Abstract
Total factor productivity (TFP) is the most commonly used measure of firm performance and a valuable tool for policy making. Although cross-country TFP analyses have been performed by many researchers, lack of homogeneous data sources obstructed conducting conclusive analyses with micro-level datasets. This study compares average TFP performances of Turkish firms and aggregate TFP in Turkey with firms in a group of selected countries using data collected by the World Bank. Data was collected through firm-level surveys that follow a standard methodology. Cross-country comparison of TFP performances shows that there are significant differences between average and aggregate TFP across countries. While aggregate TFP in Turkey ranks high among the peers, the rank of Turkey’s average TFP is lower. Also, relative to the peer group, productivity distribution is more dispersed in Turkey and large firms are more productive than small firms. This finding is consistent across various methodologies implemented to measure TFP. Despite low average productivity in Turkey, there are some industries such as Textile, Chemicals, Basic Metals and Machinery where Turkish firms rank among the top.
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Notes
- 1.
These surveys are conducted under the name of “Enterprise Surveys”. Detailed information about the data collected by the surveys and the methodology followed for collection of the data are available at http://www.entreprisesurveys.org
- 2.
Countries included in the analysis, by region are: Eastern Europe and Central Asia: Armenia, Azerbaijan, Belarus, Bosnia-Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Macedonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Poland, Romania, Russia, Serbia, Slovakia, Tajikistan, Turkey, Ukraine, Uzbekistan; Middle East and North Africa: Algeria, Egypt, Jordan, Morocco, Syria, Yemen; Latin America: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay; South and East Asia–Pacific: India, Indonesia, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Thailand, Vietnam; Sub-Saharan Africa: Angola, Botswana, Burundi, Cameroon, Ivory Coast, Congo, Ethiopia, Ghana, Guinea, Guinea-Bissau, Kenya, Madagascar, Mali, Mauritania, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, South Africa, Swaziland, Tanzania, Uganda, Zambia.
- 3.
Estimation equation: \( \log {A}_{it}=\log {Y}_{it}-\widehat{\alpha}\log {K}_{it}-\widehat{\beta}\log {L}_{it}-\widehat{\lambda}\mathit{\log }{E}_{it}-\widehat{\phi}\log {M}_{it} \)
- 4.
Estimation equation: \( \log {\mathrm{A}}_{\mathrm{it}}=\log {\mathrm{VA}}_{\mathrm{it}}-\widehat{\upalpha}\log {\mathrm{K}}_{\mathrm{it}}-\widehat{\upbeta}\log {\mathrm{L}}_{\mathrm{it}}\ where\ {\mathrm{VA}}_{\mathrm{it}}={\mathrm{Y}}_{\mathrm{it}}-{\mathrm{M}}_{\mathrm{it}}-{\mathrm{E}}_{\mathrm{it}} \)
- 5.
Alternative to GDP deflator, Producer Price Index (PPI) obtained from IMF is also used to deflate the nominal variables. The estimation results obtained with PPI are very similar to those obtained with GDP deflator.
- 6.
For the related references see Şeker and Saliola (2018).
- 7.
Presenting the correlation matrix for only 12 countries would yield insignificant correlation coefficients for some of the specifications as the data points are too low. When we look at the correlation coefficients that were estimated for the 12 selected countries; those that were significant, were also large and positive.
- 8.
Comparison of average TFP measured with and without probability weights, which are available upon request, give very similar TFP estimates.
- 9.
These averages are calculated from the 80 countries analyzed in Saliola and Şeker (2011). Average for ECA region includes only countries from the region that are surveyed in 2007–2008 period; similarly average for LAC covers 2005–2006 period; average for AFR covers 2005–2006 period. Countries in South Asia-Pacific were surveyed between 2006–2009 and in Middle East were surveyed between 2006–2009. Their averages were not included due to widespread year coverage which made it difficult to summarize as regional average.
- 10.
- 11.
- 12.
In this table average productivity levels are presented in real TFP levels rather than log(TFP). The number of observations and averages get too small when we break down the data at industry level. Thus, taking the logarithms create superfluous dispersion in TFP levels.
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Şeker, M. (2018). Total Factor Productivity in Turkey: A Comparative Analysis. In: Bilgin, M., Danis, H., Demir, E., Can, U. (eds) Consumer Behavior, Organizational Strategy and Financial Economics. Eurasian Studies in Business and Economics, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-76288-3_25
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