Optimal minimum wage in a competitive economy

This paper studies the use of a minimum wage law to implement the optimal redistribution policy when a distorting tax-transfer scheme is also available. The authors build a static general equilibrium model with a Ramsey planner making decisions on taxes, transfers, and minimum wage levels. Workers are assumed to differ only in their productivity. The authors find that optimal redistribution may imply the use of only taxes and transfers, only a minimum wage, or the proper combination of both policies. The key factor driving their results is the reaction of the demand for low-skilled labor to the minimum wage law. Hence, an optimal minimum wage appears to be most likely when low-skilled households are scarce, the complementarity between the two types of workers is large, or the difference in productivity is small.

Fiscal policy and minimum wage for redistribution: an equivalence result

In this paper, we derive conditions under which a minimum-wage law combined with anonymous taxes and transfers and an agent-specific tax-transfer scheme are equivalent policies.

Estimating dynamic equilibrium economies: linear versus nonlinear likelihood

This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. The authors report two main results. First, both for simulated and for real data, the sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, even if relatively small in absolute values, have important effects on the moments of the model. The authors conclude that the nonlinear filter is a superior procedure for taking models to the data.

On the solution of the growth model with investment-specific technological change

Recent work by Greenwood, Hercowitz, and Krusell (1997 and 2000) and Fisher (2003) has emphasized the importance of investment-specific technological change as a main driving force behind long-run growth and the business cycle. This paper shows how the growth model with investment-specific technological change has a closed-form solution if capital fully depreciates. This solution furthers our understanding of the model, and it constitutes a useful benchmark to check the accuracy of numerical procedures to solve dynamic macroeconomic models in cases with several state variables.

A, B, C’s, (and D’s) for understanding VARs

The dynamics of a linear (or linearized) dynamic stochastic economic model can be expressed in terms of matrices (A, B, C, D) that define a state-space system. An associated state space system (A, K, C, S) determines a vector autoregression (VAR) for observables available to an econometrician. We review circumstances in which the impulse response of the VAR resembles the impulse response associated with the economic model. We give four examples that illustrate a simple condition for checking whether the mapping from VAR shocks to economic shocks is invertible. The condition applies when there are equal numbers of VAR and economic shocks.

Convergence properties of the likelihood of computed dynamic models

This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, economists approximate the policy functions of the agents in the model with numerical methods. But this implies that, instead of the exact likelihood function, the researcher can evaluate only an approximated likelihood associated with the approximated policy function. What are the consequences for inference of the use of approximated likelihoods? First, we show that as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we prove that the approximated likelihood converges at the same rate as the approximated policy function. Third, we find that the error in the approximated likelihood gets compounded with the size of the sample. Fourth, we discuss convergence of Bayesian and classical estimates. We complete the paper with three applications to document the quantitative importance of our results.

Irrigation-Advisor-A Decision Support System for Irrigation of Vegetable Crops

Climate change will intensify water scarcity, and therefore irrigation must be adapted to save water. Operational tools that provide watering recommendations to end-users are needed. This work presents a new tool, Irrigation-Advisor (IA), which is based on weather forecasts and is able to separately determine soil evaporation and crop transpiration, and thus is adaptable to a broad range of agricultural situations. By calculating several statistical indicators, IA was tested against the FAO-56 crop evapotranspiration (ETcFAO) methodology using local crop coefficients. Additionally, IA recommendations were compared with current standard practices by experienced farmers (F). Six field experiments with four widely cultivated species (endive, lettuce, muskmelon and potato) were performed in Southeast Spain. Irrigation water applied, crop yield, aboveground biomass and water productivity were determined. Crop water needs underestimations (5%–20%) were detected when comparing IA against ETcFAO, although the index of agreement proved reasonable adjustments. The IA recommendations led to water savings up to 13% when compared to F, except for lettuce, with a 31% surplus in irrigation when using IA. Crop yield was not compromised and water productivity was increased by IA. Therefore, IA mimicked the farmers0 irrigation strategies fairly well without deploying sensors on-site. Nevertheless, improvements are needed for increasing the accuracy of IA estimations.The authors thank the owners of the experimental fields (Madrid and Galindo families) and David Hortelano (CEBAS-CSIC) for his help with field determinations. This research: including the APC, was funded by the Spanish MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD (MINECO) with FEDER co-financing, projects “RiegoAsesor” and “Preciriego” (grant numbers RTC-2015-3453-2 and RTC-2017-6365-2) and by the European Commission with project “SHui” (grant number: 773903)

High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia

B-cell Acute Lymphoblastic Leukaemia is one of the most common cancers in childhood, with 20% of patients eventually relapsing. Flow cytometry is routinely used for diagnosis and follow-up, but it currently does not provide prognostic value at diagnosis. The volume and the high-dimensional character of this data makes it ideal for its exploitation by means of Artificial Intelligence methods. We collected flow cytometry data from 56 patients from two hospitals. We analysed differences in intensity of marker expression in order to predict relapse at the moment of diagnosis. We finally correlated this data with biomolecular information, constructing a classifier based on CD38 expression. Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher's Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse

Analysis of the information about Doctoral Degrees presented in the Spanish universities websites.

Globalization has intensified competition, as evidenced by the growing number of international classification systems (rankings) and the attention paid to them. Doctoral education has an international character in itself. It should promote opportunities for graduate students lo participate in these international studies. The quality and competitiveness are two of the most important issues for universities. To promote the interest of graduates to continue their education after the graduate level, it would be necessary to improve the published information of ihe doctoral programs. It should increase the visibility and provide high-quality, easily accessible and comparable information which includes all the relevant aspects of these programs. The authors analysed the website contents of doctoral programs, it was observed a lack of quality of them and very poor information about the contents, so that it was decided that any of them could constitute a model for creating new websites. The recommendations on the format and contents in the web were made by a discussion group. They recommended an attractive design; a page with easy access to contents and easy to find on Ihe net and with the information in more than one language. It should include complete program and academic staff information. It should also be included the study's results which should be easily accessible and includes quantitative data, such as number of students who completed scholars, publications, research projects, average duration of the studies, etc. It will facilitate the choice of progra