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Given the concern about the low growth rates in African countries, this paper deals with the issue of how to increase the said growth rates by using South Africa as a case study. This paper attempts to answer this question by examining the determinants of total factor productivity (TFP) and productivity growth. We utilise the theoretical insights from the Solow (1956) growth model and its extension by Mankiw, Romer and Weil (1992). Our empirical methodology is based on the London School of Economics Hendry's General to Specific Instrumental Variable method and Gregory and Hansen's (1996a; 1996b) structural break technique. Our findings imply that variables like human capital, trade openness, foreign direct investment, financial efficiency, democracy and financial reforms improves TFP and productivity growth in South Africa. Importantly, the key determinants appear to be democracy and financial liberalisation.

In this paper, we present a methodology of GDP growth rate decomposition adapted for the Russian economy. We calculated the indicators for structural unemployment (NAWRU) and total factor productivity in Russia. We estimated the structural, foreign trade and cyclical components of GDP growth rates under various macroeconomic scenarios for the period from 2018 through 2024. The study shows that a significant contribution to growth rates for the period 2018 through 2024 will be made by the sum of the business cycle and random shock component, which, combined with the revitalization of investments in 2017, may indicate the beginning of a new cycle of economic growth in Russia. In the scenarios reviewed, the contribution from the foreign trade component will be negative from 2018 to 2024. The calculations indicate further stagnation of structural growth rates in the Russian economy from 2018 to 2024 at the level of approximately 1.5 p.p. in all of the basic macroeconomic scenarios reviewed. This points to the inexpediency in postponing structural reforms to create conditions for Russia's economy to achieve growth rates that exceed world averages.

From the end of 1984 through the middle of 1986, the monetary aggregate Ml growth rate has been extremely rapid by historical standards. The author argues that much of this rapid Ml growth reflects a transfer of funds out of banks' nonmonetary liabilities into banks' negotiable order of withdrawal (NOW) accounts. Prompting this transfer was the fall in market rates relative to rates offered on NOWs. In addition, the level of compensating balances that banks required of their corporate customers appears to have become more sensitive to market rates. Two measures are suggested to increase the usefulness of Ml targeting. The first is to use a shift‐adjusted Ml series, that is, a series adjusted for the flow of funds between NOW accounts and time deposits of banks. The second is to adjust Ml targets in light of interest rate changes.

Develops an analytical framework based on the economics of steady and non-steady growth states for explaining variations in growth performance in India. Follows 2 approaches to do this: (1) based on a decomposition of growth, and (2) based on an identification and assessment of 4 sets of variables. (Abstract amended)

The initial exponential growth rate of an epidemic is an important measure that follows directly from data at hand, commonly used to infer the basic reproduction number. As the growth rates λ(t) of tested positive COVID-19 cases have crossed the threshold in many countries, with negative numbers as surrogate for disease transmission deceleration, lock- downs lifting are linked to the behavior of the momentary reproduction numbers r(t), often called R0. Important to note that this concept alone can be easily misinterpreted as it is bound to many internal assumptions of the underlying model and significantly affected by the assumed recovery period. Here we present our experience, as part of the Basque Coun- try Modeling Task Force (BMTF), in monitoring the development of the COVID-19 epidemic, by considering not only the behaviour of r(t) estimated for the new tested positive cases— significantly affected by the increased testing capacities, but also the momentary growth rates for hospitalizations, ICU admissions, deceased and recovered cases, in assisting the Basque Health Managers and the Basque Government during the lockdown lifting mea- sures. Two different data sets, collected and then refined during the COVID-19 responses, are used as an exercise to estimate the momentary growth rates and reproduction numbers over time in the Basque Country, and the implications of using those concepts to make deci- sions about easing lockdown and relaxing social distancing measures are discussed. These results are potentially helpful for task forces around the globe which are now struggling to provide real scientific advice for health managers and governments while the lockdown measures are relaxed. ; Marie Skłodowska-Curie grant agreement No 792494