Is competition for FDI bad for regional welfare?

We investigate the impact on regional welfare of policy competition for FDI when a multinational firm can strategically react to differences in statutory corporate tax rates and shift taxable profits to lower-tax jurisdictions. We show that competing governments may have an incentive to tax discriminate between domestic and multinational firms even in the presence of profit shifting opportunities for the latter. In particular, tax discrimination leads to higher welfare for the region as a whole than lump-sum subsidy competition when the difference in statutory corporate tax rates and/or their average is high enough. We also find that policy competition increases regional welfare by changing the firm's investment decision when profit shifting motivations might induce the firm to locate in the least profitable country

Competition for FDI in the presence of a public firm and the effects of privatization

In this paper we investigate tax/subsidy competition for FDI between countries of different size when a welfare-maximizing and relatively inefficient public firm is the incumbent in the largest market. First, we analyze how the presence of a public firm affects the investment decision of a multinational operating in the same sector as the former and willing to serve both markets. When the public firm stops exporting to the small country due to the investment of the multinational in the region (or does not export altogether), policy competition between the two countries is irrelevant to the foreign firm's choice. But if the country receiving FDI has to pay a subsidy, only the multinational will be better off provided that it would have invested there anyway absent policy competition. By contrast, when the public firm exports to the small country, policy competition increases the attractiveness of the big country. Second, we show that privatizing the public firm makes the big country a relatively more attractive location for the investment. However, when the privatized firm stays in the market, welfare always decreases. After privatization, policy competition decreases the attractiveness of the big country, which may be willing to tax the multinational in order to discourage FDI from taking place there, and gives the small country the opportunity of benefiting from the investment

Tax Competition for Foreign Direct Investments and the Nature of the Incumbent Firm

In this paper we investigate tax/subsidy competition for FDI between countries of different size when a domestic firm is the incumbent in the largest market. We investigate how the nature (public or private) of the incumbent firm affects policy competition between the two governments seeking to attract FDI. We show that the country hosting the incumbent always benefits from FDI if the domestic firm is a public welfare-maximizing firm, while its welfare may decrease when it is a private firm, as already shown by Bjorvatn and Eckel (2006). We also show that, contrary to the case of a private domestic incumbent, a public firm acts as a disciplinary device for the foreign multinational that will always choose the efficient welfare-maximizer location. Finally, an efficiency-enhancing role of policy competition may only arise when the domestic incumbent is a private firm, while tax competition is always wasteful when the incumbent is a public firm.Foreign Direct Investment; Tax/subsidy competition; Public firm; International mixed oligopoly

Privatization and policy competition for FDI

In this paper, we provide an explanation of why privatization may attract foreign investors interested in entering a regional market. Privatization turns the formerly-public firm into a less aggressive competitor since profit- maximizing output is lower than the welfare-maximizing one. The drawback is that social welfare generally decreases. We also investigate tax/subsidy competition for FDI before and after privatization. We show that policy competition is irrelevant in the presence of a public firm serving just its domestic market. By contrast, following privatization, it endows the big country with an instrument which can be used either to reduce the negative impact on welfare of an FDI-attracting privatization or to protect the domestic industry from foreign competitors.foreign direct investment, tax competition, public firm, privatization.

On the FDI-atrracting property of privatization.

In this paper, we provide an explanation of why privatization may attract foreign investors willing to enter a regional market. Privatization turns the formerly-public firm into a less aggressive competitor since prot-maximizing output is lower than the welfaremaximizing one. The drawback is that social welfare generally decreases. We also investigate tax/subsidy competition for FDI and put forward its potentially positive role. On the one hand, it may reduce the negative impact on welfare of an FDI-attracting privatization. On the other hand, it may prevent a welfare-reducing investment by the foreign firm. This sheds light on the substitute/complementary relationship between the two policies and thetwo objectives of governments.Foreign Direct Investment; Privatization; Policy Competition

Modeling diversity by strange attractors with application to temporal pattern recognition

This thesis belongs to the general discipline of establishing black-box models from real-word data, more precisely, from measured time-series. This is an old subject and a large amount of papers and books has been written about it. The main difficulty is to express the diversity of data that has essentially the same origin without creating confusion with data that has a different origin. Normally, the diversity of time-series is modeled by a stochastic process, such as filtered white noise. Often, it is reasonable to assume that the time series is generated by a deterministic dynamical system rather than a stochastic process. In this case, the diversity of the data is expressed by the variability of the parameters of the dynamical system. The parameter variability itself is then, once again, modeled by a stochastic process. In both cases the diversity is generated by some form of exogenous noise. In this thesis a further step has been taken. A single chaotic dynamical system is used to model the data and their diversity. Indeed, a chaotic system produces a whole family of trajectories that are different but nonetheless very similar. It is believed that chaotic dynamics not only are a convenient means to represent diversity but that in many cases the origin of diversity stems actually from chaotic dynamic. Since the approach of this thesis explores completely new grounds the most suitable kind of data is considered, namely approximately periodic signals. In nature such time-series are rather common, in particular the physiological signal of living beings, such as the electrocardiograms (ECG), parts of speech signals, electroencephalograms (EEG), etc. Since there are strong arguments in favor of the chaotic nature of these signals, they appear to be the best candidates for modeling diversity by chaos. It should be stressed however, that the modeling approach pursued in this thesis is thought to be quite general and not limited to signals produced by chaotic dynamics in nature. The intended application of the modeling effort in this thesis is temporal signal classification. The reason for this is twofold. Firstly, classification is one of the basic building block of any cognitive system. Secondly, the recently studied phenomenon of synchronization of chaotic systems suggests a way to test a signal against its chaotic model. The essential content of this work can now be formulated as follows. Thesis: The diversity of approximately periodic signals found in nature can be modeled by means of chaotic dynamics. This kind of modeling technique, together with selective properties of the synchronization of chaotic systems, can be exploited for pattern recognition purposes. This Thesis is advocated by means of the following five points. Models of randomness (Chapter 2) It is argued that the randomness observed in nature is not necessarily the result of exogenous noise, but it could be endogenally generated by deterministic chaotic dynamics. The diversity of real signals is compared with signals produced by the most common chaotic systems. Qualitative resonance (Chapter 3) The behavior of chaotic systems forced by periodic or approximately periodic input signals is studied theoretically and by numerical simulation. It is observed that the chaotic system "locks" approximately to an input signal that is related to its internal chaotic dynamic. In contrast to this, its chaotic behavior is reinforced when the input signal has nothing to do with its internal dynamics. This new phenomenon is called "qualitative resonance". Modeling and recognizing (Chapter 4) In this chapter qualitative resonance is used for pattern recognition. The core of the method is a chaotic dynamical system that is able to reproduce the class of time-series that is to be recognized. This model is excited in a suitable way by an input signal such that qualitative resonance is realized. This means that if the input signal belongs to the modeled class of time-series, the system approximately "locks" into it. If not, the trajectory of the system and the input signal remain unrelated. Automated design of the recognizer (Chapters 5 and 6) For the kind of signals considered in this thesis a systematic design method of the recognizer is presented. The model used is a system of Lur'e type, i.e. a model where the linear dynamic and nonlinear static part are separated. The identification of the model parameters from the given data proceed iteratively, adapting in turn the linear and the nonlinear part. Thus, the difficult nonlinear dynamical system identification task is decomposed into the easier problems of linear dynamical and nonlinear static system identification. The way to apply the approximately periodic input signal in order to realize qualitative resonance is chosen with the help of periodic control theory. Validation (Chapter 7) The pattern recognition method has been validated on the following examples — A synthetic example — Laboratory measurement from Colpitts oscillator — ECG — EEG — Vowels of a speech signals In the first four cases a binary classification and in the last example a classification with five classes was performed. To the best of the knowledge of the author the recognition method is original. Chaotic systems have been already used to produce pseudo-noise and to model signal diversity. Also, parameter identification of chaotic systems has been already carried out. However, the direct establishment of the model from the given data and its subsequent use for classification based on the phenomenon of qualitative resonance is entirely new

Nonlinear analysis of the Colpitts oscillator with applications to design

This paper reports a methodological approach to the analysis and design of sinusoidal oscillators based on bifurcation analysis. The simple Colpitts oscillator is taken as an example to demonstrate this nonlinear approach for both the nearly sinusoidal and chaotic modes of operation. In particular, it is shown how regular and irregular (chaotic) oscillations can be generated, depending on the circuit parameters

A general method to predict the amplitude of oscillation in nearly-sinusoidal oscillators

In this paper, a general methodology for predicting the amplitude of oscillation in nearly sinusoidal oscillators is presented. The method relies on the recently proposed projection technique for the computation of the center manifold and on the Hopf normal form theory to approximate the corresponding limit cycle in state space. The Colpitts oscillator is selected as a case study and, for this circuit, a closed-form expression for the amplitude of oscillation is derived as a function of the circuit parameters

Tax competition for foreign direct investments and the nature of the incumbent firm

We investigate tax/subsidy competition for FDI between countries of different size when a domestic firm is the incumbent in the largest market and we study how the nature (public or private) of the incumbent firm affects policy competition. We show that, differently from the case of a private firm, the country hosting the incumbent always benefits from FDI if the domestic firm is a public welfare-maximizing firm. We also show that the public firm acts as a disciplinary device for the foreign multinational that will always choose the efficient welfare-maximizing location. An efficiency-enhancing role of policy competition may then arise just when the domestic incumbent is a private firm, while tax competition is always wasteful in the presence of a public firm

Assessing Dependences within Multivariate Time Series Partializing the Knowledge of Thirds

A method to estimate from multivariate measurements the dependences within a network of coupled dynamical systems is proposed. The method is non-parametric and resorts to a statistics of the eigen-spectrums of the time series partial correlation matrices. The method is successfully validated on numerically generated data, demonstrating its capability to distinguish between direct and indirect dependences