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NBER WORKING PAPER SERIESWEALTH ACCUMULATION AND THE PROPENSITY TO PLANJohn AmeriksAndrew CaplinJohn LeahyWorking Paper8920/papers/w8920NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts AvenueCambridge, MA 02138May 2002Caplin thanks the Center for Experimental Social Science at New York University and the C.V. Starr Center at NYU for financial support. Leahy thanks the NSF for financial support. We thank Bob Barsky, Chris Carroll, Doug Fore, Xavier Gabaix, Thomas Juster, Larry Kotlikoff, David Laibson, Kevin Lang, Donghoon Lee, Annamaria Lusardi, Jennifer Ma, Ben Polak, Matthew Shapiro, and Richard Thaler for insightful suggestions. We gratefully acknowledge financial support for our survey provided by the TIAA-CREF Institute. All opinions expressed herein are those of the authors alone, and not necessarily those of TIAA-CREF or any of its employees. The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research.© 2002 by John Ameriks, Andrew Caplin and John Leahy. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including ©notice, is given to the source.Wealth Accumulation and the Propensity to PlanJohn Ameriks, Andrew Caplin and John LeahyNBER Working Paper No. 8920May 2002JEL No. E2, D1ABSTRACTWhy do similar households end up with very different levels of wealth? We show that differences in the attitudes and skills with which they approach financial planning are a significant factor. We use new and unique survey data to assess these differences and to measure each household's "propensity to plan.'' We show that those with a higher such propensity spend more time developing financial plans, and that this shift in planning effort is associated with increased wealth. The propensity to plan is uncorrelated with survey measures of the discount factor and the bequest motive, raising a question as to why it is associated with wealth accumulation. Part of the answer lies in the very strong relationship we uncover between the propensity to plan and how carefully households monitor their spending. It appears that this detailed monitoring activity helps households to save more and to accumulate more wealth.John Ameriks Andrew Caplin John LeahyTIAA-CREF Institute Department of Economics Department of Economics 730 Third Avenue (730/24/01)New Y ork University Boston UniversityNew Y ork, NY 10017-3206269 Mercer Street270 Bay State Road Tel: 212-916-4693New Y ork, NY 10003Boston, MA 02215 Fax: 212-916-6849and NBER and NBERjameriks@ Tel: 212-998-8950Tel: 617-353-6675 Fax: 212-995-3932Fax: 617-353-4449andrew.caplin@ jleahy@/user/caplina//leahy/1IntroductionAccording to the life cycle model,the central determinants of wealth accumulation are age,household structure,lifetime earnings,and a relatively small set of preference pa-rameters,such as the discount rate and the bequest motive.Yet recent empirical research in the behavioral tradition suggests that other variables,with no explicit role in the life cycle model,are strongly related to wealth accumulation.For example,Madrian and Shea[2001]show that default rules in defined contribution pension plans can have a strong influence on wealth accumulation.Lusardi([1999],[2000])finds that households who have given little thought to retirement have far lower wealth than those who have given the subject more thought.We view such empirical results as extremely provocative,but somewhat disconnected from the main stream of research on life cycle saving.In large part,this separation is a result of data limitations.While there are many data sets relevant to examination of the life cycle model,available data typically include few variables of direct relevance to testing behavioral hypotheses.In this paper,we overcome these limitations using new data from two recent surveys, both of which were completed by some2,000TIAA-CREF participant households.As we describe in section2below,these surveys produced high quality data on household portfolios of assets(both inside and outside of pension plans)and debts,and on lifetime earnings profiles.In addition,following the pioneering work of Barsky,Juster,Kimball, and Shapiro[1997](henceforth BJKS),the surveys included questions designed to provide measures of classical preference parameters,such as the discount factor.The surveys also contained questions regarding household behavior related tofinancial planning,as well as questions intended to measure a variety individual and household behavioral and psychological characteristics.The main empirical results in this paper focus on the relationship betweenfinancial planning and wealth accumulation.Ourfirst set offindings confirm and enrich Lusardi’s earlier results:in section3we describe the robust positive relationship betweenfinancial planning and wealth accumulation.In section4we establish that the line of causation runs from planning to wealth accumulation,rather than vice versa.We use a set of nonfinancial survey questions to identify variation in the underlying“propensity to plan”of the survey respondents.We construct our measure of the propensity to plan based on survey questions in the nonfinancial arena that are correlated withfinancial planning. These questions were asked precisely to provide natural instruments for exploring the direction of causation in the relationship between planning and wealth.We show that1differences in planning effort associated with variation in this propensity are in turn strongly associated with differences in wealth accumulation.Why are differences in the propensity to plan associated with differences in wealth accumulation?In contrast with the results of Lusardi[2000],section5shows that differ-ential patterns of equity holding are not responsible for the connection between planning and wealth accumulation.Rather,ourfindings suggest that planners save more.Section 6explores whether or not the connection betweenfinancial planning and wealth accu-mulation is due to a correlation with measures of classical preference parameters,such as the discount factor.Wefind no evidence to support this hypothesis.If differences in the propensity to plan are unrelated to differences in the discount factor,why are they associated with differences in wealth accumulation and savings?The following simple story suggests one possible explanation:A close friend of the authors was recently surprised tofind that the size ofhis bank account had declined dramatically over the last year.To understandhow this could have happened,he carefully reviewed his spending,and wasshocked at how much money seemed to have dissipated in various directions.To ensure that this pattern did not repeat itself,he resolved to keep a closerwatch on his day-to-day spending.The end result was an increase in savings.If this is not an isolated case,it suggests there may be a link between how closely one monitors one’s spending and the level of savings.If in addition there is a high correlation between such monitoring behaviors and the propensity to plan,then this could provide an intuitive explanation for ourfindings.The survey results presented in section7suggest that this may indeed be an important line of explanation.The larger goal of our research project is to dig deeper into what determines indi-vidual differences in wealth accumulation.Currently,“the discount factor”stands in as a convenient mathematical representation for most of these diffeful as this abstraction may be for certain purposes,it does not provide much in the way of guidance to policymakers.Yet if savings and wealth accumulation are indeed impacted by shifts in the propensity to plan,this suggests entirely new mechanisms by which to encourage saving.Do the high school curriculum mandates analyzed by Bernheim,Garret,and Maki[1997]impact the propensity to plan?Does this explain their apparent impact on the savings rate?Are there alternative policies that may be even more effective at impacting the propensity to plan and the savings rate?22The Survey and the Sample2.1The SampleThe data used in this paper are drawn from two surveys sent to a sample of TIAA-CREF participants:the Survey of Participant Finances,fielded in January2000(henceforth SPF),and the Survey of Financial Attitudes and Behavior(henceforth FAB),fielded in January2001.The SPF was designed to examine in detail the type and the amount of financial assets owned by a large group of TIAA-CREF participants.The FAB explored these participants’financial preferences,expectations,and attitudes.The survey sam-ples are not representative of the TIAA-CREF population.The sampling procedure is described in greater detail in a companion paper on retirement consumption(Ameriks, Caplin,and Leahy[2002]).The surveys address many aspects of wealth accumulation.In this paper,we focus attention on wealth accumulation during the accumulation phase of the life cycle.We consider households to be in this phase if neither the respondent,nor partner if applicable, are at or above65years of age.1Of the2,064households whofilled out the FAB,1,191 satisfied this criterion,and they make up the under-65universe from which all other samples discussed in the paper are drawn.Note that because early retirement may itself be a consequence of planning-related shifts in wealth,we do not restrict our universe to those who are currently working.In most of the statistical analysis and regressions in this paper,we limit attention to a subsample of our universe that supplied complete data on all variables of interest. As afirst step in ensuring data completeness,we remove all households receiving life-annuity income from TIAA-CREF from the sample,simply because it is not clear how to interpret the TIAA-CREF asset values reported by annuitants.Of the1,067remaining households in the under-65universe,513supplied complete data and could be included in the regression analysis.Of these,we remove from the regression analysis an additional10 with nonpositive net worth,and3extreme outliers with more than$5million infinancial assets.We refer to the500remaining households as the regression sample.1While respondents always reported their own age,there was a high nonresponse rate for year of birth for the spouse/partner;the spousal age restriction is enforced only if we have the spouse’s age data.3Table1Demographic Characteristics of2001Survey RespondentsUnder65RegressionUniverse Sample Characteristic(n)(%)(n)(%) AgeBelow3512010.16112.2 35-391099.25811.6 40-441109.26112.2 45-4917314.58216.4 50-5424420.510621.2 55-5921518.17014.0 60-6422018.56212.4 GenderFemale55046.220541.0 Male64153.829559.0 Marital StatusCurr.married77765.233266.4 Prev.married19316.26112.2 Never married22118.610721.4 EducationCollege or below34228.712825.6 Masters or Prof.46639.120641.2 Ph.D.38332.216633.2 OccupationTeaching faculty39733.316232.4 Mgmt.,Sen.Admn.24920.910320.6 Other Tech./Prof.30425.515030.0 Other23119.48517.0 Num.children074762.730360.6 116213.66613.2 220517.29619.2 377 6.5357.0 Source:Authors’tabulations of2000SPF and2001FAB survey data. Notes:The“under65universe”is all respondents to the FAB survey who were under age65and,if applicable and available,whose spouse reported an age of less than65(1,191respondents/households).Some respondents in this sample did not report data for all the above characteristics.The“regression sample”is all individuals in the under65universe who:(1)provided complete informa-tion regarding the demographic characteristics above,(2)provided complete information regarding their household’s net worth,(3)provided complete in-formation on their past,present,and expected future labor earnings,(4)have no life annuity income from TIAA-CREF,(5)have positive net worth,and(6) have less than$5million in grossfinancial assets.42.2Basic Demographic and Economic VariablesTable1shows the basic demographic characteristics of households in both the under-65universe and in the regression sample.We tabulate answers to questions concerning the respondent’s gender,marital status(married,never married,previously married), number of dependent children,and age.We also tabulate educational and occupational characteristics.It is clear from the table that our sample is far from representative.In particular,re-spondents are extremely well-educated:the vast majority completed college,and roughly 1in3have Ph.Ds.In terms of employment,roughly1in3are teaching faculty,with the majority of the others having management or professional positions.The“other”employment category corresponds to secretarial,maintenance,and other support posi-tions.Finally,note that there appears to be little difference between the working and the regression samples in terms of most demographic characteristics,although the regression sample is somewhat younger and contains fewer who are widowed or divorced,possibly due to the removal of annuitants.Table2summarizes households’economic characteristics.Data on earnings is from the FAB in which we asked households to provide estimates of their overall taxable income from employment in1999.2The asset and debt information is drawn from the SPF.We record not only the total level of wealth,but also the division between retirement assets and nonretirement assets.Within the nonretirement assets,we separate out real estate wealth,which comprises both owner-occupied and investment assets.With regard to debt,we distinguish between mortgage debt,and all other forms of debt,including credit card and educational debts.In Ameriks,Caplin,and Leahy[2002]we compare characteristics of our sample with those of working households in the1998Survey of Consumer Finances(SCF).Net worth is some2.5–3times higher in our sample,while debt levels are generally lower.There is also far greater homogeneity in our sample than in the SCF.In contrast with the SCF, the vast majority of households in our sample have significant nonretirementfinancial assets,and very few have high levels of personal debt.2We use1999income from the FAB,since this corresponds most closely to the wealth data from the SPF.5Table2Financial and Earnings Data for Surveyed HouseholdsMean Median Std.Dev.#Obs.Sample and measure($000)($000)($000)(N)Under65universe∗Net worth705379933671Grossfinancial assets5752701,004735 Ret.fin.assets424210795885Non-ret.fin.assets188******** Real estate assets2501605301,145Total debt89552751,048 Mortgage debt79462591,124Personal debt80511,089 1998Employment income7767621,1331999Employment income8170681,144Expected2005emp.income8775821,015 Regression sample∗∗Net worth700394810500Grossfinancial assets555306661500 Ret.fin.assets390216450500Non-ret.fin.assets16544308500 Real estate assets230153319500Total debt8560106500 Mortgage debt7950104500Personal debt6012500 1998Employment income8168625001999Employment income857267500Expected2005emp.income938080500 Source:Authors’tabulation of2000and2001survey data.Notes:“Grossfinancial assets”is the sum of all retirement account balances,mutual funds(except real estatemutual funds),directly held stocks,directly held bonds,checking accounts,savings accounts,and CDs.“Networth”is total assets minus mortgage debt,outstanding educational loans,outstanding personal loans,andcredit card balances.All aggregates exclude the value of real estate mutual funds,whole life insurance policies,trusts,and educational savings accounts(Education IRAs and529plans).Respondents were instructed toprovide values as of December31,1999.Note these data include only the information reported by respondentson the surveys,and may therefore differ from data reported in Ameriks,Caplin&Leahy(2002).*For the under65universe,statistics are tabulated for all individuals who provided complete data for eachindividual item(in each row).The number of observations in each row varies,as item response varies.**The“regression sample”members are the500individuals in the under65universe who:(1)providedcomplete information regarding the demographic characteristics in Table1above,(2)provided completeinformation regarding their household’s net worth,(3)provided complete information on their past,present,and expected future labor earnings,(4)have no life annuity income from TIAA-CREF,(5)have positive networth,and(6)have less than$5million in grossfinancial assets.2.3Data QualityWe believe our data on portfolios of assets and debts to be of high quality.For example, our survey requests a quantitative division of assets in defined contribution retirement plans into separate classes,such as cash and equities.In contrast,for example,the Fed-eral Reserve Board’s triennial Surveys of Consumer Finances do not ask households to6provide numerical information on the breakdown of retirement assets into different asset classes.Rather,respondents provide qualitative answers;any numerical data on portfolio shares derived from the data must be obtained using additional assumptions concerning the interpretation of these qualitative answers.The survey separates employer-sponsored TIAA-CREF accounts from all other retirement assets(which are themselves broken down into other sub-categories)and from nonretirement assets.Within each such cate-gory we asked for a precise quantitative breakdown describing how much of the total was held in various different forms.At a minimum,these breakdowns were designed to allow us to discriminate between cash assets,fixed income assets,equities,and other assets. We also asked comprehensive numerical questions concerning real estate assets,and all forms of debt.Where relevant,we asked for information on the assets of the respondent’s spouse or partner.Our response rates were generally very high;well in excess of90%for most of the larger asset categories.We also had high response rates on the breakdown of these assets among different types of investment instruments.As indicated in table2,when we look across all of these responses and insist on having sufficient information to calculate net worth,we retain671of the1,191households in the under-65universe.Asking quantitative questions and getting quantitative answers is not by itself an assurance of high data quality.Greater assurance of accuracy can be found by compar-ing one of our self-reported data items against accounting records.We have appended accounting information from TIAA-CREF to the survey responses of all respondents with retirement assets at TIAA-CREF.3Yet before comparing the self-reports and the accounting data,we must take account of two important points of difference between the two types of data.Thefirst issue involves the treatment of individual IRAs.In the SPF, we asked respondents to report the total of their TIAA-CREF employer-sponsored re-tirement assets,and separately to record all(both TIAA-CREF and non-TIAA-CREF) of their individual IRA holdings.In contrast,the TIAA-CREF data we use combine TIAA-CREF assets in employer-sponsored plans with some types of TIAA-CREF IRAs that the individual may hold,making it inappropriate for us to compare the reported employer-sponsored plan total with this data.A second important difference arises in cases in which both the respondent and the respondent’s partner have TIAA-CREF as-sets.In these cases,the survey may have beenfilled in by the partner rather than by the addressee,breaking the connection between the self-reports and the accounting records.3The anonymity and confidentiality of the survey respondents has been,and continues to be,strictly enforced and maintained.The identities of specific respondents remain unknown to all of the investiga-tors.7Both of these issues must be addressed before it is valid to compare the two sources of data.With respect to the treatment of IRAs,the accounting data include an indicator of the existence of the problematic IRA accounts.Before comparing the self-reports and accounting numbers,we condition on the individual who responded to the survey having no IRAs,since this condition is necessary for the two numbers to coincide.With respect to households in which both partners have TIAA-CREF assets,we restrict attention to those for whom data on the age and gender of the respondent agree with those from the corresponding accounting record.With these issues handled,table3reports results of a log-log regression of the reported TIAA-CREF asset totals on the accounting totals for the738sample households for whom the comparison is relevant,and whose records and self-reports indicated at least$10,000in TIAA-CREF retirement assets.(We asked respondents to report amounts in thousands;the“greater than$10,000”rule is applied to reduce the influence of rounding errors.)Table3OLS Regression:Reported TIAA-CREF Assets on Accounting DataSample&RHS Variables Coeff.Std.Err.Pr>|t|Under65universeln(TCData)0.9920.0060.000Constant-0.0060.0320.859Regression sampleln(TCData)0.9970.0090.000Constant-0.0140.0470.761Source:Authors’calculations using2001survey data and1999accounting data.Note:This is a log-log regression of respondent’s report of the value of his or herTIAA-CREF assets on actual accounting data for the respondent.For both theunder65universe and the regression sample,the data include only those whoreported and had more than$10,000in TIAA-CREF assets,with no immediate(payout)annuities,or TIAA-CREF IRAs,and whose reported age and gendermatched the age and gender recorded in the TIAA-CREF database.For theuniverse,738observations are included in this regression;the R2is.958;rootMSE is0.247.For the regression sample,381observations are included,the R2is.968;root MSE is0.214.The coefficient on the TIAA-CREF accounting data is extremely close to1,while the constant term is statistically insignificant,suggesting a very high correlation between the self-reports and the accounting data.The average absolute deviation between the response and the accounting data is on the order of10%,while the median is less than 2%.We note that Gustman and Steinmeier[2001]document far larger discrepancies between the pension benefits reported by respondents to the HRS and a careful estimate8of the benefits that these same respondents have accumulated based on administrative records.In the regressions that follow,unless otherwise indicated,we calculate net worth and grossfinancial assets using self-reported data for all asset categories,including TIAA-CREF assets.We report also in section4on the results of regressions in which we replace self-reported TIAA-CREF asset total with accounting data(and in which we restrict the sample to avoid the obvious cases described above in which the TIAA-CREF data is likely to be inappropriate).In the context of that analysis,we provide a more detailed discussion of the various possible reasons for differences between the accounting data and the self-reports.3Wealth and Planning:the Correlation3.1Prior LiteratureA basic assumption in the life cycle model is that households form complete contingent plans prescribing consumption and asset holdings in all possible states of nature.Yet there is survey evidence suggesting that this is very far from ing data from the Retirement Confidence Survey,Yakoboski and Dickemper[1997]document a pervasive lack of planning for retirement.Theyfind that only36%of current workers in their survey have tried to determine how much they need to save to fund a comfortable retirement. They also report that37%of current workers report having given little or no thought to their retirement.If one translates the notion of poor planning into the language of the life cycle model, it presumably corresponds to greater uncertainty about the level of consumption implied by different states of nature.If anything,one might expect such an increased uncertainty to give rise to an increase in wealth accumulation,especially for households for whom the precautionary motive is large.Yet Lusardi[1999],using data from the Health and Retirement Survey(HRS),found that those who have given“little or no”thought to retirement havefinancial wealth significantly lower than those who have given the subject more thought,even when one controls for the usual suspects in the life cycle model,such as age and lifetime income.Of course this does not answer the question of causation: maybe it is wealth that drives thinking about retirement rather than vice versa.It is this subject that is addressed in Lusardi[2000],and to which we turn our sights in the next section.In the remainder of this section,we explore whether or not our data indicate a con-9nection betweenfinancial planning activities and wealth accumulation.In contrast with HRS households,our households generally appear to have done significant amounts of financial planning,as described in the next section.In addition,our households are relatively homogeneous,wealthy,and well-educated.Despite these differences,it turns out that Lusardi’s insight generalizes:financial planning and wealth accumulation are strongly positively correlated.3.2Defining Financial PlanningClearly,the HRS question concerning“thinking about retirement”is unsatisfactory,since it makes no direct mention offinancial planning per se.To highlight this topic,we posed our questions at the very beginning of the survey,and they were preceded by the statement:We are interested in your behavior related to planning for your household’slong-termfinancial future,and the types of advice(if any)you may have usedin developing yourfinancial plan.How best to measurefinancial planning?At this exploratory stage we do not know precisely how planning is supposed to influence wealth,nor do we know what constitutes an effective form of planning.Absent such a complete model,we focus our attention on two different approaches to measurement,based respectively on the input and output sides of the planning activity.With respect to the input side,we believe that most people have a sense of when they are and when they are not engaged infinancial planning activities.We asked survey participants to respond to the following general statement:•Question1a:I have spent a great deal of time developing afinancial plan.Answers to this question and to many other questions on the survey were placed on a qualitative1-6scale.Survey participants were asked to indicate which of six statements (1=disagree strongly,2=disagree,3=disagree somewhat,4=agree somewhat,5= agree,6=agree strongly)best characterized their reaction to the statement.Turning to the output side,it seems clear that one of the essential outputs of the planning activity is a well-articulatedfinancial plan.Hence we asked households a yes/no question concerning their preparation of just such a clearly defined plan.•Question2a:Have you personally gathered together your household’sfinancial information,reviewed it in detail,and formulated a specificfinancial plan for your household’s long term future?[yes/no]10For those who say yes to this question,we ask them also to specify the age at which this activity wasfirst undertaken,since one might expect the impact of planning on wealth to depend on how long one has had that plan in place.In regressions based on this second measure,we include both an indicator for whether or not a plan has been developed,and a measure of the time for which any such plan has been in place.Answers to questions1a and2a are presented in table4,which shows that the majority of respondents agreed(to some degree)that they had spent a great deal of time developing afinancial plan,and at the same time claimed to have put together just such a detailed plan:the correlation between these two measures of planning in our regression sample is 0.48.In self description,our sample is far more involved with long term planning than are their counterparts in the HRS,where only one-third of respondents claim to have given a lot of thought to retirement,even though all of them are within ten years of retiring(Lusardi[1999]).Table4Responses to Basic Planning Questionsamong Regression Sample MembersQ2a ResponseHas NoDetailed plan Detailed Plan Total Q1a Response(n)(%)(n)(%)(n)(%)Disagree strongly30.89 6.712 2.4Disagree287.74735.17515.0Disagree somewhat4011.03526.17515.0Agree somewhat15241.63828.419038.1Agree10528.84 3.010921.8Agree strongly3710.110.7387.6Total365100.0134100.0499100.0Source:Authors’tabulations of2001FAB survey data.Note:One individual in the regression sample did not respond to the detailed planningquestion(Q2a).One point to note about our questions is that while they measure strictly personal characteristics,we will use them in regressions for household wealth.To assess the importance of this distinction,we asked two questions on the survey designed to gauge the importance of the respondent in householdfinancial and spending decisions.•Question3h:I take the lead in making investment decisions in my household.•Question3o:I take the lead in making discretionary spending decisions in my household.11。
感官动词用法(A)感官动词(及物)有:see/notice/look_at/watch/notice/observe/liste n_to/hear/feel(Vt)/taste(Vt)/smell(Vt)(B)连缀动词(含感官不及物)be/get/become/feel/look/s oun d/smell/taste/kee p/stay/seem/ appear/grow/turn/prove/remain/go/ru n——、see, hear, feel, watch, look, 主语所处的状态。
其意思分别为主语往往是物,而不是人。
例如: 这五个动词均可作连系动词,后面接形容词作表语,说明"看/听/闻/尝/摸起来……"。
除look之外,其它几个动词的这些花闻起来很香。
These flowers smell very sweet.The tomatoes feel very soft. 这些西红柿摸起来很软。
二、这些动词后面也可接介词like短语,like后面常用名词。
例如:Her idea sou nds like fu n. 她的主意听起来很有趣。
三、这五个感官动词也可作实义动词,除look(当"看起来……"讲时)只能作不及物动词外,其余四个既可作及物动词也可作不及物动词,其主语通常是人。
例如:She smelt the meat.她闻了闻那块肉。
I felt in my p ocket for cigarettes. 我用手在口袋里摸香烟。
四、taste, smell作不及物动词时味”。
例如:,可用于"taste / smell + of +名词”结构,意为"有……味道/气The air in the room smells of earth. 房间里的空气有股泥土味。
五、它们(sound除外)可以直接作名词,与have或take构成短语。
有关通知类的中考英语作文标题,An Important Notice。
Dear Students,。
We hope this notice finds you well and focused on your studies. As the midterm exams approach, we would like to bring your attention to some important information and guidelines to ensure a smooth and successful examination process.1. Examination Schedule:The midterm exams will take place from May 15th to May 20th.Please refer to the attached schedule for your specific exam dates and times.Arrive at least 30 minutes before the start of eachexam to allow time for registration and seating arrangements.2. Exam Venue:All exams will be held in the school auditorium.Make sure to familiarize yourself with the layout of the auditorium beforehand to avoid any confusion on the exam day.3. Required Materials:Bring all necessary stationery items, including pens, pencils, erasers, rulers, and calculators (if allowed).Mobile phones, smartwatches, and any otherelectronic devices are strictly prohibited during exams.4. Exam Regulations:Follow all instructions given by the invigilatorsduring the exam.Maintain silence throughout the duration of the exam.Any form of cheating, including looking at others' papers, using unauthorized materials, or communicating with other students, will result in immediate disqualification.5. Health and Safety Measures:Adhere to all COVID-19 protocols, including wearing masks and maintaining social distancing.Hand sanitizers will be provided at the entrance of the exam venue. Please sanitize your hands upon entry.6. Special Accommodations:Students with special needs or requirements during exams should inform the school administration in advance to make necessary arrangements.7. Results Announcement:The midterm exam results will be published on the school website on May 30th.Individual feedback sessions with teachers will be arranged for students who require further clarification or assistance.8. Final Words:We understand that exams can be stressful, but remember to stay calm and focused.Trust in your preparation and give your best effort in each exam.If you have any questions or concerns regarding the midterm exams, please don't hesitate to contact the school administration.Wishing you all the best of luck!Sincerely,。
丢了东西发公告的英语作文Losing something is always a distressing experience, and it becomes even more frustrating when the lost item holds significant value or importance. In such situations, one of the most common ways to seek help is by posting a notice or announcement in public places. This act of reaching out to the community for assistance not only reflects the desperation of the individual who has lost the item, but also demonstrates the power of human connection and empathy. In this essay, we will explore thesignificance of posting a public notice when something is lost, the potential impact it can have, and the emotions involved in the process.First and foremost, posting a public notice when something is lost serves as a means of reaching out to a wider audience in the hopes of finding the lost item. By sharing details such as the description of the lost item, the location and time it was last seen, and contact information, the individual increases the chances ofsomeone coming across the notice and providing relevant information. This act not only demonstrates the proactive approach taken by the individual in trying to recover the lost item, but also serves as a call for help from the community at large. It is a testament to the belief in the inherent goodness of people and their willingness to assist others in times of need.Moreover, posting a public notice can also have a significant impact on the emotional well-being of the individual who has lost something. It provides a sense of empowerment and control in a situation that can often feel overwhelming and helpless. The act of taking matters into one's own hands and actively seeking help from others can alleviate feelings of despair and hopelessness. It allows the individual to feel that they are doing everything in their power to recover the lost item, and that they are not alone in their search. In this sense, posting a public notice becomes a form of emotional support and a source of reassurance during a challenging time.Additionally, the act of posting a public notice whensomething is lost can also foster a sense of community and solidarity. It encourages people to come together and look out for one another, reinforcing the idea that a problem shared is a problem halved. It prompts individuals to keep an eye out for the lost item, to spread the word within their own networks, and to offer any assistance they can. This collective effort not only increases the likelihood of finding the lost item, but also strengthens the bondswithin the community. It serves as a reminder that even in the face of adversity, people can come together and make a positive impact through their collective actions.On the flip side, the act of posting a public notice when something is lost can also be a source of anxiety and vulnerability for the individual. It requires them to share personal details and information in a public forum, which can feel exposing and unsettling. There is a fear of judgment or skepticism from others, as well as concerns about potential misuse of the shared information. Additionally, the act of reaching out to strangers for help can be daunting, as it involves placing trust in the goodwill and integrity of others. These emotions andconcerns add another layer of complexity to the act of posting a public notice and highlight the internal struggle faced by the individual in their pursuit of finding thelost item.In conclusion, the act of posting a public notice when something is lost is a powerful and multi-faceted endeavor. It reflects the individual's proactive approach in seeking help, provides emotional support and reassurance, fosters a sense of community and solidarity, and yet also brings about feelings of vulnerability and anxiety. It is a testament to the resilience and hope of the human spirit, and a reminder of the impact that collective action and empathy can have. Ultimately, it is a reflection of the interconnectedness of humanity and the willingness to extend a helping hand to those in need.。
notices的英语作文Title: The Importance of Notices。
Notices play a crucial role in our daily lives, serving as informational tools that keep us informed about various events, updates, and important announcements. Whether it'sa notice posted on a bulletin board, a notification on a website, or an announcement broadcasted over a public address system, notices serve to communicate vital information to individuals or groups. In this essay, wewill explore the significance of notices in different contexts and how they contribute to effective communication.First and foremost, notices are essential ineducational institutions. Schools, colleges, anduniversities regularly use notices to inform students, faculty, and staff about important dates, academic schedules, upcoming events, and administrative announcements. These notices help maintain order and ensure that everyone is aware of any changes or developmentswithin the institution. For instance, notices about exam schedules, holidays, and extracurricular activities are crucial for students to plan their academic and personal activities effectively.Similarly, notices are indispensable in the workplace. Employers often use notices to communicate policies, safety guidelines, meeting schedules, and other work-related information to employees. Notices also serve as a means of recognizing employee achievements, disseminating company news, and fostering a sense of community within the organization. Clear and timely notices can improve employee morale, enhance productivity, and facilitate smooth operations within the workplace.Moreover, notices play a vital role in public spaces and communities. Government agencies, local authorities, and community organizations use notices to inform residents about public services, road closures, community events, and regulatory changes. Notices posted in public areas such as parks, libraries, and community centers help disseminate important information to a wide audience, promoting civicengagement and participation. Additionally, notices regarding public health advisories, environmental concerns, and emergency notifications are crucial for ensuring the safety and well-being of individuals and communities.In the digital age, online notices have become increasingly prevalent and accessible. Websites, social media platforms, and email newsletters are common channels for distributing notices to a wide audience. Online notices offer the advantage of reaching people quickly and efficiently, allowing for instant updates and real-time communication. However, it's important to ensure that online notices are clear, concise, and easily accessible to all users, including those with disabilities or limited internet access.Despite the convenience of digital notices, traditional forms of notices, such as posters, flyers, and bulletin boards, still hold significance in certain contexts. These tangible notices provide a physical presence that can attract attention and convey information in a tangible way. Moreover, in areas with limited internet connectivity ortechnological resources, traditional notices remain a vital means of communication.In conclusion, notices play a crucial role in facilitating communication and disseminating information in various settings, including educational institutions, workplaces, and public spaces. Whether in traditional or digital form, notices serve to keep individuals informed, promote transparency, and foster community engagement. By paying attention to notices and ensuring effective communication channels, we can stay informed and actively participate in our communities.。
英语失物招领启事范文IntroductionLosing something important can be a very stressful experience. It can be even more stressful if you are in a foreign country and don’t speak the language very well. In such situations, it is important to know how to write a lost and found notice in English. This document provides a sample lost and found notice in English that you can use as a guide.Sample Lost and Found NoticeLost: Black leather walletI lost my black leather wallet on the evening of June 10th, 2021. The wallet contained my driver’s licen se, credit cards, and some cash. The wallet is about 4 inches by 3 inches in size and has a zipper closure. There is a small silver emblem on the front of the wallet that says “LV”.If you have found my wallet, please contact me at the following phone number:**********************************************.Thank you for your help.ExplanationThe sample lost and found notice above follows a standard format that is commonly used for such notices. Here is an explanation of each section of the notice:TitleThe title of the notice should be “Lost” or “Found”, depending on whether you are looking for something that you lost or trying to return something that you found.DescriptionThe description should provide as much detail as possible about the lost or found item. In the sample notice above, the description includes the following details:•What was lost: a black leather wallet•When it was lost: on the evening of June 10th, 2021•What was in the wallet: a driver’s license, credit cards, and cash•What the wallet looks like: 4 inches by 3 inches in size, zipper closure, small silver emblem on the front that says “LV”Contact InformationThe contact information should include a phone number and/or email address where you can be reached. Make sure to include the appropriate country code if you are providing a phone number. In the sample notice above, the contact information is as follows:•Phone number: 555-1234•Emailaddress:******************Thank YouIt is always a good idea to thank the person who finds your lost item or helps you return a found item. In the sample notice above, the thank you message is as follows:“Thank you for your help.”ConclusionWriting a lost and found notice in English can be a daunting task, especially if you are not familiar with the language. However, by following the sample notice provided above, you can create a clear and concise notice that will help you recover your lost item or return a found item to its rightful owner. Remember to provide as much detail as possible, include your contact information, and express your gratitude to anyone who helps you.。
1,什么叫文艺复兴The Renaissance (“rebirth” in French) was a cultural and intellectural movement that spanned roughly the 14th through the 17 century, beginning in Italy and later spreading to the rest of Europe 2,Renaissance is considered as the great flowering of art, architecture, politics, and the study of literature, and is also usually seen as the end of the Middle Ages and the beginning of the modern world.2,什么叫玄学派诗歌the Metaphysical school of poetryI. Definition: A school of highly intellectual(智力的)poetryTime: the early 17th centuryMajor features: mysticism in content and fantasticality in form; peculiar conceit(奇思妙想), unique way of reasoning and comparisonMain themes: life, death, love, religion, universeRepresentatives: John Donne, Andrew Marvell and George HerbertSignificance: greatly influenced the modernists of the 20th centuryII. Metaphysical conceits悬想比喻,奇喻,别出心裁的比喻Conceit: an elaborate metaphor that offers a surprising or unexpected comparison between two seemingly highly dissimilar things. This can involve original images or familiar images used in an unfamiliar way.Literature in This Age: The 18th century marked the beginning of an intellectual movement throughout in Europe known as Enlightenment. It was a progressive intellectual movement throughout Western Europe in the 18th century and Russia in the 19th Century.In late 17th and early 18th century England, there was a change of taste, which was part of a general movement in Europe, seen perhaps most impressive in 17th century France. The dominant literary theory of this period was “Neoclassicism”.Literary Genre文学流派Generally speaking, literature of the 18th century was very complex. We may classify it under three general heads: the reign of classicism, the pre-romantic poetry, and the beginning of modern novel.3,什么是启蒙运动Enlightenment (1) a progressive intellectual movement(2) flourished in France and swept through the whole Western Europe(3) aims at enlightening the whole world with the light of modern philosophical and artistic ideas; celebrated reason (4) called for a reference to order, reason and rules4,什么是前浪漫主义Pre-RomanticismWhen did Pre-romanticism appear? in the latter half of the 18th centuryWhat are the major features of Pre-romanticism?1)Romantic Revival;2)Strong protest against the bondage ofClassicism; 3)Claims of passion and emotion;4)Renewed interests in medievalliterature.Who are the representatives? William Blake and Robert BurnsWhat’s the significance?marked the decline of classicismPaved the way for the coming of romanticism in England5,什么叫Byron hero: Byronic hero was created by Byron in the Romantic period of the English literature. Such a hero is a proud, rebellious figure of noble origin. Passionate and powerful, he is to right all the wrongs in a corrupt society, and he would fight single-handedly against all the misdoings. These heroes rise against tyranny and injustice, but they are merely lone fighters striving for personal freedom and some individualistic ends.1. epic 史诗a long narrative poem, grand in style, about heroes and heroic deeds, embodying heroic ideals of a nation or race in the making. Beowulf is the English national epic that was passed from mouth to mouth and written down by many unknown hands.3. alliteration 头韵the repetition of the same sound or sounds at the beginning of two or more words that are close to each other. It is a feature of Beowulf and other Old English poems.4. alliterative verse 头韵诗poetry written in alliteration. Nearly all Old English verse, including Beowulf, is heavily alliterative, and the pattern is fairly standard – with either two or three stressed syllables in each line alliterating.5. kenning 隐喻语a metaphor usually composed of two words and used for description and association. Beowulf is full of kennings, such as ―helmet bearer‖ for ―warrior‖ and ―swan road‖ for ―sea‖.8. romance 传奇a type of literature that was popular in the Middle Ages, usually containing adventures and reflecting the spirit of chivalry. Sir Gawain and the Green Knight was a great verse romance, but its author remains unknown.11. heroic couplet 英雄双韵体two successive lines of rhymed poetry in iambic pentameter. Geoffrey Chaucer’s masterpiece The Canterbury Tale was written in heroic couplet.12. ballad meter 民谣体traditionally a four-line stanza containing alternating four-stress and three-stress lines, usually with a refrain and the rhyme scheme of abcb. Robert Burns’ ―A Red, Red Rose‖ is a great love ballad.14. English Renaissance 英国文艺复兴the literary flowering of England in the late 16th century and early 17th century, with humanism as its keynote. William Shakespeare’s Hamlet is considered the summit of this renaissance. 15. Elizabethan literature 伊丽莎白时代的文学literature written in the Elizabethan Age (1558-1603). William Shakespea re’s Romeo and Juliet was a masterpiece of this period.16. sonnet 十四行诗a fixed form consisting of fourteen lines of 5-foot iambic verse. It first flourished in Italy in the 14th century. William Shakespeare was a great English sonnet writer famous for his 154 sonnets.20. rhyme scheme 押韵格式the pattern of end-thymes in a stanza or poem, generally described by using letters of the alphabet to denote the recurrence of rhyming lines. For example, heroic couplets are ―aabbcc‖ and so on.21. quatrain 四行诗节a stanza of four lines, rhymed or unrhymed. It is the commonest of all stanzaic forms in English poetry. Robert Burns’ ―A Red, Red Rose‖ has four quatrains.24. verse drama 诗剧drama written in the form of verse. It was most widely used in the Elizabethan Age. William Shakespeare’s dramas are all verse dramas, Hamlet being the most famous.25. blank verse 无韵诗,素体诗unrhymed iambic pentameter, the most widely used of English verse forms and usually used in English dramatic and epic poetry. William Shakespea re’s play Hamlet is written in blank verse.27. essay 散文a composition, usually in prose, which may be of only a few hundred words or of book length and which discusses, formally or informally, a topic or a variety of topics. It is one of the most flexib le and adaptable of all literary forms. Francis Bacon is a great essayist; his ―Of Studies‖ isa model of good essay.28. English Romanticism 英国浪漫主义a literary movement that aimed at free expression of the writer’s ideas and feelings and flourished in the early 19th century England. A great representative of this movement is Percy Bysshe Shelley, the author of ―Ode to the West Wind‖.Sonnet 18One of the best known of Shakespeare’s sonnets, Sonnet 18 is memorable for the skillful and varied presentatio n of subject matter, in which the poet’s feelings reach a level of rapture unseen in the previous sonnets. The poet here abandons his quest for the youth to have a child, and instead glories in the youth’s beauty.Initially, the poet poses a question—‖Shall I compare thee to a summer’s day?‖—and then reflects on it, remarking that the youth’s beauty far surpasses summer’s delights. The imagery is the very essence of simplicity: ―wind‖ and ―buds.‖ In the fourth line, legal terminology—‖summer’s lease‖—is introduced in contrast to the commonplace images in the first three lines. Note also the poet’s use of extremes in the phrases ―more lovely,‖ ―all too short,‖ and ―too hot‖; these phrases emphasize the young man’s beauty.Although lines 9 through 12 are marked by a more expansive tone and deeper feeling, the poet returns to the simplicity of the opening images. As one expects in Shakespeare’s sonnets, the proposition that the poet sets up in the first eight lines—that all nature is subject to imperfection—is now contrasted in these next four lines beginning with ―But.‖ Although beauty naturally declines at some point—‖And every fair from fair sometime declines‖—the youth’s beauty will not; his unchanging appearance is atypical of nature’s steady progression. Even death is impotent against the youth’s beauty. Note the ambiguity in the phrase ―eternal lines‖: Are these ―lines‖ the poet’s ver ses or the youth’s hoped-for children? Or are they simply wrinkles meant to represent the process of aging? Whatever the answer, the poet is jubilant in this sonnet because nothing threatens the young man’s beautiful appearance.Then follows the concludi ng couplet: ―So long as men can breathe, or eyes can see, / So long lives this, and this gives life to thee.‖ The poet is describing not what the youth is but what he will be ages hence, as captured in the poet’s eternal verse—or again, in a hoped-for child. Whatever one may feel about the sentiment expressed in the sonnet and especially in these last two lines, one cannot help but notice an abrupt change in the poet’s own estimate of his poetic writing. Following the poet’s disparaging reference to his ―pupil pen‖ and ―barren rhyme‖ in Sonnet 16, it comes as a surprise in Sonnet 18 to find him boasting that his poetry will be eternal.John Keats认为,夜莺的歌声是美妙绝伦的,是不朽的,是永恒的,将世世代代的唱下去。
考研英语作文noticeAttention all students,。
Please be informed that there will be a change in the schedule for the upcoming sports event. Due to unforeseen circumstances, the event will now be held on the following day. We apologize for any inconvenience caused and appreciate your understanding.In addition, the library will be closed for maintenance this weekend. We apologize for any inconvenience caused and encourage you to plan your study time accordingly.Furthermore, there will be a meeting for all club members tomorrow afternoon. Attendance is mandatory, as important matters regarding upcoming events will be discussed. Please make sure to mark your calendars and be present at the designated venue on time.Moreover, the cafeteria will be offering a new menustarting next week. We have taken into consideration the feedback received from students and have introduced a variety of healthy and delicious options. We hope you will enjoy the new additions to the menu.Additionally, the university bookstore will be having a special sale on textbooks and stationery items. This is a great opportunity for students to purchase required materials at discounted prices. Don't miss out on this chance to save money!Lastly, the university is organizing a charity event next month. We encourage all students to participate and contribute to this noble cause. Whether it's volunteering your time or donating items, every effort counts. Let's come together as a community and make a positive impact.Thank you for your attention and cooperation. Have a great day!Best regards,。
NBER WORKING PAPER SERIESOWNERSHIP FORM AND TRAPPED CAPITAL IN THE HOSPITAL INDUSTRYHenry HansmannDaniel KesslerMark McClellanWorking Paper8989/papers/w8989NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts AvenueCambridge, MA 02138June 2002We would like to thank Alex Whalley and Arran Shearer for exceptional research assistance, and David Cutler, Ed Glaeser, and participants in the NBER Not-for-Profits conference for many helpful suggestions. Funding from the US National Institutes on Aging through the NBER and from th e Center for Social Innovation at the Stanford Graduate School of Business is greatly appreciated. The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research or the U.S. Government.© 2002 by Henry Hansmann, Daniel Kessler and Mark McClellan. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.Ownership Form and Trapped Capital in the Hospital IndustryHenry Hansmann, Daniel Kessler and Mark McClellanNBER Working Paper No. 8989June 2002JEL No. G3, I1, L3ABSTRACTOver the past 20 years, demand for acute care hospital services has declined more rapidly than has hospital capacity. This paper investigates the extent to which the preponderance of the nonprofit form in this industry might account for this phenomenon. We test whether rates of exit from the hospital industry differ significantly across the different forms of ownership, and especially whether secular nonprofit hospitals reduce capacity more slowly than do other types of hospitals. We estimate the effect of population changes (a proxy for changes in demand) at the zip-code level between 1985 and 1994 on changes in the capacity of for-profit, secular nonprofit, religious nonprofit, and public hospitals over the same period, holding constant metropolitan statistical area (MSA) fixed effects and other 1985 baseline characteristics of residential zip codes. We find that for-profit hospitals are the most responsive to reductions in demand, followed in turn by public and religiously affiliated nonprofit hospitals, while secular nonprofits are distinctly the least responsive of the four ownership types.Henry Hansmann Daniel Kessler Mark McClellanY ale Law School Graduate School of Business and Council of Economic Advisers P.O. Box 208215Hoover Institution Washington, DC 20502New Haven, CT 06520Stanford University and NBERStanford, CA 94305markmc@and NBERfkessler@I. INTRODUCTIONHospital care is the most prominent of several important service industries – concentrated heavily in health care, education, and the arts – in which nonprofit firms account for a substantial share of total production. Nonprofit firms do not, however, provide all of the nation’s hospital care; the industry is also heavily populated with both for-profit and governmental firms. The mix of ownership forms in this industry has fed a long-standing debate among economists, sociologists, and legal scholars about the patient and social welfare implications of ownership, with particular attention to differences between nonprofit and for-profit institutions. In this vein, several studies (reviewed in Kessler and McClellan 2002) have examined the effects of ownership on quality of care, operating efficiency, prices, costs, and the volume of charity care. We explore here a different but related issue: the effects of ownership on the rapidity of exit in the face of declining demand.In recent years, hospital care in the U.S. has been characterized by rapidly falling demand, likely due in large part to technological advances in medical procedures and to managed care (Cutler 2000). Between 1980 and 1999, inpatient non-federal days in short-stay hospitals in the U.S. fell from 293,830,162 to 160,560,460, or 45.4 percent (Health United States 2001, Tables 1 and 91). Capacity, however, has declined significantly less rapidly. The number of non-federal hospital beds fell from 1,247,188 to 938,746 over this period, or 24.7 percent (Health United States 2001, Table 108).The preponderance of the nonprofit form in the hospital industry may largely explain this divergence between the rate of decline in demand and the rate of contraction of capacity. The managers of nonprofit hospitals, lacking a class of owners to whom they are accountable, face no3external pressure to maximize profits, or even (like a public utility) to produce at least a market rate of return on the firm’s invested capital. Rather, when cash flow is positive, they are free to reinvest all of it, expanding capacity to the point where net revenues (and hence the marginal rate of return on existing investments) is zero. And, even if cash flow is negative, they are free to maintain capacity by drawing down on accumulated net assets (which, among nonprofit hospitals, are often substantial). Indeed, managers of nonprofit hospitals may even feel it is their duty to behave in this fashion, believing in good faith that all of the firm’s revenues and net assets must be dedicated to providing the maximum amount of hospital care possible.A natural and potentially more efficient alternative would be for the hospital to return its (potential) free cash flow, beyond what can be reinvested with an appropriately high social rate of return, to patients in the form of lower prices. But for hospital managers this alternative is rendered even less morally salient than it might otherwise be by the fact that the bulk of hospital revenue today comes, not directly from patients, but rather from third-party insurers. (Another alternative would be to donate net revenues or assets to other charities with greater social need, as is sometimes done with the proceeds from conversion from nonprofit to for-profit form.) Reluctance to reduce capacity is likely to be strongly reinforced, moreover, by pressure from a hospital’s affiliated staff physicians, whose income may be threatened by reduction or elimination of the hospitals’ facilities (Pauly and Redish 1974).To be sure, the managers of for-profit firms, and particularly of broadly-held business corporations, also have at times both the incentive and the opportunity to engage in empire-building. But the market for corporate control acts as an ultimate check on such tendencies, as demonstrated by the numerous hostile takeovers of the 1980s, which were in part directed at4reducing overcapacity and excessive reinvestment (Jensen 1988). No similar check exists for nonprofit firms, which cannot be the subject of a hostile takeover.It follows that a nonprofit firm not only can, but might well be expected to, maintain capacity and even expand in circumstances where its for-profit competitors choose to contract or exit the market entirely. And this can remain true even if for-profit firms are significantly more cost efficient, and even if the nonprofit firm receives no special subsidies (Hansmann, 1996a). In short, nonprofit firms have a tendency to act as capital traps, in which capital remains inefficiently embedded over long periods.The social welfare implications of such behavior are theoretically indeterminate. On one hand, “slow” adjustment of capacity to demand may be optimal. Altering the level of a factor of production whose costs are as sunk as those of a hospital bed is socially costly. On the other hand, “slow” adjustment may be socially wasteful. Maintaining a hospital bed is costly, in both financial and nonfinancial terms. Substantial research, starting with Roemer (1961), has suggested that high levels of bed capacity per patient lead to longer lengths of stay and higher costs. More recent research indicates that hospitals that treat relatively few cases of any particular type of patient – a potential consequence of high capacity – may deliver lower-quality care. Kessler and McClellan (2000), for example, find that elderly heart attack patients from markets with high levels of capacity per unit cardiac patient experience both generally higher levels of Medicare expenditures and higher mortality rates (although somewhat lower rates of cardiac complications).Thus, it is important to understand how ownership affects capacity choice. If ownership forms respond differently to changes in demand, then public policies that favor one ownership5form over another may affect welfare not just by altering the mix of ownership forms itself, but also by affecting the aggregate level of industry capacity. Moreover, identifying differences in the response of capacity to demand by ownership type can help distinguish among competing general models of nonprofit firm behavior.Despite the importance of the subject, however, little empirical work has focused on the impact of ownership form on capacity choice. Two studies have examined the differential supply response of nonprofit and for-profit firms, and of hospitals in particular, to rapid increases in demand. Both studies found that the market share of for-profit as opposed to nonprofit firms was significantly higher in areas that had recently experienced rapid growth in population. (Steinwald and Neuhauser 1970; Hansmann 1987.) These studies concluded that this pattern is explained, in important part, by the difficulties that nonprofit firms face in obtaining rapid access to capital.1 The long-term pattern of development in the hospital industry, prior to the implementation of Medicare and Medicaid in 1966, was that the ratio of for-profit to nonprofit firms increased during periods of rapidly increasing demand, and then fell -- owing, in part, to conversions from for-profit to nonprofit form -- as demand growth slackened (Steinwald and Neuhauser 1970). The sluggish nonprofit supply response to increasing demand was thus largely a short-term phenomenon.The principal problem facing the hospital industry today, however, may not be to hasten capacity expansion to meet growing demand, but just the opposite: to hasten the elimination of excess capacity already in place. Moreover, the problem is not peculiar to the hospital industry. The nonprofit form frequently performs a transitional role in the early stages of a service industry’s development, serving as an important source of production until adequate demand-6side financing is organized, and the service is sufficiently standardized or regulated, to permitfor-profit firms to serve as efficient providers. After that, the nonprofit firms lose their special raison d’etre, yet retain a substantial market share owing to trapped capital. Savings banking is a conspicuous example from the past (Hansmann, 1996b); health insurance and health maintenance organizations are contemporary examples; and higher education may be an example in the future (Hansmann 1996a). The problem of trapped capital is generic in the nonprofit sector.We have spoken so far of the effects of nonprofit versus for-profit ownership on the rate of capacity adjustment, but the underlying model of behavior described above can explain other differences in capacity choice between ownership forms. Hospital care, like some other important services such as education, is also provided in substantial part by governmental institutions. Supply response in general, and trapped capital in particular, may be less of a problem for these public firms than it is for nonprofit firms. A public hospital is not a nonprofit entity, but rather a proprietary entity, with the government as the owner. And the government has other pressing uses for its funds besides providing hospital care. Consequently, when the private sector becomes capable of providing services formerly provided by the government, governments may face political incentives to exit. Consistent with this logic, after Medicare and Medicaid relieved much of the need for public and nonprofit hospitals to provide uncompensated care, there was substantial exit by public institutions, whose share of total beds dropped from roughly 31% to 24% between 1971 and 1992, while for-profit hospitals simultaneously increased their market share from 6% to 12%. In striking comparison, nonprofit hospitals, rather than1 See Lakdawalla and Philipson (1998) for related work on other constraints on nonprofits’ ability to expand.7exiting in large numbers like the public institutions, actually increased their market share slightly during that period, from 63% to 64%. (Hansmann 1996a.)For similar reasons, one might expect nonprofit hospitals to differ among themselves in terms of supply response. The most conspicuous divide in this respect is between religiously-affiliated and nonreligious hospitals. Like public hospitals, religiously-affiliated hospitals often have an owner of sorts, if not in the formal legal sense then at least in the functional sense that they are commonly associated with another entity, the church, that both exercises substantial control over them and stands to benefit from economies achieved in the hospitals’ operations (and can serve as a source of funds and act as entrepreneur when expansion or entry is called for). By this reasoning, religiously-affiliated hospitals would show greater supply response than non-religious nonprofit hospitals.In this paper we seek to test whether rates of exit from the hospital industry differ significantly across the different forms of ownership, and especially whether secular nonprofit hospitals, which supply the majority of industry capacity, are much slower to reduce capacity than are other types of hospitals. We examine the relative responsiveness of the different types of hospitals to changes in demand for hospital services, using changes in the size of the elderly population as a proxy for changes in demand. We present estimates of the effect of population changes at the zip-code level between 1985 and 1994 on changes in the capacity of for-profit, secular nonprofit, religious nonprofit, and public hospitals over the same period, holding constant metropolitan statistical area (MSA) fixed effects and other 1985 baseline characteristics of residential zip codes. We decompose the effect on each ownership form’s capacity of population into four mutually exclusive and exhaustive sources: changes due to opens and closes89of hospitals, changes due to conversions, changes due to mergers and spinoffs, and changes due to changes in hospitals’ bed size. We also investigate whether the responsiveness of different ownership forms’ capacity to population differ according to zip codes’ baseline 1985 hospital market characteristics.Sections II through IV present the statistical models, the sources of data, and theempirical results. Section V concludes with some observations about the implications of our results for the hypothesized model of nonprofit firms’ behavior described above, and for economic efficiency in sectors dominated by nonprofit firms.II. MODELSFor every residential zip code z = 1, …, Z in a MSA in 1985 and 1994, we construct a measure of the hospital capacity serving that zip code. We assume that z is served by every nonfederal, general medical /surgical hospital j = 1, …, J z within 35 miles of the patient’sresidence with at least five heart attack (AMI) admissions, and every large, nonfederal, general medical/surgical teaching hospital within 100 miles of the patient’s residence with at least five AMI admissions.2 We allocate a hospital’s beds B j to z in inverse proportion to the distance between the hospital and the center of z , such that the capacity of z in 1985 is defined as∑∑===z j J j Z z jz jz j z D D B C 1185,85,1, 2 We explain the reason for these a priori constraints in Kessler and McClellan (2000); because markets for cardiac care are generally much smaller than the constraints, they are not restrictive.10 where D jz is the distance from j to z for every z = 1, …, Z j that is served by j . We define C z,94 analogously, and the log growth of capacity in zip code z as dlnC z = ln(C z,94) – ln(C z,85). The normalizing factor∑=j z jz D 111assures that for any hospital, the sum across the zip codes that it serves of the weights allocating its capacity equals 1, i.e. .111*111=∑∑==j j Z z jz Z z jz D DWe decompose capacity in two ways to explore whether market conditions and changes in demand for hospital services have different effects on the capacity provided by hospitals of different ownership types. First, we decompose each residential area’s capacity by its form of ownership. We define the secular nonprofit capacity of z , C z SNP , as,1*1185,85,85,∑∑===z j J j Z z jz jz j j SNP z D D SNP B Cwhere SNP j = 1 if j is a secular nonprofit hospital. We define for-profit capacity C z,85FP , religious nonprofit capacity C z,85RNP , and public capacity C z,85P (and their associated growth rates) analogously. Estimates of the effect of market conditions and changes in demand on eachownership form’s capacity will show how different types of organizations respond to exogenous shocks.Second, we decompose each area’s change in capacity by the source of the change. We categorize changes in total capacity and changes in each ownership form’s capacity as due to one or more of four exhaustive and mutually exclusive causes: opens and closes of new hospitals; conversions (i.e., changes in ownership status); mergers and demergers; and changes in bed size for hospitals not experiencing any of the three changes above. We construct an area’s (counterfactual) change in capacity due to (for example) opens and closes as follows. Define each hospital’s 1985 capacity B j,85 as its actual 1985 capacity; define each hospital’s 1994 capacity B j,94 = B j,85, unless the hospital opened or closed, in which case define B j,94 = B j,94. Then, recalculate each area’s capacity and change in capacity using these counterfactual hospital capacities.We model area changes in capacity as a function of 1985-1994 changes in a zip code’s Medicare enrollee population (to proxy for changes in demand for hospital services), the 1985 demographic characteristics of each zip code, the 1985 hospital market characteristics of each zip code, and MSA fixed effects. Thus, our effects are identified from within-MSA changes in population and within-MSA differences in market characteristics. We allow hospital capacity to respond asymmetrically to population increases and decreases. Our basic models specify the log change in residential zip code z’s capacity of ownership form k as a linear function of these factors:dlnC z k = αk + β+dlnP z+ + β-dlnP z- + X zφ + M zγ + εz,(1)where αk is an MSA-specific constant term; dlnP z+ is z’s log change in population if z’s population expanded, 0 otherwise; dlnP z- is z’s log change in population if z’s population contracted, 0 otherwise; X z is a vector of six variables denoting the proportion of z’s population11in 1985 who were female, black, age 70-74, age 75-79, age 80-89, and age 90-99 (omitted group is the proportion of population that were white males aged 65-69); M z is a vector of six variables denoting whether z in 1985 was in a highly-concentrated hospital market (in the top quartile of the distribution of Hirschman-Herfindahl indices), and whether z in 1985 had above the median density of patients admitted to large hospitals, teaching hospitals, hospitals that were members of multi-hospital systems, for-profit versus nonprofit hospitals, and public versus nonprofit hospitals;3 and εz is an independently-distributed error term, with E(εz | dlnP z, X z, M z) = 0. We weight each observation by the number of beds of ownership type k in zip code z, C z k.We reestimate model (1) including controls for baseline 1985 beds per capita as a control variable, to investigate the extent to which our results are sensitive to differences in baseline capacity that are correlated with ownership status across areas:(2) dlnC z k = αk + β+dlnP z+ + β-dlnP z- + X zφ + M zγ + θlnC z,85 + εz.We also estimate two expanded models to investigate whether the responsiveness of capacity to population shifts varies by 1985 baseline characteristics of hospital markets. First, we estimate models that interact lnC z with dlnP, to investigate the extent to which capacity is differentially responsive to demand in high- versus low-capacity areas:(3) dlnC z = αk + β+dlnP z+ + β-dlnP z- + δ+ lnC z,85 *dlnP z+ + δ- lnC z,85 *dlnP z- ++ θlnC z,85 + X zφ + M zγ + εz.Second, we estimate models that interact M z with dlnP z:(4) dlnC z = αk + β+dlnP z+ + β-dlnP z- + δ+ M z*dlnP z+ + δ-M z*dlnP z- + X zφ + M zγ + εz.3 See Kessler and McClellan (2001) for a detailed description of how these variables were constructed.12Estimates from this model will show, for example, the extent to which capacity of different ownership forms respond differently to demand in more versus less competitive markets.III. DATAWe use data from four sources. First, we use data on U.S. hospital characteristics collected by the American Hospital Association (AHA). The response rate of hospitals to the AHA survey is greater than 90 percent, with response rates above 95 percent for large hospitals (>300 beds). We exclude rural hospitals and hospitals owned by the federal government (primarily Veterans’ Administration hospitals) because the process governing capacity decisions for these hospitals may differ from other hospitals. We analyze the capacity of only those hospitals that ever reported providing general medical or surgical services (for example, we exclude psychiatric and rehabilitation hospitals from analysis). To assess hospital size and bed capacity per patient, we use total general medical/surgical beds, including ICU/CCU and emergency beds. We classify hospitals as teaching hospitals if they report at least 20 full-time residents.Second, we use a hospital system database constructed from multiple sources (see Madison 2001 for a detailed discussion). The AHA survey contains extensive year-by-year information on hospital system membership status. Our validity checking indicated that the universe of systems and system hospitals, and the timing of hospitals’ system membership, as defined by AHA did not conform to discussion of hospital systems in the trade press such as Modern Healthcare. We therefore created our own system database based on a combination of the AHA and other sources. We classify hospitals as for-profit, secular nonprofit, religious13nonprofit, or publicly-owned. We classify all public hospitals as non-system hospitals because system membership of public hospitals in our data did not reflect reliably actual transfer of control to an outside entity.Third, we use Medicare enrollment data to calculate the size and age/gender/race distribution of each non-rural zip code’s elderly population. The Health Care Financing Adminstration’s HISKEW enrollment files include demographic information on virtually all elderly Americans (including those enrolled in Medicare HMOs) because of the extremely high rate of takeup in the Medicare program.Fourth, we use comprehensive Medicare claims data on the hospital choices of virtually all elderly Medicare beneficiaries with heart attack in 1985, matched with the three data sources described above, to estimate a model of patients’ demand for hospital services as a function of travel distances between patients and hospitals, the characteristics of patients, and the characteristics of hospitals. With these estimates, we construct measures of patient flows to hospitals of different broad types (ownership status, size, teaching status, system membership status) that are based only on the arguably exogenous factors described above. Then, we calculate a vector of six indicator variables describing the hospital market characteristics of each zip code in 1985 as described above (see Kessler and McClellan 2000, 2002 for a detailed discussion).Table 1 describes how hospital capacity under different ownership forms has changed over the 1985-1994 period, and the sources of those changes. Table 1 decomposes changes in the number of hospital beds (and the facilities experiencing changes in beds) into four exhaustive and mutually exclusive categories: changes due to conversions (changes in ownership status),14changes due to opens and closes, changes due to mergers and spinoffs, and changes due to changes in bed size (absent a conversion, open/close, or merger/spinoff). The most salient feature of the hospital industry during our study period was a massive contraction in capacity.In percentage terms, religious nonprofit hospitals experienced the greatest contraction in bed capacity (32.5 percent), with public hospitals close behind (29.6 percent). For-profit beds contracted by 21.4 percent, while nonreligious nonprofit beds contracted the least, by 20 percent. These simple percentages do not give a clear picture of relative supply response, however, because the environments in which these four forms of ownership are found tend to differ. In particular, at the beginning of the study period, nonprofit hospitals, in comparison with for-profit hospitals, tended to be concentrated in areas with unusually high levels of capacity, where the need for reduction in capacity was presumably greatest. In results not presented in Table 1, the correlation coefficient between M z5 (=1 if the concentration of for-profit/nonprofit admissions in the zip code were above the median, 0 otherwise) and log(capacity per person in 1985) is .062, p < .01, and the correlation coefficient between M z6 (=1 if the concentration of public/nonprofit admissions in the zip code were above the median, 0 otherwise) and log(capacity per person in 1985) is .025, p < .01.In recent years, much attention has been focused on conversions of nonprofit hospitals to for-profit form – attention that has been due, in part, to concerns about private profiteering that accompanying some of these conversions (see, e.g., Sloan, Taylor, and Conover 2000). Table 1 suggests that the pattern of conversion activity is not well described, however, as an overall shift of capacity from nonprofit to for-profit form. Rather, Table 1 shows that conversions were in fact a net source of increase in secular nonprofit hospital beds between 1985 and 1994, and it15further shows that, in aggregate, net conversion activity in that period was out of both public and religious nonprofit hospitals and into both for-profit and secular nonprofit hospitals.To provide a more refined view of conversion activity, Table 2 tabulates the conversions in our data according to the ownership status of the hospitals involved both before and after the conversion. Those data show that, while there is a substantial number of conversions directly from secular nonprofit to for-profit form, there are also nearly two-thirds as many conversions in the reverse direction – likely as a response to declining profitability. The principal net conversion activity across ownership forms is, instead, from public hospitals to secular nonprofit hospitals, which accounts for nearly all of the overall net increase in secular nonprofit hospitals through conversion during the period in question.4Table 3 shows how residential zip codes’ hospital capacity responds to changes in population, according to the size of the zip codes’ MSA, and previews the results of our regression models. Table 3 reports the 1985-1994 percentage change in the four hospital ownership types’ capacity by MSA size for fast- growing versus slow-growing MSAs. Specifically, Table 3 groups zip codes according to the quartile of the zips’ urban 1985 elderly population, with 80 MSAs in each quartile. Then, within each population quartile, each of the 80 MSAs is classified into one of two groups of 40 MSAs, depending on its 1985-1994 growth in elderly population.Table 3 shows that, in the second and third population quartiles, variation across MSAs in supply response is consistent with the predictions that follow from the organizational incentives discussed in Section I. Capacity for all ownership types decreased in each group of MSAs, as16。