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1 A Journey to Ellis Island: Profiling Historical Immigration Using SAS and JMP Time Series Methods Archie J. Calise, Queensborough College of the City University of New York Joseph Earley, Loyola Marymount University, Los Angeles ABSTRACT This paper uses the Time Series Forecasting System(TSFS) from SAS along with JMP smoothing procedures to estimate and perform in-sample forecasts for the historical movement of immigration to the Port of New York Ellis Island facility (Figure 1). Results indicate that the upheaval in Europe due to World War I along with the quota changes resulting from the National Origins Act of 1924 were primary factors in modeling the movement of immigrants to the Port of New York Ellis Island immigration facility. Figure 1 PortNY year INTRODUCTION AND HISTORICAL PERSPECTIVE Ellis Island has become synonymous with immigration to the United States. This relatively small island, just 27.5 acres represented the start of the American dream for thousands of immigrants. It has been estimated that nearly half of all Americans can trace their family roots to at least one member who came to the United States through Ellis Island. Ellis Island was only one of over fifty ports of entry to the United States before the age of air travel. These included ports of entry in Boston, Baltimore, Philadelphia, Charleston, Galveston, and Angel Island in San Francisco. Ellis Island functioned as the immigration center for the Port of New York from 1892 to Prior to 1855, immigration was the responsibility of the state that the person entered. In 1855, New York started to process immigrants at Castle Garden in Battery Park. This facility was an old fort Castle Clinton built to protect the harbor during the war of The records on immigration to the United States are recorded beginning in From 1820 to 1867 alien passengers arriving at seaports were recorded. From 1868 to 1897 immigrant aliens were recorded at seaports when arriving in the United States. From 1898 to the present, immigrant aliens were admitted for permanent residence to the United States. The arrival of immigrants through Canada and Mexico by land was not recorded until Thus the complete picture of immigration to the United States can be said to be less than exact No records are known to have been kept of the immigration processing through Castle Garden or Ellis Island. The lists of passenger manifests are the only actual records available of immigrants arriving at Ellis Island. This was not the case at some of the other immigration sites. The immigrants arriving in the United States through Ellis Island were processed through the Great Hall. They went through many inspections on the island. They had to be disease free and had to prove their ability to earn their way in the United States. Inspectors would examine them for any sign of illness and would detain them if any were found. They would also be asked many questions about their past and their future intentions in the United States. In some cases, relatives already in the United States had to vouch for them. In a small number of cases about two percent they were sent back to their homeland. In 1924, the National Origins Act was passed which reduced the flow of immigration to the United States. This Act set up quotas by country and required immigrants to start the process in their own country by receiving a visa to immigrate. Thus, the need for Ellis Island as the main immigration station was reduced. The subsequent restrictions to immigration made Ellis Island not necessary, and it was closed in

2 STATISTICAL ANALYSIS: REGRESSION An important statistical tool for analyzing the Ellis Island immigration data was regression analysis. The SAS System contains numerous procedures which may be used to estimate regression equations. Regression analysis is the study of the relationship between a dependent variable, Y, and one or more independent variables, X's. A linear regression equation may be expressed as: Y i = β o + β 1 X 1 + β 2 X 2 + β 3 X β k X k + µ i where Y i is the dependent variable X i are the independent variables β i are the regression coefficients µ i is the error term or residual Regression analysis allows the researcher to determine the influence which each respective independent variable has on the dependent variable, ceteris paribus. In addition, a correctly specified regression model allows for the use of numerous statistical tests and summary statistics, such as R-square, which indicate how well the model fits the data. For most of these tests to be statistically correct, there are a number of implicit assumptions imposed on the model which must be satisfied (Gujarati, 2003). These model assumptions should be tested for validity. Pending results from these tests, there are a variety of econometric methods which may be used to deal with assumption violations. Numerous regression models were estimated for the Ellis Island immigration data. Some of the regressors used to explain the movement of immigration to Ellis Island (Port_NY variable) were: nominal and real Gross Domestic Product, nominal and real GDP per capita, population of the United States, the Standard and Poor's stock index and a dummy variable (WAR_LEGAL) constructed to take into account the effect of World War I and the restrictive National Origins Act of Following are summary statistics for several of these models. The dependent variable for the regression is the Port_NY immigration variable. Port_NY REAL_GDP intercept R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Port_NY REAL_GDP WAR_LEGAL intercept R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

3 Port_NY WAR_LEGAL intercept R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) GRANGER CAUSALITY ANALYSIS A Granger causality test was performed using the Port_NY immigration data with the War_Legal dummy variable. While regression analysis cannot prove that the disruption of immigration to the United States was caused by World War I and the reduction in quotas resulting from the National Origins Act of 1924, the time series method developed by Granger was used to test for a weaker type of causality. The Granger causality method involves performing bivariate regressions, using lagged values of the War_Legal dummy variable in the presence of lagged values of the Port_NY immigration variable, and vice-versa, to determine whether of not the War_Legal variable provides any useful information. Using an F-test, on the null hypothesis that the the War_Legal lags are not significant, we conclude that due to the low p-value of the null hypothesis should be rejected. Thus we may say that the War_Legal variable "Granger causes" the Port_NY immigration variable. Pairwise Granger Causality Tests: Null Hypothesis: Obs F-Statistic Probability WAR_LEGAL does not Granger Cause PORT_NY PORT_NY does not Granger Cause WAR_LEGAL Finally, in order to adjust for the autocorrelation problem, an AR(1) error term structure was added to the regression model, with the following results. Port_NY intercept WAR_LEGAL AR(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

4 STATISTICAL ANALYSIS: SMOOTHING METHODS In addition to exploring the immigration time series with regression analysis, smoothing methods were used for estimation and forecasting. For the class of smoothing methods, primary interest was to determine which method would be most effective for short term forecasting. The SAS system comes with an extremely useful tool called the Time Series Forecasting System. The TSFS may be use to fit numerous pre-selected and other models based on user-selected criterion such as R-square or mean square forecast error. The following results are for the Holt-linear exponential smoothing method which was selected by the TSFS as the best model, using the root-mean-square error as the criterion. Parameter Estimates Term Estimate Std Error t Ratio Prob> t Level Smoothing Weight <.0001 Trend Smoothing Weight Model Summary DF 59 Sum of Squared Errors Variance Estimate Standard Deviation Akaike's 'A' Information Criterion Schwarz's Bayesian Criterion RSquare RSquare Adj LogLikelihood Stable Invertible Yes No Figures 2, 3 and 4 are several graphs developed for the linear Holt exponential smoothing model using the TSFS. They illustrate model predictions, forecasts and prediction errors for this TSFS selected smoothing model. Figure 2 4

5 Figure 3 Figure 4 CONCLUSIONS The Time Series Forecasting System from SAS and the smoothing methods from JMP gave us a powerful, highresolution and easy to use software combination for our investigation of the movement of immigrants to Ellis Island. Our principal conclusion was that the upheaval in Europe due to World War I along with the quota changes resulting from the National Origins Act of 1924 were the primary factors which influenced the movement of immigrants to the Port of New York Ellis Island immigration facility. 5

6 COPYRIGHT INFORMATION SAS and JMP are registered trademarks of the SAS Institute, Inc. in the USA and other countries. Indicates USA registration. Other brand or product names are registered trademarks or trademarks of their respective companies. REFERENCES Granger, Clive, "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods", Econometrica, Vol. 37, 1969, pp Gujarati, Damodar (2003), Basic Econometrics, Fourth Edition, New York: McGraw-Hill Irwin, Inc. Moreno, Barry(2004), Encyclopedia of Ellis Island, Westport, Connecticut: Greenwood Press. SAS Institute Inc. (1991 and 1993), SAS/ETS Software: Applications Guides 1 and 2, Version 6, First Edition, Cary, N.C.: SAS Institute Inc. CONTACT INFORMATION Joseph Earley Loyola Marymount University Los Angeles, California Work Phone: Fax: jearley@lmu.edu 6