. Consequently, neither the original nor the secondary data used for analysis can be publicly disclosed or shared. For details regarding access to these datasets, inquiries should be directed to TUIK.

Notes

  1. Invoicing in Turkish Lira protects Turkish exporters from exchange-rate risk, whereas contracting in importers’ or vehicle currency exposes Turkish exporters to exchange-rate risk. Exports invoiced in either US dollars or Euros account for 79% of the total in our dataset. Therefore, most Turkish exporters face exchange rate risk. Although we do not explicitly address this issue, the CIA can reduce the risk of exchange rate exposure by shortening the time lag between the contract date and the payment date.

  2. We focus on textile and clothing exports because intra-firm trade in this product category is expected be minimal. In intra-firm trade, exporters and importers can share the risk in their transactions, so determining the payment method may become more complicated than our theoretical model assumes. For instance, using the Turkish trade data, Saygılı (2023) points out that the share of import content of exports for textile and clothing sectors is 22.7 percent and below the overall average (25.8 percent). That share reaches 38.5 percent in the manufacture of machinery and equipment. This suggests that the textile and clothing sector has lower foreign value-added content and the presence of intra-firm trade is moderate compared to other sectors (especially manufacturing sectors). Demir and Javorcik (2018) also investigate the Turkish exports of these industries. Nevertheless, Yoshida et al. (2022) confirm a concave impact of productivity on the probability of choosing the CIA based on Turkish export data from all industries.

  3. As banks act as a middleman between an exporter and an importer, LC allows an exporter to receive payment with minimal risk. However, using LC requires importers and exporters to gain the trust of banks. Thus, LC differs qualitatively from CIA, despite the fact that both methods eliminate payment risk for exporters. Therefore, we do not explicitly address LC. In fact, LC is rarely used in our dataset compared to OA, and its share decreases over time, as shown in Fig. 1. This means that Turkish exporters in this industry cannot easily employ LC in practice.

  4. Others include Niepmann and Schmidt-Eisenlohr (2017), Türkcan and Avsar (2016, 2018), Demir and Javorcik (2020), Im (2020), Avsar et al. (2022), and Crozet et al. (2022). However, these studies primarily use country-level trade finance data.

  5. Melitz’s (2003) theoretical mechanism is used in many fields of international economics. For example, see Mejean et al. (2010) for the location choice of foreign direct investment and Demidova and Krishna (2008) for tariff scheme selection.

  6. Note that \(G=0\) if \(a={a}^{*}\equiv {f}^{\frac{1}{m-1}}w{\left[\left(1-\frac{\widetilde{\lambda }}{1+r}\right)\left(\frac{\mu -1}{{\mu }^{m}}\right)Q\right]}^{-\frac{1}{m-1}}\).

  7. We have FTS data for the years 2002–2017 and AISS data for the years 2009–2017. Therefore, we combine the data from 2009–2017.

  8. These shares are calculated based on the number of transactions in total textile and clothing exports.

  9. We use Rauch (1999)'s classification to distinguish between homogenous and differentiated goods.

  10. The index is normalized on a scale of 1 to 6 to produce the final score for a destination country.

  11. We do not include the firm fixed effect as our exporter’s productivity variable is relatively stable during the sample period and the firm fixed effect offsets the majority of the impact of exporter productivity, which is our main explanatory variable.

  12. We thank an anonymous referee for suggesting to include the export value in the regression.

  13. The export value significantly correlates with productivity, implying the multicollinearity in column [8]. We also conducted estimations with the export value and without the productivity level and the square, confirming that the result does not change significantly, both qualitatively and quantitatively.

  14. See the TUIK website for the detail (https://data.tuik.gov.tr/Bulten/Index?p=Small-and-Medium-Sized-Enterprises-Statistics-2019-37548&dil=2).

  15. The threshold level of ln Productivity for a change of the sign is determined by \(-\alpha /\beta\), given these coefficients have opposite signs. For the group of importing countries with lax law enforcement, the threshold value is 20 (0.0260 divided by 0.0013). This value exceeds the maximum value of ln Productivity (18.65 in Table 3); therefore, the productivity effect is always positive for these destination countries.

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Authors and Affiliations

  1. Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya, Turkey

    Kemal Türkcan

  2. Faculty of Economics, Shiga University, Hikone, Japan

    Yushi Yoshida

  3. Faculty of Economics, Chuo University, 742-1 Higashinakano Hachioji-Shi, Tokyo, 192-0393, Japan

    Taiyo Yoshimi

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  1. Kemal Türkcan
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  2. Yushi Yoshida
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Correspondence to Taiyo Yoshimi.

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We appreciate the constructive feedback from the RIETI research group “Exchange Rate and International Currency” participants. Yoshida gratefully acknowledges the financial support from Shiga University's Joint Research Project Fund, 2019–2022, and Heiwa-Nakajima Foundation. Yoshimi gratefully acknowledges financial support from KAKENHI 20KK0289. Yoshimi and Yoshida would also like to thank KAKENHI 20H01518 for their financial assistance. We would like to thank Weiyang Zhai for his excellent research assistance on this project. Finally, we would like to thank the Turkish Statistical Institute (TUIK) for allowing us to obtain and process the data on their premises. All errors are ours.

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Türkcan, K., Yoshida, Y. & Yoshimi, T. Exporter’s Productivity and the Cash-In-Advance Payment: Transaction-Level Analysis of Turkish Textile and Clothing Exports. Open Econ Rev (2024). https://doi.org/10.1007/s11079-024-09752-x

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