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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.IZA Discussion Paper No. 8144 April 2014 ABSTRACT Additive Nonparametric Regression in the Presence of Endogenous RegressorsIn this paper we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and cognitive outcomes. Some of our (average) results are consistent with the literature (e.g., negative returns to child care when mothers have higher levels of education). However, as our estimators allow for heterogeneity both across and within groups, we are able to contradict many findings in the literature (e.g., we do not find any significant differences in returns between boys and girls or for formal versus informal child care).JEL Classification: C14, C36, I21, J13

In this paper we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient and free from curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and cognitive outcomes. Some of our (average) results are consistent with the literature (e.g., negative returns to child care when mothers have higher levels of education). However, as our estimators allow for heterogeneity both across and within groups, we are able to contradict many findings in the literature (e.g., we do not find any significant differences in returns between boys and girls or for formal versus informal child care).

Using patent statistics related to solar power on a panel of eleven countries from 1990 to 2008, we build a reduced-form model to analyze the role that public policies play in fostering innovation. We conclude that public expenditure on R&D and feed-in tariffs have a significant effect on the development of solar energy. We also find a significant effect of electricity price, attributable to rising energy prices. Using patent citations, we estimate the knowledge flows available to inventors in each country over time and we find that the marginal productivity of R&D has a positive and significant effect on innovation.

When determining a claimant's eligibility for Social Security Disability Insurance (SSDI) benefits, the Social Security Administration (SSA) assesses whether his/her health condition (e.g., back/spine problems) sufficiently impairs his/her functional capacity (e.g., ability to lift/carry weight) so that the he/she is unable to meet the requirements (e.g., need to lift/carry 25 pounds) of his/her previous occupation and other possible occupations. Using data from the Survey of Income and Program Participation (SIPP) and Occupational Requirements Survey (ORS), we compare the occupational requirements of workers with and without a given health condition in order to understand what to expect from claimants with that health condition. Although sample sizes are limiting and ORS data collection is not yet complete, we find some evidence that workers' occupational requirements accommodate their health conditions. This evidence suggests that claimants with these health conditions may be able to fulfill the requirements in these occupations. We do find some evidence of the opposite causality: Workers may experience health conditions later in life from occupational requirements that may have caused their health conditions. This evidence is a caution against using data without onset information to inform claimant-ability expectations. Overall, this report provides evidence that national surveys with occupation, health, and function questions have the potential to inform revisions to the SSA disability determination process by providing information on people with health conditions who are working and meeting the requirements of a variety of occupations.