INVESTIGATION OF THE INFLUENCE OF UNEMPLOYMENT ON ECONOMIC INDICATORS


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1 INVESTIGATION OF THE INFLUENCE OF UNEMPLOYMENT ON ECONOMIC INDICATORS Ilona Tregub, Olga Filina, Irina Kondakova Financial Universiy under he Governmen of he Russian Federaion 1. Phillips curve In economics, he Phillips curve (named by William Phillips) is a hisorical inverse relaionship beween he rae of unemploymen and he rae of inflaion in an economy. Saed simply, lower unemploymen in an economy is correlaed wih a higher rae of inflaion. Economerics model π caπ bu bu υ 1,2,.. ; a, b, с, 0 Where: b is a posiive consan, U is unemploymen, U n is he naural rae of unemploymen or NAIRU, π and π e are he inflaion and expeced inflaion respecively. ε is a disurbed erm, is ime period, c, a are he coefficiens 1.1. Correlaion analysis Firs sep of analyzing economerics model is Correlaion Analysis. We sared wih Philips curve and saisical daa of China. For his macroeconomic law we need daa abou expeced inflaion and inflaion raes, unemploymen rae, inflaion rae we ook as CPI. To make correlaion analysis we use Excel file. Firs of all, we should undersand which variable is dependen and which variables are independen. For our analysis we should consruc marix of correlaion and diagrams wih rend, a he end we should make a conclusion abou dependence beween variables. Nex sep is o pu daa ino cells and columns. Afer ha I find coefficien of correlaion using he correlaion funcion. In my example we make correlaion analysis for Fisher equaion, in his law we have one dependen variableinflaion rae and wo independen variables expeced inflaion rae and unemploymen rae. We sar wih esing coefficien of correlaion beween expeced inflaion and inflaion raes. We should chose he saisical funcion and allocae wo necessary arrays. Afer ha we obain our coefficien of correlaion, in my case, he coefficien of correlaion is 0,9i means ha here is srong posiive dependence beween inflaion and expeced inflaion raes, as one indicaors increases, anoher also increases. The same
2 procedure we made for oher independen variable. And we obain following resuls: i means ha here is srong posiive dependence beween inflaion and expeced inflaion raes r 1 =0,90 r 2 =0,50 I means ha here is medium negaive dependence beween inflaion and unemploymen raes For full correlaion analysis we need o consruc scaer plo o deerminae he funcion of dependence beween wo variables. On he scaer plo we name dependen and independen variables, in my case i is nominal and real ineres raes. Also, i is beer o reduce he numbers in he formulas o wo decimal place value. To sum up, in our scaer plo i is necessary o show diagram beween wo variables, line of rend, equaion of he funcion, square of R and he name of variables. 30,00 25,00 20,00 Inflanion rae 15,00 10,00 5,00 0,00 5,00 0,00 5,00 10,00 15,00 20,00 25,00 5,00 Expeced Inflanion rae y = 1,21x 1,49 R² = 0,81 30,00 25,00 20,00 Inflanion rae 15,00 10,00 5,00 0,00 1,00 1,50 2,00 2,50 3,00 3,50 4,00 4,50 5,00 5,00 Unemploymen rae y = 4,37x + 20,43 R² = 0,25 Making Correlaion Analysis we can come up o conclusion ha inflaion rae depends posiively on expeced inflaion rae and medium negaively on unemploymen
3 rae. As inflaion rae and unemploymen rae have negaive relaion (as one increase, anoher decreases), consequenly, saisical daa of China proves Phillips curve Regression analysis. Afer esing dependence beween variables and proved ha in our equaion we have posiive dependence beween variables, we should make regression analysis o find esimaion of coefficiens, sandard deviaion of coefficiens and disurbed erms, R 2 and F. All hese we need o consruc esimaed equaion for our model. Firs of all, we have he second varian of basic model; we have one dependen variable and wo independen variables. b a c 1,73 1,12 4,83 Esimaion of Coeffiiens 0,81 0,12 3,08 Sandard Deviaion of Coeffiiens R 2 0,85 2,92 #Н/Д δ ε F 60,00 21,00 #Н/Д 1020,66 178,61 #Н/Д Afer ha i is neccesary o make regression analysis. To do his we should find in Excel file Daa Analysis, selec Saisical and hen Regression. We will ge he window which we shoul fulfill wih our daa. Here we should pu our saisical daa of variables and chose free cell where we wan o see our resul. Moreover, i is imporan o noe ha when we selec our arrays of daa, we should selec all cells excep of he las array of each variable. Also, we pu sicks near he represenaion of graphs of remains. A he end press Ener and see he resul as graphs and ables. This informaion is necessary for furher ess and makes esimaed equaion. Below is our resul of Fisher equaion wih esimaion of coefficiens, sandard deviaion of coefficiens and disurbed erms, R square and value of F. Esimaed equaion 4,83 1,12 1,73 3,08 0,12 0,81 2,92 0,85 60,00 Third sep is Fes. This we need o esimae random or no random our square R. Using his funcion is necessary o ge value of F criical. F cri =3,47. As our F criical is less ha F saisical we can come up o conclusion ha R 2 is no random and qualiy of specificaion is high. 2. Okun's Law Okun's law is an empirically observed relaionship relaing unemploymen o losses in a counry's producion. Okun's law saes ha a one poin increase in he unemploymen rae is associaed wih wo percenage poins of negaive growh in real GDP. This law is named afer Arhur Okun, he economis who, in 1962, was he firs o make deailed observaions abou his relaionship. Socalled "naural unemploymen"
4 refers o he fac ha here will always be a leas a cerain amoun of unemploymen in a free marke economy, because of volunary changes in employmen and oher reasons no relaed o economic hardship. In is mos basic form, Okun's law invesigaes he saisical relaionship beween a counry's unemploymen rae and he growh rae of is economy. The economics research arm of he Federal Reserve Bank of S. Louis explains ha Okun's law "is inended o ell us how much of a counry's gross domesic produc (GDP) may be los when he unemploymen rae is above is naural rae." I goes on o explain ha "he logic behind Okun's law is simple. Oupu depends on he amoun of labor used in he producion process, so here is a posiive relaionship beween oupu and employmen. Toal employmen equals he labor force minus he unemployed, so here is a negaive relaionship beween oupu and unemploymen (condiional on he labor force)." I is mos imporan o noe ha Okun's law is a saisical relaionship ha relies on a regression of unemploymen and economic growh. As such, running he regression can resul in differing coefficiens ha are used o solve for he change in unemploymen, based on how he economy grew. I all depends on he ime periods used and inpus, which are hisorical GDP and employmen daa. The law has indeed "evolved," or changed over ime o fi he curren economic climae and employmen rends a he ime. One version of Okun's law has saed very simply ha when unemploymen falls by 1%, GNP rises by 3%. Anoher version of Okun's Law focuses on a relaionship beween unemploymen and GDP, whereby a percenage increase in unemploymen causes a 2% fall in GDP. All he necessary informaion for he counry we have chosen (he USA) concerning he Okun s law I downloaded from he websie of The World Bank (hp://daa.worldbank.org/counry/unied saes). For his model we needed daa on Curren unemploymen rae, Unemploymen rae a previous momen of ime and Growh rae of oupu. Thus, I jus found needed indicaors in a given Excel file.
5 The unemploymen rae a curren momen of ime (U ) depends posiively on he unemploymen rae a previous momen of ime (U 1 ) and negaively on he growh rae of oupu from previous o curren momen of ime (G y ). The iniial form of he economeric model is: U a 0 a1g y a1g y a 2U 1 a 0, a1 0,0 a 2 1 E ( ) 0 ( ) cons Where U is he unemploymen rae a curren momen of ime; U 1 is he unemploymen rae a previous momen of ime; G y is he normal growh rae; G y is he growh rae of oupu from previous o curren momen of ime; a 0, a 1, a 2, are coefficiens; ε is disurbance erm Correlaion analysis To analyze he correlaion beween variables and o build he scaer diagram, we consruced a able in Excel wih iniial daa: As he model has more han wo variables, i is needed o creae he marix of pair correlaion. Marix of pair correlaion U U 1 Gy U 1 U 1 0,81 1 Gy 0,34 0,20 1 The Scaer Diagram showing he relaionship beween Curren unemploymen rae (U ) and he Unemploymen rae a previous momen of ime (U 1 ) The Scaer Diagram showing he relaionship beween Curren unemploymen rae (U ) and he Growh rae of oupu (G y )
6 Conclusion Correlaion coefficien beween curren unemploymen rae (U ) and unemploymen rae a previous momen of ime (U 1 ) is equal o 0,81. I indicaes ha here is a srong posiive linear relaionship beween curren unemploymen rae (U ) and unemploymen rae a previous momen of ime (U 1 ). Correlaion coefficien beween curren unemploymen rae (U ) and growh rae of oupu (G y ) is equal o 0,34. I indicaes ha here is a very weak negaive linear relaionship beween curren unemploymen rae (U ) and growh rae of oupu (G y ). The Scaer Diagram showing he relaionship beween Curren unemploymen rae (U ) and he unemploymen rae a previous momen of ime (U 1 ) indicaes ha he relaion beween wo variables is linear and posiive. The Scaer Diagram showing he relaionship beween curren unemploymen rae (U ) and growh rae of oupu (G y ) indicaes ha he relaion beween wo variables is linear and negaive Regression analysis a 1 a 2 a 0 0,45 1,00 1,28 S ai 0,04 0,06 0,35 R 2 0,93 0,45 #Н/Д F 182,43 27 #Н/Д 72,80 5,39 #Н/Д F cri 3,33 F cri < F coefficien a 0 is 1,28 coefficien a 1 is 0,45 coefficien a 2 is 1,00 sandard deviaion of a 0 is 0,35
7 sandard deviaion of a 1 is 0,04 sandard deviaion of a 2 is 0,06 sandard deviaion of disurbance erm ε is 0,45 R 2 is equal o 0,93 and F is equal o 182,43 Thus, he esimaed form of he economeric model is U R 2 1,28 0,45G (0,35) 0,93 0,04 y a G 1 F 182,43 1U Conclusion R 2 is 0,93, i is close o 1, hus, regression line including G y and U 1 in he model explains values of U. F is equal o 182,43 and F criical is equal o 3,33. Value of F criical is much lower han value of F, hus, we can conclude ha R 2 is no random and he qualiy of specificaion is high. Hence, i can be concluded ha his model is applicable o he USA under he period from 1981 o y 1 (0,06) (0,45)
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