How University Endowments Respond to Financial Market Shocks: Evidence and Implications

Endowment payouts have become an increasingly important component of universities’ revenues in recent decades. We test two leading theories of endowment payouts: (1) universities smooth endowment payouts, or (2) universities use endowments as self-insurance against financial shocks. In contrast to both theories, endowments actively reduce payouts relative to their stated payout policies following negative, but not positive, shocks. This asymmetric behavior is consistent with “endowment hoarding,” especially among endowments with values close to the benchmark value at the start of the university president’s tenure. We also document the effect of negative endowment shocks on university operations, including personnel cuts.

Program Design and Student Outcomes in Graduate Education

Doctoral programs in the humanities and related social sciences are characterized by high attrition and long time-to-degree. In response to these long-standing problems, the Andrew W. Mellon Foundation launched the Graduate Education Initiative (GEI) to improve the structure and organization of PhD programs, and in turn reduce attrition and shorten time-to-degree. Over a 10-year period starting in 1991, the Foundation provided a total of $80 million to 51 departments at 10 major research universities. This paper estimates the impact of the GEI on attrition rates and time-to-degree. Our analysis is based on a competing-risk duration model and student-level data spanning the start of the GEI, including data on students at a set of control departments. We estimate that, on average, the GEI had modest impacts on student outcomes in the expected directions: reducing attrition rates, reducing time-to-degree, and increasing completion rates. The overall impacts of the GEI appear to have been driven in part by reductions in cohort size, increases in financial aid, and increases in student quality

Fast and Efficient Lossless Image Compression

(c) 1993 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We present a new method for lossless image compression that gives compression comparable to JPEG lossless mode with about ve times the speed. Our method, called FELICS, is based on a novel use of two neighboring pixels for both prediction and error modeling. For coding we use single bits, adjusted binary codes, and Golomb or Rice codes. For the latter we present and analyze a provably good method for estimating the single coding parameter

Arithmetic Coding for Data Compression

(c) 1994 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Arithmetic coding provides an e ective mechanism for remov- ing redundancy in the encoding of data. We show how arithmetic coding works and describe an e cient implementation that uses table lookup as a fast alternative to arithmetic operations. The reduced-precision arithmetic has a provably negligible e ect on the amount of compression achieved. We can speed up the implemen- tation further by use of parallel processing. We discuss the role of probability models and how they provide probability information to the arithmetic coder. We conclude with perspectives on the comparative advantages and disadvantages of arithmetic coding

Fast Progressive Lossless Image Compression

Copyright 1994 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.173910We present a method for progressive lossless compression of still grayscale images that combines the speed of our earlier FELICS method with the progressivity of our earlier MLP method We use MLP s pyramid based pixel sequence and image and error modeling and coding based on that of FELICS In addition we introduce a new pre x code with some advantages over the previously used Golomb and Rice codes Our new progressive method gives compression ratios and speeds similar to those of non progressive FELICS and those of JPEG lossless mode also a non progressive method The image model in Progressive FELICS is based on a simple function of four nearby pixels We select two of the four nearest known pixels using the two with the middle non extreme values Then we code the pixel s intensity relative to the selected pixels using single bits adjusted binary codes and simple pre x codes like Golomb codes Rice codes or the new family of pre x codes introduced here We estimate the coding parameter adaptively for each context the context being the absolute value of the di erence of the predicting pixels we adjust the adaptation statistics at the beginning of each level in the progressive pixel sequenc

Error Modeling for Hierarchical Lossless Image Compression

(c) 1992 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We present a new method for error modeling applicable to the MLP algorithm for hierarchical lossless image compression. This method, based on a concept called the variability index, provides accurate models for pixel prediction errors without requiring explicit transmission of the models. We also use the vari- ability index to show that prediction errors do not always follow the Laplace distribution, as is commonly assumed; replacing the Laplace distribution with a more general symmetric exponential distribution further improves compression. We describe a new compression measurement called compression gain, and we give experimental results showing that the MLP method using the variability index technique for error modeling gives signi cantly more compression gain than other methods in the literature

Analysis of Arithmetic Coding for Data Compression

Arithmetic coding, in conjunction with a suitable probabilistic model, can pro- vide nearly optimal data compression. In this article we analyze the e ect that the model and the particular implementation of arithmetic coding have on the code length obtained. Periodic scaling is often used in arithmetic coding im- plementations to reduce time and storage requirements; it also introduces a recency e ect which can further a ect compression. Our main contribution is introducing the concept of weighted entropy and using it to characterize in an elegant way the e ect that periodic scaling has on the code length. We explain why and by how much scaling increases the code length for les with a ho- mogeneous distribution of symbols, and we characterize the reduction in code length due to scaling for les exhibiting locality of reference. We also give a rigorous proof that the coding e ects of rounding scaled weights, using integer arithmetic, and encoding end-of- le are negligible

Parallel Lossless Image Compression Using Huffman and Arithmetic Coding

We show that high-resolution images can be encoded and decoded e ciently in parallel. We present an algorithm based on the hierarchical MLP method, used either with Hu man coding or with a new variant of arithmetic coding called quasi-arithmetic coding. The coding step can be parallelized, even though the codes for di erent pixels are of di erent lengths; parallelization of the prediction and error modeling components is straightforward

Design and Analysis of Fast Text Compression Based on Quasi-Arithmetic Coding

We give a detailed algorithm for fast text compression. Our algorithm, related to the PPM method, simpli es the modeling phase by eliminating the escape mechanism and speeds up coding by using a combination of quasi-arithmetic coding and Rice coding. We provide details of the use of quasi-arithmetic code tables, and analyze their compression performance. Our Fast PPM method is shown experimentally to be almost twice as fast as the PPMC method, while giving comparable compression

Insecticide Toxicity, Synergism, and Resistance in the German Cockroach (Dictyoptera: Blattellidae)

The toxicity of, synergism of, and resistance to insecticides in four strains of German cockroach, Blattella germanica (L.), were investigated. Toxicity of nine insecticides by topical application to the susceptible strain varied \u3e2,000-fold, with deltamethrin (LD50 = 0.004 μg per cockroach) and malathion (LD50 = 8.4 μg per cockroach) being the most and least toxic, respectively. Resistance to pyrethrins (9.5-fold) in the Kenly strain was unaffected by the synergists piperonyl butoxide (PBO) or S,S,S-tributylphosphorotrithioate( DEE), suggesting that metabolism is not involved in this case. Malathion resistance in the Rutgers strain was suppressible with PBO, implicating oxidative metabolism as a resistance mechanism. The Ectiban-R strain was resistant to all the pyrethroids tested, and cypermethrin resistance was not suppressible with PBO or DEE. These findings support results of previous studies that indicated this strain has a kdr-like mechanism. Bendiocarb resistance in both the Kenly and Rutgers strains was partially suppressed by either PBO or DEE, suggesting that oxidative and hydrolytic metabolism are involved in the resistance. Trends between the effects of the synergists on the susceptible versus resistant strains are discussed