The Nexus Between Internal Investment and Economic Growth in Kenya

Abstract

Countries with a high investment GDP ratio benefit from better, competitive products and services. Which increases capital stock for production, more employment, and income; in turn reducing social and income disparities. The Kenyan government envisaged a sustained economic growth of 10% by investing in priority sectors; to become an industrialized middle-income country by the year 2030; though un-achieved to date. To examine the nexus between internal investments and economic growth, the study used annual time-series observations from the years 1996 to 2017; where internal investments are from the government; private domestic; and public-private partnership; and exogenous variables were rates of real interest; social discount; commercial lending interest; and the country risk premium on lending for investment decisions. The inference used stationarity; cointegration; significance; causality; variance decomposition of forecast error; and impulse response function. Stationarity tests suited the ARDL model which also supports small size observations. Findings were; a significant and positive influence on economic growth from lags of real GDP, government, private domestic, except public-private partnership investments. Anticipation for growth lies with; significant pairwise causality (real GDP with public investment); significant block exogeneity (public investment); endogeneity (real GDP), and exogeneity (public investment) influence; and short-run private domestic investment recovery. Keywords: ARDL, Economic Growth, Public Investment, Private Domestic Investment, Public-Private Partnership Investment, Investment Decisions.

E‐trading and services quality effects on stock exchange in a developing country: A study of Dhaka stock exchange

“…Stock exchanges around the world have embraced automation to enhance trade transparency, efficiency, and accessibility for market participants (Dutta et al, 2017). E‐trading systems are defined narrowly as systems that automate the process of trading, unlike the broad definition (Aduda et al, 2014). There are often subtle differences between fully automated systems, as their architectures are often complicated.…”