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Fax: 1-847-281-9855 E-mail: order@davidpublishing.com Chi nese Busi ness Revi ew Volume 10, Number 1, January 2011 (Serial Number 91) Contents Behavioral Economics Students and Retirement Saving Predictors1 Chris Kalin, Oliver Schnusenberg Regional Economics Banks Earnings, Risks and Returns in China21 Cheng Fan Fah, Annuar Nasir Foreign Direct Investments, Environmental Sustainability, and Strategic Planning: A Local Perspective29 Pasquale Pazienza, Caterina De Lucia, Vincenzo Vecchione, Elena Palma An Analysis of the Possible Economic Effects of HIV/AIDS in Swaziland Using the SAM and CGE Models41 Mphumuzi Angelbert Sukati Enterprise Management Absorptive Capacity and Knowledge Flows for Large International Firms: A Survey51 Luigi Aldieri Marketing Implications of E-banking in Entrepreneurial MarketingCase From Albania67 Kozeta Sevrani, Klodiana Gorica Chinese Business Review, ISSN 1537-1506 January 2011, Vol. 10, No. 1, 1-20 1 Students and Retirement Saving Predictors Chris Kalin Wells Fargo Bank, Los Angeles, USA Oliver Schnusenberg The University of North Florida, USA Overthepastcentury,Americanshavehadsignificantincreasesinpersonalincomeyetpersonalsavingshas decreased.Theresearchersexaminecollegestudentshabitsandbeliefsconcerningsavingforretirement.Toour knowledge,nostudyhasexaminedretirementsavingsamongAmericansinthisagegroup.Wetrytodetermine whether a students retirement savings beliefs and habits can be predicted. Using a unique survey, five indexes are constructed to assess students risk-aversion level, financial background, general savings habits, financial literacy, andattitudestowardsaving.Thisindexisusedtopredictasimilarlyconstructedindexofretirementsavings behavior.Wefindthatouroverallmodelispredictiveofastudentsbeliefsandhabitsinregardstosavingfor retirement.Inaddition,thestudentslevelofrisk-aversion,savingsbeliefsandfinancialliteracywereeach independentlypredictiveofretirementsavingsbehavior.Variousdemographicinfluencesontheconstructedrisk and savings variables as well as interactions are also investigated. Keywords: retirement savings, financial literacy, risk aversion, students Introduction and Motivation ThepurposeofthisstudyistoinvestigatethesavingsbehaviorofAmericanstudentsviaasurvey instrument.Thisstudycontributestotheexistingliteratureinseveralways.First,nostudytodatehas investigatedthefactorsaffectingthesavingsbehaviorofAmericancollegestudents.Fromaneducational viewpoint, such an investigation is important because it could indicate a need for further education in the area or personal finance beginning at the high school level. Second,wedevelopamodelthatidentifiesrelevantpredictorsforthesavingsbehavioramongcollege students.Thiscouldrevealnewandexcitingareasforfutureresearch.Furthermore,anunderstandingof relevantpredictorsmightalsoresultinfocusingfinancialeducationoncertainsegmentsthepopulationto increase the savings rate. Lusardi (2008) finds that offering financial education increase both financial and total networthsharplyforfamilieswithloweducation.Moreover,Lusardialsofindsthatretirementseminars increase total wealth for both high and low education families. Whilepreviousstudieshaveinvestigatedthedifferentdemographicinfluencesonsavingsbehaviorand savingsbehaviorasawhole,nopreviousstudyhasinvestigatedwhatfactorsrelatedtoupbringingand predispositiontorisk-takingaffectapersonsretirementsavingsbehavior.Investigatingcollegestudentsis particularly useful to accomplish this purpose since this is the population that will shortly enter the workforce and should begin saving for retirement. Thus, identifying the factors that affect this groups retirement savings

Chris Kalin, senior analyst, Wells Fargo Bank. OliverSchnusenberg,Ph.D.,associateprofessoroffinance,DepartmentofAccountingandFinance,CogginCollegeof Business, The University of North Florida. STUDENTS AND RETIREMENT SAVING PREDICTORS 2 behavior can shed additional light on the education needed to achieve a comfortable retirement. Review of Related Literature Most Americans would prefer to retire sooner than later, yet few actually have developed a plan to achieve this goal. Over the past century, Americans have seen huge increases in personal income. From 1980 to 2005, personal income rose 265%, yet there has been no increase in personal savings. In fact, savings have decreased with the rise of personal income. People often come up with excuses such as I have bills to pay now, I cant afford to save, or I can always save for retirement later. For most Americans, saving for retirement does not becomeanimportantobjectiveuntiltheyareintheir50s,atwhichtimeitmaybetoolatetoaccumulatea significantamountofwealth(Hennessy,2006;Hrung,2002).Indeed,aspointedoutbyMitchellandUtkus (2004)surveysrepeatedlyfindthatfewerthan40%ofUSworkershavecalculatedhowmuchtheywillneed during retirement, 30% have not saved anything for retirement, and only 20% feel very confident about having enough money to live comfortably in retirement (Employee Benefit Research Institute (EBRI), 2003). All workers today need to plan for retirement even more than prior generations. Ettorre (1995) finds that workers are currently saving about 1/3 of that required for a comfortable retirement. The Congressional Budget Office has conducted studies and revealed that a substantial portion of todays workers are not saving enough to cover even their basic needs in retirement. Although many individuals claim that we have the strongest economy in the world, we are the only country with a negative personal savings rate. As shown in Figure 1, in the early 1980s, Americas personal savings rate waswellover10%.Sincethattime,thesavingsratehasfluctuatedgreatly.Figure1alsoillustratesthatthe personal savings rate appears to increase during recessions, with a very pronounced spike during the recent global financial crisis. On average,however, thesavings rate israther low, and some authors point out that Americans spend more than they earn each year (Alford, Farnen, & Schachet, 2004; Ettorre, 1995; Schieber, 2004). Figure 1. Personal saving rate (PSAVERT). Source: U.S. Department of Commerce: Bureau of Economic Analysis. Saving early is the key to retirement wealth accumulation. By saving early, an individual can greatly reduce the percentage of their income that they must save each year to achieve a comfortable retirement, one where they do not struggle to meet their basic needs. This is due to the phenomenon known as compound interest, earning interest on interest, which significantly decreases the total savings that must be contributed by the investor if he STUDENTS AND RETIREMENT SAVING PREDICTORS 3orshebegins investingearly.Byimplementingasavingstrategyatayoung age,whenthe total dollaramount will likely be small, the investor forms a habit which will likely continue throughout their working years as their income increases. Although the total dollar amount of savings may be higher among people with higher incomes, Dynan (2004) noted that the savings rate is not tied to income (19-2) (Schieber, 2004; Keister, 2003). Zhong(1994)foundthatOlderpeoplesavemorethanyoungerpeoplemoreeducatedpeoplesave more than less educated people (13-8). Yet, those who actually save are often still not rich in retirement. This is because employees tend to choose investments that are less risky. Bischopink (1994) found that the majority of those who participate in retirement savings plans are slow to accumulate wealth because they tend to choose conservativeinvestmentswithlowreturnsratherthanplacingtheirmoneywithprofessionalinvestment managerswhotakeonmoreriskbutachievehigherreturns.Employeeschoseconservativeinvestments because they are scared of the greater potential loss that comes with riskier investments. This demonstrates the importanceofsavingearly.Bysavingearly,theinvestorcanaffordtotakeonmoreriskastheyhavemore yearstorecoveranylosses.Whenexaminedoverlongertimeperiods(i.e.,tentotwentyyears),higherrisk investments tend to have higher returns than less risky investments (Hoesly, 1995). Therearethreemainsourcesofincomeinretirement.Retirementincomecomesfrompersonalsavings including Individual Retirement Accounts, employer-sponsored plans such as the defined benefit (pension) and defined contribution (401k or 403b) plans, and social security. Expenses in retirement are different from expenses in the working years. In retirement, there are no social securitytaxesto bepaid, lessspending onfoodasmost havemoretimeto cookmealsathome,lessclothing expenses and lessmortgage expenses. The amount saved from certain expenses not incurred in retirement are oftenstillnot enoughtocoverthesignificantlyincreasedmedicalexpensesretirees face(Scholz, Seshadri,& Khitatrakun, 2006). Today, more than ever, it is vital that Americans begin to think about retirement savings. In the past, the primary option for retirement saving was the defined benefit plan such as a pension plan. Under such plans, the employer provides a predetermined amount to the employee every year after retirement. Many workers prefer the defined benefit plan because under such plans the employer, not the employee, is responsible for assuring that adequate money has been set aside to fund retirement payments to its retirees. Historically, the majority of workersreliedsolelyonthecompanypensiontofundtheirretirementneeds.In1980,90%ofallretirement savings were put into defined benefit plans. Recently,therehasbeenamajorshiftawayfromthetraditionaldefinedbenefitplanstowarddefined contribution plans such as the 401(k) and IRA. Under defined contribution plans the employee, not the employer, must first off choose whether or not they want to save for retirement, secondly determine how much they need to save to adequately fund their expenses in retirement and thirdly chose the direction that their money is invested. From 1992 to 1998, the number of employees covered by a defined benefit plan dropped from 40% to 20% while those enrolled in a defined contribution plan jumped from 37% to 57%. In 1999, 88% of all retirement savings contributions were to defined contribution plans (Wise, 2006; Bischopink & Meister, 1994). Amajordifferencebetweenthedefinedbenefitanddefinedcontributionplansisevidencedinthe structureofpaymentsdistributedinretirement.Anemployeecoveredbyadefinedbenefitplanisguaranteed retirement income from the employer until he or she dies. Whether the retiree lives one year after retirement or forty,theyareguaranteedacertainamountofincomeeachyear.Alternatively,underadefinedcontribution plantheretireehastodeterminehowtomakethebalanceoftheiraccountatretirementlasttheirentirelife; STUDENTS AND RETIREMENT SAVING PREDICTORS 4 Often a difficult task as it is impossible to know exactly how manyyears after retirement one will live. If the investor lives longer than they had calculated, they run out of money and are forced to bare the consequences. Another reason why now more than ever workers need to plan early for retirement is due to the significant populationofbabyboomers.Babyboomersincludeanyonebornbetween1946and1964.Those76million Americans born in this eighteen year window make up nearly one-third of the entire United States population. Soon,therewillbeamajorchangeinthemixofworkerstoretireesaseveryeightsecondsanotherbaby boomer turns 55 years old (16-1). Although the current mix of workers to retirees is 3.3 to 1, the 2006 OASDI Trusteesreportindicatesthatbythetimeallthebaby-boomershavereachedage65in2030,theratioof workers to retirees will only be 2.2 to 1. With fewer workers paying into the system and more retirees drawing benefits, the current system will fail unless strategic changes are implemented. There is evidence to suggest that the baby boomers have not saved enough for retirement. Wysocki (1995) in the Wall Street Journal says that, boomers earning $100,000 will need $653,000 in todays dollars to retire at 65 in comfort but are only saving about 31% of the amount needed (18-2). Bernheim (1996) found that baby boomer households were saving only one-third the amount necessary to be adequately prepared for retirement. Even if Social Security and Medicare do not run out as projected, baby boomers are still not saving enough for retirement (Hennessy, 2006; Alford et al., 2004; Ettorre, 1995; Scholz et al., 2006; Schieber, 2004). TheuncertaintyofthecurrentSocialSecuritysystemisanotherreasonworkersmustplansoonerfor retirement.Thoughtheprogramisunder-fundedanditsfutureisseriousdoubt,manyworkersstillfeelthat SocialSecuritywillsupporttheminretirement.Ifthereare nochangesin thecurrentsystem,SocialSecurity and Medicare taxes, which are currently at 6.2% on the first $94,200 and 1.45% on any additional income will need to be raised to 28% of an employees paycheck by 2030. This means that an average worker in the 28% federal tax bracket will have to be pay nearly 60% of their income in taxes to support the current system. 80% ofworkersmistakenlybelievethatevenifthesystemstaysfunded,theamounttheywillreceivefromSocial Securitywillbeenoughtosupportthemajorityoftheirretirement.TheOASDImonthlystatisticsreportfor June2010revealsthattheaverageretireereceives$1,069.20permonthfromSocialSecurity,orjustunder $13,000peryear.Thisisnotenoughtobethesolesourceofretirementincomeformostretirees (http://www.ssa.gov/policy/docs/quickfacts/stat_snapshot/) (Selnow, 2003; Ettorre, 1995; Atchley, 1998). AmajorfactorthatcausesAmericanstoplacelittlevalueonsavingearlyforretirementisthelackof financialliteracypossessedbythegeneralpopulation.Studentsareneverrequiredtoeducatethemselveson personal finance. Once they enter the workforce, they will likely not take the time to become more financially literate and will not adequately prepare for their retirement. Workersmayfindthemselvesconfusedoroverwhelmedatthenumberofretirementsavingsoptions availabletothem.Theymaygetfrustratedandignorethetopicofinvestinguntilalatertime.Bischopinks (1994)studyrevealedthatemployerswhoeducateemployeesabouttheplansavailabletothemwillhavea higher percentage of workers who participate in the plans. Selnow (2003) also notes that People aware of the need to save and familiar with investment options are more likely to set aside money for later (13-8). Workers whoarenot educated often havefalseexpectationsaboutretirementandtheamount they need tosave before they get there (Atchley, 1998; Alford et al., 2004). There are also psychological factors that play a role in a persons decision to not save early, or adequately, forretirement.Formostpeople,itismoresatisfyingtospendnowratherthansaveforanuncertainfuture.In doingthis,theyarechoosingpleasurenowoverlongtermpain.Weareaninstantgratificationsocietyandthe STUDENTS AND RETIREMENT SAVING PREDICTORS 5benefitsofsavingforretirementcannotberealizedintheshort-term.Selnow(2003)notedthatitistoughto promote within the labor force a willingness to set aside scarce resources for some distant age that the worker may ormaynotreach,forrewardsthattheworkermayormaynotachieve,atapricetodaythattheworkermayor may not wish to pay (13-3). Young workers feel that they will always be young. Many workers view retirement savings as a gamble and wonder why they should save when they might not even make it to retirement. To add to the uncertainty, the markets might crash causing them to be worse off than if they had never saved at all. While there is no immediate tangible reward for saving for retirement and no immediate penalty for not, the futurebenefitsaregreatandthefuturepenaltiesaresevere.Finke(2006)studiedtimepreferenceinrelationto savingforretirementandfoundthatthosewhovalueretirementplaceahighvalueonfutureconsumption.His study revealed the strong correlation between preventative behavior and saving for retirement. He noted that those who wear seatbelts, exercise, and read nutrition labels are more likely to save for retirement than those who do not. In addition, he discovered that a low proportion of smokers and drug users save for retirement. Also, he found that thosewhoreceivegreaterutilityfromtakingpartinriskybehaviortendtoplacelessemphasisonsavingfor retirement. As noted by Finke, time preference for money and risk-aversion levels are significant predictors of the importance a person places on saving for retirement (Hennessy, 2006; Selnow, 2003; Hoesly, 1995). Keister (2003) found that the number of siblings a person has is a significant predictor of the importance one places on saving for retirement. The more siblings one has, the less time each child has devoted exclusively to them from their parents. Parents with more children tend to save less and have less time to educate each child about saving. Also, the more siblings one has the less likely their parents are to pay for college, give them a car, or put the down payment on their first home. This causes them to have to pay for these items with no financial assistancefromtheirparents.Theyareforcedtostarttheirfinancesoffindebtandoncesomeoneisindebt, they often remain there for the rest of their lives. In addition, the more siblings one has the less likely they are to receive an inheritance when their parents pass away. Keister finds that overall, the more siblings one has the less wealth accumulation they are likely to achieve. One of the primary retirement savings vehicles available today is the 401(k) plan. These plans were made available after the 1978 legislation and are defined contribution plans which qualify under section 401(k) of the Internal Revenue Code. Although they are fairly new in relation to other retirement savings plans, 401(k) plans arenowthemostpopulartaxsubsidyintheUnitedStates.UnliketheIRA,the401(k)isemploymentbased, meaning you can only contribute to the plan of your current employer. 401(k)plansaregainingpopularityastheyallowtheemployeetocontributeupto25%oftheirpre-tax salary, up to $15,500 in 2008, to the plan. Interest is earned on that pre-tax money and taxes are not paid until thetimeofwithdrawal.Bycontributingtoa401(k)plan,employeesactuallylowertheamounttheypayin federaltaxeseachyearastheirreportedincomereflectsthedeductiontheyhaveplacedintheir401(k).For example, a worker in the 28% tax bracket who decides to place the maximum allowed ($15,500) into his or her 401(k) plan would save $4,500 per year in federal taxes. Theseplansarealsoawiseinvestmentasmostemployerswillmatchtheemployeescontribution.On average, the employer will match 3% of an employees salary placed into the plan. That equates to an automatic 100%returnoninvestmentwithnorisk.Someoneearning$50,000ayearcontributingonly3%willplace $1,500 a year in their 401(k) plan. In addition, the employer will place another $1,500 in the form of a company match into the plan raising the total to $3,000 per year being placed into the plan. AsseeninTable1,Wise(2006)foundthat401(k)participationratesaredirectlylinkedtoageand STUDENTS AND RETIREMENT SAVING PREDICTORS 6 earnings. Despite the wide availability of 401(k) plans, there are many employees who still do not participate. Inordertoachievehigherparticipationamongemployees,manyemployersarechoosingtouseautomatic enrollment.Underthisoption,allnewemployeesareautomaticallyenrolledinthe401(k)planunlessthey choosetoopt-out.Thishasproventobeaneffectivewaytoincreaseparticipationasmanyemployeesnever sign up for the 401(k) plan because they do not know how or do not want to take the time fill out the paperwork. Studies conducted by Selnow (2003) reveal that 9 out of 10 employees automatically enrolled in a 401(k) plan were still enrolled 6 months later. In addition, participation numbers remain 1/3 higher after 3 years than those not under an automatic enrollment plan. Table 1 401(k) Participation by Age and Earnings (Percent) Age Annual earnings $100KAll 40 (6)0.6 Mean2.03751.9688 Median2.00002.0000 The descriptive statistics for the sample based on the demographic questions of the survey are displayed in Table 2. As shown in Table 2, there was about an even gender split, with 49% females and 51% males. Also as expected, over 74% of the students are between 18 and 25 years old, and only 14% of them are married (about 12% ofthesamplehaschildren).About 24% ofthesamplemajoredinfinance,and 77% ofthesamplehas a GPA between 2.6 and 3.5. Only 29% of the sample is employed full-time,1but 47% of those working full-time

1Only about 11% of the sample indicated that they do not work at all. The vast majority work part-time. STUDENTS AND RETIREMENT SAVING PREDICTORS 9or part-time have employers that offer 401(k) plans. Most frequently, students in the sample do not have debt (23%),while21%ofthesamplehasmorethan$25,000indebt.Notably,about36%ofthestudentsinthe samplehaveaconservativepoliticalorientation.AlthoughnotdisplayedinTable2,97%ofthosesurveyed were juniors or seniors. Participantscompleted5independentmeasures.Theseincludedthestudentslevelofriskaversion, financial background, general savings behavior, financial literacy and general savings beliefs. The risk aversion scaleincludednineitems.ResponsestoeachofthequestionswereeitherLikert-typeorforcedchoice. Risk-aversionquestionsincludedIngeneral,wouldyouconsideryourselfathrill-seeker?Thisscalewas designed to determine whether or not the students level of risk-aversion could be used to predict the value they place on saving for retirement. Next,participantscompletedthefinancialbackgroundscalewhichcontainedfouritems.Responsesto each of the questions were either Likert-type or forced choice. Questions in this category included Was your family financially constrained when you were growing up? This scale was designed to determine whether or not a students financial upbringing is predictive of the value they place on saving for retirement. Then, participants completed the general savings behavior scale which contained seven items. Responses to each of the questions were either Likert-type or forced choice. Questions included Do you save money on a frequentbasis(forexample,quarterly,monthlyorweekly)?Thisscalewasdesignedtoseeifastudents current savings habits can predict whether or not they will be saving for retirement. Next, participants completed the financial literacy scale which contained ten items. Responses to each of the questionswereeitherLikert-typeorforcedchoice.Toassessthestudentsfinancialliteracy,participantswere askedtorespondtosuchquestionsasIamfamiliarwithmostofthesavingsoptionsandvehiclesavailable today. This scale was designed to test whether or not a students level of knowledge regarding financial products such as the 401(k) and IRA can be used to predict the value they will place on saving for retirement. Finally, participants completed the general savings beliefs scale which contained seventeen items. Responses to each of the questions were either Likert-type or forced choice. This section included such questions as I feel guiltywhenIdonotsave.Thisscalewasdevelopedtodeterminewhetherornotastudentsgeneralsavings beliefs, aside from their habits, can predict the value they place on saving for retirement. The dependent variable was the students retirement savings habits and beliefs. Participants completed this scale which contained twenty-two items. Responses to each of the questions were either Likert-type or forced choice. This scale included questions such as At what age do you believe a person should begin to save for retirement? This scale incorporated both the students beliefs toward retirement savings as well as their actual savings behavior. The responses to each question were assigned a numerical value. For each of the six categories of questions, the scores were then aggregated for each student. The scoring was conducted in such a manner that the resulting scaleswereasfollows:Ahigherscoreontherisk-aversionscaleindicatedalessrisk-averseperson;ahigher score on the financial background scale indicated the student had greater exposure to sound financial upbringing; ahigherscoreonthegeneralsavinghabitsscaleindicatedthatthestudentiscurrentlysavingmore;ahigher scoreonthefinancialliteracyscaleindicatedthatthestudenthasagreaterknowledgeofsavingsoptions available;ahigherscoreonthegeneralsavingsbeliefsscaleindicatedthatthestudentbelievesthatsavingis important,regardlessofwhetherornottheyareactuallysaving;andahigherscoreontheretirementsavings habits and beliefs scale indicated that the student places a higher value on saving for retirement. STUDENTS AND RETIREMENT SAVING PREDICTORS 10 Table 3 Correlations of Demographic Variables and Six Financial Indexes Risk aversion Financial back- ground Savings beliefs Financial literacy General savings habits Retirement savings habits Gender AgeGPAMarriedFinance major Child- ren Debt Conser-vative Full- time employ-ment Income U.S. Raised Sib- lingsEmployer offers 401(k) Risk aversion1.000 Financial background 0.0091.000 Savings beliefs 0.1320.1141.000 Financial literacy 0.1230.0160.389**1.000 General savings habits -0.214**0.218**0.197*0.1381.000 Retirement savings habits 0.1140.0240.217**0.263**0.348**1.000 Gender-0.108-0.013-0.206**-0.228**0.1240.0851.000Age0.133-0.215**0.1490.210**0.0500.184*-0.091 1.000GPA-0.0510.0760.264**0.241**0.187*-0.0080.035 0.118 1.000 Married-0.086-0.1270.1480.280**0.1370.1520.023 0.413**0.225**1.000 Finance major 0.019-0.032-0.0300.060-0.0050.106-0.081 0.040 -0.130 0.0231.000 Children-0.114-0.1310.1460.244**0.0500.0340.088 0.408**0.1440.572**0.0401.000 Debt0.024-0.162*0.0510.1260.0770.199*-0.013 0.377**-0.090 0.378**0.164*0.251**1.000 Conservative-0.0430.0930.0950.1430.0690.077-0.095 -0.139 -0.060 0.0250.160*0.083-0.110 1.000 Full-time employment -0.070-0.1080.1130.195*0.0440.171*-0.006 0.348**-0.087 0.244**-0.0050.288**0.298**-0.0581.000 Income0.008-0.1450.261**0.240**0.1020.226**0.000 0.564**0.0800.605**0.0950.423**0.502**0.0090.543**1.000 U.S. raised0.028-0.054-0.086-0.0070.0180.120-0.023 -0.043 -0.093 -0.105-0.218**-0.161*-0.098 0.178*-0.074-0.0231.000 Siblings0.023-0.1500.0900.044-0.0110.171*0.110 0.041 0.1420.169*-0.1010.0640.133-0.0360.0230.1360.0441.000Employer offers 401(k) -0.085-0.1130.1380.1100.0210.112-0.101 0.134 -0.066 0.151-0.0530.173*0.245**0.0210.384**0.417**-0.0130.068 1.000 Notes. * Significant at the 5% level. ** Significant at the 1% level. Thecorrelationsbetweenthesixindexesjustdescribedandthevariousdemographicvariablesare displayedin Table 3.The followingrelationships are statisticallysignificantatleastat the5% level.Students with a high degree of risk aversion tend to have less than $2,500 in debt. Moreover, less risk-averse individuals aretypicallyraised intheU.S.andareconservativeintheirpolitical orientation.Also,students withlessrisk aversion do not display good general savings habits. Students who are risk averse tend to exhibit well-pronounced general savings habits, which is encouraging. Studentsfinancialbackgroundisalsopositivelyassociatedwithgeneralsavingshabits.Interestingly,older studentsandstudentswithlessdebtseemtohavelessofafinancialbackground.Thosestudentsthatbelieve savingingeneral isimportanttendto befinanciallyliterate,havegood generalandretirementsavingshabits, haveahigherincomeandahigherGPA.Moreover,believingthatsavingisimportantappearstobemore pronounced for male students. Those students that are financially literature are also mostly male. They have a higher GPA, are older, and are more likely to be married with children. They are more likely to work full-time and have a higher income. Lastly, financially literature students have better retirement savings habits and beliefs. If a student exhibits good general savings habits, that student is likely to have better retirement savings habits and to have a higher GPA. Similarly, those students with good retirement savings habits are older, have more debt, STUDENTS AND RETIREMENT SAVING PREDICTORS 11aremorelikelytoworkfull-timeandhaveahigherincome,andaremorelikelytohavesiblings.Thismakes sense, since these students probably observed how difficult it is to save for retirement by their parents example. Inadditiontotheitemsalreadymentionedwhendiscussingthecorrelationsthusfar,olderstudentsare morelikelytobemarried,tohavechildren,andtohavemoredebt.Theyalsohaveahigherincomeandare more likely to work full-time. MarriedstudentsalsoappeartohaveahigherGPA.Giventhetimeconstraintsofgoingtoschool,thisis interesting.However,maybeforamarriedstudenttoenterintoatimecommitmentbygoingbacktoschoolis positively correlated with the seriousness of their studies. These students are older, more financially literate, have a higher GPA, and more likely to work full-time with a higher income. They are also more likely to have siblings. The correlations for finance majors are a little disappointing. There are no correlations between the finance majors and any of the six created savings and risk indices. However, finance majors tend to have more debt and are more politically conservative. Generally, those students with more debt have less of a financial background, and are older and married with children, and are more likely to work for employers who offer a 401(k). In summary, the correlations reported in Table 3 confirm what one would expect in terms of correlations. However, two correlations are interesting: (1) the finding that male students are more financially literature, and (2) the finding that finance majors appear to not reap any practical value from their education. Empirical Results In order to further investigate the relationships between the various variables, we first conduct a variety of t-tests to investigate significant differences between groups. Next, we regress the retirement savings index on the riskaversion,financialbackground,savingsbeliefs,financialliteracy,andgeneralsavingshabitsindexesfor several different groups. Lastly, we conduct ANOVA analyses to investigate the impact of variable interactions. Table 4, Panel A presents the results for t-tests for differences in means for all six constructed indexes. A t-statistic is computed for each of the demographic variables. Panel B presents Levenes F-test for equality ofvariances.IfthedifferenceinvariancesinPanelBissignificant,thecomputationsinPanelAassume unequal variances. ThefirstrowinTable4,PanelAdisplaysthet-testsforretirementsavingshabits.Severalsignificant differences can be identified. Students older than 25, married, with incomes greater than $30,000 annually who workfull-timehavebetterretirementsavingsbeliefsandhabitsthantheircounterparts.Thisfindingisnot surprising,sinceemployedindividualsaremoreabletosaveforretirement,theneedforwhichisevenmore pronounced when married. Those students that have a better financial background as a result of their upbringing tend to have less debt and fewer children. It is also interesting that these students tend to be younger. Perhaps, due to their upbringing, they are more aware of the financial obligations associated with have children. Maybe younger students have a better financial background because of the increasing media-coverage of financial instruments in recent years. Thefindingsforgeneralsavingsbeliefsareverypronounced.Itseemsthatmalesgreaterthan25yearsold, married or with a high GPA value savings more. Moreover, savings beliefs are also more pronounced for students who work full-time for employers, earning more than $30,000 per year, that offer 401(k)s. This largely confirms the results previously reported in Table 3. Thelastrow of Table 4, Panel A indicates that students whoexhibit good generalsavingshabitsalsotendtobeolder,haverelativelyhighGPAandaremarried.Interestingly,whileit appears that those students working full time believe that savings is important, they dont seem to do so in practice. STUDENTS AND RETIREMENT SAVING PREDICTORS 12 Table 4 TestsforDifferencesinMeansandFTestsforEqualityofVariancesBetweenDemographicCharacteristics and Savings Variables Panel A: t tests for differences in Meansa 401(k)Siblings US raisedIncomeb Full-time employmentConservative DebtcChildrenFinance major MarriedGPAdAgeeGender Retirement savings habits 1.4141.665*1.519 3.297***2.186**0.9731.913*0.0561.3371.939*0.1282.110** 1.070 Risk Aversion -1.0710.2880.348 -0.059-0.883-0.5440.179-1.1900.241-1.088-0.2570.196-1.370 Financial background -1.429-1.252-0.681 -1.164-1.3701.169-1.751*-1.659*-0.400-1.6090.875-3.268*** -0.157 Savings beliefs 1.746*-0.008-1.085 2.885***1.4321.200-0.3531.797*-0.3821.878*2.127**2.663*** -2.651***Financial literacy 1.396-0.300-0.090 3.371***2.505**1.813*0.3363.042*** 0.7573.672***2.172**3.314*** -2.939***General savings habits 0.265-1.2220.222 1.1310.5520.8720.7670.578-0.0661.738*2.599***1.760*1.570 Panel B: F test for equality of variancesa 401(k)Siblings US raisedIncomeb Full-time employmentConservative DebtcChildrenFinance major MarriedGPAdAgeeGender Retirement savings habits 1.2410.1070.0250.0240.5020.0774.228**0.0451.5800.1865.579**0.6280.120 Risk aversion 0.1161.3700.0680.0240.0280.0420.0000.8830.0610.2651.4891.1460.031 Financial background 0.2954.414**3.543* 12.994***5.563**0.0050.6241.6380.07210.901***0.0196.490**3.239* Savings beliefs 10.982***0.2900.6901.1841.0020.1092.5193.993**0.2893.032*3.897**0.3410.248 Financial literacy 0.0222.0790.1520.9421.2620.0550.5660.3360.4190.2841.5691.8870.401 General savings habits 0.5791.2970.0160.5101.1330.0050.8870.0150.5570.0050.0450.0810.745 Notes.*Significantatthe10%level.**Significantatthe5%level.***Significantatthe1%level.aForthedemographic variables401(k),Siblings,USraised,Full-timeemployment,Conservative,Children,Financemajor,Married,andGender,the groups were defined using a yes or no format. For example, the t-test for 401(k) is the difference in the indexes of the left-most column for those whose employers offer a 401(k) and the index value for those whose employers do not offer a 401(k). The first groupisalwaystheaffirmativegroup.ThefirstgroupforGenderisfemale. bThefirstgroupforIncomeisthosemakingmore than$30,000ayear;thesecondgroupiseveryoneelse. cThefirstgroupforDebtisthosewithmorethan$2,500indebt;the second group is everyone else. d The first group for GPA is those with a GPA greater than 3.0; the second group is everyone else. e The first group for Age is those older than 25; the second group is everyone else. The findings for financial literacy are interesting. First, those students earning more than $30,000 a year, with children,married, with a higher GPA aremore financially literate. Additionally, older students and male studentsalsoappeartobemorefinanciallyliterate.Themostinterestingfindingisperhapsthatpolitically conservative students are more financially literate than those with other political orientations. Interestingly, risk aversion does not seem to differ across any of the demographic groups investigated here. Thus far, we have investigated the relationship between the six constructed risk and savings variables and the demographicvariables.Inordertoinvestigatethedirectrelationshipbetweenourfivepredictorvariables(risk aversion,financialbackground,savingsbeliefs,financialliteracyandgeneralsavingshabits)andthedependent variable(retirementsavingshabitsandbeliefs),weconductregressionsfortheentiresampleandforvarious subsamples based on the demographic variables. The regression results are presented in Table 5 through Table 9. STUDENTS AND RETIREMENT SAVING PREDICTORS 13Table 5 Regression Results Based on Gender Entire sample; n = 160, F = 7.78***, Adjusted r squared = 0.18 PredictorCoefficientt-statistic Risk aversion0.6732.16** Financial background-0.193-0.90 Savings beliefs0.2570.83 Financial literacy0.2932.14** General savings habits0.4544.65*** Female sample; n = 79, F = 4.38***, Adjusted r squared = 0.18 PredictorCoefficientt-statistic Risk aversion0.1821.64 Financial background-0.156-1.47 Savings beliefs0.1741.47 Financial literacy0.0610.507 General savings habits0.3783.18*** Male sample; n = 81, F = 4.42***, Adjusted r squared = 0.18 PredictorCoefficientt-statistic Risk aversion0.7731.74* Financial background0.1670.48 Savings beliefs0.0500.12 Financial literacy0.5102.67*** General savings habits0.4432.99*** Notes. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level. Source: Regression results are from predicting retirement savings behavior from general savings habits, financial literacy, financial background, risk aversion, and savings beliefs. The first panel in Table 5 presents the regression results for the entire sample of 160 students. As shown in Table 5, financial literacy and general savings habits are positive and significant predictors of retirement savings habits.Thismakessense,asmorefinanciallyliteratestudentsandstudentswhoarealreadysavingunderstand theimportanceofsavingforretirement.Interestingly,the lessrisk-averseastudentis,themoreheorshewill saveforretirement,onaverage.Giventhatoursampleconsistsofrelativelyyoungstudents,theymaybe investing primarily in equities when saving for retirement, which could explain their lack of risk aversion. The next panel of Table 5 shows the results for female student and male students. For female students, only general savings habits are positively associated with greater retirement savings. For male students, risk aversion, financial literacy, and general savings habits are significantly associated with greater retirement savings. Table 6 presents the regression results by age and GPA. It is interesting to note that the regression results for students older than 25 and 25 or younger mirror those for female students and male students, respectively. For students with a GPA greater than 3.0, risk aversion, financial literacy, and general savings habits result in higher retirement savings or at least the belief that saving for retirement is important. For students with a lower GPA, the results are almost identical, but risk aversion apparently does not affect retirement savings. STUDENTS AND RETIREMENT SAVING PREDICTORS 14 Table 6 Regression Results Based on Age and GPA Age > 25; n = 41, F = 3.25***, Adjusted r squared = 0.22 PredictorCoefficientt-statistic Risk aversion0.3620.61 Financial background-0.348-0.90 Savings beliefs0.5700.84 Financial literacy0.2260.88 General savings habits0.7103.47*** Age 3.0; n = 63, F = 4.41***, Adjusted r squared = 0.22 PredictorCoefficientt-statistic Risk aversion1.2312.14** Financial background0.0410.10 Savings beliefs-0.418-0.81 Financial literacy0.4832.14** General savings habits0.6613.52*** GPA $2,500; n = 97, F = 4.92***, Adjusted r squared = 0.17 PredictorCoefficientt-statistic Risk aversion0.2620.76 Financial background-0.216-0.94 Savings beliefs0.1560.41 Financial literacy0.3742.32** General savings habits0.3763.26*** Debt = $30,000; n = 43, F = 4.18***, Adjusted r squared = 0.27 PredictorCoefficientt-statistic Risk aversion-0.004-0.01 Financial background-0.159-0.54 Savings beliefs0.4250.57 Financial literacy0.1390.58 General savings habits0.6073.47*** Income < $30,000; n = 117, F = 3.52***, Adjusted r squared = 0.10 PredictorCoefficientt-statistic Risk aversion0.9052.46** Financial background-0.089-0.30 Savings beliefs0.1590.45 Financial literacy0.2241.31 General savings habits0.3733.12*** Employer offers 401(k); n = 75, F = 6.36***, Adjusted r squared = 0.27 PredictorCoefficientt-statistic Risk aversion0.5711.39 Financial background-0.458-1.62 Savings beliefs1.0682.23** Financial literacy0.3552.04** General savings habits0.3592.95*** Employer does not offer 401(k); n = 85, F = 3.34***, Adjusted r squared = 0.12 PredictorCoefficientt-statistic Risk aversion0.8001.71* Financial background0.0110.03 Savings beliefs-0.177-0.41 Financial literacy0.2561.20 General savings habits0.5463.57*** Notes. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level. Source: Regression results are from predicting retirement savings behavior from general savings habits, financial literacy, financial background, risk aversion, and savings beliefs. Sofar,wehaveonlyinvestigatedrelationshipsbetweenindividualvariablesandtheretirementsavings predictors. However, given many of the relationships, it is also important to investigate whether there are any interaction effects of the demographic variables on the six savings and risk constructs. In order to examine this issue, we conducted ANOVA analyses for each possible interaction between the six constructed variables and thedemographicvariables.Thesignificantinteractioneffectsandtheirassociatedconstructsarepresentedin Table 10. The first column of Table 10 identified the significant interaction of two demographic variables; the secondcolumnliststheconstructtheinteractionissignificantfor.Giventherelationshipsidentifiedthusfar, we can identify which specific demographic characteristic influences the construct. Genderappearstohavesignificantinteractionswithseveralvariables,asshowninthefirstfourrowsof Table 10. First, older males apparently especially value savings in general. Second, single males have a better financial background. Males with children and young males are also especially financially literate. The next variable with a lot of significant interactions with other variables is age. These findings are very interesting.First,young,non-financemajorswithaconservativepoliticalorientationhaveabetterfinancial STUDENTS AND RETIREMENT SAVING PREDICTORS 18 background. However, older students with children are more financially literate (perhaps by necessity). Table 10 Significant Interactions Between Demographic Variables and Six Savings Indexes VariableFSignificance Gender*AgeSavings beliefs2.4450.049 Gender*MarriedFinancial background4.0920.045 Gender*ChildrenFinancial literacy4.0280.020 Gender*401(k)Financial literacy4.6910.032 Age*FinMajFinancial background2.6510.035 Age*ChildrenFinancial literacy2.6300.026 Age*PoliticalFinancial background3.8220.005 GPA*FinMajRetirement savings habits5.4640.001 Married*ConservativeGeneral savings habits4.9390.028 Retirement savings habits7.1870.008 FinMaj*DebtRisk aversion2.4340.050 FinMaj*ConservativeFinancial literacy3.9510.049 FinMaj*SiblingsRisk aversion2.8380.040 Children*401(k)Financial literacy4.5480.035 Debt*IncomeSavings beliefs2.8150.000 The findings for finance majors are also interesting in terms of their interaction. Finance majors with a GPA greater than 3.0 apparently understand the importance of retirement savings. Moreover, finance majors with more than$2,500indebtandfinancemajorswithsiblingsarelessriskaverse.Thiscouldsimplyindicatethatfinance majors understand financial relationships and, consequently, they realize that leveraging has risk associated with it. TherearethreeotherinterestingrelationshipsinTable10.First,marriedandpoliticallyconservative students exhibit better retirement savings habits. Second, students with children whose employers offer a 401(k) aremorefinanciallyliterate,perhapsbynecessityofconstraintsnaturallyimposedbyhavingchildren.Third, students earning more than $30,000 a year with no debt believe that saving is very important.2 Summary and Conclusion Theoverallmodelcontainingthestudentsrisk-aversionlevel,financialbackground,generalsavings habits,financialliteracyandgeneralsavingsbeliefsispredictiveofthestudentsretirementsavings.The students risk-aversion level is predictive of the students retirement savings independent of the overall model. Thisistobeexpectedbecausesomeonewhoisariskyindividualwouldlikelynotbesavingforretirement since not saving for retirement is certainly a big risk. Also, the students general savings habits are predictive of theirretirementsavingsindependentoftheoverallmodel.Thisisimportantbecausetheviewsastudenthas towardsavingingeneralwillshapehisorherviewstowardsavingforretirement.Additionally,andperhaps most importantly, the students financial literacy is a significant predictive factor of their retirement savings in many of the models analyzed here. If the student does not know what options are available to them, or if they

2Interestingly, the interaction term was not significantly related to actual savings behavior. STUDENTS AND RETIREMENT SAVING PREDICTORS 19are confused by the number of retirement savings products, they are likely not going to save. Certain variable are not significant independently in predicting a students retirement savings. One is the studentsgeneralsavingbehavior.Eventhoughastudentmayhaveanimplementedsavingstrategyfortheir incomenow,itdoesnotnecessarilytranslateintoanimplementedretirementsavingsstrategy.Also,the students financial background does not predict their retirement savings. A students financial background does notpredisposethemtobemoreorlesslikelytobesavingforretirement.Thisindicatesthatyoucantakea student who grew up with a wealthy family at the beach and a student who grew up in poverty in the inner-city and through proper education, both should be equally as likely to value saving for retirement. There are certain limitations associated with this study. One is the fact that the investigated institution is a non-traditionalschoolmeaningthattheaverageageofstudentsishigherthanatatraditionaluniversity. Non-traditional students are therefore more likely to be working full-time while pursuing their degree and more likely to already be saving for retirement. Another limitation is that some of the questions may not be assessing the variablescorrectly.Forexample,in theriskaversionscalecontained questions thatassessedthestudents risk-aversion level in a round-about way. Questions such as I smoke regularly or I always wear my seatbelt may not truly assess the students level of risk-aversion. Inconclusion,furtherresearchneedstobeconducted.Aninterestingextensionoftheresultspresented here would be to investigate the relationships following the global financial crisis to see its impact on savings behaviorin general andsavingsbeliefsin particular.Theresults presentedhere implythat education playsan importantroleinactualretirementsavingsbehavior.Most studentsarenoteducatedinthisarea,evenifthey are finance majors. One approach to increase retirement savings may be to educate students about the benefits of retirement savings, the importance of good general savings habits, and the possible implications to excessive risk taking on the ultimate value of their retirement account. References Alford, S., Farnen, D., & Schachet, M. (2004). Affordable retirement: Light at the end of the tunnel. Benefits Quarterly, 20, 7-14. Atchley, R. C. (1998). Doomsday 2029? Social security projections dont tell the whole story. Journal of the American Society of CLU & ChFC, 52, 30-34. 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Chinese Business Review, ISSN 1537-1506 January 2011, Vol. 10, No. 1, 21-28 21 Banks Earnings, Risks and Returns in China Cheng Fan Fah, Annuar Nasir University Putra Malaysia, Serdang, Selangor, Malaysia Thisstudyaimstofindtheeffectoffinancialrisks,pricerisksandmarketrisksontheEarningResponse Coefficients(ERC)forChinaCommercialBanks.Theresearchmethodologiesusethetraditionalcumulative abnormalreturnsandtheunexpectedearningasthemaindependentandindependentvariables.Theevidences show that: (1) There is a strong returns-to-earnings relation for banks; (2) The liquidity risk has information content beyond earnings changes in the returns-to-earnings relation. This probably due to the reason that managers of banks find the level of liquidity that fulfilled the need of investors and at the same time earns good profits for the banks. Keywords: earnings response coefficients, liquidity, credit, interest, solvency risk Introduction Mostcommercialbanksintheworldareoperatinginfreeenterprisesystem.However,Chinabanking systemwasfollowingamonobankmodelbefore1978,wherePeopleBankofChina(PBOC)combinedthe rolesofcentralandcommercialbanking.AllthebankswhichweretakenoverorrestructuredintoPBOC system or under PBOC administration or Ministry of Finance to ensure the national production plans would be fulfilled with no incentives to compete with one another. Throughout the history of the Peoples Republic, the banking system has exerted close control over financial transactions and the money supply. Thereformationafter1978hadexpandedthebankingsystembyestablishingseverallargestate-owned banks:TheBankofChina(BOC,1912),ChinaConstructionBank(CCB,1954),AgriculturalBankofChina (ABC,1979),andIndustrialandCommercialBankofChina(ICBC,1984).Thesebankssplittingthelending functions from the PBOC were initially designated to serve their sector of economy. In the mid-1980s the banking system still lacked some of the services and characteristics that were considered basic in most countries. Interbank relations were very limited, and interbank borrowing and lending was virtually unknown. Checking accounts were usedbyveryfewindividuals,andbankcreditcardsdidnotexist.By1985,thebigfourbankswereallowedto competeinallsectors,buttheircompetitionamongthemwaslimitedbecausetheyservedmainlyaspolicy lending conduits for the government and lacked incentive to compete until mid 1990s (Yang, 2002). PBOC has encouraged banks to diversify their portfolios by increasing their services to the private sector and individual consumers. By July 2000, a personal credit rating system was launched in Shanghai to be used toassessconsumercreditriskandsetratingsstandards.ThisisanimportantmoveindevelopingChinas consumer credit industry, and increase bank loans to individuals. The government of China has allowed several small banks to raise capital through bonds or stock issues.

Cheng Fan Fah, associate professor, Department of Economics, Faculty of Economics and Management, University Putra Malaysia. Annuar Nasir, dean, professor, Faculty of Economics and Management, University Putra Malaysia. BANKS EARNINGS, RISKS AND RETURNS IN CHINA 22 Followed the listing of Shenzhen Development Bank and Pudong Development Bank, China Minsheng Bank, then the only private bank in China, was listed on the Shanghai Stock Exchange in December 2000. There were many more banks got listed after that (see section 3 on the sample of banks). Years of government-directed lending has presented Chinese banks with large amounts of non-performing loans.AccordingtothePBOCBanksreport,non-performingloansaccountfor21.4%to26.1%oftotal lending of Chinas four big banks in 2002. In 1999, four asset management companies (AMC) were formed to repackage the non-performing loans into viable assets and sell them off to the investors. Therefore, the research questions are (1) What is the current financial situation of China commercial banks? (2) Which risk factors are significant in the earnings-returns relationship? TheobjectiveofthispaperistoinvestigatetheeffectsoffinancialrisksonearningsresponseforChina commercialbanks.Specifically,thestudywillexaminetherelationshipofstandardizedunexpectedearnings, interestrisk,liquidityrisk, creditrisk,solvencyrisk,stock pricerisk, marketriskandexchangerateriskwith the abnormal returns of these banks. Thepaperisdividedintofourchapters.Section2dealswiththeliteraturereview.Section3containsan explanationoftheresearchdesign,hypotheses,dataandvariableselection:Methodologicalissueswerealso discussed. The findings and discussion were presented in section 4 while paper end with conclusions in section 5. Literature Review Abodyofacceptedfindingshasemergedintheinstitutionallymoredevelopedcapitalmarketsfromthe research following that lead from two pioneering studies (Ball & Brown, 1968; Beaver, Clarke, & Wright, 1979) aboutthecommonstockpricerevaluationeffectfromchangesinaccountingearnings.Thepreviousstudy focusesoncommonstockpricedirectionfrommagnitudeofearningschanges.Thelatterstudyfocuseson documentedthemagnitudeofcommonstockpricechangesarisingfrommagnitudeofearningschanges.A strongandsignificantresponseintheformofrisk-adjustedsharepricechangesinstudiesusingquarterly earnings changes immediately around the date of announcement of earnings changes will be appeared. This was proved by a very high rank correlation ranging from 0.85 to 1.00 in the US capital market in the strength of the relation.Therearesimilarfindingswithsubstantialrevaluationeffectcontingentonaccountingearnings changes was revealed in the findings in other developed capital markets (Cheng & Ariff, 2007). Themodestattempttoanswerwhethertheacceptedfindingsinseveralinstitutionallymoredeveloped capitalmarketsabouttherelevanceofaccountingearningsbyCheng,ArifandShamsher(2001):Thereare some70emergingmarketsintheworld,andtheliteratureonthesemarketsisgrowingsteadily.Therelation between changes in unexpected earnings and share price changes in US markets was measured using firms had beenshowninthatstudy.Althoughasignificantprice-to-earningsrelationisevident,butconsistencyand magnitude of the relation were not as large as those reported in any institutionally more developed markets. If price reaction is measured over long periods the price adjustment is stronger. This shows that emerging markets maybeaplaceofspeculativetrading.OthersstudiesonsimilartopicsareHagerman,ZmijewskiandShah (1984), Ariff, Loh and Chew (1997) and Ball, Kothari and Watts (1993). The returns-to-earnings relation over the past 30 years have provided evidence that earnings and earnings related information explains stock returns had been shown on above studies. One of the more striking consequences of the 2007-2009 financial crises has been the collapse of lending BANKS EARNINGS, RISKS AND RETURNS IN CHINA 23in the U.S. According to the IFS data, private sector credit from commercial banks slowed from annual rates of 8% or greater from 2003 through the first quarter of 2008, to just over 2% by the end of 2008, and thereafter actually registered negative growth for the first time in the decade. By referring to quarterly bank-specific data forthelargest100U.S.banks,Barajas,Chami,Cosimano,andHakura(2010)findthatcapital,ratherthan liquidity,constrainedtheirlending.Thismaybeinpartbecausetheliquiditymeasuresdonotadequately capture the effect of the collapse of the repo market. Banks took actions to increase capital both by (1) slowing the growth of lending and (2) raising profit margins by not fully passing through the Federal Reserves cuts in the cost of funding. U.S. banks are found to optimally choose capital based on the expected demand for loans in the future and the marginal cost of holding capital. There are several studies have analyzed the four financial risks, which is credit risk, interest risk, solvency riskandliquidityriskontheearningsresponsecoefficientsandtherelationshipbetweentheseeffectsonthe stockpricingandreturns.Amulti-factormodeltoexaminetheinterestratesensitivityofafinancial intermediaryscommonstockandre-specifyinanattempttoestimateeachfactorsinfluencewasusedby Giliberto (1985). The joint interaction of exchange rate and interest rates on bank stock pricing was analysed by Choi, Elyasiani and Kopecky (1992). Faff and Howard (1999) examined the relationship between interest rates changesandthereturnsonthesharesofAustraliabanksandfinancecompanies.DennisandJeffrey(2002) relatethecommonstockreturnsofpubliclytradedAustraliabankstoamarketindex,interestrate,andthe trade-weighted Australia dollar exchange rate. The relationship between default risk and firm size, book-to-market ratio and stock returns during a severe crisiswasstudiedbyBystrom,WorasinchaiandChongsithipol(2005).ChengandAriff(2007)examine whether four financial risk factors correlated with the abnormal return of bank shares. There had mixed results in finding difference factors affecting the banks performances on all above studies. Inthisstudy,weextendtheresearchontheearningsrespondcoefficienttoChinabankswhichhave developed and expended rapidly. This study examine 6 risks in China banks that are interest risk, solvency risk, liquidity risk, credit risk, price risk and market risk. Should the financial risks effect the banks stock revaluation similar to studies in United States, Australia and other part of the world? Do the bank managers and regulators able to convince their investors that earnings and earnings alone are the only facts to consider for bank stocks valuation?In ordertoextendtheliteraturetosettle thiscontestabilityissue oftheorytobanking firms,which are different in many respects is worthwhile to carry out this type of research. Research Methodology Research Design Many previous studies on earnings response acknowledged on the existence of strong correlations among the changes in stock price and the changes of earnings. This research is to study the impact of earnings to share price for China commercial banks and the magnitude of earnings response coefficient that stock prices change affects by risk determinants. Abnormal Return The abnormal return was calculated by using the difference between current year return and previous year returnandwhichiscommonlyusedinaccountingliterature.Sharpes(1964)MarketModel:Analysisof Abnormal Returns as a standard general equilibrium relationship for asset returns was used. Abnormal Returns BANKS EARNINGS, RISKS AND RETURNS IN CHINA 24 (AR) are: ARit=Rit[i+itRmt](1) with Rit = (Pit - Pit-1)/Pit-1 and Rmt = (It - It-1)/It-1. In addition, I referred to markets composite index. The market parameters i and it were estimated by ordinary least square regression over trading periods, -71 months to -11 months(parameterestimationperiod)relativetotheannouncementmonth.Thewindowsofanalysisforthe ARsweretakenas12months.Thewindowsofanalysiswerefromthemonthofearningsannouncementsto 11months before the announcements. Analysis of Unexpected Annual Accounting Earnings Unexpectedannualearningswerecomputedusingthenaiveexpectationmodel,whichassumedthatthe next periods expectation is simply the current periods annual earnings. This was consistent with the study to investigatetheeffectofpricechangesatapointintime.Unexpectedannualearnings(UEs)werecomputed using naive model: UEit = [Eit Ei (t-1)]/ Ei(t-1) Tostudythereturns-to-earningsrelation,thecoefficientwastestedintheregressionanalysisbetween unexpected earnings as independent variable and abnormal return as the dependent variables. The significance oftheslopecoefficient(b)andthecoefficientofthedeterminationortheRsquarewereusedtomeasurethe inferences regarding the information content of annual earnings. The model being used: CARi=0+1*UEi+i (2) where, CARi: Cumulative abnormal returns over 12 months window; UE: Unexpected annual earnings; i : A random disturbance term assumed to be normally distributed. The slope coefficient of the regression 1 is called the Earnings Response Coefficient (ERC). Risk Determinant Factors The seven financial risk factors were identified. The financial ratios from the balance sheets were grouped as factorsusedforfactoranalysis.Thesevenfinancialriskswereidentifiedareinterestriskfactor,liquidityrisk factor, credit risk factor, solvency risk factor, stock price risk factor, market risk factor and exchange risk factor. In this study, the following ratios were used to measure the seven financial risks. Interest rate risk, Ir : Short Term Liability/ Total liability; Liquidity risk, Lr : Net loans / Total assets; Credit risk, Cr : Loan loss reserve / Gross loans; Solvency risk, Sr: Total capital ratio; Stock price risk, i : Standard deviation of stock returns; Market risk, m: Standard deviation of market returns; Exchangeraterisk,Ei:StandarddeviationofexchangerateofRenminbi(RMB,Yuan)toUnitedStates dollar (USD, $). Therelationbetweenabnormalreturnasdependentvariableandunexpectedearnings,interestraterisk, liquidityrisk,creditrisk,solvencyrisk,stockpricerisk,marketriskandexchangerateriskasindependent variables were tested in the regression: CARi=0+1UEi+2Iri+3Lri+4Cri+5Sri+6i+7m+8Ei+i(3) BANKS EARNINGS, RISKS AND RETURNS IN CHINA 25Nine simple regressions were performed according to equation (3). The research question is to identify these factors whether have the information content over and above the informationfromtheearningsdisclosures(UE).Thepanelordinaryleastsquareregressionwasusedfor regressions and determined the key factors to be more significantly in adding information to price determinants. Data Collection The China commercial banks data set was organized from the monthly closing prices, annual earnings and balance sheets information in the sources: Bankscope, financial data in the China Stock Exchange; the financial information from the Company Annual Reports; and the annual earnings announcements obtained from China Stock Exchange website. Table 1 Total Assets, Deposits, Equity, Loans and Income of Selected Banks in 2008 (RMB Million) NoBankTotal assetsDeposits &S-T fundingEquityLoansNet income1Bank of Nanjing93,71677,19811,29639,0571,456 2Bank of Ningbo103,26387,5708,80548,4661,332 3Bank of Beijing Co Ltd417,02133,814365,451187,6905,417 4Shenzhen Development Bank Co Ltd474,440442,87316,401281,715614 5Hua Xia Bank731,637666,03927,421345,6683,071 6Industrial Bank Co Ltd1,020,899886,35449,022489,98611,385 7China Minsheng Banking Corporation1,050,141946,15853,810646,4437,831 8China CITIC Bank Corporation Ltd1,188,1521,056,36095,661651,35213,354 9Shanghai Pudong Development Bank1,309,4251,199,95041,702681,26712,516 10China Merchants Bank Co Ltd1,571,7971,420,23279,781852,75420,946 11Bank of Communications Co.Ltd2,682,9472,445,281150,0951,298,77628,490 12Bank of China Limited6,951,6805,972,449489,8873,189,65265,894 13China Construction Bank Corporation7,555,4526,867,357467,5623,683,57592,642 14Indust. & Commercial Bank of China9,757,1468,900,520606,6304,436,011111,226 Total34,907,71731,002,1552,463,52416,832,412376,173 Average2,493,4082,214,440175,9661,202,31526,869 Data from year 2002-2008 were used and the population of sample consisted of 14 listed and traded banks over the test period. Table 1 shows the general information about these 14 banks in year 2008. These banks are Bank of Nanjing, Bank of Ningbo, Bank of Beijing Co Ltd, Shenzhen Development Bank Co Ltd, Hua Xia Bank, IndustrialBankCoLtd,ChinaMinshengBankingCorporation,ChinaCITICBankCorporationLimited, Shanghai Pudong Development Bank Co. Ltd, China Merchants Bank Co Ltd, Bank of Communication Co. Ltd., Bank of China Ltd., China Construction Bank Corporation, Industrial & Commercial Bank of China (ICBC). Table1showsthebankstotalassets,deposits,equity,incomeandloansofselectedbanksin2008.The sample consists of banks whose total assets vary from RMB 93 billion (Bank of Nanjing) to RMB 9,757 billion (Industrial & Commercial Bank of China). The average total asset is RMB 2,493 billion. Findings and Discussion Returns-to-Earnings for Banks Table 2 shows the regression results of the returns-to-earnings relation of the fourteen banks in the period 2002to2008.Theregressionsarebetweenrisk-adjustedcumulativeabnormalreturnsasdependentvariable BANKS EARNINGS, RISKS AND RETURNS IN CHINA 26 and the unexpected annual earnings, the seven risk factors as the independent variables. To estimate the returns to earnings relation, the independent variables were regressed one by one. The results in Table 2 are shown the firsteightregressionmodelsthenfollowbyafinalregressionmodelwhichconsistsofalltheindependent variables that significantly affect the returns-to-earnings relation. From the model 1, the regression result indicates that the coefficient for UE is positive. The value for UE coefficientis0.188anditst-statisticis2.331,whichissignificantat0.05level.TheR-squareinmodel1is 0.133. The 14 banks exhibited strong return-to-earnings relation. Table 2 Regression Results for Returns-to-Earnings Relation for Banks in China From Period 2004 to 2008 IndependentModel variable123456789 Constant, 0-0.136-0.03-0.011-0.01-0.02-0.046-0.112-0.07-0.513 (-2.857)(-0.069)(-0.313)(-0.041)(-0.053)(-0.356)(-1.297)(-0.167)(-1.955) (0.08)(0.946)(0.757)(0.968)(0.958)(0.724)(0.205)(0.869)(0.061) UE, 10.1880.200 (2.331)(2.519) (0.027*)(0.018*) Interest0.121 Risk, 2(0.528) (0.602) Liquidity-0.654-0.570 Risk, 3(-2.068)(-1.460) (0.048*)(0.046*) Credit-0.05 Risk, 4(-0.423) (0.675) Solvency-0.132 Risk, 5(-1.051) (0.302) Price-0.398 Risk, 6(-0.371) (0.713) Market-1.62 Risk, 7(-1.417) (0.167) Exchange-5.212 Risk, 8(-0.334) (0.741) Adj R-sq0.133-0.0110.102-0.0290.004-0.0310.034-0.0320.166 F-Stat5.4350.6754.2780.1791.1040.1382.0090.1123.893 (0.027*)(0.418)(0.048*)(0.675)(0.302)(0.713)(0.167)(0.741)(0.033*) DW2.1372.1812.2232.1282.2572.131.9012.1902.205 Notes. Number in each bracket is t-statistic and p-value, significant at (*) 0.05 level. BANKS EARNINGS, RISKS AND RETURNS IN CHINA 27Risk Determinants of the Returns-to-Earnings Relation for Banks The seven risk factors were subsequently added one by one into the regression of risk adjusted cumulative abnormal returns and unexpected annual earnings. Model 2, model 4, model 5, model 6, model 7 and model 8 showthecoefficientforinterestrisk,creditrisk,solvencyrisk,stockrisk,marketriskandexchangeraterisk factorsareinsignificant,exceptthecoefficientforliquidityriskfactorinmodel3.Model3indicatesthatthe coefficientoftheliquidityriskfactorhasat-statisticof-2.068andap-valueof0.048whichissignificantat 0.05 level. The coefficient of the liquidity risk factor has a negative sign which shows that the lower the bank liquidityriskfactorthebankshavethelowerabilitytofundtheirfinancialneeds.Italsomeanthatforbanks that are having the same unexpected earning and the one with lower/higher liquidity risk, the higher/lower the investors valuation of the bank share prices in response to the earnings changes. Themodel9indicatesthecoefficientfortheliquidityriskfactorhasat-statistic1.460andap-valueof 0.046.Thisresultsuggeststhatwithin95%confidence,thecoefficientforliquidityriskfactorisgreaterthan beingzero.Therefore,liquidityriskfactoristobetakenasindicatingashavingadirectionalandalsoa magnitude effect after the earning variable. In this study, there is no econometric problem and the data used are pooled data. Therefore, these data do not have auto correlation problem. The Durbin-Watson statistics lie between 1.901-2.257 shows that the data do not have autocorrelation problem. The values of Variance Inflation Factors (VIF) lie between 1.00-1.10, which arebelowsignificantlevel.Hence,thereisnomulticollinearityproblem.Generally,thereisnoeconometric problem and the residuals do not display serial correlation or heteroscadesticity. Conclusion The main purpose of this paper is to study the effect of financial risks on the earnings response coefficients for selected China commercial banks. The findings in this study for China provide new evidences in different and unique way related to its particular historical background and government decision on economy. ThisstudydiscoversthatliquidityriskhasinformationcontentbeyondearningsforChinabanks.Inthis paper, there was a negative sign for liquidity risk which means when liquidity risk increases, the share response to earnings of the banks decreases. There is a trade off between liquidity and profitability. The finding in this studyisconsistentwiththegeneraltheoryonassets-liabilitiesmanagementof banks.Managers ofbanksfind the level of liquidity that fulfilled the need of investors and at the same time earn good profits for the banks. In conclusion, this study has shown the strong returns-to-earnings relation for banks. The liquidity risk has informationcontentbeyondearningschangesinthereturns-to-earningsrelation.Whereastheother6risk factorswerenotsignificantprobablyduetothereasonthatfirstly,theinvestorswerenotconcernedwiththe otherfactoredriskvariables.Secondly,thebankswereverywellmanagedbytheirmanagersthattheother financial risk variables are stable to have no significant effect on share value. References Ariff, M., Loh, A. L. C., & Chew, P. M. K. (1997). The impact of accounting earnings disclosures on stock prices in Singapore. Asia Pacific Journal of Management, 14, 17-29. Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. 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Journal of Accounting, Business & Management, 14, 1-16. Cheng, F. F., Arif, M., & Shamsher, M. (2001). Accounting earnings and share revaluation: Further exploration. Capital Market Review, 9(2), 21-48. Choi, J. J., Elyasiani, E., & Kopecky, K. J. (1992). The sensitivity of bank stock returns to market, interest and exchange rate risks. Journal of Banking and Finance, 16, 983-1004. Dennis,S.A.,&Jeffrey,A.(2002).StructuralchangesinAustraliabankrisk.JournalofInternationalFinancialMarket, Institutions and Money, 12, 1-17. Faff,R.W.,&Howard,P.F.(1999).InterestrateriskofAustraliafinancialsectorcompaniesinperiodofregulatorychange. Pacific-Basin Finance Journal, 7, 83-101. Giliberto,M.(1985).Interestratesensitivityinthecommonstocksoffinancialintermediaries.JournalofFinancialand Quantitative Analysis, 20, 123-126. Hagerman,R.L.,Zmijewski,M.E.,&Shah,P.(1984).Theassociationbetweenthemagnitudeofquarterlyearningsforecast errors and risk-adjusted stock returns. Journal of Accounting Research, 22(2), 526-540. Sharpe. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19, 425-442. Yang, X. (2002). General financial history of China. Beijing: China Finance Publishing House. Chinese Business Review, ISSN 1537-1506 January 2011, Vol. 10, No. 1, 29-40 29 Foreign Direct Investments, Environmental Sustainability, and Strategic Planning: A Local Perspective Pasquale Pazienza, Caterina De Lucia, Vincenzo Vecchione University of Foggia, Foggia, Italy Elena Palma University of Bari, Bari, Italy The present work constitutes further explorations on Foreign Direct Investments (FDI) and environmental quality linkages and a starting point in the strategic planning process in the metropolitan area of Bari (the main province oftheApuliaregioninsouthernItaly)inthecontextoftransfrontiercommercialcooperation.Apaneldata analysisisusedtoshoweithertheexistenceoftherelationshipsacrossFDI,economicandenvironmental variablesortheirnatureacrossprovincesandtime(fixedorrandomeffectmodel).Modelresultssuggestthat diversificationofpoliciesacrossprovinceswouldbemoreeffectiveinthestrategicplanningprocessofa metropolitan city. Keywords: FDI, strategic planning, environment, panel data Introduction Overthelastyears,theincreasingadoptionofstrategicplanningprocessesinmetropolitanurban contextsindicatethatplanningandpublicparticipationpolicystrategiesarefundamentaltosustainable practisestoeitherre-qualifyurbanareas(i.e.,Hamburg,Birmingham,LiverpoolandManchester)or re-launchinternationaldynamicpoles(i.e.,Lion,Barcelona,AmsterdamandTurin).Evidenceofthese processes are also taking place in new EU countries (Karnitis & Kucinskis, 2009). TheMetropolitanareaofBari,themainprovinceintheApuliaregioninSouthernItaly,hasrecently adoptedanurbanstrategicplanningprocesstobuildaconcreteprojectforregionaldevelopmentin2015. Thisprojectaimsatprioritisethoseactionswhichfavourtheinteractionacrossamultitudeofstakeholders and are strategic to boost development over 31 municipalities which constitute the metropolitan area of Bari. TheneedtoexpansionandrequalificationoftheMetropolitanareaofBariandthedevelopmentofApulia regionasawholeshouldbeseeninthecontextofitsgeographicalpositionwhichfavoursinternational transfrontier cooperation with Eastern European countries. The increased globalisation process across the Apulia region, its Metropolitan area of Bari and Eastern Europeancountriesiscrucialtofavourtheenhancementofcapitalinflowandoutflowandaccelerate

Pasquale Pazienza, Ph.D., Department of Economics, Mathematics and Statistics, University of Foggia. Caterina De Lucia, Ph.D., Department of Economics, Mathematics and Statistics, University of Foggia. Vincenzo Vecchione, professor, Department of Economics, Mathematics and Statistics, University of Foggia. Elena Palma, Ph.D., Department of Geographical and Commodity Science, University of Bari. FOREIGN DIRECT INVESTMENTS, ENVIRONMENTAL SUSTAINABILITY 30 economic growth patterns according to sustainability and strategic planning visions. Thepresentworkanalysesthisframeworkasfollows:Section2illustratesanoverviewofthe MetropolitanAreaofBari;Section3focusesontheopennessofaregionaleconomicsystemtothe trans-border commercial cooperation to investigate the impact of FDI with an eye to the main environmental qualityissuesintheprovinceofBariandApuliaregion;Section4showsanempiricalanalysisacross provinces and sectors to test the existence of relationships between environmental quality, FDI and economic growthintheApuliaregion;andfinally,section5discussespolicyimplicationsfortheurbanstrategic planning process and concludes. An Economic Overview of the Metropolitan Area of Bari in the Apulia Region The metropolitan area of Bari represents an area formed by 31 municipalities of the province of Bari. This area generates a value added of around 16 billion Euros derived mostly from the service (77.5%) and industry (19.7%) sectors. Agriculture contributes to the creation of value added in a small percentage (2.8%). As shown in Figure 1, most of the municipalities whose service sector performs at its highest contributes to the formation of their value added (60%-80%), a value which is higher than the national average. Figure 1. Sectoral value added. Source: the authors elaboration on CCIA data, 2007. The metropolitan area of Bari employs almost 90,000 workers across the 135,000 enterprises registered tothelocalChamberofCommerce(CameradiCommercio,Industria,ArtigianatoeAgricolturaCCIA). MostofthelabourforceisemployedintheIndustryandCommercesectors(48%),withaminorityinthe Building,AgricultureandICTsectors.Traditionandinnovationortraditionwithininnovationistherefore considered the expression of the local economy. Figure 2 shows the composition of firms across municipalities of the metropolitan area of Bari. The area is characterised by a rural activity of its surroundings. The agro-industry districts of the area, with good export FOREIGN DIRECT INVESTMENTS, ENVIRONMENTAL SUSTAINABILITY 31performances,agricultureemploymentandcapacityofinnovationaremainlydevelopedaroundthe municipalities of Rutigliano, Turi and Toritto. Local districts in terms of number of people employed are found across the municipalities of Bari, Modugno and Bitonto, where the ASI Consortium, one of the most important industrialparksoftheAdriaticSeaRegion,islocated.Furthermore,mostofmedium-sizedenterprisesare situated in the municipalities of Bari, Molfetta and Valenzano which are specialised predominantly in the ICT sector.This,togetherwiththeMechanicssector,hasbeenparticularlyvitaloverthelastdecadetothe industrialdevelopmentofthemetropolitanareaofBariintermsofexportperformanceandinnovation capacity. Tourism, a growing sector with great opportunities to attract foreign capital, is instead active across the municipalities of Bari, Polignano a Mare and Giovinazzo. Figure2.SectoralnumberoffirmsacrossmunicipalitiesinthemetropolitanareaofBari.Source:theauthors elaboration on CCIA data, 2007. These are the main attraction poles in the Metropolitan Area of Bari for providing recreative activities. The servicesector(i.e.,commerce,marketinganddistribution)mostlyrelatedtowholesalebusinesses,aswellas FoodandFlowercommodityindustriesarealsovigorousacrosstheentiremetropolitanarea,althoughtheir export performances strongly depend to business cycle trends. Finally, energy sector is expanding over the last yearsduetoregionalinvestmentsintheeco-buildingsector.Theemerginglocaleconomicsystemencounters various problems to be fully structured in the regional context. Firstly, the loss of the entire value chain within theregionalterritory;Secondly,thelackoflocaland/orinternationalcollaboration,innovationand internationalisation, mainly due to the presence of few large enterprises and a multitude of small-sized firms. To overcome these problems, the Apulia region has promoted, by means of the Regional Law 23/2007, the creation of regional industrial districts whose processes and implications for the local economy are still in progress. FOREIGN DIRECT INVESTMENTS, ENVIRONMENTAL SUSTAINABILITY 32 Transfrontier Commercial Cooperation in the Metropolitan Area of Bari The last Italian Economic Census in 2001 showed a clear improvement of the economic performance (in termsofworkersandaddedvalue)oftheAdriaticSeaRegionsofItalywithrespecttotheTyrrhenianSea ones over the last ten years. Import-exportperformanceofthemetropolitanareaofBarihasrecordedasurplusofthebalanceof paymentmainlyduetoexportsofFood,Mechanicandotherindustriescommodities.Forthisreason,the province of Bari is ranked 29th across 113 Italian provinces (including Valle DAosta region which does not haveanyprovince)forthevalueofitsexports(3billionEurosin2007).Albania,GreeceandMontenegro representthemostimportantcommercialpartnersforthemetropolitanareaofBarieventhoughAlbaniais the largest area devoted to exporting commodities. The metropolitan area of Bari is leader in the Apulia region for the attraction of net FDI. However, the empowermentofthemetropolitanareaandtheApuliaregiontowardssustainabilityissuessuggeststhat certainlinksmaybecreatedbetweenFDIandenvironmentalquality.Theselinkswouldinturnaffectthe strategicchoicesofpolicymakerswhenimplementingaplanningprocess.Thequantitativeanalysis conductedinthenextsections,involvesthereforethestudyoftherelationshipsbetweenFDI,economic growth (GDP) and environmental quality. Overview of FDI, Economic Growth and Environmental Linkages The debate over FDI, economic growth and environment issues is relatively new. Many studies focus their attentiononthecause-effectnexusofstringentenvironmentalpolicies onfirmscompetitivenessasmigration from/attractiontoagivenlocation.Theoretically,aprocessofarbitrage(factorpriceequalisation)drivesup emissions in countries with more abundant environmental resources and lower pollution regulation, to the point whereindustriesmigrate,anddrivedownemissionsinthosecountriesthatlosetheirindustries.Comparative advantages may therefore be seen as pollution haven and industry flight hypothesis (De Lucia, 2007). A further aspect to consider is the link between FDI and pollution. Most of the literature concerned with thisissueisbornundertheEnvironmentalKuznetsCurve(EKC)hypothesis.TheEKCbecamefamous because of its similarity to the inverted U-shaped relationship between inequality and income levels advanced by Simon Kuznets (1955). The EKC hypothesis purposes an inverted U-shaped between various indicators of environmentaldegradationandpercapitaincome.Thisimpliesthatonecountryseconomicgrowthwill redresstheenvironmentalimpactsoftheearlystagesofeconomicdevelopmentandthatgrowthand technologicalprogresswillleadtoimproveenvironmentalperformancesinthedevelopedcountries(De Lucia,2002).EKCstudieshavecapturedtheinternationalcommunitysattentiongiventheincreasing interest in the economic growth, trade and environmental quality. The debate is still on-going. FDI are seen asdependingonintheincomeeffect(onecountryspreferencesfortheenvironment)andthescaleeffect (improvementsintheenvironmentalvariableasGDPincreases)ofthehostregion.However,onceforeign capitalsenterthehostregion,thesecancertainlyhaveeffectsontheEKCscharacteristics.FDIcould accelerateeconomicgrowth(Li,Liu,&Parker,2001;Chen&Demurger,2002;Liu&Wang,2003; Tvaronaviius & Tvaronaviiene, 2008) or provide technology improvements and therefore advance income effects(Thompson,2002;Lemoine&nal-Kesenci,2004).Thesewouldinturnaffectandreinforcethe decision making process towards sustainability issues. FOREIGN DIRECT INVESTMENTS, ENVIRONMENTAL SUSTAINABILITY 33FDI: Some Descriptive Statistics Issues for Apulia Region and the Metropolitan Area of Bari Figure3illustratestheinflowandoutflowoffirmsintheApuliaregionovertheperiod1988-2000. Computations are based on the Italian National Institute of Statistics (ISTAT) data. Although a positive trend canbedrawnfrombothinflowandoutflowoffirms,amassiveincreaseoffirmsoutflowratesispresent. While at the end of 1980s until mid 1990s a net inflow of firms prevails, from 1996 to 2000 a net outflow of just about 3 firms occurs in Apulia region. Figure 3. Inflow and outflow of foreign firms in Apulia. Source: the authors elaborations on ISTAT data, 1988-2000. Thetrendarguablyfollowstheeconomicbusinesscycleduringthoseyears.Atthebeginningofthe 1980s, the enhancement of the ICT and tertiary sector worldwide provided a rapid increase of new industries in which the Apulia Region benefitted until mid 1990s. Once the international business cycle reached its peak duringthe1990s,theexistenceofnegativeexpectationsandstructuralproblems(Pazienza&Vecchione, 2008) favoured the increase of foreign firms outflow from the Apulian territory. Figure 4. Inflow and outflow of foreign turnover in Apulia region. Source: the authors elaborations on ISTAT data, 1988-2000. Figure 4 shows the turnover of foreign firms. The turnover inflow remained almost below 1,000 billion Euros until mid 1990s to jump at a peak of more than 5,000 billion Euros in 1998-1999. During those years, FOREIGN DIRECT INVESTMENTS, ENVIRONMENTAL SUSTAINABILITY 34 in fact, massive investments in the industrial sectors took place (ICE, 1999). The firms turnover outflow, on the other hand, was of just about 50 million Euros over the same time span. The description now turns to briefly analyse the trend in FDI in Apulia region and the metropolitan area ofBari.Figure5illustratesFDIinApuliaregionduringthetimeperiod1999-2005.Duringtheseyears,an increasingtrendinforeigninflowandoutflowofinvestmentsintheApuliaregionispresent.Bothflows reachpeakvaluesin2005.Manyreasonscanbeadoptedtoexplainsuchphenomenon.Oneofthemost apparent is the effect of the European Union Structural Funds system of which Apulia Region benefits. This would favour higher business climate expectations foreign and home firms. Figure 5. FDI in Apulia region. Source: the authors elaborations on ICE data, 1999-2005. In the metropolitan area of Bari a similar trend is also evident (see Figure 6). Capital outflow is massive (64 billion Euros) at the beginning of the time span considered to stabilise (not above 2 billion Euros) by the end of the same period. Capital inflow assumes, on the other hand, a weak increasing trend over time. Figure 6. FDI in the metropolitan area of Bari. Source: the authors elaborations on ICE data, 1999-2005. Environmental Quality: Issues for Apulia Region and the Metropolitan Area of Bari Inthissection,adescriptionofairqualitylevelsandregulationissuesforApuliaregionandthe metropolitanareaofBariarepresented.Italy,asotherEuropeancountries,hasrecentlyadoptedvarious Capital inflow Capital outflow Foreign outflow Apulian inflow Apulian outflow Foreign inflow FOREIGN DIRECT INVESTMENTS, ENVIRONMENTAL SUSTAINABILITY 35internationalregulationstocombatglobalproblemssuchastheKyotoProtocol,theMontrealProtocol,the European Air Quality Directives for long range transboundary flux emissions, or the latest renewable energy policy adopted by the European Council in March 2007 (the so-