Relevant bibliographies by topics / Unobserved ability

Academic literature on the topic 'Unobserved ability'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Unobserved ability"

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Belzil, Christian, and Jorgen Hansen. "Unobserved Ability and the Return to Schooling." Econometrica 70, no. 5 (September 2002): 2075–91. http://dx.doi.org/10.1111/1468-0262.00365.

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Blackburn, M., and D. Neumark. "Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials." Quarterly Journal of Economics 107, no. 4 (November 1, 1992): 1421–36. http://dx.doi.org/10.2307/2118394.

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Gittleman, Maury, and Brooks Pierce. "Inter-Industry Wage Differentials Job Content and Unobserved Ability." ILR Review 64, no. 2 (January 2011): 356–74. http://dx.doi.org/10.1177/001979391106400208.

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BJÖRKLUND, ANDERS, BERNT BRATSBERG, TOR ERIKSSON, MARKUS JÄNTTI, and ODDBJÖRN RAAUM. "Interindustry Wage Differentials and Unobserved Ability: Siblings Evidence from Five Countries." Industrial Relations 46, no. 1 (January 2007): 171–202. http://dx.doi.org/10.1111/j.1468-232x.2007.00461.x.

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Tarvid, Alexander. "Unobserved Heterogeneity in Overeducation Models: Is Personality More Important than Ability?" Procedia Economics and Finance 5 (2013): 722–31. http://dx.doi.org/10.1016/s2212-5671(13)00084-1.

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Ding, Weili, and Steven F. Lehrer. "Understanding the role of time-varying unobserved ability heterogeneity in education production." Economics of Education Review 40 (June 2014): 55–75. http://dx.doi.org/10.1016/j.econedurev.2014.01.004.

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Bradlow, Eric T. "Teacher’s Corner: Negative Information and The Three-Parameter Logistic Model." Journal of Educational and Behavioral Statistics 21, no. 2 (June 1996): 179–85. http://dx.doi.org/10.3102/10769986021002179.

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The 3-parameter logistic model is commonly used to describe the relationship among an unobserved latent trait (ability), unobserved item properties, and an observed binary outcome. We show that for certain values of the item properties and latent ability, the observed information about ability contained in the binary response is negative. This result has implications for maximization procedures, such as Newton-Raphson; approximate sampling methods, such as the Metropolis-Hastings algorithm; and Bayesian adaptive testing. All of these typically utilize the observed information. This result is contrasted with the fact that observed negative information does not occur in the limiting case with no guessing (2-parameter logistic model). The probability of negative information is expressed by a simple formula. This research extends the work of Samejima (1973) and Yen, Burket, and Sykes (1991).
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Gao, Jianfei, Mohamed A. Zahran, Amit Sheoran, Sonia Fahmy, and Bruno Ribeiro. "Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3922–29. http://dx.doi.org/10.1609/aaai.v34i04.5806.

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We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that the states we wish to predict are unobserved in the training data. Specifically, given a parametric model over the transition rates of a CTMC and some known transition rates, we wish to extrapolate its steady state distribution to states that are unobserved. A technical roadblock to learn a CTMC from its steady state has been that the chain rule to compute gradients will not work over the arbitrarily long sequences necessary to reach steady state —from where the aggregate statistics are sampled. To overcome this optimization challenge, we propose ∞-SGD, a principled stochastic gradient descent method that uses randomly-stopped estimators to avoid infinite sums required by the steady state computation, while learning even when only a subset of the CTMC states can be observed. We apply ∞-SGD to a real-world testbed and synthetic experiments showcasing its accuracy, ability to extrapolate the steady state distribution to unobserved states under unobserved conditions (heavy loads, when training under light loads), and succeeding in difficult scenarios where even a tailor-made extension of existing methods fails.
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Assunção, Juliano J., and Maitreesh Ghatak. "Can unobserved heterogeneity in farmer ability explain the inverse relationship between farm size and productivity." Economics Letters 80, no. 2 (August 2003): 189–94. http://dx.doi.org/10.1016/s0165-1765(03)00091-0.

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Taber, Christopher R. "The Rising College Premium in the Eighties: Return to College or Return to Unobserved Ability?" Review of Economic Studies 68, no. 3 (July 2001): 665–91. http://dx.doi.org/10.1111/1467-937x.00185.

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Dissertations / Theses on the topic "Unobserved ability"

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Zayats, Yaraslau Mroz Thomas A. "Schooling, wages and the role of unobserved ability in the Philippines." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,368.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.
Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Economics." Discipline: Economics; Department/School: Economics.
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Faridi, Rushad. "Microcredit Programs and Evaluation of Women's Success." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11167.

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Microcredit programs are of great interest to economists and policymakers because of their potential for reducing poverty, particularly among women. The first chapter mainly investigates the effectiveness aspect of microcredit programs. Using program evaluation methods, we find significant improvement in women's economic condition after participating in these programs. This study also corrects for the self-selection bias that might arise due to the fact that women decide on whether to participate in the programs or not. The second chapter studies the determinants of women's economic performance in microcredit programs. These determinants are in the form of different types of characteristics of women: their own characteristics, such as age or schooling or the characteristics of the household or village they live in. One obstacle to measure the effect of observed characteristics is the problem of omitted variable bias, typically caused by unavailability of data on unobserved ability of individuals. In the absence of suitable instruments, this study finds information about unobserved ability from the marriage market. It is found that incorporating estimates of women's unobserved characteristics significantly changes the estimated effect of women's observed characteristics and substantially removes the omitted variable bias. Microcredit programs originated from Bangladesh and now three major microcredit programs are operating: Grameen Bank, BRAC and RD-12. The third chapter investigates how these different microcredit programs have been performing relative to each other. Using similar program evaluation technique as in chapter 1, we measure program impact on women's economic welfare for these programs separately. We find that BRAC outperforms Grameen Bank and RD-12 significantly. This result is interesting since it contradicts the popular notion that Grameen Bank is the most successful microcredit program. This study also tries to find the determinants of economic success of women participating in these programs, separately for each program. These results provide more insights into different aspects of microcredit program.
Ph. D.
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Wilson, Hugh David Economics Australian School of Business UNSW. "The Firm Size Effect: An Application of Hierarchy Theories." Awarded by:University of New South Wales. School of Economics, 2000. http://handle.unsw.edu.au/1959.4/32642.

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In this thesis the positive relationship between firm size and wages is investigated through the application of hierarchy theories. Many different explanations have been proposed for this relationship, but have met only limited success at best. The strongest finding to date is that unobserved ability is a significant factor. The question of interest here is ???why do wages increase as the size firm increases???? Hierarchy theories take a different approach towards the analysis of firms in comparison to the alternate theories which have dominated previous investigations. As a result of their focus on the organisational relationships within a firm???s internal structure, hierarchy theories offer certain insights to the size-wage relationship which to date have been unnoticed. An empirical investigation into the size-wage differential incorporating structural considerations into an augmented wage equation offers strong support for the propositions of hierarchy theories. I find that half of the firm size effect for workers can be explained by controlling for some aspects of management structure, and that span of control has a discontinuous effect on wages. These results are completely consistent with the existing findings on unobserved ability and have the added attraction of providing economic as well as statistical explanatory power.
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Hofman, Stanislav. "Meziodvětvové mzdové rozdíly v České republice." Master's thesis, 2013. http://www.nusl.cz/ntk/nusl-324173.

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This thesis examines inter-industry wage differentials in the Czech Republic, using the European Union - Statistics on Income and Living (EU-SILC 2009) survey as our primary data source. Findings show that even after controlling for large number of workers and jobs characteristics wage differences based on industry affiliation still persist. The variation of the inter-industry wage differentials amounts to approximately 5 percent with the maximum wage level difference of 25 percent between the financial sector and agriculture. By applying two distinct methodologies we tested the hypothesis that the inter-industry wage differentials are actually caused by higher concentration of workers with better unmeasured abilities in higher-paying industries. Neither of the two methods rejected the unobserved ability hypothesis. Finally, our analysis also shows that the inter-industry wage differentials can be to a certain extent attributed to rent-sharing and different labour turnover costs across sectors.
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Books on the topic "Unobserved ability"

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Blackburn, McKinley L. Unobserved ability, efficiency wages, and interindustry wage differentials. Cambridge, MA: National Bureau of Economic Research, 1991.

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2

Munasinghe, Lalith. Wage dynamics and unobserved heterogeneity: Time preference or learning ability? Bonn, Germany: IZA, 2004.

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Munasinghe, Lalith. Wage dynamics and unobserved heterogeneity: Time preference of learning ability? Cambridge, MA: National Bureau of Economic Research, 2005.

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Book chapters on the topic "Unobserved ability"

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Bartholomew, David J. "Models for Ability." In Unobserved Variables, 21–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39912-1_4.

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Conference papers on the topic "Unobserved ability"

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Zheng, Wei, Xiaofeng Zhu, Yonghua Zhu, and Shichao Zhang. "Robust Feature Selection on Incomplete Data." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/443.

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Feature selection is an indispensable preprocessing procedure for high-dimensional data analysis,but previous feature selection methods usually ignore sample diversity (i.e., every sample has individual contribution for the model construction) andhave limited ability to deal with incomplete datasets where a part of training samples have unobserved data. To address these issues, in this paper, we firstly propose a robust feature selectionframework to relieve the influence of outliers, andthen introduce an indicator matrix to avoid unobserved data to take participation in numerical computation of feature selection so that both our proposed feature selection framework and exiting feature selection frameworks are available to conductfeature selection on incomplete data sets. We further propose a new optimization algorithm to optimize the resulting objective function as well asprove our algorithm to converge fast. Experimental results on both real and artificial incompletedata sets demonstrated that our proposed methodoutperformed the feature selection methods undercomparison in terms of clustering performance.
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Ojinnaka, M. A., J. J. Beaman, and S. Fish. "Early Kick Detection Using Real-Time Data Analysis With a Lumped Parameter Model." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9931.

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The risk of kick and lost circulation at the wellbore open-hole increases with water depth due to narrow pressure margins. The safety of drilling operations and mitigation of risk to drilling personnel, equipment, and the environment hinges on the ability of the drilling crew to detect these undesirable events in their early stages and quickly bring the well under control. This paper presents an approach for the estimation of unobserved bottom-hole phenomena during drilling ahead operations by means of combining multiple surface measurements with predictions from a hydraulic model of the well. Bond graph technique is used to formulate a lumped-parameter hydraulic model of the drilling ahead process, the model is linearized, and an estimation method is applied to the proposed stochastic model. This methodology was tested offline with drilling ahead data from a well where a kick occurred and the results showed kick detection earlier than traditional methods allowed.
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Gao, Kyle Yingkai, Achille Fokoue, Heng Luo, Arun Iyengar, Sanjoy Dey, and Ping Zhang. "Interpretable Drug Target Prediction Using Deep Neural Representation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/468.

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The identification of drug-target interactions (DTIs) is a key task in drug discovery, where drugs are chemical compounds and targets are proteins. Traditional DTI prediction methods are either time consuming (simulation-based methods) or heavily dependent on domain expertise (similarity-based and feature-based methods). In this work, we propose an end-to-end neural network model that predicts DTIs directly from low level representations. In addition to making predictions, our model provides biological interpretation using two-way attention mechanism. Instead of using simplified settings where a dataset is evaluated as a whole, we designed an evaluation dataset from BindingDB following more realistic settings where predictions of unobserved examples (proteins and drugs) have to be made. We experimentally compared our model with matrix factorization, similarity-based methods, and a previous deep learning approach. Overall, the results show that our model outperforms other approaches without requiring domain knowledge and feature engineering. In a case study, we illustrated the ability of our approach to provide biological insights to interpret the predictions.
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Reports on the topic "Unobserved ability"

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Blackburn, McKinley, and David Neumark. Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials. Cambridge, MA: National Bureau of Economic Research, October 1991. http://dx.doi.org/10.3386/w3857.

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2

Munasinghe, Lalith, and Nachum Sicherman. Wage Dynamics and Unobserved Heterogeneity: Time Preference of Learning Ability? Cambridge, MA: National Bureau of Economic Research, January 2005. http://dx.doi.org/10.3386/w11031.

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3

Ding, Weili, and Steven Lehrer. Understanding the Role of Time-Varying Unobserved Ability Heterogeneity in Education Production. Cambridge, MA: National Bureau of Economic Research, February 2014. http://dx.doi.org/10.3386/w19937.

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4

Lochner, Lance, and Youngki Shin. Understanding Earnings Dynamics: Identifying and Estimating the Changing Roles of Unobserved Ability, Permanent and Transitory Shocks. Cambridge, MA: National Bureau of Economic Research, April 2014. http://dx.doi.org/10.3386/w20068.

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