Bounds on Quantiles in the Presence of Full and Partial Item Nonresponse

Microeconomic surveys are usually subject to the problem of item nonresponse, typically associated with variables like income and wealth, where confidentiality and/or lack of accurate information can affect the response behavior of the individual. Follow up categorical questions can reduce item nonresponse and provide additional partial information on the missing value, hence improving the quality of the data. In this paper we allow item nonresponse to be non-random and extend Manski’s approach of estimating bounds to identify an upper and lower limit for the parameter of interest (the distribution function or its quantiles). Our extension consists of deriving bounding intervals taking into account all three types of response behavior: full response, partial (categorical) response and full nonresponse. We illustrate the theory by estimating bounds for the quantiles of the distribution of amounts held in savings accounts. We consider worst case bounds which cannot be improved upon without additional assumptions, as well as bounds that follow from different assumptions of monotonicity.item nonresponse;bracket response;bounds and identification

Nonparametric Bounds in the Presence of Item Nonresponse, Unfolding Brackets and Anchoring

Household surveys often suffer from nonresponse on variables such as income, savings or wealth.Recent work by Manski shows how bounds on conditional quantiles of the variable of interest can be derived, allowing for any type of nonrandom item nonresponse.The width between these bounds can be reduced using follow up questions in the form of unfolding brackets for initial item nonrespondents.Recent evidence, however, suggests that such a design is vulnerable to anchoring effects.In this paper Manski's bounds are extended to incorporate the information provided by the bracket respondents allowing for different forms of anchoring.The new bounds are applied to earnings in the 1996 wave of the Health and Retirement Survey.The results show that the categorical questions can be useful to increase precision of the bounds, even if anchoring is allowed for.microeconomics;nonresponse

Nonparametric Modeling of the Anchoring Effect in an Unfolding Bracket Design

Household surveys are often plagued by item non-response on economic variables of interest like income, savings or the amount of wealth. Manski (1989,1994, 1995) shows how, in the presence of such non-response, bounds on conditional quantiles of the variable of interest can be derived, allowing for any type of non-random response behavior. Including follow up categorical questions in the form of unfolding brackets for initial item non-respondents, is an effective way to reduce complete item non-response. Recent evidence, however, suggests that such design is vulnerable to a psychometric bias known as the anchoring effect. In this paper, we extend the approach by Manski to take account of the information provided by the bracket respondents. We derive bounds which do and do not allow for the anchoring effect. These bounds are applied to earnings in the 1996 wave of the Health and Retirement Survey (HRS). The results show that the categorical questions can be useful to increase precision of the bounds, even if anchoring is allowed for.unfolding bracket design;anchoring effect;item nonresponse;bounding intervals;nonparametrics

Nonparametric Bounds on the Income Distribution in the Presence of Item Nonresponse

Item nonresponse in micro surveys can lead to biased estimates of the parameters of interest if such nonresponse is nonrandom. Selection models can be used to correct for this, but parametric and semiparametric selection models require additional assumptions. Manski has recently developed a new approach, showing that, without additional assumptions, the parameters of interest are identified up to some bounding interval. In this paper, we apply Manski’s approach to estimate the distribution function and quantiles of personal income, conditional on given covariates, taking account of item nonresponse on income. Nonparametric techniques are used to estimate the bounding intervals. We consider worst case bounds, as well as bounds which are valid under nonparametric assumptions on monotonicity or under exclusion restrictions.nonparametrics;bounds and identification;sample non-response

Powering AGNs with super-critical black holes

We propose a novel mechanism for powering the central engines of Active Galactic Nuclei through super-critical (type II) black hole collapse. In this picture, ~$10^3 M_\odot$ of material collapsing at relativistic speeds can trigger a gravitational shock, which can eject a large percentage of the collapsing matter at relativistic speeds, leaving behind a "light" black hole. In the presence of a poloidal magnetic field, the plasma collimates along two jets, and the associated electron synchrotron radiation can easily account for the observed radio luminosities, sizes and durations of AGN jets. For Lorentz factors of order 100 and magnetic fields of a few hundred $\mu G$, synchrotron electrons can shine for $10^6$ yrs, producing jets of sizes of order 100 kpc. This mechanism may also be relevant for Gamma Ray Bursts and, in the absence of magnetic field, supernova explosions.Comment: 4 pages, 1 figur

Nonparametric Modeling of the Anchoring Effect in an Unfolding Bracket Design

Household surveys are often plagued by item non-response on economic variables of interest like income, savings or the amount of wealth. Manski (1989,1994, 1995) shows how, in the presence of such non-response, bounds on conditional quantiles of the variable of interest can be derived, allowing for any type of non-random response behavior. Including follow up categorical questions in the form of unfolding brackets for initial item non-respondents, is an effective way to reduce complete item non-response. Recent evidence, however, suggests that such design is vulnerable to a psychometric bias known as the anchoring effect. In this paper, we extend the approach by Manski to take account of the information provided by the bracket respondents. We derive bounds which do and do not allow for the anchoring effect. These bounds are applied to earnings in the 1996 wave of the Health and Retirement Survey (HRS). The results show that the categorical questions can be useful to increase precision of the bounds, even if anchoring is allowed for.

Bounds on Quantiles in the Presence of Full and Partial Item Nonresponse

Microeconomic surveys are usually subject to the problem of item nonresponse, typically associated with variables like income and wealth, where confidentiality and/or lack of accurate information can affect the response behavior of the individual. Follow up categorical questions can reduce item nonresponse and provide additional partial information on the missing value, hence improving the quality of the data. In this paper we allow item nonresponse to be non-random and extend Manski’s approach of estimating bounds to identify an upper and lower limit for the parameter of interest (the distribution function or its quantiles). Our extension consists of deriving bounding intervals taking into account all three types of response behavior: full response, partial (categorical) response and full nonresponse. We illustrate the theory by estimating bounds for the quantiles of the distribution of amounts held in savings accounts. We consider worst case bounds which cannot be improved upon without additional assumptions, as well as bounds that follow from different assumptions of monotonicity

Nonparametric Bounds on the Income Distribution in the Presence of Item Nonresponse

Item nonresponse in micro surveys can lead to biased estimates of the parameters of interest if such nonresponse is nonrandom. Selection models can be used to correct for this, but parametric and semiparametric selection models require additional assumptions. Manski has recently developed a new approach, showing that, without additional assumptions, the parameters of interest are identified up to some bounding interval. In this paper, we apply Manski’s approach to estimate the distribution function and quantiles of personal income, conditional on given covariates, taking account of item nonresponse on income. Nonparametric techniques are used to estimate the bounding intervals. We consider worst case bounds, as well as bounds which are valid under nonparametric assumptions on monotonicity or under exclusion restrictions.