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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; revised, November 18}
 
\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, and 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, the
innovative, the ``game changers.''
\pause

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

\item And for those who ARE of that caliber, is current policy
reasonably welcoming?

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Previous Work}

To my knowledge, previous work on this topic has been limited.
\pause

One older source is (North, 1995): 
\pause

\begin{table}
\begin{tabular}{|l|c|}
\hline
department quality & \% foreign-born \\ \hline
\hline
highest quarter & 37.2\% \\ \hline
second quarter & 44.5\% \\ \hline
third quarter & 47.5\% \\ \hline
lowest quarter & 50.6\% \\ \hline
\end{tabular}
\caption{Foreign-student enrollments in Ph.D. engineering programs}
\end{table}

\end{frame}

\begin{frame}
\frametitle{Our Approaches}

\pause

We will approach the question via analyses of:
\pause

\begin{itemize}

\item wages
\pause

\item dissertation awards
\pause

\item patents

\end{itemize}
\end{frame}

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

\begin{itemize}

\item Foreign workers exploitable during sponsorship period.
\pause

\item Underpayment found to be 15-20\% in (Matloff, 2003) and 33\% in
(Ong, 1997).
\pause

\item Due to loopholes, legally required {\it prevailing wage}
is typically well below real \underline{market wage} (Matloff,
2003).\footnote{Similar loopholes for legal definition of ``actual
wage.''}
\pause

\item Congressionally-commissioned employer surveys, (NRC, 2001) and
(GAO, 2003), found many employers admitting to paying H-1B workers less
than comparable Americans.
\pause

\item GAO even noted role of loopholes:  

\begin{quote}
... [employers] hired H-1B workers in part because these workers 
would often accept lower salaries...however, these employers said 
they never paid H-1B workers less than the required wage. 
\end{quote}

\end{itemize}

\end{frame}

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

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

\begin{itemize}

\item Use as baseline wage 20\% above 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 (not managers).
\pause

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

\item Proxy for green card, cit.:  Count those over 32, by which time 
most good (EB-1, EB-2) sponsored workers should have green cards.
\pause

\item No public data on employer name.
\pause

\item /A


\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{Factor Impacts on Probability of Earning $>$ \$150K}

\begin{tabular}{rr}
coef. & conf. int. \\
\hline
$\beta_0$ (const.) & -3.85 $\pm$ 0.28 \\
$\beta_1$ (Age) & 0.005 $\pm$ 0.006 \\
$\beta_2$ (MS) & 0.71 $\pm$ 0.12 \\
$\beta_3$ (PhD) & 1.42 $\pm$ 0.18 \\
$\beta_4$ (spons.) & 0.06 $\pm$ 0.13 \\
$\beta_5$ (China) & -1.45 $\pm$ 0.43 \\
$\beta_6$ (India) & 0.16 $\pm$ 0.23 
\end{tabular}

\pause

\begin{itemize}

\item MS, PhD: large pos. impact
\pause

\item China: large neg. impact
\pause

\item India: small pos. or neutral (larger in lin. regress of tot. wage) 
\pause

\item other foreign: small pos. or neutral
\pause

\item no evidence of overall ``foreign genius'' 

\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Why Negative Impact in China Case?}

\begin{itemize}

\item 
Possibly reflects 
\begin{CJK}{UTF8}{gbsn}填鸭\end{CJK}
\begin{CJK}{UTF8}{bsmi}子\end{CJK}---``tian yazi,'' Chinese term for rote-memory
learning.
\pause

\item Governments of China, Japan, S. Korea and Taiwan have all tried to
remedy this.

\item Language effects?

\end{itemize}

\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\footnote{names used as proxy} 
\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 talents. 
\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,
are available at \url{http://heather.cs.ucdavis.edu/BGIT.html}

\end{frame}

\end{document} 
