Exploring Changes in the Labor Market of Health Care Service Workers in Texas and the Rio Grande Valley I. Introduction

Transcription

1 Ina Ganguli ENG-SCI 103 Final Project May 16, 2007 Exploring Changes in the Labor Market of Health Care Service Workers in Texas and the Rio Grande Valley I. Introduction The shortage of healthcare workers throughout the United States, in particular the shortage of nurses, is affecting some parts of the country more than others. Experts have identified many reasons for the national shortage, including the aging of the nurse and nurse education workforce, declines in enrollment in nursing education programs, as well as decreases in job satisfaction among nurses and deterioration of the image of nursing (Goodin, 2003). The state of Texas, the second largest state in terms of area and population, and which has the highest percentage of individuals without health insurance in the country at 27 percent, is also facing the problem (DeNavas-Walt et al, 2006). One of the poorest areas of Texas and in the country, the Rio Grande Valley (RGV) region is one of the areas in Texas experiencing a growing need for health sector workers. The RGV is located in southern Texas along the border with Mexico (see Figure 1 below). By 2014, it is estimated that employment at public and private hospitals will have increased by 44 percent, and employment at physicians offices by 46 percent over 2004 levels in the RGV area 1. (Whittaker, 2007). In this paper, I will explore the factors relating to the growing demand for healthcare workers in the RGV by analyzing trends in demographic characteristics between 1990 and 2000 across Texas ZIP Code Tabulation Areas (ZCTAs). As the RGV is only one area in the large state of Texas, I analyze these trends for Texas as a whole in order to identify the spatial and temporal trends that might be differentially affecting the RGV region over other parts of the state. The plan for the paper is as follows: First, I will describe my data. Next, I provide some background on the RGV area and I will do some exploratory spatial data analysis. Lastly, I will test a simple model which seeks to explain the growth in health care sector employment by changes in demographic factors, such as the education and age structure, as well factors related to the supply and demand for health workers, such as number of nursing schools and hospitals. Figure 1. Rio Grande Valley Area of South Texas (Highlighted in Blue) Data Source: 108th CD Census 2000 TIGER/Line files 1 Projection for Hidalgo, Starr, and Willacy counties. Total employment is only projected to increase by 29 percent.

2 II. Data My main sources of data for this analysis are the 1990 and 2000 Censuses. There are three main sources from which I have accessed this data: (1) the Texas State Data Center and Office of the State Demographer, (2) the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), and (3) The US Census Long Form Files on the Geolytics CDs. I joined data from these sources and years to create one shapefile with all the variables I used for my analysis in terms of changes from 1990 to I have also gathered spatial data for hospital locations and nursing schools from the American Hospital Directory and Texas Nursing Association online directories using the most recent information available. Using the ZIP Code information, I geocoded these institutions and created shapefiles. As mentioned earlier, my analysis is based on data from ZIP Code Tabulation Areas (ZCTAs) in the state of Texas. I have also gathered information and additional useful data for Metropolitan Statistical Areas (MSAs) in Texas, but there were too few MSA with data for both 1990 and 2000 to undertake this analysis. Since ZCTAs are perhaps too small for this analysis, in the future, I hope to gather more complete data for a more appropriate areal unit. The variables that I will use in my analysis, which I created for changes from by ZCTA, include: Percent of total population that is foreign born Percent of total population above age 65 Percent of total population with a post-secondary degree (including Associate, Bachelor s, or other Graduate or Professional Degree) Employment in health care services sectors as a percent of total population I will also draw upon the following variables for either the year 2000 or the most current period available: Percent of total population in 1999 below 200% of poverty level Number of hospitals Number of nursing schools % of total population that is non-white Total population per square mile in land area only III. Background & Exploratory Spatial Data Analysis The RGV area has been undergoing a striking social and economic transformation in recent years. Historically one of the poorest parts of the country, the RGV continues to have high levels of poverty, but it is also one of the fast growing regions in terms of population and economic growth. The McAllen-Edinburg-Mission Metropolitan Area in the RGV ranked 4 th in the country in terms of percent population change from the 1990 to 2000 Censuses 3, with a change of 48.5%. Economic growth has also been rapid as more and more businesses are developing or relocating to the RGV, especially after the North American Free Trade Agreement (NAFTA) came into effect on January 1, Most of the RGV is made up of Hispanic populations. The region has also seen a large influx of both legal and illegal immigrants from Mexico and Central America in recent years. The rapid data are based on 2000 boundaries. 3 Census 2000 Ranking Tables for Metropolitan Areas: 1990 and 2000; Table 5: Metropolitan Areas Ranked by Percent Population Change: 1990 to

3 demographic and economic changes in this area are partly due to the immigration from these regions, but also due to the migration of people to the RGV from other parts of the U.S. with the development of maquiladoras and the growth of other businesses in the area. The RGV is also becoming more and more popular with older Americans as a retirement destination, due to the warm weather, low cost of living, and expanding medical care facilities. Figure 2a below shows a quantile map of the percent foreign born in Texas, and we can see that there is clustering of higher percentage foreign born in the border region. The histogram in Figure 2b shows the distribution of per capita income among ZCTAs in Texas, with the yellow highlighted bars corresponding to the ZCTAs with yellow hatching in the quantile map in Figure 2a. The histogram displays that the areas with high percentages of foreign born are also areas with low per capita income. We can see that the entire RGV is in the bottom part of the distribution of per capita income. Figure 2a. Percentage Foreign Born by Texas ZCTA, 2000 Source: 2000 U.S. Census, GeoLytics Figure 2b. Histogram of Per Capita Income, 2000 (Highlighted bars correspond to ZCTAs highlighted in 2a) 3

4 The factors described above, i.e. the fast growing population, high levels of poverty, high numbers of immigrants and elderly, all may play a role in the increasing demand for health care workers in the RGV. Although the health care system is growing rapidly, the health care worker pool isn t growing as quickly as medical investment in the region. In Figure 3 below we can see the change in the employment of health care service workers as a percent of total population from 1990 to The dark purple ZCTAs had an increase in employment of health care workers as a percentage of total population above 1.5 standard deviations. We can see that there are many areas around the state that have had high increases in employment in this sector, and most of the RGV area is in this category. The map also shows plots of hospitals (red dots) in ZCTAs across Texas. Most hospitals are clustered around the large cities of Dallas, Houston, and San Antonio, but there are also a number of hospitals in the RGV not far from the U.S.- Mexico border. We can also see the locations of colleges and universities with nursing programs on the map (green triangles). There seems to be a correspondence between the areas with a large clustering of hospitals and the location of nursing schools, including in the RGV. To see how much spatial autocorrelation there in my variables of interest, I plotted Moran s I coefficients for the change in the percent of foreign born and the change in the percent of health care employment using a first-order queen s contiguity matrix. I combined univariate and bivariate Moran scatterplots to produce a scatterplot matrix, which is shown in Figure 4. This 4

5 provides an overview of the spatial pattern for each variable with itself, and with spatial lags of the other variable. Figure 4. Scatterplot Matrix of Moran s I Coefficients, Change in Health Service Employment and Change in Percent Foreign Born We can see from the Moran s I scatterplots that the there seems to be some positive spatial autocorrelation for the change in the percent foreign born, with a Univariate Moran s I coefficient of The Univariate Moran s I coefficient for the change in employment in the health service sector is only , which indicates that there may be some positive spatial autocorrelation. Finally, the Multivariate Moran s I Scatter plots show that there is some indication of negative spatial autocorrelation between these variables, i.e. areas with an increase in health service sector employment are near areas with a decrease in percentage of foreign population. However, the small Moran s I indicates that that this is probably not a significant relationship. IV. Model In this section I will estimate regression models to predict changes in health service sector employment. I chose to first estimate a spatial lag regression model, since I believe that the type of spatial autocorrelation that is occurring as a result of migration and diffusion across ZCTAs. Since the area of analysis is very small, and since residents of Texas are known to drive a lot, I would imagine that demand for health care workers in one ZCTA would be affected by the economic and demographic characteristics of nearby ZCTAs. However, the small size of the ZCTA may not be appropriate for analyzing employment changes, since the local labor market probably spans several ZCTAs. Therefore, I also estimate a spatial error model to see which model better accounts for these issues. 5

6 In these models, I included the following variables: PER_HEACH (change in employment in health services sector as a percentage of total population); PER65UP 1 (change in percentage of the population above age 65); PER_FOR 1 (change in percentage of the population that is foreign born); PER_EDUCH (change in employment in education services sector as a percentage of the population); PER_HE_C (change in percentage of the population that has a post-secondary degree); COUNT_ (number of hospitals in ZCTA); ZBLV2P_P (change in Percent of total population in 1999 below 200% of poverty level); ZNWHITEP (% of total population that is non-white population); ZDENSITY (Total population per square mile in land area only); and ZG_DOC(Number of Primary Care Physicians; Clinically Active Physicians in ZCTA). First, I will estimate a classic OLS regression without the spatial lag: Table 1. Classic OLS SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION Data set : Hospital_Counts Dependent Variable : PER_HEACH Number of Observations: 2394 Mean dependent var : Number of Variables : 10 S.D. dependent var : Degrees of Freedom : 2384 R-squared : F-statistic : Adjusted R-squared : Prob(F-statistic) : e-014 Sum squared residual: Log likelihood : Sigma-square : Akaike info criterion : S.E. of regression : Schwarz criterion : Sigma-square ML : S.E of regression ML: Variable Coefficient Std.Error t-statistic Probability CONSTANT PER65UP PER_FOR PER_EDUCH PER_HE_C COUNT_ ZBLV2P_P ZNWHITEP ZDENSITY e ZG_DOC DIAGNOSTICS FOR SPATIAL DEPENDENCE FOR WEIGHT MATRIX : hos_queen.gal (row-standardized weights) TEST MI/DF VALUE PROB Moran's I (error) Lagrange Multiplier (lag) Robust LM (lag) Lagrange Multiplier (error) Robust LM (error) Lagrange Multiplier (SARMA) We can see from the regression results in Table 1 that the model does a very poor job in explaining the change in employment in the health service sector. The R-squared value is very low, which means that the model does not explain much of the variation in the dependent variable. However, several of the explanatory variables are significant, indicating that they might play a role in explaining the change in employment in the health service sector, but not a 6

7 large one, as the magnitudes are all very small. The only coefficient a large magnitude is the one on the variable for the number of hospitals. However, the standard error is large, and therefore the coefficient is not significant. We can also see from the spatial diagnostics that the Moran s I is not very large, indicating there is not much spatial autocorrelation. Using the residual from this regression, the LISA statistics can help us understand to what extent there is spatial clustering. The Moran scatter plot shows that there isn t much global autocorrelation of the residuals, while the LISA maps show us where there is clustering: Figure 5. Moran s I Scatterplot and LISA Cluster Map We can see from the LISA cluster map that the RGV area is a high-high area, indicating that there is model underprediction here. This means that the change in health employment would be higher than would be expected after accounting for the explanatory variables. Next, I estimated a spatial lag regression by including a lag of the dependent variable using a first-order queen s contiguity matrix. The results in Table 2 below show that this improved the fit of the model, as the R-squared increased slightly, but the overall fit is still not good. We can see that many of the variables are significant, although they again have small magnitudes. We can see that the lagged dependent variable has the greatest magnitude of all the coefficients (0.24) and is significant. It indicates that an increase in the percent of the population employed in the health services sector in a neighboring ZCTA is associated with an increase in that ZCTA, on average. Other variables that are positively associated with a change in employment in the health sector are the percentage of the population below 200% of poverty level and the percentage of the population with post-secondary degrees. We can see that many of the independent variables are negatively associated with an increase in the dependent variable. For example, the change in the percentage of the population foreign born, and the change in the percentage above 65 are both negatively associated with a change in employment in the health service sector. Although very small, the direction of this coefficient is surprising, as I expected areas with more elderly individuals to have greater demand for health care services, and therefore I expected to see a positive relationship. I had also expected to see a significant coefficient on the number of primary care physicians, since I believed that the number of doctors is positively related to the demand for health care workers, since they are 7

8 complements in the labor market. However, I think the area of analysis (ZCTA) and my measures for demand and supply of health care workers are not accurately capturing the dynamics of the health care services labor market. Table 2. Spatial Lag Regression SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION Data set : Hospital_Counts Dependent Variable : PER_HEACH Number of Observations: 2394 Mean dependent var : Number of Variables : 11 S.D. dependent var : Degrees of Freedom : 2383 R-squared : F-statistic : Adjusted R-squared : Prob(F-statistic) : e-022 Sum squared residual: Log likelihood : Sigma-square : Akaike info criterion : S.E. of regression : Schwarz criterion : Sigma-square ML : S.E of regression ML: Variable Coefficient Std.Error t-statistic Probability CONSTANT W_PER_HEACH COUNT_ PER_HE_C PER_EDUCH PER_FOR PER65UP ZG_DOC ZBLV2P_P ZNWHITEP ZDENSITY e e DIAGNOSTICS FOR SPATIAL DEPENDENCE FOR WEIGHT MATRIX : hos_queen.gal (row-standardized weights) TEST MI/DF VALUE PROB Moran's I (error) Lagrange Multiplier (lag) Robust LM (lag) Lagrange Multiplier (error) Robust LM (error) Lagrange Multiplier (SARMA) Finally, I estimated the spatial regression ( spatial errors ) model. In Table 3 below, the results show that this model is a worse fit than the lagged variable model. The log likelihood value, although very negative, and the R-squared values are both higher in the lagged variable model than in this model. Thus, although all the models I estimated are poor predictors of the change in health care service employment, the lagged variable model seems to do the best job in fitting the model. Table 3. Spatial Error Model SUMMARY OF OUTPUT: SPATIAL ERROR MODEL - MAXIMUM LIKELIHOOD ESTIMATION Data set : Hospital_Counts Spatial Weight : hos_queen.gal Dependent Variable : PER_HEACH Number of Observations: 2394 Mean dependent var : Number of Variables : 10 S.D. dependent var : Degree of Freedom : 2384 Lag coeff. (Lambda) :

9 R-squared : R-squared (BUSE) : - Sq. Correlation : - Log likelihood : Sigma-square : Akaike info criterion : S.E of regression : Schwarz criterion : Variable Coefficient Std.Error z-value Probability CONSTANT PER_HE_C PER_EDUCH PER_FOR PER65UP COUNT_ ZG_DOC ZDENSITY e e ZNWHITEP ZBLV2P_P LAMBDA V. Conclusion In sum, the results of my regression analysis show that there are important factors that I am not considering when analyzing changes in employment in the health care service sector in Texas with my regression models. Alternatively, it could be that I need to take a different approach to analyzing the dynamics of the health care workforce shortage altogether. The dependent variable that I used in my analysis the change in employment in the health service sector as a percentage of the total population may not be the appropriate variable to use to answer my research questions relating to demand and supply of health care workers in Texas regions. Data on wages of health care workers in particular, would help me see the dynamics between prices and quantities in the labor market. Furthermore, I believe that the level of my analysis, at the ZCTA level, is much too small to capture the condition of the surrounding health labor market. Despite these limitations, this analysis was a very useful and effective way for me to begin understanding the spatial dynamics over time concerning the health care labor market, immigration, education, and economic development in the RGV region of South Texas and Texas as a whole. In the future, I hope to be able to gather informative measures for a more appropriate areal unit to continue my analysis of this issue. With national attention focused on this region with regard to the immigration debate as well as the shortage of health care workers, along with the dramatic economic and social transformations occurring there, I hope that this project will help guide my further research in this area. I believe that understanding the spatial and temporal dynamics behind these issues will be very important in helping to identify future directions for policy in the region. 9

10 References American Hospital Directory, < Goodin, Heather Janiszewski, The Nursing Shortage in the United States of America: An Integrative Review of the Literature, Journal of Advanced Nursing, 43(4), , GeoLytics, 2000 and 1990 U.S. Census Long Form CDs. Geospatial Data Warehouse, U.S. Department of Health and Human Services, Health Resources and Services Administration. < DeNavas-Walt, Carmen, Bernadette D. Proctor, and Cheryl Hill Lee, U.S. Census Bureau, Current Population Reports, P60-231, Income, Poverty, and Health Insurance Coverage in the United States: 2005, U.S. Government Printing Office, Washington, DC, Texas Census 2000 Shapefiles, Texas State Data Center and Office of the State Demographer. < Texas Nurses Association, < Whittaker, Matt, Wealth Care, The Monitor, McAllen, TX, May 6,