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Industrial structure and total factor productivity: the Tunisian manufacturing sector between 1998 and 2004


Using a panel of manufacturing firms operating in 138 delegations across the Tunisian coast and observed over the 1998–2004 period, we study the impact of industrial structure on regional economic growth measured by total factor productivity. The results of an unbalanced panel data-based model indicate that the diversity of the industrial scene seems to be a local growth-promoting factor for high-tech sectors. Specialization often articulates the impact of diversity, while competition positively affects productivity.

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  1. 1.

    Puga (2010) stress that the underlying reason to look hard at agglomeration economies in production is that if agglomeration increases productivity, then it can potentially increase earnings, income and standards of living. Krugman (2005) explains, when we consider countries, a 5 % difference in productivity will translate into (roughly) a 5 % difference in living standards.

  2. 2.

    Agglomeration economies have been used to justify cluster policies by national and local governments in many country such as Germany, Brazil, Japan, Southern Korea, Spanish Basque country or more recently France (Martin et al. 2011)

  3. 3.

    Olley and Pakes (1996).

  4. 4.

    For recent reviews of the empirical literature, see Rosenthal and Strange (2004) and Graham and Kim (2008).

  5. 5.

    As an example, we mention Baccouche et al. (2008) who propose to study the impact of FDI on TFP from a panel of Tunisian manufacturing firms over the period of 1998–2004 and Baccouche and Kouki (2003) and Goaied and Mouelhi (2000) who proposed to estimate the firm-level technical efficiency and to decompose the TFP growth in Tunisian textile, clothing and leather industries during the period of 1983–1994.

  6. 6.

    Ellison and Glaeser (1997) and Batisse (2002a, (2002b) use also the same index to study industrial concentration.

  7. 7.

    Law no 93–120 of 27th December 1993— Official Government Gazette no 99 of 28/12/1993.

  8. 8.

    For more detail, see Ackerberg et al. (2007).

  9. 9.

    Barbesol and Briant (2008) use a simple average productivity.

  10. 10.

    see Fig. 1 and Table 3 in the Appendix.

  11. 11.

    For the sectoral level, the same level was used (digit-2) considered by the APII (Agency for the Promotion of Industry and Innovation) and the CGDR (general commissariat of regional development). Information about digit-3 does not exist.

  12. 12.

    In fact, the passage to the governorate scale can result in a considerable decrease in the number of observations. In addition, the use of data across the governorate can hide significant heterogeneity (employment and other variables used in the model) between the various delegations belonging to the same administrative unit (governorate).

  13. 13.

    Trend is a tendency term introduced in the production function in order to take into account autonomous technical progress.

  14. 14.

    To take into account heterogeneity, some studies include sector-defining variables in the model.

  15. 15.

    Cingano and Schivardi (2004) have also introduced simultaneously an indicator of competition and an indicator of size in the regression.

  16. 16.

    The correlation coefficient between firm size and local competition is but 0.098, which leads us not to interpret firm size as a proxy of local competition. We tried in a first step to estimate the same basic model without the indicator of average firm size and in a second step without the indicator of competition while keeping all other dependent variables in the model. The results do not seem too affected. These results are not reported in the paper but are available under request.

  17. 17.

    The reader can see Baccouche et al. (2008) for the case of Tunisia and Bouoiyour and Toufik (2007) for the case of Morocco.


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I want to thank Baccouche Rafik Mohamed Amara and the two anonymous referees of Ann Reg Sci for their comments and suggestions.

Author information

Correspondence to Khaled Thabet.



See Fig. 1 and Tables 3, 4, 5, 6, 7.

Fig. 1

The eleven coastal governorates: 1 Tunis, 2 Ariana, 3 Ben Arous, 4 Manouba, 5 Bizerte, 6 Zaghouan, 7 Nabeul, 8 Sousse, 9 Monastir, 10 Mahdia et 11 Sfax

Table 3 Number of delegation by governorate
Table 4 Average annual growth between 1998–2004
Table 5 Descriptive statistics
Table 6 Parameter estimates of production function
Table 7 Average annual growth of TFP per sector and governorate

 Correlation matrix

  Log(sp) Log(comp) Log(div) Log(size) FDI Log(RD) Log(K) Log(L)
Log(sp) 1.000
Log(comp) \(-\)0.369 1.000
Log(div) \(-\)0.297 \(-\)0.016 1.000
Log(size) \(-\)0.074 \(-\)0.098 \(-\)0.088 1.000
FDI 0.241 \(-\)0.368 0.000 0.228 1.000
Log(RD) 0.215 \(-\)0.208 0.000 \(-\)0.166 0.074 1.000
Log(K) 0.236 \(-\)0.480 0.123 \(-\)0.273 0.039 0.405 1.000
Log(L) 0.439 \(-\)0.394 0.096 \(-\)0.308 0.151 0.392 0.745 1.000

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Thabet, K. Industrial structure and total factor productivity: the Tunisian manufacturing sector between 1998 and 2004. Ann Reg Sci 54, 639–662 (2015). https://doi.org/10.1007/s00168-015-0670-4

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