America's secret competitive advantage is a dirty secret

Michael E Porter, a leading management thinker, identifies seven uniquely American competitive advantages to explain the country’s pre-eminence. He points to America’s environment for entrepreneurship, its science, technology, and innovation machine, its institutions for higher learning, its strong commitment to competition and free markets, its efficient capital markets (especially for risk capital), and its fundamental dynamism and resilience that embraces a willingness to restructure and take losses. In this paper, I argue there is another critical competitive advantage for America in relation to its other democratic peers. It is the fact that in American presidential and congressional elections, about half of the electorate never turns out to vote. And the unique competitive advantage arises from the fact that unlike in other Western democracies, the people who end up staying away from voting in the U.S. belong overwhelmingly to its poorest, least educated sections. Before considering why this should amount to a competitive advantage, I look at reasons why the poor in America vote in far lesser proportions than their numbers. For instance, the rules governing voter-eligibility are determined by state, as well as, federal laws. Historically, many of the southern states have had a nasty record of officially and unofficially making it more difficult for blacks and poor whites to vote, a position that largely prevailed until the Voting Rights Act of 1965. Other reasons include the disenfranchisement of its prison population, which, at two million, is the largest in the world. Moreover, felony disenfranchisement laws in most states make it difficult for ex-felons to vote. As a result, about 5.4 million offenders and ex-offenders (about 2.5 percent of the electorate) were excluded from the voting rolls in the 2004 presidential election. Also, the fact that Election Day in the U.S. is not a national holiday makes it difficult for those holding low paying jobs (where wages are paid by the hour) to go out and vote. The upshot of it all is that the average voter turnout in America has been historically lower compared to its peers, and the poor in America vote in far lesser proportions than their numbers. For instance, during the presidential elections of 2008, despite extraordinary efforts by the Obama campaign to mobilize poor and minority voters, a CNN exit poll found that only 18 percent of those who turned out to vote, earned an income of less than $30,000 per annum against the 34 percent of American households that belong to this category. Why should this amount to a competitive advantage for the American economy? A critical factor which determines the economic success of a country is how well it strikes a balance between its short term needs and long term requirements. The short term interests veer towards more spending and consumption, while the long term interests lie in greater investment for the future and in shaping an environment conducive to creation of wealth.Essentially, the poor and the disadvantaged within a country would tend to have a short term outlook. Their interest would lie in having the government spend more on generous social security benefits and subsidies and in laws that protect labour. They would be far less enthused by the investments and sacrifice required to further the economic well-being of the country over the long term, or in promoting the entrepreneurial class. I argue that it is the short term considerations that hold sway in democracies where the poor vote in large numbers. I cite the example of India where populist policies have always pulled in the votes, with negative long term economic consequences. America’s overriding economic success has much to do with its “national consensus”, built around old-fashioned virtues like respect for property rights, free trade and free markets, lower taxes, flexible labour laws, and a culture that fosters individual responsibility and celebrates individual success. I contend that this consensus has been kept alive in large measure by keeping its poor away from voting. In contrast, Western Europe and Canada have seen an alternative consensus emerge which emphasises more frequent state intervention in economic matters, a comprehensive social security net, and a tax regime with a higher burden on the rich—in the cause of a more equitable society. Arguably, this alternative consensus could emerge in Europe because the national elections in these countries do not effectively (and insidiously) keep out the poor as they do in the U.S. In the concluding parts, I look at some of the recent trends in the voting patterns in U.S. national elections and predict a swing towards the more liberal values of the Democratic Party. I argue that the current “Republican revolution” that began with Reagan in 1980 may have come to an end for now. I conclude with the contention that America is headed towards a future where it becomes more equitable (like Europe) but at likely cost to its hitherto extraordinary competitive edge.competitive advantage; disenfranchisement and economic competitiveness; American competitive advantage

A VECM Model of Stockmarket Returns

Observations of security prices and other financial time series usually include not only the close (C), but also an open, a high and a low (O,H,L) price for a specified interval. The multivariate vector of values (H,L,O,C) is obviously more informative than just the open or close (O, C) for modelling volatilities and volatility predictions. In this paper we capture the return generation process of security prices by using all the quoted prices (H, L, O, C) via a vector error correction (VECM) model. The results of the empirical models using daily DJI index data for a 11 year period (1990-2000) indicate some interesting stylised facts regarding the market returns. We show, via the return generation process (RGP) proposed, that the "cointegrating returns" exhibit significant explanatory power. Some insights are also provided as to why logarithmic returns tend to be non-normally distrbutedCointegration (CI); VECM; VAR; return generation process (RGP).

The Senior Communicator of the Future – Competencies and Training Needs

Sanchez (2005) proposed the future leading communicator as: “the true professional [who] will be an adroit strategist, a creative technician and a skilled facilitator – a friend of technology and an exponent of life-long learning. The future is a global voyage into the art and science of communication, where the successful communicator will be like the men and women of the Renaissance, pulling it all together, but in the high tech environment of the 21st century.” (pp.10-11) Since the 1980s, starting from Broom and Dozier’s seminal studies on the nature of public relations employment and professionalism, there has been discussion of the career paths, competencies and training needs of public relations and corporate communication professionals. More recently, the Arthur W. Page Society (2007) has scoped the role of the Chief Communication Officer’s role in the Authentic Enterprise which placed the communicator at C-Level (Executive Board) or close to it (the marzipan layer) of the corporation. The research to be reported in this paper analyses the responses of leading European and international senior-level communicators as to the knowledge, skills, relationships, 360-degree vision, and managerial abilities that senior communications professionals will need in five years’ time, and what it takes to prepare the next generation of leaders in globally integrated organizations. The paper will also reflect on recent academic and practice literature about the nature of these competencies and discusses the potential methods and routes of their delivery. It will also consider the current operating situation, the challenges facing senior corporate communicators and their future needs. The outcomes will include recommendations for consideration by educators and employers, especially those operating in cross-cultural environments

Hamiltonian System Approach to Distributed Spectral Decomposition in Networks

Because of the significant increase in size and complexity of the networks, the distributed computation of eigenvalues and eigenvectors of graph matrices has become very challenging and yet it remains as important as before. In this paper we develop efficient distributed algorithms to detect, with higher resolution, closely situated eigenvalues and corresponding eigenvectors of symmetric graph matrices. We model the system of graph spectral computation as physical systems with Lagrangian and Hamiltonian dynamics. The spectrum of Laplacian matrix, in particular, is framed as a classical spring-mass system with Lagrangian dynamics. The spectrum of any general symmetric graph matrix turns out to have a simple connection with quantum systems and it can be thus formulated as a solution to a Schr\"odinger-type differential equation. Taking into account the higher resolution requirement in the spectrum computation and the related stability issues in the numerical solution of the underlying differential equation, we propose the application of symplectic integrators to the calculation of eigenspectrum. The effectiveness of the proposed techniques is demonstrated with numerical simulations on real-world networks of different sizes and complexities

Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms

In temporal ordered clustering, given a single snapshot of a dynamic network in which nodes arrive at distinct time instants, we aim at partitioning its nodes into $K$ ordered clusters $\mathcal{C}_1 \prec \cdots \prec \mathcal{C}_K$ such that for $i<j$, nodes in cluster $\mathcal{C}_i$ arrived before nodes in cluster $\mathcal{C}_j$, with $K$ being a data-driven parameter and not known upfront. Such a problem is of considerable significance in many applications ranging from tracking the expansion of fake news to mapping the spread of information. We first formulate our problem for a general dynamic graph, and propose an integer programming framework that finds the optimal clustering, represented as a strict partial order set, achieving the best precision (i.e., fraction of successfully ordered node pairs) for a fixed density (i.e., fraction of comparable node pairs). We then develop a sequential importance procedure and design unsupervised and semi-supervised algorithms to find temporal ordered clusters that efficiently approximate the optimal solution. To illustrate the techniques, we apply our methods to the vertex copying (duplication-divergence) model which exhibits some edge-case challenges in inferring the clusters as compared to other network models. Finally, we validate the performance of the proposed algorithms on synthetic and real-world networks.Comment: 14 pages, 9 figures, and 3 tables. This version is submitted to a journal. A shorter version of this work is published in the proceedings of IEEE International Symposium on Information Theory (ISIT), 2020. The first two authors contributed equall

Bayesian Inference of Online Social Network Statistics via Lightweight Random Walk Crawls

Online social networks (OSN) contain extensive amount of information about the underlying society that is yet to be explored. One of the most feasible technique to fetch information from OSN, crawling through Application Programming Interface (API) requests, poses serious concerns over the the guarantees of the estimates. In this work, we focus on making reliable statistical inference with limited API crawls. Based on regenerative properties of the random walks, we propose an unbiased estimator for the aggregated sum of functions over edges and proved the connection between variance of the estimator and spectral gap. In order to facilitate Bayesian inference on the true value of the estimator, we derive the approximate posterior distribution of the estimate. Later the proposed ideas are validated with numerical experiments on inference problems in real-world networks