In aggregate unadjusted data, measured Solow residuals exhibit large seasonal variations. Total Factor Productivity grows rapidly in the fourth quarter at an annual rate of 16 percent and regresses sharply in the first quarter at an annual rate of -24 percent. This paper considers two potential explanations for the measured seasonal variation in the Solow residual: labor hoarding and increasing returns to scale. Using a specification that allows for no exogenous seasonal variation in technology and a single seasonal demand shift in the fourth quarter, we ask the following question: How much of the total seasonal variation in the measured Solow residual can be explained by Christmas? The answer to this question is surprising. With increasing returns and time varying labor effort, Christmas is sufficient to explain the seasonal variation in the Solow residual, consumption, average productivity, and output in all four quarters. Our analysis of seasonally unadjusted data uncovers important roles for labor hoarding and increasing returns which are difficult to identify in adjusted data.
Africa is far behind East and Southeast Asian countries in attracting FDI from multinational corporations (MNCs). To improve their positions, most African states are now pursuing neoliberal economic policies that focus on private sector development and export trade. In light of their chosen economic path, this exploratory paper proposes an institutionally-driven management framework for African states to create and promote national competitive advantages to enhance their attractiveness to FDI. The proposed framework highlights the government’s role in managing the four critical environmental forces—techno-economic, politico-institutional, socio-demographic, and cultural—that drive national productivity and innovative capacity, especially in the early phases of the national push for economic growth and development.
Leadership training continues to be identified consistently as a priority need in law enforcement (Brand, 2010, p. 18). This need is especially acute for first-line supervisors who play a central role in efficient service delivery (Moriarty, 2009, p. 20). The California Commission on Peace Officer Standards and Training (POST) (2008) has been responsive to this need for more than two decades through the Sherman Block Supervisory Leadership Institute (SBSLI). In June 2011, the Institute launched its 300th session.
Remarks by Charles L. Evans, President and Chief Executive Officer, Federal Reserve Bank of Chicago Swiss National Bank Research Conference Zurich, Switzerland
Economic data are used primarily in two ways. Academic economists typically use data to build models of the economy in order to understand how the economy works. Business analysts, on the other hand, use economic data to forecast future economic activity. These two activities and groups of people are not truly distinct groups, nevertheless the two activities do involve some substantive differences. The problem facing the business analyst, and to a large extent the policymaker or businessman who has to make decisions based on the economic outlook, is how each piece of new information should be assessed. Does it portend higher growth or lower, a recession or a boom, slow growth or stasis? Such assessments are crucial to running a successful business and to the proper ongoing evaluation of economic policy. Yet economic analysis rarely focuses on precisely these questions. In the current article, we develop an organized structure for evaluating economic indicators and apply that structure to a wide variety of financial indicators and a selected group of real indicators as well. This process is fundamentally more eclectic than the usual econometric analysis which looks for or constructs a best indicator, where best typically refers to winning some narrowly defined contest of general purpose forecasting ability measured over some preselected time span.' Unfortunately, experience tells us that such a search is likely to end in failure. Economic history is full of examples of indicators, such as stock prices and various monetary aggregates, which work for a short period of time after their discovery and then fail dramatically just as they become widely used. There are many reasons for this, but one stands out. As the following analysis will show, indicators do well at different things and at different times. Without an understanding of the limitations this implies, these best indicators are often stretched well beyond their capabilities. What the business analyst really needs to know is the type of information that an indicator possesses and the types of purposes to which it can reasonably be put. Indicators, like people, perform better or worse depending on the context in which they operate. Efficient usage requires matching indicators both with appropriate questions and with other complementary indicators. For instance, some indicators, such as the Purchasing Managers' Index of the National Association of Purchasing Management (NAPM), do well at predicting short run changes in activity, but do not do very well at pinning down the level of activity over longer time spans. Other indicators, such as the growth in real M2, forecast short run phenomena poorly, but do better at predicting average activity over a longer time
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