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Author: Pepper, John V.

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1.	Manski, Charles F. Pepper, John V.
1.	Monotone Instrumental Variables with an Application to the Returns to Schooling NBER Technical Working Paper No. 224, National Bureau of Economic Research, February 1998. Also: http://www.nber.org/papers/t0224 Cohort(s): NLSY79 Publisher: National Bureau of Economic Research (NBER) Keyword(s): Armed Forces Qualifications Test (AFQT); Educational Returns; Schooling; Treatment Response: Monotone, Semimonotone, or Concave-monotone; Variables, Independent - Covariate; Variables, Instrumental Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement, with much debate about whether some covariate is or is not a "valid instrument" in an application of interest. There is therefore good reason to consider weaker but more credible assumptions. assumptions. To this end, we introduce monotone instrumental variable (MIV) A particularly interesting special case of an MIV assumption is monotone treatment selection (MTS). IV and MIV assumptions may be imposed alone or in combination with other assumptions. We study the identifying power of MIV assumptions in three informational settings: MIV alone; MIV combined with the classical linear response assumption; MIV combined with the monotone treatment response (MTR) assumption. We apply the results to the problem of inference on the returns to schooling. We analyze wage data reported by white male respondents to the National Longitudinal Survey of Youth (NLSY) and use the respondent's AFQT score as an MIV. We find that this MIV assumption has little identifying power when imposed alone. However combining the MIV assumption with the MTR and MTS assumptions yields fairly tight bounds on two distinct measures of the returns to schooling. Published as: Manski, Charles F. and John V. Pepper. "Monotone Instrumental Variables With An Application To The Returns To Schooling," Econometrica 68,4 (July 2000): 997-1010. Also: http://www.jstor.org/stable/2999533
	Bibliography Citation Manski, Charles F. and John V. Pepper. "Monotone Instrumental Variables with an Application to the Returns to Schooling." NBER Technical Working Paper No. 224, National Bureau of Economic Research, February 1998.

2.	Manski, Charles F. Pepper, John V.
2.	Monotone Instrumental Variables: with an Application to the Returns to Schooling Working Paper 308, Thomas Jefferson Center for Political Economy Working Paper Series, University of Virginia, January 1998 Cohort(s): NLSY79 Publisher: Department of Economics, University of Virginia Keyword(s): Armed Forces Qualifications Test (AFQT); Educational Returns; Schooling; Treatment Response: Monotone, Semimonotone, or Concave-monotone; Variables, Independent - Covariate; Variables, Instrumental Permission to reprint the abstract has not been received from the publisher. Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement, with much debate about whether some covariate is or is not a "valid instrument" in an application of interest. There is therefore good reason to consider weaker but more credible assumptions. To this end, we introduce monotone instrumental variable (MIV) assumptions. A particularly interesting special case of an MIV assumption is monotone treatment selection (MTS). IV and MIV assumptions may be imposed alone or in combination with other assumptions. We study the identifying power of MIV assumptions in three informational settings: MIV alone; MIV combined with the classical linear response assumption; MIV combined with the monotone treatment response (MTR) assumption. We apply the results to the problem of inference on the returns to schooling. We analyze wage data reported by white male respondents to the National Longitudinal Survey of Youth (NLSY) and use the respondent's AFQT score as an MIV. We find that this MIV assumption has little identifying power when imposed alone. However combining the MIV assumption with the MTR and MTS assumptions yields fairly tight bounds on two distinct measures of the returns to schooling.
	Bibliography Citation Manski, Charles F. and John V. Pepper. "Monotone Instrumental Variables: with an Application to the Returns to Schooling." Working Paper 308, Thomas Jefferson Center for Political Economy Working Paper Series, University of Virginia, January 1998.

3.	Manski, Charles F. Pepper, John V.
3.	Monotone Instrumental Variables: With an Application to the Returns to Schooling Econometrica 68,4 (July 2000): 997-1010. Also: http://www.jstor.org/stable/2999533 Cohort(s): NLSY79 Publisher: Blackwell Publishing, Inc. => Wiley Online Keyword(s): Schooling; Treatment Response: Monotone, Semimonotone, or Concave-monotone; Variables, Independent - Covariate; Variables, Instrumental Introduction: For fifty years econometric analyses of treatment response have made extensive use of instrumental variable (IV) assumptions holding that mean response is constant across specified subpopulations of a population of interest. Yet the credibility of mean independence conditions and other IV assumptions has often been a matter of considerable disagreement, with much debate about whether some covariate is or is not a "valid instrument" in an application of interest. There is therefore good reason to consider weaker but more credible assumptions. To this end, we introduce monotone instrumental variable (MIV) assumptions holding that mean response varies weakly monotonically across specified subpopulations. We study the identifying power of these MIV assumptions and give an empirical application. The findings reported here add to the literature developing nonparametric bounds on treatment effects.
	Bibliography Citation Manski, Charles F. and John V. Pepper. "Monotone Instrumental Variables: With an Application to the Returns to Schooling." Econometrica 68,4 (July 2000): 997-1010.

4.	Manski, Charles F. Pepper, John V. Petrie, Carol V.
4.	Informing America's Policy on Illegal Drugs: What We Don't Know Keeps Hurting Us Washington DC: National Academy Press, 2001. Cohort(s): NLSY79 Publisher: National Academy Press Keyword(s): Data Analysis; Drug Use; Longitudinal Data Sets; Longitudinal Surveys; NLS Description Adequate data and research are essential to judge the effectiveness of the nation's efforts to cope with its illegal drug problem. Given the importance of the illegal drug problem and the continuing controversy about how best to confront it, there is a pressing need for the nation to assess the existing portfolio of data and research and to initiate stronger efforts where necessary. Accordingly, at the request of the U.S. Office of National Drug Control Policy, the National Research Council established the Committee on Data and Research for Policy on Illegal Drugs. The committee was given charge to: 1. Assess existing data sources and recent research studies that support policy analysis; 2. identify new data and research that may enable the development of more effective means of evaluating the consequences of alternative drug control policies; and 3. explore ways to integrate theory and findings from diverse disciplines to increase understanding of drug abuse and the operation of drug markets.
	Bibliography Citation Manski, Charles F., John V. Pepper and Carol V. Petrie. Informing America's Policy on Illegal Drugs: What We Don't Know Keeps Hurting Us. Washington DC: National Academy Press, 2001..

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