Sunday, June 22, 2014

Immigration in the Heartland

Blogging has slowed down with other summer plans, and I still probably won't post anything at length in the next few days as I make final touches on a research proposal and update the qv package for Stata.

Meanwhile, some really nice journalism by Damien Cave and Todd Heisler from the New York Times. They have been traveling along Interstate 35 and reporting on the evolving relationship between immigrants and Heartland America. Their journey and all of the updates can be traced on Twitter at #thewaynorth. The most recent report, titled "Living with Immigration", documents the gradual, sometimes reluctant, acceptance of immigrants in many communities.

What I love about this type of work is how it gives a face to the surge of U.S. immigrants in non-traditional places. Academic research on the this phenomenon, often referred to as the "new destination" literature, has been fruitful. But since I like to sometimes mix in non-academic sources when teaching introductory courses, I always appreciate high quality journalism on social issues.

Monday, June 9, 2014

Brain drain and adaptation of Taiwanese immigrnats in the U.S.

I happen to be re-reading Hsian-Shui Chen's Chinatown No More: Taiwan Immigrants in Contemporary New York (1992, Cornell University Press) this week .  In studying various types of Taiwanese immigrants in New York City during the late '80s, Chen concluded that what appeared to be a homogeneous Chinese enclave on the surface had diverse compositions. The lives of Taiwanese immigrants, in particular, have little interaction with the Manhattan Chinatown and its associated  immigrant.

Looking back at his conclusion two decades later, heterogeneity in immigrant communities is hardly news to us who study international migration now. Still, this book intrigues me the most with the discussion about the scale of brain drain, and immigrants' adaptation in the U.S. society.

Brain Drain
On the first subject, I have to start with this brief on page 129.

"In 1974, some 22,366 college graduates were produced in Taiwan. Of these, 2,285 went abroad to study, and only 486 returned after fishing their studies. Through 1986 [sic], an average of 4,632 Taiwan students went abroad each year, but only 793 came back (World Journal, January 26, 1986)..."

Let me put these numbers in perspective: about one-tenth of the college graduates in Taiwan went abroad during the 70s and 80s, with probably 90% of them studying U.S. institutions. These were not the average young people in Taiwan, but from the more competitive and motivated segment of their cohort. Then over three quarters of them stayed in the States after completing their studies. Even for me lived through part of this history, it is still striking to learn the actual scale of exodus.

On the sending end, this has to have depleted Taiwan of its human capital and wasted the educational investment made on these individuals. Common knowledge in Taiwan was that these young people were primarily from the best universities in Taiwan. So the exodus would be analogous to the U.S. losing half of their Ivy League graduates, or for Britain to send half of those trained in Cambridge and Oxford away for good.

There are also implications on the receiving end. While a few thousand immigrants every year is a drop in a bucket for the entire U.S, the aggregate impact is likely substantial at the ethnic group level. Take a conservative estimate of 2,000 per year for two decades, which yields 40,000 immigrants with post-graduate degrees. Putting this number in context, U.S. Census shows that the total Taiwan-born population in the States was 75,353 in 1980 and 244,102 in 1990 (tripled in a decade!). Even by using the 1990 number, the Taiwanese American group would still have one-sixth of its population with advanced degrees earned in the U.S. And this has not yet factored in the associated chained migration effect that brings along family members that are also likely to be more educated than average, which could further bolster the overall educational level. In fact, Portes and Rumbaut (2001:83) used the 2000 U.S. Census and estimated that 66.7% of the Taiwan-born population in the States have college degrees (second to India's 69.1%). That is much higher than the 35%~45% for the U.S. born.

But these students did not necessarily fare well, at least according to Chen's account. Some of his interviewees with advanced degrees struggled to find or hold on to a professional job. Two of three professionals whose stories Chen detailed had held non-professional jobs, such as clerks and self-employment, before finding stable work in the public sector--one in the IRS and another in the municipal government. It was obvious that their advanced training did not help much in transitioning them into the job market.

Language and culture differences also appeared to played a part, as both interviewees appeared to remain distanced from English-speaking social circles. During times bouncing between jobs, their job search depended much on relatives and social ties in the ethnic community.

The role of ethnic communities is indeed intriguing. The once heated enclave economy debate in sociology (eg. Model 1992; Portes and Jensen 1987; Sanders and Nee 1987; Zhou and Logan 1989) revolved around the issue of whether ethnic sectors of the market provide better returns to human capital. While the debate has examined the question from numerous angles, one overlooked aspect is the potential differentiated effects due to differences in skill levels. In Chen's account of Taiwanese immigrants, the less-skilled pretty much viewed the ethnic sector as their main option for employment. Even when occasionally venturing outside of the sector, they still mostly adhered to paths carved out by predecessors from the ethnic community.

Career options for the U.S. educated, however, appeared to have a different composition. The enclave economy served as a fall back option while they could seek self-employment or low-level white collar jobs. They are rarely, however, satisfactory with the work environment, which resembles a secondary labor market sector. Meanwhile, the U.S. earned credentials offered them the flexibility to search office jobs in formal organizations outside of the ethnic community. Work in the mainstream economy does provide better pay, reasonable hours, and job security, but the language and cultural barriers appear to limit how far the immigrant can go in these organizations.

Tuesday, June 3, 2014

A few R functions to summarize lmer results

As I am wrapping up with the growth curve models at hand, here are a couple of R functions to share with whoever is still using lmer from the pre-1.0 version lme4 (not ready to upgrade as yet--too many codes would require updating). These functions were written to summarize results from different lmer models.

The first three functions separately extract the model summary statistics (lmer.stats), the fixed effect parameters (lmer.fixef), and the random effect parameters (lmer.ranef) into data frames. The last function lmer.append can combine these results into an aggregated data frame, which can then be saved as a spreadsheet using the xlsx package.

Note: the if condition in lmer.ranef needs revision to make the columns consistent if you have more than one covariance term in any of the models. Otherwise R won't be able to aggregate the data frames.

lmer.stats<-function( {
    label<-deparse(substitute(    # identifier
    df<-data.frame(label, "AIC"=A, "BIC"=B, "LL"=ll, "DEV"=dv, "df"=dg, "N"=obs.TIME, "CHILD"=obs.CHILD, "SCHOOL"=obs.SCHOOL)

    label<-deparse(substitute(     # identifier


    if (ncol(dfr)==4)    {    # random slope models have more columns
    }     else {

    vars$Variable<-gsub("\\)", "", vars$Variable)    # deal with (Intercept)
    vars$Variable<-gsub("\\(", "", vars$Variable)
    label<-deparse(substitute(     # identifier

lmer.append<-function(...,append=TRUE)    {
    if (!append){
    } else {
        L.stats<<-rbind(L.stats, lmer.stats(...))
        L.ranef<<-rbind(L.ranef, lmer.ranef(...))
        L.fixef<<-rbind(L.fixef, lmer.fixef(...))

Added 06/09/2014:
Someone just reminded me that the functionality of lmer.stat is similar to the base routine anova(). Say you have lmer model estimates A1, A2, and A3, anova(A1,A2,A3) returns a data frame that summarizes the degrees of freedom, AIC, BIC, log-likelihood, and results of deviance tests in relation to the first model, in this case, A1. Still, that does not include the other statistics that lmer.stats provides.