Macroeconomic Determinants of Total Factor Productivity: An Analysis on the Example of OECD Countries
Nadide Yiğiteli, Fahriye ÖztürkThis study examines the macroeconomic determinants of total factor productivity (TFP), a critical factor for sustainable economic growth, within the scope of the dataset, including the 1990–2017 period and 27 OECD countries. The primary goal is to provide an up-to-date perspective on the literature within the scope of the analyzed period and country and provide an infrastructure for policy sets that will contribute to long-term economic growth. We use a two-stage empirical application in this context. First, the economic stability index, innovation index, technology diffusion index, education index, and economic freedom index are computed. Subsequently, the effect of these variables on TFP is investigated using panel regression methods. Results reveal that the technology diffusion index, which depicts the mechanisms of technology transfer from country to country, is the most important factor determining TFP. This variable is followed by the education and innovation indices. Another important finding of the study is that the economic stability and freedom indices have a significant impact on TFP. However, the magnitude of these effects is smaller than that of other variables.
Toplam Faktör Verimliliğinin Makroekonomik Belirleyenleri: OECD Ülkeleri Örneğinde Bir Analiz
Nadide Yiğiteli, Fahriye ÖztürkÇalışmada, 1990-2017 dönemini ve 27 OECD ülkesini içeren veri seti kapsamında sürdürülebilir ekonomik büyüme için temel bir etken olan toplam faktör verimliliğinin (TFV) makroekonomik belirleyicileri analiz edilmektedir. Çalışmanın amacı, analiz edilen dönem ve ülke kapsamında literatüre güncel bir bakış açısı sunmak ve sürdürülebilir ekonomik büyümeye katkı sağlayacak politika setlerine yönelik altyapı sağlamaktır. Bu kapsamda, çalışma iki aşamalı bir ampirik uygulama içermektedir. İlk olarak, ekonomik istikrar endeksi, yenilik endeksi, teknoloji yayılım endeksi, eğitim endeksi ve ekonomik özgürlük endeksi hesaplanmakta olup sonraki aşamada ise panel regresyon yöntemleri kullanılarak bu değişkenlerin TFV üzerindeki etkisi araştırılmaktadır. Bulgular, TFV’yi belirleyen en önemli faktörün teknolojinin ülkeden ülkeye transfer mekanizmalarını gösteren teknoloji yayılım endeksi olduğunu göstermektedir. Bu değişkeni, eğitim ve yenilik endeksleri ve takip etmektedir. Ekonomik istikrar endeksi ile ekonomik özgürlük endeksinin de TFV üzerinde anlamlı bir etkisinin bulunduğu ancak bu etkilerin büyüklüğünün diğer değişkenlere göre daha sınırlı olduğu çalışmanın diğer önemli bulgularıdır.
Productivity shows the relationship between output and inputs in the production function. In this context, productivity expresses the ability to maximize output based on a specific input vector or minimize input based on a particular output vector in the production process. Total factor productivity (TFP) is defined as the productivity of all inputs contributing to the output production. In economies with restrictions on production factors and decreasing returns, sustainable economic growth cannot be achieved by increasing the number of production inputs such as capital and labor. In this case, the primary method to make economic growth sustainable is to increase the productivity of inputs. The increase in productivity based on the measurement method is the easiest one because of increased technical efficiency and technological progress. Countries can boost their production by utilizing their existing capacity. With technological advancement, the potential production limit expands. Moreover, the utilization of a greater portion of current potential or the adoption of new technologies is constrained by the limits of social, political, and institutional structures and economic growth conditions.
Within the scope of the dataset, including the period 1990–2017 and 27 OECD countries, this study examines the macroeconomic determinants of TFP, a fundamental determinant of sustainable economic growth. The study includes a two-stage analysis in this context. To begin, the economic stability index, innovation index, technology diffusion index, education index, and economic freedom index are computed. The effect of these variables on TFP is then investigated using panel regression methods in the following stage. Real exchange rate volatility and inflation rate variables are included in the macroeconomic stability index. The innovation index is obtained by high tech product exports, the number of researchers and technicians in the reserach and development (R&D) sector, scientific and technical journal articles, the total number of patents, and R&D expenditures. Additionally, the technology diffusion index is calculated, taking into account the variables of openness and foreign direct investment. The average years of education were scaled as an indicator of the education index and the economic freedom score as an indicator of institutional quality using the same methodology used to calculate indexes. The economic freedom score includes the average value of 12 variables: property rights, judicial effectiveness, government integrity, tax burden, government spending, fiscal health, business freedom, labor freedom, monetary freedom, trade freedom, investment freedom, and financial freedom variables. In this context, the calculated indexes are the model’s independent variables, and the TFV index is the dependent variable.
Findings show that the coefficients of the explanatory variables of the innovation index, technology diffusion index, education index, and economic freedom index are positive. Therefore, countries with larger values of these variables tend to have higher TFP increases. Meanwhile, the coefficient of macroeconomic stability index is negative, which means higher values indicate a more unstable economic structure. Therefore, the negative value of this index suggests that TFP will also increase as stability increases. As a result, the signs of the predicted coefficients receive the expected values according to the economic theory and are statistically significant at the 1% significance level. The variable with the greatest impact power on TFV within the studied period and model framework is determined as the technology diffusion index. This variable is followed by the education index and the innovation index. The study’s findings are consistent with the fundamental theoretical framework of endogenous growth theories based on human capital, R&D, foreign direct investment, and international trade. Additionally, the results are consistent with the view that the classical schools will increase productivity through international specialization, division of labor, and resource allocation.
The study’s findings indicate that countries seeking to move forward with stable income and economic growth should pursue policies that improve technology production and distribution conditions. Meanwhile, the country’s ability to mass produce advanced technologies is critical for the spread of technology transfer effects. Training a qualified workforce capable of utilizing advanced technology points for effective educational policies in terms of both quality and quantity is important. Developing skills and increasing awareness of new technologies necessitates the activation of lifelong learning processes. Attracting foreign direct investments that will contribute to technology transfers and capability development is also seen as an important policy tool. Meanwhile, innovative ecosystem that will motivate technological progress is determined by social, political, cultural, and macroeconomic variables. Ensuring ease of doing business and establishing institutional structures with precise, clear, and objective rules are important policy areas.
