Exporting Spillovers: Firm-Level Evidence from Argentina

LAURIER Business & Economics

DEPARTMENT OF ECONOMICS WORKING PAPER SERIES

2008-02 EC:

EXPORTING SPILLOVERS: FIRM-LEVEL EVIDENCE FROM ARGENTINA

Facundo Albornoz

and

Maurice Kugler

Department of Economics Tel: 519.884.1970 Wilfrid Laurier University, Fax: 519.888.1015 Waterloo, Ontario, Canada www.wlu.ca/sbe N2L 3C5

Exporting Spillovers: Firm-Level Evidence from Argentina∗

Facundo Albornoz† Maurice Kugler‡ February 13, 2008

Abstract We investigate whether exporting firms generate possibilities for productivity enhancement by other firms through spillovers. While spillovers have been analyzed when domestic learn from foreign-owned firms, we consider the possibility of learning from firms that export, irrespective of ownership origin. We find evidence consistent with learning from exporters to upstream producers. Foreign-owned firms that do not export do not generate spillovers. Therefore, our results suggest that export activity, as opposed to foreign direct investment (FDI) per se,isassociatedwithknowledgediffusion to input suppliers.Indeed, the results suggest that FDI subsidies to foster technology spillovers may well be dominated by certain export promotion strate- gies. In addition, removing barriers to exports can prove less costly than removing barriers to FDI inflows. JEL-Classification: O30, F10 Keywords: Exporting, Spillovers, FDI, Supply-Chain Linkages

∗We would like to thank for helpful conversations Matthew Cole, Rob Elliott, Ricardo Hausmann, Dani Rodrik, and Ernesto Stein. †University of Birmingham. ‡Wilfrid Laurier University, the Centre for International Governance Innovation, and Center for International Development at Harvard University.

1 1 Introduction

The observation that multinational corporation (MNC) affiliates tend to have higher productivity than manufacturers surrounding them, has led re- searchers to investigate whether foreign direct investment (FDI) inflows cre- ate learning opportunities for firms in the host country. Also, it is now a well established fact that exporter firms, whether domestically or foreign owned, are more productive, more skill intensive and more innovative (e.g. Bernard, Jensen, Redding, and Schott (2007)). The motivation of the current paper is that, as in the case of MNC subsidiaries, the superior productivity of exporter firms may generate learning opportunities for other firms. While there are many empirical studies of learning within firms associated to export status, there is little evidence about learning between firms associated with export activity. In this paper, we investigate the evidence on technology spillovers from exporters, both domestic and foreign owned, using firm level data from Argentina’s manufacturing survey data between 1992 and 2001. The evidence on FDI spillovers in developing countries shows that the presence of MNCs in downstream sectors is associated with higher productivity of domestic firms in upstream sectors (e.g. Kugler (2006) and Javorcik (2004)). Furthermore, in some contexts, evidence on spillovers is associated with the activities of exporting MNC subsidiaries only and not the rest of subsidiaries (e.g. Blyde, Kugler, and Stein (2004)). This may be due to both less concern about technology leakage by exporting MNC subsidiaries as they are not competing for the host country market and to more scope for demonstration effects as exporters tend to have higher productivity. Firm heterogeneity means that there is potential learning from activities from other domestic firms, and not only MNCs. More generally, it may well be that the generation of spillovers by a firm is not due to its affiliation with a MNC but rather to its exporter status. We explore this possibility and find robust evidence consistent with spillovers from exporters only. In terms of policy, the findings are of interest because it may be much less costly at the margin to remove barriers to export activity than to FDI inflows, and removal of such barriers may prove a catalyst to export-led growth (e.g. Hausmann, Hwang, and Rodrik (2007) and Pack (1987)).

2 Data

Our analysis is based on data from the manufacturing ﬁrm-level surveys conducted by the National Institute of Statistics and Censuses in Argentina

2 (INDEC). The data constitute a representative sample of the manufacturing sector and account for more than 50% of total sector sales and 60% of total exports. The surveys cover the periods 1992-1996 and 1998-2001 respectively and collect data on about 1200 ﬁrms, with roughly 700 of appearing in both samples. For details on the data set see INDEC (2002). We use a balanced panel of 673 ﬁrms providing information on sector, ownership structure, exports, imports, output, labor, skill structure, R&D expenditures, three-digit ISIC sector and TFP. TFP is the only variable that is not directly provided by the survey. The TFP variable is calculated from measures of capital stock1, labor2 and real gross output3, and implementing the Olley and Pakes (1996) method.4 Table 1 reports the summary statistics for the resulting data set.

3 Export linkages

We develop measures of exporter presence within a sector and in downstream sectors following Aitken and Harrison (1999), Javorcik (2004) and Blyde, Kugler, and Stein (2004). X First, WITHINjt measures the presence of exporters in the sector in the following way:

P XF Y X i∀i∈j ijt ijt WITHINjt = P (1) i∀i∈j Yijt

Where XFijt is an indicator variable taking the value 1 if exports represent 5 more than 5% of total sales. Yijt stands for firm’s output. Notice that X WITHINjt increases with the output of exporters and their number in the sector. 1The capital stock includes machinery and equipment; office, accounting and computing machinery; electrical machinery and apparatus; motor vehicles, trailer and semi-trailers; and other transport equipment. 2Labor is measured by the number of workers. 3Real gross output is calculated by adjusting the reported sales for changes in inven- tories of finished goods and deflating the resulting value by the Producer Price Index for the appropriate three-digit ISIC sector. 4For details on the calculations of the capital stock and the TFP see Albornoz and Ercolani (2007). 5We use 5% as the threshold, rather than the mere observation of foreign sales in a particular year, to avoid misclassification error. Also, we have checked that the results are not sensitive to using of higher shares of foreign sales to define exporters. This is consistent with the observation by Bernard et al. (2007) that for most exporter firms sales abroad account for a relatively small share of revenue.

3 X Second, UP ST REAMjt is a measure of the presence of exporters in the sectors that are being supplied by the sector to which the ﬁrm in question belongs. This variable is a proxy for the knowledge acquired through contacts with costumers. The implicit assumption is that exporters, as ﬁrms with higher productivity and requiring high quality inputs, may share technical knowledge with their suppliers. The indicator takes the form:

X X UP ST REAMjt = δjkWITHINkt (2) k if k6=j

where δjk is the proportion of sector j output supplied to sector k, derived from Argentina’s I/O matrix. In order to investigate whether the impact of spillovers varies across the F type of source firms, we define: WITHINjt where F stands alternatively for Domestic X, MNC X and MNC. We construct these variables in a similar way than in (1) except for the fact that XFijt indicates different types of firms. XFijt takes the value of 1 if the source firm is a domestically owned exporter (Domestic X), an exporter belonging to a MNC (MNC X) or MNC subsidiaries irrespectively of export status, respectively. F We define as well UP ST REAMjt in a way that immediately follows from (2). Table 2 provides the summary statistics for the linkages variables.

4 Estimation

To examine the relationship between ﬁrm productivity and linkages with exporters, we estimate the following estimation:

∆ ln TFPi,t =β0 + γ1lnT F Pi,t−1 + γ2SRi,t−1 + γ3R&Di,t−1+

γ4FSi,t−1 + γ5WITHINj(i),t−1 + γ6UP ST REAMj(i),t−1+

τ + τj(i) + αi + εi,t,

where ∆TFPit = ln(TFPi,t) − ln(TFPi,t−1) and TFPi,t stands for total factor productivity of firm i at time t. To rule out that the estimated coefficients simply reflect co-movement due to transitory shocks, we include aggregate time trends (τ). Furthermore, we include sector specific time trends (τj(i)) where j(i) stands for the sector to which firm i belongs, and we al- low for firm specific fixed effects (αi). As pointed out by Hanson (2005), if there were shocks which at the same time boost sectoral and downstream exports while making domestic plant productivity higher, and we do not con- trol for sector specific time trends, then we may interpret wrongly positive

4 estimated coefficients on the sectoral export activity variables, WITHINj(i)t 6 and UP ST REAMj(i)t, as reflecting spillovers. In addition to our key export measures, we include classic potential deter- minants of productivity such as the share of foreign capital (FS), skill ratio (SR) and the share of R&D over output. Finally, all variables in the right hand side are lagged to avoid contemporaneous feedback effects.

5 Results

In Table 2, we report only results based on the Two-step System General- ized Methods of Moments analysis (System GMM).7 In the first column, we estimate a specification without discriminating export activity by firm ownership. The variable UP ST REAM has a positive association with plant TFP , even after controlling for both aggregate and sector specific time trends. The variable WITHIN measuring export activity in the plant’s own sector does not have an effect on TFP . There is no evidence of firm learning from exporter firms in the same sector. When we discriminate exporters by ownership, we find that both domestic and foreign owned exporter activities in downstream sectors are associated with higher plant productivity. Comparing columns 2 and 3, the evidence is consistent with a larger effect of MNC affiliate exporters than domestic exporters. A one standard deviation increase in downstream activity of domestic exporters is associated with an increase in the rate of productivity growth of 1.7 percentage points. And, if the exporter firms are MNC affiliates, the associated rise in productivity is 3.2 percentage points. In the last column, we consider whether non-exporter MNC affiliate activity either within sector or in downstream sectors is associted with higher productivity by other firms. We find no evidence of a correlation between MNC activities per se and productivity of upstream firms.. As to other correlates, foreign ownership is insignificant in all specifica- tions suggesting that if foreign owners select to invest in higher productivity

6One way to mitigate this problem would be to find an instrumental variable for sectoral exports. But this would require finding a variable which is correlated with export profitability, both within industry and in downstream industries, but not with domestic plant productivity. 7We have carried out several robustness checks. We have estimated our main specification using fixed effects, first and second differences OLSs. In addition, we have repeated the analysis by estimating an augmented production function, including capital and labor in the right-hand side, rather than first estimating TFP using the Olley-Pakes method and then using TFP as dependant variable in the second stage. All these variations, available upon request, yielded similar qualitative results.

5 plants, they do not raise their productivity (Arnold and Javorcik (2005)). Lastly, we ﬁnd that past R&D investments are signiﬁcantly associated with productivity growth.

6 Conclusion

We find evidence consistent with the existence of positive spillovers from downstream exporting activity. Our results suggest that spillovers are associated with export status, rather than ownership origin. Furthermore, the results here are consistent with domestic exporters also generating learning opportunities for input suppliers. Hence, productivity spillovers are associated with export activity rather than FDI among Ar- gentina’s manufacturers. In part, the evidence we uncover may reflect a productivity boost associated with the absorption of export knowhow from other exporters. It is worth noticing that our results survive after controlling for industry specific time trends and therefore they are more robust than those generally presented in the FDI spillover literature, which are subject to the problem that FDI increments (within the firm’s sector or downstream) would need to be orthogonal to all other potential factors affecting sectoral productivity. This is the critique by Hanson (2005) to the bulk of the literature on FDI spillovers mentioned above.

References

Aitken, B. J., and A. E. Harrison (1999): “Do Domestic Firms Beneﬁt from Direct Foreign Investment? Evidence from Venezuela,” The Ameri- can Economic Review, 89(3), 605–618.

Albornoz, F., and M. Ercolani (2007): “Learning by exporting: do ﬁrm characteristics matter? Evidence from Argentinian panel data,” Discus- sion Papers Series, Department of Economics, University of Birmingham, (07-17).

Arnold, J. M., and B. S. Javorcik (2005): “Gifted Kids or Pushy Par- ents? Foreign Acquisitions and Plant Performance in Indonesia,” CEPR Discussion Paper 5065.

Bernard, A., B. Jensen, S. Redding, and P. Schott (2007): “Firms in International Trade,” Journal of Economic Perspectives, 21(3).

6 Blyde, J., M. Kugler, and E. Stein (2004): “Exporting vs. Outsourc- ing by MNC Subsidiaries: Which Determines FDI Spillovers?,” Discus- sion Paper Series In Economics And Econometrics 0411, University of Southampton.

Hanson, G. (2005): “Comment,” in Does Foreign Direct Investment Pro- mote Development?, ed. by T. H. Moran, E. M. Graham, and M. Blomstrm, pp. 175–78. Peterson Institute.

Hausmann, R., J. Hwang, and D. Rodrik (2007): “What you export matters,” Journal of Economic Growth, 12(1), 1–25.

INDEC (2002): “Segunda Encuesta Nacional de Innovaci´ony Conducta Tecnol´ogica,” Serie de Estudios, (38).

Javorcik, B. S. (2004): “Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers through Backward Linkages,” American Economic Review, 94(3), 605–627.

Kugler, M. (2006): “Spillovers from foreign direct investment: Within or between industries?,” Journal of Development Economics, 80(2), 444–477.

Olley, S., and A. Pakes (1996): “The Dynamics Of Productivity In The Telecommunications Equipment Industry,” Econometrica, 64, 1263–1297.

Pack, H. (1987): Productivity, Technology and Industrial Development. Ox- ford University Press.

7 Tables

Variable Mean Std. Dev. Min Max Employment 252 480 10 5977 Outputa 33532 107920 29 2.35×103 R&Db 0.01 0.03 0 0.69 Skill ratio (SR)c 0.06 0.09 0 1.00 Foreign share (FS) d 0.11 0.29 0 1.00 Export ratioe 0.10 0.20 0 1.00 Capital stocka 16855 77266 1.021 3.02 × 106 TFP 10.80 1.04 3.99 15.20 TFP Exporter 11.09 0.95 3.9 15.02 TFP MNC 11.55 1.06 7.93 15.21 TFP Exporter MNC 11.58 1.02 7.93 15.02 ∆ ln TFPi,t 0.0001 0.52 -1.01 1.09 a: Expressed in thousands of constant 1993 pesos b: Expenditure on R&D as a proportion of total output. c: Proportion of professional workers out of total workforce. d: Proportion of shares that are foreign-owned. e: Total export sales as a proportion of production. Table 1: Summary statistics, 673 ﬁrms in balanced panel.

Variable Mean Std. Dev. Min Max UPSTREAMX 0.34 0.24 0 1 WITHINX 0.80 0.19 0 1 UPSTREAMDomestic X 0.22 0.20 0 .93 WITHINDomestic X 0.48 0.23 0 1 UPSTREAMMNC X 0.12 0.14 0 1 WITHINMNC X 0.32 0.27 0 1 UPSTREAMMNC 0.13 0.15 0 1 WITHINMNC 0.35 0.27 0 1

Table 2: Summary statistics on spillovers

8 Regressand: ∆ lnTFPi Regressors: (1) (2) (3) (4) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ lnTFPi,t−1 .088 .149 .180 .178 (.012) (.013) (.114) (.024) SRi,t−1 .009 .024 .012 .494 (.014) (.014) (.014) (.005) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ R&Di,t−1 .066 .082 .084 .118 (.021) (.023) (.025) (.031) FSi,t−1 .249 .104 .373 .215 (67.21) (24.66) (16.95) (67.97) X WITHIN j,t−1 .001 (.001) X ∗∗∗ UPSTREAM j,t−1 .005 (.002) Domestic X WITHIN j,t−1 -.001 (.001) Domestic X ∗∗∗ UPSTREAM j,t−1 .007 (.002) MNC X WITHIN j,t−1 -.002 (.003) MNC X ∗∗∗ UPSTREAM j,t−1 .023 (.006) MNC Non-X WITHIN j,t−1 .002 (.018) MNC Non-X UPSTREAM j,t−1 .003 (.032) Constant .216 .128 .686 .861 (76.19) (68.77) (20.56) (65.13) Year Dummies Yes Yes Yes Yes Year-sector dummies Yes Yes Yes Yes Hansen test χ2 169.9∗∗∗ 194.1∗∗∗ 170.60∗∗∗ 109.30∗∗∗ AB test for AR(1) 4.39∗∗∗ 4.25∗∗∗ 4.19∗∗∗ 4.51∗∗∗ AB test for AR(2) 3.81∗∗∗ 3.99∗∗∗ 4.04∗∗∗ 4.23∗∗∗ Observations 4766 4766 4766 4766 Number of Groups 697 697 697 697 Standard errors in (brackets). ***,** signiﬁcant at the 1% and 5% levels respectively.

Table 3: Dynamic Panel-data Estimation, Two-step System Generalized Methods of Moments analysis.