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Families understand that it is unwise to repeatedly spend much more than they take in. But Washington continues its shopping spree on the taxpayer credit card with seemingly no regard to the stack of bills the nation has already piled up.
To view the interactive version of this chart, please update your browser to its most current version or switch to an alternate browser.
Source: Congressional Budget Office.
COMMENTARY ON OCTOBER 27, 2013 The Only Worthy Topic for Budget Talks - Spending Control
BACKGROUNDER ON FEBRUARY 12, 2013 How the United States' High Debt Will Weaken the Economy and Hurt Americans
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Milla Jovovich
Birth Name: Milla Jovovich
Born: 12/17/1975
Birth Place: Soviet Union
Born: 12/17/1975
Birth Place: Soviet Union
Born on Dec. 17, 1975 in Kiev, Ukraine, Jovovich was raised by Bogich, a pediatrician, and Russian actress Galina Longinova. The family left Kiev in 1981, landing first in London and eventually settling in Sacramento, CA. Jovovich's parents struggled to make ends meet in their new home, with her mother resorting to cleaning houses - among her clients was director Brian DePalma - when her Russian acting credits failed to find her roles in the States. Following a divorce with her mother in 1991, Jovovich's father was incarcerated for five years due to his role in an insurance fraud scheme; he was released in 1999. Meanwhile, at just nine years old, Jovovich expressed interest in following i... Continue Bio >>
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Milla Jovovich Happy Sunday everyone! Hope you're having a great weekend! I just got my brand new @samaeyewear prescription sunglasses! So I can finally protect my eyes from the sun AND see simultaneously! Thanks so much guys, I'm so happy with these! #ladiary
Sunday 22nd of February 2015 04:04:55 PM
Sunday 22nd of February 2015 04:04:55 PM
Milla Jovovich Hey Everyone! I hope you've all been having a great week so far! Well, I am less than 6 weeks away from having a baby!!! Our doc said this little one is growing so fast that our due date could be moving up by as much as a week! We are so excited, especially me because this little girl is getting to be so big, it's becoming more and more difficult to function normally! lol!
I have to go back to work in June on one film and then in August we start filming "Resident Evil-The Final Chapter" so I've been trying to really lead the healthiest lifestyle possible (with out torturing myself!) to make sure I stay in the best (pregnancy) shape I can. The part that makes this so much easier to achieve is that the healthier my body is, the healthier my baby will be, so it's win/win for everyone!
Now let's get to #fooddiary! What have I eaten this week? Well, one of my good friends from the fashion industry, fellow model Brighdie Noriega (@sironabrighdie) and founder of the Sirona Cleanse (http://sironacleanse.com)
has been kind enough to allow me the absolute treat of sampling her food all week long! "Sirona Cleanse" is a small, boutique company and Brighdie makes everything herself for her clients which she has delivered every morning fresh to their home.
Well, I don't need to "cleanse" or lose weight at the moment, so I just added her gorgeous, vegan dishes to my normal meals and I couldn't be more impressed with the high quality of the produce, as well as, the amazing taste of literally every dish I've tried so far. I'm actually so sad that she can only manage to send food one week a month at this point! The food is so delicious, nutrient dense and totally balanced, so you know your body is getting everything it needs every single day you eat it. It's been so great to have her wonderful soups and salads to munch on in my fridge, I can't even tell you! And of course it's allowed me to get so much more done around the house because she's cut my cooking time down by more than half! lol! Yay Brighdie! You're so incredible and I really can't wait for when my next session comes in a month! Also, It's exciting to know that my newborn will be getting all the benefits of Brighdie's cooking via breastmilk after she's born! We're both going to be glowing because we're eating so well!
On top of my friend's amazing meals, Monday I pounded 6 chicken breasts super thin and quickly fried them in refined coconut oil with some salt and pepper. After they were cooked, I squeezed some lemon juice on them and I have yummy, healthy protein to eat for most of the week! I also made another batch of edamame dip and a white bean puree dip so I have as much healthy, easy food in the fridge as possible to snack on. I am also absolutely loving buckwheat, so I boiled some up and added some fried onion and carrot to it and it's so yummy. It's a super great alternative to rice, because its gluten free and has so many health benefits! Of course it goes with out saying that I make smoothies for lunch. I will actually be adding a #millaskitchen section to my website www.milla.com and permanently add my #fooddiary pics to it, so anyone interested can just go there and see what my smoothie ingredients are.
I have taken some pics of a few Sirona Cleanse dishes below, they are all the gorgeous, veggie dishes in glass. I also took a photo of my latest smoothie ingredients and my super fast and simple chicken breast recipe after its finished.
Ok, well I think that's it! Oh and follow me on Instagram: https://instagram.com/MillaJovovich/ if you like my pics! Have a great weekend! All the best! -m #countdowntodelivery #millaskitchen #lafooddiary
Friday 20th of February 2015 12:36:33 PM
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Katherine Duchess of Lancaster 1350?-1403Overview
Publication Timeline
Most widely held works about
Most widely held works by
Katherine Swynford : the history of a medieval mistress
by Jeannette Lucraft
1 edition published in 2006 in English and held by 2 WorldCat member libraries worldwide Katherine Swynford - sexual temptress or powerful woman at the centre of the medieval court? This book unravels the many myths and legacies of this fascinating woman, to show her in a whole new life. Katherine was sister-in-law to Geoffrey Chaucer and governess to the daughters of Blanche of Lancaster and John of Gaunt. She also became John of Gaunt's mistress - a role that she maintained for 20 years - and had four illegitimate children by him, from one of whom Henry Tudor was descended. In a move surprising in the fourteeth century, John of Gaunt eventually married her, making her Duchess of Audience Level
Related Identities
Associated Subjects
Autobiographies Biography Chantries Chaucer, Geoffrey, Chaumpaigne, Cecilia Courts and courtiers England English fiction Governesses Great Britain Historical fiction John,--of Gaunt, Duke of Lancaster, Katherine,--Duchess of Lancaster, Kings and rulers--Paramours Love stories Man-woman relationships Mistresses Nobility Political fiction Rape victims Richard--II,--King of England,
Alternative Names
Catherine 1350?-1403 Duchess of Lancaster
Catherine, Duchess of Lancaster, 1350?-1403
Katharine 1350?-1403 Duchess of Lancaster
Katharine, Duchess of Lancaster, 1350?-1403
Katherine duchesse de Lancastre
Lancaster, Katherine 1350?-1403 Duchess of
Lancaster, Katherine, Duchess of, 1350?-1403
Roelt, Katherine 1350?-1403
Roet, Katherine 1350?-1403
Roët, Katherine de
Swynford, Catherine 1350?-1403
Swynford, Katherine 1350?-1403
Swynford, Katherine ca.1350-1403


text: How to create a good scareMany horror games and movies are scary, but are sometimes predictable in their scares. Nothing gets the audience more than a good unpredictable scare. But how to do so? When is the perfect time to scare someone? I have some ideas I think might work,1. The anti-climax: If you played Kyo Oni, you might remember the cage in the attic and it's little surprise. This is an example of using the cliched predictable scares for your advantage. It's like a fake out technique with a little extra. Another example would be after a scare to instantly include another one, throwing the player off guard. An example would be Mika from Ao Oni hiding in the closet alot (she really likes it). End the chase, only to have another one start instantly.2. The little details: Any good horror game or movie needs to pay attention to the details, the small building up parts that add up to the final thing. Lets say you saw some bugs earlier in the game, then you saw alot in the background during a scene of sorts, t


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Gluten-Free Pound Cake with Cranberries
Using a gluten-free baking blend means this cake doesn't require an array of alternative flours.
- Servings: 8
Photography: Johnny Miller
Source: Everyday Food, December 2011
- 1 cup (2 sticks) unsalted butter, room temperature, plus more for pan
- 3/4 cup granulated sugar
- 1/4 cup packed light-brown sugar
- 1 tablespoon finely grated orange zest
- 4 large eggs, room temperature
- 1 tablespoon pure vanilla extract
- 1/2 teaspoon fine salt
- 2 cups gluten-free all-purpose flour blend (spooned and leveled)
- 1/2 teaspoon ground cinnamon
- 1/2 cup chopped pecans
- 1/2 cup dried cranberries
- Gluten-free confectioners' sugar, for dusting
Preheat oven to 350 degrees. Lightly butter a 4 1/2-by-8 1/2-inch loaf pan. In a large bowl, using an electric mixer, beat butter, granulated and brown sugars, and orange zest on medium-high until very light and fluffy, 7 minutes. Add eggs, one at a time, beating well after each addition and scraping down bowl as needed. Beat in vanilla and salt. With mixer on low, gradually add flour blend and cinnamon and beat until just combined (do not overmix). Fold in pecans and cranberries.
Transfer batter to pan, smooth top, and bake until a toothpick inserted in center of cake comes out with a few moist crumbs attached, about 1 1/4 hours (cover with foil if browning too quickly). Let cool in pan 15 minutes. Turn out onto a wire rack and let cool completely. Dust with confectioners' sugar before serving.
Cook's Note
Reviews (2)
Laura C 19 Jun, 2013
Made this without the nuts (allergy), and used the Williams-Sonoma Cup4Cup flour. Added a little bit of psyllium husk (thanks Gluten Free Girl) and this loaf was MARVELOUS. Just like I remember from my non-gluten free days. Will make many variations of this in future. One thing to watch- you don't have to bake it for 1h 15, mine was done in about 1 hour.
LESR 22 Apr, 2013
Good Recipe. I was skeptical given that there were no reviews. As such I analysed the recipe and realized that there was no baking powder, and further on comparing other gluten free recipes, it did not call for xanthem gum. However this came out great. I didn't add the cranberries, etc. as I wanted a more traditional pound cake. Nice


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Quirky News
Kids get chance to sleep with the FA Cup
Lucky children are being given not only the chance to lift the FA Cup - but to also wake up in bed clutching it.
Children who visit The FA Cup Trophy Tour - which will visit key cities across the country - will be chosen at random to win the chance to have the iconic trophy in their house for the night.
It means they can even drift off to sleep dreaming of the heroes who have lifted The FA Cup before them.
Six-year-old Finley Burridge from London was the first to have his dream come true, describing the experience as "amazing".
The FA's Sir Trevor Brooking said: "The FA Cup is one of the most recognisable pieces of silverware in world sport.
"We hope that a new wave of football mad youngsters can fall in love with the competition like generations of children before them."


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NASCAR Odds: Ford 300 From Homestead-Miami Speedway |
Written by Drew Sharper |
Thursday, 19 November 2009 10:47 |
Ford 300 Odds The NASCAR Nationwide Series wraps up its 2009 season on Saturday with the 2009 Ford 300 from Homestead-Miami
Points leader Kyle Busch simply needs to start the race in order to win the season championship, but he also enters the race as the favorite along with rival Carl Edwards. Busch and Edwards have 3/1 odds win the race according to oddsmakers at the online sports book SPORTSBETTING.com. Other short odds to win are Joey Logano (4/1) and Brad Keselwoski (4/1). Carl Edwards won last year's event and is coming off a win last week. Edwards has placed in the top nine in his last 11 races and in 19 of his last 20. Edwards has five wins on the year. Logano has competed in 21 races this year in the Nationwide series and has five wins during this season. Logano has finished outside the top 10 in his last two races. Keselowski is third in the standings, 250 points behind Busch. Keselowski has four wins this year and has finished in the top 10 in 16-straight races and has three wins during that stretch. Carl Edwards 3/1 Joey Logano 4/1 Brad Keselowski 4/1 Danny Hamlin 7/1 Matt Kenseth 7/1 Ryan Newman 10/1 Jeff Burton 10/1 Mike Bliss 15/1 Field 3/1 |


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All Times EST |
Wednesday, Feb. 13 |
George Washington at Fordham, Noon
Saint Louis at La Salle, Noon
Rhode Island at Temple, Noon
Lehigh at American, 7 p.m.
Lafayette at Army, 7 p.m.
Stony Brook at Binghamton, 7 p.m.
Bucknell at Colgate, 7 p.m.
Saint Joseph's at Duquesne, 7 p.m.
Maine at Hartford, 7 p.m.
Holy Cross at Navy, 7 p.m.
Villanova at Seton Hall, 7 p.m.
New Hampshire at UMBC, 7 p.m.
Boston U. at Vermont, 7 p.m.
Oklahoma St. at West Virginia, 7 p.m.
Butler at Charlotte, 7 p.m.
W. Kentucky at Florida Atlantic, 7 p.m.
Georgia Southern at W. Carolina, 7 p.m.
Louisiana-Lafayette at South Alabama, 8:05 p.m.
Marquette at Cincinnati, 7 p.m.
Wright St. at Detroit, 7 p.m.
Indiana at Michigan St., 7 p.m.
Texas at Kansas St., 8 p.m.
Valparaiso at Loyola of Chicago, 8 p.m.
Houston Baptist at Lamar, 8 p.m.
Kansas at TCU, 8 p.m.
UALR at Arkansas St., 8:05 p.m.
Fresno St. at New Mexico, 9 p.m.
Wyoming at Nevada, 9:30 p.m.
Colorado St. at San Diego St., 10 p.m.
Air Force at UNLV, 10 p.m.
Thursday, Feb. 14 |
Drexel at Hofstra, Noon
Canisius at Fairfield, 6 p.m.
Virginia at Boston College, 7 p.m.
Old Dominion at Delaware, 7 p.m.
Iona at Saint Peter's, 7 p.m.
North Texas at Louisiana-Monroe, 6:15 p.m.
Northwestern St. at SE Louisiana, 6:15 p.m.
FIU at Troy, 6:15 p.m.
Stephen F. Austin at Nicholls St., 6:30 p.m.
Gardner-Webb at Campbell, 7 p.m.
Presbyterian at Coastal Carolina, 7 p.m.
SMU at East Carolina, 7 p.m.
N.C. State at Florida St., 7 p.m.
Towson at Georgia St., 7 p.m.
UNC Asheville at High Point, 7 p.m.
South Carolina at Kentucky, 7 p.m.
Winthrop at Liberty, 7 p.m.
Radford at Longwood, 7 p.m.
UTEP at Marshall, 7 p.m.
Clemson at Maryland, 7 p.m.
Wake Forest at North Carolina, 7 p.m.
George Mason at UNC Wilmington, 7 p.m.
Duke at Virginia Tech, 7 p.m.
Seattle at Louisiana Tech, 8 p.m.
UAB at Memphis, 8 p.m.
Mississippi St. at Mississippi, 8 p.m.
Utah Valley at New Orleans, 8 p.m.
UCF at Southern Miss., 8 p.m.
Rice at Tulane, 8 p.m.
Missouri at Vanderbilt, 8 p.m.
South Dakota at Oakland, 6:30 p.m.
UMKC at IPFW, 7 p.m.
S. Dakota St. at IUPUI, 7 p.m.
Indiana St. at Evansville, 8 p.m.
Cleveland St. at Green Bay, 8 p.m.
Penn St. at Iowa, 8 p.m.
Youngstown St. at Milwaukee, 8 p.m.
N. Dakota St. at W. Illinois, 8 p.m.
Purdue at Wisconsin, 8 p.m.
Missouri St. at Creighton, 8:05 p.m.
Wichita St. at Drake, 8:05 p.m.
Illinois St. at S. Illinois, 8:05 p.m.
Ohio St. at Nebraska, 9 p.m.
Sam Houston St. at Oral Roberts, 6 p.m.
Texas A&M-CC at Cent. Arkansas, 6:30 p.m.
Tulsa at Houston, 8 p.m.
Iowa St. at Oklahoma, 8 p.m.
Idaho at Texas-Arlington, 8 p.m.
Florida at Texas A&M, 9 p.m.
San Jose St. at New Mexico St., 8:30 p.m.
N. Colorado at N. Arizona, 8:35 p.m.
Santa Clara at BYU, 9 p.m.
Utah St. at Denver, 9 p.m.
Saint Mary's (Cal) at Gonzaga, 9 p.m.
Weber St. at Montana, 9 p.m.
Idaho St. at Montana St., 9 p.m.
Portland St. at S. Utah, 9 p.m.
Long Beach St. at Cal State Fullerton, 10 p.m.
Pacific at CS Northridge, 10 p.m.
San Diego at Loyola Marymount, 10 p.m.
San Francisco at Portland, 10 p.m.
North Dakota at Sacramento St., 10 p.m.
UC Irvine at UC Riverside, 10 p.m.
UC Davis at Hawaii, Mid
Friday, Feb. 15 |
Niagara at Marist, 5 p.m.
Brown at Columbia, 7 p.m.
Yale at Cornell, 7 p.m.
Manhattan at Loyola (Md.), 7 p.m.
Harvard at Penn, 7 p.m.
Dartmouth at Princeton, 7 p.m.
Rider at Siena, 7 p.m.
Northeastern at William & Mary, 8 p.m.
Colorado at Arizona, 9 p.m.
Utah at Arizona St., 9 p.m.
California at UCLA, 9 p.m.
Washington at Oregon, 10 p.m.
Washington St. at Oregon St., 10 p.m.
Stanford at Southern Cal, 11 p.m.
Saturday, Feb. 16 |
Army at Navy, 11:30 a.m.
St. Francis (Pa.) at Quinnipiac, Noon
Robert Morris at Sacred Heart, 1 p.m.
New Hampshire at Boston U., 1 p.m.
Bryant at Wagner, 1 p.m.
Villanova at Georgetown, 2 p.m.
Fairleigh Dickinson at LIU Brooklyn, 2 p.m.
CCSU at Mount St. Mary's, 2 p.m.
Pittsburgh at Syracuse, 2 p.m.
Stony Brook at Vermont, 2 p.m.
Canisius at Iona, 2 p.m.
Houston Baptist at NJIT, 2 p.m.
Saint Peter's at Fairfield, 2 p.m.
American at Holy Cross, 3 p.m.
St. Francis (N.Y.) at Monmouth (N.J.), 3 p.m.
Maine at Binghamton, 4 p.m.
UConn at Rutgers, 4 p.m.
Harvard at Princeton, 6 p.m.
Brown at Cornell, 7 p.m.
Dartmouth at Penn, 7 p.m.
Yale at Columbia, 7 p.m.
Colgate at Lehigh, 7 p.m.
Lafayette at Bucknell, 7 p.m.
Chattanooga at Coll. of Charleston, Noon
North Florida at Jacksonville, 1 p.m.
Mercer at ETSU, 1:30 p.m.
Wofford at Appalachian St., 2 p.m.
Presbyterian at Charleston Southern, 2 p.m.
Furman at Davidson, 2 p.m.
Coppin St. at Delaware St., 2 p.m.
Morgan St. at Md.-Eastern Shore, 2 p.m.
Elon at UNC Greensboro, 2 p.m.
Radford at Liberty, 3 p.m.
Arkansas St. at Louisiana-Monroe, 3 p.m.
Northwestern St. at Nicholls St., 3 p.m.
Stephen F. Austin at SE Louisiana, 3 p.m.
Alabama St. at Southern U., 3 p.m.
Alabama A&M at Alcorn St., 3:30 p.m.
Winthrop at High Point, 3:30 p.m.
Gardner-Webb at Coastal Carolina, 4 p.m.
Jacksonville St. at E. Kentucky, 4 p.m.
Howard at Florida A&M, 4 p.m.
Grambling St. at Jackson St., 4 p.m.
N.C. Central at Savannah St., 4 p.m.
N.C. A&T at S.C. State, 4 p.m.
UNC Asheville at Campbell, 4:30 p.m.
Stetson at N. Kentucky, 4:30 p.m.
Kennesaw St. at S.C.-Upstate, 4:30 p.m.
Florida Gulf Coast at Lipscomb, 5 p.m.
SE Missouri at UT Martin, 5 p.m.
Tennessee Tech at Morehead St., 5:15 p.m.
W. Kentucky at FIU, 5:30 p.m.
Florida Atlantic at Louisiana-Lafayette, 6 p.m.
North Texas at Troy, 6 p.m.
Samford at Georgia Southern, 7 p.m.
Seton Hall at South Florida, 7 p.m.
Idaho at Louisiana Tech, 8 p.m.
Belmont at Tennessee St., 8 p.m.
Michigan St. at Michigan, Noon
Cent. Michigan at Miami (Ohio), 1 p.m.
Buffalo at E. Michigan, 2 p.m.
South Dakota at IPFW, 2 p.m.
UMKC at Oakland, 2 p.m.
Kent St. at Toledo, 2 p.m.
Ill.-Chicago at Valparaiso, 2:35 p.m.
Youngstown St. at Green Bay, 3 p.m.
N. Dakota St. at IUPUI, 3 p.m.
Cleveland St. at Milwaukee, 3 p.m.
Bradley at N. Iowa, 3 p.m.
Indiana St. at S. Illinois, 3:05 p.m.
Bowling Green at N. Illinois, 4 p.m.
W. Michigan at Ohio, 4 p.m.
Wichita St. at Creighton, 4:05 p.m.
Ball St. at Akron, 5 p.m.
Missouri St. at Drake, 5 p.m.
Murray St. at E. Illinois, 5 p.m.
Austin Peay at SIU Edwardsville, 5 p.m.
S. Dakota St. at W. Illinois, 5:30 p.m.
Illinois St. at Evansville, 6 p.m.
Loyola of Chicago at Wright St., 6 p.m.
Ark.-Pine Bluff at Texas Southern, Noon
TCU at Baylor, 2 p.m.
Sam Houston St. at Cent. Arkansas, 3 p.m.
MVSU at Prairie View, 4 p.m.
McNeese St. at Lamar, 5 p.m.
Kansas St. at Oklahoma St., 5 p.m.
Texas St. at UTSA, 5 p.m.
Utah Valley at Texas-Pan American, 5:30 p.m.
Middle Tennessee at UALR, 5:30 p.m.
Texas A&M-CC at Oral Roberts, 6 p.m.
Seattle at Texas-Arlington, 7 p.m.
New Mexico at Boise St., 4 p.m.
Air Force at Colorado St., 4 p.m.
Idaho St. at Montana, 4 p.m.
Weber St. at Montana St., 4 p.m.
North Dakota at N. Arizona, 4:35 p.m.
UC Davis at CS Northridge, 5 p.m.
Wyoming at Fresno St., 5 p.m.
San Francisco at Gonzaga, 5 p.m.
BYU at Pepperdine, 5 p.m.
N. Colorado at Sacramento St., 5 p.m.
Cal Poly at UC Santa Barbara, 5 p.m.
San Jose St. at Denver, 6 p.m.
Cal State Fullerton at UC Irvine, 8 p.m.
Long Beach St. at UC Riverside, 8 p.m.
Utah St. at New Mexico St., 8:30 p.m.
E. Washington at S. Utah, 9 p.m.
Chicago St. at CS Bakersfield, 10 p.m.
Saint Mary's (Cal) at Portland, 10 p.m.
Pacific at Hawaii, Mid
Sunday, Feb. 17 |
Loyola (Md.) at Rider, 1 p.m.
Albany (N.Y.) at Hartford, 2 p.m.
James Madison at Hofstra, 2 p.m.
Manhattan at Marist, 2 p.m.
Temple at Saint Joseph's, 2 p.m.
Niagara at Siena, 2 p.m.
Cincinnati at St. John's, 5 p.m.
Virginia Tech at Clemson, 1 p.m.
Southern Miss. at East Carolina, 1 p.m.
Georgia at Florida, 1 p.m.
North Carolina at Florida St., 1 p.m.
Georgia St. at UNC Wilmington, 1 p.m.
La Salle at VCU, 1 p.m.
DePaul at Louisville, 1:30 p.m.
LSU at Mississippi St., 1:30 p.m.
George Washington at Charlotte, 2 p.m.
South Carolina at Mississippi, 2 p.m.
Northeastern at Old Dominion, 2 p.m.
UMass at Richmond, 2 p.m.
UTEP at UCF, 2 p.m.
Delaware at William & Mary, 2 p.m.
Boston College at Miami, 2:05 p.m.
Alabama at Auburn, 2:30 p.m.
Georgia Tech at N.C. State, 2:30 p.m.
Vanderbilt at Tennessee, 2:30 p.m.
Wake Forest at Duke, 3 p.m.
SMU at Memphis, 3 p.m.
Houston at Tulane, 3 p.m.
Marshall at UAB, 3 p.m.
Towson at George Mason, 5 p.m.
Maryland at Virginia, 5 p.m.
West Virginia at Iowa St., 1:30 p.m.
St. Bonaventure at Butler, 2 p.m.
Fordham at Saint Louis, 2 p.m.
Rhode Island at Xavier, 2 p.m.
Oklahoma at Kansas, 2:30 p.m.
Notre Dame at Marquette, 2:30 p.m.
Northwestern at Minnesota, 3 p.m.
Iowa at Purdue, 5 p.m.
Missouri at Arkansas, 3 p.m.
Rice at Tulsa, 3 p.m.
Texas at Texas Tech, 3:30 p.m.
Stanford at UCLA, 3:30 p.m.
Colorado at Arizona St., 4 p.m.
UNLV at San Diego St., 4 p.m.
California at Southern Cal, 5 p.m.
Utah at Arizona, 7 p.m.
Washington St. at Oregon, 7 p.m.


text:
All computers require installation of cooling fans. They play a very important job by providing an instant cooling service to your computer. Internal computer temperature rises constantly from working hard drives, video cards, and motherboards. If your cooling fan doesn't work properly, your computer parts can overheat and melt. Many computer cases come with extra slots for cooling fans. Due to power increase in computer components, there is a bigger need for cooling fans.
Here are a few tips on how to buy PC cooling fans:


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Insurance choice and the demand for prescription drugs.
I. IntroductionOver the past decade or so, expenditure on outpatient prescription drugs has become one of the fastest-growing components of health care expenditure in the U.S. [11]. Much of the debate over the Medicare Catastrophic Coverage Act of 1988 and the current debate over health care reform is concerned with the rising cost and utilization of prescription drugs particularly among the elderly. There is of course a general debate over the extent to which the problems of adverse selection and/or moral hazard arising from universal coverage will render futile the cost estimates for the sundry forms of health insurance to be covered under any reform. The case of prescriptions is particularly interesting, as witness the pressure placed on pharmaceutical companies by administration officials in 1993.
This paper analyzes the influence that health insurance has on elderly individuals' decisions to use prescription drugs. We create a data base from a survey of health insurance and medicine use in the Commonwealth of Pennsylvania conducted during the summer of 1990 by researchers affiliated with the Medicine, Health, and Aging Project at Penn State [23]. Pennsylvania is particularly interesting in this regard because of the generous provisions of the PACE (Pharmaceutical Assistance Contract for the Elderly) program, which pays for all outpatient prescriptions for low income elderly, less a $4.00 copayment per 30 days dosage.
Our analytic framework is dictated by three considerations. First, we wish to produce quantitative estimates of insurance effects over a wide range of prescription benefit provisions. Most prior research on prescription drug demand has focused on the effects of relatively minor alterations in insurance benefits such as the addition of a $.50 or $1.00 copayment. Findings from these studies are not generalizable to situations in which individuals gain (or lose) prescription coverage or face other major changes in prescription benefits.
Second, we wish to address the question of whether proscription demand is driven more by the own-price effects of drug coverage or by the cross-price effects of Medicare supplementation for ambulatory physician visits. Outpatient physician visits are covered under Medicare Part B, but are subject to deductible and coinsurance provisions which may reduce their use. Since physicians control access to prescription medicine, it follows that changes in the price of physician care may affect drug demand as well.
The third consideration is methodological rather than substantive. Elderly who have prescription coverage obtain it from three basic sources: employer-sponsored plans, individual Medigap policies, or public programs including Medicaid and pharmaceutical assistance plans like PACE. Except for employer-sponsored plans, the elderly must seek coverage on their own. It is reasonable to suspect that those who do are more likely to need prescription drugs than those who do not seek coverage. Unless controlled for, self selection can lead to serious bias in models designed to measure the moral hazard engendered by insurance.
Most of the empirical research on the demand for prescription drugs involve studies of differences in utilization following small changes in insurance coverage. Several studies conducted in the 1960s and early 1970s concluded that Medicaid copayments reduce drug utilization rates [7; 2; 21], but methodological shortcomings make the results difficult to generalize. Later studies using interrupted time-series designs have produced similar findings. A South Carolina study observed a significant decline in drug use following imposition of a $.50 drug copayment in that state's Medicaid program in 1977 [12; 17]. A study by Soumerai et al. [22] found significant reductions in prescription fill rates among New Hampshire Medicaid recipients following imposition of a three-per-month [R.sub.x] limit and a $1.00 copayment.
Similar before-and-after studies (some with controls, some without) have been conducted in Great Britain following changes in National Health Service patient cost-sharing provisions for prescription drugs that occurred at various times from 1969 through 1986 [16; 20; 1; 8; 14; 19]. Estimated [R.sub.x] price elasticities from these studies range from a low of -.06 [19] to a high of -.64 [14], with a preponderance of estimates in the -.10 to -.20 range.
Changes in cost-sharing for prescription medicine among privately insured groups in the United States have also been shown to have significant effects on drug utilization in the expected direction. An early study by Weeks [25] found that the introduction of prepaid drug benefits in an employee health plan increased the average number of prescriptions filled. A recent study by Harris, Stergachis and Ried [6] discovered that progressively higher copayment levels led to proportionate reductions in drug use among non-aged HMO enrollees.
The Rand Health Insurance Experiment (HIE) produced two papers on the relationship between drug utilization and insurance coverage [10; 9]. Unfortunately, the health insurance packages offered to HIE participants did not vary according to prescription drug benefits, so that it proved impossible to estimate directly the price effect of [R.sub.x] coverage on utilization levels. Moreover, the sample frame excluded the elderly. Despite these shortcomings, the HIE provides important clues to the possible impact of insurance on medicine use. In their 1985 paper, Leibowitz, Manning, and Newhouse [10] examined the relationship between [R.sub.x] utilization and patient cost-sharing (for all medical services, not just drugs). They found that HIE enrollees with generous insurance filled significantly more prescriptions than did those with less generous coverage; in fact, the degree of price responsiveness did not differ much between drugs and other medical services. Their conclusion that "drugs, like medical care expenditures in general, respond to cost-sharing faced by consumers" [10, 1068] has been widely debated on grounds that the HIE results cannot distinguish between the own-price effect of insurance on the covered service in question (prescription drugs) and the cross-price effect of coverage for services that complement drug therapy (physician visits). A second Leibowitz paper [9] reported no significant relationship between insurance plan generosity and utilization rates for over-the-counter medicine (which may potentially substitute for [R.sub.x] products).
Finally, we take note of a study by Cameron et al. [3] which uses Australian data to estimate a series of health services demand equations including equations for prescription drug use. This study is relevant to our purposes mainly because it demonstrates the difficulty of estimating insurance effects in the presence of self-selected coverage (variation in own-price for prescribed medicine in Australia arises from private insurance supplements of the national insurance plan). The authors first estimate a model with endogenous insurance variables and find that more generous insurance coverage has a positive and significant effect on prescription utilization. They then reestimate the models using instrumental variables for the insurance options and find implausibly large insurance coefficients and a sign reversal (persons subject to a $2.00 prescription copayment are predicted to use nearly double the number of prescribed medicines compared to those for whom drugs are free).
II. Empirical Model
The estimation of insurance effects on the demand for health care in choice-based samples is difficult because the determinants of insurance coverage are strongly correlated with the determinants of use. Persons with poor health have higher utilization rates, but are also likely to purchase larger amounts of insurance. For the analyst this creates a modeling problem since the insurance variables will capture both the demand-inducing effect of better coverage and residual effects associated with insurance selection. To make matters worse, the standard econometric remedies for endogeneity - instrumental variables and Heckman selection-control models - perform poorly in this context. Experience similar to Cameron's has been reported by Newhouse and Phelps [13] and others. A recent Monte Carlo experiment reported by Hartman [6] shows that two-stage estimators are inherently unstable. The problem appears to arise from the fact that the instrument created in the first-stage stage equation is highly collinear with the exogenous variables in the second stage. The coefficient estimates produced in the second stage are thus extremely sensitive to minor changes in the specification of either equation.
Our approach to modeling the demand for prescription drugs controls for self-selectivity in another way. It is a variation of a longitudinal model developed by Wolfe and Goddeeris [26] in which past utilization is used to proxy for the unobserved determinants of insurance choice. We test for the endogeneity of insurance choice using Hausman tests and find that insurance choice is correlated with the error term when our proxy ms excluded and uncorrelated when it is included. Thus the Hausman test is able to discriminate between the two models, and justifies, to that extent, the use of the designated proxies. On that basis we can measure the moral hazard associated with insurance coverage directly from the insurance coefficients.
Formally, we propose a model of prescription demand of the following sort,
[M.sub.it] = [[Beta].sub.1][X.sub.it] + [[Beta].sub.2][I.sub.it] + [e.sub.it] (1)
where M is the number of prescription drugs reported purchased by the ith individual within a certain time period (t), X is a vector of personal characteristics, and I is a vector of insurance dummies. Although [[Beta].sub.2] captures the moral hazard effect, OLS estimates of equation (1) are biased because the error term is of the form:
[e.sub.it] = [v.sub.it] - [h.sub.it] (2)
where v is random noise and h is health status over the period in which the insurance is in force. The value of [h.sub.it] is not directly observable until after the insurance decision has been made but is presumed by the individual (and the modeler) to be related to [h.sub.it-1]. The better the health the fewer the M, but also the less likely is insurance. Hence I is correlated with the error term.
Wolfe and Goddeeris deal with this difficulty by assuming that h is first-order autoregressive. Performing the Cochrane-Orcutt transformation yields an equation in which lags of all the variables are included on the right hand side and the error term consists only of [v.sub.it] and the residual from the AR(1) model of [h.sub.it] and hence is uncorrelated with the regressors.
This model is a specific example in the class of models where health status is forecasted linearly from some information set Z:
E([h.sub.it][where][Z.sub.it-1]) = [[Beta].sub.3][Z.sub.it-1] (3)
where Z is the set of information available to the ith individual at time t - 1. In the above scenario, Z consists of all lagged variables, but because of the autocorrelation, [[Beta].sub.3] is constrained. Our data set, described in the next section, is limited in that we do not have access to all the variables at time t - 1 (specifically, we do not have longitudinal data on prescription drug use). We do, however, have data on a number of health indicators which we believe will encompass the information available at time t - 1. Because we eschew the parameterization suggested by the Cochrane-Orcutt transformation, the estimates of [[Beta].sub.3] are unconstrained.
The regression model is simply:
[M.sub.it] = [[Beta].sub.1][X.sub.it] + [[Beta].sub.2][I.sub.it] + [[Beta].sub.3][Z.sub.it-1] + [v.sub.it] + [u.sub.it] (4)
where u is the prediction error on h. Under the current error structure, the estimate of [[Beta].sub.2] is consistent.
We can test the appropriateness of equation (4) via a Hausman test. We run this regression without [Z.sub.t-1] and use Hausman procedures to test for endogeneity of the insurance choice variables. It will turn out (as described below) that exogeneity can be rejected, and from this we infer that the difficulties discussed above indeed exist. We then add [Z.sub.t-1] to the list of regressors and reperform the test, which in this case cannot reject exogeneity. Hence the Hausman test is able to discriminate between the two specifications and leads to our belief that the expanded model provides consistent estimates.
We estimate three versions of equation (4). First is an OLS model with observations on prescription drug use for all individuals (including those with no reported use). Second is a Probit probability-of-use model with prescription utilization coded as a binary variable (1 = reports use of prescription drugs; 0 = reports no drug use). Third is an OLS level-of-use equation estimated over the subset of individuals reporting one or more prescription during the study period.
III. Data
Our data set is a panel constructed from survey responses and Medicare claims records for 4066 elderly Pennsylvanians who were asked about their demographic characteristics, current health status, insurance coverage and medicine use in a mail questionnaire administered in the late summer and early fall of 1990. The survey sample was randomly selected from Medicare enrollment files for Pennsylvania. Disabled beneficiaries under age 65 were excluded from the sample frame as were elderly HMO enrollees.(1) The survey questionnaire was sent to 6,502 persons. After reminders and repeat mailings 4,508 responded (69.3 percent). Further detail on the survey instrument and its implementation is contained in Stuart et al. [23].
We selected calendar year 1988 as the t - 1 period for the Z variables. Thus when constructing the analytical file for the study we eliminated 442 individuals who were not covered for Medicare Parts A and B continuously from January, 1988 through the survey date. The characteristics of the reduced sample are shown in Table I. Respondents reported an average of 1.23 prescriptions or prescription refills during a two-week recall period. Approximately half (54 percent) filled at least one prescription and among that group use averaged two-and-a-half [R.sub.x] scripts. These utilization rates are considerably above those reported in the most current National Medical Expenditure Survey [11], but then the percentage of elderly Pennsylvanians with prescription coverage is also significantly above the national mean [23].
The X vector is represented by various demographic characteristics including age, sex, race, education, annual income, and marital status. The income breakdown includes two categories used to identify individuals putatively eligible for public programs, Medicaid (income [less than] $6,000) and PACE (income [less than] $12,000 if single, [less than] 15,000 if married). The next income bracket ($12,000 to $15,000) is limited to single persons given the PACE restriction just noted. The X vector also contains measures of self-reported health status contemporaneous to the survey date. These included general questions about physical and mental health, questions about specific health conditions (here summarized into two measures), and queries regarding smoking and drinking behavior.
As noted, all respondents are Medicare beneficiaries. They supplement Medicare in several ways including Medicaid, PACE, and private insurance plans. We characterized the latter according to source of coverage (employer-sponsored and individual Medigap plans), type of benefit (ambulatory physician visits and prescription drugs), and single or multiple plans. Individuals may be included in more than one category. In fact most are.
The survey did not elicit information on the scope of drug benefits provided in the private plans. Medicaid represents the most comprehensive drug coverage on the list. During the study period elderly Medicaid recipients were charged a $.50 copayment for certain classes of drugs, but most prescription medicine was free. PACE imposed a $4.00 copayment per prescription or prescription refill (both limited to dosages of 30 days). The modal employer-sponsored plan included prescription benefits of some type. Few individual Medigap policies provide this coverage. Most private plans that supplement Medicare Part B coverage of physician visits pay the Medicare 20 percent coinsurance; few cover the Part B deductible or excess charges above Medicare limits.
The [Z.sub.t-1] vector captures prior health status and is proxied by 1988 Medicare utilization. We had a wide choice of Medicare variables for this purpose and selected two, days hospitalized in 1988 and total Medicare Part A payments for that year.
The final set of model variables represent health care resources available to survey respondents. These include measures of physician, hospital, nursing home, and pharmacy supply relative to county population levels. Data for these variables were obtained from the Area Resource File and the Pennsylvania Department of Health and were linked to the person-level analytical file via respondent addresses and the county FIPS code.
IV. Results
We are interested in the effects that the six types of insurance have on the use of pharmaceuticals. Enrollment in employer-sponsored plans is considered exogenous in all models, leaving us with five potential endogenous insurance dummies. Our first task is to use the Hausman test to deliver models free of endogeneity problems. The Hausman test consists of two steps (see e.g., Godfrey [4]). First, five probit regressions are run with the insurance dummies as dependent variables. The residuals from these regressions are collected and added as covariates to the utilization equation (with the insurance choice dummies also included). If the coefficients of the residuals are significantly different from zero, exogeneity is rejected.
Following this procedure, the probits were estimated, using as right-hand side variables all of the variables discussed above. Also included, to aid in identification, were county-specific measures of rurality.(2) The residuals from these models were then inserted into the prescription utilization equations containing the prior utilization ([Z.sub.t-1]) measures and those without them. We rejected exogeneity in every model that did not contain the [Z.sub.t-1] measures. For example, in the version of the first model (use estimated over the entire sample) that contained no [Z.sub.t-1] variables, the F-test for the coefficients on the residuals was 9.307 (p [less than] .0001). We conclude that exogeneity is rejected at any conventional level of significance. When this same model is estimated with the [Z.sub.t-1] variables included, the F-statistic was 0.2015 with a p-value of 0.96. Exogeneity in this case is substantively accepted. The Hausman test has discriminated between the two versions of the utilization model and the [Z.sub.t-1] measures are shown to be jointly adequate for the representation of unobserved determinants of insurance choice.(3)
To then estimate the selection-purged models, we run the regressions with the auxiliary regressors (the probit residuals) removed. Our findings are presented in Table II.
Overall, the models perform quite well, explaining between 15 and 20 percent of the variance in prescription use among respondents. Many of the individual variables, however, have very low significance. These include ethnicity, educational attainment, income,(4) marital status, mental health rating, and health care resources in the county. By contrast, all of the indicators of current physical health are significant in both the full-sample utilization equation (column 1) and the probability of use model (column 2). Moreover, the magnitudes of the health status indicators are in accord with expectations: people in better (self-perceived) health use fewer medications. The prior utilization [Z.sub.t-1] measures also have the expected signs and are statistically significant in two of the three models.
Among the background variables are three idiosyncratic coefficients: alcohol users, smokers, and age all have negative coefficients when positive might have been expected. In the case of age, the result is not entirely unexpected given the evidence of morbidity compression found in our month-to-death models described in Stuart et al. [23]. If very old survivors are healthier than their younger counterparts, then on average older people within any given cross-section will use fewer prescription drugs (even when the time trend for any individual is positive). The signs on the other two variables are unexpected. The highly significant negative effects of drinking and [TABULAR DATA FOR TABLE II OMITTED] smoking on the probability of filling any prescription is particularly problematic. It seems unlikely that these behaviors would have protective effects on health (although there is some evidence that light drinking reduces risk of heart attacks). Perhaps instead these coefficients reflect a general recklessness towards health.
We are, of course, primarily interested in the coefficients of the insurance dummies. The most striking result is for PACE enrollees. According to the full-sample estimate (column 1) PACE beneficiaries fill 0.29 more prescriptions or refills per two-week period than do elderly who are not covered by an employer-sponsored plan and have neither prescription coverage nor Medicare supplementation for ambulatory physician visits (the excluded category for the insurance vector). Since the model has been putatively purged of adverse selection bias, we conclude that this is the result of the price subsidy that PACE beneficiaries enjoy.
The results for other insurance and subsidy programs are less substantive and less precise. In the full-sample estimates, private coverage for ambulatory physician care and prescription drugs also increase pharmaceutical use by about one-tenth of a prescription in a two-week period, although the test statistics indicate only borderline significance. Employer-sponsored insurance appears to have a negative effect on prescription use which is most notable in the probability-of-use equation (column 2). This anomaly is more apparent than real since prescription and physician coverage effects are independently assessed. The negative coefficients on [R.sub.x] use among those with employer-based coverage may reflect some residual health status effects associated with previous employment. It is unlikely that these findings are due to usage restrictions or drug review imposed by employers for the simple reason that such utilization control programs were not common during the study period. The failure to find significant effects associated with multiple Medigap coverage should be interpreted in the same light as the employment insurance coefficients. However, we are surprised by the consistently negative (albeit insignificant) signs on the Medicaid coverage variable. Medicaid offers the most generous package of prescription benefits available to elderly Pennsylvanians and it would be perverse in the extreme for recipients to reduce their use of prescribed medicines because of the benefit. In all likelihood, these coefficients are an artifact of some other characteristic of Medicaid enrollment not captured in the model.
Further insight into the effect of insurance on drug utilization can be gained by comparing the probability-of-use and level-of-use-by-users results in columns 2 and 3 of Table II. Here it becomes evident that insurance does not simply shift the demand curve outward. Rather the demand-inducing effect appears to be limited to those who would not use any prescription medicine in the absence of insurance. For example, at mean values for the background variables, enrollment in PACE raises the probability of prescription use by 11 percent while ambulatory physician coverage raises it by 5 percent. In both cases the effects are significant at p [less than] .001. By contrast, neither form of coverage has a statistically significant effect on the number of medicines used by persons filling one or more prescriptions. Indeed, none of the insurance dummies was significant in the use-by-users equation.
These findings shed new light on the demand for prescription drugs by the elderly. It appears clear that drug use is price sensitive both to direct subsidies and to coverage of complementary physician services. The actual degree of price sensitivity is impossible to compute for most of the insurance variables because of heterogeneous benefits within the coverage groups (even Medicaid recipients face different out-of-pocket prices depending on the drugs prescribed). PACE is an exception. During the study period PACE imposed a flat $4.00 copay per prescription or refill regardless of the actual charge. Between July and December 1990 (when the survey was fielded), the average usual and customary charge per PACE prescription was $24.98 [15]. Most pharmacies offer elderly patrons a standard 10 percent "senior citizen discount" which would reduce the average noninsured prescription charge to $22.48. The average subsidy is thus $18.48 or 82.2 percent. Based on our full-sample regression results, PACE beneficiaries responded to this price reduction with a 27.6 percent increase in the quantity of prescriptions purchased,(5) thus yielding an own-price elasticity of -.34.
V. Concluding Remarks
Whether this elasticity estimate can be generalized beyond the PACE program is an open question. We are confident that the PACE coefficients truly reflect price sensitivity and not adverse selection, but they reflect it within the context of a particular program with unique features. PACE differs from most private Medicare supplements, whether employer-sponsored or individually-purchased, in that the program reimburses pharmacies directly. Some authors have conjectured that "card programs" (like PACE) induce greater demand than indemnity plans with similar benefit coverage because there is no need to pay up front and beneficiaries are protected from the risk of lost or misplaced receipts [18]. PACE also differs from other public-sector pharmaceutical assistance plans and state Medicaid programs in that it places few administrative restrictions on drug benefits, with one notable exception. There is no restrictive formulary. Both branded and generic products are covered. There are no limits on the number of prescriptions that can be filled at one time nor are there limits on refills. However, coverage is limited to one-month's supply at a time; each additional month's supply is subject to another copay.
This last feature both lowers the value of the PACE subsidy for maintenance drugs and makes it appear as if program beneficiaries are refilling more prescriptions than are individuals whose prescriptions provide two or three month's supply. Because our survey did not distinguish between initial prescriptions and refills, we cannot rule out the possibility that some of the higher demand attributed to the PACE price subsidy is really an artifact of this coverage restriction. To the extent this bias is present in our results it should be most evident in the use-by-user equation, since there are the people most likely to be refilling maintenance prescriptions. We simply note that the PACE coefficient in this equation is small and insignificant whereas the PACE coefficient in the probability-of-any-prescription-fill equation is large and highly significant.
Caveats aside, the results of this analysis strongly suggest that if prescription drug coverage for the elderly were improved - say through an expansion of Medicare benefits - that the demand for drugs would increase. But by how much? Surely by less than a doubling of drug expenditures on behalf of the previously uninsured, as recently projected by Waldo [24]. Nor is the impact likely to be as large as the 3.4% increase per 10% decline in out-of-pocket expense predicted by our own PACE coefficients (for reasons described above). The importance of our findings lies less in the point estimates than in the ability of the estimates to show that higher utilization rates are due to the insurance coverage itself and are not just an artifact of adverse selection.
1. Medicare HMO enrollees were excluded because HCFA does not maintain complete utilization and payment records for them.
2. The rurality measure is the 10-level Human Resource Profile County Adjacency Code classification scheme compiled by the Health Resources Administration and available on the Area Resource File.
3. A precisely analogous pair of results is obtained using the linear probability model in the first stage.
4. The coefficients on the categorical income variables capture the effect of income net of insurance coverage. As expected, the price effects of insurance - particularly the comprehensive coverage afforded by Medicaid and PACE - are stronger than income effects on prescription demand.
5. The percentage quantity increase is obtained by dividing the PACE coefficient (0.29) by the intercept (1.05).
1. Birch, S., "Relationship Between Increasing Prescription Charges and Consumption in Groups Not Exempt From Charges." Journal of the Royal College of General Practitioners, January 1986, 154-56.
2. Brian, E. and S. Gibbens, "California's Medical Copayment Experiment." Medical Care, December 1974 (supplement), 1-56.
3. Cameron, A. Colin, P. Trivedi, F. Milne, and J. Piggott, "A Microeconomic Model of the Demand for Medical Care and Health Insurance in Australia." Review of Economic Studies, January 1988, 85-106.
4. Godfrey, L. G. Misspecification Tests in Econometrics. Cambridge: Cambridge University Press, 1988.
5. Harris, B., A. Stergachis, and L. Ried, "The Effect of Drug Co-payments on Utilization and Cost of Pharmaceuticals in Health Maintenance Organizations." Medical Care, October 1990, 907-17.
6. Hartman, Richard, "A Monte Carlo Analysis of Alternative Estimators in Models Involving Selectivity." Journal of Business and Economic Statistics, January 1991, 41-49.
7. Knapp, D., "Paying for Outpatient Prescription Drugs and Related Services in Third-party Programs." Medical Care Review, August 1971, 826-59.
8. Lavers, R., "Prescription Charges, the Demand for Prescriptions and Morbidity." Applied Economics August 1989, 1043-52.
9. Leibowitz, Arleen, "Substitution Between Prescribed and Over-the-counter Medications." Medical Care, January 1989, 85-94.
10. -----, W. Manning, and J. Newhouse, "The Demand for Prescription Drugs as a Function of Cost-sharing." Social Science and Medicine, 1985, 1063-69.
11. Moeller, J. and N. Mathiowetz, Prescribed Medicines: A Summary of Use and Expenditures by Medicare Beneficiaries. Washington, D.C.: National Center for Health Services Research and Health Care Technology Assessment, September 1989.
12. Nelson, A., C. Reeder, and W. Dickson, "The Effect of a Medicaid Drug Copayment Program on the Utilization and Costs of Prescription Services." Medical Care, August 1984, 724-36.
13. Newhouse, Joseph, and C. Phelps. "New Estimates of Price and Income Elasticities of Medical Care Services," in The Role of Health Insurance in the Health Services Sector. New York: NBER, pp. 261-313.
14. O'Brian, B., "The Effect of Patient Charges on the Utilisation of Prescription Medicines." Journal of Health Economics, March 1989, 109-32.
15. Pennsylvania Department of Aging. PACE: Pharmaceutical Assistance Contract for the Elderly. Biannual Report to the Pennsylvania General Assembly, July 1990-December 31, 1990. Harrisburg, Penn.: Pennsylvania Department of Aging, 1991.
16. Phelps, Charles and J. Newhouse, "Coinsurance, the Price of Time and the Demand for Medical Services." Review of Economics and Statistics, August 1974, 334-42.
17. Reeder, C. and A. Nelson, "The Differential Impact of Copayment on Drug Use in a Medicaid Population." Inquiry, Winter 1985, 396-403.
18. Rubinstein, E., "Employer Strategies in Managing Prescription Drug Costs." Business and Health. January 1991, 2-6.
19. Ryan, M. "Estimating the Effects of Prescription Charges on the Use of NHS Prescription Drugs in England, 1979-1985." Discussion Paper 3/89, Health Economics Research Unit, University of Aberdeen.
20. Schnell, B., "Pharmaceutical Services Under the British National Health Service." Drugs and Health Care, 1975, 70-71.
21. Smith, M. and D. Garner, "Effects of a Medicaid Program on Prescription Drug Availability and Acquisition." Medical Care, July 1974, 571-81.
22. Soumerai, S., D. Ross-Degan, J. Avorn, et al. "Payment Restrictions for Drugs under Medicaid: Effects on Therapy, Cost, and Equity." New England Journal of Medicine, August 27, 1987, 550-56.
23. Stuart, Bruce, et al. An Analysis of Determinants and Consequences of Prescription Drug Coverage for Pennsylvania Elderly. Final report to the Pew Charitable Trust under grant n. 88-01439-00. Vol. 1. University Park, Penn.: Medicine, Health, and Aging Project, The Pennsylvania State University, 1991.
24. Waldo, Daniel, "Estimating the Cost of a Medicare Outpatient Prescription Drug Benefit." Health Care Financing Review, Spring 1994, 103-12.
25. Weeks, H., "Changes in Prescription Drug Utilization After the Introduction of a Prepaid Drug Insurance Program." Journal of the American Pharmaceutical Association, 1973, 205.
26. Wolfe, John and J. Goddeeris, "Adverse Selection, Moral Hazard, and Wealth Effects in the Medigap Insurance Market." Journal of Health Economics, November 1991, 433-59.
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Author: | Stuart, Bruce C. |
Publication: | Southern Economic Journal |
Date: | Apr 1, 1995 |
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Whilst the purchased items are in your possession, we ask that you take care to insure that the clothing is kept in the condition in which it was received. When returning your items to Poshtotz Ltd please ensure that all items are returned as they were sent, for example; in adequate packaging, with labels, hangers, suits and suit covers in place.
Please be aware that any missing items will delay the refund or exchange process.
Poshtotz Ltd recommends that our customers return goods using Royal Mail 1st Class Recorded delivery, thus ensuring the item is traceable should a problem arise within the Royal Mail System.
Customers should also obtain a proof of purchase for their own personal record.
Please use the returns form that was enclosed in your received package, filling in details as appropriate for example; your username, address, instructions etc.
We will allow 14 days from the date of purchase for refunds and 30 days from the date of purchase for exchanges.
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text:
Birmingham City Football Club
- Nickname:Blues
- Founded:1875
- Manager:Lee Clark
- Ground:St Andrew's
- Capacity:30016
- Last Game: 28-Feb-2015 15:00
- Birmingham City1
Brentford0 - Full match report
- Birmingham City News
- Fixtures
- Results
- Information
- League Table
- News Archive
- Statistics
- Squad
- Championship News
Yeung plans City takeover
16 Jul 2007 - 11:21:08
Hong Kong businessman Carson Yeung has confirmed he plans to launch a full takeover of Birmingham City.
Yeung will officially complete his purchase of 29.9 per cent of the club's shares on Monday and has set out his plan to buy 'the whole club' from current owners Ralph and David Gold and David Sullivan.
The Blues board had previously refused to confirm whether Yeung was planning a complete takeover at St Andrew's.
"I don't have any politics and I only want to buy for interest because I love football," he told the Daily Mirror.
"I will not let the Birmingham fans down. I like the city of Birmingham and have a lot of good plans for the future.
"David Gold and David Sullivan have accepted our plan. As soon as we can I would like to own the whole club.
"We can buy some good players in January but maybe by then we might already be in a healthy position so we will wait and see.
"We can spend a lot of money in the right way but first I want to meet (manager) Steve Bruce as soon as possible.
"I have a lot of confidence in Steve Bruce, he is a very straight man and we could have a good relationship."
Related Birmingham City News
Related Birmingham City News


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Latest update : 2012-01-08
Questioning the Arab League's Syrian monitors
Concerns over the Arab League observer mission in Syria. The online tools available to help follow the American Presidential campaign. And two Finnish divers fishing under ice, upside down.
Questioning the Arab League's Syrian monitors
Since the deployment of Arab league peace monitors to Syria on the 27th December, regime opponents have been doing what they can to provide online coverage of the mission. This footage is from Wednesday and apparently shows them filming security forces' checkpoints in Deraa, where the anti-regime unrest began 10 months ago.
And in this other video, apparently filmed in Homs, a woman stops a member of the delegation to ask them if they know what has happened to her husband, as she has had no news from him since he was taken to prison.
As this footage from Hama suggests, the presence of Arab League observers has clearly done little to curb the repression. We see residents going to meet the observers when all of a sudden gun fire rings out.
The effectiveness of the mission has indeed been harshly criticized. Even more so because the government is doing everything in its power to confuse the issue; in this online message, soldiers, who have since defected, say their superiors gave them police cards so they could pass themselves off as such should they encounter any observers.
Al Manar, the Lebanese satellite television channel affiliated to Hezbollah, and which backs the Damascus regime, has uploaded this report. It is subtitled in French and shows members of the delegation saying they were targeted by armed anti-government gangs for saying they supported Bashar al-Assad.
Online tools to follow the American presidential campaign
The Republican Party caucus process kicked off in Iowa on Tuesday night, officially launching the US Presidential race with elections scheduled for November. And there are many non-partisan online tools available to help web users decide on who will get their vote. Here's a quick round up of the ways in which you can follow the campaign online.
A YouTube channel called "YouTube politics" has been set up, devoted entirely to the Presidential Election, hosting each of the candidates various campaign videos. There is also an infographic which you can use to compare, in real time, the presidential hopefuls' popularity on the video sharing platform.
On Social debating site "Two Sides" web users can compare their views and opinions on a given question to those of the candidates. All sorts of themes are touched on, from the abolition of the death penalty to the reality of global warming; in just a few clicks you can find out where each presidential hopeful stands on each of the topics.
American daily "The Washington Post" is using Twitter to follow campaign trends. The newspaper has developed an application called "Mention Machine" which counts, again in real time, the number of times the candidates are mentioned on Twitter and other media. This tool gives an overview of the candidates, with stats to boot.
And finally Google, has set up a participative platform to gather, amongst other things, all the press articles, catalogued by theme, that mention the name of one of the Presidential contenders. It's also keeping a log of online trends. It's a way of monitoring candidate related searches and also recording the number of views the candidates' campaign ads have received.
Are Twitter followers a protectable customer list?
The website PhoneDog is seeking 340 000 dollars in damages from one of its former employees Noah Kravitz, in a dispute over a Twitter thread he created when he worked for the Californian company. When he quit in 2010, Kravitz took the account and it's thousands of followers with him. His ex-employer is now suing him saying the Twitter list was a customer list, raising the question of whether or not employees have rights to the social network accounts they run on behalf of their company.
Now trending on social networks
A web user has been passing themself of on Twitter as Wendi Deng Murdoch, even fooling administrators of the social network. The account was set up in the name of Rupert Murdoch's wife shortly after the media tycoon made his debut on the social network, and was initially certified as being 'authentic' by Twitter. The hoax was finally revealed on Tuesday. Obviously embarrassed, the social network apologized for the confusion surrounding the fake account, but refused to make any comment on its verification process.
Video of the day
This video is called « fishing under ice » and as its name would suggest, it was filmed under ice, in a lake in Finland. What makes the clip even more extraordinary is that the two divers decided to use the underwater surface of the ice to do their fishing upside down. It's beautiful to watch and takes us to a world somewhere between dream and reality.


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Skip Maine state header navigation
Q1. Describe how the School
Administrative Unit (SAU) affords reasonable opportunity for public comment on
the NCLB consolidated application and considers such comment, prior to
submission of the application. Sec.
Q2. Describe the process the
SAU uses to assist the State Educational Agency (SEA) to identify migrant
children upon enrollment. Sec. 1309(2)
Q3. For contracted NCLB
services, describe the process of developing and paying for the contract and
identify the location and the personnel responsible at the SAU for maintaining
the original, signed contract. EDGAR
Q4. If there is a non-public
school receiving Title IA, Title IIA, and/or Title III services, describe the
consultation process to determine these services. Sec. 1120(b), Sec. 9501(b)(3)
Q5. Describe the process the
SAU uses to procure and pay for services for non-public schools. Sec.1120(c), Sec.
Q6. Describe the SAU's data
review process and how this informs the design of the Title IA program. Sec. 1112(f)
& Sec. 1115(c)(2)
Q7. If the SAU has any Title
IA CIPS schools, describe how the results of the annual review of CIPS plans at
each school is used for future school improvement planning. Sec.1116(a)(1)(D)
Q8a. Describe how the needs
of parents are determined for Title IA parent training programs. Sec. 1118(e)
Q8b. Describe the training program
developed for parents of Title IA students. Sec. 1118(e)
Q8c. Describe how Title IA
parental training programs are evaluated for effectiveness.
Sec. 1118(e)
Q9. If the SAU is required to have a Parent
Involvement Reservation [Title IA allocation greater than $500,000], describe
how Title IA parents at each school assist in determining how these funds will
be expended. Sec. 1118(a)(3)
Q10. If the SAU has Education
Technicians paid with Title IA funds, describe how they are supervised? Sec. 1119(g)(3)
Q11. Describe how the SAU ensures
that all Title IA staff, including Education Technicians, has the opportunity to participate in Professional
Development (PD) offerings. Sec. 1119(g)(3)
Describe the notification and dissemination process for the NCLB report card
and summary. How are parents notified
when the report card is available? Where
is the information located? Include the
weblink if available online. Sec.
http://www2.ed.gov/programs/titleiparta/reportcardsguidance.doc - See
A-3 and A-4.
Q13. If PD activities are
carried out under Title IIA, describe how the SAU's teachers,
paraprofessionals, principals, other relevant school personnel, and parents
collaborate in the planning of the PD activities. Sec. 2122 (b) (7)
Q14. Provide a summary of
results of the staff needs assessment for professional development and hiring,
as identified by the SAU and school staff.
Sec. 2122(c)(1)
Such needs assessment shall be conducted with the
involvement of teachers, including teachers participating in programs under
Title 1A, and shall take into account the activities that need to be conducted
in order to give teachers the means, including subject matter knowledge and
teaching skills, and to give principals the instructional leadership skills to
help teachers, to provide students with the opportunity to meet challenging
State and local student academic achievement standards.
Q15. Provide the name of the
current Homeless Liaison and the date of the most recent Homeless Liaison
training attended by this Liaison. Sec. 722 (f)(6)
Q16. Describe the process used to educate other
staff about McKinney Vento.
Sec 722 (g)(6)(C)
Q17. Describe the process used to educate parents
and youth about McKinney Vento. Sec 722 (g)(6)(A)
Q18. Describe outreach efforts employed to
educate and collaborate with other community partners who work with homeless
families and youth. Sec 722 (g)(5)(A)(B)
Q19. Describe how assistance is provided to
families and youth with the dispute process.
Sec 722 (g)(3)(E)
Q20. If the SAU has staff paid with Title III funds, describe how the
SAU ensures that Title III funds are not used for non-allowable activities such
as administering the ACCESS for ELLs©. Sec.
Documents to be provided:
D1. SAU's Lau Plan
The SAU is required to have a
current Lau Plan that ensures equitable opportunity for participation of
English Learner. Title VI Civil Rights Act, 1964 USC Sec. 2000d and Equal
Education Opportunities Act, 1974, 20 USC.
Sec. 1703(a)-(f)
D2. A copy of the Title IA consultation
notes/minutes with non-public school officials if a non public school accepts
Title IA services. Sec.1120 (b)
http://www2.ed.gov/programs/titleiparta/psguidance.doc - See
example responses throughout the document
D3. Yearly parent
notification of the SAU's student directory information policy, either through
Family Educational Rights and Privacy Act (FERPA) or NCLB. Sec.9528
D4. Parent notification of
their right to request the professional qualifications of their child's teacher
in Title IA schools. Sec.1111(h)(6)(A)
D5. Letter or notice to parents in a Title IA
school if their child has been assigned, or has been taught for four or more
consecutive weeks by, a teacher who has not met the highly qualified teacher
requirements. Sec. 1111(h)(6)(B)
D6. The written notice to all staff concerning
Staff Code of Conduct covering procurement issues including conflict of
interest, gratuities and violations. EDGAR 80.36 (b)
D7. For staff paid with NCLB federal
funds, the most recent copies of bi-annual certification(s) for single funding
stream staff and monthly activity reports for staff paid with multiple funding
streams. Sec. OMB Circular A-87(h)(4)
D8. Title IA letter/notice to parents/guardians
to inform them that their child has been selected for Title IA services in
Targeted Assistance Schools. Sec.1118 (c) (4)
D9. The SAU Parent Involvement Policy. Sec.1118
(a) & (e)
The SAU is required to have a written Parent
Involvement Policy that is developed jointly with parents and describes how the
SAU will: a. Involve parents in the
Title IA plan (Consolidated Grant Application); b. Assist schools in parent
involvement activities to improve student academic achievement; c. Build capacity for parent involvement; d. Coordinate
parent involvement with other programs such as Head Start; e. Conduct, with the
involvement of parents, an annual review of the policy.
http://www2.ed.gov/programs/titleiparta/parentinvguid.doc See Appendix D
D10. School Level Parent
involvement Policy. Sec.1118(c)(1) & (e)
Each Title IA school will have a written Parent
Involvement Policy that describes how the school will: a. Hold an annual Title
IA parent meeting; b. Provide
opportunities for meetings with parents on a regular basis for decisions
relating to the education of their child, such as parent conferences; c. Offer meetings at flexible times; d.
Provide information about the Title IA program, school curriculum,
assessments and proficiency levels; and
e. Involve parents in the planning and review of the Title IA program.
http://www2.ed.gov/programs/titleiparta/parentinvguid.doc - See
Appendix E, pgs. 53-54
D11. Title IA School Written Parent/School Compact.
Sec. 1118(d)(1)
Each Title IA school will have a written Parent/School
Compact that: a. Indicates how the
school will provide instruction that enables the student to meet academic
achievement standards; b. Indicates how
the parents will support their child's learning; and c. Addresses the importance of ongoing communication.
http://www2.ed.gov/programs/titleiparta/parentinvguid.doc - See
Appendix E, pgs. 51-53
D12. Sample of any Title 1A communications
sent to parents directly related to their child's progress. Sec. 1118(d)(2)
D13. The process and procedures for annual Highly
Qualify Teacher (HQT) review of teacher records. Sec.1119 & Maine HQ
This is often a section of the SAU's Professional
Development Plan, Local Certification Committee Plan, or is kept with the HQT
records and forms.
D14. Financial – A copy of
the most recent reimbursement invoice and expenditure run from the SAU's
accounting system to support the reimbursement for Title IA and at least one
other title – Title IIA, Title III or Title VI. EDGAR Part 80.21 and Administrative Letter 42,
Policy Code KL, dated 6/7/11.
www.4pcamaine.org - Federal Grant Reimbursement System Login
D15. Financial system
expenditure reports for the past fiscal year for Titles IA, IIA, III and VI.
Summarize salary/benefit runs by Title by Staff and provide detail runs by
Title for all other expenditures. EDGAR 76.702, 76.730, Part 80, Subpart C
NCLB Law: http://www2.ed.gov/policy/elsec/leg/esea02/index.html
NCLB Non-Regulatory
Guidance: http://www.maine.gov/education/nclb/guidance/home.html
EDGAR: http://www2.ed.gov/policy/fund/reg/edgarReg/edgar.html
Maine Doe NCLB Homepage: http://www.maine.gov/education/nclb/


text:
Many of us were involved in the discussions with EPSB last year about the potential replacement K-9 school. At the May 2014 Parent Council meeting, we decided we needed to hear more from our parents as a community. What did others feel and think about this potential project? Are our views as a Parent Council the views of the parents as a whole? What information can we give to EPSB to show our position going forward?
To attempt to gather this information, several volunteers developed a survey. We provided paper copies of the survey to every student to take home. We also posted it on School Zone. An email was also sent out to 50+ members of the Parent Council email notification list. Thank you to everyone who helped prepare and distribute the survey.
We received, through paper and web, 85 individual responses from parents. THANK YOU for the fantastic response.The most resounding feedback: parents love VP and are in no hurry to leave.
Here are the results:


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You guys. YOU GUYS. The playoffs are this weekend and the Seahawks are playing Green Bay. I know that this probably means very little to most of you, but it's basically a defining moment in my marriage and if we can get through this weekend WE CAN GET THROUGH ANYTHING.
We're actually both pretty psyched, because no matter who wins, one of our teams is going to the Super Bowl.
Unfortunately, it also means that I've spent the entire week reading NFL analysis and predictions, and I forgot that it was Friday, and that I have a round-up do. So better late than never, here's the week that was:
The city of Bainbridge (just across the water from downtown Seattle) has banned cheese inside City Hall today in anticipation of this weekend's football game. In response, a radio station in Milwaukee is refusing to play music by Seattle artists until after the weekend. THERE ARE NO WINNERS IN THIS FIGHT.
What if the Harry Potter books were all about Hermione and her struggles of being a witch in a wizard's world? Answer: they would be totally awesome. Check out Hermione Granger and the Goddamn Patriarchy.


text:
Series starts 1/5
Game 1: Thursday, January 5th | ||
Pacers | 99 | Final |
Warriors | 89 | |
Recap »Boxscore » |
Game 2: Friday, February 10th | ||
Warriors | 95 | Final |
Pacers | 107 | |
Recap »Boxscore » |
7:30 PM ET, January 5, 2006
ORACLE Arena, Oakland, CA
Top Performers
Ind: S. Jackson 27 Pts, 6 Reb, 3 Ast, 1 Blk
GS: M. Dunleavy 19 Pts, 6 Reb, 4 Ast, 2 Stl
OAKLAND, Calif. (AP) -- The undermanned Indiana Pacers pulled off the unthinkable: winning at Golden State on national television without their big names and rumors swirling about the future of Ron Artest.
Stephen Jackson had 27 points, five rebounds and three assists and the Pacers jumped to a quick lead they nearly relinquished in the waning moments of a 99-89 victory over the Warriors on Thursday night.
"It's a huge win," coach Rick Carlisle said. "As big a win as I can remember in a long time."
Star center and leading scorer Jermaine O'Neal arrived just in time to witness it.
O'Neal missed his third straight game because of pneumonia and a sore back, but flew in from Indianapolis to rejoin the team and sat on the bench in street clothes. Jamaal Tinsley missed his fourth straight game for Indiana, which improved to 7-9 on the road.
"There is absolutely nothing we can go through that we haven't seen," said O'Neal, who plans to practice Saturday and gauge whether he'll be ready for Sunday's game at Sacramento. "The last thing I want to do is mess up the chemistry these guys have right now."
Derek Fisher made two free throws with 1:46 left to get Golden State within 91-89, before Jackson scored and was fouled on the other end but missed a free throw.
Austin Croshere added 17 points, nine rebounds, two assists and two steals for the Pacers, who bounced back from a 106-86 loss at Denver a night earlier that started their four-game West Coast swing.
Beat-up Indiana had lost five of six, but played like a healthy team with a winning streak, opening up a 20-point lead after the first quarter and holding a comfortable cushion until the final minutes. Fred Jones added 11 points for the Pacers before fouling out with 4:11 left.
Artest is sidelined with a sore right wrist and away from the team, waiting to be traded after telling management last month he needs a fresh start.
Holding off the Warriors late told the Pacers a lot about themselves and what they can accomplish.
"That's what we want to start doing for 2006," Jackson said. "The best thing about us is we have to keep playing and counter runs. We've been going through a lot. This is a big road win on national TV. The fact we were able to hold this thing together, that's big for us."
Baron Davis had 16 points, seven assists and seven rebounds and rookie Ike Diogu added 16 points and a career-high 12 rebounds for his first double-double. Mike Dunleavy had 19 points, six rebounds and four assists for Golden State, which looked sluggish and uninspired for most of the night after returning from a three-game road trip. Richardson had 17 points.
Dunleavy hit a 3 with 7:19 to go to get Golden State within six, but the Warriors didn't get closer until two free throws by Richardson at 2:21 to make it 91-87.
Every time the Warriors threatened, the Pacers had an answer.
"You can't get down by 20," Richardson said. "This league is too tough to do that."
Diogu scored back-to-back baskets to pull the Warriors within 62-52 with 7:05 remaining in the third, then had a big block on the other end.
Golden State won three straight on the heels of a six-game losing streak before Tuesday's 104-94 defeat at Memphis. The Warriors lost two players to injury in this game: Troy Murphy and Calbert Cheaney.
Murphy, Golden State's top rebounder and third-leading scorer, sprained his left ankle with 4:11 left in the third period and didn't return. He had eight points and five rebounds in 27 minutes.
Murphy's 3 with 5:54 left in the second quarter pulled Golden State within 40-28, but Indiana answered with five straight points and a 16-11 run to end the half for a 56-41 lead.
The Pacers made eight of their first 11 shots to Golden State's 3-for-11 as Indiana jumped to an 18-7 lead. Golden State got almost no production from its talented backcourt. Richardson and Davis were a combined 3-for-10 for nine points and three assists. Fisher missed all five of his shots and had just one assist.
"We're having a miserable time starting games," Warriors coach Mike Montgomery said. "We don't seem to play until we're down 10 or 20. I just don't understand it."
Game notes
Montgomery was evasive about Golden State's possible interest in acquiring Artest. Yet Carlisle acknowledged the Warriors are apparently one of many teams interested in Artest, who has been on the inactive list for the last 13 games and isn't traveling with the club. "I've heard Golden State mentioned, but I've heard 17 or 18 other teams mentioned. What I can tell you is whoever ends up with him is going to get a top-10 player who's a real difference maker," Carlisle said. ... Golden State waived G Aaron Miles and Indiana waived C Samaki Walker. ... Cheaney strained his left quadriceps early in the second quarter and didn't return.
Team Stat Comparison
Points | 99 | 89 |
Rebounds (Offensive-Total) | 8-41 | 8-38 |
Assists | 16 | 16 |
Turnovers | 14 | 13 |
Steals | 8 | 6 |
Blocks | 5 | 6 |
Fast Break Points | 2 | 13 |
Fouls (Tech/Flagrant) | 26 (0/0) | 23 (0/0) |
Largest Lead | 22 | 1 |
Top Performers
Game Leaders
Points | S. Jackson 27 | M. Dunleavy 19 |
Rebounds | J. Foster 11 | I. Diogu 12 |
Assists | A. Johnson 4 | B. Davis 7 |
Steals | D. Granger 3 | M. Dunleavy 2 |
Blocks | A. Johnson 2 | J. Richardson 2 |
Team rosters: Indiana | Golden State |
Next 5 Games
Indiana | Golden State |
01/08 @SAC 6:00pm 01/09 @LAL 7:30pm 01/11 MIL 7:00pm 01/13 WSH 7:00pm 01/14 @CHI 7:30pm |
01/11 MIA 7:30pm 01/12 @PHX 7:00pm 01/14 LAL 7:30pm 01/16 SEA 1:00pm 01/20 CLE 7:30pm |
Complete Schedule: Indiana | Golden State |


text:
1967 Austin Healey 3000 Right Hand Drive '67 Austin Healey 3000 BJ8, right hand drive in red with black interior. This car is equipped with overdrive,... More
1967 Austin Healey BJ8 '67 Austin Healey 3000 BJ8 in black with black interior. This car comes equipped with a manual transmission, overdrive and wire... More
1966 Austin-Healey 3000 MK III ALL ORIGINAL BJ8, One owner, all org. 40k true survivor 1966 Austin Healey 3000 mklll BJ8. Ice blue, blue... More
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Austin Healey 3000 MK3 overdrive 1964 running car needs some work Running 1964 Austin Healey3000 MK3 overdrive. A perfect restoration project for hobbyists. New delivered... More
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'59 Austin Healey 100-6, 2+2, Clean, New: Top, Upholstery, Brakes all around, Top Hardware, Excellent Condition. Very good rubber, Engine shows well and runs... More
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Here's your chance to own a final production year 1959 Austin Healey 100-6. The 100-6 is also known as one of the "Big Healey's" by... More
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The 100-6 is a two-seat roadster produced by British Motor Corporation from 1956-1959. It was a replacement of the Healey 100 and was... More
In 1959, Austin-Healey improved the popular 100 series with front disc brakes and an enlarged engine displacement of 2.9 liters. The resulting Austin-Healey 3000... More
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Austin-Healeys evolved dramatically during the marques brief life. Its hard to accept today that from the debut of the first four-cylinder BN1 in 1952... More
1962 Austin-Healey BT7, We found this Austin Healy tucked away in the garage of a teacher in New York State that had cared for... More
1962 Austin Healey 3000 MKII BN7 Two Seater Tri-Carb; the two seater tri-carb is a very unique and limited production Austin Healey. It... More
vehicle highlights Doge 340 8 cylinder engine with comp.cam 300 horse power (1070 miles on build) Dodge automatic transmission (fresh build) shift kit seamless fenders custom front end new paint new... More
1966 AUSTIN HEALEY 3000 BJ8, Vin # HBJ8L32718, 69,000 original miles,heritage certificate,black with red coves,restored 2 years ago with receipts, red leather interior. Runs... More
Up for sale in our Tampa Showroom is this gorgeous 1964 Austin-Healey Sebring 3000 Replica. This beautiful car was a replica of the classic... More
This 1961 Austin Healey 3000 is a hard to find and desirable tri-carb example for restoration. Taken apart and rusty but comes very complete... More
1956 Austin-Healey, restored and good driver. Modified to disc brakes and motor. Bob Vincent, 701 Randi Cir, Dayton, TN 37321. PH: 423-240-4295.... More
1963 Austin-Healey 3000 MK II, A never restored original example of the first Healey with rollup windows and a folding top. The car is... More
1958 Austin-Healey 100-Six BN6 Two-Seater Chassis No. BN6-L/3560 Engine No. 26D/RU/H70587 Body No. 3062 Numbers-Matching as per British Motor Industry Heritage Trust Certificate Ideal "Driver" example of the ever popular Big Healey... More
1960 Austin Healey 3000 BN7 Two Seater TOTALLY RESTORED!!! With a no expense spared nut and bolt ground up rotisserie restoration by noted Healey... More
1967 Austin Healey 3000 BJ8 Mk III Sports Convertible Very sharp, final year Mark III Big Healey with all the right attributes! Fully restored by experts,... More
Incredible Singular Enthusiast Ownership Perfect & Documented History From Day One Expertly Restored by Knut Holzer of BMC An Easy Concours Championship Contender The Austin Healey 3000 has... More
1968 Austin-Healey 3000 MK III BJ8 RoadsterTo be OFFERED AT AUCTION at Auctions America's Fort Lauderdale event, March 27-29, 2015. Click here to view... More
1964 Austin-Healey Mark III 3000 BJ8To be OFFERED AT AUCTION at Auctions Americaâs Fort Lauderdale event, March 27-29, 2015. Click here to view this... More
1956 Austin-Healey 100 Le Mans RoadsterTo be OFFERED AT AUCTION at Auctions Americaâs Fort Lauderdale event, March 27-29, 2015. Click here to view this... More
1967 Austin-Healey Sprite MK IV, The healey was my uncles car he wanted to restore . He ended up giving it to me and... More
1964 Austin-Healey 3000 MK II, Extensive mechanical refurbishing completed over the last 18 months by regional Healey expert including brake master cylinder, brake lines... More
To be offered at Bonhams' premier Amelia Island Auction at the Fernandina Beach Golf Club in Fernandina Beach, FL on Thursday March 12th beginning... More
To be offered at Bonhams' premier Amelia Island Auction at the Fernandina Beach Golf Club in Fernandina Beach, FL on Thursday March 12th beginning... More
To be offered at Bonhams' premier Amelia Island Auction at the Fernandina Beach Golf Club in Fernandina Beach, FL on Thursday March 12th beginning... More
To be offered at Bonhams' premier Amelia Island Auction at the Fernandina Beach Golf Club in Fernandina Beach, FL on Thursday March 12th beginning... More