The Effect of Outsourcing on the Change of Wage Share

Transcription

1 Clemson Unversty TgerPrnts All Theses Theses The Effect of Outsourcng on the Change of Wage Share Tanq L Clemson Unversty Follow ths and addtonal works at: Recommended Ctaton L, Tanq, "The Effect of Outsourcng on the Change of Wage Share" (2017). All Theses Ths Thess s brought to you for free and open access by the Theses at TgerPrnts. It has been accepted for ncluson n All Theses by an authorzed admnstrator of TgerPrnts. For more nformaton, please contact kokeefe@clemson.edu.

2 THE EFFECT OF OUTSOURCING ON THE CHANGE OF WAGE SHARE A Thess Presented to the Graduate School of Clemson Unversty In Partal Fulfllment of the Requrements for the Degree Master of Arts Economcs by Tanq L December 2017 Accepted by: Dr. Scott Baer, Commttee Char Dr. Mchal M.Jerzmanowsk Dr. Matthew S. Lews

3 ABSTRACT The technology breakthroughs and tarff reductons of the last several decades have reduced the costs of trade. The classcal Heckscher-Ohln model cannot explan the pattern of the change n nonproducton wage share: the relatve wages of nonproducton and producton workers ncreased steadly snce 1980s. The ncreasng wage gap leads to ncreasng ncome nequalty, growng poverty and strans the socal fabrc. Hence, we need an alternatve approach to explan the ncreasng wage gap between nonproducton and producton workers. Feenstra developed a trade n ntermedate model whch estmates the how much outsourcng contrbutes to the total change n nonproducton wage share and compared t wth the contrbuton of computers. He estmated the change n nonproducton wage share n hs book, Advanced Internatonal Trade (2004). Usng the NBER productvty data (Bartelsman and Gray 1996) and mported ntermedate nputs data (Feenstra and Hanson 1990) to run hs regresson, he found that outsourcng and hgh-tech captal are the man factors. Specfcally, he reported that the outsourcng contrbuted 15-24% and computers (hgh-tech captal) contrbuted 13-31% to the total change n nonproducton wage share from 1979 to Moreover, whether outsourcng s more or less mportant than computers, depends on how we measure the computers. If we measure computers wth the share of nvestments, t wll be more mportant than outsourcng. If we measure computers wth ex-post or ex-ante rental prces, t wll be less mportant than outsourcng.

4 For ths paper, I use an updated data and compare my results wth Feenstra s. The NBER data I use s for the years 1958 to 2009 (Bartelsman and Gray 2014) and the ntermedate nputs data s updated through I fnd that outsourcng contrbuted 17-28% whle computers contrbuted 9-45%. If we measure computers as the share of nvestment, t contrbutes 45%, whch s more mportant than outsourcng. In other measurements lke ex-post, computers contrbute 14%, whch s less mportant than outsourcng. The fact that my fndngs are smlar to Feenstra s, provdes a robustness check on hs orgnal fndngs and gves us more confdence n them.

5 TABLE OF CONTENTS TITLE PAGE... ABSTRACT... Page 1. INTRODUCTION LITERATURE REVIEW Methodology Development Data Resources MODEL RESULTS Changes n Wages and Employment The Relatve Demand for Nonproducton Workers CONCLUSION REFERENCES v

6 1. Introducton In the evoluton of the global economy, the last several decades -- whch have often been descrbed as the second golden age of world trade have been characterzed by dramatc decreases of transportaton costs and subsequent rounds of b- and mult-lateral reducton of trade barrers. The assocated ncrease n the volume of nternatonal commerce has not been confned to fnal goods; there has also been a sgnfcant ncrease of trade n the producton actvtes. What we mean by ths s that dfferent stages of producton can occur at dfferent places and the producton stages need not be ted to where the ultmate consumer s located. Thus, the fragmentaton of producton s beng asssted by the fall n transportaton costs. Not only has transport costs fallen, but mproved (communcaton) technology has ncreased the fragmentaton of the producton process. These relatvely new features of globalzaton nvte us to look at the consequences of outsourcng and fragmentaton of the producton process. The classcal models of nternatonal trade have trouble explanng the observed pattern of trade and the mpact trade has on factor prces; n partcular, the Heckscher-Ohln model cannot account for the observed rse n the skll-premum that have taken place n many countres. Therefore, we need an alternatve approach to estmate and explan the ncreasng wage gap between nonproducton and producton workers. The wage gap between nonproducton and producton workers leads to ncreasng ncome nequalty, growng poverty and strans the socal fabrc. 1

7 Snce around 1980 many economes, especally the Unted Sates, have experenced a sgnfcant ncrease n the wages of sklled workers (nonproducton) relatve to those wth less ablty (producton workers). In the Unted States the wage share of nonproducton/producton ncreased from 1.53 n 1979 to 1.72 n Moreover, the relatve employment of nonproducton/producton ncreased along wth the wage share from 0.36 n 1979 to 0.45 n Ths means that there must be an outward shft n the demand for nonproducton workers. As Feenstra llustrated n hs book, Advanced Internatonal Trade (2004), outsourcng and hgh-tech captal are the man factors of ths outward shft. We know that f companes decde to purchase ntermedate nputs overseas and move the labor-ntensve actvtes there as well, ths wll defntely reduce the employment n the home country, lke the Unted States, whch s the outsourcng effects on the employment n the home country. And we can expect that ths effect would affect dfferently the employment of nonproducton workers verse the employment of producton workers. In ths way, outsourcng has the smlar quanttes effects to use of computer servces on the reducton of the change of the employment share of nonproducton/producton workers. In ths paper, we wll fnd whch one s more mportant for the change of the wage share of nonproducton/producton workers by extendng Feenstra s models used n hs book. 2

8 I follow the approach of Feenstra, who regressed the change n nonproducton wage share on shpments of each ndustry; the captal/shpment rato; outsourcng, measured by mported ntermedate nputs; and the share of computers and other hgh-tech captal. Feenstra used NBER productvty data (Bartelsman and Gray 1996) and mported ntermedate nputs data (Feenstra and Hanson1990) to run hs regresson. He found that the outsourcng contrbuted 15-24%. Whle computers contrbuted 13-31% to the total change n nonproducton wage share from 1979 to Moreover, f we measured computers wth ex-post or ex-ante rental prces, computers contrbuted 13% and 8% respectvely. If we measured computers wth the share of nvestment, t contrbuted 31%. For ths paper, I use an updated data set and I compare these results to what Feenstra found n hs book; NBER data year range s 1958 to 2009 (Bartelsman and Gray 2014) and ntermedate nputs data year range updated to And the results from my regresson are smlar wth Feenstra s but the magntude s dfferent. For outsourcng, my result s 17-28% and for computers, t s 9-45%. Under ex-post and ex-ante rental prces measurements they are 14% and 9% respectvely. And under the share of nvestment measurement, t s 45%. Compared to Feenstra s results, we can see that outsourcng contrbuton ncreased slghtly from 1990 to 1997, but the contrbuton of computers under the share of nvestment measurement ncreased dramatcally from 1990 to 1997 (from 31% to 45%). Whether outsourcng s more or less mportant than computer servces depends on how 3

9 we measure computers and other hgh-tech captal. All n all, wth the new verson data, the result s consstent wth Feenstra s results n the drecton of the effects of outsourcng and computers. My fndngs mean that we can have more confdence n Feenstra s orgnal estmates 2. Lterature Revew Outsourcng has a qualtatvely smlar effect on reducng the relatve demand for unsklled labor wthn an ndustry as does sklled ntensvely technology change, lke the ncreased of computers. Ths pont of vew was frst mentoned by Feenstra n hs book, Advanced Internatonal Trade-Theory, and Evdence. 2.1 Methodology Development Some researchers used Heckscher-Ohln-Vanek (HOV) model to compute the change n the factor of trade and prces n the early 1980s. And they were faled. However, the result from the HOV model suggests another method to fnd the cause: model the 4

10 ntermedate nputs trade nstead of relyng on an HOV equaton. The method sometmes called producton sharng, or we can call t n a smpler way, outsourcng. Bernard and Jensen estmated the ndustry-level decomposton of the change n the share of employment and wages of nonproducton workers from 1973 to Then he compared both of the change n the share of employment and wages, between ndustres wth the change wthn ndustres. The results showed that the trade shfts the composton of actvty wthn an ndustry. And later they dd the estmate agan but wth plant-level data. The results suggested that trade has an effect on factor demand and wages by shftng the demand for labor wthn ndustres. Ths provdes that outsourcng s the cause of those shfts. 2.2 Data Resources In ths paper, we need to use three part of data. Frst, we need NBER productvty dataset to estmate the changes n wages and employment from 1979 to 2009 (Bartelsman and Gray 2014). Second, we need mported ntermedate nputs dataset. Ths dataset made by Feenstra and Hanson (1990). We need ths dataset and combned wth trade data to form the nput-output matrx. The last, we also need hgh-technology captal dataset. Ths dataset made by Bernd and Morrson (1995). 5

11 3. Model Smple Model: Trade n Intermedate Inputs In ths paper, we wll use the same model n Feenstra s book. We assume that there are two nputs y, = 1,2. To produce nputs y, = 1,2, we use unsklled labor L, sklled labor H, and captal K. Then we have a lnear homogeneous producton functon: y = f (L, H, K ) = 1,2 (1) Let suppose y 1 s the unsklled labor ntensve nput and y 2 s the sklled labor ntensve nput. So, y 1 wll represent the unsklled labor ntensve actvtes lke assemblng components. And y 2 wll represent the sklled labor ntensve actvtes lke marketng, R&D and, servces. Both of y 1 and y 2 are needed to the produce fnal manufacturng product. However some of those actvtes occurred n factory, whch could be outsourced overseas. It s mported from aboard. It also means that some of actvtes y 2, those sklled labor ntensve actvtes, can be exported aboard to support producton overseas. Now, let us denote x 1 < 0 as the mports of nput 1 and x 2 > 0 as the exports of nput 2. And p = (p 1, p 2 ) denote the prce vector of the traded ntermedate nputs. 6

12 Therefore, the bundle producton functon of the fnal manufacturng product s y n = f n (y 1 x 1, y 2 x 2 ) And the total factor usage s L n = L 1 + L 2, H n = H 1 + H 2, K n = K 1 + K 2 (2) Wth perfect competton assumpton, we solve the optmal output for the value of output from the fnal manufacturng product plus the net of trade by maxmzng the revenue functon subject to the resource constrants (equaton 1 and 2) G n (L n, H n, K n, p n, p) max p n f n (y 1 x 1, y 2 x 2 ) + p 1 x 1 + p 2 x 2, subject to x,l,h,k y n = f n (y 1 x 1, y 2 x 2 ), L n = L 1 + L 2, H n = H 1 + H 2, K n = K 1 + K 2 Next, we derve the revenue functon G n (L n, H n, K n, p n, p) foreach ndustry n = 1,.., N, where p = (p 1, p 2 ). Because G n (L n, H n, K n, p n, p) s lnearly homogeneous n prces, so we can re-wrte t as p n G n (L n, H n, K n, 1, p p n ) Then we get the real value-added functon 7

13 Y n = G n (L n, H n, K n, 1, p p n ) (4) Ths functon measures the y n plus the real net exports. Then we assume that the levels of captal and output are fxed, we get the short-run cost functon, C n (w, q, K n, Y n, p p n ) max w L n + qh n, L n,h n subject to Y n = G n (L n, H n, K n, 1, p p n ) (5) To keep track of mport prce we wll measure the expendture on mported ntermedate nputs for each ndustry. And we denote all the structural varables by z n (we treated as an error terms n our cost functon before), whch have effects on costs n each ndustry (nclude outsoucrng varable, one of z n varables, and computers and other hgh-tech captal, others z n varables). So we need to re-wrte our cost functon nto C n (w, q, K n, Y n, z n ). Next, we use the costs functon from the Feenstra s book, and translog the cost functon (drop the ndustry subscrpt n) M K ln C = α 0 + =1 α ln w + β k ln x k + 1 k=1 γ 2 =1 j=1 j ln w ln w j + 1 K K M K k=1 (6) δ 2 l=1 kl ln x k ln x l + =1 k=1 φ k ln w ln x k M M Where w s the wages of the optmal nputs = 1,, M and x k s ether the quanttes of the fxed nputs or outputs k=1,,k, or other structural parameters. Take 8

14 the frst dervatves, ln C ln w = ( C w )(w C). C w s the demand for the nput and ( C w ) (w C) s the payments to factor relatve to total costs. We wll denote the payments to factor relatve to total costs as costs shares s. Dfferentate equaton (6) wth repect to ln w, we obtan M K j=1 k=1 (7) s = α + γ j ln w j + φ k ln x k, = 1,, M In ths paper, we assume that cost functon s the same across all ndustres. In cost functon C n (w, q, K n, Y n, z n ) we have two type of nputs- unsklled labor and sklled labor. However n the share equaton (7), we focus on the share for sklled labor, captal, output, and other structural varables z n. We can also estmate the change of the wage share of nonproducton workers by takng the dfference between two years n ndustres s nh = φ 0 + φ k ln K n + φ γ ln Y n + φ z z n, n = 1,, N, (8) Where z n s the vector of structural varables and φ z s the coeffcents of the vector. Ths model wll let us to observe that how much of ncrease s contrbuted by captal changes, output changes, and the structural varables f there s an ncrease n the wage share of nonproducton workers. 9

15 4. Results 4.1 Changes n Wages and Employment Froom 1979 to 1995, the real wages of full-tme workers wth 12 years of educaton decreased by 13.4% and the real wages of full-tme workers less than 12 years of educaton decreased by 20.2%. In the meantme, the real wages of full-tme workers less than 16 years or more years of educaton ncreased by 3.4%, so the waged gap between the sklled workers and unsklled workers s 16.8%-23.6%, whch ncreased a lot compared wth

16 Used the formula below: Producton worker wage rate Non producton worker wage rate producton worker wage bll producton workers Non producton worker wage bll Non producton workers (total pay roll (total employment - producton, worker wage bll - producton workers and the data from NBER productvty database, we get the Fgure 1 and Fgure2. ) ) ndustry 11

17 Fgure 1 Note: The x-axs s the year range from and the y-axs s the relatve wages of nonproducton/producton workers, U.S. Manufacturng Fgure 2 Note: The x-axs s the year range from and the y-axs s the relatve employment of nonproducton/producton workers, U.S. Manufacturng From Fgure 1, we can see that the relatve wage of nonproducton/producton workers n 1990 s around 1.63 and t s around 1.53 n However, n 2009, the relatve wage of 12

18 nonproducton/producton worker ncreases to It means that the wage gap has stll ncreased snce And the ncreasng the wage of nonproducton workers should lead a decrease n the demand for nonproducton workers. However, Fgure 2 shows that from 1958 to 1990 the relatve employment of nonproducton/producton workers ncreases along wth the relatve wages of nonproducton/producton workers. And from 1990 to 2009, the relatve employment of nonproducton/producton workers decreased along wth the relatve wages of nonproducton/producton workers. Therefore, there s only one explanaton can explan these facts s that there has been an outward shft n demand of nonproducton workers snce 1980 and an nward shft n demand of nonproducton workers snce 1990s. Wth the shft n demand, t makes sense that the relatve employment of nonproducton/producton workers ncreased and decreased along wth the relatve wages of nonproducton/producton workers. The outward shft proves that there are some factors affectng the demand for nonproducton workers, whch are outsourcng and technology breakthroughs. However, we want to determne that among those ncreases how much s contrbuted by outsourcng and how much s due to hgh-tech equpment. 13

19 4.2 The Relatve Demand for Nonproducton Workers Wth the queston above, we estmate equaton (8) above wth 447 ndustres wthn the U.S. manufacturng sector, over The data are from the NBER Productvty Database at the four-dgt Standard Industral Classfcaton (SIC) level. In our regresson, we focus on nonproducton workers and use t as a proxy for producton workers. Our dependent varable s the change n the share of nonproducton workers n total wages wthn each ndustry. From 1979 to 1997, the producton wage share decreased substantally. At the same tme, the nonproducton wage share has merely ncreased slghtly and the captal share ncreased by an average rate of around 1 percent a year snce For our regresson, we wll weght regressons by the ndustry share of the total manufacturng wage bll. Hence, larger ndustres wll receve more weght n regressons. Our regressors are the 1.shpments of each ndustry, 2. The captal/shpments rato, 3.outsourcng, 4.the share of computers and other hgh-tech captal n the captal stock. We expect that outsourcng has the smlar effects wth computers and other hgh-tech captal yet t has more mpact on the change n the share of nonproducton workers. 14

20 Table 1: Dependent Varable Change n Nonproducton Wage Share, (1) Mean (2) Regresson (3) Regresson (4) Regresson ln(k/y) (0.01) (0.01) (0.009) ln(y) (0.006) (0.006) (0.006) Outsourcng (0.093) (0.09) (0.09) Computer and other hgh-tech captal measured wth ex-post rental prces: Computer share (0.091) Other hgh-tech share (0.14) Computer and other hgh-tech captal measured wth ex-ante rental prces: Computer share (0.17) Other hgh-tech share (0.07) Computers measured as share of nvestment: Computer share (0.009) Hgh-tech share (ex-post rental (0.03) prces) Constant (0.03) (0.03) (0.05) R N Note: the mean of dependent varable equals (5) Contrbuton 9-11 % 8-10 % 17-28% 14% -- 9% 0.3% 45% 4% 38-50% 15

21 Column (1) reports the mean values of the dependent varables and ndependent varables from 1979 to For column (2) we regress the computer and other hgh-tech captal shares by measurng wth ex-post rental prces. And for column (3) we regress the computer and other hgh-tech captal shares by measurng wth ex-ante rental prces. For column (4) we regress the computers by measurng as shares of nvestment. As we expected before, outsourcng has a postve effect on the change n share of nonproducton workers, whch s smlar to what the computer share does. In column (5), we dvde the total changes n the nonproducton wages shares by multplyng the regresson coeffcents by the mean values for the change n each varable. We can see that outsourcng s contrbuted about 17-28% of the total changes n the nonproducton wages shares. However, the results for computers depends on the measurements. If we measure computers and other hgh-tech captal as a share of the captal stock usng ex-post rental prces, they account for 14% of the total changes n the nonproducton wages shares. If we measure computers and other hgh-tech captal as a share of the captal stock usng ex-ante rental prces, they only account for 9.3% of the total changes n the nonproducton wages shares. In both cases above, we see that the contrbuton of outsourcng s larger than the contrbuton of computers and other hgh-tech captal. But, 16

22 f we measure computers as a share of nvestment, t wll account for 45% of the total changes n the nonproducton wages shares. In ths case, the contrbuton of computers exceeds the contrbuton of outsourcng a lot. Hence, whether outsourcng s more or less mportant than computers depends on how we measure computers and other hgh-tech captal. Regardless of the measurements of computers and other hgh-tech captal, we can say that both outsourcng and computers servces are mportant to explan the total changes n the nonproducton wages shares. 5. Conclusons Trackng the ntermedate nputs can help us to easly fnd the shft happened n relatve demand for nonproducton workers wthn an ndustry. We regress the change n the share of nonproducton workers n total wages wthn each ndustry on shpments of each ndustry, the captal/shpments rato, outsourcng, mported ntermedate nputs, and the share of computers and other hgh-tech captal n the captal stock. Then we can argue that the effects of outsourcng are smlar to the effects of computers and other hgh-tech captal. And both of them have postve mpacts on the change of the wage share of nonproducton workers. Next, we see that n some cases outsourcng s more mportant than computers and other hgh-tech captal. But n some cases outsourcng s less 17

23 mportant than computers and other hgh-tech captal. It depends on how to measure computers and other hgh-tech captal. References Berman, E., Bound, J., & Grlches, Z. (1993). Changes n the Demand for Sklled Labor wthn U.S. Manufacturng Industres: Evdence from the Annual Survey of Manufacturng. Bartelsman, E., & Gray, W. (2014). The NBER Manufacturng Productvty Database. Berman, E., Bound, J., & Grlches, Z. (1993). Changes n the Demand for Sklled Labor wthn U.S. Manufacturng Industres: Evdence from the Annual Survey of Manufacturng. Bernard, A. B., & Jensen, J. (1997). Exporters, skll upgradng, and the wage gap. Journal of Internatonal Economcs, 42(1-2), Feenstra, R., & Hanson, G. (1996). Globalzaton, Outsourcng, and Wage Inequalty. Feenstra, R. C. (2004). Advanced nternatonal trade: theory and evdence. Prnceton: Prnceton Unversty Press. 18

24 U.S. Exports - SAS and STATA Format. (n.d.). Retreved November 27, 2017, from U.S. Imports - SAS and STATA Format. (n.d.). Retreved November 27, 2017, from 19