Background: A recent increase in the adoption of mobile phone technology generated a great deal of interest and optimism regarding its effect on economic development in sub-Saharan Africa (SSA), particularly on the enhancement of agricultural development.Aim: In this study the impact of mobile phone technology on agricultural productivity in SSA is examined.Setting: The empirical assessment uses a panel data set covering 41 countries over a period of 25 years.Methods: We employed an econometric approach and panel data covering 41 countries and a 25 year-period (1990–2014) to investigate the effect of the adoption of mobile phone technology and other socio-economic variables on agricultural total factor productivity (TFP). The use of regression analyses allowed us to estimate and measure the contribution of certain variables to agricultural TFP growth in SSA.Results: The results show that the uptake of mobile phone technology had a positive effect on agricultural TFP growth in SSA.Conclusion: Mobile phone technology has been established to be one of the drivers of agricultural productivity in SSA.Implication: The implications of this study are that governments, NGOs, and businesses working on improving agricultural productivity and food security in SSA need to continue endorsing mobile technology as a means to improve agricultural productivity.
This paper utilizes the Least Squares Dummy Variables (LSDV) technique in investigating the effect of financial depth on economic growth within a sample of middle-income countries, over the period 2005–2017. The research finds that financial depth has a negative impact on real GDP growth within middle-income countries. This result is robust to the use of alternative measures of financial depth, the use of per capita GDP growth as a proxy for economic growth, the inclusion of dummy variables to control for the 2007–2010 global financial crisis, the exclusion of countries with high average growth as well as across income levels. Based on its findings, this study recommends the need for robust regulations to ensure that the credit facilities of domestic financial institutions are channeled towards productive investments rather than debt servicing.
This study utilizes static and dynamic models in examining the short run and long run impacts of government spending on labour force participation and unemployment within the West African Monetary Zone (WAMZ) over the period 1991-2018. While the static models are estimated using the Pooled Ordinary Least Squares (POLS) technique and the Least Squares Dummy Variables (LSDV) technique, the dynamic models are estimated using the GMM-IV technique. The GMM-IV technique better addresses endogeneity issues relative to the other techniques utilized and also, the parameters obtained from the technique are confirmed to be consistent by the Arellano-Bond test for zero autocorrelation. Accordingly, this technique is given preference in this paper. The results from the technique reveal that government spending increases the labour force participation rate but has an ambiguous impact on unemployment rate. In the long run, the parameter estimates largely remain unchanged in terms of their sign and significance; however, they increase in size. Based on these findings, this paper firstly recommends that policy makers intensify efforts in increasing government spending; as a reduction may impact negatively on the labour force participation rate. Secondly, this paper recommends the formulation and implementation of fiscal policies that are robust enough to reduce the unemployment rate as they increase the labour force participation rate.
Motivated by the sharp increases in public spending following the global financial crisis and the COVID-19 pandemic, we employ the GMM Panel VAR approach at annual frequency between 2004-2014 to investigate the response of alternative income distribution variables to shocks imposed on tax revenues and three key components of social expenditures: social protection, health and education. We confirm the potential of fiscal policy to reduce income inequality, but point to the differential approaches to do so in middle-and high-income countries. We find that the particular expenditure component under consideration matters in terms of the impact on inequality and on different parts of the income distribution, as well as in terms of the implied time profile. In middle-income countries, positive education spending shocks are the most effective in achieving better distributional outcomes. By contrast, in high-income countries, positive health spending and tax shocks have a more pronounced favourable distributional impact. JEL codes: E62, H53, O15
This paper employs the R software in identifying the most suitable ARMA model for forecasting the growth rate of the exchange rate between the US dollar and a unit of the British pound. The data is systematically split into two distinct periods identified as the in-sample period and the out of sample period. The best model selected for the in-sample period is used to make forecasts for the out of sample period. Both traditional and rolling window forecasting methods are employed. This research uses the MSE, MAE, MAPE and correct sign prediction criterion to compare the forecasting performance of the rolling window forecasting method and the traditional forecasting method. The results obtained suggest that the traditional forecasting method performs better judging by MSE, MAE and MAPE. In other words, the traditional forecasting method is more suitable for predicting the magnitude (i.e., size) by which the US /UK exchange rate changes over time. However, the results also suggest that the rolling window forecasting method performs better based on the correct sign prediction criterion. In other words, the rolling window forecasting method is more appropriate for predicting the changes in the sign of the US /UK exchange rate.
This study utilizes static and dynamic panel models in investigating the impact of social benefits on the aggregate level of income inequality as well as on the income shares of different income groups within the EU-15 countries over the period 1995-2015. While the static panel regression models are estimated with Pooled Ordinary Least Squares (POLS) and Least Squares Dummy Variables (LSDV) techniques, the dynamic panel regressions are estimated using dynamic GMM-IV technique. Diagnostic tests indicate that the results from the GMM-IV technique are consistent and the associated instrumental variables are valid; hence this study gives preference to the results from this technique. The results indicate that social benefits generally have a significantly negative impact on the aggregate level of inequality, a positive impact on the income shares of the low and middle income groups, and a negative impact on the income shares of the high income groups. In the long run, the sign and significance of the parameter estimates remain unchanged but their sizes increase considerably. This research considers a variety of theories and finds that there exists much ambiguity in the theoretical literature. Based on its findings, this study recommends that policymakers address rising income inequality by intensifying efforts towards raising social benefits and ensuring that the welfare system is efficiently managed.
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