Click for next page ( 228

The National Academies of Sciences, Engineering, and Medicine
500 Fifth St. N.W. | Washington, D.C. 20001

Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 227
APPENDIX E Multivariate Analysis of the Determinants of Financial Air] Given to ]-l Visa Holders by the Chinese Government and American Universities Tables E-1 and E-2 give the results of the multivariate analyses of the determinants of financial aid from American universities and the Chi- nese government for ]-1 visa holders. The first column in each table lists the factors found to be significant determinants of the amount of money given each visa holder. The second column indicates the weight given to each factor. Because the dependent variable is the number of dollars provided per year, the "B" (Beta) coefficients in this column can be interpreted as the dollar amounts that should be added to or subtracted from the intercept given at the bottom of the table in order to derive an estimate of the amount of money a student with certain characteristics would be expected to receive. The third column shows t-statistics that indicate whether the coefficient in column 2 is significant. T-statistics larger than 1.96 indicate that the probability of obtaining a coefficient that large by chance is less than 5 percent. Since; all the t-statistics shown are greater than 1.96, all factors listed are highly significant. When a sample is very large, as in the case N = 19,859, statistical significance is more frequently obtained. The R2 shown at the bottom of the table indicates the proportion of the variation in the amount of money received that could be explained by the available factors. For the analysis of the financial aid from the Chinese government, this was 13 percent; for the universities, it was 17 percent. This leaves a significant portion of the variation unexplained. However, given that the unit of analysis frequently was zero, and given the limited number of independent variables, these are quite satisfac- tory results. Following are two specific examples of how characteristics of individ- uals would affect the amount of money each would have been expected to receive from an American university or the Chinese government. These examples are intended to show how the same individual would have been evaluated differently for funding by the Chinese government 227

OCR for page 227
228 APPENDIX E and a U.S. university based on the data available to CSCPRC staff for 1979 to 1983. Example 1: A new research scholar came to the United States to study engineering in 1981. He is 25 years old and has just gradu- atedfrom a university in China. The intercept is given, followed by addition or subtraction of amounts based on the applicant's characteristics. As shown below, the applicant is expected to receive more money from the Chinese govern- ment than from the U. S. university. Chinese Government U.S. University Intercept $2,981.67 $224.73 1981 -895.94 1,540.82 Age -833.74 Research scholar 1,973.28 1,130.42 Engineering 2,313.86 - 461.04 Expected level of funding 5,539.12 2,454.93 Example 2: A female university professor in China is a continuing student studying law on a J-1 visa. She is 38 years old, and is applying for funding in 1980. As shown below, this student would be expected to receive some support from a U.S. university, but nothing from the Chinese government. In fact, the predicted amount from the PRC is negative. (Anyone who would receive a negative estimated amount of funding would be very unlikely to have received any support from that particular source of funds.) Intercept 1980 Continuing Age Female Student Law University professor Expected level of funding Chinese Government '' ~ '' $2,981.67 -775.88 -631.09 - 1,267.30 -637.49 1,357.37 -1,053.18 -25.90 up.. university $224.73 996.73 287.50 209.27 3,239.27 1,280.97 -1,243.15 4,995.32

OCR for page 227
APPENDIX E 229 TABLE E-1 Determinants of the Yearly Amount of Money Given to a ]-1 Student or Scholar by a U.S. University, 1979 Through 1983 Independent Variable Estimate of B t-statistic Year 1980 996.73 5.17 1981 1,540.82 8.80 1982 1,754.02 10.10 1983 1,703.61 9.83 Continuing student or scholar 287.50 4.29 Female 209.27 2.38 Category Student 3,239.27 20.24 Professor 2,610.27 12.14 Teacher 1,420.59 3.49 Research scholar 1,130.42 7.63 Field of study A g r i c u 1 t u r e 967.61 - 4.61 Architecture 1,631.91 - 3.17 Computer science 879.72 4.50 E n g i n e e r i n g 461.04 - 3.72 Health sciences 1,508.57 10.17 Law 1,280.97 3.36 Life sciences 2,017.43 13.55 Mathematics 1,415.81 7.56 Physical sciences 2,208.23 17.64 Social sciences 643.89 3.49 Occupation Government official - 1,434.54 -9.92 University professor 1,243.15 - 11.37 Secondary teacher - 1,756.63 - -4.98 Media 1,323.87 - 2.82 Business management - 1,705.98 -6.09 Other organization - 1,607.84 - 10.89 Intercept 224.73 0.52 R2 = .167 Adjusted R2 = .166, N = 19,859 NOTE: Variables not found to be significant are not included. SOURCE: USIA data tape.

OCR for page 227
230 APPENDIX E TABLE E-2 Determinants of the Yearly Amount of Money Given to a J-1 Student or Scholar by the Chinese Government, 1979 Through 1983 Independent Variable Estimate of B t-statistic Year 1980 -775.88 -5.35 1981 -895.94 -6.60 1982 - 1,086.54 -8.08 1983 -792.19 -5.90 Continuing student or scholar -631.09 - 12.17 Age in years -33.35 - 12.07 Female -637.49 -9.49 Category Student 1,357.37 10.88 Professor 1,247.59 7.46 Teacher 1,957.77 6.26 Research scholar 1,973.28 17.07 Field of study Agriculture 1,569.54 9.69 American studies - 1,537.77 -2.27 Architecture 2,275.32 5.80 Computer science 1,721.11 13.05 Engineering 2,313.86 39.88 Humanities 343.68 2.29 Law - 1,053.18 -3.69 Library science 981.48 2.17 Occupation Agricultural worker - 2,205.72 - 6.98 Intercept 2,981.67 14.90 R2= 126 Adjusted R2 = .125, N = 19,859 NOTE: Variables not found to be significant are not included. SOURCE: USIA data tape.