Explore the vast range of titles available.

This paper analyzes the role of specialized high-skilled labor in the disproportionate growth of the service sector. Empirically, the importance of skill-intensive services has risen during a period of increasing relative wages and quantities of high-skilled labor. We develop a theory in which demand shifts toward more skill-intensive output as productivity rises, increasing the importance of market services relative to home production. Consistent with the data, the theory predicts a rising level of skill, skill premium, and relative price of services that is linked to this skill premium.

Software project cost prediction is a very important task during building and developing software projects. This process helps software project engineers to accurately manage and plan their resources in terms of cost estimation. However, Need for accurate cost development prediction model for a software project is not a simple procedure. Predicting the cost required while developing software engineering projects is the most difficult challenge that attracts the attention of researchers and practitioners. This paper adopts a new model in estimating the cost of building or developing software engineering projects using a machine learning approach. The results proves that machine learning methods can be used to predict program cost with high accuracy rate compared with traditional software estimation techniques. The proposed model in this research was trained on the NASA (National Aeronautics and Space Administration) data set, which contains the characteristics of 60 projects in addition to the real cost of the projects. An analysis of the results of the implementation for the proposed methods showed that the cost Predicting process using K-Nearest Neighbours algorithm (KNN), Cascade Neural Networks (CNN) and Elman Neural Networks (ENN) It has the ability to predict the costs required to build or develop software engineering projects, K-Nearest Neighbours algorithm has shown high accuracy for Predict the required cost to develop Software Engineering projects Compared to Cascade Neural Networks and Elman Neural Networks ENN.

This paper gives an overview of current thinking by economists about the consequences of ongoing technological progress for labour markets, and discusses policy implications. In economics, the impact of technological progress on labour markets is understood by the following two channels: (i) the nature of interactions between differently skilled workers and new technologies affecting labour demand and (ii) the equilibrium effects of technological progress through consequent changes in labour supply and product markets. The paper explains how the ongoing Digital Revolution is characterized by a complex interplay between worker skills and digital capital in the workplace, and consequent changes in job mobility for workers and in output prices affecting consumer demand for goods and services. In particular, it explains how current worker–technology interactions and the equilibrium effects they entail combine to create economy-wide job polarization with winners and losers from ongoing technological progress. The paper therefore concludes by discussing a set of policy interventions to ensure that the benefits of the Digital Revolution are broadly shared.

We examine the drivers of monthly changes in maize prices across 18 Tanzanian markets. Local prices respond three to four times faster to the main regional market (Nairobi) than to the international benchmark (US Gulf). More importantly, shocks from Nairobi account for only one third of the explained variation in domestic prices; the remaining two-thirds is accounted for by domestic influences (including harvest cycles, weather shocks, and trade policies). Further, we show that remoteness and the local agroecology systematically influence the behavior of food prices.

Autism Spectrum Disorder (ASD) is becoming a growing concern worldwide. Parents are often not aware of the different nature of children with ASD and attempt to treat him/her the same way as other children. However, that causes more and more isolation of such children from the social interactions around them, resulting in more secluded and people-phobic behaviors. Nevertheless, similar to other children, children with ASD also like to play with toys. This observation has led to the use of toys in a way that mere playful activities could become sources of learning and skill-building, somewhat serving or assisting in the role of a human teacher. Robots have been observed to be fascinating for all children and compensating for a human companion to a certain extent. In this paper, a short study has been presented involving a humanoid robot programmed for a number of teaching and therapeutic behaviors, such as exercises, singing, explaining, and playing with children. Tests were performed on a small group of 15 children with ASD (ages 7–11) using these activities at a local school for children with special needs for a number of weeks. The objective of the study was to quantify the improvement in a number of behavior and learning parameters when children performed the activities with NAO robot present with the teacher, as opposed to the same type of activities performed by the teacher alone. The performance improvement was quantified in terms of the NAO robot activity as independent variable, and following dependent behavioral variables observed from the responses of children: (a) number of trials, (b) activity response time, (c) response type, and (d) behavior retention. Quantified findings from these tests are reported in this paper against average performance values (based on teachers and psychologists’ evaluation). The results of the study have been found to be very encouraging which demonstrates the capability of robotic toys to improve the learning process for children with ASD. The results of this study also encourage the low-cost development and usage of such robotic toy systems for teaching and therapeutic applications that help such children to become better members of society.

We show that unexpected price-level movements generate sizable wealth redistribution in the Euro Area (EA), using sectoral accounts and newly available data from the Household Finance and Consumption Survey. The EA as a whole is a net loser of unexpected price-level decreases, with Italy, Greece, Portugal, and Spain losing most in per capita terms, and Belgium and Malta being net winners. Governments are net losers of deflation, while the household (HH) sector is a net winner in the EA as a whole. HHs in Belgium, Ireland, Malta, and Germany experience the biggest per capita gains, while HHs in Finland and Spain turn out to be net losers. Considerable heterogeneity exists also within the HH sector: relatively young middle class HHs are net losers of deflation, while older and richer HHs are winners. As a result, wealth inequality in the EA increases with unexpected deflation, although in some countries (Austria, Germany, and Malta) inequality decreases due to the presence of relatively few young borrowing HHs. We document that HHs' inflation exposure varies systematically across countries, with HHs in high-inflation EA countries holding systematically lower nominal exposures.

Heat pumps are a key technology for improving energy efficiency as they can significantly reduce energy costs and emissions. Given the significant role of heat pumps in carbon neutrality pathways, and pressure for related national energy efficiency programs, it is important to examine economic profitability of heat pump investments and their relative environmental and social benefits. This paper aims to answer the following main research question: are areas with lower housing prices and income less likely to invest into energy efficiency? The paper finds that in Finland heat pumps are already very profitable and converting buildings’ heating systems into heat pumps creates major environmental and economic benefits for the residents. The cost of heating and heat pump investment costs does not vary between locations whereas housing prices, rents and income do. Neighborhoods with lower housing prices have less motivation and capability to invest into heat pumps. Urban areas with positive housing price development, higher income and better financing options will likely invest into energy efficiency without subsidies. Potential subsidies should be allocated into areas with lower housing prices, because emissions are evenly distributed, and lower income areas pay relatively more for energy. Energy efficiency subsidies could be tied into housing prices or more specifically into property tax, which is universally collected in most countries. Property tax could be used to guide energy efficiency investments into locations where they would not be carried out otherwise. For areas that do not need subsidies, this paper recommends that awareness should be increased, because the economic and carbon emission reduction potential of energy efficiency measures is still not well understood.

In the 1970s, large increases in the price of oil were associated with sharp decreases in output and large increases in inflation. In the 2000s, even larger increases in the price of oil were associated with much milder movements in output and inflation. Using a structural VAR approach, Blanchard and Gali (in J. Gali and M. Gertler (eds.) 2009, International Dimensions of Monetary Policy, University of Chicago Press, pp. 373–428) argued that this reflected a change in the causal relation from the price of oil to output and inflation. They then argued that this change could be due to a combination of three factors: a smaller share of oil in production and consumption, lower real wage rigidity, and better monetary policy. Their argument, based on simulations of a simple new-Keynesian model, was informal. Our purpose in this paper is to take the next step, and to estimate the explanatory power and contribution of each of these factors. To do so, we use a minimum distance estimator that minimizes, over the set of structural parameters and for each of two samples (pre- and post-1984), the distance between the empirical SVAR-based impulse response functions and those implied by a new-Keynesian model. Our empirical results point to an important role for all three factors.

The purpose of this research is to develop a computer-driven decision support system (DSS) to select optimal additive manufacturing (AM) machines for metal powder bed fusion (PBF) applications. The tool permits to evaluate productivity factors (i.e., cost and production time) for any given geometry. At the same time, the trade-off between feature resolution and productivity analysis is visualized and a sensitivity analysis is performed to evaluate future cost developments. This research encompasses a decision support system that includes a data structure and an algorithm which is coded in “MathWorks Matlab,” considering cost structures for metal-based AM (i.e., machine cost, material cost, and labor cost). Results of this research demonstrate that feature resolution has a crucial effect on the total cost per part, but displays decreasing impacts for higher build volume rates. Based on assumptions of business consultancies, productivity can be increased, resulting in a potential decline of cost per part of up to 55% until 2025. Using this DSS tool, it is possible to evaluate the most optimal AM production systems by selecting between several input parameters. The algorithm allows industry practitioners to retrieve information and assist in decision-making processes, including cost per part, total cost comparison, and build time evaluations for typical commercial metal PBF systems.

Most sovereign defaults are partial, with heterogeneous post-default outcomes, and commodity prices are an important determinant of sovereign default and the subsequent restructurings. In the case of emerging countries, as a result of direct dependence of government on revenues from commodity exports, declines in commodity prices reduce government’s resources to service the external debt thereby increase the chances of default. In this paper, we construct a country-specific commodity price index with time-varying weights based on commodity exports to quantify the impact of commodity prices on the partial default rate measured by debt arrears. We show that declines in commodity prices have a significant, positive effect on the default rate. The overall predicted effects for a one-standard deviation decrease in a composite of the level and change of the price index at its 1st, 2nd, and 3rd quartile, on average, are 14.2, 12.5, and 9.3 percentage points respectively. We also show that for a country-specific one-standard deviation decrease in the composite price index, the predicted effect varies from insignificant to an increase of 33.8 percentage points. The country-specific effect on the default rate generally increases in magnitude with a country’s dependence on commodity exports, while it depends heterogeneously on external indebtedness—increasing in magnitude for low levels (below a threshold of about 30 percent) of debt and decreasing thereafter.