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In-work transfers are often seen as a good trade-off between redistribution and efficiency as they alleviate poverty among low-wage households, while increasing financial incentives to work. In the context of the recent economic downturn, they have been advocated to offset the disincentive effect of wage cuts and to cushion the negative redistributive impact of earnings losses and cuts in the minimum wage. We study this double effect for Ireland, a country deeply affected by the economic crisis, and for which existing in-work support policies are of limited scope. The employment and poverty effects of alternative policies are analysed thanks to counterfactual simulations built using a micro-simulation model, the Living in Ireland Survey 2001 and labour supply estimations. We focus on an extension of the existing scheme, the Family Income Supplement and its replacement by the refundable tax credit in force in the United Kingdom.

Labor supply data seldom include detailed information on hours and wages in secondary job or overtime work. Based on survey information on hours and wages in overtime work and second job which is merged to administrative register information on income taxes and deductions we estimate a “Hausman labor supply model,” which allows for a detailed treatment of nonconvexities. Including explicit information on overtime pay and second job wages increase the estimated elasticities compared to a standard labor supply model without this information. However, allowing a more flexible treatment of nonconvexities the estimated elasticities are reduced; even below the estimates of the baseline results. In simulations we show that these findings have significant consequences when evaluating the degree of self-financing of various tax reforms.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

The newsvendor problem is fundamental to many operations management models. The problem focuses on the trade-off between the gains from satisfying demand and losses from unsold products. The newsvendor model and its extensions have been applied to various areas, such as production plan and supply chain management. This chapter examines the study about newsvendor problem. In this research, there is a review of the contributions for the multiproduct newsvendor problem. It focuses on the current literature concerning the mathematical models and the solution methods for the multiitem newsvendor problems with single or multiple constraints, as well as with the risks. The objective of this research is to go over the newsvendor problem and bring into comparison different newsvendor models applied to the flower industry. A few case studies are described addressing topics related to the newsvendor problem such as discounting and replenishment policies, inventory inaccuracies, or demand estimation. Three newsvendor models are put into practice in the field of flower selling. A full database of the flowers sold by an anonymous retailer is available for the study. Computational experiments for practical example have been conducted with use of the CPLEX solver with AMPL programming language. Models are solved, and an analysis of different circumstances and cases is accomplished.

Since 2012, with the Growth Decree 2.0, the Italian Government has been engaged in promoting the establishment and the growth of new innovative startups with a high technological value. The general objective of the policy is to promote sustainable growth, technological advancement and innovation. In the Decree framework, startups have to own an innovative character that is identified by at least one of the following three criteria: investments in research and development, qualified workforce, holding patents or registered software. Such three characteristics can be considered as innovation inputs able to create the conditions for a competitive advantage and greater economic performance. The research objective is to analyze how innovation inputs in innovative startups affect their economic performance. Each input factor can have a different impact on the company profitability. To this end, we apply the methodology SMAA for strategic management analysis and assessment (or SMAA-S) to detect the relation between innovation and profitability in Italian startups.