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COPYRIGHT 2004 International Academy of Business and Economics
ABSTRACT
A number of recent studies have compared the performance of neural networks to a variety of statistical techniques for the classification problems. In this paper, we compared the prediction of occupational attainment using a backpropagation neural network model and a multinomial logit model Both techniques use variables related to education, experience minority status, disability status, marital status, sex, and geographic region as inputs to perform the prediction. The neural network training and performance evaluation is also discussed in detail Although a comparison of the predictive ability of both models showed similar results, this paper presents neural networks as a more robust alternative for occupational attainment prediction application.
1. INTRODUCTION
During the last two decades, several research studies have focused on investigating the determinants of occupational attainment, as well as the race and sex differences in occupational attainment for the determination of occupational segregation (Broom, Jones, McDonell, and Williams, 1980; Brown, Moon, and Zoloth, 1980a; Gabriel, Williams, and Schmitz, 1990; Meng and Miller, 1995; Miller and Volker, 1985; Schmidt, and Strauss, 1975). As defined by Brown, Moon, and Zoloth (1980a)"an individual's occupational attainment is a function of employer's willingness to hire that person and of the individual's desire to work in specific occupations. Willingness of employers to hire an individual depends on human capital. The individual's desire for a particular occupation can be expressed by at least three of the arguments in a utility function: income, taste for the work involved and family size. The interaction of these supply and demand factors leads to the individual's employment in a particular occupation." The most common technique employed in occupational attainment prediction involves the estimation of parameters using statistical models with discrete dependent variables, namely multinomial logit, and ordered probit. Although these qualitative response techniques are widely used in the social sciences, it is well known that the predictive power of these models is rather poor (Greene, 1993).
A useful and robust approach for solving applied business and engineering problems that are distinguished by lack of availability or applicability of any mathematical model is neural networks, also known as artificial neural networks, connectionist models, parallel distributed processing models, and neuromorphic systems. Neural networks are developed as generalizations of mathematical models of human cognition, based on the following assumptions (Fausett, 1994): (1) information processing occurs at many simple elements called neurons or neural processing elements (NPEs), (2) signals are passed between neurons over connection links, (3) each connection link has an associated weight, which, in a typical neural net, multiplies the signal transmitted, (4) each neuron applies an activation function (usually nonlinear) to its net input (sum of weighted input signals) to determine its output signal. Since neural networks utilize an architecture and information processing manner similar to the brain, some of the networks show similar characteristics that are associated with the brain, for example, the ability to learn from examples, to generalize from situations, to classify examples into categories, and to self organize information. It has been determined that while under ideal statistical conditions the neural network can perform as well as a multiple regression model, under less than ideal conditions (e.g. when the model is not correctly specified, existence of missing data, outliers, heteroscedasticity, or autocorrelation), neural networks' performance is superior (Denton, 1995).
The utility of neural networks in practical applications for a wide variety of problems has been documented extensively in engineering, management science, and financial management literature (Sharda, 1994). This paper describes how we developed a neural network model to predict occupational attainment. We also compare the accuracy of the neural network prediction with that obtained by a multinomial logit model. Utilizing a neural network software called NeuralWorks Professional II Plus (NeuralWare, 1993), we designed, developed, and validated a neural network that makes use of data from the Current Population Survey (CPS).
Section 2 is a review of literature detailing several practical applications of neural networks in business and engineering. Section 3 describes the various studies conducted in the area of occupational attainment. We also discuss in this section the motivation to use a neural network for the occupational attainment prediction problem. Section 4 explains the issues involving the multinomial logit model, and provides the basic architecture, training, and performance evaluation of the neural network. Results are discussed in Section 5, followed by conclusions in Section 6.
2. LITERATURE REVIEW: NEURAL NETWORKS
A wide range of interesting applications of neural networks motivated and helped us in our research. Lu et al. (Lu, Chen, Kim, and Hwang, 1996) compared the effectiveness of neural networks and the multinomial logit model, and concluded that the ANNs perform better than logit regressions in franchising decision-making. Fletcher and Goss (1993) used back-propagation (BP) neural network model to predict the bankruptcy of a firm, and reported improvement in the prediction accuracy over the logit model. Salchenberger, Cinar and Lash (1992) used a neural network and the logit model to forecast bank failures using the bank's financial ratios as inputs. The neural network performed as well or better than the logit model. Wu, Fang, King, and Nuttle (1995) applied neural network technology for the decision surface modeling of apparel retail operations. Denton (1995) compared neural networks to linear regression forecasting models and...
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