1. 1 J. de Loecker Do Exports Generate Higher Productivity? Evidence from Slovenia (Journal of Int’l Economics, Sep. 2007) presented by Yunrong Li

2. 2  About the paper  Criticism

3. 3 About the Paper 1. Introduction 2. Data and preliminary analysis 3. Export entry and productivity gains 4. Productivity gains and export destination 5. Conclusion

4. 4 1.Introduction  Empirical finding: positive correlation between the export status of a firm and its productivity  WHY ?  Self-selection hypothesis: only more productive firms export and are able to compete on international markets.  Learning-by-exporting hypothesis: exporting enables firms to learn from their buyers and competitors through contracts.  Motivation for this paper: Test for learning-by-exporting hypothesis  Key issue of the test: control for self-selection process.

5. 5 Why Slovenia ?  Experienced a successful transition from a socially planned economy into a market economy in less than a decade.  High growth rate of GDP  Opening up to Western countries; substantial increase in exports in a very short time.  A natural experiment to test the hypothesis of learning-by-exporting.

6. 6 2. Data and Preliminary Analysis  Firm level data in the entire manufacturing sector between 1994 and 2000  The sample consists of 6391 firms and 29,804 observations  In total, 1,872 firms joined the export market at different points of time.  7% of these entrants are new firms; remaining 1,700 firms are crucial to verify the impact of “ starting to export ” on productivity.

7. 7 2. Data and Preliminary Analysis  If exporters have different characteristics from the non-exporters.  OLS regression model: where x is the characteristics of firm i at period t in industry k; EXP is a export dummy; l is the log of number of employees of firm i.  Interest lies in

8. 8 2. Data and Preliminary Analysis  Test result: exporters differ significantly from non-exporters.  Exporters: pay higher wages; sell more; operate on a larger scale; invest more; more capital intensive.  These results are in line with Bernard and Jensen (1995) for the USA, Bernard and Wagner (1997) for Germany, Isgut (2001) for Colombia.

9. 9 3. Export Entry and Productivity Gains  3.1 Productivity Dynamics and Export Status  3.2 Identifying productivity gains arising from exporting  3.3 Productivity gains upon export for companies joining the Slovenian manufacturing industry

10. 10 3.1 Productivity Dynamics and Export Status The method of estimating productivity is based on the work of Olley and Pakes (1996). Two innovations their method:  Control for simultaneity bias without using instruments available.  Control for potential selection bias. This issue is important for Slovenia, because selection is likely to be an intrinsic part of the transition process.

11. 11 3.1 Productivity Dynamics and Export Status Innovation of this paper:  Allow for market structure (demand condition, factor market, etc.) to be different for exporters.  Deflate the “value added” with a Slovenia PPI. But this is not enough to control for output and factor price differences for exporters  Hence, the paper estimates the production function for each 2-digit NACE sector separately (Assuming firms within the same sector face the same input prices)

12. 12 3.1 Productivity Dynamics and Export Status  The model for estimating productivity: y, l, k denote the log of output as measured by value added, labor and capital. and are OP-EXP estimators for labor and capital for industry j. stands for a measure of productivity.

13. 13 3.1 Productivity Dynamics and Export Status  Conventional productivity index: weigh firms ’ productivity by their market share.  Normalize the first year 1994 to 1.  Comparison: by 1999, productivity index increase by 16% for exporters and only 10% for non-exporters.

14. 14 3.2 Identifying productivity gains arising from exporting  Key issue: control for self-selection process while testing for learning-by-exporting hypothesis.  Solution: create a counterfactual control group for each starter, “ matching technique ”.  We don ’ t observe the counterfactual controls; therefore, we need to match each starter with a control group who has similar characteristics as the starter but don’t export.

15. 15 3.2 Identifying productivity gains arising from exporting  How to select the control group: To find a group as close as possible to the starter (treatment group) in terms of the predicted probability to start exporting.  We cut off the firms who always export during the entire periods.  Apply the ‘ propensity score matching ’ method by Rosenbaum and Rubin (1983a,1984b and 1984)

16. 16 3.2 Identifying productivity gains arising from exporting

17. 17 3.2 Identification productivity gain from exporting

18. 18 3.2 Identifying productivity gains arising from exporting  Econometric Model:  Re-scale the time periods: a firm starts exporting at s=0, note that different firms may start to export in different periods.  is the productivity of firm i at period s START i takes on 1 if a firm starts to export, and 0 for s≠0

19. 19 3.2 Identifying productivity gains arising from exporting  The probability model of starting to export: Where is normal cdf; -1 mean before starting to export; PRIVATE takes on 1 if the firm i is privately owned; a set of year dummies and industry dummies is also included to control for aggregate demand and supply shocks.  Denote the predicted prob. by p i  Select a matching firm j for i based by:

20. 20 3.2 Identifying productivity gains arising from exporting  Difference in difference Method (DID)  N firms start exporting at s=0  a set of controls C(i) for firm i, and denote the number of controls in C(i).  Every starter is matched with a set of control firms.  The matching is always performed once a firm starts to export and s={0, 1, …,S}

21. 21 3.2 Identifying productivity gains arising from exporting  Calculate the effect of learning-by-exporting in two ways:  Where w ij =, note that N changes with s, so,  We can also calculate for year-to-year produc. growth rate and growth rate compared to pre-export level

22. 22 3.3 Productivity gains upon export for companies joining the Slovenian manufacturing industry

23. 23 3.3 Productivity Gains upon Export Entry in Slovenia Manufacturing Industry specific result

24. 24 4. Productivity gains and export destination  Eaton and Kortum (2004 and 2005) use destination to understand the importance of fixed cost in entering export market.  The first paper to investigate productivity gains from exporting by distinguishing between various destinations.  8 groups: Africa, Asia, North-America, South-America, Western Europe, Southern Europe, Central and Eastern Europe, others (Australia and New Zealand)  On average 90 percent of firms export to Western Europe, Southern Europe and Central and Eastern Europe. 1/3 firms export to Asia and North America.  The pattern across industries is quite stable, which implies that the difference across industries can ’ t be solely explained by difference in destination.

25. 25 4.Productivity gains and export destination  Divide the destinations into low income and high income regions.  Split the firm sample into two groups: only export to high income regions or low income regions.  Estimate the productivity gains from export entry separately for these two groups of firms.  Only run the regression on the entire manufacturing sector.  Estimate the instantaneous productivity gains, since the destination info is only cross sectional.

26. 26 4.Productivity gains and export destination  Result:  Firms exporting only to high income regions experience a higher productivity gains than the overall sample, and so much so do firms exporting to low income regions.

27. 27 4.Productivity gains and export destination  Evidence for learning-by-exporting hypothesis: If only more productive firms could export, then export destination would not matter much. But we found that destination affects firms productivity gains.  Comment: This reasoning should be based on assuming that before exporting, both kinds of firms (export to high or low) have similar productivity level, because it might be that more productive firms can export to high income regions.

28. 28 5. Conclusion  Estimate total factor productivity based on Olley and Pakes (1996)  Introduce a matching technique to build up a counterfactual control group.  On average, exporters become more productive by 8.8% than the control group.  Productivity increases in future years following the decision to export (can be seen from cumulative effect).  The magnitude and timing of the learning effects are quite different across sectors.  Significant higher productivity gains for firms exporting to high income regions.

29. 29 Criticism  The part I appreciate:  Exporters gain produc. from exporting because exporters can learn from their importers and competitors through contracts, but not because of increasing return to scale.  Van Biesebroeck (2006): exploit increasing return to scale with access to larger market demand. But this paper finds out that in Slovenia, even firms with no significant increasing return to scale can still gain from exporting.

30. 30 Criticism  Question left unanswered:  Assume that all differences between exporters and non-exporters are captured by the observables including productivity;  But productivity gains from starting to export can be driven by unobservables which are highly correlated with exporting status. However, this can ’ t be tested in the framework of this paper.

31. 31 Criticism  Some ideas:  This paper divides exporters into two groups: only export to high income regions or only to low income regions. This makes a big drop in the sample size. I would advise to include those firms who export, say more than 60%, of their total output to high/low income regions.  Geography is an important issue. Firms lie in better regions have higher probability of starting to export.