Trade and the skill premium in developing countries: the role of intermediate goods and some evidence from Peru

The rise in income inequality in developing countries after trade liberalization has been a puzzle for trade theory, which predicts the opposite effect. The authors present a model with imported intermediate goods in which the relative wages of skilled labor can rise due to higher imports of inputs or due to skill-biased technological change. The evidence from Peru in the post-liberalization phase in the early 1990s supports the skilled-biased technological change hypothesis. The authors find that most of the decrease in the blue-collar wage share in the manufacturing industries can be explained by the increase in machinery imports that followed liberalization, suggesting that the skilled-biased technology is embodied in imported machinery.Peru ; Economic development ; Latin America ; Trade

Can capital-skill complementarity explain the rising skill premium in developing countries? evidence from Peru

The factors behind the increase in the relative wages of skilled workers in developing countries are still not well understood. The authors use data from Peru to analyze the determinants of within-industry share of skilled workers. They use a translog cost function for gross output and are therefore able to incorporate the effects of materials, both domestic and imported, in addition to capital. The authors find that capital accumulation can explain a large fraction of the increase in the wage bill share and relative wages of skilled labor. This finding is contrary to the commonly held view that unobservable technological change is responsible for the rising skill premium in both developing and developed economies. A test for separability indicates that a gross output cost function is the appropriate one to use, and therefore share equations based on value-added cost functions could be misspecified.

Can capital-skill complementarity explain the rising skill premium in developing countries? Evidence from Peru

The factors behind the increase in the relative wages of skilled workers in developing countries are still not well understood. The authors use data from Peru to analyze the determinants of within-industry share of skilled workers. They use a translog cost function for gross output and are therefore able to incorporate the effects of materials, both domestic and imported, in addition to capital. The authors find that capital accumulation can explain a large fraction of the increase in the wage bill share and relative wages of skilled labor. This finding is contrary to the commonly held view that unobservable technological change is responsible for the rising skill premium in both developing and developed economies. A test for separability indicates that a gross output cost function is the appropriate one to use, and therefore share equations based on value-added cost functions could be misspecified

Trade and the skill premium in developing countries: the role of intermediate goods and some evidence from Peru

The rise in income inequality in developing countries after trade liberalization has been a puzzle for trade theory, which predicts the opposite effect. The authors present a model with imported intermediate goods in which the relative wages of skilled labor can rise due to higher imports of inputs or due to skill-biased technological change. The evidence from Peru in the post-liberalization phase in the early 1990s supports the skilled-biased technological change hypothesis. The authors find that most of the decrease in the blue-collar wage share in the manufacturing industries can be explained by the increase in machinery imports that followed liberalization, suggesting that the skilled-biased technology is embodied in imported machinery

Synchronous Reference Frame Based Active Filter Current Reference Generation Using Neural Networks

The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system in recent years. The significant harmonics are almost always 5th, 7th, 11th and the 13th with the 5th harmonic being the largest in most instances. Active filter systems have been proposed to mitigate harmonic currents of the industrial loads. The most important requirement for any active filter is the precise detection of the individual harmonic component\u27s amplitude and phase. Fourier transform based techniques provide an excellent method for individual harmonic isolation, but it requires a minimum of two cycles of data for the analysis, does not perform well in the presence of subharmonics which are not integral multiples of the fundamental frequency and most importantly introduces phase shifts. To overcome these difficulties, this paper proposes a Multilayer Perceptron Neural Network trained with back-propagation training algorithm to identify the harmonic characteristics of the nonlinear load. The operation principle of the synchronousreference- frame-based harmonic isolation is discussed. This proposed method is applied to a thyristor controlled dc drive to obtain the accurate amplitude and phase of the dominant harmonics. This technique can be integrated with any active filter control algorithm for reference generation

Application of Neural Networks for Data Modeling of Power Systems with Time Varying Nonlinear Loads

Nowadays power distribution systems typically operate with nonsinusoidal voltages and currents. Harmonic currents from nonlinear loads propagate through the system and cause harmonic pollution. The premise of IEEE 519 is that there exists a shared responsibility between utilities and customers regarding harmonic control. Maintaining reasonable levels of harmonic voltage distortion depends upon customers limiting their harmonic current injections and utilities controlling the system impedance characteristics. Measurements of current taken at the point of common coupling (PCC) to a customer are expected to determine whether the customer is in compliance with IEEE 519. These measurements yield the combination of nonlinear load harmonics and nonlinear current due to supply voltage harmonics and typically the customer is required to take corrective actions to compensate the harmonics. This paper presents a neural network scheme whereby, it is possible to do data modeling of the customer\u27s impedance and predict the resulting voltage distortion at the PCC if the customer were to take corrective actions. Experimental results from field measurements are provided. The proposed scheme is applicable to single as well as three phase systems

Echo State Networks for Determining Harmonic Contributions from Nonlinear Loads

This paper investigates the application of a new kind of recurrent neural network called Echo State Networks (ESNs) for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The interaction between loads connected to a point of common coupling (PCC) is a highly dynamic process. The determination of true harmonic current injection by individual loads is further complicated by the fact that the supply voltage waveform at the PCC is distorted by other loads at the PCC or further upstream and is therefore rarely a pure sinusoid. Harmonics in a power system are classified as either load harmonics or as supply harmonics. The principles of ESN are based on the use of a Recurrent Neural Network (RNN) as a dynamic reservoir. In order to compute the desired output dynamics, only the weights of connections from the reservoir to the output units are calculated. This is simply a linear regression problem. Experimental results presented in this paper confirm that attempting to predict the Total Harmonic Distortion (THD) of a load by simply measuring the load\u27\u27s current may not be accurate. The main advantage of this new method is that only waveforms of voltages and currents at the PCC have to be measured. This method is applicable for both single and three phase loads

Neural Network Based Method for Predicting Nonlinear Load Harmonics

Generation of harmonics and the existence of waveform pollution in power system networks are important problems facing the power utilities. The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system in recent years. Interaction between loads and sources in a power distribution network is a complex process and often not possible to explain analytically without making assumptions. The determination of true harmonic current distortion of a load is further complicated by the fact that the supply voltage waveform at the point of common coupling (PCC) is rarely a pure sinusoid. This paper proposes a neural network based method to find a way of distinguishing between load contributed harmonics and supply harmonics, without disconnecting any load from the network. A neural network structure with memory is used to model the admittance of the nonlinear load. Once training is achieved, the neural network predicts the true harmonic current of the load if it could be supplied with a clean sine wave. The main advantage of this method is that only waveforms of voltages and currents have to be measured and is applicable for single phase as well as multiphase loads. This could be integrated into a commercially available power quality instrument or be fabricated as a standalone instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument