Pension reform with migration and mobile capital: is a Pareto improvement possible?

This paper shows that in a two-country two-overlapping-generations model with migration, capital mobility and an immobile production factor (land), a locally welfare-improving pension reform at the cost of the neighboring country is possible if land plays a minor role in production. Furthermore, differences in the size of the PAYG pension schemes between the countries distort the international allocation of labour and capital. As a result, a Pareto-improving pension reform is possible if countries employ PAYG pension schemes of different size, provided that a federal government exists that redistributes benefits and losses of the reform both intergenerationally and internationally.     

Mutation of HRM practices in Russia: an application of CRANET methodology

This article describes the challenges facing established practices and patterns of human resource management (HRM) during the economic recession. It is based on the results of the CRANET survey, administered in Russia in the third quarter of 2008, on the 2008 CRANET data available for Bulgaria and on survey of companies' executives, implemented in the first half of 2010. We found that Russian HRM practices that are based on low formalization of performance assessment, great versatility of payment arrangements, and high flexibility of working and contractual arrangements enabled companies to adapt to the recession conditions without massive layoffs.     

Different paths to economic reform in Russia and China: causes and consequences

Economic reform in Russia has been described as ‘shock therapy’ because rapid industrial privatization, price liberalization and democratic reforms of the political system were introduced simultaneously. However, shock therapy led to insider control of most manufacturing firms, with important consequences for foreign investment. In contrast, China’s ‘gradualist’ reforms, without privatization or significant democratization, facilitated foreign joint ventures as the dominant means of reforming State-owned enterprises incrementally. This paper proposes an explanation for these contrasting reform paths in Russia and China and then traces their consequences for inward foreign domestic investment (FDI), exporting and for corporate governance in the short and long term. The impact of national level policies on enterprises is demonstrated in a matched pair of cases in Russia and China. Conclusions are drawn for policy, for practice and for future research.     

Insider-controlled Firms in Russia

Privatisation Vouchers in Russia were heavily invested in the holders' own firms. Using data from a recent survey, we show that insider control in firms privatised in 1992-4 through the voucher process (as distinct from the earlier leased buy-out method) is insecure and dependent on managers' support. For employees, investment in insider control appears to have been motivated by employment income insurance rather than expected excess returns on the equity. Managers are predominantly the same individuals as before privatisation and display considerable hostility to outside investors, probably because they fear dismissal should outsiders gain control. Despite insider control, firms are shedding labour quite rapidly through voluntary resignations. Employment dynamics appear to be unrelated to insider equity ownership. This revised version was published online in July 2006 with corrections to the Cover Date.     

INDUCING VARIETY: A THEORY OF INNOVATION CONTESTS

This article analyzes the design of innovation contests when the quality of an innovation depends on the research approach, but the best approach is unknown. Inducing a variety of research approaches generates an option value. We show that suitable contests can induce such variety. The buyer‐optimal contest is a bonus tournament, where suppliers can choose only between a low bid and a high bid. This contest implements the socially optimal variety for a suitable parameter range. Finally, we compare the optimal contest to scoring auctions and fixed‐prize tournaments.     

The QLBS Q-Learner goes NuQLear: fitted Q iteration,inverse RL,and option portfolios

The QLBS model is a discrete-time option hedging and pricing model that is based on Dynamic Programming (DP) and Reinforcement Learning (RL). It combines the famous Q-Learning method for RL with the Black–Scholes (–Merton) (BSM) model's idea of reducing the problem of option pricing and hedging to the problem of optimal rebalancing of a dynamic replicating portfolio for the option, which is made of a stock and cash. Here we expand on several NuQLear (Numerical Q-Learning) topics with the QLBS model. First, we investigate the performance of Fitted Q Iteration for an RL (data-driven) solution to the model, and benchmark it versus a DP (model-based) solution, as well as versus the BSM model. Second, we develop an Inverse Reinforcement Learning (IRL) setting for the model, where we only observe prices and actions (re-hedges) taken by a trader, but not rewards. Third, we outline how the QLBS model can be used for pricing portfolios of options, rather than a single option in isolation, thus providing its own, data-driven and model-independent solution to the (in)famous volatility smile problem of the Black–Scholes model.     

Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique

Number of patents may be developed on the basis on different natural and science and technological factors. Number of patents prediction based on the different factors in many countries is analyzed in this investigation. These factors represent natural and science resources. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the number of patents prediction. Five inputs are considered: research and development (R&D) resources, natural resources, quality of academic institutions, quality of collaboration with the private sector and quality of education. As the ANFIS output, number of patents is considered. The ANFIS process for variable selection is also implemented in order to detect the predominant factors affecting the prediction of number of patents. Results show that the R&D is the most influential factor for the number of patents prediction.