Conditional Convergence And Future Tfp Growth In Israel

This study formulates and estimates a model for long-term forecasting of the total factor productivity (TFP) component of Israel's GDP and for assessing the impact of policy variables on it. To do this, we first estimate GDP per worker in a cross-sectional regression of 66 countries, including Israel, where the explanatory variables are fundamental factors such as geography and culture, as well as policy-influenced variables such as human capital quality, infrastructure levels, and institutional quality. We then calculate for each country the gap between the predicted GDP per worker, based on the values of the explanatory variables in 2010 and the regression coefficients, and the actual GDP per worker. This gap reflects the potential for Israel's GDP per worker to grow faster than the average global growth rate, particularly through faster TFP growth. In the second stage, we estimate TFP growth equations where the TFP growth rate depends on the initial labor productivity gap. The use of fundamental variables and policy variables together in a conditional convergence framework is a novel contribution to the economic growth literature. We find that Israel's average gap between predicted and actual GDP per worker is positive but small. From this, we conclude that Israel's TFP has very limited potential to grow faster than the global average without changes in policy variables. The baseline TFP growth forecast for 2020–60 is 0.55%, slightly lower than Israel's historical TFP growth rate in 2000–19.