The Millionaire Next Door

Thomas J. Stanley, William D. Danko

• Editorial: RosettaBooks
• Año: 2010
• ISBN:
  9780795314865
• Categoría: Personal Development
• Categoría: Personal Finance

How do the rich get rich? An updated edition of the "remarkable" New York Times bestseller, based on two decades of research ( The Washington Post ). Most of the truly wealthy in the United States don't live in Beverly Hills or on Park Avenue. They live next door. America's wealthy seldom get that way through an inheritance or an advanced degree. They bargain-shop for used cars, raise children who don't realize how rich their families are, and reject a lifestyle of flashy exhibitionism and competitive spending. In fact, the glamorous people many of us think of as "rich" are actually a tiny minority of America's truly wealthy citizens—and behave quite differently than the majority. At the time of its first publication, The Millionaire Next Door was a groundbreaking examination of America's rich—exposing for the first time the seven common qualities that appear over and over among this exclusive demographic. This edition includes a new foreword by Dr. Thomas J. Stanley—updating the original content in the context of the financial crash and the twenty-first century. "Their surprising results reveal fundamental qualities of this group that are diametrically opposed to today's earn-and-consume culture." — Library Journal

APPENDIX 1
HOW WE FIND MILLIONAIRES

How do we go about finding millionaires to survey? A “C” student of ours once tried to answer this question in a marketing research course. He suggested that we merely obtain a list of people who drive luxury cars. As readers know by now, however, most millionaires do not drive luxury cars. Most luxury car drivers are not millionaires. No, this method will not work!

TARGETING BY NEIGHBORHOOD

The method used in our most recent study, as well as many others we have conducted, was developed by our friend Jon Robbin, the inventor of geocoding. Mr. Robbin was the first to classify—or code—each of the more than 300,000 neighborhoods in America. Using this system, one can code more than 90 percent of America’s 100 million households.

Mr. Robbin coded these neighborhoods first according to the average income for each. Next, he estimated the average net worth of each neighborhood by first determining the average interest income, net rental income, et. al. generated by households in each neighborhood. Then, using his mathematical “capitalization model,” he estimated the average net worth that would be required to generate such incomes. Once he had determined the estimated average net worth for each neighborhood, he assigned each a code. A code of one was assigned to the neighborhood with the highest estimated average net worth; a two was assigned to the neighborhood with the next highest average net worth, and so on. (Also see Thomas J. Stanley and Murphy A. Sewall, “The Response of Affluent Consumers to Mail Surveys,” Journal of Advertising Research [June/July 1986], pp. 55–58.)

We use this estimated net-worth scale to help us find millionaires to survey. First, we select sample neighborhoods that rank significantly higher than average along the estimated net-worth scale. A commercial mailing list company calculates the number of households in each of our chosen high–net worth neighborhoods. Next, the list company randomly selects heads of households within the selected neighborhoods. These are the people we survey.

In our most recent national study, conducted from June 1995 through January 1996, we selected 3,000 heads of households. Each received an eight-page questionnaire, a form letter asking for his participation and guaranteeing the anonymity and confidentiality of the data we collected, and a dollar bill as a token of our appreciation, along with a business reply envelope in which to return the completed questionnaire. A total of 1,115 surveys were completed in time to be included in our analysis. An additional 322 surveys could also be accounted for: 156 address unknown, 122 incomplete, and 44 otherwise usable surveys returned after data analysis had commenced. Overall, the response rate was 45 percent. Out of the 1,115 respondents, 385, or 34.5 percent of the total, had a household net worth of $1 million or more.

TARGETING BY OCCUPATION

We supplemented this survey with alternative surveys. Often we employ what is called the ad hoc method, in which we survey a narrowly defined population segment, as opposed to people who live within affluent neighborhoods in general. These population segments include affluent farmers, senior corporate executives, middle managers, engineers/architects, health-care professionals, accountants, attorneys, teachers, professors, auctioneers, entrepreneurs, and others. Ad hoc surveys are useful because even the best geocoding methods typically ignore affluent people who live in rural areas.

APPENDIX 2
1996 MOTOR VEHICLES: ESTIMATED PRICE PER POUND

APPENDIX 3
BUSINESSES/OCCUPATIONS OF SELF-EMPLOYED MILLIONAIRES

Accountant

Accounting/Auditing Services

Advertising Agency

Advertising Specialty Distributor

Advertising/Marketing Advisor

Aerospace Consultant

Agriculture

Ambulance Service

Antique Sales

Apartment Complex Owner/Manager

Apparel Manufacturer-Sportswear

Apparel Manufacturer-Infant Wear

Apparel Manufacturer-Ready-to-Wear

Apparel Retailer/Wholesaler-Ladies’ Fashions

Artist-Commercial

Attorney

Attorney-Entertainment Industry

Attorney-Real Estate

Auctioneer

Auctioneer/Appraiser

Audio/Video Reproduction

Author-Fiction

Author-Text Books/Training Manuals

Automotive Leasing

Baked Goods Producer

Beauty Salon(s) Owner-Manager

Beer Wholesaler

Beverage Machinery Manufacturer

Bovine Semen Distributor

Brokerage/Sales

Builder

Builder/Real Estate Developer

Business/Real Estate Broker/Investor

Cafeteria Owner

Candy/Tobacco Wholesaler

Caps/Hats Manufacturer

Carpet Manufacturer

Citrus Fruits Farmer

Civil Engineer and Surveyor

Clergyman-Lecturer

Clinical Psychologist

Coin and Stamp Dealer

Commercial Laundry

Commercial Real Estate Management Company

Commercial Laboratory

Commercial Property Management Company

Commodity Brokerage Company-Owner

Computer Consultant

Computer Applications Consultant

Construction

Construction Equipment Dealer

Construction Equipment Manufacturing

Construction-Mechanical/Electrical

Construction Performance Insurance

Consultant

Consulting Geologist

Contract Feeding

Contractor

Convenience Food Stores Owner

Cotton Gin Operator

Cotton Farmer

Cotton Ginning Owner/Manager

CPA/Broker

CPA/Financial P...