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\title{Are They the Best and the Brightest? \\
Analysis of Employer-Sponsored Tech Immigrants}

\author{
Norm Matloff \\
Department of Computer Science \\
University of California at Davis \\
Davis, CA 95616 USA \\
matloff@cs.ucdavis.edu 
}

\date{Berkeley Center for Globalization and Information Technology \\
November 15, 2010}
 
\begin{document} 

\begin{frame}
\titlepage

\end{frame}

\begin{frame}
\frametitle{The Setting}

\pause

\begin{itemize}

\item ``[restrictive U.S. immigration policy is] driving away the
world's best and brightest''---Bill Gates, 2007
% International Educator, May/June 2007
\pause

\item ``We should not [send our] bright and talented international
students...to work for our competitors abroad upon graduation''--NAFSA 
(Nat. Assoc. of Foreign Student Advisers)
% http://www.nafsa.org/public_policy.sec/international_student_1/immigration_reform_issues/comprehensive_immigration_3
\pause

\item ``...we should be stapling a green card to the diploma of any
foreign student who earns an advanced degree at any U.S. university... The 
world’s best brains are on sale. Let’s buy more!''---{\it New York
Times} columnist Tom Friedman, 2009
% NYT, June 27, 2009
\pause

\item Industry wants more H-1B work visas, fast-track green cards for
STEM foreign students.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Questions}

\begin{itemize}

\item We all support the immigration of outstanding talents.
\pause

\item But are most of those sponsored by the tech industry of that caliber?
\pause

\item And for those that ARE of outstanding talent:
\pause

   \begin{itemize}

   \item What nations do they come from?
   \pause

   \item Does existing policy serve them well?

   \end{itemize}

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Quantitative Approaches}

\pause

We will approach the question via analyses of:

\begin{itemize}

\item wages
\pause

\item dissertation awards
\pause

\item patents

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Wage Issues}
\pause

Due to wording of law, the legally required {\it prevailing wage}
is typically well below the actual \underline{market wage}.  
\pause

\begin{itemize}

\item Employers claim to hire foreign workers due to their ``hot'' skill
sets.  A skill typically brings a 15\% premium, more in combination.
But prevailing does not factor in skills.  
\pause

\item Nor is educational level factored in.  Can legally get a Master's worker
for a Bachelor's price.
\pause

\item Underpayment found to be 15-20\% in (Matloff, 2003)
% N. Matloff, On
% the Need for Reform of the H-1B Nonimmigrant Work Visa in
% Computer-Related Occupations, {\it University of Michigan Journal of Law
% Reform} Fall 2003}
and 33\% in (Ong, 1997).
% P. Ong and E.
% Blumenthal, in Darrell Hamamoto and Rodolfo Torres (ed.), {\it New
% American Destinies:  A Reader in Contemporary Asian and Latino
% Immigration, 1997}}
\pause

\item NRC
% National Research Council,  Building a Workforce
% for the Information Economy, National Academies Press, 2001
and GAO
% H-1B Foreign Workers: Better Tracking Needed to
% Help Determine H-1B Program's Effects on U.S. Workforce}, 2003
employer surveys found many employers admitting to (\underline{legally})
paying H-1B workers less than comparable Americans.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Solutions}
\pause

How does one use wages to assess talent, given
underpayment of the foreign workers?  One can:
\pause

\begin{itemize}

\item Use as a baseline wage 20\% above the legal prevailing wage.
\pause

\item Consider only workers who were originally sponsored by employers
but now have green cards or citizenship.
\pause

\item Consider nonmonetary evidence of outstanding talent, such as
awards and patents.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Nationality Issues:  China}
\pause

Much has been written about the adverse effects of rote-memory oriented
education on innovation in East Asia. 
\pause

\begin{itemize}

\item The governments of China, Japan, S. Korea and Japan have all tried
to remedy it.
\pause

\item A frequent complaint of Chinese Nobel laureate C.N. Yang.
\pause

\item Prof. Chen Lixin, China:  The system ``results in the phenomenon of high
scores and low ability.''
\pause

\item The Chinese use the phrase, 
\begin{CJK}{UTF8}{gbsn}填鸭\end{CJK}
\begin{CJK}{UTF8}{bsmi}子\end{CJK}, ``stuff the duck,''
to poke fun at rote-memory learning.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Nationality Issues, cont'd.}
\pause

\begin{itemize}

\item Much demonization of China these days, in the press and Congress.
\pause

\item ``...U.S. dominance in
high-performance computing is at risk.  One could argue that this hits
the foundation of our economic future''---Wu-chun Feng, professor at
Virginia Polytechnic Institute and State University, on China's building
the world's fastest supercomputer, 2010
\pause

\item Relates to the industry lobbyists' cry, ``Don't let them return
home and work for our competitors!''
\pause

\item So, are the Chinese hired in the U.S. tech industry ``the best and
the brightest''?  Or are most victims of 
\begin{CJK}{UTF8}{gbsn}填鸭\end{CJK}
\begin{CJK}{UTF8}{bsmi}子\end{CJK}? 

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{First Wage Analysis:  PERM Data}

\pause

\begin{itemize}

\item DOL files of employer applications for green card
sponsorship.
% http://www.flcdatacenter.com/CasePerm.aspx
\pause

\item Enables analysis by employer and nationality.
\pause

\item Accounts for region via prevailing wage.
\pause

\item Lacks data on education, age.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{PERM Analysis}

\pause

\begin{itemize}

\item I calculated the median wage ratio:

$$
\textrm{WR = median of} ~~~
   \frac{\textrm{actual wage}}{\textrm{emp. claimed prev. wg.}}
$$

\pause

\item By law, must have $WR \geq 1$.
\pause

\item But, denominator too small by factor of 1.15 to 1.33 (see above).
\pause

\item So, only (median) values higher than, say 1.25, indicate a firm is
hiring mainly the ``best and brightest.''

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{PERM Results}
\pause

Median WR for some prominent firms, in general and for software
engineers:
\pause

\bigskip

\begin{tabular}{lrr}
firm & WR & WR, s.e. \\
\hline
Microsoft & 1.18 & 1.15 \\
Intel & 1.13 & 1.08 \\
Google & 1.12 & 1.15 \\
Cisco & 1.04 & 1.04 \\
Oracle & 1.13 & 1.15 \\
HP & 1.20 & 1.08 \\
Motorola & 1.00 & 1.00 \\
Qualcomm & 1.00 & 1.00 \\
eBay & 1.05 & 1.02 \\
PayPal & 1.15 & 1.09 \\
\end{tabular}

\end{frame}

\begin{frame}
\frametitle{Conclusions from PERM Data}
\pause
A few firms pay a 10-15\% premium.

\pause
Not enough to cover the 15-33\% deficiency in prevailing wage.

\pause
In any case, not ``genius'' level.  (Some workers have $WR > 2$.)
\end{frame}

\begin{frame}
\frametitle{Second Wage Analysis:  2000 Census Data}

\pause

\begin{itemize}

\item 5\% PUMS sample
\pause

\item Looked at all programmers, software engineers and electrical
engineers in California.
\pause

\item Proxy for employer sponsorship:  Entered country after age 17.
\pause

\item Considered only those over 30, by which time sponsored workers
should have green cards and thus be free of exploitation.
\pause

\item No public data on employer name, worker nationality.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{PUMS Analysis/Results}
\pause

Logistic regression:

$$
\textrm{probability of } Salary > \$150K = 
$$

$$
\textrm{logit}(\beta_0 
+ \beta_1 Age
+ \beta_2 MS
+ \beta_3 PhD
+ \beta_4 TmpVisa
+ \beta_5 China
+ \beta_6 India)
$$

\end{frame}

\begin{frame}
\frametitle{PUMS Analysis/Results, cont'd.}

\begin{tabular}{rr}
coef. & conf. int. \\
\hline
$\beta_0$ (const.) & -4.11 $\pm$ 0.51 \\
$\beta_1$ (Age) & 0.01 $\pm$ 0.01 \\
$\beta_2$ (MS) & 0.71 $\pm$ 0.23 \\
$\beta_3$ (PhD) & 1.36 $\pm$ 0.34 \\
$\beta_4$ (spons.) & 0.06 $\pm$ 0.25 \\
$\beta_5$ (China) & -1.34 $\pm$ 0.79 \\
$\beta_6$ (India) & 0.07 $\pm$ 0.43 
\end{tabular}

\pause

Substantial negative effect for China here, possibly reflecting 
\begin{CJK}{UTF8}{gbsn}填鸭\end{CJK}
\begin{CJK}{UTF8}{bsmi}子\end{CJK}.

Or could be due to language, etc.  

\pause

Some possible positive Indian effect at PhD level (analysis not shown
here).

\pause

But anyway, no evidence of ``foreign genius'' from any nationality.

\end{frame}

\begin{frame}
\frametitle{ACM Dissertation Awards}

\pause

\begin{itemize}

\item Assoc. for Computing Machinery, the main professional CS body
\pause

\item 58 awards since 1982
\pause

\item 2 from China, 8 from India (judging from names)
\pause

\item Among PhDs, the PUMS data indicate that about 12\% are from China
and 23\% from India.  So, Indians, Chinese underrepresented.
\pause

\item 25 of the 58 foreign, slightly underrepresented.
\pause

\item Again, no evidence that the foreign students are outperforming the
domestic ones.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Patents}

\pause

\begin{itemize}

\item Several recent papers on foreign-nationals in patent apps. in the
U.S., e.g. (Wadhwa {\it et al}, 2007), (Kerr and Lincoln, 2010).
\pause

\item But those merely find that immigrants participate in a lot of
patents---hardly surprising, given their prevalence in the tech
industry.
\pause

\item But only one has calculated {\it per capita} patents by
immigrants, (Hunt, 2010).  She finds after controlling for field and
education level:

\begin{quote}
...both main work visa groups and student/trainee visa holders have 
statistically significantly lower patenting probabilities than
natives...
\end{quote}
\pause

This is especially interesting in that the second group is the one the
industry lobbyists highlight.

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Final Remarks}
\pause

\begin{itemize}

\item Most of the sponsored foreign workers appear to be of ordinary
talent.
\pause

\item But again, some are indeed truly outstanding, including various
Nobel laureates such as C.N. Yang, Fields Medal winners such as S.T.
Yau, plus many whose names are never in the newspaper but really are
superb intellects.
\pause

\item We should facilitate the immigration of such talents.
\pause

\item Recently there has been some concern about long green card waits
for employer-sponsored workers.  However, for PhDs, who have their own
category, the wait continues to be short.

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Resources}

These slides, and the R programming code used to compile the statistics,
will be available at \url{http://heather.cs.ucdavis.edu/BGIT.html}.

(``Footnotes'' in comment lines in {\bf .tex} file.)

\end{frame}

\end{document} 
