PREDICTING THE WAGES OF EMPLOYEES USING SOCIO-ECONOMIC AND DEMOGRAPHIC DETERMINANTS: A CASE OF PAKISTAN

Transcription

1 Proc. 10 th Int. Conference on Statstcal Scences Lahore, Pakstan March 7-9, 2013, Vol. 24, pp PREDICTING THE WAGES OF EMPLOYEES USING SOCIO-ECONOMIC AND DEMOGRAPHIC DETERMINANTS: A CASE OF PAKISTAN Khadja Fatma, Ayesha Saleem and Zahoor Ahmad Department of Statstcs, Unversty of Gujrat, Gujrat, Pakstan Emal: ufatma43@yahoo.com aysha.stat@gmal.com zahoor.ahmed@uog.edu.pk ABSTRACT In ths study an attempt s made to develop a model to predct the monthly wages of regular pad employees on the bass of soco-economc and demographc determnants. Further t s nvestgated that whch varables play more mportant role n predcton of monthly wages of employees. The data was obtaned from Pakstan Labour Force Survey conducted by Federal Bureau of Statstcs, Pakstan. The dependent varable s monthly wages of employees and ndependent varables are soco-economc and demographc characterstcs of the employees. Multlayer Perceptron (MLP) Neural Network Model has been used for analyss purpose. The results showed that the demographc characterstcs; age, gender and area of workng, and the soco-economc determnants; educaton, job status and workng hours are playng mportant role n predctng earnng of employees. KEYWORD Earnng, Soco-economc determnants, Demographc Determnants, Pakstan Labour Force Survey, Multlayer Perceptron. 1. INTRODUCTION AND BACKGROUND The development of labor market has always been on the prorty agenda of every country n the world. Ths s also one of the mportant objectves among developng countres, to measure the wage of employees because earnngs have a sgnfcant role n the wellbeng of the households. The dstrbuton of personal earnngs has been changed n dfferent perods across dfferent countres (Karoly, 1992; Brandoln, Rosola and Torrn 2010). Ths dsparty n wages holds due to changes n factors that can affect ndvdual earnngs. These factors nclude both ndvdual and labor market characterstcs. Now countres are tryng to focusng on to enhancng the ndvduals capacty to work more. Because t s beleved that economc growth can be ncreased by enhancng the qualty of workforce by concentratng on human resources. As human captal plays an mportant role n the development of a country. Snce human captal s not only acknowledged for growth and reducng poverty, but t s also a basc requrement for human development. Presently IMF (2004) reported that ndcators of human captal are also ncluded n Mllennum Developmental Goals (MDGs) specfcally educaton. 179

2 180 Predctng the wages of employees usng soco-economc In the recent past many studes had been done whch emprcally analyzed the determnants of employees earnngs wth dfferent perspectve (Klndt, Deaton and Landes 1981; Khan and Irfan 1985; Haque 1977; Nasr and Mahmood 1998; Xao 2001; Aslam 2002; Panday 2003; Nazl 2004; Awan and Hussan 2007; Fard, Hussan, and Bashr, 2010; Haq, Nawaz, Mahtab and Cheema 2012). These studes explore many soco-economc and demographc determnants that can affect earnng of any employee. Prevous studes reported that hgher educaton returns hgher wages (Khan and Irfan 1985; Nasr 1998; Aslam 2002; Awan and Hussan 2007; Jaffry, Ghulam and Shah 2007). Haque (1977), Ashraf and Ashraf (1993) and Nazl (2004) ponted out that there are dfferent returns to earnngs for dfferent level of educaton because one year of schoolng rases earnngs rapdly. Ths s due to postve relaton between the educaton and development. Improvement n natonal progress wll be more when people receve more educaton snce educaton generate productve labor force that contrbutes n the economc growth of a country. Except educaton, there are many soco-economc determnants that may affect ndvduals earnngs. Such as tranng of employees, occupaton, job agreement and total number of workng hours because wages may depend on these varables too. Techncal tranng of employees wll also cause to ncrease n the monthly earnngs. Snce ndvdual that complete hs or her tranng wll get more wage as compare to those that dd not complete ther tranng even n the same establshment. Now talkng about the total number of workng hours, usually ths varable s gnored n some studes lke Nasr (1998). But ndvduals that work more may get more wages comparatve to other peoples that work less. Therefore t s mportant to consder ths varable regardng wages. Ths s also beleve that occupaton may also cause through whch earnngs may dffer among ndvduals. As ndvduals that may work n busness, ndustry or offce may get dfferent wages as compare to other occupatons. Nazl (2004) emprcally nvestgated that sgnfcant dfference exsts n earnngs across occupatons. In the same way job status may also cause to generate varaton n the earnngs amongst ndvduals. Other than soco-economc varables some demographc varables are also nfluental for ndvdual s earnngs these varables can be person s age, gender, martal status and area of workng. It s also a well-establshed fact that age has sgnfcant effect on person s earnng. Snce experence s ncreased wth ncreased age therefore earnng s ncreased. Even n some studes lke Nasr (1998) takes person s age as the proxy of experence. Gender s also one of the mportant factors n determnng the earnngs. In prevous lterature many studes are presented on the topc related to gender wage dfferentals (Ashraf and Ashraf 1993; Nasr and Mahmood 1998; L and Urmanbetova 2002; Yasn, Fan and Yaseen 2010). These studes reported that labor market s structured dfferently for males and females wth specal reference to Pakstan where females consttute almost half of the populaton and nvolved n producton ether drectly or ndrectly, but do not have access even to ther own earnngs (Yasn, Fan and Yaseen 2010). In Pakstan socety women are assgned to non-work actvtes more as compare to men. In a study

3 Khadja, Ayesha and Ahmad 181 Bergman (1974) reported that employer can be based f he uses an ndvdual s gender to dsqualfy her from an occupaton. Women as compare to men are restrcted n low payng jobs usually servces sectors. Smlarly regonal dfferences (urban/rural) are also a leadng factor regardng wages. As labor markets may have dfferent performance across the regon. Indvduals havng jobs n urban areas are consdered to have more earnngs as compare to those who work n rural areas. As urban areas may have greater lkelhood for jobs and expected to have hgher earnngs, therefore t s mportant to consder the regonal dfferences for earnngs of ndvdual. In ths present study the man focus s to develop a model to predct the monthly wages of the regular pad employees on the bass of the soco-economc and demographc determnants. Further the relatve mportance of these varables s analyzed that determnes the monthly wages of employees that s whch varables play more mportant role. Further n 1998 a study s conducted by the Nasr (1998) regardng personal earnngs of employees consderng the smlar survey by FBS. As data consdered n mentoned study was qute old and ths present study s conducted by consderng the latest data avalable. After the ntroducton and background source of data and ts characterstcs are dscussed n secton 2 ths secton also provdes the nformaton about technque employed to fulfll the objectves. Secton 3 represents the results of analyss and ts nterpretaton and the man conclusons that are drawn from ths study are gven n secton DATA AND METHODOLOGY 2.1. Source of Data The data for ths study has been obtaned from Labour Force Survey (LFS) for year that was conducted by Federal Bureau of Statstcs (FBS), Pakstan. The populaton of LFS s consstng of all the rural and urban areas of four provnces of Pakstan excludng FATA and mltary restrcted areas. A stratfed two-stage sample desgn s used and developed by FBS for ths survey. A sample of households conssts of ndvduals s consdered. In the LFS data, for some cases, nformaton s mssng on dfferent varables. So after excludng mssng cases for any varable the ultmately sample sze of regular pad employees s Data Descrpton The dependent varable consder for ths study s monthly wages of regular pad employees that were earn from the man work. Independent varables are prmarly related to soco-economc and demographc determnants of employees. Further detal of ndependent varables s gven as follows. I. Demographc Determnants ) Age ) Gender ) Martal Status(MS) v) Mgraton Status v) Area of Workng

4 182 Predctng the wages of employees usng soco-economc II. Soco-economc Determnants ) Tranng ) Occupaton ) Total number of workng hours (THW) a. Job Type Type of job s categorzed as ) Permanent job (PJ) ) Contract job (CJ) b. Level of educaton Level of educaton s categorzed as follows ) No formal educaton (NFE) ) Below matrculaton (BM) ) Matrculaton but less than ntermedate (MBI) v) Intermedate but less than degree (IBD) v) Degree and above (DA) Except two varables, age and total number of workng hours, all the other varables are bnary. Table A1 gven n the Appendx A contans the complete descrpton of the above varables used n ths study Neural Network Model: Neural Network (NN) s an nformaton processng paradgm that s nspred by the way bologcal nervous systems, such as the bran, process nformaton. The key element of ths paradgm s the novel structure of the nformaton processng system. It s composed of a large number of hghly nterconnected processng elements (neurons) workng n unson to solve specfc problems. All neural networks have an nput layer and an output layer, but the number of hdden layers may vary The Multlayer Perceptron (MLP) Neural Network Model: The followng dagram llustrates a MLP network wth three layers:

5 Khadja, Ayesha and Ahmad 183 Ths network has an nput layer (on the left), one hdden layer (n the mddle) and an output layer (on the rght) wth two neurons. Dfferent functons of all these layers are gven below Input Layer: A vector of predctor varable values (x 1 x p ) s presented to the nput layer. The nput layer normalzes the values of these predctors nto a specfc range.e. -1 to 1, 0 to 1 etc. The nput layer dstrbutes the values to each of the neurons n the hdden layer. In addton to the predctor varables n nput layer, there s a constant nput of 1.0, called the bas that s fed to each of the hdden layers; the bas s multpled by a weght and added to the sum gong nto the neuron. The basc reason to normalze the data s that, f nputs are generally on wdely dfferent scales then predctors wth large values may overcome the predctors wth small values. So t s useful to put the predctors nto same scale. Some normalzaton methods are gven as follows. a. Standardzed normalzaton b. Adjusted normalzaton c. Sgmod normalzaton d. Hyperbolc tangent normalzaton Hdden Layer: Arrvng at a neuron n the hdden layer, the value from each nput neuron s multpled by a weght (w j ), and the resultng weghted values are added together producng a n combned value w x. The weghted sum j n w x j s fed nto an actvaton functon, whch outputs a value. The outputs from the hdden layer are dstrbuted to the output layer Output Layer: When the output of hdden layer s appear at a neuron n the output layer, the value from each hdden layer neuron s multpled by a weght, and the resultng weghted values are added together producng a combned value. The weghted sum s agan fed to an actvaton functon, whch outputs a value Actvaton Functon: The actvaton functon "lnks" the weghted sums of unts n a layer to the values of unts n the succeedng layer. Dfferent types are actvaton functons are used by MLP n hdden and output layer. The most useable actvatons are gven as follows. a. Identty: Ths s an actvaton functon only for output layer f there s any scale dependent varable then ths s the actvaton functon for unts n the output layer. It takes real-valued arguments and returns them unchanged. Ths functon has the form: f ( x) x

6 184 Predctng the wages of employees usng soco-economc b. Hyperbolc tangent: Ths actvaton functon can be used n both hdden and output layer dependng on type of problem. It takes real valued arguments and transforms them to the range ( 1, 1). Ths functon has the form: e f ( x) e x x e e x x c. Softmax: Ths actvaton functon s appears only for the output layer f dependent varable s categorcal. It takes real valued arguments and converts nto the range (0, 1). If sum ths functon over all the categores then ts sums to one. Ths functon has the form: exp( xk ) f ( x) exp( x ) j j d. Sgmod: Ths s another actvaton functon that can appear n both hdden and output layer. It takes real-valued arguments and changes them to the range (0, 1). Ths functon has the form: f ( x) 1/ (1 e x ) 3. RESULTS AND DISCUSSION MLP a neural network model s also used to predct the monthly wages of regular pad employees on the bass of soco-economc and demographc determnants. Result of the Analyss s gven n Appendx A. Descrptve statstcs of quanttatve varables are gven n the table A2 and table A3 contans the descrptve analyss of bnary varables. Further tables contan the results of the MLP model. Case processng summary of MLP model s gven n table A4 representng that total number of cases avalable s MLP model automatcally dvdes the cases nto two parts; one s for tranng propose and other for testng. 70.2% cases (12226) are used for tranng and remanng 29.8% cases (5201) are used for testng. Full descrpton of MLP model can be seen from fgure A1 that s the graphcal representaton of the neural network model. It can be seen n the gven pcture that MLP network s conssts of three layers. The number of nodes n the frst layer (nput layer) s equal to the number of ndependent varables and bas. As n the fgure total number of nodes n the nput layer s 29 whereas ndependent varables used n the analyss are 15. Number of nodes n the nput layer are more than number of varables used n the analyss ths s because 13 varables are bnary and n the layer each category have one node therefore these 13 varables make 26 nodes (13*2=26) and two varables are quanttatve (26+2=28), so nput layer contans the 29 (28 + Bas) nodes. Second layer (hdden layer) contans 10 nodes and n the last layer (output layer) number of nodes are equal to the number of categores n the dependent varable. As the dependent varable s quanttatve therefore t s just 1. The network automatcally chooses hyperbolc tangent actvaton functon for the hdden layer to lnk the weghted sum of values wth the next layer (output layer) and dentty actvaton functon s appled to the output layer to produce the resultant value. In ths fgure, grey and blue lnes show the relatonshps between nput, hdden and output nodes, these relatonshps are estmated n the form of

7 Khadja, Ayesha and Ahmad 185 weghts. Grey lnes show postve weghts and blue lnes show negatve weghts, the dependent varable wage has negatve relatonshp wth node 4 where as t has postve relatonshp wth the remanng nodes of the hdden layer. As n the parameter estmaton table (table A6) the weghts for wage column are postve for all the nodes except for the 4 th node. The model summary table (table A5) presents nformaton about the results of tranng and testng sample. In ths table the value of sum of square of error for testng sample ( ) s much less than the tranng sample ( ). The error decreased from tranng sample to testng sample. The same stuaton appears for the relatve error. The value of relatve error s also decreased for tranng sample (0.578) to testng sample (0.571). The value s small and t s a good ndcaton of the model predcton. The mportance of ndependent varables can be vsualzed from the table A7 and fgure A2. The mportance of an ndependent varable s measure how much the network model predcted value change by changng dfferent values of ndependent varables. Normalzed mportance value s calculated as the mportance values dvded by the largest mportance values and ths fgure expressed as percentages. The mportance chart s bascally a bar chart of the values gven n the mportance table, where bars are sorted n descendng value of mportance. It appears that varable of age and educaton degree above s the most mportant varables n predctng the wages of employees. Whereas permanent job, gender, no formal educaton, total number of workng hours, area of workng, educaton ntermedate below degree, educaton Matrculaton below nter, below matrculaton are the correspondng mportant varables for earnngs. But contract job, Mgraton, tranng complete, martal status and occupaton (busness, ndustry, and offce) have least effect on predcton of earnngs of employees. The manual calculaton and further detal of MLP model to predct the earnngs of employees s also gven n the Appendx. The purpose of ths manual calculaton s that by usng ths model one can predct the wage of an employee when varables for any near future are gven. 4. COMMENTS AND CONCLUSION The basc objectve of ths study s to develop a model by utlzng soco-economc and demographc determnants and to explore that whch varables play more mportant role that determnes the monthly wages of employees. So the developed model s gven n the Appendx B and the relatve mportance as t s gven n the mportance graph (fgure A2) we can see that from frst ten varables three are related to the demographc determnants that are age, gender and area of workng. On the bass of these results we can say that n demographc determnants age, gender and area of workng are playng more mportant role as compare to the mgraton and martal status. Whereas from socoeconomc determnants all the fve categores of educaton, permanent job and workng hours are appears n the frst ten varables. Through ths we can conclude that n socoeconomc determnants contract job, tranng complete and occupaton does not have as much mpact on employees earnngs as other soco-economc determnants.

8 186 Predctng the wages of employees usng soco-economc REFERENCES 1. Ashraf, J. and Ashraf, B. (1993). An Analyss of the Male-Female Earnngs Dfferental n Pakstan. The Pakstan Development Revew, 32(4), Aslam, M. (2002). Rates of Return to Educaton by Gender n Pakstan. Global Poverty Research Group (GPRG), WPS Awan, M.S. and Hussan, Z. (2007). Returns to educaton and gender Dfferentals n Wages n Pakstan. The Lahore Journal of Economcs, 12(2), Bergmann, B. (1974). Occupatonal Segregaton, Wages and Profts when Employers Dscrmnate by Race or Sex. Eastern Economc Journal, I (Aprl-July), Brandoln, A., Rosola, A. and Torrn, R. (2010). The Dstrbuton of Employees Labor Earnngs n the European Unon: Data, Concepts and Frst Results. ECB/CEPR/IFW Labor Market Workshop on Wages n a Tme of Adjustment and Restructurng European Central Bank. 6. Fard, M.Z., Hussan, S. and Bashr, F. (2010). Impact of Educaton on Students Earnngs: A Case of Publc Sector Unverstes n Pakstan. Internatonal Research Journal of Fnance and Economcs, 5(42), Haq, M.A.U., Nawaz, M.A., Mahtab, N. and Cheema, A.K.H. (2012). Determnants of wage Growth: An Emprcal Analyss of Prvate Formal Sector n Bahawalpur Dvson. Busness and Economc Research, 2(1), Haque, N.U. (1977). Economc Analyss of Personal Earnngs n Rawalpnd Cty. The Pakstan Development Revew, 26(4), Baldacc, E., Clements, B., Gupta, S. and Cu, Qang (2004). Socal Spendng, Human Captal, and Growth n Developng Countres: Implcatons for achevng the Mllennum Developmental Goals. Internatonal Monetary Fund (IMF) Workng Paper, WP/04/217, Fscal Affars Department. 10. Jaffry, S., Ghulam, Y. and Shah, V. (2007). Returns to educaton n Pakstan. The Pakstan Development Revew, 46(2), Karoly, L.A. (1992). Changes n the Dstrbuton of Indvdual Earnngs n the Unted States: The Revew of Economcs and Statstcs, 74(1), Khan, S.R. and Irfan, M. (1985). Rate of Returns to Educaton and the Determnants of Earnngs n Pakstan. The Pakstan Development Revew, 24(3&4), Krby, S. and Rley R. (2004). The Returns to Schoolng and Job Specfc Experence: The Role of ICT Technology. Natonal Insttute of Economc and Socal Research, London. 14. Klndt, T.H., Deaton, B.J. and Landes, M.R. (1981). The Determnants of Wage Increase n New Manufacturng Plants n Rural Areas. Southern Journal of Agrcultural Economcs, 13(1), L, H. and Urmanbetova, A. (2002). The Effect of Educaton and Wage Determnaton n Chna s Rural Industry. School of Economcs Georga Insttute of Technology, Atlanta, Chna. 16. Nasr, Z.M. (1998). Determnants of Personal Earnngs n Pakstan: Fndngs from the Labour Force Survey The Pakstan Development Revew, 39(3), Nasr, Z.M. and Mahmood, R. (1998). Personal Earnngs Inequalty n Pakstan: Fndngs from the HIES The Pakstan Development Revew, 37(4), Nazl, H. (2004). The Effect of Educaton, Experence and Occupaton on Earnngs: Evdence from Pakstan. The Lahore Journal of Economcs, 9(2), 1-30.

9 Khadja, Ayesha and Ahmad Panday, P. (2003). An Emprcal Analyss of the Determnants of Earnngs and Employment: Does Trade Protecton Matter? Journal of Economc Development, 28(1), Xao, J. (2001). Determnants of employee Salary Growth n Shangha: An Analyss of Formal Educaton, On-The-Job Tranng, and Adult Educaton wth a Three-level Model. The Chna Revew, 1(1), Yasn, G., Fan, M. I. and Yaseen, A. (2010). Determnants of Gender Based Wage Dscrmnaton n Pakstan: A confrmatory Factor Analyss Approach. Internatonal Research Journal of Fnance and Economcs, 55,

10 188 Predctng the wages of employees usng soco-economc APPENDIX A Table-A1: Varable Descrpton Varables Descrpton Wage Monthly earnng from man job Mgraton status 1 f mgrate 0 otherwse. Area of work 1 f works n urban area 0 otherwse. Tranng complete (TC) 1 f completed techncal tranng 0 otherwse. Gender 1 f male 0 otherwse. Martal Status (MS) 1 f marred 0 otherwse. No formal educaton (NFE) 1 f NFE 0 otherwse. Below Matrculaton (BM) 1 f BM 0 otherwse. Matrculaton but below ntermedate (MBD) 1 f MBD 0 otherwse. Intermedate but below degree (IBD) 1 f IBD 0 otherwse. Degree above (DA) 1 f DA 0 otherwse. Occupaton (Busness, Industry, Offce) 1 f occupaton (busness, ndustry, offce) 0 otherwse. Permanent Job (PJ) 1 f PJ 0 otherwse. Contract Job (CJ) 1 f CJ 0 otherwse. Age Current age of the ndvdual. Total number of workng hours (THW) Total numbers of workng hours durng last week n man occupaton. Table-A2: Descrptve Statstcs of Quanttatve Varables Varables Total Mnmum Maxmum Mean Std. Devaton Age Total number of hours worked (last week) Table-A3: Descrptve Statstcs of Bnary Varables Varables Categores Frequency Percent Non mgrants Mgraton Mgrants Status Rural Area of Work Urban Tranng Complete Gender Martal Status No Formal Educaton No complete tranng have ever complete tranng Female Male Otherwse Marred and lve wth spouse Otherwse Have no formal educaton

11 Khadja, Ayesha and Ahmad 189 Varables Categores Frequency Percent Otherwse Below Have educaton below matrculaton Matrculaton Matrculaton but Below Intermedate Intermedate but Below Degree Degree Above Occupaton Permanent Job Contract Job Otherwse Have educaton matrculaton but below ntermedate Otherwse Have educaton ntermedate but below degree Otherwse Have educaton degree above Otherwse Workng n Busness, Industry or Offce Otherwse Have permanent job Otherwse Have contract job Multlayer Perceptron Network Table-A4 Case processng summary Case Processng Summary N Percent Sample Tranng % Testng % Vald % Excluded 0 Total Tranng Testng Table-A5: Model Summary Model Summary Sum of Squares Error Relatve Error.578 Stoppng Rule Used 1 consecutve step(s) wth no decrease n error Tranng Tme 00:00: Sum of Squares Error Relatve Error.571

12 190 Predctng the wages of employees usng soco-economc Input Layer Hdden Layer 1 Predctor Table-A6: Parameter Estmates of Neural Network Model Parameter Estmates Predcted Output Hdden Layer 1 Layer H(1:1) H(1:2) H(1:3) H(1:4) H(1:5) H(1:6) H(1:7) H(1:8) H(1:9) Wages (Bas) [Mgraton=0] [Mgraton=1] [Area=0] [Area=1] [TC=0] [TC=1] [Gender=0] [Gender=1] [MS=0] [MS=1] [NFE=0] [NFE=1] [BM=0] [BM=1] [MBI=0] [MBI=1] [IBD=0] [IBD=1] [DA=0] [DA=1] [Occupaton=0] [Occupaton=1] [PJ=0] [PJ=1] [CJ=0] [CJ=1] Age THW (Bas).491 H(1:1).076 H(1:2).011 H(1:3) H(1:4).419 H(1:5).875 H(1:6).134 H(1:7).337 H(1:8).331 H(1:9).015

13 Khadja, Ayesha and Ahmad 191 Fgure-A1: MLP Network Illustraton

14 192 Predctng the wages of employees usng soco-economc Table-A7: Independent Varable Importance Importance Normalzed Importance Mgraton Status % Area % Tranng Complete % Gender % Martal Status % No Formal Educaton % Below Matrc % Matrc but Below Inter % Inter but Below Degree % Degree Above % Occupaton (Busness Industry Offce) % Permanant Job % Contract Job % Age: % total number of hours worked % Fgure-A2: Independent Varable Importance Chart

15 Khadja, Ayesha and Ahmad 193 APPENDIX B Predcton of Wages Usng MLP Model Manually To predct the wages of employee usng MLP model, the values of ndependent varables and weghts estmated by the model are requred. The weghts for hdden and output layer are gven n table A6. As the model have three layers; nput layer, hdden layer and output layer. So n frst step hdden layer receves the weghted sum of ncomng sgnals (nformaton from ndependent varables) sent by the nput layer and processes t n the hdden layer usng Hyperbolc Tangent actvaton functon x x e e f ( x). The hdden layer n turn sends an output sgnal towards the neurons n x x e e output layer. So processng on the weghted nformaton receved by the output layer s done on the same way as n hdden layer that s by applyng an actvaton functon on t. In ths model output layer utlze an dentty actvaton functon f ( x) x, because wages s a scale dependent varable. In end model provde a rescaled value depends upon whch type of rescalng method s utlzed for the scale dependent varable. In exstng archtecture of the MLP standardzed method s employed to rescale the varable. Detaled of ths predcton processed s gve the followng, manual calculaton for each layer s gven below. For llustraton, let us consder the estmaton of an employee. As stated above, for predcton the values of the ndependent varable are requred. The values for ndependent varables for an employee are gven n the followng table. The weghts for predcton wll be used from table A6. Varables Values Mgraton Status 1 Area of Workng 1 Tranng Complete 0 Gender 1 Martal Status 0 No Formal educaton 0 Below Matrculaton 1 Matrculaton Below Intermedate 0 Intermedate Below Degree 0 Degree and Above 0 Occupaton 1 Permanent Job 0 Contract Job 1 Age 60 Total workng Hours 48

16 194 Predctng the wages of employees usng soco-economc Calculatons for Input to Hdden Layer Let x denote the th ndependent varable, wj ndependent varable and the jth hdden layer node. Let hdden layer then n our case j j n y bas w x ; 1,2,..., n and j 1,2,...,9 Now we calculate the results at each node of hdden layer. For frst node 15 y bas w x 1 1 denotes the weght of th y j represent the jth node of = (1)(0.329) + (1)(0.495) + (0)(0.492) + (1)(-0.403) + (0)(0.455) + (0)(-0.153) + (1)(-0.081) + (0)(-0.147) + (0)(-0.218) + (0)(0.294) + (1)(0.019) + (0)(0.369) + (1)(-0.175) + (60)(0.056) + (48)(0.37) = For second node 15 y bas w x 2 2 = (1)(0.444) + (1)(0.466) + (0)(-0.296) + (1)(-0.365) + (0)(0.243) + (0)(0.209) + (1)(0.109) + (0)(0.117) + (0)(0.22) + (0)(-0.094) + (1)(0.051) + (0)(0.346) + (1)(-0.489) + (60)(0.455) + (48)(0.155) = For thrd node 15 y bas w x 3 3 = (1)(-0.418) + (1)(0.174) + (0)(0.128) + (1)(0.47) + (0)(-0.098) + (0)(0.435) + (1)(0.23) + (0)(-0.276) + (0)(0.471) + (0)(0.396) + (1)(-0.474) + (0)(0.404) + (1)(-0.315) + (60)(0.02) + (48)(0.396) = For forth node 15 y bas w x 4 4 = (1)(-0.259) + (1)(-0.327) + (0)(0.522) + (1)(0.576) + (0)(-0.041) + (0)(0) + (1)(-0.081) + (0)(0.367) + (0)(0.282) + (0)(-0.325) + (1)(-0.487) + (0)(-0.074) + (1)(-0.314) + (60)(-0.157) + (48)(0.314) = 4.978

17 Khadja, Ayesha and Ahmad y bas w x 5 5 = (1)( 0.426) + (1)(0.161) + (0)(-0.246) + (1)(-0.339) + (0)(-0.242) + (0)(-0.182) + (1)(0.337) + (0)(-0.544) + (0)(-0.481) + (0)(-0.539) + (1)(0.084) + (0)(-0.349) + (1)(0.318) + (60)(0.388) + (48)(0.041) = y bas w x 6 6 = (1)( ) + (1)( 0.449) + (0)( ) + (1)( 0.334) + (0)( ) + (0)( 0.352) + (1)( ) + (0)( 0.505) + (0)( 0.402) + (0)( 0.019) + (1)( 0.353) + (0)( ) + (1)( ) + (60)( 0.013) + (48)( ) = y bas w x 7 7 = (1)( ) + (1)( ) + (0)( 0.096) + (1)( ) + (0)( ) + (0)( ) + (1)( ) + (0)( 0.038) + (0)( ) + (0)( ) + (1)( 0.348) + (0)( ) + (1)( -0.06) + (60)( 0.4) + (48)( ) = y bas w x 8 8 = (1)( ) + (1)( ) + (0)( 0.093) + (1)( ) + (0)( 0.018) + (0)( 0.437) + (1)( 0.212) + (0)( 0.074) + (0)( 0.112) + (0)( ) + (1)( 0.084) + (0)( 0.201) + (1)( 0.507) + (60)( 0.243) + (48)( ) = y bas w x 9 9 = (1)(-0.04) + (1)(-0.428) + (0)(-0.386) + (1)(-0.213) + (0)(0.11) + (0)(0) + (1)(-0.102) + (0)(-0.407) + (0)(-0.204) + (0)(0.089) + (1)(-0.432) + (0)(-0.451) + (1)(0.356) + (60)(0.357) + (48)(0.413) = Now we have to use the actvaton functon tangent hyperbolc to obtan the results of each node from the hdden layer For frst node tanh y = tanh( ) 1 1 For second node tanh tanh y = 2 For thrd node tanh y = tanh 15 1

18 196 Predctng the wages of employees usng soco-economc For forth node tanh y = 4 tanh For ffth node tanh y tanh For sxth node tanh y tanh For seventh node tanh y tanh For eghth node tanh y tanh For nnth node tanh y tanh(40.72) 1 9 Calculaton for Hdden to Output Layer th Let zk denote the result for k node of output layer. 9 z bas w tanh y k jk j j1 In case of scale dependent varable number of nodes n output layer s one. Therefore the above equaton takes the form So 9 z bas w tanh y j1 jk j z = (1)(0.076) + (1)(0.011) + (1)(-0.052) + ( )(0.419) + (1)(0.875) + (-1)(0.134) + (1)(0.337) + ( )(0.331) + (1)(0.015) = The actvaton functon for output layer s dentty so the value remans the same. But the MLP model provdes the standardzed value for the scale dependent varable. So to un-standardzed ths value utlzes x z. Here and are obtaned from the predcted values from the model to un-standardzed the value. Now = and = Thus un-standardzed value s So the estmated wage of employee s rupees. It s suggested that f data on these ndependent varable s avalable for the comng years one can easly predct the wage of an employee.