Partial Identification in Matching Models for the Marriage Market

We study partial identification of the preference parameters in models of one-to-one matching with perfectly transferable utilities, without imposing parametric distributional restrictions on the unobserved heterogeneity and with data on one large market. We provide a tractable characterisation of the identified set, under various classes of nonparametric distributional assumptions on the unobserved heterogeneity. Using our methodology, we re-examine some of the relevant questions in the empirical literature on the marriage market which have been previously studied under the Multinomial Logit assumption

Identification in One-to-One Matching Models with Nonparametric Unobservables

This paper considers a one-to-one matching model with transferable utilities, in two-sided markets. In the model, the agents have preferences over some observable agent characteristics (called types) on the other side of the market. There are other observed characteristics aggregated at the level of types that determine the systematic preferences over these types. These systematic preferences enter the agent utilities in the form of a linear index. Agents also have idiosyncratic taste shocks. This paper shows the identification of systematic preference parameters over types, without making any parametric assumptions on the distribution of the unobserved taste shocks. The matching model reduces to two separate discrete-choice problems linked together by market clearing conditions, satisfied in the presence of equilibrium transfers. However, transfers are endogenous and unobserved which makes the discrete-choice problem non-standard. This paper gives conditions under which transfers are simply functions of the linear indices. This insight along with variation across i.i.d. markets is used to reduce the matching model to a semiparametric multi-index model with an unknown link function. Identification is shown under appropriate exclusion restrictions on the regressors

Identification in One-to-One Matching Models with Nonparametric Unobservables

This paper considers a one-to-one matching model with transferable utilities, in two-sided markets. In the model, the agents have preferences over some observable agent characteristics (called types) on the other side of the market. There are other observed characteristics aggregated at the level of types that determine the systematic preferences over these types. These systematic preferences enter the agent utilities in the form of a linear index. Agents also have idiosyncratic taste shocks. This paper shows the identification of systematic preference parameters over types, without making any parametric assumptions on the distribution of the unobserved taste shocks. The matching model reduces to two separate discrete-choice problems linked together by market clearing conditions, satisfied in the presence of equilibrium transfers. However, transfers are endogenous and unobserved which makes the discrete-choice problem non-standard. This paper gives conditions under which transfers are simply functions of the linear indices. This insight along with variation across i.i.d. markets is used to reduce the matching model to a semiparametric multi-index model with an unknown link function. Identification is shown under appropriate exclusion restrictions on the regressors

Identification and inference in discrete choice models with imperfect information

We study identification of preferences in a single-agent, static, discrete choice model where the decision maker may be imperfectly informed about the utility generated by the available alternatives. We impose no restrictions on the information frictions the decision maker may face and impose weak assumptions on how the decision maker deals with the uncertainty induced by those frictions. We leverage on the notion of one-player Bayes Correlated Equilibrium in Bergemann and Morris (2013; 2016) to provide a tractable characterisation of the identified set and discuss inference. We use our methodology and data on the 2017 UK general election to estimate a spatial model of voting under weak assumptions on the information that voters have about the returns to voting. We find that the assumptions on the information environment can drive the interpretation of voter preferences. Counterfactual exercises quantify the consequences of imperfect information in politics

Identification and inference in discrete choice models with imperfect information

In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives. By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties. Simulations reveal that the obtained bounds on the preference parameters can be tight in several settings of empirical interest

Identification and inference in discrete choice models with imperfect information

In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives. By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties. Simulations reveal that the obtained bounds on the preference parameters can be tight in several settings of empirical interest

Study of maternal and perinatal outcome in women with congenital heart disease

Background: Pregnancy induced changes in cardiovascular hemodynamics are generally well tolerated. However, the reversible but prolonged hemodynamic stress of pregnancy can have negative effects on the diseased heart. In our study we studied various uncorrected and corrected congenital cardiac lesions and their antenatal, intrapartum course, their ability to withstand labor and postpartum complications. Methods: Our study was a retroprospective observational cross-sectional study conducted at KEM hospital, Mumbai with enrolment of total 27 subjects over a period of 18 months between August 2020 to December 2021.The study included all the registered and emergency patients either post abortal or post-delivery with congenital cardiac diseases including the ones who were surgically corrected. Results: The present study was conducted to study demographic features and maternal and perinatal outcomes of pregnancy in these women. Majority of the subjects were diagnosed with heart disease at the age more than 20 years (40.74%) and very few at age less than 5 years. In the present study only 7.4% study subjects were aware about pre-marital counselling, and preconceptional counselling. Among the lesions ASD was commonest reported among 51.85% study subjects. ICU admission was required among 11.11% study subjects. Labour analgesia given to only 18.52% study subjects. We did not observe any association between surgically corrected CHD and ICU admissions, postnatal complications, and outcomes, p>0.05. Conclusions: This study concluded that pre-pregnancy diagnosis, counselling, appropriate referral, routine antenatal supervision and delivery at an equipped centre improve the overall outcomes

A Survey on Challenges to the Media Cloud

Content of a media over Internet consumes significant amount of energy. Numerous application media applications, services and devices have introduced and clients are consuming more and more media. Media processing requires great capacity and capability.[1] Cloud computing has proven a best technology for providing various services, great computing power, massive storage and bandwidth with modest cost. Integration of Media and Cloud can become very beneficial for both and hence becomes media cloud. In this paper we have discussed several challenges of media cloud. Those include Integration, Storage, Processing and Delivery

Identification and Estimation in Many-to-one Two-sided Matching without Transfers

In a setting of many-to-one two-sided matching with non-transferable utilities, e.g., college admissions, we study conditions under which preferences of both sides are identified with data on one single market. Regardless of whether the market is centralized or decentralized, assuming that the observed matching is stable, we show nonparametric identification of preferences of both sides under certain exclusion restrictions. To take our results to the data, we use Monte Carlo simulations to evaluate different estimators, including the ones that are directly constructed from the identification. We find that a parametric Bayesian approach with a Gibbs sampler works well in realistically sized problems. Finally, we illustrate our methodology in decentralized admissions to public and private schools in Chile and conduct a counterfactual analysis of an affirmative action policy

Partial identification in matching models for the marriage market

We consider the one-to-one matching models with transfers of Choo and Siow (2006) and Galichon and Salanié (2015). When the analyst has data on one large market only, we study identification of the systematic components of the agents’ preferences without imposing parametric restrictions on the probability distribution of the latent variables. Specifically, we provide a tractable characterisation of the region of parameter values that exhausts all the implications of the model and data (the sharp identified set), under various classes of nonparametric distributional assumptions on the unobserved terms. We discuss a way to conduct inference on the sharp identified set and conclude with Monte Carlo simulations