chap13 - Cameron School of Business

深圳湾学校2023-2024学年秋季学期八年级期中考试-英语学科试卷（考试时间：70分钟满分：75分）班级：姓名分数：温馨提示：请将所有答案按要求填写在答题卡上。

第一部分选择题（50分）Ⅰ. 单项选择（15分）i. 选出可以替换划线部分的最佳选项（共7小题，每小题1分）( ) 1. -- Use your intelligence, you will win the game.-- Thanks! I will!A. energyB. wisdomC. internet( ) 2. -- Don’t lie to me. I have the ability to read your heart.-- Of course I won’t lie to you.A. look forward toB. am able toC. am worth( ) 3. -- Lily, my new chair is comfortable.-- Wow! Let me have a try.A. small and easyB. nice and softC. convenient and important ( ) 4. -- When he was aware of his mistakes, it was too late.-- What a pity.A. realizedB. promisedC. challenged( ) 5. -- A wise man can learn from his mistakes.-- I agree with you.A. braveB. hard-workingC. smart( ) 6. -- James Cameron’s new movie is coming out soon. Shall we watch it together?-- Sure! I look forward to seeing it in the cinema.A. don’t want to seeB. can’t wait to seeC. am too busy to see ( ) 7. -- The price includes dinner, lunch, and breakfast.-- It is still too expensive. I am afraid I can’t afford.A. putsB. starsC. coversii. 选出可以填入空白处的最佳选项（共8小题，每小题1分）( ) 8. -- There is no salt left. Jim, would you like to get ________?-- OK, Mum!A. oneB. someC. any( ) 9. -- ________ knows how to make the machine work except Steven.-- Then we need to ask him for help.A. EverybodyB. SomebodyC. Nobody( ) 10. -- Basketball is popular around the world. ________ people play it every day.-- I like basketball too!A. Two millionsB. Millions ofC. Million of( ) 11. -- In the early ________ century, the first train began to carry passengers.-- That’s a long time ago.A. nineteenB. ninthC. nineteenth( ) 12. -- Jim is 10 years old. He is ________ student in my class.-- When is his birthday?A. youngestB. the youngestC. the younger( ) 13. -- Whose story is ________ Tom’s or Jack’s?-- I like Tom’s story.A. more interestingB. interestingC. the most interesting( ) 14. -- Alice is not as ________ as Mike.-- But Alice is smarter.A. tallB. tallerC. the tallest( ) 15. -- With a high-speed train station built in Luzhou, it is. ________ for us to travel to Chengdu.-- Great! I want to go to Chengdu in summer holidays.A. much easyB. much more easierC. much easierⅡ. 完形填空（10分）阅读下面短文，从短文后每小题的A、B、C三个选项中选出能填入相应空白处的最佳选项。

MICHAL HERZENSTEIN,SCOTT SONENSHEIN,and UTPAL M.DHOLAKIAThis research examines how identity claims constructed in narrativesby borrowers influence lender decisions about unsecured personal loans.Specifically,do the number of identity claims and their content influ-ence lending decisions,and can they predict the longer-term perfor-mance of funded loans?Using data from the peer-to-peer lending website,the authorsfind that unverifiable information affects lendingdecisions above and beyond the influence of objective,verifiable informa-tion.As the number of identity claims in narratives increases,so doesloan funding,whereas loan performance suffers,because these borrow-ers are less likely to pay back the loan.In addition,identity content playsan important role.Identities focused on being trustworthy or successfulare associated with increased loan funding but ironically are less predic-tive of loan performance than other identities(i.e.,moral and economichardship).Thus,some identity claims aim to mislead lenders,whereasothers provide true representations of borrowers.Keywords:identities,narratives,peer-to-peer lending,decision makingunder uncertainty,consumerfinancial decision making Tell Me a Good Story and I May Lend Y ou Money:The Role of Narratives inPeer-to-Peer Lending DecisionsThe past decade has witnessed a growing number of business models that facilitate economic exchanges between individuals with limited institutional mediation. Consumers can buy products on eBay,lend money on peer-to-peer(P2P)loan auction sites such as , and provide zero-interest“social loans”to entrepreneurs through .In all these cases,strangers decide whether to engage in an economic exchange and on what terms,using only information provided by the borrowers. *Michal Herzenstein is Assistant Professor of Marketing,Lerner College of Business and Economics,University of Delaware(e-mail: michalh@).Scott Sonenshein is Assistant Professor of Man-agement,Jones Graduate School of Business,Rice University(e-mail: scotts@).Utpal M.Dholakia is Professor of Management, Jones Graduate School of Business,Rice University(e-mail:dholakia@ ).The authors thank Rick Andrews,Richard Schwarz,and Greg Hancock for their statistical assistance.They further thank the editor John Lynch,the associate editor,and the two JMR reviewers for their insight-ful comments.Financial support from the Lerner College of Business and Economics at the University of Delaware and the Jones Graduate School of Business at Rice University is greatly appreciated.David Mick served as associate editor for this article.Objective quantitative data about exchange partners often are difficult to obtain,insufficient,or unreliable.As a result, decision makers may turn to subjective,unverifiable,but potentially diagnostic qualitative data(Michels2011). One form of qualitative data useful to decision makers in such economic exchanges are the narratives constructed by potential exchange partners.A narrative is a sequentially structured discourse that gives meaning to events that unfold around the narrator(Riessman1993).For example,a narra-tive might explain a person’s past experiences,current sit-uation,or future hopes(e.g.,Thompson1996;Wong and King2008).By providing an autobiographical sketch that explains the vicissitudes of their life,the narrative authors provide a window into how they conceptualize themselves (Gergen and Gergen1997)and a portrait of how they con-struct their identity.However,as a result of either ambiguity or the strategic use of the medium to influence others(e.g., Schau and Gilly2003),narratives offer only one of several possible interpretations of self-relevant events(Sonenshein 2010).As a result,narrators can relay interpretations of their circumstances that convey the most favorable identities (Goffman1959).©2011,American Marketing AssociationISSN:0022-2437(print),1547-7193(electronic)S138Journal of Marketing ResearchV ol.XLVIII(Special Issue2011),S138–S149Role of Narratives in Peer-to-Peer Lending Decisions S139RESEARCH MOTIVATIONS AND CONTRIBUTIONS The idea that narratives may involve the construction of a favorable identity poses two key questions for research. First,whereas narratives can provide diagnostic informa-tion to a decision maker who is considering an economic exchange,the veracity of the narrator’s story is difficult to determine.Because a narrative offers the possibility of describing either an authentic,full,true self or a partial, inauthentic,misleading self,potential exchange partners are left to intuit the truth of the presentation.Accordingly a key question to consider is,given their potential for diagnostic and misleading information,to what extent do narratives influence economic exchange transactions?Previous con-sumer research has largely focused on narratives of con-sumption experiences(Thompson1996)or consumption stories(Levy1981),but scholars have not examined the role of narratives in economic exchanges.We believe that narratives may be a particularly powerful lens,in that they allow the consumer to attempt to gain better control over the exchange and thus can provide a means to help con-summate the exchange.Second,to what extent do narratives affect the perfor-mance and outcomes of an economic exchange?Narrative scholars claim that the construction and presentation of a narrative can shape its creator’s future behavior(Bruner 1990)but rarely examine the nature of this influence empir-ically.Such an examination would be critical to understand-ing how a mixture of quantitative and qualitative factors shapes outcome quality(e.g.,Hoffman and Yates2005). We examine these two questions using data from the online P2P loan auctions website,,by studying borrower-constructed narratives(particularly the identities embedded in them),the subsequent decisions of lenders, and transaction performance two years later.We define identity claims as the ways that borrowers describe them-selves to others(Pratt,Rockmann,and Kaufmann2006). Borrowers can construct an identity based on a range of ele-ments,such as religion or success.The elements become an identity claim when they enter public discourse as opposed to private cognition.With this framework,we make several contributions.First,by developing and testing theory around how nar-ratives supplement more objective sources of information that decision makers use when considering afinancial trans-action,we draw attention to how narrators can intentionally exploit uncertainty and favorably shape circumstantial facts to obtain resources,such as access to money in unmediated environments.The narrative,as a supplementary,yet some-times deal-making or deal-breaking,information source for decision makers is predicated on compelling stories versus objective facts.It thus offers a means for people to recon-struct their pasts and describe their futures in positive ways. Second,by linking narratives to objective performance measures,we show how narratives may predict the longer-term performance of lending decisions.Because the deci-sion stakes are high in this unmediated and unsecured financial arena,lenders engage in highly cognitive pro-cessing(Petty and Wegener1998).The strong disincen-tive of potentialfinancial loss leads to cognitive process-ing,which tends to produce accurate attributions about a person and the probabilities of future events(Osborne and Gilbert1992).Because of this motivation for accuracy,we suspect lenders use narratives to help them make invest-ment decisions.Third,from a practice perspective,the recentfinancial crisis has exposedflaws in the criteria used to make lend-ing decisions.Quantitativefinancial metrics,such as credit scores,have proven unreliable for predicting the ability or likelihood of consumers to repay unsecured loans(Feldman 2009).A narrative perspective on the consummation and performance offinancial transactions offers the promise of improving systems for assessing borrowers.RESEARCH SETTINGWe conducted our research on (hereinafter, Prosper),the largest P2P loan auction site in the United States,with more than one million members and$238mil-lion in personal loans originated since its inception in March2006(as of June2011).On Prosper,borrowers and lenders never meet in person,so we can assess the role of narratives in overcoming the uncertainty that arises during financial transactions between unacquainted actors.The process of borrowing and lending money through a loan auction on Prosper is as follows:Before posting their loan request,borrowers give Prosper permission to ver-ify relevant personal information(e.g.,household income, home ownership,bank accounts)and access their credit score from Experian,a major credit-reporting ing this and other information,such as pay stubs and income tax returns,Prosper assigns each borrower a credit grade that reflects the risk to lenders.Credit grades can range from AA, which indicates that the borrower is extremely low risk(i.e., high probability of paying back the loan),through A,B,C, D,and E to HR,which signifies the highest risk of default. Borrowers then post loan requests for auction.When post-ing their loan auctions,borrowers choose the amount(up to $25,000)and the highest interest rate they will pay.They also may use a voluntary open-text area,with unlimited space,to write anything they want—that is,the borrower’s narrative(see Michels2011).After the listing becomes active,lenders decide whether to bid,how much money to offer,and the interest rate. A$1,000loan might befinanced by one lender who lends $1,000or by40lenders,each lending$25for example. Most lenders bid the minimum amount($25)on individual loans to diversify their portfolios(Herzenstein,Dholakia, and Andrews2011).After the auction closes,listings with bids that cover the requested amount are funded.If a list-ing receives bids covering more than its requested amount, the bids with the lowest interest rates win.If the auc-tion does not receive enough bids,the request remains unfunded.Prosper administers the loan,collects payments, and receives fees of.5%to3.0%from borrowers,as well as a1%annual fee from lenders.We employed three dependent variables in our study. First,loan funding is the percentage of the loan request to receive a funding commitment from lenders.For exam-ple,if a loan request for$1,000receives bids worth$500, loan funding is50%.If it receives bids worth$2,000,loan funding equals200%.A higher loan funding value signi-fies greater lender interest.Second,percentage reduction infinal interest rate captures the decrease in the inter-est rate between the borrower’s maximum specified rate and thefinal rate.For example,if a borrower’s maximumS140JOURNAL OF MARKETING RESEARCH,SPECIAL ISSUE 2011T able 1DEFINITIONS OF IDENTITIES AND EXAMPLES FOR DATA CODINGIdentityDefinitionExamplesTrustworthy (Duarte,Siegel,and Young 2009)Lenders can trust the borrower to pay back the money on time.“I am responsible at paying my bills and lending me funds would be a good investment.”(Listing #17118)Successful (Shafir,Simonson,and Tversky 1993)The borrower is someone with a successful business or job/career.“I have [had]a very solid and successful career with an Aviation company for the last 13years.”(Listing #18608)Hardworking (Woolcock 1999)The borrower will work very hard to pay the loan back.“I work two jobs.I work too much really.I work 26days a month with both jobs.”(Listing #18943)Economic hardship (Woolcock 1999)The borrower is someone in need because of hardship,as a result of difficult circumstances,bad luck,or other misfortunes that were,or were not,under the borrower’s control.“Unfortunately,a messy divorce and an irresponsible ex have left me with awful credit.”(Listing #20525)Moral (Aquino et al.2009)The borrower is an honest or moral person.“On paper I appear to be an extremely poor financial risk.In reality,I am an honest,decent person.”(Listing #17237)Religious (Weaver and Agle 2002)The borrower is a religious person.“One night,the Lord awaken me and myspouse our business has been an enormous success with G-d on our side.”(Listing #21308)interest rate is 18%and the final rate is 17%,the per-centage reduction in interest rate is 18−17 /18=5 56%.rate decreases only if the loan request receives full greater lender interest results in greater reduc-of the interest rate.Third,loan performance is the payment status of the loan two years after its origination.We further classify the types of identity claims made by prospective borrowers.Borrowers in our sample employed six identity claims in their narratives:trustworthy,economic hardship,hardworking,successful,moral,and religious.In Table 1,we provide definitions and illustrative examples of each identity claim.Borrowers provided an average of 1.53 SD =1 14 identity claims in their narratives.RESEARCH HYPOTHESESWho Is Likely to Provide More Identity Claims in Their Narratives?Narratives,when viewed as vehicles for identity work,provide opportunities for people to manage the impres-sions that others hold of them.Impression management the-ory posits that people want to create and maintain specific identities (Leary and Kowalski 1990).Narratives provide an avenue for impression management;through discourse,people can shape situations and construct identities that are designed specifically to obtain a desired outcome (Schlenker and Weigold 1992).Some scholars argue that people use narratives strategically to establish,maintain,or protect their desired identities (Rosenfeld,Giacalone,and Riordan 1995).However,the use of impression manage-ment need not automatically signal outright lying;people may select from a repertoire of self-images they genuinely believe to be true (Leary and Kowalski 1990).Nevertheless,strategic use of impression management means that,at a minimum,people select representations of their self-image that are most likely to garner support.In economic exchanges involving repeated transactions,each party receives feedback from exchange partners thateither validates or disputes the credibility of their self-constructions (Leary and Kowalski 1990),so they can determine if an identity claim has been granted.Prior transactions also offer useful information through feedback ratings and other mechanisms that convey and archive rep-utations (Weiss,Lurie,and MacInnis 2008).However,in one-time economic exchanges,such feedback is not avail-able.Instead,narrators have a single opportunity to present a convincing public view of the self,and receivers of the information have only one presentation to deem the presen-ter as credible or not.We hypothesize that in these conditions,borrowers are strategic in their identity claims.Borrowers with satisfac-tory objective characteristics are less likely to construct identity claims to receive funding;they feel their case stands firmly on its objective merits alone.In contrast,borrowers with unsatisfactory objective characteristics may view narratives as an opportunity to influence the attribu-tions that lenders make,because in narratives,they can counter past mistakes and difficult circumstances.In this scenario,borrowers make identity claims that offset the attributions made by lenders about the borrower being fundamentally not a creditworthy person.These disposi-tional attributions are often based on visible characteristics (Gilbert and Malone 1995).The most relevant objective characteristic of borrowers is the credit grade assigned by Prosper,derived from the borrower’s personal credit history (Herzenstein,Dholakia,and Andrews 2011).With more than one identity claim,borrowers can present a more com-plex,positive self to counteract negative objective informa-tion,such as a low credit grade.Thus:H 1:The lower the borrower’s credit grade,the greater isthe number of identities claimed by the borrower in the narrative.Role of Narratives in Peer-to-Peer Lending Decisions S141Impact of the Number of Identity Claims onLenders’DecisionAlthough economists often predict that unverifiable information does not matter(e.g.,Farrell and Rabin1996), we suggest that the number of identity claims in a bor-rower’s narrative play a role in lenders’decision making, for at least two reasons.First,borrower narratives with too few identities may fail to resolve questions about the borrower’s disposition.If a borrower fails to provide suf-ficient diagnostic information for lenders to make attri-butions about the borrower(Cramton2001),lenders may suspect that the borrower lacks sufficient positive or dis-tinctive information or is withholding or hiding germane information.Second,the limited diagnostic information provided by fewer identity claims limits a decision maker’s ability to resolve outcome uncertainties.Research on perceived risk supports this reasoning;decision makers gather informa-tion as a risk-reduction strategy and tend to be risk averse in the absence of sufficient information about the decision (e.g.,Cox and Rich1964).In the P2P lending arena,the loan request and evaluation process unfold online with-out any physical interaction between the parties.Further-more,on Prosper,borrowers are anonymous(real names and addresses are never revealed).This lack of seemingly relevant information is especially salient,because many decision makers view unmediated online environments as ripe for deception(Caspi and Gorsky2006).To the extent that the identity claims presented in a narrative reduce uncertainty about a borrower,lenders should be more likely to view the listing favorably,increase loan funding,and decrease thefinal interest rate.Therefore,the number of identity claims in a borrower’s narrative may serve as a heuristic for assessing the borrower’s loan application and lead to greater interest in the listing.Although we suggest that the number of identities bor-rowers claim result in favorable lending decisions,we also argue that these identity claims may persuade lenders erroneously,such that lenders fund loans with a lower likelihood of repayment.Borrowers can use elaborate multiple-identity narratives to craft“not-quite-true”stories and make promises they mightfind difficult to keep.More generally,a greater number of identity claims suggests that borrowers are being more strategic and positioning them-selves in a manner they believe is likely to resonate with lenders,as opposed to presenting a true self.Therefore,we posit that,consciously or not,borrowers who construct sev-eral identities may have more difficulty fulfilling their obli-gations and be more likely to fall behind on or stop loan repayments altogether.Thus,despite the high stakes of the decision,lenders swayed by multiple identities are more likely to fall prey to borrowers that underperform or fail (Goffman1959).H2:Controlling for objective,verifiable information,the more identities borrowers claim in their loan requests,the morelikely lenders are to(a)fund the loan and(b)reduce itsinterest rate,but then(c)the lower is the likelihood of itsrepayment.Role of the Content of Identity Claims onLender Decision Making and Loan PerformanceWe also examine the extent to which select identities affect lenders’decision making and the longer-term per-formance of loans.With a limited theoretical basis for determining the types of identities most likely to influ-ence lenders’decision making,this part of our study is exploratory.Research on trust offers a promising starting point(Mayer,Davis,and Schoorman1995),because it sug-gests that identities may reduce dispositional uncertainty and favorably influence lenders.Trust is a crucial element for the consummation of an economic exchange(Arrow 1974).Scholars theorize that trust involves three compo-nents:integrity(borrowers adhere to principles that lenders accept),ability(borrowers possess the skills necessary to meet obligations),and benevolence(borrowers have some attachment to lenders and are inclined to do good)(Mayer, Davis,and Schoorman1995).We theorize that trustworthy,religious,and moral iden-tities increase perceptions of integrity because they lead lenders to believe that borrowers ascribe to the lender-endorsed principle of fulfilling obligations,either directly (trustworthy)or indirectly by adhering to a philosophy (religious or moral).Specifically,a moral identity tells potential lenders that the person has“a self-conception organized around a set of moral traits”(Aquino et al.2009, p.1424),which should increase perceptions of integrity.A religious identity signals a set of role expectations to which a person is likely to adhere,and though religions vary in the content of these expectations(Weaver and Agle 2002),many of them include principles oriented against lying or stealing and toward honoring contractual agree-ments.A hardworking identity should increase perceptions of integrity,because hardworking people are determined and dependable,which often makes them problem solvers (Witt et al.2002),meaning that they will do their best to meet their obligations,a disposition likely to resonate with lenders.We also reason that the religious and moral identities invoke in lenders a sense of benevolence,which is a foun-dational principle of many religions and moral philoso-phies.Similarly,the economic hardship identity may invoke benevolence,because the borrower exhibits forthrightness about his or her past mistakes and thus suggests to lenders that the borrower is trying to create a meaningful relation-ship based on transparency.We theorize that an identity claim of success can increase perceptions of ability and the belief that the narrator is capable of fulfilling promises(Butler1991).Lenders are more likely to lend money to a borrower if they perceive that the person is capable of on-time repayment(Newall and Swan2000).A successful identity likely describes the past or present,but it also can serve as an indication of a probable future(i.e.,the borrower will continue to be successful),which helps“fill in the blanks”about the bor-rower in a positive way.In contrast,economic hardship likely constructs the borrower as someone who has had a setback,which ultimately undermines perceptions of ability and thus negatively affects lenders’decisions.We have offered some preliminary theory in support of these specific relationships between identity content and loan funding/interest rate reductions,but this examinationS142JOURNAL OF MARKETING RESEARCH,SPECIAL ISSUE2011remains exploratory,so we pose these relationships as exploratory research questions(ERQ):ERQ1:Which types of identity claims influence lending deci-sions,as indicated by(a)an increase in loan fundingand(b)a decrease in thefinal interest rate?We also explore the impact of the content of identity claims on loan performance.We envision two potential sce-narios.In thefirst,identities are diagnostic of the borrower or serve as self-fulfilling prophecies.Examining the ability aspect of trustworthiness,we anticipate a negative relation between an economic hardship identity and loan perfor-mance(borrowers validate their claim of setbacks)but a positive relation between a successful identity and loan per-formance(borrowers prove their claim of past success). Moreover,we expect the four integrity-related identities—trustworthy,hardworking,moral,and religious—to indicate better loan performance.After a self-presentation as having integrity,the borrower probably has a strong psychological desire for consistency between the narrative and his or her actions(Cialdini and Trost1998).That is,in their narra-tives,borrowers may make an active,voluntary,and public commitment that psychologically binds them to a partic-ular set of beliefs and subsequent behaviors(Berger and Heath2007).Because these four identities speak to funda-mental self-beliefs versus predicted outcomes(e.g.,success or hardship),they can strongly motivate borrowers to live up to their claims.Thus these identities,regardless of their accuracy,can become true and predict the performance of the lending decision.In the other scenario,however,identities improve the lender’s impression of the borrower,thereby allowing bor-rowers to exert control over the provided impressions (Goffman1959).Borrowers(or narrators,more generally) construct positive impressions and may misrepresent them-selves and send signals that may not be objectively war-ranted.Despite the belief that self-constructing identities are helpful for a lending decision,they actually may have no impact or even be harmful to lenders.These mixed pos-sibilities lead to another exploratory research question: ERQ2:How are the content of identity claims and loan perfor-mance related?STUDYDataOur data set consists of1,493loan listings posted by borrowers on Prosper in June2006and June2007.We extracted this data set using a stratified random sampling ing a web crawler,we extracted all loan listings posted in June2006and June2007(approximately5,400 and12,500listings,respectively).A significant percentage of borrowers on Prosper have very poor credit histories, and most loan requests do not receive funding.To avoid overweighting high-risk borrowers and unfunded loans,we sampled an equal number of loan requests from each credit grade.To do so,wefirst separated funded loan requests from unfunded ones,then divided each group by the seven credit grades assigned by Prosper.We also eliminated all loan requests without any narrative text,for three reasons. First,including loan requests without narratives could con-found the borrower’s choice to write something other than narratives in the open text box with the choice to write nothing at all.Second,the vast majority of listings lack-ing a narrative do not receive funding.Third,loan requests without text represent only9%of all loans posted in June 2006and4%of those posted in June2007.We nevertheless used the“no text”loans in our robustness check.We randomly sampled posts from the14subgroups (2funding status×7credit grades).In2006,we sampled 40listings from each subgroup(until data were exhausted) to obtain513listings;in2007,we sampled70listings from each subgroup to obtain980listings,for a total of 1,493listings.Each listing includes the borrower’s credit grade,requested loan amount,maximum interest rate,loan funding,final interest rate of funded loans,payback sta-tus of funded loans after two years,and open-ended text data.Before combining the data from2006and2007,we tested for a year effect but found none,which supports their combination.Dependent VariablesThefirst dependent variable,loan funding,ranges from 0%to905%in our data set,but requiring an equal inclu-sion of all credit ratings skews these statistics.The mean percentage funded(including all listings)is105.74%(SD= 129 2)and that for funded listings is205.45%(SD=119 6). Because it was skewed,we log-transformed loan fund-ing as follows:Ln(percent funded+1).The second depen-dent variable,percentage reduction in thefinal interest rate, ranges from0%to56%in our data set.The mean per-centage reduction in interest rate for all listings is6.4% (SD=10 7)and for funded listings is11.88%(SD=12 75). Because the distribution is skewed,we log-transformed it (we provide the distributionfigures in the Web Appendix, /jmrnov11).The third dependent variable is loan performance,mea-sured two years after loan funding.For each funded loan in our data set,we obtained data about whether the loan was paid ahead of schedule and in full(31.1%of funded list-ings),was current and paid as scheduled(40.5%),involved payments between one and four months late(7.1%),or had defaulted(21.3%).This dependent variable may appear ordered,but the likelihood ratio tests reveal that a multino-mial logit model fares better than an ordered logit model for analyzing these data(for both the number and content of identities).Thus,in the following analysis,we employ a multinomial logit model.Independent VariablesWe read approximately one-third of all narratives and developed our inductively derived list of six identity claims (Miles and Huberman1994):trustworthy,economic hard-ship,hardworking,successful,moral,and religious,as we define in Table1.Two research assistants examined the same data and determined these six identities were exhaustive.Next,five additional pairs of research assistants (10total)coded the entire data set.We coded each iden-tity as a dichotomous variable that receives the value of1 if the identity claim was present in a borrower’s narrative and0if otherwise.A pair of research assistants read each listing in the data set,independently atfirst,then discussed them to determine the unified code for each listing.Accord-ing to20randomly sampled listings from our data set,used。

Knowledge Transfer Through Inheritance: Spin-out Generation,Development and SurvivalRajshree Agarwal*Dept. of Business AdministrationUniversity of Illinois at Urbana Champaign350 Wohlers Hall, 1206 S. Sixth St.Champaign, IL 61820Email: agarwalr@Raj EchambadiDept. of MarketingCollege of Business AdministrationUniversity of Central FloridaP.O. Box 161400Orlando, Fl 32816-1400Email: rechambadi@April M. FrancoDept. of EconomicsW210 John Pappajohn Business BuildingThe University of IowaIowa City, IA 52242-1000Email: afranco@MB SarkarDept. of ManagementCollege of Business AdministrationUniversity of Central FloridaP.O. Box 161400Orlando, Fl 32816-1400Email: msarkar@All authors contributed equally. The names are arranged in alphabetical orderThird round submission toThe Academy of Management Journal* Contact Author.Knowledge Transfer Through Inheritance: Spin-out Generation,Development and SurvivalABSTRACTThis paper examines the role of inherited knowledge in an organization’s formation and as a subsequent source of its competitive advantage. We investigate how knowledge inherited from an industry incumbent by a “spin-out” (an entrepreneurial venture by an ex-employee) influences the spin-out’s development and performance. Using data from the disk drive industry from 1977 to 1997, we show that the incumbent’s capabilities related to technology and market pioneering predict spin-out formation by its employees. Results show that incumbents with both strong technological and market pioneering know-how generate fewer spin-outs than firms with strengths in only one area. The incumbent’s capabilities at the time of spin-out founding positively affects the spin-out’s knowledge capabilities and result in spin-outs having higher probabilities of survival relative to other industry entrants.Organizational researchers have long considered the effects of historical antecedents on industry entrants’ heterogeneity. For diversifying firms, there is evidence that pre-entry experience influences a firm’s resources and capabilities and, therefore, its performance in new product markets (Carroll, Bigelow, Seidal &Tsai, 1996; Klepper & Simons, 2000). While not much is known about the birth and development of capabilities in new ventures (Helfat & Lieberman, 2002; Zahra, Ireland, & Hitt, 2000), research suggests that routines and resources transfer from old to new organizations with personnel migration (Aldrich & Pfeffer, 1976; Almeida & Kogut, 1999; Pfeffer & Leblebici, 1973). Findings indicate that previous employment affiliations influence not only new venture formation (Burton, Sørensen, & Beckman, 2002; Shane & Khurana, 1999) but also product-market strategies (Boeker, 1997) and firm survival (Bruderl, Preisendorfer, & Ziegler, 1992; Phillips, 2002). However, despite past work suggesting that historical ties between an incumbent and a new entrant manifest themselves through knowledge diffusion, some gaps remain.First, while emerging research speculates that new ventures exploit knowledge they inherit from their founders’ past employer (Klepper & Sleeper, 2000; Phillips, 2002), we lack an explicit theoretical and empirical knowledge connection linking pre-entry affiliation with new venture creation, capability formation, and survival. For example, past literature has conceptualized parent-progeny transfer of routines as a function of the relative position of the employee and has assumed that an underlying process of knowledge transfer occurs, without explicitly testing for such inheritance (see Phillips, 2002). Therefore, it is unclear whether knowledge is in fact inherited and how pre-entry affiliation shapes a start-up’s knowledge endowments. Second, as noted by Huber (1991), there is little research that has systematically linked inherited knowledge to firm performance. Therefore, it is unclear whether initial endowments from inheritance have long-term effects on a firm’s evolution and performance.This paper addresses each of these gaps by developing and testing a framework linking knowledge inheritance to the formation, development, and life chances of a spin-out -- defined as an entrepreneurial new venture (the progeny) founded by former employees of an incumbentfirm (the parent) which competes in the same industry as the parent and has no equity relationships with any industry incumbent. Such spin-outs are legion in high-technology industries such as semi-conductor (Braun & MacDonald, 1978; Dosi, 1984), disk drive (Christensen, 1993), and laser (Klepper & Sleeper, 2000) manufacturers, as well as in the legal services (Phillips, 2002) industry, where generation after generation of executives have left their employers to launch new entrants. Tending to operate at the forefront of innovation, they often commercialize technology that their parents had developed but chosen to either ignore or underexploit (Bhide, 2000; Christensen, 1993; 1997). Spin-outs therefore pose a special threat to incumbents, who not only risk losing proprietary knowledge but also run the danger of being upstaged by knowledge that they themselves have created. We analyze how inherited knowledge from parent firms affected spin-outs’ formation and evolution using data on the rigid disk drive industry, which has been called the “fruit fly of industries” due to its rapid technological changes (Christensen, 1993). A significant percentage of new entrants in this industry were spin-outs, thus making it a particularly appropriate setting for our study.Our paper contributes to the under-researched phenomenon of employee entrepreneurship (Klepper 2001). We offer a strategic management view of knowledge inheritance and its role in seeding the entrepreneurial process – a perspective that is quite distinct from dominant economic (Moen, 2001; Zucker, Darby & Brewer, 1998), and sociological perspectives (Aldrich & Pfeffer, 1976; Boeker, 1997) on knowledge spillovers. We also contribute to emerging ideas surrounding strategic entrepreneurship (Hitt et al., 2001) by adopting a symbiotic perspective of value-creation and value-appropriation. Finally, by suggesting that the organization of capabilities may be as important a source of performance heterogeneity as the capabilities themselves, our research advances understanding of the resource-base (Leiponen, 1999).K NOWLEDGE T RANSFER AND THE S PIN-OUT P HENOMENONKnowledge is not only the fountainhead of innovative firm entry (Schumpeter, 1934) but is also fundamental to a firm’s evolution and growth (Spender, 1996). A critical competitive asset, tacit knowledge enables a firm to build new resource positions to take advantage ofchanging conditions (Eisenhardt & Martin, 2000; Grant, 1996). The ability to reconfigure resources ahead of competitors is especially relevant in high-technology markets, where frequent technological disruptions create both opportunities in new subfields and threats of obsolescence (Mitchell, 1994; Cohen & Levinthal, 1990; Teece, Pisano, & Shuen, 1997). Underlying strategic renewal in such markets are investments in developing two key interrelated capabilities, namely, R&D and marketing (Daneels, 2002; Teece, 1986).While technological capabilities reflect a firm’s ability to generate new scientific discoveries and technological breakthroughs, market pioneering capabilities enable a firm to commercialize technological innovations ahead of competitors. The potential value created by a firm’s R&D efforts can be unlocked and appropriated through marketing by understanding and satisfying new customer needs quickly (Jaworski & Kohli, 1993; Narver & Slater, 1990). Market pioneering not only involves complex marketing skills that are qualitatively different from those required by later entrants (Bowman & Gatignon, 1995) but is critical in markets with short product life cycles, where prices tend to drop sharply after an initial period of time (Hatch & Macher, 2002). The two capabilities are thus complementary in that a firm needs to create technological innovations as well as design “killer applications” that enable it to appropriate market value (Cohen & Levinthal, 1990; Moran & Ghoshal, 1999; Teece, 1986).In the process of investing in such value-creating and appropriating capabilities, firms enhance their employees’ capabilities too. As a firm utilizes its human capital to develop new know-how (Hitt et al., 2001; Lepak & Snell, 1999), its employees simultaneously acquire technological (scientific knowledge), social (personal contacts and network ties), and cultural (value placed by society on symbols of prestige) capital from their employers (Becker, 1964; Long et al., 1998; Yli-Renko, Autio, & Sapienza, 2001). Prior research suggests that an organization’s tacit knowledge, which is integral to acquiring complex scientific or business process skills, is not only socially embedded in organizational routines (Liebeskind, 1996; Nelson & Winter, 1982) but also resides in individual employees and their skills (Hitt et al., 2001; Szulanski, 1996). A complex and critical part of technology relates to its “softer” side,which goes beyond codified knowledge available in scientific papers, formulae, technical specifications, blueprints, or hardware and is often held by individual employees in the form of tacit knowledge and competence assets (Kogut & Zander, 1992; Teece, 1988). This recursive and interdependent relationship between a firm’s tacit knowledge and its human capital (Lepak & Snell, 1999) implies that investments in R&D and marketing increase not only the organization’s resource base but also its employees’ human capital.Unlike tangible assets, however, personnel are under limited organizational control, and free to quit at will (Coff, 1997). Human capital is mobile and, therefore, so is the knowledge that employees possess (Aldrich & Pfeffer, 1976; Boeker, 1997). The impediments to spillovers are further lowered in some states such as California that do not enforce non-compete clauses. Further, market mechanisms are generally ineffective in protecting knowledge spillovers caused by employee mobility. While firms can increase employees’ exit costs and impose “golden handcuffs” (Liebeskind, 1996), these incentive mechanisms are subject to agency costs. Problems of moral hazard (Wiggins, 1995) and information asymmetries (Anton & Yao, 1995) create contractual problems between employees and their employers. As a result, incentives provided by incumbent firms to lock in their employees (and their knowledge) may not be effective because the potential rewards are greater in entrepreneurial ventures.Due to the uncertainties and costs of protecting knowledge, technology-rich firms have been referred to as “precarious monopolies” (Stinchcombe & Heimer, 1988). By forming their own entrepreneurial ventures, employees can expropriate the outputs of their previous employer’s investments in capability development. Organizations thus often emerge from other organizations (Stinchcombe, 1965), as employees leave to found new spin-out organizations using knowledge gained as a result of employment with the industry incumbent.Knowledge Capabilities and Spin-out GenerationAbundant Knowledge: Anecdotal evidence suggests that some firms seem to be “entrepreneurial hotbeds” in spawning progeny (Burton et al., 2002). What these firms seem to have in common is an abundance of knowledge. There are two reasons why firms with abundantknowledge would be associated with a higher potential for spin-out generation. First, the place of employment may influence an employee’s ability to perceive a prospect. Since exploiting an opportunity is endogenous to discovering it (Shane, 2000), and knowledge asymmetry lies at the heart of entrepreneurship (Venkataraman, 1997), possessing unique and idiosyncratic information is a source of advantage since it enables one to spot potential opportunities ahead of others. Working with firms that are at the cutting-edge helps create a “knowledge corridor” that facilitates opportunity recognition (Hayek, 1945; Venkataraman, 1997). The stock of prior knowledge has an influence on one’s ability to understand, infer and creatively extend new knowledge to new frontiers in a way that those lacking prior information cannot replicate (Roberts, 1991). In the context of technological innovations, for example, Shane (2000) noted that prior information triggers the discovery of a new entrepreneurial opportunity. Further, the quality of research discussions and social interactions are likely to be substantively different in a premier research institution than in others. Scientists have been known to undergo short-term financial sacrifices to apprentice for firms that are on the technological frontier in order to enhance their knowledge (Franco & Filson, 2000; Hitt et al., 2001).Similarly, by accumulating and developing rule-like responses, interpretive schemas, and outcome evaluations (Greve & Taylor, 2000), firms can develop decision heuristics that enhance their market pioneering knowledge. For example, prospector organizations that achieve growth by entering new markets and frequently expanding product offerings may engage in higher levels of market-oriented behavior and, in the process, develop distinctive competencies in pioneering new market segments (Matsuno & Mentzer, 2000; Slater & Olson, 2001). As employees internalize the organization’s culture (Inzerille & Rosen, 1983; Meek 1988), such strategic orientations may provide prospective founders with procedural and declarative knowledge related to assessing market needs and identifying new market opportunities earlier than competitors. Thus, employees of firms with greater knowledge capabilities are more likely to perceive the next generation of technologies and markets earlier than their counterparts in firms that lag behind.Second, raising venture capital for an entrepreneurial venture is fraught with information asymmetry between the entrepreneur and the venture capitalist, causing problems of moral hazard and adverse selection. (Brav & Gompers, 1997). The newer the technology and more nascent the market, the greater the information asymmetry and associated uncertainty about the venture’s prospects. In such situations, investors depend on certification cues to make quality judgments, and the prospective entrepreneur’s institutional affiliation assumes heightened importance in assuring venture quality and mitigating concerns about the liabilities of newness (Gompers & Lerner, 2001; Shane & Khurana, 1999; Stuart, Hoang, & Hybels, 1999). Past research indicates that employment affiliation with a marquee firm transfers status and thus has a legitimizing effect (Podolny, 1994; Stuart et al., 1999). Accordingly, employees of well-reputed firms benefit from enhanced social capital in the form of reputation, networks, role models, and “entrepreneurial capital” (Aldrich, Renzulli & Langton, 1998). Research findings indicate that entrepreneurs’ prior jobs influence perceptions of their skills and trustworthiness (Davis, 1991; Eisenhardt & Schoonhoven, 1996). Further, innovations in emerging areas of technology have been perceived to be more important when affiliated with high-status organizations (Podolny & Stuart, 1995). In the absence of unambiguous measures of quality, institutional affiliations may thus serve an endorsement role and influence perceptions relating to the quality and promise of a proposed entrepreneurial venture. These, in turn, help to mobilize the financial and other resources necessary to undertake an entrepreneurial venture (Burton et al., 2002; Higgins & Gulati, 2003). As a result, the place of prior employment can influence access not only to opportunities but also resources (Granovetter, 1985), since investors are likely to be more willing to back ventures in which the founder’s employer is a technological or marketing leader.Thus, employees at firms with better capabilities have a higher potential to create new ventures due to both opportunity recognition and investor confidence, but whether they realize these opportunities and commit to leaving employment to start-up a new venture is likely to depend on how well their employer firm uses the abundance of knowledge it generates.Underutilization of Abundant Knowledge: When an organization’s strategy for acquiring knowledge emphasizes either technological know-how or market pioneering know-how and ignores the other, many identified but unexploited opportunities result. In such situations, employees may be more likely to leave and start their own ventures. Research on individual risk- taking behavior suggests that individual action may be spurred by divergence between organizational and individual goals (Greve, 1998). While top management tends to emphasize goals that are salient to external stakeholders who provide critical resources to the organization, employee aspirations could be different. Christensen (1993) showed how a firm’s dependence on existing customers both hampered efforts to reorient market strategies and frustrated engineers whose technological inventions were not commercialized. When incumbent firms develop promising technological inventions but fail to marshal the resources needed to take the technologies to market, their behavioral inertia and inaction may result in a growing gap between employees’ aspirations and their current situation (Kahneman & Tversky, 1979). Similarly, as organizations develop their "scouting" and "prospecting" abilities, they may uncover new opportunities, and firms with superior market pioneering know-how will be able to home in on these better. Some of these opportunities may require the firms to develop technologies or venture in directions that they are not willing to go, causing again a divergence between the professional personnel and top management. Thus, if firms do not simultaneously develop their technological and market pioneering know-how, they create frustration among employees, who perceive their organizations as systematically missing out on either value-creating or value- appropriating opportunities.Further, an incumbent’s unwillingness to pursue certain technologies may also cause employees to perceive reduced entry and survival barriers for their own ventures. Thus, undeveloped technologies or new market opportunities, particularly those that are substantial and path breaking, increase employees’ confidence in venturing out and their entrepreneurial propensity (Eisenhardt, 1989). Underexploitation causes some employees to act on the potential created by the abundant opportunities by leaving and starting their own firm. Therefore,H1: The probability of spin-out generation is likely to be higher when a firm possesseshigher levels of either (a) technological or (b) market pioneering know-how. Simultaneous Creation and Appropriation of Knowledge: The complementary nature of technological and market pioneering know-how (Griffin & Hauser, 1996; Teece, 1986) not only creates a valuable synergy that increases a firm’s effectiveness but also inhibits competitive imitation (Grant, 1991). First, by responding to opportunities, organizations prevent aggravation and frustration among employees from their “shelved” inventions (Christensen, 1993; Garvin, 1983). Better job satisfaction and increased prospects reduce the chances of employees leaving (Benkhoff, 1997). Second, incumbent organizations that possess both high-end technological and market pioneering know-how exhibit a “willingness to cannibalize” (Chandy & Tellis, 1998; Kamien & Schwartz, 1982). Their preemptive entry into new technical subfields deters spin-out formation by raising entry barriers. In essence, these organizations commit themselves to preventing the underexploitation of their knowledge resources. Through a combination of incentives aimed at employee retention and competitive deterrence, incumbents with high capabilities in both areas can reduce the incidence of spin-out generation. In other words, an abundance of underutilized knowledge can beget spin-outs, but spin-outs are deterred when the knowledge of a firm is put to good use.1 Thus,H2: The greater the level of both technological know-how and market pioneering know-how in an incumbent, the lesser is the likelihood of spin-out generation.Spin-out’s Knowledge InheritanceThe knowledge that a spin-out inherits from the incumbent firm that employed its founders should have an imprinting effect of the spin-out. Organizational sociologists have theorized about the interorganizational transfer of rules, routines, and resources (Brittain & Freeman, 1980; Hannan & Freeman, 1986). This literature posits that, similar to the reproduction and transmission of biological genes, organizational blueprints transfer through the career experiences of founders (Winter, 1991). Applying the transfer of resources to the domain of organizational speciation, it is plausible that progeny inherit knowledge-based resources from1 We would like to thank an anonymous reviewer for helping us articulate this thought.their parents (Phillips, 2002). When employees leave to start a new venture, they walk out with tacit knowledge that goes beyond codified information. Parental know-how at the time of spin-out inception thus represents knowledge that potentially can be inherited by the spin-out. Moreover, the knowledge inherited from the parent may have a long term effect on the spin-out’s knowledge capabilities.Founding conditions, related to both the environment and the founder are imprinted on a new organization (Stinchcombe, 1965) and have an impact on the organization at various levels—structure, strategy, technology, and routines—and have long-term consequences for a firm’s actions and performance (Baum, Calabrese, & Silverman, 2000; Eisenhardt & Schoonhoven, 1991; Sastry & Coen, 2000; Shane & Stuart, 2002). Moreover, an organization’s absorptive capacity—its ability, efficiency, and aspiration to learn, discover, and acquire new knowledge—is also linked to its level of prior related knowledge (Cohen & Levinthal, 1990). This implies that the knowledge inherited by its founders from a parent organization positions a spin-out on a developmental path that affects its long-term competitive positioning. Thus, the parent’s knowledge at the time of spin-out inception is related to the spin-out’s knowledge over time. Further, the same arguments also relate to the within-group variance among spin-outs’ capabilities. Smart parents are more likely to create smart progenies. Starting with a good model can have an impact on subsequent firm performance (Cyert, Kumar, & Williams, 1993), and superior endowments at birth result in long-term robust performance. Thus,H3: The levels of the spin-out firm’s (a) technological and (b) market pioneering know-how over time will be positively related to the level of its parent’s (a) technological and(b) market pioneering know-how at the time of the spin-out’s inception.Spin-out Knowledge Capabilities and SurvivalDue to inherited knowledge and entrepreneurial origin, spin-outs may differ in their knowledge and survival probabilities vis-à-vis other entrants. We follow Helfat and Lieberman’s (2002) distinction between entrants, as opposed to the coarse-grained distinction between de novo and de alio entrants based on pre-entry experience (Carroll et al. 1996; Klepper & Simons, 1996). Among de novo entrants representing new start-ups, we distinguish between spin-outs andnon-spin-out de novo entrants, since spin-outs inherit knowledge from an industry incumbent. 2 Among de alio entrants, we distinguish between diversifying entrants and incumbent-backed ventures. Diversifying entrants are established firms in other industries that enter the focal industry, while incumbent-backed ventures represent separate legal entities with incumbent ties (e.g., subsidiaries, joint ventures, franchisees, spin-offs).3Entrant Knowledge Capabilities: Among the four entrant categories, both incumbent-backed ventures and spin-outs benefit from direct knowledge transfer from an incumbent. Just as spin-outs inherit knowledge from the parent firm through founders, incumbent-backed ventures obtain knowledge through a cooperative relationship with the incumbent at birth, and perhaps on a continuing basis. Thus, given the links to incumbent firms, knowledge difference between these two groups is an empirical issue. Diversifying and non-spin-out de novo entrants, however, learn either by doing or by indirect grafting mechanisms such as recruiting employees who work in the industry (Boeker, 1997; DiMaggio & Powell, 1983). Compared with these two types of entrants, spin-outs should have an advantage because the knowledge brought in by ex-employee founders will be less vulnerable to the problem of “stickiness” and will therefore be more effectively transferred internally within the organization (Szulanski, 1996).The adoption of complex technology and business processes involves a conscious process of reconstruction, diffusion, and integration into new routines within an organization. Stickiness, reflects difficulties encountered by organizations in effecting internal transfer of knowledge. This causes knowledge, particularly the tacit component, to lie inert in some part of the organization - acquired, yet not readily accessible or retrievable, and therefore not deployable and convertible into value when required (Whitehead, 1929). As a result, “organizations may not necessarily know all that they know” (Szulanski, 2000, p. 10) and thus fall short of fully exploiting their know-how (von Hippel, 1994).2 We refrain from using the term “entrepreneurial spin-offs” used by Helfat and Lieberman (2002) and Klepper (2001, 2002) to avoid confusion with the term “spin-off” as defined in the finance literature.3 As Helfat and Lieberman (2002) elaborated, incumbent-backed ventures are hybrids between diversifying and de novo entrants that are set up by established firms. While the established firm may have a financial stake or representation on the board of directors, these are nonetheless new companies and separate legal entities.The directness of knowledge transfer through employee-founders reduces stickiness and increases a firm’s ability to integrate and successfully acquire knowledge. First, the general management role of entrepreneurial founders enables them to adopt a holistic picture. Employees may be limited in their ability and motivation to transfer relevant resources across different departments of the organization, leading to an under-use of knowledge. Fisher, Maltz and Jaworski (1997) found that functional identification may actually decrease information dissemination through the organization. Acting as knowledge brokers between functional domains and various employees, founders on the other hand can increase the likelihood of employees adopting a new practice (Lenox & King, 2003). By being in a more influential position to bring about progressive routinization of best practices, founders have an advantage over other lower-level employees in effecting the knowledge transfers. Second, founders have the incentives and motivation to share their knowledge and transform it into best practices so as to appropriate full benefits from their know-how, while agency problems and competitive incentive structures in organizations can create certain exchange dynamics in internal knowledge markets that may discourage employees from sharing knowledge (Davenport & Prusak, 1998). Since power in the organization depends on having non-replicated knowledge, employees may prefer not to lose their knowledge monopoly. Founders face no such divergence between their own and the organization’s goals, causing useful knowledge to be disseminated within the organization more easily.Further, founding teams of spin-outs are likely to have a knowledge advantage over employees hired individually into new firms. Typically, multiple employees from diverse backgrounds (and often from different incumbents) come together to start a new firm, thus creating synergy and increasing the potential value of their combined know how (Dess & Shaw, 2001). Armed with insider knowledge, these prospective entrepreneurs are likely to conduct an active search for specific pockets of complementary knowledge in their employing firm or their social networks in the industry to create synergy among the individual components of know-how. Accordingly,。

Macroeconomics R. GLENN HUBBARD COLUMBIA UNIVERSITY ANTHONY PATRICK O’BRIEN LEHIGH UNIVERSITY MATTHEW RAFFERTY QUINNIPIAC UNIVERSITY Boston Columbus Indianapolis New York San Francisco Upper Saddle RiverAmsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City So Paulo Sydney Hong Kong Seoul Singapore Taipei TokyoAbout the AuthorsGlenn Hubbard Professor Researcher and Policymaker R. Glenn Hubbard is the dean and Russell L. Carson Professor of Finance and Economics in the Graduate School of Business at Columbia University and professor of economics in Columbia’s Faculty of Arts and Sciences. He is also a research associate of the National Bureau of Economic Research and a director of Automatic Data Processing Black Rock Closed- End Funds KKR Financial Corporation and MetLife. Professor Hubbard received his Ph.D. in economics from Harvard University in 1983. From 2001 to 2003 he served as chairman of the White House Council of Economic Advisers and chairman of the OECD Economy Policy Commit- tee and from 1991 to 1993 he was deputy assistant secretary of the U.S. Treasury Department. He currently serves as co-chair of the nonpar-tisan Committee on Capital Markets Regulation and the Corporate Boards Study Group. ProfessorHubbard is the author of more than 100 articles in leading journals including American EconomicReview Brookings Papers on Economic Activity Journal of Finance Journal of Financial EconomicsJournal of Money Credit and Banking Journal of Political Economy Journal of Public EconomicsQuarterly Journal of Economics RAND Journal of Economics and Review of Economics and Statistics.Tony O’Brien Award-Winning Professor and Researcher Anthony Patrick O’Brien is a professor of economics at Lehigh University. He received a Ph.D. from the University of California Berkeley in 1987. He has taught principles of economics money and banking and interme- diate macroeconomics for more than 20 years in both large sections and small honors classes. He received the Lehigh University Award for Distin- guished Teaching. He was formerly the director of the Diamond Center for Economic Education and was named a Dana Foundation Faculty Fel- low and Lehigh Class of 1961 Professor of Economics. He has been a visit- ing professor at the University of California Santa Barbara and Carnegie Mellon University. Professor O’Brien’s research has dealt with such issues as the evolution of the U.S. automobile industry sources of U.S. economiccompetitiveness the development of U.S. trade policy the causes of the Great Depression and thecauses of black–white income differences. His research has been published in leading journals in-cluding American Economic Review Quarterly Journal of Economics Journal of Money Credit andBanking Industrial Relations Journal of Economic History Explorations in Economic History andJournal of PolicyHistory.Matthew Rafferty Professor and Researcher Matthew Christopher Rafferty is a professor of economics and department chairperson at Quinnipiac University. He has also been a visiting professor at Union College. He received a Ph.D. from the University of California Davis in 1997 and has taught intermediate macroeconomics for 15 years in both large and small sections. Professor Rafferty’s research has f ocused on university and firm-financed research and development activities. In particular he is interested in understanding how corporate governance and equity compensation influence firm research and development. His research has been published in leading journals including the Journal of Financial and Quantitative Analysis Journal of Corporate Finance Research Policy and the Southern Economic Journal. He has worked as a consultantfor theConnecticut Petroleum Council on issues before the Connecticut state legislature. He has alsowritten op-ed pieces that have appeared in several newspapers including the New York Times. iii Brief Contents Part 1: Introduction Chapter 1 The Long and Short of Macroeconomics 1 Chapter 2 Measuring the Macroeconomy 23 Chapter 3 The Financial System 59 Part 2: Macroeconomics in the Long Run: Economic Growth Chapter 4 Determining Aggregate Production 105 Chapter 5 Long-Run Economic Growth 143 Chapter 6 Money and Inflation 188 Chapter 7 The Labor Market 231 Part 3: Macroeconomics in the Short Run: Theory and Policy Chapter 8 Business Cycles 271 Chapter 9 IS–MP: A Short-Run Macroeconomic Model 302 Chapter 10 Monetary Policy in the Short Run 363 Chapter 11 Fiscal Policy in the Short Run 407 Chapter 12 Aggregate Demand Aggregate Supply and Monetary Policy 448 Part 4: Extensions Chapter 13 Fiscal Policy and the Government Budget in the Long Run 486 Chapter 14 Consumption and Investment 521 Chapter 15 The Balance of Payments Exchange Rates and Macroeconomic Policy 559 Glossary G-1 Index I-1ivContentsChapter 1 The Long and Short of Macroeconomics 1WHEN YOU ENTER THE JOB MARKET CAN MATTER A LOT ........................................................ 11.1 What Macroeconomics Is About........................................................................... 2 Macroeconomics in the Short Run and in the Long Run .................................................... 2 Long-Run Growth in the United States ............................................................................. 3 Some Countries Have Not Experienced Significant Long-Run Growth ............................... 4 Aging Populations Pose a Challenge to Governments Around the World .......................... 5 Unemployment in the United States ................................................................................. 6 How Unemployment Rates Differ Across Developed Countries ......................................... 7 Inflation Rates Fluctuate Over Time and Across Countries................................................. 7 Econo mic Policy Can Help Stabilize the Economy .. (8)International Factors Have Become Increasingly Important in Explaining Macroeconomic Events................................................................................. 91.2 How Economists Think About Macroeconomics ............................................. 11 What Is the Best Way to Analyze Macroeconomic Issues .............................................. 11 Macroeconomic Models.................................................................................................. 12Solved Problem 1.2: Do Rising Imports Lead to a Permanent Reductionin U.S. Employment. (12)Assumptions Endogenous Variables and Exogenous Variables in EconomicModels ........................................................................................................ 13 Forming and Testing Hypotheses in Economic Models .................................................... 14Making the Connection: What Do People Know About Macroeconomicsand How Do They KnowIt .............................................................................................. 151.3 Key Issues and Questions of Macroeconomics ............................................... 16An Inside Look: Will Consumer Spending Nudge Employers to Hire................................ 18Chapter Summary and Problems ............................................................................. 20 Key Terms and Concepts Review Questions Problems and Applications Data Exercise Theseend-of-chapter resource materials repeat in all chapters.Chapter 2 Measuring the Macroeconomy 23HOW DO WE KNOW WHEN WE ARE IN ARECESSION ........................................................... 23Key Issue andQuestion .................................................................................................... 232.1 GDP: Measuring Total Production and Total Income ..................................... 25 How theGovernment Calculates GDP (25)Production and Income (26)The Circular Flow of Income (27)An Example of Measuring GDP (29)National Income Identities and the Components of GDP (29)vvi CONTENTS Making the Connection: Will Public Employee Pensions Wreck State and Local Government Budgets.................................................................... 31 The Relationship Between GDP and GNP........................................................................ 33 2.2 Real GDP Nominal GDP and the GDP Deflator.............................................. 33 Solved Problem 2.2a: Calculating Real GDP . (34)Price Indexes and the GDP Deflator (35)Solved Problem 2.2b: Calculating the Inflation Rate ..........................................................36 The Chain-Weighted Measure of Real GDP ....................................................................37 Making the Connection: Trying to Hit a Moving Target: Forecasting with “Real-Time Data” .................................................................................. 37 Comparing GDP Across Countries................................................................................... 38 Making the Connection: The Incredible Shrinking Chinese Economy ................................ 39 GDP and National Income .............................................................................................. 40 2.3 Inflation Rates and Interest Rates ....................................................................... 41 The Consumer Price Index .............................................................................................. 42 Making the Connection: Does Indexing Preserve the Purchasing Power of Social Security Payments ................................................................ 43 How Accurate Is theCPI ............................................................................................... 44 The Way the Federal Reserve Measures Inflation ............................................................ 44 InterestRates .................................................................................................................. 45 2.4 Measuring Employment and Unemployment .. (47)Answering the Key Question ............................................................................................ 49 An Inside Look: Weak Construction Market Persists.......................................................... 50 Chapter 3 The Financial System 59 THE WONDERFUL WORLD OFCREDIT ................................................................................... 59 Key Issue and Question .................................................................................................... 59 3.1 Overview of the Financial System ...................................................................... 60 Financial Markets and Financial Intermediaries ................................................................ 61 Making the Connection: Is General Motors Making Cars or Making Loans .................... 62 Making the Connection: Investing in the Worldwide Stock Market . (64)Banking and Securitization (67)The Mortgage Market and the Subprime Lending Disaster (67)Asymmetric Information and Principal–Agent Problems in Financial Markets...................68 3.2 The Role of the Central Bank in the Financial System (69)Central Banks as Lenders of Last Resort ..........................................................................69 Bank Runs Contagion and Asset Deflation ....................................................................70 Making the Connection: Panics Then and Now: The Collapse of the Bank of United States in 1930 and the Collapse of Lehman Brothers in2008 (71)3.3 Determining Interest Rates: The Market for Loanable Funds and the Market forMoney .......................................................................................... 76 Saving and Supply in the Loanable Funds Market ........................................................... 76 Investment and the Demand for Loanable Funds ............................................................ 77 Explaining Movements in Saving Investment and the Real Interest Rate (78)CONTENTS .。

盘点美国排名前⼗的供应链（物流）硕⼠项⽬（上）（世毕盟留学）盘点美国排名前⼗的供应链（物流）硕⼠项⽬（上）供应链管理（物流）专业专注于管理原材料、半成品和成品，负责的过程从最初的源头⼀直到最终消费者，其⽬的在于创造价值，使供应和需求能达到⼀个动态的平衡。

看起来很复杂，但是通过⼀个很简单的例⼦你就可以明⽩。

你在电商那买了⼀部最新的⼿机，从⼿机零件的制造商开始，到⼯⼚把零件组装成⼀部⼿机，到成品⼿机运输到电商仓库，从电商仓库运输到你⼿中。

这条供应链可以⾮常长（会⽐我描述得长得多），我可以给你讲得⾮常细致，中间会涉及到很多环节，但是这⾥我们不需要赘述，需要明⽩的是该专业是会利⽤现代化技术和理论来优化各个环节，实现速度和效率的最⼤化的综合性学科。

这个过程会涉及到很多⽅⾯的知识，⽐如⼯业设计，系统⼯程，运筹，采购，信息技术，市场营销等等，现在⼜加⼊了可持续发展和风险控制的部分，全球化的趋势也在使该⾏业有着⽇新⽉异的变化。

作为⼀个交叉性的学科，物流学综合了多门学科的研究⽅法和特点，包括运筹学、信息管理学、市场，甚⾄会计和⾦融等等学科。

所以，对学⽣的本⾝背景相⽐其他的商务硕⼠要⾼⼀些，尤其是对申请者的⼯作经历，⽐如有些学校在物流研究⽣录取上⾯明确有⼀年以上的⼯作经验，同时在学术背景上要求学⽣在本科学习的阶段已经学习过相关的课程。

这个要求也是物流专业和商科其他专业⽅向之间存在的⼀个⽐较明显的差别，⼀般商科的⽅向即使对⼯作经验⽐较亲睐，但在MS⽅向中，不会硬性的要求申请者有⼏年的职业⼯作经验的积累。

因此，在申请的竞争上⾯，物流专业是属于商科专业中竞争⽐较激烈的⽅向，对申请者本⾝的条件和背景有着更加严格的要求和标准。

我们先来看⼀下2017 U.S. News上美国供应链（物流）项⽬的专业排名，总的来说，专排前⼗⾥综排也在前⼗的只有2所，MIT和Stanford，作为⼀个⾮基础学科，这也是情理之中的事。

1. Michigan State University (Broad) 密歇根州⽴⼤学供应链管理科学硕⼠(MSSCM)是⼀个很独特的研究⽣项⽬，它提供了有关供应链实践技术的更深层次的知识，这个项⽬有⼀个特点是它的课程设置可允许在学⽣们全职⼯作的时候完成。

Political Uncertainty and Corporate InvestmentCyclesBRANDON JULIO and YOUNGSUK YOOK∗November5,2010ABSTRACTWe document cycles in corporate investment corresponding with the timing of na-tional elections around the world.During election years,firms reduce investment ex-penditures by an average of4.8%relative to non-election years,controlling for growthopportunities and economic conditions.The magnitude of the investment cycles varieswith different country and election characteristics.We investigate several potential expla-nations andfind evidence supporting the hypothesis that political uncertainty leadsfirmsto reduce investment expenditures until the electoral uncertainty is resolved.Thesefind-ings suggest that political uncertainty is an important channel through which the politicalprocess affects real economic outcomes.∗Julio is at London Business School and Yook is at the Graduate School of Business at Sungkyunkwan Uni-versity.Patrick Bolton,Murillo Campello,Ethan Cohen-Cole,Alex Edmans,Zsuzsanna Fluck,Paolo Fulghieri, Dirk Hackbarth,Cam Harvey(Editor),Li Jin,Tae-Young Kim,Stewart Myers,Bang Nguyen-Dang,Meijun Qian,Philipp Schnabl,Vikrant Vig,Michael Weisbach,Toni Whited,two anonymous referees,and an asso-ciate editor provided useful comments as did seminar participants at the China Europe International Business School,Georgetown University,Hong Kong Baptist University,Hong Kong Polytechnic University,Korea Uni-versity,London School of Economics,Nanyang Technological University,Norwegian School of Economics and Business Administration,Seoul National University,Sungkyunkwan University,University of North Carolina at Chapel Hill,the2008Chinese International Conference in Finance,the2008AsianFA-NFA International Con-ference,the2009Paris Spring Corporate Finance Conference,the2009Singapore International Conference on Finance,the2009European Finance Association Conference,the2010American Finance Association Confer-ence,and the2010Finance Down Under Conference.“...how unrealistic any theory of investment opportunity is which leaves the polit-ical factor out of account”.Joseph A.Schumpeter(1939)The relationship between politics and economic outcomes has a long history in research and public debate.One important way in which politics is hypothesized to influence real decisions is through the channel of uncertainty and instability.In particular,the incentives and uncer-tainties associated with possible changes in government policy or national leadership have implications for the behavior of both politicians andfirms.The effects of policy uncertainty are especially relevant in light of the recentfinancial crisis and recession.There is a great deal of uncertainty as to how governments will shape policy to stimulate investment in the short run and formulate regulatory and economic policy in the long run.It has been argued that this uncertainty itself may be hindering a recovery by inducingfirms to delay investment until the uncertainty related to futurefinancial regulation and macroeconomic policy is resolved.1In this paper,we examine the effects of political uncertainty on the investment behavior of firms in the context of national elections.Elections in which the national leader is determined provide an interesting setting to study the effects of political uncertainty on investment for two important reasons.First,while standard models of policy typically assume a single welfare maximizing planner that makes policy choices over the entire life of the economy,the real world is characterized by leaders who face limited terms and may be replaced by other lead-ers with different policy preferences.Election outcomes are relevant to corporate decisions as they have implications for industry regulation,monetary and trade policy,taxation,and,in more extreme cases,the possible expropriation or nationalization of privatefirms.Second, investigating the impact of political uncertainty on investment is a challenging task due to the potential endogeneity between uncertainty and economic growth as the economic downturn itself has arguably generated a great deal of political uncertainty.Elections around the world provide a natural experimental framework for studying political influences in corporate in-vestment,allowing us to disentangle some of the endogeneity between economic growth and political uncertainty.If political uncertainty is higher when changes in national leadership are more probable,elections provide a recurring event that helps isolate the impact of policy uncertainty on investment from other confounding factors.The timing of elections is out of the control of any individualfirm and evenfixed in time by constitutional rules for a largeproportion of observations in our sample.In addition,elections around the world take place in different years over time,allowing us to net out any global trends in corporate investment. Using national elections in48countries between1980and2005,we examine changes in cor-porate investment as political uncertaintyfluctuates by comparing corporate behavior in the year leading up to the national election outcomes with that in non-election years.The intuition underlying the relationship between electoral uncertainty and investment is simple:if an election can potentially result in a bad outcome from afirm’s perspective,the option value of waiting to invest increases and thefirm may rationally delay investment until some or all of the policy uncertainty is resolved.The relationship between uncertainty and real investment has been modeled by Bernanke(1983)and Bloom,Bond and Van Reenen(2007), among others.In these models,firms become cautious and hold back on investment in the face of uncertainty.Others have modeled the effects of political uncertainty in a macroeco-nomic context.Rodrik(1991)and Pindyck and Solimano(1993)are prominent examples of this literature in which the uncertainty brought about by political factors leadsfirms to choose lower levels of investment expenditures.Chen and Funke(2003)model the private invest-ment decision in emerging markets in the face of policy uncertainty.More recently,Bloom, Flotoetto and Jaimovich(2009)model business cycles as a function of variation in levels of macroeconomic uncertainty.The idea that political instability can deter investment on the aggregate level is supported by empirical evidence.Barro(1991)and Alesina and Perotti(1996)find that measures of political instability and violence are correlated with cross-country differences in investment rates.Pindyck and Solimano(1993)and Mauro(1995)find evidence that political uncertainty and an index measuring bribery and corruption are negatively related to investment spending at the aggregate level.However,some difficulties arise in interpreting the aggregate evidence. First,it is not clear whether the various measures of political instability are exogenous to the economic conditions and the aggregate investment.Second,as discussed in Pindyck and Solimano(1993),the models of investment under uncertainty are less clear about how un-certainty affects long-run equilibrium investment rates,defined as the ratio of investment to capital stock,as uncertainty affects both the optimal capital stock and investment in the long run.The predictions of the models are less ambiguous when there are temporary shocks to the level of uncertainty as the uncertainty mainly works through investment rather than capitalstock in the short run.Indeed,Bernanke(1983)shows that events whose long-run implica-tions are uncertain can generate investment cycles by increasing the returns to waiting for new information,particularly when the source of uncertainty periodically renews itself over time.A temporary increase in uncertainty surrounding national elections creates incentives that may induce immediate declines in investment expenditures.Our empirical investigation provides results consistent with the political uncertainty hy-pothesis.We document novel and robust evidence that political uncertainty around national elections induces cycles in corporate investment.In the period leading up to the election,in-vestment expenditures decline by an average of4.8%,controlling for growth opportunities, cashflows,and economic conditions.To address the concern that the results may be driven by elections that are notfixed in time by constitution,we repeat the analysis by estimating our investment regressions only for countries withfixed election timing andfind similar re-sults.Additionally,we examine the determinants of early elections andfind a strong positive correlation between economic growth and the probability of holding an early election.To the extent that afirm’s investment expenditures are positively correlated with economic growth, this suggests that the inclusion of endogenously timed elections in the regressions has the net effect of reducing the dampening effect of electoral uncertainty on investment as the elections are generally called during periods of relatively high economic performance.Across countries,wefind that the temporary decline in investment expenditures is larger in countries with civil law origins,fewer checks and balances,less stable governments,and in countries with a higher ratio of central government spending to GDP.Within countries,the cycles are more pronounced forfirms in industries considered to be more sensitive to political outcomes.Elections in which the outcome is“close”as measured by voting results lead to deeper investment cycles than elections in which the victor wins by a large margin.We also find that investment rates drop more in election years in which the incumbent national leader is classified as“market-friendly”by the World Bank.We also show that the election-year drop in investment is followed by a small,temporary increase in investment in the year imme-diately following the election as the uncertainty over election outcomes subsides.However, the overall magnitude of the post-election increase in investment is smaller than that of the earlier decline.We also measure changes in cash holdings,finding temporary increases in cash balances in the year prior to the election in the amount of4.3%of the average cash toassets ratio,controlling forfirm and economic conditions.The increase in cash holdings is similar in magnitude to the election-year decline in investment,suggesting that the funds that would have been used as investment are temporarily held as cash until the election uncertainty is resolved.Political uncertainty is not the only mechanism whereby real outcomes can be affected around the timing of elections.There are two plausible alternative explanations in the lit-erature suggesting election-induced cycles in investment.Thefirst is the political business cycles hypothesis.Starting with Nordhaus’s(1975)model of political business cycles,there has been much debate over whether incumbents manipulatefiscal and monetary policy instru-ments to influence the level of economic activity prior to an election in order to maximize the probability of reelection.Thus,one alternative explanation for our results is that corporate investment is reacting to changing macroeconomic fundamentals.While the political business cycle hypothesis predicts that average economic activity should be higher just before the elec-tion,the actions used to stimulate the economy could have a crowding-out effect on private investment.The second alternative explanation is related to the value of political connections. Somefirms may have incentives to change their investment behavior to help ensure that their political connections remain in office through the election cycle.Bertrand,Kramarz,Schoar and Thesmar(2006)investigate the behavior of politically connectedfirms around municipal elections in France,andfind that thefirms managed by connected CEOs boost their invest-ment during election years,particularly in politically contested areas,likely in an attempt to help their connection get re-elected.We conduct formal tests of these alternative hypotheses andfind no evidence that they are operating in our sample offirms.We therefore view the political uncertainty hypothesis to be the explanation among existing theories that bestfits the patterns in the data.Thesefindings have two important contributions.First,we document a new stylized fact regarding corporate investment around the world.That is,there is a tendency forfirms to reduce investment in election years.These results demonstrate an important link between the political process and real outcomes.Second,the results suggest that political uncertainty mat-ters for afirm’s real investment and savings decisions.This provides an interesting illustration of the impact of uncertainty in general as an important determinant of investment dynamics.As far as we know,we are thefirst to examine the effects of national elections onfirm-level investment behavior around the world.The remainder of the paper proceeds in the following manner.Section II develops the empirical predictions and discusses the identification strategy.Section III summarizes the firm characteristics and the election data.Section IV presents our main empirical results related to corporate investment cycles around elections,including various subsample analyses, multiple robustness checks,and an examination of changes in corporate cash holdings around the election period.Section V concludes.I.Hypothesis Development and Empirical StrategyWhen a particular investment project is characterized by some degree of irreversibility and uncertainty over future cashflows or discount rates,the value of the investment project will be affected by the same factors that influence the pricing offinancial options,in particular,the volatility or uncertainty of the value of the underlying asset.The application of option pricing to capital budgeting has generated many empirical predictions and insights on how investment dynamics change in the face of uncertainty.Some classic examples include McDonald and Seigel(1986),who examine the valuation of operating options and the value of waiting to invest.They demonstrate that even moderate amounts of uncertainty can more than double the required rate of return for investment projects.Ingersoll and Ross(1992)model the timing decision in the face of interest rate uncertainty.They argue that,under the assumptions of irreversibility and uncertainty,the simple net present value(NPV)rule is not optimal from a value-maximizing perspective.Uncertainty increases the value of waiting to invest through what Bernanke(1983)termed the“bad news”principle.That is,an increase in uncertainty causes reductions in current investment only if there is some probability of a bad outcome.In the context of national elec-tions,this suggests thatfirms will delay investment in anticipation of possible negative changes in the country’s macroeconomic policy,taxation,monetary policy,or the general regulatory environment.However,in some cases,the outcome of an election could be construed as good news,regardless of who wins in the end.For example,if the current government is corruptor incompetent,firms could view a likely change in power as good news and hence may not reduce investment prior to the realization of the election outcome since any different outcome may be better than the current state of affairs.The bad news principle is more subtle in this case.For example,suppose afirm is choosing among several mutually exclusive investment projects,each with a positive expected return.Also suppose that the outcome of an upcoming election will increase the expected return of each of the investment projects,regardless of the outcome.Thefirm still has an incentive to delay investment if the outcome would reorder the rankings of the individual projects in terms of expected returns.Thus,the bad news principle does not require the possibility of extreme policies such as nationalization of private assets to induce changes in investment.Even positive changes in policy may induce an incentive forfirms to wait to invest as the outcome will still have implications for howfirms allocate investment spending across various investment opportunities.If political uncertainty matters forfirms,then the recurring nature of the political uncer-tainty around elections can generate cycles in investment spending.This is an application of Bernanke’s bad news principal that the possibility of a bad election outcome induces afirm to hold off on its investment projects.This leads to our primary hypothesis that investment expenditures are expected to decline in the year leading up to the election.That is,we expect the average effect of electoral uncertainty to be a temporary decline in the conditional mean investment rate for allfirms in the sample.The bad news principle also suggests that the value of waiting to invest will vary fromfirm tofirm and across countries.Within countries,the magnitude of the investment cycle may vary across elections,depending on the the degree of uncertainty regarding election outcomes.The spread between potential outcomes as well as the likelihood of each outcome will generate heterogeneity in the size of observed investment cycles.Across countries,we hypothesize that investment cycles will be more pronounced in countries with a higher probability of policy changes or a larger variation in possible policy outcomes after the election.Since we are investigating national elections,we expect the effect of elections to be larger for countries with more centralized governments.Political institutions may matter as well.Countries in which political decisions are more constrained by various checks and balances are less likely to experience large policy swings following a change of power.For example,presidential systems are typically considered to have greater checks and balances but lessflexibility inpolicy making relative to parliamentary systems,suggesting that perhaps large policy swings are more common in parliamentary systems.2We also expect that countries with less stable governments in general will experience larger changes in investment around elections.Within countries,we hypothesize that the drop in investment expenditures will be larger when the election outcome is more uncertain.In particular,we expect that cycles will be more pronounced for elections with close outcomes relative to those with large margins of victory. The amount of uncertainty regarding the impending election outcome is unobservable,but we do observe the election results and vote counts for each ing the size of the margin of victory as a proxy for the degree of outcome uncertainty in any given election, we examine whether investment cycles vary with the degree of uncertainty across elections within countries.We also investigate the political platform of the incumbent leader during the election year.The political platform of an incumbent with respect to economic policy may have asymmetric implications on investment cycles.Firms are likely to view a possible shift in leadership from a market-friendly leader to a socialist leader as worse news than a possible shift in the other direction.Our empirical strategy employs the timing of national elections around the world to test the political uncertainty hypothesis.It is important to note that the timing of elections is not a direct measure of political uncertainty.Hence,an important identification assumption is that political uncertainty is indeed higher on average in the period leading up to an election compared to other time periods.There is evidence fromfinancial markets that the uncertainty related to elections and political changes are reflected in asset prices.Bialkowski,Gottschalk and Wisniewski(2008)and Boutchkova,Doshi,Durnev and Molchanov(2010)examine the stock market volatility around national elections andfind that volatility is significantly higher than normal during the election period.Boutchkova et al.(2010)find that the return volatility is higher around elections forfirms operating in politically sensitive industries,suggesting that the increased volatility reflects a higher political risk.Bernhard and Leblang(2006)document changes in bond yields,exchange rates,and equity volatility around elections and other po-litical changes and show that these changes are larger during elections with less predictable outcomes.This evidence provides support for our identification assumption that political un-certainty is higher than normal during elections.Our empirical analysis produces two broad sets of results.In ourfirst set of results,we employ the variation across elections,countries,andfirms to help us identify the uncertainty channel to explain the reduction in corporate investment.Our basic approach in thisfirst step is to examine variation in corporate investment around the timing of national elections and to demonstrate that these changes are larger for events in which the uncertainty related to election outcomes is higher.We recognize that other mechanisms may be at play during election periods that can lead to changes in investment behavior.Therefore,our second set of results attempts to distinguish the political uncertainty channel from other existing hypotheses, namely the political business cycle hypothesis and the possible effects of political connections.II.Data DescriptionA.Election DataThis study considers248national elections in48countries held between1980and2005in which the outcome determined the national leader directly or indirectly.The detailed election information is obtained from a variety of sources.The primary source for election and regime change data is the Polity IV database maintained by the Center for International Develop-ment and Conflict Management at the University of Maryland.This database contains annual information on the regime and authority characteristics of all independent states with total populations greater than500,000.The second major source of information is the World Bank Database of Political Institutions.This source provides information about electoral rules and the classification of political platforms for the elected leaders and candidates.We supplement the election data with various internet sources3for cases in which the election information is missing from the Polity IV database or the Database of Political Institutions.Thefirst task for the election data collection is to identity the chief executive of each coun-try and the national elections associated with the selection of the chief executive.In a country with a presidential system,the supreme executive power is normally vested in the office of the president.Thus,presidential elections are naturally considered in our analysis for coun-tries with presidential systems.In a parliamentary system,the executive power is normallyvested in a cabinet responsible to parliament.In such a country,the prime minister or pre-mier,being the head of the cabinet and leader of the parliament,functions as the actual chief executive of the nation.Thus,legislative elections are utilized for countries with parliamen-tary systems as the outcome of such legislative election has the foremost influence over the appointment of prime minister.4Some countries use a hybrid system combining elements of both parliamentary and presidential democracy;a president and a prime minister coexist with both presidential and legislative elections held nationally.In such cases,the constitutional framework and practice is examined in greater detail to understand how executive power is divided between the two leaders,and the election associated with the leader who exerts more power over executive decisions is selected for the study5.As a robustness check,we repeat our analysis excluding the four countries for which the classification requires some discretion (Finland,France,Pakistan and Poland)andfind that the results are unchanged.The resulting data set comprises31countries with legislative elections,16countries with presidential elections,and one country(Israel)with prime ministerial elections.6Table I presents the classification of political systems and the number of elections utilized for each of the48countries in our sample.The table also shows the origin of each country’s legal system,as reported by La Porta,Lopez-de-Silanes,Shleifer,and Vishny(1998).[TABLE I HERE]Another important characteristic of national elections is whether the timing of the elec-tions is exogenously specified by electoral ernments under some electoral systems can be dissolved before the expiry of its full term for various reasons,and an election is then normally called to form a new government.This complicates the interpretation of our empirical results as the timing of elections may be endogenously connected to the country’s economic performance over time.Ito(1990),for example,documents that Japanese general elections have coincided with the periods of economic expansion,suggesting that the govern-ment opportunistically selected the timing of elections.To deal with the possible endogeneity of election timing,we classify countries as having either exogenous timing or endogenous timing.All countries with a record of early elections are classified as having endogenous tim-ing.All presidential elections,with the exception of Sri Lanka,are held on a regular basis and are classified as having exogenous timing.This leaves unclassified seven countries withparliamentary systems and one country with hybrid system.In order to classify those remain-ing countries,we refer to electoral laws and practices as well as the classification provided by Alesina,Cohen and Roubini(1992)7.Accordingly,three of the remaining countries,Czech Republic,Finland,and New Zealand,are classified as having endogenous timing8and the rest are classified as having exogenous timing.Table I reports the election timing classification for every country in our sample.Panel A of Table II summarizes the election data.Elections are held every3.8years on average and the average nominal term of a chief executive is4.4years.The next row reports the political platform of incumbent governments in the election years.The classification is based on the World Bank Database of Political Institutions,which refers to various sources including Political Handbook yearbooks in order to identify party orientation with respect to economic policy.9The World Bank classifies a government as being right-leaning if the political party is defined as conservative,Christian democratic,or right-wing by these sources. Left-leaning parties are those that are defined as communist,socialist,social democratic,or left-wing.Centrist parties are those that advocate strengthening private enterprise in a social-liberal context.We define the the incumbent political party as being”market-friendly”if the incumbent government in the election year is classified as right-leaning or centrist by the World Bank.Accordingly,63.3%of the incumbent administrations in the year leading up to an election are classified as market-friendly,and the remaining36.7%are classified as left-leaning.We also summarize the distribution of historical vote counts to give a sense for the degree of uncertainty surrounding a given election.On average,the winner of an election obtains41.9%of the total vote,followed by the runner-up at28.7%,and the third-place candidate receives12.2%of the total.The table also shows that45.6%of the elections are classified as having exogenous timing.Table II also shows that54%of the elections lead to the replacement of the national leader and43%of the elections result in change in the ruling party.[TABLE II HERE]B.Country-Level DataWe obtain institutional and macroeconomic data from various sources.The World Devel-opment Indicators from the World Bank is our primary source for the macroeconomic vari-ables including real GDP,central government spending,inflation,and real interest rate.We obtain data on the money supply(M1)from Political Risk Service’s International Country Risk Guide(ICRG).ICRG also reports the government stability ratings on a monthly basis for the countries in our sample.The government stability index assigns numbers between0and 12,where higher values indicate more stable governments.This time-varying index assesses the government’s ability to carry out its declared programs,and its ability to stay in office.The Database of Political Institutions provides a measure of the effectiveness of checks and balances in each political system on an annual basis10.The basic idea is to capture the number of decision makers whose agreement is necessary for the approval of policy changes. The measure is a count of the number of veto players in the political system at a given point in time based on the prevailing electoral rules and laws.It also takes into account whether the executive and legislative branches of government are controlled by the same party,which effectively reduces the checks and balances relative to having different parties controlling different branches of government.In presidential systems,the count is increased by one for the president and increased by one for each additional legislative body.For parliamentary systems,the count is increased by one for the prime minister and increased by the number of parties included in the governing coalition.The number is reduced if the party of the executive is the same as the largest party in any particular chamber of government.Table II shows that the average of checks in the sample is3.95with the standard deviation of1.95.The index of central bank independence(CBI)measures the extent to which the central bank is independent from the political power.This annual,time-varying index is taken from Cukierman,Webb,and Neypati(1992)for the period between1980and1989,and from Polillo and Guillen(2005)for the period between1990and2000.Initially,Cukierman,Webb,and Neypati(1992)constructed the index for72industrial and developing countries for the period between1950and1989.11Later,Polillo and Guillen extended the index to the period between 1990and2000according to the definition of Cukierman,Webb,and Neypati(1992).The index is a continuous score ranging between zero and one,where one indicates maximum。

辽宁省大连市2024-2025学年高一英语上学期期末考试试题留意事项：1. 答卷前，考生务必将自己的名字、准考证号填写在答题卡上。

2. 作答时，务必将答案写在答题卡上。

写在本试卷及草稿纸上无效。

第一部分听力（共两节，满分30分）第一节（共5小题；每小题1. 5分，满分7. 5分）听下面5段对话。

每段对话后有一个小题，从题中所给的A、B、C三个选项中选出最佳选项。

听完每段对话后，你都有10秒钟的时间来回答有关小题和阅读下一小题。

每段对话仅读一遍。

1. What will the woman do this evening?A. Go to the theater.B. Go over her lessons.C. Listen to the music.2. How does the man find the problem?A. Quite difficult.B. Very interesting.C. Too easy.3. Where will Steve probably be during the holiday?A. On the sunny beach.B. In the mountains.C. At home.4. Who is good at fixing the bicycle?A. The man speaker.B. The woman speaker.C. Tony.5. What are the two speakers talking about?A. Young volunteers.B. Cleaning up after the party.C. How to work together.其次节（共15小题；每小题1. 5分，满分22. 5分）听下面5段对话或独白。

每段对话或独白后有几个小题，从题中所给的A、B、C三个选项中选出最佳选项。

听每段对话或独白前，你将有时间阅读各个小题，每小题 5秒钟；听完后，各小题将给出 5 秒钟的作答时间。

社交电商营销策略研究国内外文献综述目录社交电商营销策略研究国内外文献综述 (1)1. 国内研究现状 (1)（1）对社交电商的定义和特点的研究 (1)（2）对社交电子商务发展现状研究 (1)（3）对社交电子商务发展模式研究 (1)（4）对社交电商发展策略的研究 (2)2. 国外研究现状 (2)（1）对于社交电商概念的研究 (2)（2）对社交电商特征的研究 (2)3. 文献评述 (3)参考文献 (3)1.国内研究现状如果我们梳理关于社交电商的国内文章，我们会发现国内对社交电商的研究要多得多，但以社交电子商务营销策略为切入点的研究较少，相关研究也是基于宏观视角和国外案例展开，关于中国的国情和社交电子商务平台的具体研究很少。

总的来说，国内研究人员对社交电商的下列几方面进行了研究: （1）对社交电商的定义和特点的研究王胜平,陈康,付银霜（2020）等研究发现，社交电子商务是基于互联网发展起来的电商平台，其经营以与用户密切沟通交流为主要特点，可以在当前的商业网络环境中推动消费者的新消费需求[3]。

叶舜雅（2017）认为，社交性，需求被动性，内容自我可持续性，信息双向沟通和信任转移是社交电子商务的特点，与普通的电子交易不同，社交电商用户的需求是被动的[4]。

由于社区中朋友的购买和推荐，用户往往倾向于购买。

（2）对社交电子商务发展现状研究余蓉（2020）提出社交电商的最终目标是让用户消费。

他们也分析总结了社交电子商务现在的发展过程中存在移动支付受到限制，流量消耗过大，行业标准不完善等问题[5]。

李响（2017）通过研究国外社交电子商务网站的发展现状后指出，基于社交网络和兴趣偏好的电子商务是未来电子商务行业的发展方向[6]。

邹黎（2020）认为目前中国社交电商发展的资金和资源不足，同类别产品的竞争过于激烈，对单一平台过度依赖等问题和局限性[7]。

（3）对社交电子商务发展模式研究杨世武,苑心怡（2020）在对我国社交电商的发展现状研究后认为，当前，社交与电商结合的发展模式还没有形成一套行业标准。

2023年中考英语模拟试卷注意事项1．考试结束后，请将本试卷和答题卡一并交回．2．答题前，请务必将自己的姓名、准考证号用0．5毫米黑色墨水的签字笔填写在试卷及答题卡的规定位置．3．请认真核对监考员在答题卡上所粘贴的条形码上的姓名、准考证号与本人是否相符．4．作答选择题，必须用2B铅笔将答题卡上对应选项的方框涂满、涂黑；如需改动，请用橡皮擦干净后，再选涂其他答案．作答非选择题，必须用05毫米黑色墨水的签字笔在答题卡上的指定位置作答，在其他位置作答一律无效．5．如需作图，须用2B铅笔绘、写清楚，线条、符号等须加黑、加粗．Ⅰ. 单项选择1、—Why did you come to discuss the question？—Because it was a question which was worth ______ consideration.A．nervous B．serious C．anxious D．strict2、The country life he was used to greatly since 2008.A．change B．has changed C．changing D．have changed3、My back hurt badly I went to see a doctor.A．so B．or C．and D．but4、—I don’t care __________. In this camp, there’s only one hairstyle-short! Understand?—Yes, madam!A．what you are used to liking B．what you used to be likeC．what are you used to liking D．what did you use to be like5、--Could you tell me ___________--- It’s about ten minutes’ drive.A．how long it takes him to drive to schoolB．how far it is from your home to schoolC．how soon your sister get back from school6、—I think using Wechat every night is a waste of time.—Yes. In fact, there are ▲ meaningful things to do.A．less B．more C．the least D．the most7、There are fewer and fewer tigers in India. The situation will continue _______ humans stop hunting them for their fur and bones.A．if B．unless C．because D．since8、—Tommy, there are so many mistakes in your chemistry paper, what will you do with it?—Oh, I am sorry, Miss Lee. , I'll do more exercises and learn from the mistakes to make progress.A．In case that B．In case C．In that case9、Taiji is my favorite sport and I often play it ________ healthy.A．to keep B．keeps C．keeping D．kept10、We should look up to those scientists sent Tianzhou-1 Cargo Spaceship to space.A．who B．what C．whichⅡ. 完形填空11、On a snowy evening, a rich lady was standing by the road. She was worried. Her 1 had broken down. Just then a poor man named Robert came. He was on his way back home from work as usual.The lady wondered, “Is the man going to 2 me? He looks very cold and hungry.” But to her surprise, he stopped and said with a smile. “What’s wrong, madam?” The lady told him what was happening.After a while, the car was at last fixed by Robert. The lady wanted to pay him. “No, that is 3 , madam.” he sa id, “I was just helping someone in 4 . If you really want to pay me back, I hope whenever you see someone in trouble, you should give him a hand.” A few minutes later, the lady 5 a shabby(破旧的)house by the road. She remembered Robert’s words, so she stopped. The hostess(女主人)warmly asked her 6 . The lady could see that was a 7 family and that they needed help. When the hostess was making tea in the kitchen, the lady 8500 dollars on a table and went away quietly. Robert came home later than before, thinking how 9 their life was. Their baby was to be born the next month but there was not 10 money. His wife went up to him, gave him a kiss and said softly, “Don’t worry, dear! A stranger has helped us out!”Let’s always be ready to help others because helping others is helping ourselves.1．A．bike B．motorbike C．bus D．car2．A．help B．save C．kill D．find3．A．nothing B．something C．everything D．anything4．A．picture B．rain C．need D．danger5．A．saw B．heard C．smelt D．felt6．A．out B．in C．away D．along7．A．terrible B．sad C．rich D．poor8．A．dropped B．forgot C．left D．lay9．A．exciting B．hard C．happy D．interesting10．A．many B．some C．enough D．fewⅢ. 语法填空12、动词应用1．(grow)up is not always easy．When we face difficulties，a spirit of depending on yourself is more useful than 2．(cry)for help．That’s what Hong Zhanhui’s story of growing from boy to man with family hardship 3．(tell) us．Hong 4．(bear) in 1982 in a poor family in Xihua County(县)，Henan Province．When he was only 11，his father5．(become) badly ill and one day he came back with an unwanted baby girl．A year later，Hong’s mother left home．She no longer wanted 6．(live) such a poor life and face her sick husband．So everything hard fell onto the young boy’s shoulders：to take care of his father and the sister Chenchen，and to go on to study．Although his life was hard，Hong 7．(never go) away from his father and sister．He worked in part—time jobs to feed his family．He climbed tall trees to get birds’ eggs for his sister．He walked two hours at weekends to the market to buy different things to sell around his school．He said that he 8．(make) a good life in the future. A few years later，he studied at a college．To take care of Chenchen，he had worked hard to rent(租)a room near his college for several years.After Hong’s story went public，he became a hero in people’s eyes．But Hong refused offers from others．He said he 9．(feel) encouraged by kind offers，but he could depend on his own work．Through his hard life，he 10．(grow) up from boy to man．Ⅳ. 阅读理解A13、阅读下列短文，从每题所给的A、B、C、D项中，选出最佳选项。

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