Replication code for "The Labor Market Outcomes of Bilinguals in the United States: Accumulation and Return Effects"

This is the code repository for the Bilinguals Project. The R-code consists of four files: i) preprocessing file for the Census data; ii) the Sparse Principal Component Analysis (SPCA); iii) the three-way matching implementation; and iv) regression analysis and decomposition.

Data sources

Data is obtained from two data sources:

• Census data: from Integrated Public Use Microdata Series (IPUMS) doi:10.18128/D010.V8.0. The following variables have been obtained for years 2005-2019: YEAR, SAMPLE, SERIAL, CBSERIAL, HHWT, CLUSTER, REGION, STATEFIP, METRO, STRATA, GQ, PERNUM, PERWT, SEX, AGE, MARST, BIRTHYR, RACE, RACED, HISPAN, HISPAND, BPL, BPLD, CITIZEN, YRIMMIG, YRSUSA1, LANGUAGE, LANGUAGED, SPEAKENG, EDUC, EDUCD, EMPSTAT, EMPSTATD, LABFORCE, CLASSWKR, CLASSWKRD, OCC2010, IND1990, WKSWORK2, UHRSWORK, INCTOT, INCWAGE, INCOTHER, POVERTY, PWSTATE2. A detailed description of the variables is provided in the Census codebook (here).
• Skill codes: from Occupation Information Network (O*NET) doi:10.26209/MJ1461305.

Installation

The R scripts are written in R 4.0.2 and use the packages listed below. Launch a command line session and cd into the folder containing the input files. Then, specify the CRAN mirror and install packages like:

$ R -e 'chooseCRANmirror(graphics=FALSE, ind=76);install.packages(c("readr","tidyverse","readxl","psych","psychTools","GPArotation","rospca","dplyr","plot3D", "writexl","psy","fastDummies","ggalt","ggsci","scales","multiwayvcov","lmtest","Hmisc","sandwich","oaxaca","stargazer","dineq"))'

Data handling and visualisation

• 'tidyverse', including 'readr' and 'readxl' (read data), 'tibble' (update dataframes), 'dplyr' (manipulate data), 'ggplot2' (create graphics)
• 'ggalt' (extra coordinate systems, 'geoms', statistical transformations, scales and fonts for 'ggplot2')
• 'fastDummies' (dummies from categorical variables)
• 'ggsci' (color palettes for 'ggplot2')
• 'scales' (breaks and labels for axes and legends)

Sparce principal component analysis

• 'rospca' (robust sparse PCA using the ROSPCA algorithm)
• 'GPArotation' (gradient projection algorithm rotation for factor analysis)
• 'psych' (multivariate analysis and scale construction using factor analysis and principal component analysis)
• 'psychTools' (support functions for 'psych')
• 'psy' (Cronbach's coefficient alpha)

Regression analysis and wage gap decomposition

• 'lmtest' (testing linear regression models)
• 'sandwich' (robust and cluster errors)
• 'multiwayvcov' (multi-way clustering)
• 'Hmisc' (miscellaneous functions for data analysis)
• 'oaxaca' (Oaxaca-Blinder wage gap decomposition)
• 'stargazer' (convert output to LaTeX code, HTML code and ASCII text)
• 'dineq' (decomposition of income inequality, including 'rif' function)

Implementation

• The original file downloaded from the IPUMS website is very large (~16 GB) and the preprocessing takes an excess of 20 minutes to run (data-processing.R). The output file bilinguals_processed.csv (~6.2 GB) includes the processed data which performs the following transformation of the dependent variable: monetary values are discounted to 2005 dollars and calculated as yearly wages for 52weeks/year.
• The SPCA might be performed by supplying the input file Skills_2010.xlsx to the SPCA.R script. The script implements the SPCA algorithm that reduces down the 35 skills obtained from O*NET into three main components that were interpreted as representative of the cognitive, manual, and interpersonal dimensions of jobs. The finding was validated through exploratory factor analysis (EFA). Launch a command line session and cd into the folder containing the input files. Then, run the script and obtain skills_factors.xlsx like:

$ Rscript SPCA.R

• The threeway_matching.R merges the bilinguals_processed.csv and skills_factors.xlsx files and performs the three-way matching of bilinguals, late learners, and monolinguals to reduce the sample space and the bias in the data. The script outputs the bilinguals_matched.csv file and visualization of the resulting bias reduction. To run the script, navigate to the folder containing the input files and perform the following command:

$ Rscript threeway_matching.R

• regression_analysis.R evaluates the wage gap between monolinguals and bilinguals and late learner and then decomposes the difference into endowments and returns effects for the matched sample. Log-wages regression include the following covariates: three types of skills, demographic characteristics, time and state fixed effects. To investigate distributional differences in returns to language skills, the script runs and visualize the trends of language-group coefficients using recentered influence function (RIF) transforms of the dependent variable at each percentile. The distributional effects of skill accumulations are divided into endowments and returns estimates from unconditional quantile regressions in the spirit of Oaxaca-Blinder wage gap decomposition. For the code execution, run:

$ Rscript regression_analysis.R

• EPS.eps executes the mapping of skills into skill groups, namely, cognitive, manual and interpersonal skills.