Abstract
The issue of hunger in America was considered resolved in the 1970s after attracting so much attention in the 1960’s. President Johnson’s War on Poverty was thought to have solved the problem once and for all. The poverty rate in the United States began to fall drastically with the onslaught of the Great Society Programs in the 1960’s and continued to do so until the early 1970s when the poverty rate started creeping up again. In the early 1980’s the poverty rate again began to level off, only to rise again during the Reagan and Bush eras, cf. Jorgenson (1990).
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Notes
The problem of course, is that quantifying “hunger” is very difficult since it is a multidimensional problem. This is quite obvious when one peruses the survey used to construct the hunger count (see Table 7.2).
For the child support stochastic frontier efficiency score, Good and Pirog-Good (1990) did not report the score for Washingtion, D.C., we used the U.S. average for this value for D.C.
Amemiya (1973) suggested using H = 1/(1 + exp Q) which can be rewritten as Q = log(1-H)-log H. To generalize this transformation, we can use Q = a log H + c (log H)2+ b log(1-H). In particular, we choose c = 1 equated with (2.2), the equality still holds even if all the coefficients of the LHS and the RHS were doubled. Therefore, we choose one coefficient, c, to be one.
Under HA, we employ the following diffuse prior pdf for the parameters: While under HB, the prior pdf is: with The prior pdf for β2 given σ is the form of a k2 dimensional multivariate Cauchy pdf with zero location vector and matrix V′ V/T, a matrix suggested by the form of the information matrix.
For simplicity of exposition, we begin from Hypothesis A and will consider relative merits of adding extra variables. However, if we want to be very general, we can begin from the following assumptions and move toward the larger models. Consider the following two hypotheses. By introducing UNEMP, or PERSINC., or other variables for X, and by computing, we can choose the most important variable to explain the difference in Q. Using this variable in place of EDHS in Hypothesis A, we can continue to increase the regression span
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© 1998 Springer-Verlag Berlin Heidelberg
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Ryu, H.K., Slottje, D.J. (1998). A New Method for Estimating Limited Dependent Variables: An Analysis of Hunger. In: Measuring Trends in U.S. Income Inequality. Lecture Notes in Economics and Mathematical Systems, vol 459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58896-9_7
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DOI: https://doi.org/10.1007/978-3-642-58896-9_7
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