Is there Evidence of Learning‐by‐Exporting in Turkish Manufacturing Industries?

Abstract

Exporting has always been thought of as one tool to improve productivity and, consequently, to spur economic growth in low‐ to middle‐income economies. However, empirical evidence of this so‐called ‘learning‐by‐exporting’ effect has been limited. This article determines whether learning‐by‐exporting is evident in two Turkish manufacturing sectors—the textile and apparel (T&A) and the motor vehicle and parts (MV&P) industries. A semi‐parametric estimator that controls for problems associated with simultaneity and unobserved plant heterogeneity is used to test the learning‐by‐exporting hypothesis. After controlling for these issues, our results suggest statistically stronger learning‐by‐exporting effects in the T&A than in the MV&P industry. The highly concentrated and capital‐intensive nature of the MV&P industry is the main reason for the lower learning‐by‐exporting effect in this sector. From a policy perspective, this implies that targeting export‐enhancing policies to industries with significant learning‐by‐exporting effects may lead to more productivity gains and would better stimulate an export‐led growth.

Key Words:

• Learning‐by‐exporting
• heterogeneity
• productivity
• trade liberalization

Jel Classifications:

• F10
• F14
• D21
• L60

Acknowledgement

We would like to thank Omer Gebizlioglu, Ilhami Mintemur and Emine Kocberber at the State Institute of Statistics in Turkey for allowing us access to the data for this study, and Erol Taymaz for helpful discussions about the data.

Notes

1. See Yasar (2002).

2. See Olley & Pakes (1996) for the mathematical representation of the exit decision. Note further, that the nature of the Markov perfect equilibrium strategy in this model is such that the optimal investment and entry/exit decisions of firms in each time period depends on the perceptions of the distribution of future market structures, given current information available (see Olly & Pakes, 1996, pp. 1272–1273). This is consistent with the formal definitions of Markov Perfect Equilibrium provided by Ericson & Pakes (1995) and Maskin & Tirole (1988).

3. The value function is increasing in both capital stock and export intensity, which implies that Ω _t (EI_{t –1},K_t ) is decreasing in K and EI. This will result in a situation in which plants with larger capital stocks and export will stay in the market even at the low realizations of productivity because they anticipate larger future returns. Thus, self‐selection caused by exit behavior will lead to the expectation that productivity will decline in the size of capital stock and export intensity. This causes a downward bias in capital stock and export intensity.

4. Note that the investment decision here is only conditioned on the current capital stock and the expression in (4) does not necessarily imply a positive relationship between investment and capital stock.

5. See Griliches & Marisse (1998), Pavcnik (2000) and Levinsohn & Petrin (1999, 2000) for further information on the application of Olley & Pakes (1996) model.

6. More specifically, we use the gross investment data (domestic plus imported capital purchases less sales, with maintenance investment added in), deflated by a capital price index, and summed over time using depreciation rates, to compute the aggregated capital data. In order to calculate the initial benchmark we use three‐year averages of the growth rate of investment. After calculating the capital benchmark, we cumulated the capital stock from that point using the investment data and a depreciation rate. In other words, the capital stock is estimated by applying the perpetual inventory method (PIM) on the fixed assets, which requires estimated years of lifetime capital equipment: K_t = K_{t +1}(1 − δ) + I_{t −1}, where K_t is the capital stock in period t; δ is the depreciation rate of capital; and I_t is the level of the investment during the period.

7. In the interest of space, we do not fully present the detailed differences between exporters and non‐exporters. Detailed information about the export premia in Turkish apparel, textile and motor vehicle and parts industries based on more detailed plant characteristics is available from the authors upon request.

8. In 2000, Turkey ranked 7th and 14th in world apparel, and textile exports. In 2001, Turkey’s share of world apparel, and textile exports was 3.40% and 2.66% (UN, 2002).

9. Dummy variables for year, region and plant size were included in the estimated production function to control for unobserved macroeconomic shocks, regional differences and scale effects. We do not report them because their relevance to our research question is limited. The complete set of findings is available from the authors.

10. Yasar et al. (2006) examine the short‐run and the long‐run dynamics of the relationship between export levels and productivity for these industries. They find that permanent and temporary productivity shocks induce larger export level responses than permanent and temporary export shocks on productivity. They also find that for the T&A industry, temporary export shocks usually result in faster short‐run productivity adjustments, as compared to the effects of productivity shocks on short‐run exports. The converse is true for the MV&P industry. Their results are consistent with our findings.