How Education Level, Gender, and Social Network Correlate With Migrant Workers Starting Income in China s Urban Cities *

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1 US-Chna Educaton Revew B, January 2016, Vol. 6, No. 1, do: / / D DAVID PUBLISHING How Educaton Level, Gender, and Socal Network Correlate Wth Mgrant Workers Startng Income n Chna s Urban Ctes * Dandan Chen The George Washngton Unversty, Washngton, D.C., USA Jngnng Guan Unversty of Wsconsn-Madson, Madson, USA The research queston for ths study s how educaton level, gender, and socal network affect mgrant workers startng ncome for ther frst job n Chna s urban ctes. Our objectve s to reveal the nterplay of educaton level, gender, and socal network n determnng mgrant workers ncome, whch are to the core of the current academc debate. The explanatory factor analyss was used to dentfy relevant varables, and a multvarate regresson analyss was conducted to reveal the relatonshps among educaton level, gender, socal network, and mgrant workers startng ncome n Chna s urban ctes. Keywords: mgrant workers, educaton, gender, socal network, ncome, urban development Introducton The year of 2010 marked the frst tme n Chna s hstory for the urban populaton to surpass the rural one (Bloomberg, 2012), wtnessng the nternal mgrant populaton, made an unprecedented jump to nvolve more than 200 mllon people. By the end of 2013, ths nternal mgrant populaton was reported to have grown up to 245 mllon, whch equals at least one mgrant n every sx resdents of ths naton (The Natonal Health and Famly Plannng Commsson, 2014). The term mgrant workers has been frequently used wthout an unequvocal defnton. However, t s wdely adopted to generally refer to, n Chna s nternal mgraton, the people who grew up n rural areas and have moved nto urban ctes to work. Ths populaton has caught much attenton n the current mgraton studes relatng to Chna due to the sgnfcant scale, dversty, and complexty of ths group. Ths research paper ams to provde some emprcal evdence that would support exstng polcy suggestons urgng mgrant workers workng condtons to be enhanced. These polcy suggestons have appealed for makng such a dfference by effectvely mprovng mgrant workers educaton background and ther soft sklls lke networkng sklls, regardless of ther gender. The research queston for ths study s how educaton level, gender, and socal network affect mgrant workers startng ncome n Chna s urban ctes. Our objectve s to reveal the nterplay of educaton level, gender, and socal network, whch are to the core of the current academc debate. * The deas and opnons expressed n the artcle are those of the authors and do not necessarly represent the vews of UNESCO and do not commt the organzaton. Dandan Chen, M.A., Graduate School of Educaton and Human Development, The George Washngton Unversty. Jngnng Guan, M.A., Department of Educatonal Leadershp and Polcy Analyss, Unversty of Wsconsn-Madson.

2 64 MIGRANT WORKERS STARTING INCOME IN CHINA S URBAN CITIES Lterature Revew Accordng to Roberts (2001), factors lke the educaton level, gender, age, martal status, company ownershp, and place of orgn could have an mpact on mgrant workers ncome. Nevertheless, ths study was based on the data on Shangha s mgrant populaton n 1993, whch was a long tme ago; besdes, t only reflected the stuaton n one sngle cty. Smlarly, a study conducted by Lu and Song (2006) found that the educaton, age, work duraton, experence, hukou (household regstraton status), and company ownershp are sgnfcant determnants of mgrants hourly wages, based on a survey conducted n 2005 n Chna s Tanjn Cty. Interestngly, ths study sngled out the gender, assertng the gender does not sgnfcantly affect mgrant workers hourly wages. In the meantme, contrarly, a study conducted by Wang (2005) on the gender-wage dfferental for mgrant workers clamed females tend to get lower ncome than ther male counterparts, so a gender dsparty does exst. Unfortunately, the data sets for both studes are naccessble to the publc, whch makes t very challengng to verfy ther fndngs or to help reach a consensus. Recently, Wang, Lao, and Zhou (2014) asserted that the socal network (.e., the network of socal connectons each ndvdual person possesses) s a sgnfcant determnant of mgrant workers ncome, usng the survey data collected by the government n However, ther study focused on some very specfc aspects of the socal network effect tself n leu of the correlatons between the socal network and other determnants, and consequently how the socal network gets to play wth other socal factors n determnng mgrant workers ncome has yet to be unveled. Overall, through lterature revew, the educaton level and the socal network have been found frequently confrmed as two crtcal determnants of mgrant workers ncome, whle how the socal network nteracts wth some other factors has yet to be clearly demonstrated. Also, the effect of the gender has been argued to be an mportant factor. Ths study thus focuses on the correlatons among the educaton level, socal network, gender, and mgrant workers ncome to brdge the lterature gap and to support the current polcymakng. Methodology Framework Ths research was bult upon the theoretcal framework of Costello and Osborne (2005), whch clarfes the myths n processng approprate the exploratory factor analyss approprately to select the most relevant varables for further analyss. Costello and Osborne s framework, smply put, asserted that, n the exploratory factor analyss, the maxmum lkelhood factor analyss s the best choce for data that are relatvely normally dstrbuted, whereas the prncple axs factor analyss should be used nstead when the assumpton of normal dstrbuton s severely volated. The best choce for researchers n retanng varables, accordng to Costello and Osborne, s the scree test, whch retans varables above the break pont where the scree curve flattens out. In addton, accordng to the two authors, oblque rotaton should render a more accurate soluton than orthogonal rotaton n socal scence studes as some correlaton among socal factors would be consdered n the former, whch should be the case when analyzng socal ssues. Analyss Selecton of data set. There are lmted natonal data and restrcted access to census data on mgrant workers. Addtonally, when nvestgatng the factors that nfluence mgrant workers ncome, a survey should

3 MIGRANT WORKERS STARTING INCOME IN CHINA S URBAN CITIES 65 be desgned to collect data (Lu & Song, 2006; Han & Yuan, 2009). Takng nto account of the fndngs from the lterature revew, the only open-access survey data found to be most relevant to ths study s the data from the Chna Urban Labor Survey, collected n 2001 (Chna Survey Data Network [CSDN], 2008), by local offces of Chna s Natonal Statstcal Bureau. Ths data set ncluded detaled nformaton from questonnare tems on the ndvdual and household demographcs based on a sample of 8,109 ndvduals who aged above 16 and already joned the workforce n fve large urban ctes: Shangha, Shenyang, Wuhan, X an, and Fuzhou. Selecton of observatons. Consderng that the year of 1996 s the turnng pont of Chna s labor market and that the orgnal data set ncludes a varable that asks whether the respondents started ther frst job before 1996, we decded to use ths varable to preserve only the nformaton provded by the respondents who started ther frst job after Ths study also ams to target those mgrant workers who were employed rather than unemployed the tme when the survey was conducted, and who were hred by others rather than self-employed for the frst job, as suggested n the data set. Thus, only the observatons that were coded as employed by others were preserved. In ths way, a total of 634 from the orgnal 2,998 observatons were selected to be the observatons for the further analyss n ths study. Selecton of regresson model. The regresson analyss s conducted usng fxed effects model. Frst, the followng regresson model s assumed: Y X (1) Y s the monthly ncome mgrant workers () earn for ther frst job at the begnnng stage. X s the vector of ndependent varables we targeted, namely, the educaton level, socal network, and gender. represents the stochastc error. Second, the regresson model n the followng equaton s consdered: Y X n e The selected data for ths study vary among dfferent mgrant workers, ncludng the varance among the varables to be controlled n ths study. n and e, replacng the error n (1), capture the varance and the error respectvely. There are two ways to properly deal wth n above so as not to bas the coeffcent for the varable X : fxed effects model and random effects model. The former s better for a data set that s not normally dstrbuted, whch s the case here, and therefore, the fxed effects model was adopted for ths study. Usng the measure of mean for each of ts varables except By deductng the model (3) from the model (2), Y X, we got the regresson model (3) as follows: (2) (3) gets deleted. Consequently, the potental bas from the between-entty varance can be resolved through ths subtracton. The model we get through ths process was a fxed effects model ths study was lookng for (as cted n Km & Park, 2012). To answer the research queston for ths study, the fxed effects regresson model was specfed as below: Income Educaton Level Gender Socal Network VaranceWthn Enttes n e The Income was the dependent varable, and Educaton Level, Gender, and Socal Network were the ndependent varables.. Varance Wthn Enttes represents the effects of some other mportant factors that were held constant usng the fxed effects. The null hypotheses consdered for ths study are: 1. The coeffcent of Educaton Level equals zero n n

4 66 MIGRANT WORKERS STARTING INCOME IN CHINA S URBAN CITIES ( 1 0 ); 2. The coeffcent of Gender equals zero ( 2 0 ); and 3. The coeffcent of Socal Network equals zero ( 0 3 ) when holdng all other varables as covered n varance wthn enttes constant. Selecton of varables. Now that the fxed effects regresson equaton was clarfed, the correspondng varables n the survey data set need to be dentfed for data processng. Gven the avalable varables n the data set, only the mgrant workers begnnng monthly ncome for ther frst job s found to the nterest of ths study, and thus ths ndcator would be used as a proxy ndcator for mgrant workers startng ncome. The varables dsplayng mgrant workers educaton level and ther gender, as already exstng n the data set, were desgnated as the varables to be used for the analyss of Educaton Level and Gender. Please note, the varable for gender was found necessary to be used as a dummy varable, and so the orgnal varable was recoded, usng 0 for male and 1 for female. Results and Dscusson In the orgnal data set, 1 there are multple tems relatng to the socal network and varous other factors whch were expected to be studed. To ntegrate the most relevant varables n the data set nto the fxed effects regresson desgn, the Shapro-Franca test was used n the frst place to examne the assumpton of normal dstrbuton, only to fnd that these subset data were not normally dstrbuted, as shown n Table 1. Table 1 Results From Shapro-Franca Test Varable Observatons W V Z p-value Year of brth (a1_a) Educaton level (b1) Years of stay (a14) In ths condton, followng the theoretcal framework from Costello and Osborne (2005), the prncpal axs factor analyss, scree test, and oblque rotaton method were used to dentfy the relevant varables. Gven the results, as shown n Table 2, only the varables that met these condtons were retaned: 1. The value n unqueness was smaller than 0.50; 2. The value n loadng for factors was larger than 0.50; 3. The egenvalue was above 1; 4. The varable led above the break pont of the scree plot n the scree test. Table 2 Results From Prncpal Axs Factor Analyss Varable Factor 1 Factor 2 Unqueness a10 * Note: Egenvalue < 1 a11 * a15 * Hukou a16 * a a a (Table 2 to be contnued) 1 The lnk to download the orgnal data set (downloadable after regstraton): /DownLoadChannel_new/detal.aspx?ClassID=4&DataID=9

5 MIGRANT WORKERS STARTING INCOME IN CHINA S URBAN CITIES 67 Socal network a44_ a44_2 * a44_3 * a44_ a45_ a45_2 * a45_3 * a45_ c102_ c103_11 * c104_ Famly c105_11 * c102_ c103_12 * c104_12 * c105_12 * Notes. Blank represents factor loadng smaller than 0.3; the symbol * ndcates ths varable s dentfed to be relevant and s to be used for the analyss. The varables fnally selected for the fxed effects regresson analyss were lsted below n Table 3. Table 3 Selected Varables for the Fxed Effect Regresson Analyss Varables used n ths study Varable names n CSDN s survey data Dependent varable Monthly ncome at the begnnng for the frst job f44_a_1: What s your monthly ncome (ncludng wage, monthly bonus, and subsdes)? (only look at the frst job at the begnnng stage) Gender a2: Intervewers, please wrte down the sex of the respondent : 1. Male 2. Female Independent varables to be targeted Socal network a44_2: How many relatves dd you know when you just came to ths cty? a45_2: How many relatves do you know now n ths cty? a44_3: How many hometown fellows dd you know when you just came to ths cty? a45_3: How many hometown fellows do you know now n ths cty? Educaton level b1: What s your educaton level? (code) Year of brth a1_a: When were you born? Year Month (only look at the number for year) Martal status a3: What s your marrage status? 1. Sngle 2. Marred 3. Dvorced 4. Wdowed a10: Dd you have cty hukou or rural hukou before you were 16 years old? 1. Cty hukou 2. Rural hukou a11: When dd you manly lve before you were 16 years old? 1. Ths cty and ths communty 2. Ths cty and other communty Independent 3. Other cty 4. Other town or county 5. Other vllage 6. Others varables to be Hukou status a15: Whch knd of hukou do you have now? held constant 1. Cty hukou 2 Rural hukou a16: Where s your hukou now? 1. Ths cty ths communty 2. Ths cty and other communty 3. Other cty 4. Other town or county 5. Other vllage 6. Others Years of stayng n ths cty a14: How long have you been lvng n ths cty? year Famly background c103_11: What s the type of the work unt for your father s prmary job? (code) (Table 3 to be contnued)

6 68 MIGRANT WORKERS STARTING INCOME IN CHINA S URBAN CITIES Independent varables to be held constant Independent varables used to select observatons c103_12: What s the type of the work unt for your mother s prmary job? (code) c104_11: What s the ndustry of your father s prmary job? c104_12: What s the ndustry of your mother s prmary job? c105_11: What s your father s occupaton? Famly background 1. Peasants 2. Workers 3. Self-employed 4. Owner of prvate busness 5. Admnstrators 6. Professonals 7. Admnstrators and professonals 8. Others c105_12: What s your mother s occupaton? 1. Peasants 2. Workers 3. Self-employed 4. Owner of prvate busness 5. Admnstrators 6. Professonals 7. Admnstrators and professonals 8. Others Whether you started the frst a19: Dd you start your frst job before 1996? job after Yes 2. No Whether you are employed a36: What s your workng status now? now (code) Whether you were f4_1: Are you self-employed or hred by others? employed by others for your 1. Self-employed 2. Hred by others frst job Four varables were dentfed to represent Socal Network, and one varable was desgnated for Educaton Level and Gender respectvely. Consderng the results from the lterature revew, the varables to be held constant were relatng to the year of brth, martal status, hukou status, years of stayng n ths cty, and famly background. A fxed effects regresson analyss was then conducted to estmate the effect of the educaton level, gender, and socal network on mgrant workers begnnng monthly ncome from ther frst job, holdng other factors 2 2 constant. The results n Table 4 showed that R = 0.59, and the adjusted R = The p-value was smaller than 0.01 for the varable for Gender ( a2 ) and was smaller than 0.05 for the varable for Educaton Level ( b1 ). These p-values showed that Gender s sgnfcant enough at the 0.01 level and Educaton Level sgnfcant at the 0.05 level to reject the null hypotheses 1 and 2. They suggested that female mgrant workers tend to earn lower begnnng monthly ncome than ther male counterparts, whereas a hgher educaton level would offset ths effect. The p-values for the four varables representng Socal Network ( a44_2, a44_3, a45_2, and a45_3 ) were larger than 0.05, whch faled to reject the null hypothess 3, suggestng Socal Network should not be a sgnfcant factor n ths analyss. Table 4 Results From the Two Regresson Analyses Startng monthly ncome (f44_a_1) Coef. Robust S.E. t p > t 95% conf. nterval Gender (a2) ** Socal network 1 (a44_2) Socal network 2 (a45_2) Socal network 3 (a44_3) Socal network 4 (a45_3) Educaton level (b1) * Intercept 1, ** , Notes. * p < 0.05; ** p < In the Stata output of ths analyss, the F statstc and the overall sgnfcance value were mssng, perhaps ths was because too many varables were computed n ths analyss. Nevertheless, an F-test for the jont sgnfcance of the dentfed varables for Educaton Level, Gender, and Socal Network demonstrated the three

7 MIGRANT WORKERS STARTING INCOME IN CHINA S URBAN CITIES 69 factors made a sgnfcant effect on mgrant workers begnnng monthly ncome (Pro > F = < 0.05), when holdng other factors constant. Concluson Ths paper studed how educaton level, gender, and socal network correlate wth mgrant workers startng ncome, based on the survey data for Chna s fve urban ctes: Shangha, Shenyang, Wuhan, X an, and Fuzhou. A fxed effects regresson model was bult to gude the exploraton: Income Educaton Level Gender Socal Network VaranceWthn Enttes Here are the null hypotheses for ths study: 1. The coeffcent of Educaton Level equals zero ( 1 0 ); 2. The coeffcent of Gender equals zero ( 2 0 ); 3. The coeffcent of Socal Network equals zero ( 0 3 ) when holdng all other varables as covered n varance wthn enttes constant. In the fxed effect regresson analyss, the varable for Gender was found sgnfcant enough at the 0.01 level and Educaton Level sgnfcant at the 0.05 level to reject the null hypotheses 1 and 2; the varables representng Socal Network were larger than 0.05, whch faled to reject the null hypothess 3. These fndngs suggested that female mgrant workers tend to earn lower begnnng monthly ncome than ther male counterparts, whereas a hgher educaton level would offset ths effect; the network of socal connectons one mgrant worker possesses do not have a sgnfcant mpact on ths person s begnnng monthly ncome. The results of ths study suggested that there s ndeed a gender dsparty among mgrant workers n urban ctes regardng ther startng monthly ncome, wth female mgrant workers earnng less than the male mgrant workers. However, ths ncome gap s not that obvous for male and female mgrant workers who possess a hgher level of educaton. The results also suggested that the socal network should not be a sgnfcant determnant of mgrant workers startng monthly ncome. References Bloomberg. (2012, January 17). Chna s urban populaton exceeds countrysde for frst tme. Bloomberg News. Retreved from Chna Survey Data Network (CSDN). (2008). Chna Urban Labor Survey, Retreved from center. org/csdn_en/downloadchannel_new/detal.aspx?classid=4&dataid=9 Costello, A. B., & Osborne, J. W. (2005). Best practces n exploratory factor analyss: Four recommendatons for gettng the most from your analyss. Practcal Assessment, Research & Evaluaton, 10(7), 1-9. Han, L., & Yuan, X. (2009). The ncome ncrease of the rural mgrant worker and ts determnants. Populaton Journal, 1, Km, K., & Park, D. (2012). Impacts of urban economc factors on prvate tutorng ndustry. Asa Pacfc Educaton Revew, 13, Lu, Z., & Song, S. (2006). Statstcal analyss on mcro-determnants that affects wages of rural-urban mgrants. Modern Fnance and Economcs, 10(26), Roberts, K. D. (2001). The determnants of occupatonal choce of labor mgrants to Shangha. Chna Economc Revew, 12(1), The Natonal Health and Famly Plannng Commsson. (2014). Development report on Chna s mgrant populaton. The Natonal Health and Famly Plannng Commsson.

8 70 MIGRANT WORKERS STARTING INCOME IN CHINA S URBAN CITIES Wang, C., Lao, H., & Zhou, X. (2014). The mpact mechansm of socal networks on Chnese rural-urban mgrant workers behavor and wages. Economc and Labour Relatons Revew, 25(2), Wang, M. (2005). Zhonguo chengsh laodongl shchang shangde xngbe gongz chay (Gender wage dfferentals n Chna s urban labor market). Economc Research, 12, World Bank. (2009). Chna From poor areas to poor people: Chna s evolvng poverty reducton agenda An assessment of poverty and nequalty. Washngton, D.C.: World Bank.