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The Economic Community of West Africa States (ECOWAS) is a regional group consisting of 15 countries which was founded 1975 with the goal to promote economic trade, national cooperation, and the creation of a monetary union throughout West Africa. This paper empirically assesses the determinants of the economic development from 1996-2016 in ECOWAS using panel unit root tests, panel cointegration tests, and the estimation of the dynamic panel data regression via the Arellano–Bond estimator and Arellano–Bover and Blundell–Bond estimator. The empirical results show total factor productivity (TFP), law, and somewhat corruption are indicative of economic growth under the Arellano–Bond estimator. Under the Arellano–Bover and Blundell–Bond estimator, the results revealed that inflation, gross domestic saving (GDS), and TFP have a significant impact on economic growth in the ECOWAS. From these empirical results, improving economic growth in ECOWAS countries improves the quality of life of people and the government of each ECOWAS country become cognizant of the benefits in the implementation of pro-growth policies. The policy implication is that the governments of the ECOWAS countries should give policy priority to promote pro-growth economic policies and enhance institutions to enable economic growth.

The estimation of the employment effects of offshore safety and environmental regulation is often highly speculative and based on questionable assumptions. Nevertheless, it is still highly publicized and used as a basis for policy statements in support or, or in opposition to, proposed regulations. Much more reliable estimates of such employment effects can be made, however, based on fundamental principles of microeconomic analysis. This paper demonstrates this by developing a microeconomic model explaining the effects of offshore regulations on employment, assuming the standard profit-maximization behavior of firms. The paper finds that the most relevant and reliable measures of employment effects are: reductions in employment from operations that are terminated because of the new regulation, increases in employment because of additional labor needed to meet the new requirements, and increases in employment in equipment manufacturing when the regulation calls for the expanded use of certain equipment. The costs related to these contractions or expansions of employment can often be gleaned from information in the benefit-cost analysis that was required to accompany the proposed regulation by the regulatory agency involved. For example, the daily costs of offshore rigs and the costs of equipment can be translated to increases in employment.

The purpose of this paper is to examine the sources of economic growth for the ECOWAS countries and to disentangle the relative contribution of each source. Malmquist Productivity Index decomposition is used to distinguish between technical efficiency versus technological change. In addition, an OLS and panel regression is used to estimate the contribution of various sources of growth to increases in GDP per capita. The paper concludes that 1) there was a modest increase in Productivity Index in ECOWAS countries (11.1% between 1981 and 2015) and 2) both factor accumulation and total factor productivity drive the economic growth with technological change and efficiency change being significantly greater contributors.Keywords: Total factor productivity, Malmquist Productivity Index Economic Growth, ECOWAS, Fixed Effect, Random Effect

Railroad Economics (Dennis, Scott; Talley, Wayne)

This paper predicted fluctuations in the transportation sector using leading indicators. From 25 initial candidates, we selected seven leading indicators, using various screening techniques and modern time series models. A composite leading index was constructed and found to perform well in predicting transportation reference cycles. The leading index signals downturns in the transportation sector 10 months ahead and upturns six months ahead on average. The index predicted the latest recession in transportation with a lead of 20 months. The analysis also confi rms the predictive contents of the composite leading index (CLI) in relation to transportation growth cycles. These evaluation criteria ensure accurate forecasts of the general state of the transportation sector in a timely fashion.