CNN Tonight with Don Lemon on Bill Clinton & BLM

Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Fri Apr 22, 2016 8:46 am

The Black Male Incarceration Problem Is Real and It’s Catastrophic
by Antonio Moore
Los Angeles Attorney, Producer "Crack in the System" Documentary
02/17/2015

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“We have more work to do when more young black men languish in prison than attend colleges and universities across America.” President Barack Obama

In a recent article the widely held belief surrounding the number of black men incarcerated outnumbering the number of black men enrolled in higher education was refuted. The piece is titled “The myth that there are more black men in prison than in college, debunked in one chart”. The author appears to believe through proving this assertion about prison and education ratios to be inaccurate either a point of great progress can be demonstrated, or an overstatement of calamity about the state of African American men can be corrected.



Yet the real issue is the number of people in prison should never be similar to the number educated. For most in our country this in fact holds true, but for black men the two numbers are in fact close and that is the inescapable problem. The supposed myth on its face may in fact be incorrect. There may be more black men in college than in prison, but the truth still stands that there are a socially catastrophic number of black men behind bars in the United States. Let me give a bit of context for this discussion. Referencing the same article above.

“The Census estimates that approximately 18,508,926 people in the U.S. population are black males, of all ages...The Bureau of Justice Statistics’ National Prisoner Statistics Program reports that in that same year, 526,000 were in state or federal prisons, and, as of mid-year 2013, 219,660 were in local jails, making for a total of about 745,000 behind bars.”


To give a lens for viewing this data India is a country of 1.2 Billion people, the country in total only has around 380,000 prisoners. In fact, there are more African American men incarcerated in the U.S. than the total prison populations in India, Argentina, Canada, Lebanon, Japan, Germany, Finland, Israel and England combined.

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As stated by Nicole Porter in the piece “Politics of Black Lives Matter”

...countries have the policies and prison populations they choose. Between 1965 and 1990, a period during which overall and violent crime rates tripled in Germany, Finland, and the United States, German politicians chose to hold the imprisonment rate flat, Finnish politicians chose to substantially reduce theirs, and American politicians generally enacted policies that sent more people to prison, along with lengthened prison terms.


Today in the United States there are approximately 18 million black men, and nearly 161 million women of all races. According to the Sentencing Project the total number of women incarcerated in America is about 200,000. Even more shocking despite the population of black men being about a tenth the size, there are nearly 4 times as many black men incarcerated in comparison to women of all races in the U.S.

To give a more appropriate contrast than just black men in college and black men incarcerated, lets look at the debated education vs incarceration reality for white women and black men comparatively. According to the Census in total there are about 8.5 million white women in college, and there are just 60,000 white women incarcerated. For black men the numbers are as listed above, there are about 1.4 million black men enrolled in higher education, and a cataclysmic 745,000 behind bars, with another large sum on probation and parole.

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So in the end the contrasting of college enrollment vs. incarceration is not the key comparative. While it is part of an analysis, only by applying it with more depth can we see a fuller and more accurate picture. It is more important to look at these rates and be honest about the advantaged, and disadvantaged. The key is that as then presidential candidate Barack Obama stated “We have more work to do...” We can only take steps and start this work by asking the hard question of why historical differences in both opportunity and misfortune have left us such a disparity in access to opportunity for all.
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Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Fri Apr 22, 2016 8:15 pm

Relentless Moral Crusader Is Relentless Gambler
by Katharine Q. Seelye
New York Times
Washington
May 3, 2003

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William J. Bennett, author of "The Book of Virtues" and one of the nation's most relentless moral crusaders, is a high-rolling gambler who has lost more than $8 million at casinos in the last decade, according to online reports from two magazines.

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The Book of Virtues: A Treasury of Great Moral Stories, by William J. Bennett

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Body Count: Moral Poverty ... And How to Win America's War Against Crime and Drugs, by William J. Bennett, John J. Dilulio, Jr., and John P. Walters

"... fatherless, Godless, and jobless ... radically impulsive, brutally remorseless youngsters, including ever more teenage boys, who murder, assault, rob, burglarize, deal deadly drugs, join gun-toting gangs, and create serious disorders” (Bennett, DiIulio, & Walters, 1996, p. 27).


The Washington Monthly said on its Web site that "over the last decade Bennett has made dozens of trips to casinos in Atlantic City and Las Vegas, where he is a `preferred customer' at several of them, and sources and documents provided to The Washington Monthly put his total losses at more than $8 million."

In an article that depends on much the same reporting, the online version of Newsweek said that 40 pages of internal casino documents show that Mr. Bennett received treatment typical of high-stakes gamblers, including limousines and "tens of thousands of dollars in complimentary hotel rooms and other amenities."

Mr. Bennett, who has served Republican presidents as education secretary and drug czar, declined to be interviewed today by The New York Times, with a spokesman saying that he needed to digest the articles before responding.

The fact of Mr. Bennett's gambling is not new. He has said over the years that he likes to gamble and that it relaxes him. What is unusual is the apparent extent of his losses; neither magazine reported his winnings.

Mr. Bennett told the magazines that he has basically broken even over the years. "I play fairly high stakes," he said, adding, "I don't put my family at risk, and I don't owe anyone anything."

The magazines said they had no documentation that he was in debt but suggested that he had lost more than he had won. In response, Mr. Bennett is quoted as saying, "I've made a lot of money and I've won a lot of money," adding, "You don't see what I walk away with." He said he gave some of his winnings to charity and reported everything to the Internal Revenue Service.

The magazines said that in one two-month period, Mr. Bennett wired one casino more than $1.4 million to cover his losses.

The magazines say he earns $50,000 for each appearance in speaking fees on the lecture circuit, where he inveighs against various sins, weaknesses and vices of modern culture.

But Mr. Bennett exempts gambling from this list.

He has said in the past that he does not consider gambling a moral issue. When his interviewers reminded him of studies that link heavy gambling with a variety of societal and family ills, Mr. Bennett said he did not have a problem himself and likened gambling to drinking alcohol.

"I view it as drinking," he said. "If you can't handle it, don't do it."


Mr. Bennett is popular among social conservatives, but many of them consider gambling a serious problem. James C. Dobson, the president of Focus on the Family and a member of a federal commission that studied gambling, said in 1999: "Gambling fever now threatens the work ethic and the very foundation of the family. Thirty years ago, gambling was widely understood in the culture to be addictive, progressive and dangerous."

In the 1990's, leaders of the conservative Christian Coalition joined with other religious leaders to create the National Coalition Against Legalized Gambling. Ralph Reed, former executive director of the Christian Coalition, called gambling "a cancer on the American body politic" that was "stealing food from the mouths of children."

Friends of Mr. Bennett were reluctant today to criticize him directly.

"It's his own money and his own business," Grover G. Norquist, president of Americans for Tax Reform, a conservative advocacy group, said. "The downside of gambling losses is that the government gets a third of the money, which is unfortunate and probably a sin in and of itself," said Mr. Norquist, whose group advocates smaller government.

William Kristol, editor of The Weekly Standard and another conservative ally of Mr. Bennett, agreed that this was a matter between Mr. Bennett, his wife and his accountant.

"It would be different if he had written anti-gambling screeds," Mr. Kristol said. "I'm sure he doesn't regard gambling as a virtue but as a rather minor and pardonable vice and a legal one and one that has not damaged him or anyone else."

Mr. Kristol said that Mr. Bennett was not being hypocritical. "If Bill Bennett went on TV encouraging young people to gamble the rent money at a Las Vegas casino or was shilling for gambling interests, that would be inconsistent" with his moral crusades, Mr. Kristol said.

As Mr. Bennett told The Las Vegas Review-Journal in 1995, "I've played poker all my life and I shouldn't be on my high horse about it."
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Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Fri Apr 22, 2016 9:47 pm

The Coming Mayhem: Pre-Teens today are more violent than ever before. In their world -- nothing matters. Where are all the adults?
by Richard Rodriguez
San Francisco
January 21, 1996

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The monster has the face of a child. The nightmare figure outside our window is wearing Reeboks and is 7 years old.

In recent months, politicians and police chiefs have proudly reported a drop in the crime rate in major U.S. cities. Criminologists are warning, however, that youth crimes -- particularly violent crimes by the young -- are increasing and will continue to increase.

James Q. Wilson, for example, predicts that the growing population of teenage boys will mean an increase in murders, rapes and muggings. A new type of criminal is appearing. John J. Dilullo Jr. calls them "the super-predators." Remorseless, vacant-eyed, sullen -- and very young.

A tough street kid (16 years old) says he thinks of himself as bad. But his younger brother, 9 years old, is crueler -- "that mother scares even me."

We are entering a Stephen King novel: We are entering an America where adults are afraid of children. Where children rule the streets. Where adults cower at the approaching tiny figure on the sidewalk ahead.

A friend, a heavyweight amateur wrestler, warns me away from making a dinner reservation at a Venice Beach restaurant. "There are too many gang kids around after dark."

Adults put bars on their windows. Or move into fortified communities. Adults pay to send their children to safe, private schools. But sometimes now, the adult wonders if, perhaps, the monster is asleep in the bedroom down the hall.

It is the inner-city monster -- the black kid in the Raiders' jacket -- we imagine more quickly.
But everyday in the paper, from rural America to the suburbs, come stories of young violence. A kid shoots up the chemistry classroom. A gang stomps a homeless man to death. Two boys in Beverly Hills murder their parents.

And it may not even be a case of "he" anymore. Youth counselors tell me that the toughest kids they meet now are girls. The girls are getting meaner than the boys. We adults are left feeling like children. We don't know if we are seeing the world for what it really is.

A father who lives in a suburb of Los Angeles says that his neighborhood appears safe -- almost like the America he remembers from childhood. Kids play soccer in the park on Saturdays. Couples stroll the sidewalk on soft summer nights. "Why, then," he wonders, "did the local high school need to hire a full-time security cop this year?"

A 12-year-old tells me adults don't matter when you're in trouble. He avoids the parking lot of the mall, whether or not there are adults around. He avoids certain streets. The other day, he was accosted on the sidewalk, lost his jacket to a pair of young thieves while, all around him, adults passed oblivious.

Were we adults surprised last week to learn from a Louis Harris poll that teenagers live daily aware of danger? One in nine -- more than one in three in high-crime neighborhoods -- admit that they often cut class or stay away from school because of fear of crime. One in eight carry a weapon for protection.

A mortician in black Oakland says that kids often use his mortuary as a kind of hang-out. Kids come in to look at the corpses (most of the dead are young). The smallest ones will bring their tiny brothers and sisters, tiptoe to peer inside the coffin. Death bears a familiar face.


We adults now name the young criminals super-predators. Perhaps we should think of them as the super-alones. There are children in America who have never been touched or told that they matter. Inner-city mama is on crack. Or suburban mama gives the nanny responsibility for raising the kids. Papa is in a rage this morning. Where are the aunts to protect the child? Where is there a neighbor who cares?

The child takes the role of the adult. A friend, now in prison for armed robbery, remembers his father's drunken rages -- his father quoting the Bible with whiskey breath. My friend, one night, took a knife to his father's neck to protect a younger brother from being beaten to death.

"I don't know." The same, dull answer so many kids give you. The question may vary: What do you want to be? What is your happiest memory? Why did you murder the old lady for $20? The answer is always the same.

On the other hand, on Martin Luther King Jr.'s birthday, I was at my yuppie gym in front of a row of Stairmasters. The TV was blaring. On CNN, President Bill Clinton and Coretta Scott King were exchanging flatteries.

The buffed blond (a preternaturally youthful-looking 40-year-old) reached for the remote control. In an instant we were transported to MTV, where three black rapsters were talking about their music.

Question: Why is it that most of the people who buy black rap are white kids in the suburbs?

Answer: Because the rapster is glamorous for having a big, mean, thumping voice that extends beyond "I don't know."
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Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Fri Apr 22, 2016 10:32 pm

How Well Do You Know Your Kids?
by Pat Wingert
5/9/99

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Jocks, preps, punks, Goths, geeks. They may sit at separate tables in the cafeteria, but they all belong to the same generation. There are now 31 million kids in the 12-to-19 age group, and demographers predict that there will be 35 million teens by 2010, a population bulge bigger than even the baby boom at its peak. In many ways, these teens are uniquely privileged. They've grown up in a period of sustained prosperity and haven't had to worry about the draft (as their fathers did) or cataclysmic global conflicts (as their grandparents did). Cable and the Internet have given them access to an almost infinite amount of information. Most expect to go to college, and girls, in particular, have unprecedented opportunities; they can dream of careers in everything from professional sports to politics, with plenty of female role models to follow.

But this positive image of American adolescence in 1999 is a little like yearbook photos that depict every kid as happy and blemish-free. After the Littleton, Colo., tragedy, it's clear there's another dimension to this picture, and it's far more troubled. In survey after survey, many kids -- even those on the honor roll -- say they feel increasingly alone and alienated, unable to connect with their parents, teachers and sometimes even classmates. They're desperate for guidance, and when they don't get what they need at home or in school, they cling to cliques or immerse themselves in a universe out of their parents' reach, a world defined by computer games, TV and movies, where brutality is so common it has become mundane. The parents of Eric Harris and Dylan Klebold have told friends they never dreamed their sons could kill. It's an extreme case, but it has made a lot of parents wonder: do we really know our kids?

Many teens say they feel overwhelmed by pressure and responsibilities. They are juggling part-time jobs and hours of homework every night; sometimes they're so exhausted that they're nearly asleep in early-morning classes. Half have lived through their parents' divorce. Sixty-three percent are in households where both parents work outside the home, and many look after younger siblings in the afternoon. Still others are home by themselves after school. That unwelcome solitude can extend well into the evening; mealtime for this generation too often begins with a forlorn touch of the microwave.

In fact, of all the issues that trouble adolescents, loneliness ranks at the top of the list. University of Chicago sociologist Barbara Schneider has been studying 7,000 teenagers for five years and has found they spend an average of 3-1/2 hours alone every day. Teenagers may claim they want privacy, but they also crave and need attention -- and they're not getting it. Author Patricia Hersch profiled eight teens who live in an affluent area of northern Virginia for her 1998 book, "A Tribe Apart." "Every kid I talked to at length eventually came around to saying without my asking that they wished they had more adults in their lives, especially their parents," she says.

Loneliness creates an emotional vacuum that is filled by an intense peer culture, a critical buffer against kids' fear of isolation. Some of this bonding is normal and appropriate; in fact, studies have shown that the human need for acceptance is almost a biological drive, like hunger. It's especially intense in early adolescence, from about 12 to 14, a time of "hyper self-consciousness," says David Elkind, a professor of child development at Tufts University and author of "All Grown Up and No Place to Go." "They become very self-centered and spend a lot of time thinking about what others think of them," Elkind says. "And when they think about what others are thinking, they make the error of thinking that everyone is thinking about them." Dressing alike is a refuge, a way of hiding in the group. When they're 3 and scared, they cling to a security blanket; at 16, they want body piercings or Abercrombie shirts.

If parents and other adults abdicate power, teenagers come up with their own rules. It's "Lord of the Flies" on a vast scale. Bullying has become so extreme and so common that many teens just accept it as part of high-school life in the '90s. Emory University psychologist Marshall Duke, an expert on children's friendships, recently asked 110 students in one of his classes if any of them had ever been threatened in high school. To his surprise, "they all raised their hand." In the past, parents and teachers served as mediating forces in the classroom jungle. William Damon, director of the Stanford University Center for Adolescence, recalls writing a satirical essay when he was in high school about how he and his friends tormented a kid they knew. Damon got an "A" for style and grammar, but the teacher took him aside and told him he should be ashamed of his behavior. "That's what is supposed to happen," Damon says. "People are supposed to say, 'Hey, kid, you've gone too far here'." Contrast that with reports from Littleton, where Columbine students described a film class nonchalantly viewing a murderous video created by Eric Harris and Dylan Klebold. In 1999 this apparently was not remarkable behavior.

When they're isolated from parents, teens are also more vulnerable to serious emotional problems. Surveys of high-school students have indicated that one in four considers suicide each year, says Dr. David Fassler, a child and adolescent psychiatrist in Burlington, Vt., and author of "Help Me, I'm Sad: Recognizing, Treating and Preventing Childhood and Adolescent Depression." By the end of high school, many have actually tried to kill themselves. "Often the parents or teachers don't realize it was a suicide attempt," he says. "It can be something ambiguous like an overdose of nonprescription pills from the medicine cabinet or getting drunk and crashing the car with suicidal thoughts."

Even the best, most caring parents can't protect their teenagers from all these problems, but involved parents can make an enormous difference. Kids do listen. Teenage drug use (although still high) is slowly declining, and even teen pregnancy and birthrates are down slightly -- largely because of improved education efforts, experts say. More teens are delaying sex, and those who are sexually active are more likely to use contraceptives than their counterparts a few years ago.

In the teenage years, the relationship between parents and children is constantly evolving as the kids edge toward independence. Early adolescence is a period of transition, when middle-school kids move from one teacher and one classroom to a different teacher for each subject. In puberty, they're moody and irritable. "This is a time when parents and kids bicker a lot," says Laurence Steinberg, a psychology professor at Temple University and author of "You and Your Adolescent: A Parents' Guide to Ages 10 to 20." "Parents are caught by surprise," he says. "They discover that the tricks they've used in raising their kids effectively during childhood stop working." He advises parents to try to understand what their kids are going through; things do get better. "I have a 14-year-old son," Steinberg says, "and when he moved out of the transition phase into middle adolescence, we saw a dramatic change. All of a sudden, he's our best friend again."

In middle adolescence, roughly the first three years of high school, teens are increasingly on their own. To a large degree, their lives revolve around school and their friends. "They have a healthy sense of self," says Steinberg. They begin to develop a unique sense of identity, as well as their own values and beliefs. "The danger in this time would be to try to force them to be something you want them to be, rather than help them be who they are." Their relationships may change dramatically as their interests change; in Schneider's study, almost three quarters of the closest friends named by seniors weren't even mentioned during sophomore year.

Late adolescence is another transition, this time to leaving home altogether. "Parents have to be able to let go," says Steinberg, and "have faith and trust that they've done a good enough job as parents that their child can handle this stuff." Contrary to stereotypes, it isn't mothers who are most likely to mourn in the empty nest. They're often relieved to be free of some chores. But Steinberg says that fathers "suffer from thoughts of missed chances."

That should be the ultimate lesson of tragedies like Littleton. "Parents need to share what they really believe in, what they really think is important," says Stanford's Damon. "These basic moral values are more important than math skills or SATs." Seize any opportunity to talk -- in the car, over the breakfast table, watching TV. Parents have to work harder to get their points across. Ellen Galinsky, president of the Families and Work Institute, has studied teenagers' views of parents. "One 16-year-old told us, 'I am proud of the fact that [my mother] deals with me even though I try to push her away. She's still there'." So pay attention now. The kids can't wait.
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Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Fri Apr 22, 2016 11:49 pm

Contra-Cocaine Was a Real Conspiracy
by Robert Parry
December 3, 2013

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In the insular world of Manhattan media, there’s much handwringing over the latest blow to print publications as New York Magazine scales back from a weekly to a biweekly. But the real lesson might be the commercial failure of snarky writing, the kind that New York demonstrated in its recent hit piece on “conspiracy theories.”

What was most stunning to me about the article, pegged to the 50th anniversary of John F. Kennedy’s assassination, was that it began by ridiculing what is actually one of the best-documented real conspiracies of recent decades, the CIA’s tolerance and even protection of cocaine trafficking by the Nicaraguan Contra rebels in the 1980s.

According to New York Magazine, the Contra-cocaine story -– smugly dubbed “the last great conspiracy theory of the twentieth century” -– started with the claim by ”crack kingpin” Ricky Ross that he was working with a Nicaraguan cocaine supplier, Oscar Danilo Blandon, who had ties to the Contras who, in turn, had ties to the CIA.

Author Benjamin Wallace-Wells writes: “The wider the aperture around this theory, the harder its proponents work to implicate Washington, the shakier it seems: After several trials and a great deal of inquiry, no one has been able to show that anyone in the CIA condoned what Blandon was doing, and it has never been clear exactly how strong Blandon’s ties to the contra leadership really were, anyway.”

So, it was all a goofy “conspiracy theory.” Move along, move along, nothing to see here. But neither Wallace-Wells nor his New York Magazine editors seem to have any idea about the actual history of the Contra-cocaine scandal. It did not begin with the 1996 emergence of Ricky Ross in a series of articles by San Jose Mercury-News investigative reporter Gary Webb, as Wallace-Wells suggests.

The Contra-cocaine scandal began more than a decade earlier with a 1985 article that Brian Barger and I wrote for the Associated Press. Our article cited documentary evidence and witnesses -– both inside the Contra movement and inside the U.S. government –- implicating nearly all the Contra groups fighting in Nicaragua under the umbrella of Ronald Reagan’s CIA.

Our Contra-cocaine article was followed up by a courageous Senate investigation led by Sen. John Kerry of Massachusetts who further documented the connections between cocaine traffickers, the Contras and the Reagan administration in a report issued in 1989.

Yet, part of the scandal always was how the Reagan administration worked diligently to undercut investigations of the President’s favorite “freedom fighters” whether the inquiries were undertaken by the press, Congress, the Drug Enforcement Administration or federal prosecutors. Indeed, a big part of this cover-up strategy was to mock the evidence as “a conspiracy theory,” when it was anything but.

Big Media’s Complicity

Most of the mainstream news media played along with the Reagan administration’s mocking strategy, although occasionally major outlets, like the Washington Post, had to concede the reality of the scandal.

For instance, during the drug-trafficking trial of Panamanian dictator Manuel Noriega in 1991, U.S. prosecutors found themselves with no alternative but to call as a witness Colombian Medellín cartel kingpin Carlos Lehder, who — along with implicating Noriega — testified that the cartel had given $10 million to the Contras, an allegation first unearthed by Sen. Kerry.

“The Kerry hearings didn’t get the attention they deserved at the time,” a Washington Post editorial on Nov. 27, 1991, acknowledged. “The Noriega trial brings this sordid aspect of the Nicaraguan engagement to fresh public attention.”

Yet, despite the Washington Post’s belated concern about the mainstream news media’s neglect of the Contra-cocaine scandal, there was no serious follow-up anywhere in Big Media -– until 1996 when Gary Webb disclosed the connection between one Contra cocaine smuggler, Danilo Blandon, and the emergence of crack cocaine via Ricky Ross.

But the premier news outlets -– the likes of the Washington Post, the New York Times and the Los Angeles Times -– didn’t take this new opportunity to examine what was a serious crime of state. That would have required them to engage in some embarrassing self-criticism for their misguided dismissal of the scandal. Instead, the big newspapers went on the attack against Gary Webb.

Their attack line involved narrowing their focus to Blandon -– ignoring the reality that he was just one of many Contras involved in cocaine smuggling to the United States -– and to Ross -– arguing that Ross’s operation could not be blamed for the entire crack epidemic that ravaged U.S. cities in the 1980s. And the newspapers insisted that the CIA couldn’t be blamed for this cocaine smuggling because the agency had supposedly examined the issue in the 1980s and found that it had done nothing wrong.


Because of this unified assault from the major newspapers -– and the corporate timidity of the San Jose Mercury-News editors -– Webb and his continuing investigation were soon abandoned. Webb was pushed out of the Mercury-News in disgrace.

That let the mainstream U.S. media celebrate how it had supposedly crushed a nasty “conspiracy theory” that had stirred up unjustified anger in the black community, which had been hit hardest by the crack epidemic. The newspapers also could get some brownie points from Republicans and the Right by sparing President Reagan’s legacy a big black eye.

But Webb’s disclosure prompted the CIA’s Inspector General Frederick Hitz to undertake the first real internal investigation of the ties between the Contra-cocaine smugglers and the CIA officers overseeing the Contra war in Nicaragua.

The CIA’s Confession

When Hitz’s final investigative report was published in fall 1998, the CIA’s defense against Webb’s series had shrunk to a fig leaf: that the CIA did not conspire with the Contras to raise money through cocaine trafficking. But Hitz made clear that the Contra war had taken precedence over law enforcement and that the CIA withheld evidence of Contra drug-smuggling crimes from the Justice Department, Congress, and even the CIA’s own analytical division.

Besides tracing the extensive evidence of Contra trafficking through the entire decade-long Contra war, the inspector general interviewed senior CIA officers who acknowledged that they were aware of Contra-drug smuggling but didn’t want its exposure to undermine the struggle to overthrow Nicaragua’s leftist Sandinista government.

How bad, then, is it? Very bad. The story has never been fully told, because so many of those who would have shouted their opposition from the rooftops if a Republican President had done this were boxed in by their desire to see the President re-elected and in some cases by their own votes for the bill (of which, many in the Senate had been foreordained by the President's squeeze play in September of 1995).

-- The Worst Thing Bill Clinton Has Done: A Clinton appointee who resigned in protest over the new welfare law explains why it is so bad and suggests how its worst effects could be mitigated. by Peter Edelman


According to Hitz, the CIA had “one overriding priority: to oust the Sandinista government. . . . [CIA officers] were determined that the various difficulties they encountered not be allowed to prevent effective implementation of the Contra program.” One CIA field officer explained, “The focus was to get the job done, get the support and win the war.”

Hitz also recounted complaints from CIA analysts that CIA operations officers handling the Contras hid evidence of Contra-drug trafficking even from the CIA’s analysts. Because of the withheld evidence, the CIA analysts incorrectly concluded in the mid-1980s that “only a handful of Contras might have been involved in drug trafficking.” That false assessment was passed on to Congress and to major news organizations — serving as an important basis for denouncing Gary Webb and his disclosures in 1996.

Although Hitz’s report was an extraordinary admission of institutional guilt by the CIA, it went almost unnoticed by the big American newspapers.


8: Spreading the Word

The daily press knew how to handle this story. So what if Science buried Nelson-Rees's report in the back pages under the stodgy title "Banded Marker Chromosomes as Indicators of Intraspecies Cellular Contamination." The newspapers, properly horrified, played it on page one with headlines more to the point:

CANCER WAR SET BACK
GOOF COSTS 20 YEARS OF RESEARCH

A line of human tumor cells used by laboratories around the world for more than 20 years may have invalidated millions of dollars worth of cancer research, according to a scientist's report .... As a result, says the author, Dr. Walter A. Nelson-Rees, checks are in order for dozens of laboratories engaged in cancer research. -- Los Angeles Herald Examiner


DEAD WOMAN'S CANCER CELLS SPREADING

Dr. Walter Nelson-Rees, one of the most experienced cell biologists in the world ... has reported that many cell lines are by no means what they are thought to be by the laboratories handling them. -- Miami Herald


A SHOCKER FOR SCIENTISTS

"The main situation has probably existed for years," said the main author of the report, Walter A. Nelson-Rees, a highly respected researcher. ... Nelson-Rees said the contaminating potential of the HeLa cells is well known, but that sufficient precautions against it have apparently not been taken. -- San Francisco Chronicle


All this publicity made no sense to a number of scientists. Why was Nelson-Rees taking bows now when Stan Gartler had dropped the original bomb in 1966?

Part of the reason was that Nelson-Rees's paper was printed in Science, one of the few technical journals that nonscientists, particularly reporters, find accessible. One section, prepared by the journal's news staff, was actually written in English, and in the 7 June 1974 issue, the section carried a story that translated Nelson-Rees's article beautifully. "If Nelson-Rees is right," wrote Barbara Culliton, "a lot of people may have been spending a lot of time and money on misguided research. If, for example, you are studying the properties of human breast tumor cells, hoping to find features that distinguish breast cells from others, and are, all the while, dealing unknowingly with cervical tumor cells, you've got a problem." That was plain enough even for a newspaper reporter to understand, and to embellish and bang out for the morning edition.

But what really made Nelson-Rees a media star was the dramatic background of his shocking results: "The War." In Gartler's day, HeLa contamination had been the dirty little family secret of the tissue culture crowd. Its broader impact was not obvious. In 1974, however, "The War" had been officially declared and raging for several years. Everybody knew that the nation's most brilliant medical experts were at this very moment working feverishly against the scourge of cancer. It was a national priority.

Nelson-Rees's message made this large and serious effort seem a little silly. Sure, the institute was spending millions of dollars sending its brave recruits over the top against the enemy. But it turns out our boys were shooting with blanks!


-- "Goof" Costs 65 Years to Infinity of Cancer Research. Excerpt From: "A Conspiracy of Cells: One Woman's Immortal Legacy and the Medical Scandal It Caused", by Michael Gold


On Oct. 10, 1998, two days after Hitz’s final report was posted on the CIA’s Web site, the New York Times published a brief article that continued to deride Webb but acknowledged the Contra-drug problem may have been worse than earlier understood.

Several weeks later, the Washington Post weighed in with a similarly superficial article. The Los Angeles Times never published a story on the contents of Hitz’s findings though Los Angeles had been “ground zero” of the Ross-Blandon connection.

In 2000, the Republican-controlled House Intelligence Committee grudgingly acknowledged that the stories about Reagan’s CIA protecting Contra drug traffickers were true. The committee released a report citing classified testimony from CIA Inspector General Britt Snider (Hitz’s successor) admitting that the spy agency had turned a blind eye to evidence of Contra-drug smuggling and generally treated drug smuggling through Central America as a low priority.

“In the end the objective of unseating the Sandinistas appears to have taken precedence over dealing properly with potentially serious allegations against those with whom the agency was working,” Snider said, adding that the CIA did not treat the drug allegations in “a consistent, reasoned or justifiable manner.”

The House committee’s majority Republicans still downplayed the significance of the Contra-cocaine scandal, but the panel acknowledged, deep inside its report, that in some cases, “CIA employees did nothing to verify or disprove drug trafficking information, even when they had the opportunity to do so. In some of these, receipt of a drug allegation appeared to provoke no specific response, and business went on as usual.”

Like the release of Hitz’s report in 1998, the admissions by Snider and the House committee drew virtually no media attention in 2000 — except for a few articles on the Internet, including one at Consortiumnews.com. Because the confirmation of the Contra-cocaine scandal received so little mainstream media coverage, Gary Webb remained a pariah in his profession of journalism, making it next to impossible for him to land a decent-paying job and contributing to his suicide in 2004. [For details, see Consortiumnews.com’s “The Warning in Gary Webb’s Death.”]

What’s a Conspiracy Theory?

So, what is one to make of New York Magazine’s decision 15 years after the CIA’s confession and nearly a decade after Webb’s death to lead off its snarky ridicule of “conspiracy theories” with such a grossly inaccurate account of what was undeniably a real conspiracy?

One might have hoped that a publication that fancies itself as iconoclastic would have had the journalistic courage not to simply reinforce a fake conventional wisdom -– and have the human decency not to join in the mainstream media’s dancing on Webb’s grave. But that is apparently too much to expect of New York Magazine.

There is another problem in New York’s sneering takedown of “conspiracy theories” -– and that is the magazine lacks a decent definition of what a “conspiracy theory” is, especially given the pejorative implications of the phrase.

In my view, a “conspiracy theory” is a case of fanciful, usually fact-free speculation positing some alternative explanation for an event. Typically, a “conspiracy theory” not only lacks any real evidence but often ignores compelling evidence that goes in other directions. For instance, the current conspiracy theory about President Barack Obama being born in Kenya despite birth certificates and birth notices of his birth in Hawaii.

By contrast, a real conspiracy can be defined as a collaboration among individuals to engage in criminal or scandalous behavior usually in a secretive manner. There are many such examples involving high government officials, including Richard Nixon’s Watergate and Ronald Reagan’s Iran-Contra Affair.

The difference between a “conspiracy theory” and a real conspiracy is that the latter is supported by substantial evidence and the former is reliant on someone simply thinking something up, often with partisan or ideological motivation.

There is, of course, much gray area between those two poles. There are cases in which some evidence exists indicating a conspiracy but it’s short of conclusive proof. In such cases of legitimate doubt, aggressive investigations are warranted -– and the U.S. news media should welcome, not punish, these lines of inquiry.

Instead, the role of the mainstream press often has been to ridicule journalists and other investigators who venture into these murky waters. Often, that ridicule leads to serious cases of journalistic malfeasance as occurred with the mistreatment of Gary Webb and the Contra-cocaine story.

Other times the smug “anti-conspiracism” makes it impossible to get at the facts and to inform the American public about wrongdoing in a timely fashion. That can allow corrupt government officials to go unpunished and sometime to return to government in powerful positions.


The other important lesson to take from New York Magazine’s lumping real conspiracies and possible conspiracies in with fanciful conspiracy theories is that each case is unique and should be treated as such. Each set of facts should be examined carefully.

Just because one conspiracy can be proven doesn’t substantiate every claim of conspiracy. And the opposite is also true, just because one fact-free conspiracy theory is nutty doesn’t mean all suspected conspiracies deserve ridicule.

Through its anti-journalistic behavior, New York Magazine makes it hard to mourn its current financial predicament as it cuts back to publishing every other week. Indeed, the magazine is making a case that few tears should be shed if it disappears entirely.

Investigative reporter Robert Parry broke many of the Iran-Contra stories for The Associated Press and Newsweek in the 1980s. He is the author of America's Stolen Narrative and the editor of Consortium News.
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Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Sat Apr 23, 2016 11:38 pm

A pawn in the CIA drug game
by Rosalind Muhammad
West Coast Bureau Chief
FinalCall.com

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AN DIEGO--Ricky Donnell Ross, 36, was a trailblazer in the crack cocaine trade in Los Angeles and other parts of the U.S.

A celebrated drug dealer, Ricky reaped millions as an unknowing pawn of Central Intelligence Agency and U.S. Drug Enforcement Agency operatives, who supplied him with unlimited amounts of cocaine. His suppliers used the profits to pay for the CIA-spawned Contra war versus Nicaragua's leftist government in the 1980s. Ricky's connections were first revealed in a series of articles published by the San Jose Mercury News and in court testimony.

He granted The Final Call an exclusive interview at the Metropolitan Correctional Center, where he is awaiting sentencing on cocaine trafficking charges.

The Contra connection

In his interview, Ricky described how he was seduced into the lucrative cocaine brokering market in 981. It would be more than a decade before Ricky would learn that his key supplier, Oscar Danilo Blandon Reyes, a man whom he called a friend, had a master's degree in marketing and was a DEA informant, with connections to the CIA.

Known simply as "Freeway Rick," Ricky started out as a poor, illiterate, high school dropout from South Central Los Angeles and a talented tennis player.

At 19, Ricky said, an older teacher, who taught at a job center, turned him on to cocaine. Ricky said he looked up to the man and started selling cocaine for him.

The money was good. Ricky went solo. His teacher's Nicaraguan supplier and Oscar Danilo Blandon Reyes, supplied him.

Ricky's operation grew, soon he was one of the biggest cocaine dealers in South Central and Danilo Blandon became his sole supplier. Their business relationship grew personal, said Ricky, adding that he would spend time at Danilo's home, far from the crowded ghettoes of L.A.

Danilo schooled his portage in the art of staying "low key" and taught him how to market mass quantities of cocaine at bargain-basement prices, said Ricky.

"At first we were just getting eight ounces or so worth $16,000," he explained. "As time went on Danilo started supplying kilos (worth tens of millions of dollars). I don't know how it was possible. I didn't question him. Just took it as a blessing."

By 1984, "Freeway Rick" was a kingpin, with over a dozen crack houses in South Central, churning out $20,000 to $40,000 a day in profits. His network of drug dealers peddled a staggering 500,000 crack nuggets daily.

Ricky used cashiers' checks to but close to $6 million in property--motels, tire shops, junk yards, apartment buildings, houses. One day Ricky's partner was showing off a .22 pistol to Danilo. The next day Danilo brought him a brand new Uzi submachine gun "still in a box," and gave Ricky a .22 with a silencer.

Ricky and partner became gun dealers selling the Uzis, AK-47s, and Colt AR-15 assault rifles that became the trademark of bloody Crip versus Bloods gang wars and drive-by shooting in the 1980s.

Danilo once tried to sell his partner a grenade launcher, Ricky said.

Ricky traveled with Danilo to Detroit, Miami, Atlanta and New York. In New York, Ricky said, he met one of Danilo's dealers, who boasted of a 500-kilo-a-month operation worth about $10 million.

Ricky also knew Danilo was sending guns to the Contras. "After two or three years together, he told me that he got ran out of his country and they was trying to fight and get his country back," Ricky said.

Danilo Blandon, an illegal citizen and founder of one Contra army was once described by a federal prosecutor as one of the biggest Nicaraguan cocaine dealers in America.

Time to 'Chill Out'

In January 1987, with crack markets exploding in major cities, police went after L.A.'s crack problem. They formed the Freeway Rick Task Force dedicated to putting Ricky Ross out of business.

Ricky headed to Cincinnati with his girlfriend, who was battling crack addiction and had family there. They settled into a suburban home.

After a couple months, Ricky said, Danilo visited him and offered a cut into 13 kilos of cocaine that he needed distributed. Ricky went to work and soon monopolized Cincinnati's virgin crack market, using the same strategies and Nicaraguan drug connections.

He started selling crack as far away as Cleveland, Dayton, Indianapolis and St. Louis.

Ricky's luck ran out in 1988. One of his cocaine loads ran into a drug-sniffing dog at a New Mexico bus station and drug agents eventually connected it to him. He pleaded guilty to crack trafficking and received a mandatory 10-year prison sentence which began serving in 1990.

"Freeway Rick" becomes an informant

Federal prosecutors from Los Angeles approached Ricky days after the arrest and offered a deal. If he would help prosecutors investigating a drug scandal engulfing the Los Angeles County Sheriff's elite narcotics squads, they would help cut down his jail time.

Ricky became a government informant.

"They wanted me to talk about searches the task force made on crack houses, money at the houses, did they beat up (people) or steal money," Ricky received five years off his sentence and an agreement that his remaining drug profits would not be seized.

He was still behind bars in 1994, awaiting parole, when San Diego DEA agents targeted him for a "reverse" sting, one in which government agents provide the drugs and the target provides the cash.

Within days of his parole and return to Los Angeles in October 1994, Ricky said, Danilo called him saying he had 600 kilos of cocaine worth about $12 million and he wanted Ricky to help sell it.

Ricky said he initially declined but later gave in to the persistent phone calls and obtained a buyer for 100 kilos of the cocaine Danilo claimed he had.

On March 2, 1995, in a parking lot near San Diego, Ricky looked inside a cocaine-laden Chevy Blazer. Suddenly the place was swarming with police.

Ricky jumped into a friend's pickup, sped off and was captured after the truck swerved into a hedgerow. He has been in jail without bond since.

Ricky stood trial in March and the government's star witness against him was his old friend, Danilo.

On Danilo's testimony, Ricky and two other men were convicted by an all-white jury of conspiracy charges, conspiring to sell the DEA's cocaine. Ricky now faces life in jail, with no chance for parole.

Ricky's eyes teared as he described Danilo's testimony, "It was like he was killing me. It was nothing I could do but sit there and take it. There's a tape they played in court where (Danilo) said, "I hate n-----s, but they pay cash,' "Ricky recalled.

"I would have died for him. He's the worst. When I see how (the government) twists the rules for him and they want to give me a life sentence, to me it's sickening."

Danilo received $45,000 in government rewards and expenses for Ricky's arrest, records show.

U.S. District Judge Marilyn Huff postponed Ricky's Aug. 23 sentencing until Sept. 13 to allow his attorney, Alan Fenster, to question two inmates at the Metropolitan Correctional Center in San Diego about their knowledge of Danilo Blandon's alleged drug dealing while working for the DEA.

Atty. Fenster told The Final Call that he hopes such testimony will convince the judge that Ricky deserves a new trial because of prosecutorial misconduct.

"Our contention is that (Ricky) was minding his own business and was an unsuspecting victim" of the DEA's reverse sting, Atty. Fenster said.

"If the judge finds government misconduct was so outrageous, she has the power to dismiss the charges," the attorney added. "This was a trial by ambush. The defense was denied information on Mr. Blandon that would impeach him. The government really sandbagged us."


Ricky, who taught himself to read and write about five years ago, said he could be looked at two ways: As a villain or as a victim.

Asked if he was every concerned about how crack cocaine was affecting the Black community, Ricky admits, "Not at first. It never crossed my mind."

He feels "partially responsible" for the legions of crack babies as well as addicts who prostitute themselves to sustain their drug habits.

"I took the drugs and I transferred them from (Danilo's) hands to their hands," Ricky concedes. "I feel that I was a 'strawberry' too. I was manipulated. I was just like the prostitute."

Ultimately, he said, the U.S. government is responsible for the crack epidemic. "They put it in our hands. They financed it. It was their planes that brought it over here," Ricky said. "Their guy, Oscar Danilo, Blandon, he set up the market. They picked me. I didn't go to Nicaragua. This could go higher than the CIA. They say that drugs corrupt whole governments."
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Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Sun Apr 24, 2016 10:43 pm

Part 1 of 2

Measuring Crack Cocaine and Its Impact*
by Roland G. Fryer, Jr. Harvard University Society of Fellows and NBER; Paul S. Heaton University of Chicago; Steven D. Levitt University of Chicago; Kevin M. Murphy University of Chicago
April 2006

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* We would like to thank Jonathan Caulkins, John Donohue, Lawrence Katz, Glenn Loury, Derek Neal, Bruce Sacerdote, Sudhir Venkatesh, and Ebonya Washington for helpful discussions on this topic. Elizabeth Coston and Rachel Tay provided exceptional research assistance. We gratefully acknowledge the financial support of Sherman Shapiro, the American Bar Foundation, and the National Science Foundation.

Abstract

A wide range of social indicators turned sharply negative for Blacks in the late 1980s and began to rebound roughly a decade later. We explore whether the rise and fall of crack cocaine can explain these patterns. Absent a direct measure of crack cocaine’s prevalence, we construct an index based on a range of indirect proxies (cocaine arrests, cocaine-related emergency room visits, cocaine-induced drug deaths, crack mentions in newspapers, and DEA drug busts). The crack index we construct reproduces many of the spatial and temporal patterns described in ethnographic and popular accounts of the crack epidemic. We find that our measure of crack can explain much of the rise in Black youth homicides, as well as more moderate increases in a wide range of adverse birth outcomes for Blacks in the 1980s. Although our crack index remains high through the 1990s, the deleterious social impact of crack fades. One interpretation of this result is that changes over time in behavior, crack markets, and the crack using population mitigated the damaging impacts of crack. Our analysis suggests that the greatest social costs of crack have been associated with the prohibition-related violence, rather than drug use per se.

I. Introduction

Between 1984 and 1994, the homicide rate for Black males aged 14-17 more than doubled and homicide rates for Black males aged 18-24 increased almost as much, as shown in Figure 1. In stark contrast, homicide rates for Black males 25 and older were essentially flat over the same period. By the year 2000, homicide rates had fallen back below their initial levels of the early 1980s for almost all age groups.1

Homicide was not the only outcome that exhibited sharp fluctuations over this time period in the Black community. Figure 2 presents national time series data by race for fetal death rates, low birth weight babies, weapons arrests, and the fraction of children in foster care.2 All of these time series exhibit noticeable increases for Blacks--typically followed by offsetting declines—starting in the late 1980s or early 1990s. The fraction of Black children in foster care more than doubled, fetal death rates and weapons arrests of Blacks rose more than 25 percent, and Black low birth weight babies increased nearly 10 percent.3 Among Whites, there is little evidence of parallel adverse shocks. The poor performance of Blacks relative to Whites represents a break from decades of convergence between Blacks and Whites on many of these measures.4

The concurrent rises and declines in outcomes as disparate as youth homicide, low birth weight babies, and foster care rates presents a puzzle, especially when many standard economic and labor market measures for Blacks show no obvious deviations from trend over the same period (Blank 2001). In this paper, we examine the extent to which one single underlying factor – crack cocaine – can account for the fluctuations in all these outcomes.5

Crack cocaine is a smoked version of cocaine that provides a short, but extremely intense, high. The invention of crack represented a technological innovation that dramatically widened the availability and use of cocaine in inner cities. Virtually unheard of prior to the mid-1980s, crack spread quickly across the country, particularly within Black and Hispanic communities (Bourgois 1996, Chitwood, et al. 1996, Johnson 1991). Many commentators have attributed the spike in Black youth homicide to the crack epidemic’s rise and ebb (Blumstein 1998, Cook and Laub 1998, Cork 1999, and Grogger and Willis 2000). Sold in small quantities in relatively anonymous street markets, crack provided a lucrative market for drug sellers and street gangs (Bourgois 1996, Jacobs 1999). Much of the violence is attributed to attempts to establish property rights not enforceable through legal means (Bourgois 1996, Chitwood, et al. 1996). With respect to other outcomes, the physiological evidence regarding the damage that crack cocaine does to unborn babies suggests that crack usage might explain the patterns in fetal death and low birth weight babies (Frank et. al. 2001, Datta-Bhutatad 1998), although there is no consensus (Zuckerman 2002). The highly addictive nature of crack, combined with relatively high crack usage rates by women (Bourgois 1996, Chitwood, et al. 1996), could contribute to dysfunctional home environments leading to placement of children into foster care.

In spite of the general appreciation of the potential role that crack may have played in driving the patterns observed in the data (e.g., Bennett et al. 1996, Wilson 1990), especially with respect to the Black youth homicide spike (Cook and Laub 1998, Blumstein and Rosenfeld 1998, Levitt 2004), there has been remarkably little rigorous empirical analysis of crack’s rise and its corresponding social impact. The scarcity of research appears to be due in part to the great difficulty associated with constructing reliable quantitative measures of the timing and intensity of crack’s presence in local geographic areas. Baumer et al. (1998), Cork (1999), and Ousey and Lee (2004) use cocaine-related arrests as a proxy for crack. Ousey and Lee (2002) supplement arrest data with the fraction of arrestees testing positive for cocaine. Grogger and Willis (2000) use breaks from trend in cocaine-related emergency room visits in a sample of large cities, as well as survey responses from police chiefs in these cities to measure the timing of crack’s arrival. Corman and Mocan (2000) use drug deaths, but the data do not specify which drug is responsible.

In this paper, we take a somewhat different approach to measuring crack cocaine. Rather than relying on a single measure, we assemble a range of indicators that are likely to proxy for crack. These include cocaine arrests and cocaine-related emergency room visits as in the previous literature, but also the frequency of crack cocaine mentions in newspapers, cocaine-related drug deaths, and the number of DEA drug seizures and undercover drug buys that involve cocaine. While each of these proxies has important shortcomings, together they paint a compelling story for capturing fluctuations in crack. As demonstrated in Figure 3, the national time-series aggregates for these variables tend to follow a similar pattern, rising sharply around 1985, peaking between 1989 and 1993, and in most cases declining thereafter. In spite of the aggregate similarities, using just one of the proxies does not appear to be sufficient for describing crack: the average cross-city correlation in the measures is only about .35. By combining the various measures together into a single factor, however, we are able to generate a crack cocaine index that is not particularly sensitive to any one of the individual measures and corresponds well to the ethnographic and media accounts of crack cocaine’s spread and prevalence. Our measure captures the intensity of crack’s presence in a particular place and time and can be constructed for a wide variety of geographic areas. We estimate our crack index annually for large cities and states over the period 1980-2000. These data available to other researchers.6

We find that crack rose sharply beginning in 1985, peaked in 1989, and slowly declined thereafter. Our estimated crack incidence remains surprisingly high over time – in the year 2000 crack remains at 60-75 percent of its peak level. Crack is concentrated in central cities, particularly those with large Black and Hispanic populations. The cities experiencing the highest average levels of crack are Newark, San Francisco, Philadelphia, Atlanta, and New York. Although crack arrived early to the West Coast, the strongest impacts were ultimately felt in the Northeast and the Middle Atlantic States. The Midwest experienced relatively low rates of crack.

Our index of crack is strongly correlated with a range of social indicators. We find that the rise in crack from 1984-1989 is associated with a doubling of homicide victimizations of Black males aged 14-17, a 30 percent increase for Black males aged 18-24, and a 10 percent increase for Black males 25 and over, and thus accounts for much of the observed variation in homicide rates over this time period.7 The rise in crack can explain 20-100 percent of the observed increases in Black low birth weight babies, fetal death, child mortality, and unwed births in large cities between 1984 and 1989. In contrast, the measured impact of crack on Whites is generally small and statistically insignificant. We estimate that crack is associated with a 5 percent increase in overall violent and property crime in large U.S. cities between 1984 and 1989.8

The link between crack and adverse social outcomes weakens, however, over the course of the sample. Even though crack use does not disappear, the adverse social consequences largely do. Thus, by the year 2000, we observe little impact of crack, which accounts for much of the recovery in homicide rates and child outcomes for Blacks over the period. We hypothesize that the decoupling of crack and violence may be associated with the establishment of property rights and the declining profitability of crack distribution. The fading of adverse child outcomes may be attributable to the concentration of crack usage among a small, aging group of hardcore addicts (MacCoun and Reuter 2001, page 123).

The remainder of the paper is structured as follows. Section II provides a brief history of crack cocaine. Section III describes the data we use. Section IV presents our methodology for combining the crack proxies into a single index, presents the results of that exercise, and assesses the determinants of the timing and intensity with which crack hits a city or state. Section V analyzes the extent to which crack can account for the observed fluctuations in social indicators since 1985. Section VI concludes. The appendix outlines the sources of data used and the precise construction of the variables and crack cocaine index.

Section II: A Brief History of Crack Cocaine

Cocaine is a powerful and addictive stimulant first extracted from the coca plant in 1862. During the 19th century, cocaine had a variety of medical uses and could be purchased over the counter, including in the original version of Coca-Cola (Bayer 2000). In the 1970s, inhaled cocaine emerged as a popular but high-priced recreational drug. The street price of pure powder cocaine was roughly $100 to $200 per gram (which is equivalent to $300-$600 per gram in 2004 dollars). The high price of cocaine had two important implications: (1) cocaine use was concentrated among the affluent, and (2) retail cocaine purchases required hundreds of dollars because it was impractical to transact in fractions of grams.9

Crack cocaine is a variation of cocaine made by dissolving powder cocaine in water, adding baking soda, and heating. The cocaine and the baking powder form an airy condensate, that when dried, takes the form of hard, smokeable “rocks.”10 A pebble-sized piece of crack, which contains roughly one-tenth a gram of pure cocaine, sells for $10 on the street and provides an intense high, but one that lasts only fifteen minutes.

Crack is an important technological innovation in many regards. First, crack can be smoked, which is an extremely effective means of delivering the drug psychopharmacologically. Second, because crack is composed primarily of air and baking soda, it is possible to sell in small units containing fractions of a gram of pure cocaine, opening up the market to consumers wishing to spend $10 at a time. Third, because the drug is extremely addictive and the high that comes from taking the drug is so short-lived, crack quickly generated a large following of users wishing to purchase at high rates of frequency. The profits associated with selling crack quickly eclipsed that of other drugs. Furthermore, unlike most other drugs, crack is often sold in open-air, high-volume markets between sellers and buyers who do not know one another.

There are three primary reasons why crack may have been so devastating to the Black community. First, street gangs, which already controlled outdoor spaces, became the logical sellers of crack (Venkatesh and Levitt 2000). Gang violence, primarily as a means of establishing and maintaining property rights, grew dramatically, and potentially accounts for the sharp rise in Black youth violence. Second, the increased returns associated with drug dealing attracted young Black males to gangs and may have reduced educational investment. Third, a large fraction of crack users were young women. Prostitution was common among female crack addicts, potentially accelerating the spread of AIDS and the unwanted birth of low birth weight “crack babies.”11 Crack addicted mothers and fathers are unlikely to provide nurturing home environments for their children (and often ended up incarcerated), leading to the relinquishment of parental rights.

Section III: Data

We analyze data separately at the city-level and the state-level. The city-level analysis is carried out on the 144 cities with population greater than 100,000 in 1980. These cities are of particular interest because anecdotal evidence suggests that the problems associated with crack were concentrated in large urban areas. In addition, a number of the variables we use are collected at the city-level, making it a natural unit of analysis. Focusing on the state-level allows us to analyze outcome variables that are not available at the city level, and facilitates a linkage between our work and the large empirical literature carried out using state-level data. In all cases, annual data are used for the period 1980 to 2000.

As noted earlier, we utilize a range of measures to proxy for the prevalence of crack.12 At the city-level, these outcomes are crack-related emergency room visits, cocaine-related arrests, the frequency of crack cocaine mentions in newspapers, DEA drug buys and seizures involving cocaine, and cocaine-related deaths. The emergency room data are based on information from the Drug Abuse Warning Network (DAWN). These data initially covered 14 cities, with that number growing to 19 by the end of the sample. In later years, these data distinguish between crack and powder cocaine, but for consistency over the whole period, we do not exploit this variation.13 The proxy we use is cocaine-related emergency room visits per capita in the metropolitan area. Arrest data are collected by city police departments and are available through the FBI’s Uniform Crime Reports. Because of incomplete reporting, we follow Levitt (1998) and define our cocaine arrest measure as cocaine arrests as a fraction of total arrests in the city.14 Our measure of crack cocaine mentions in newspapers is the number of news articles in Lexis-Nexis with a city’s name and the words “crack” and “cocaine,” divided by the total number of articles with the city's name and the word “crime.” By constructing the variable as a ratio, we avoid obvious problems associated with the fact that large cities and those with local newspapers included in Lexis-Nexis will appear much more frequently in the database. The frequency of DEA drug buys and seizures per capita is taken from the System to Retrieve Drug Evidence (STRIDE) data base, which catalogs undercover drug buys and seizures carried out by DEA agents and informants, typically in support of criminal prosecutions. These data include approximately 274,000 cocaine transactions. The city in which the transaction occurred is recorded in the data set. STRIDE does not allow one to definitively distinguish between powder and crack cocaine.15 The final proxy for crack that we use is the rate per capita of cocaine-related deaths, drawn from the annual Mortality Detail File produced by the National Center for Health Statistics. The death data include accidental poisonings, suicides, and deaths due to long-term abuse for which cocaine use was coded by physicians as a primary or contributing factor. We cannot distinguish between crack cocaine and powder cocaine drug deaths in these data.

Each of these proxies suffers from important weaknesses. First, with the exception of the newspaper citations, the measures are unable to clearly differentiate between powder and crack cocaine. Second, both the cocaine arrest and DEA drug buy data are affected not just by the prevalence of crack usage, but also by the intensity of government enforcement efforts (see, for example, Horowitz 2001). Third, the DAWN emergency room data covers only a small set of metropolitan areas and the sample of hospitals that participates changes over time (Substance Abuse and Mental Health Services Administration 2003 Appendix B). Both the emergency room data and the drug death data are potentially affected by the subjective nature of physician determination of the contribution of cocaine to an emergency room visit or a death. Finally, the newspaper citation measure is the output of a relatively crude algorithm and suffers both from the criticism that in the early years of the epidemic there were other terms such as “rock cocaine” that were sometimes used to describe the product before crack cocaine became the agreed upon nomenclature, and also that the newsworthiness of crack-related stories may have declined over time, while consumption continued to rise.

The number of observations, means, and standard deviations for all of these city-level crack proxies are presented in the top panel of Table 1. Values are reported separately for the period prior to and after 1985, the year when crack use is believed to have become widespread. All of these indicators are much higher in the post-1985 period, even for those measures that do not directly distinguish between powder and crack cocaine. Crack mentions in newspapers are extremely low prior to 1985; these instances likely represent false positives in our Lexis-Nexis algorithm.16 Note that the number of observations available varies dramatically across measures due to differences in the cities covered by the data, as well as the years in which data are collected. The data appendix describes the statistical corrections that are done to account for missing values in the construction of our crack index.

When conducting our analysis at the state level, we drop the emergency room and crack citation variables since these measures are available only for a limited number of large cities. The rest of our city-level measures are available at the state-level. The state-level crack proxies are shown in the second panel of Table 1. The absolute levels of the crack proxies are generally lower in states than in large cities, but the patterns are otherwise similar.

The final two panels of Table 1 report the criminal, social, and economic outcomes that we are trying to explain using our crack index at the city-level and state-level respectively. These outcomes include age specific homicide rates, the full set of violent and property crimes tracked in the FBI’s Uniform Crime Reports, fetal death, low-birth weight babies (singleton births only), teen births and unwed births, death rates of children aged one to four, weapons arrests, the number of new admissions to prison, the per capita number of children in the foster care system, the unemployment rate, and the poverty rate. When the data are available we analyze these variables separately by race. Some of the variables are available at the state-level only.

Section III: Identifying Crack

The prevalence of crack cocaine is not directly observable. Instead, we must rely on noisy proxies in an attempt to measure crack. The primary approach that we take for extracting the information available in these proxies is to construct a single index measure of crack using factor analysis.17 In particular, we estimate an equation of the form:

(1) Image

where the subscripts i, s, and t correspond to particular proxy measures, geographic units, and years respectively. The left-hand-side variables are the proxy measures all of which are stacked into a single vector. The variable Crack is not directly observed, but rather estimated along with the ß's . One could also include covariates on the right-hand side of equation (1) – indeed we allow for city-fixed effects in our baseline estimates – but for simplicity we omit these covariates from the formal discussion.

Estimation of equation (1) generates predicted values both for a set of factors (Crackst) and a set of coefficients (also known as loadings) ßi. We focus our analysis on the single factor that has the greatest explanatory power. Because our proxies are highly correlated, our data are well-described by a one-factor model which captures roughly 50% of the variation in the proxies.18 Additional factors contribute little in terms of explaining the observed variation in our data. The factor with the greatest explanatory power is what we will call “crack,” but more accurately it is the single index that explains the largest share of the variation in our crack proxies. The loadings tell us the degree to which each estimated factor influences the different outcome variables. The crack index we obtain is a weighted average of the proxy variables underlying it, with weights given by the squares of the loadings on each proxy.

There are three advantages to combining our multiple measures into a single index. First, we are interested in describing the observed patterns of crack’s arrival and fluctuations. Having one summary measure of crack rather than five separate proxies greatly simplifies this task. Second, because each of our individual proxy measures is quite noisy, combining them into a single index substantially increases the signal-to-noise ratio. For instance, in the simplest case where ßi =1 for each proxy, the share of the variance in a given measure that is attributable to a true signal is Image are the variance of the latent crack factor and the measurement error in proxy i respectively. Under the assumption that the measurement errors across proxies are i.i.d, the signal-to-total variance ratio of an equally-weighted index of N proxies is Image

A third possible benefit of using a single crack index arises when we turn to estimating the impact of crack on outcomes such as low-birth weight babies or crime rates. Although it might seem that putting each of the individual proxies directly on the right-hand side of such regressions would be preferable (see, for example Lubotsky and Wittenberg 2004), in the plausible scenario in which one or more of our individual proxies may be endogenous to a particular outcome of interest (e.g. when Black youth homicide is on the rise, police departments may respond by intensifying enforcement against drug sellers, holding constant overall crack use), use of an index provides potential benefits by imposing the restriction that each of the proxies has an identical impact on the outcome variable. If the proxies were each entered separately as explanatory variables, one might greatly overstate the role of crack in explaining fluctuations in social outcomes.

In estimating the impact of crack on social outcomes, an attractive alternative to constructing a single index is to instead use the various proxies as instrumental variables for one another. To the extent that the proxies are correlated with the underlying latent variable crack, but the measurement error in the proxies is uncorrelated, the various crack measures are plausible instruments for one another. We present results both using our single index proxy and using an instrumental variables approach.20

One unique feature of our data that we have not yet emphasized, but proves extremely valuable in our analysis, is the fact that crack cocaine was a technological innovation which was virtually unknown until around 1985. This provides us with a well defined pre-crack period. To the extent that our proxies/index are truly reflecting crack, they should have values near zero in the pre-1985 period. In addition, variation in the proxies prior to 1985 is likely to be almost purely measurement error. Comparing the variation in the proxies before and after crack arrives will provide useful insights into the extent to which fluctuations in our proxies after 1985 can plausibly be viewed as signal rather than noise.21

Section IV: The Prevalence of Crack Across Time and Space

Table 2 presents correlations across the various crack proxies we use in the analysis. The top panel of the table reports correlations across time for the national aggregates of the proxies. Given how similar the time-series patterns are for these variables in Figure 3, it is not surprising that these correlations are high, ranging from .225 to .951. The mean of the off-diagonal elements is equal to .71. The middle panel of the table presents correlations using either the city-year or the state-year as the unit of analysis. City-fixed effects and state-fixed effects have been removed to eliminate systematic and persistent reporting differences across areas. The correlation across the proxies falls, but remains substantial. For both the city and the state sample, all the pairwise correlations remain positive and the mean of the off-diagonal elements is .32 for cities and .36 for states. In other words, when a city or state is high relative to its usual value on one of the proxies in a given year, it tends to be high on all of them – consistent with the hypothesis that a single factor is driving the increase. The bottom panel of the table reports correlations at the city-year and state-year level after removing not only city or state fixedeffects, but also all of the aggregate time series variation using year dummies. Consequently, the correlations in the bottom panel reflect whether a city-year or state-year observation that is high relative to the rest of the sample in that year on one proxy also tends to be high on the other proxies. These correlations are much lower than the others shown in the table. The mean offdiagonal element in the city sample falls to only .07; in the state sample it is .15. The DEA cocaine busts and the newspaper citation index perform particularly poorly. These results highlight the fact that the co-movement of our proxy variables is due in large part to the fact that across many areas, all the proxies tend to be high in some years and low in others.

Table 3 presents our baseline estimates of the loadings obtained using factor analysis. The greater is the value corresponding to a particular proxy, the more influence it is given in constructing the crack index. 22 The factor loadings can be negative, although that is not the case for any of our analysis. We report five sets of estimates corresponding to the correlation matrices in Table 3: national aggregates (column 1), city-level data (columns 2 and 3), and state-level data (columns 4 and 5). At the city and state level, we report results before and after year-fixed effects have been removed. For the national aggregates, the loadings are relatively equal across the five proxies. The same is true at the city level before removing year-fixed effects (column 2), except that the DEA cocaine busts proxy receives less weight than the others. When year-fixed effects are taken out, more weight is given to cocaine deaths at the expense of cocaine-related ER visits and, to a lesser extent, crack-related newspaper citations. At the state level, cocaine arrests and cocaine deaths both get high weight and DEA drug busts are given more influence after year-fixed effects are removed.

Figure 4 presents population-weighted crack indexes that correspond to the factor analysis loadings for the national-level, city-level and state-level analysis in Table 3. We show results for the specifications without year-fixed effects; the patterns are similar for the other specifications. The crack index is identified only up to a scale of proportionality, so the absolute units of the crack measure are not directly interpretable, nor can they be directly compared across the three figures.

Our results regarding the time-series pattern of crack are not particularly sensitive to the level of aggregation used in the analysis. In all three cases, the crack index is low but rising slightly until 1985, at which point there is a sharp increase to a peak in 1989. The timing of crack’s rise that we estimate corresponds nicely with the anecdotal evidence regarding the introduction of crack cocaine in the mid-1980s and its rapid proliferation. Our estimation technique in no way constrains the estimated crack prevalence to be low in the early years of the data; this result is driven purely by the data. The one noticeable difference between the three indexes is the pattern from 1989-2000. In the top figure using national aggregate data, the index falls almost 50 percent between 1989 and the end of the sample. In the city-level and state-level samples the decline is about 25 percent, with much of the decline coming in latter half of the 1990’s. Regardless of the index, one perhaps surprising result is that our crack index remains so high in 2000, a time in which many casual observers had declared the crack epidemic to have faded. Survey measures of crack usage, however, reinforce our conclusion that crack has not disappeared. The percentage of high school seniors reporting having used crack in the last twelve months fell roughly 30 percent (from 3.1 to 2.2 percent) between 1989 and 2000 (Johnston et al 2004). Eighth and tenth graders in the same survey, who were not asked about crack usage until 1990, reported the highest incidence of annual crack usage in 1999. There is also evidence that sharp declines in the price of crack have led those who use crack to consume it in greater quantities (MacCoun and Reuter 2001).

Existing evidence suggests that the impact of crack has been much greater on Blacks and Hispanics than on Whites. Because most of our proxy variables are not available separately by race, we are forced to take an indirect approach to identifying a race-specific crack index. Under the assumption that the loadings on the crack proxies are the same for Blacks and Whites, using the available data we can estimate a model in which the impact of crack varies by race. The overall per capita impact of crack in a city (or state) and year can be decomposed into
(2) Image where the P variables represent population shares by race and the coefficients are race- and city-specific crack estimates. captures how our crack index varies across cities at a given point of time as a function of the racial composition of the city; is the value the crack index would take in a hypothetical city all of whose residents were of the race in question. That coefficient does not necessarily directly reflect the relative rates of crack usage across individuals of different races if, for example, the presence of more Blacks in a city is associated with higher crack use by Whites. To estimate the relative impact of crack by race, we run a separate cross-sectional regression for each year with the crack index as the left-hand-side variable and race proportions as the right-hand-side variables, omitting a constant.

The results of this estimation are reported in Figure 5. For both city- and state-level analyses, crack among Blacks rises until 1989 and then remains relatively steady thereafter. In large cities, the estimated crack index for Whites is consistently 5-10 times lower than Blacks. In the state-level sample, Blacks are again much higher than Whites, and in both samples Hispanics appear roughly comparable to Blacks.

Figures 6 and 7 present crack estimates by region and size of central city respectively. In Figure 6, the Northeast experienced the greatest crack problem, followed by the West. In Figure 7, the time series pattern for crack in cities above and below 350,000 residents is quite similar, except that the crack levels are more than twice as high in the larger cites.23

Table 4 reports the cities and states in our sample with the highest and lowest estimated average crack prevalence over the period 1985-2000.24 Because our estimates are noisy at the city-level and the state-level due to substantial measurement error in our proxies, precise rankings must be viewed with the appropriate caution. The set of cities with the greatest crack problem includes cities one might expect: Newark, Philadelphia, New York, and Oakland, for instance. Boston, San Francisco, and Seattle, however, are perhaps surprising.25 Other cities one would expect to rank high such as New Orleans, Baltimore, Washington D.C., and Los Angeles also rank highly, but are not in the top ten. Among states, Maryland and New York head the list. The cities with the least evidence of crack tend to be smaller, geographically isolated cities, but not exclusively cities with low Black populations. Huntsville and Jackson, two of cities with the very low crack estimates, for example, are thirty and seventy percent Black respectively. The states with low crack tend to have large rural populations and few minorities.

Section V: The Impact of Crack Cocaine

In this section, we attempt to measure the impact that crack had on society by regressing a variety of outcome measures on our estimated crack index and varying combinations of covariates: (3) Image where CrackIndex is one of the crack indices we estimated above, and s corresponds to a geographic area (either national, state, or city), and t represents time. One important point to note is that our crack index is a measure of the severity of crack in an area as a whole. This severity can represent both the composition of the population as well as the intensity of use per person. For instance, since crack use appears to be more prevalent among Blacks than Whites, if two cities have the same value on the crack index, but one city has a higher proportion of Whites, the implication is that crack use per Black and crack use per White are likely to be higher in that city than the other city. Consequently, when we examine socio-economic outcomes as dependent variables, the logical specification involves defining outcomes in terms of rates per overall city population. For example, when we examine Black youth homicide, the dependent variable we use is Black youth homicides per 100,000 city residents, not Black youth homicides per Black youth.26

There are two obvious shortcomings associated with the simple OLS approach that we adopt. The first is that our crack index may suffer from measurement error, which will bias the estimates of crack’s impact, most likely in a downward direction. The second weakness of this approach is that the estimates we obtain reflect correlations, rather than true causal impacts. It is possible that omitted variables such as the erosion of social networks or the decline of two-parent households affects both crack and outcomes like homicide or children in foster care.

Instrumenting one proxy using the others provides a means of dealing with the issue of measurement error, under the assumption that the measurement error in the different proxies are uncorrelated (conditional on the other controls included in the regression). It is important to note, however, that this instrumenting strategy is unlikely to help in providing estimates that are directly interpretable as causal in nature. To the extent that omitted variables or reverse causality lead one of the crack proxies to be correlated with the error term in equation (3), it is likely that all of the crack proxies will suffer the same weakness.

Table 5 shows results from estimating equation (3) on our city-level sample using the city-level crack index. Each row of the table corresponds to a different outcome variable. The first column of the table reports the mean of the dependent variable in the sample; because our outcomes are denominated by the entire city population (not just Blacks or Whites depending on which race we are looking at), the reported means for both races are less than if the variables were per member of the group. Because Blacks make up a smaller percentage of the population, the reported means are particularly small relative to a rate per capita for Blacks. We allow the estimated coefficient on the crack index to vary by time period; columns 2-5 of the table report the coefficients for the periods 1985-1989, 1990-1994, and 1995-2000 respectively.27 All specifications include city-fixed effects, year dummies, and controls for percent of the population that is Black and Hispanic, log population, and log per capita income. Standard errors, shown in parentheses, take into account AR1 serial correlation within cities over time.

The top panel of Table 5 reports outcomes for Blacks. The crack index is positively associated with eight of the nine outcomes we examine in the 1985-1989 time period, with statistically significant estimates at the .01 level in five of the cases. A similar pattern of estimates is present in the 1990-1994 period. After 1995, however, the link between the crack index and these social outcomes for Blacks disappears--more than half of the point estimates become negative in the last period, and only the male age 18-24 homicide rate has a positive and significant coefficients. The middle panel of Table 5 shows city-level estimates for Whites. Although the crack index is generally positively correlated with these outcomes after 1985 (21 of the 27 point estimates are positive), only 5 of the 27 coefficients are statistically different than zero. The bottom panel of the table analyzes results for overall crime rates, which are not separately available by race. The crack index is positively correlated with eight of nine crime categories from 1985-1989, six significantly, and seven of the nine categories from 1990-1994, but the estimated coefficients decline in magnitude and statistical significance in the final period for all categories of crime.

To aid in interpreting the magnitude of the effects implied by the coefficients in Table 5, we report graphically the fraction of the observed variation in the outcomes that can be accounted for by the crack index over the period in which crack rose sharply (1984-1989) in Figure 8, and from the peak of crack to the end of the sample (usually 1989-2000) in Figure 9. In these figures, we compare the observed percent changes for the crime and birth-related outcomes over these time periods to the implied impact of crack calculated in one of two ways: (1) using the crack index as in Table 5, and (2) instrumenting for cocaine arrests using the other crack proxies as instruments.28 The implied impact of crack from the OLS specification in a particular year is the product of the regression coefficient in Table 5 multiplied by the average value of the crack index in that year. The impact of crack between 1984 and 1989 is simply the difference between the measured impact of crack in 1989 and in 1984. Calculating the impact of crack in the instrumental variables regressions is done in a similar manner, with the added complication related to measurement error in the cocaine arrests proxy.29 The corresponding standard error bands are shown in the figure as well.

A number of important points emerge from Figure 8. First, comparing the columns shaded white (OLS estimates) and the columns shaded black (instrumental variables estimates), in most cases the IV estimates are larger than the OLS estimates, although less precisely estimated. These generally larger IV estimates are consistent with the presence of measurement error in our index, leading to attenuation bias in our OLS results. Second, the magnitude of the actual percentage increase in homicide rates among young Black males is far greater than for the other variables considered. Our measure of crack cocaine explains a substantial part of this increase, particularly in the instrumental variables specifications. According to our IV estimates, crack can account for a 100-155 percent increase in Black male homicides among those aged 18- 24, and a change of 55-125 percent for Black male homicide among those aged 14-17. In contrast, our crack measure accounts for only small changes in older Black male homicide and White homicide. Third, for the birth/childhood outcomes, the impact of crack is larger for Blacks than for Whites, but the results are not as stark as for homicide. We estimate that the rise in crack between 1984 and 1989 accounts for roughly one-third of the increase in low birth weight Black babies, less than one-third of the Black rate of unwed births, much or all of the increase in Black child mortality and fetal death. For Whites, there is some apparent positive association between crack and low birth weight babies and child mortality. Finally, we find a positive relationship between crack and a wide range of crimes, although the magnitude is small when compared to the impact on Black youth homicide. In the OLS specifications, violent crime and property crime are both estimated to have increased by roughly 4 percent due to crack over the period 1984-1989; in the IV specifications the increase is approximately 10 percent, but very imprecisely estimated.

Figure 9 shows the estimated impact of changes in crack on the same set of outcomes for the period 1989-2000. One-half to three-fourths of the decline in homicide by Black males aged 14-17 can be attributed to the combined impact of a decline in the level of the crack index, and more importantly, the weakening of the link between crack and violence. By the year 2000, most of the crack-related spike in youth homicide in the late 1980s was gone--because the baseline level of youth homicide is almost three times higher in 1989 than in 1984, a 34 percent decrease in the latter period is equal and opposite to almost a 100 percent increase in the earlier period in terms of number of homicides. For older criminals and Whites, the impact of receding crack does not consistently explain a substantial fraction of the observed homicide declines in the 1990s. For Blacks, the adverse effects of crack on birth outcomes in the 1980s are essentially undone in the 1990s. The impacts on White birth outcomes are small and carry mixed signs. For city-level crime measures, the reductions attributable to crack in the 1990s erase much, but often not all, of the crack-driven increases in the 1980s. The notable exception to this pattern is the IV estimate on violent crime, which suggests that violent crime rose (although not statistically significantly) in the 1990s as crack receded (because the estimated impact of crack on violent crime is negative in the 1990s).

Table 6 replicates the analysis of Table 5, but using states as the unit of analysis rather than our sample of large cities. The regression results in Table 5 are quite similar to what we found for large cities; crack has the greatest impact on Black youth homicide, tends to be positively related to adverse birth outcomes for Blacks and is not significantly related to most outcomes for Whites. The impact of crack once again weakens over time. One important difference relative to the city sample is that overall crime is not positively and statistically related to crack in the state sample. In addition, there is less evidence of a strong impact of crack on Black birth outcomes outside of the large cities. A number of additional outcome measures are available at the state level: foster care rates, new prison commitments, unemployment rates, and poverty rates. None of these outcomes reveals a pattern suggestive of an important impact of crack in the expected direction. The negative relationship between crack and new prison commitments, while perhaps surprising given the enormous increase in drug-related incarceration over this period, hints at the possibility that aggressive punishment of drug sellers may have reduced the severity of the crack epidemic. In other words, the negative coefficient in the prison regression may primarily reflect reverse causality running from imprisonment to crack, rather than vice versa.

Figures 10 and 11 report the fraction of the observed variation in the outcomes at the state level that can be explained by the crack index for the periods 1984-1989, and 1989-2000. Not surprisingly given that crack was concentrated in large cities, the rise and fall of crack has less explanatory value at the state level. For instance, the crack index explains less than one-third of the overall rise in young Black male homicide at the state level, and only one-fifth of the increase in Black low birth weight babies. Overall, comparing the results in Figures 8-11, and noting that about 16 percent of the U.S. population resides in cities included in our city sample, we estimate that about 70 percent of the adverse impact of crack was felt in large cities, implying that the rates per capita were at least 10 times higher in large cities than in the rest of the country.

Section VI: Conclusion

A number of social, criminological, and economic variables experienced negative shocks in the late 1980s and early 1990s, particularly among Blacks. We find evidence consistent with the hypothesis that the rise of crack cocaine played an important contributing role. To overcome the absence of a reliable quantitative measure of crack, we construct a crack index based on a set of imperfect, but plausible proxies. This crack index reproduces many of patterns described in journalistic and ethnographic accounts including the timing of the crack epidemic and the disproportionate impact on Blacks and Hispanics. We find a strong link between our measure of crack and increased homicide rates by the young, especially among Blacks, in the late 1980s. During that time period, our crack index is also associated with adverse outcomes for babies – especially Black babies. By the early 1990s, however, the relationship between crack and unwelcome social outcomes had largely disappeared. Thus, though crack use persisted at high levels, it did so with relatively minor measurable social consequences. This finding is consistent with an initially high level of crack-related violence as markets responded to the changes in distribution methods associated with the technological shock that crack represented. After property rights were established and crack prices fell sharply reducing the profitability of the business, competition-related violence among drug dealers declined.

One explanation for the weakening relationship between the crack index and birth outcomes is the changing composition of crack users. Following its introduction, crack use was overwhelmingly a drug of adolescents and young adults, and its use was widespread. For instance, 7.2 percent of respondents in the National Household Survey on Drug Use and Health (SAMHSA 2003b) who were 18-22 in 1985 report lifetime crack usage, compared to only 2.8 percent of those who were 33-37 years old in 1985. Later cohorts also use crack at much lower rates than the first cohorts exposed to crack. In 2002, 0.6 percent of the age group that was 18- 22 in 1985 (and by 2002 was 35-39 years old) had used crack in the last month. In stark contrast, less than 0.2 percent of the 18-22 year olds in 2002 report using crack in the prior month. As crack addicts aged, fewer were in the high fertility age group. Presumably, as the dangers of crack to the fetus became clear, women intending to get pregnant may have avoided crack and those using crack may have been less likely to take a pregnancy to term.

If the rise of crack indeed exerted an important influence on social outcomes over the last two decades, then an obvious concern is that studies examining these outcomes which fail to adequately control for crack may generate misleading conclusions. Ayres and Donohue (2003), for instance, conjecture that the findings of Lott and Mustard (1997) and Lott (2000) regarding the impact of concealed weapons laws are spurious, driven by the omitted variable crack cocaine. Sailer (1999) and Joyce (2004) level the same charges at Donohue and Levitt’s (2001) analysis of the impact of legalized abortion on crime. An important application of the crack index we construct is as a control variable in future research.

Appendix

Data


This paper uses data from the 144 cities with population above 100000 in 1980 and the 50 U.S. States. The following table describes the data used in paper.

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* Cocaine deaths include accidental poisonings, suicides, and other deaths for which cocaine use was coded as a primary or contributing factor. Prior to 1989, deaths were classified using the International Classification of Diseases 9th revision (ICD-9); starting in 1999 deaths were coded using the 10th revision. Cocaine death entries under ICD-9 include ICD codes 8552, 3042, and 3056 as well as ICD codes 8501-8699, 9501-9529, 9620-9629, 972, 9801-9879, 3050-3054, 3057-3059 with a secondary code of 9685. Cocaine deaths under ICD-10 include codes F140-F149 and F190-F199, X42, X44, X62, X64, X85, Y12, and Y14 with secondary code T405. For more information on counting drug-related deaths see Lois Fingerhut & Christine Cox, "Poisoning mortality, 1985-1995", Public Health Reports 113:3, 1998.

** Cocaine busts were defined as busts with primary drug code 9041 and secondary drug codes L000, L005, L010, L269, L900, and L920

Estimating the Crack Index

The procedure for estimating the crack index is:

1. Remove city or state fixed effects from each of the crack proxies. Readjust each proxy to have a grand mean of 0 during the period from 1980-1984.

2. Normalize each of the proxies to have unit variance. This eliminates differences in units of measure across proxies.

3. The factor loadings ( i) and scores (Zst) satisfy the relationship:

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Here i indexes a proxies, s a location, and t a time period. We require a scale restriction to separately identify the loadings and scores. In our analysis, we impose that the sum of the squared loadings is one.

4. Select an initial value for the loadings.

5. Stack each of the available proxies at a particular location/time. Use least squares regression across the proxies to estimate the value of the crack index (score) at each location/time. These regressions are within a location/time, across measures.

6. The squared loadings measure the extent to which each of the proxies contributes to the overall crack index. To account for missing data, at each location/time, multiply the scores calculated in step 5 by Image, where Yi represents the loading associated with proxy i and the summation is made over all available proxies at that location/time.

7. Regress each proxy on the scores calculated in step 5 to generate a new estimate of the loading associated with that proxy. These regressions are within a particular measure, across locations/times.

8. Re-normalize the estimated loadings to satisfy the scale restriction.

9. Repeat steps 5-8 until the loadings converge.30

10. Test multiple initial choices for loadings to ensure that the convergent result is optimal in the sense of minimizing the sum of the squared residuals in (1).

It is important to note that constructed in this manner the absolute mean and variance of the crack index are arbitrary.
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Part 2 of 2

Appendix

Table 1: Estimated Crack Indices for Cities and States


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Notes: The unit of observation is a city-year in the top portion of the table and a state-year in the bottom portion. See the data appendix for precise sources and definitions of the variables. For most variables the sample period covered is 1980-2000; for some measures a shorter time series is available. The city sample is restricted to the 144 cities with population greater than 100,000 in 1980. Rates are measured relative to the total city or state population.

Table 2: Correlations in Crack Proxies

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Notes: The national correlations are the correlations across time of national aggregate annual values of the five crack proxies. The city correlations are correlations across location and time for the 144 U.S. cities with population above 100000 in 1980. The state correlations are correlations across location and time for the 50 U.S. states. The data spans the years 1980-2000.

Table 3: Estimates of Factor Loadings

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Notes: The table reports results of factor analysis to extract the first principle component of the five crack proxies. Each of the proxies was standardized to have unit variance over the period 1980-2000 and a grand mean of zero from 1980-1984 prior to estimation of the loadings. The loadings are restricted to have a squared sum of 1. The "% of Variation" row reports the percentage of the total variance in the transformed variables that can be explained by the first principle component. The city sample includes U.S. cities with a population above 100000 in 1980. The city and state estimates remove city or state fixed effects prior to factor analysis. Columns 3 and 5 also remove year fixed effects from each of the measures prior to factor analysis.

Table 4: Areas With Highest and Lowest Average Crack Levels

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Table 5: Estimated Effects of Crack on Outcome Measures, City Sample

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Notes: The “Coefficient” columns report estimated coefficients from OLS regressions of the outcome measures on the crack index interacted with indicator variables for the years 1985-1989, 1990-1994, and 1995-2000. The regressions include controls for percent of the population Hispanic, percent of the population Black, log population, and log per capita income as well as city and year fixed effects. Standard errors are in parenthesis. * denotes significance at the 5% level; ** denotes significance at the 1% level. The OLS estimates were corrected to allow AR(1) serial correlation in the error terms. The coefficient on crack in the 1980-84 period is imprecisely estimated due to the small amount of variation in crack during this period and is omitted from the table.

Table 6: Estimated Effects of Crack on Outcome Measures, State Sample

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Notes: The “Coefficient” columns report estimated coefficients from OLS regressions of the outcome measures on the crack index interacted with indicator variables for the years 1985-1989, 1990-1994, and 1995-2000. The regressions include controls for percent of the population Hispanic, percent of the population Black, log population, and log per capita income as well as state and year fixed effects. Standard errors are in parenthesis. * denotes significance at the 5% level; ** denotes significance at the 1% level. The OLS estimates were corrected to allow AR(1) serial correlation in the error terms. The coefficient on crack in the 1980-84 period is imprecisely estimated due to the small amount of variation in crack during this period and is omitted from the table.

Figure 1: Black Male Homicide Rates by Age

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The figure depicts population-weighted national averages derived from state data.

Figure 2: Outcome Measures

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The figures depict population-weighted annual national averages derived from state-level data.

Figure 3: Crack Proxies

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The figures depict population-weighted annual national averages derived from city-level data.

Figure 4: Crack Indices

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The reported indices are population weighted national estimates of the crack index.

Figure 5: Crack Indices by Race

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The race-specific indices were estimated using year-by-year cross sectional regressions of the city-level crack index on the population percentages of Blacks, Whites, and Hispanics.

Figure 6: Crack Indices by Region

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The regional indices were estimated using year-by-year cross sectional regressions of the city-level crack index on indicator variables for the specified regions.

Figure 7: Crack Indices by City Size

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The indices were estimated using year-by-year cross sectional regressions of the crack index on indicator variables for the specified city size.

Figure 8: Actual Changes in Social Outcomes and Predicted Changes Due to Crack 1984- 1989, City-Level Sample

Crime Categories by Race

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Birth Outcomes by Race

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Index Crime Categories

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† indicates that data are available from 1980 to 1998 only. The leftmost bar (“Actual Change”) plots the actual percent change in the outcome of interest between 1984 and 1989 with 1984 as the base year. To obtain the “OLS Predicted Change Due to Crack” (middle bar), let ß85-89 denote the estimated effect of crack during the 1985-1989 period, Image denote the mean crack index in 1989, and Image denote the mean value of the outcome of interest in 1984. The predicted change is constructed as Image The IV predicted changes are constructed similarly, except that the coefficients are based on an instrumental variables strategy in which cocaine arrests are used as the right-hand-side variable in equation (3) and a crack index constructed using the other crack proxies is used as an instrumental variable. In calculating the implied impact of crack using IV, only the portion of the observed variation in cocaine arrests attributable to variation in crack is relevant. It can be shown that algebraically this equates to scaling down the full variation in cocaine arrests by the signal to signal- plus-noise ratio in cocaine arrests. This signal to signal-plus-noise ratio can be computed from the variance-covariance matrix of the crack proxies, combined with a scaling adjustment across proxies that can be inferred from the ratio of the IV estimates when one proxy is used as the instrument and the other variable is instrumented versus when the roles of the two variables are reversed. OLS and IV regressions include controls for percent of the population Hispanic, percent of the population Black, log population, and log per capita income as well as city and year fixed effects. The whiskers display 95% confidence intervals for the OLS and IV estimated effects. To facilitate comparison of effect sizes across social outcomes, in some cases confidence intervals were permitted to extend beyond the bounds of the plots. The OLS estimates were estimated allowing AR(1) serial correlation in the error terms; IV standard errors were obtained via the bootstrap.

Figure 9: Actual Changes in Social Outcomes and Predicted Changes Due to Crack 1989- 2000, City-Level Sample

Crime Categories by Race

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Birth Outcomes by Race

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Index Crime Categories

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† indicates that data are available from 1980 to 1998 only. The leftmost bar (“Actual Change”) plots the actual percent change in the outcome of interest between 1989 and 2000 with 1989 as the base year. To obtain the “OLS Predicted Change Due to Crack” (middle bar), let ß85-89 denote the estimated effect of crack during the 1985-1989 period, Image denote the mean crack index in 1989, and Image denote the mean value of the outcome of interest in 1989. The predicted change is constructed asImage For outcomes that were not available through 2000 the change from 1989 through the final year of available data is reported in the final column. The IV predicted changes are constructed similarly, except that the coefficients are based on an instrumental variables strategy in which cocaine arrests are used as the right-hand-side variable in equation (3) and a crack index constructed using the other crack proxies is used as an instrumental variable. In calculating the implied impact of crack using IV, only the portion of the observed variation in cocaine arrests attributable to variation in crack is relevant. It can be shown that algebraically this equates to scaling down the full variation in cocaine arrests by the signal to signal- plus-noise ratio in cocaine arrests. This signal to signal-plus-noise ratio can be computed from the variance-covariance matrix of the crack proxies, combined with a scaling adjustment across proxies that can be inferred from the ratio of the IV estimates when one proxy is used as the instrument and the other variable is instrumented versus when the roles of the two variables are reversed. OLS and IV regressions include controls for percent of the population Hispanic, percent of the population Black, log population, and log per capita income as well as city and year fixed effects. The whiskers display 95% confidence intervals for the OLS and IV estimated effects. To facilitate comparison of effect sizes across social outcomes, in some cases confidence intervals were permitted to extend beyond the bounds of the plots. The OLS estimates were estimated allowing AR(1) serial correlation in the error terms; IV standard errors were obtained via the bootstrap.

Figure 10: Actual Changes in Social Outcomes and Predicted Changes Due to Crack 1984- 1989, State-Level Sample

Crime Categories by Race

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Birth Outcomes by Race

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Index Crime Categories

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Other Social Outcomes

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† indicates that data are available from 1980 to 1998 only and †† indicates that data are available from 1983 through 1995 only. The leftmost bar (“Actual Change”) plots the actual percent change in the outcome of interest between 1984 and 1989 with 1984 as the base year. To obtain the “OLS Predicted Change Due to Crack” (middle bar), let ß85-89 denote the estimated effect of crack during the 1985-1989 period, Image denote the mean crack index in 1989, and Image denote the mean value of the outcome of interest in 1984. The predicted change is constructed as ImageThe IV predicted changes are constructed similarly, except that the coefficients are based on an instrumental variables strategy in which cocaine arrests are used as the right-hand-side variable in equation (3) and a crack index constructed using the other crack proxies is used as an instrumental variable. In calculating the implied impact of crack using IV, only the portion of the observed variation in cocaine arrests attributable to variation in crack is relevant. It can be shown that algebraically this equates to scaling down the full variation in cocaine arrests by the signal to signal- plus-noise ratio in cocaine arrests. This signal to signal-plus-noise ratio can be computed from the variance-covariance matrix of the crack proxies, combined with a scaling adjustment across proxies that can be inferred from the ratio of the IV estimates when one proxy is used as the instrument and the other variable is instrumented versus when the roles of the two variables are reversed. OLS and IV regressions include controls for percent of the population Hispanic, percent of the population Black, log population, and log per capita income as well as state and year fixed effects. The whiskers display 95% confidence intervals for the OLS and IV estimated effects. To facilitate comparison of effect sizes across social outcomes, in some cases confidence intervals were permitted to extend beyond the bounds of the plots. The OLS estimates were estimated allowing AR(1) serial correlation in the error terms; IV standard errors were obtained via the bootstrap.

Figure 11: Actual Changes in Social Outcomes and Predicted Changes Due to Crack 1989- 2000, State-Level Sample

Crime Categories by Race

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Birth Outcomes by Race

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Index Crime Categories

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Other Social Outcomes

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† indicates that data are available from 1980 to 1998 only and †† indicates that data are available from 1983 through 1995 only. The leftmost bar (“Actual Change”) plots the actual percent change in the outcome of interest between 1984 and 1989 with 1984 as the base year. . To obtain the “OLS Predicted Change Due to Crack” (middle bar), let ß85-89 denote the estimated effect of crack during the 1985-1989 period, Image denote the mean crack index in 1989, and Image denote the mean value of the outcome of interest in 1989. The predicted change is constructed as Image The IV predicted changes are constructed similarly, except that the coefficients are based on an instrumental variables strategy in which cocaine arrests are used as the right-hand-side variable in equation (3) and a crack index constructed using the other crack proxies is used as an instrumental variable. In calculating the implied impact of crack using IV, only the portion of the observed variation in cocaine arrests attributable to variation in crack is relevant. It can be shown that algebraically this equates to scaling down the full variation in cocaine arrests by the signal to signal- plus-noise ratio in cocaine arrests. This signal to signal-plus-noise ratio can be computed from the variance-covariance matrix of the crack proxies, combined with a scaling adjustment across proxies that can be inferred from the ratio of the IV estimates when one proxy is used as the instrument and the other variable is instrumented versus when the roles of the two variables are reversed. OLS and IV regressions include controls for percent of the population Hispanic, percent of the population Black, log population, and log per capita income as well as state and year fixed effects. The whiskers display 95% confidence intervals for the OLS and IV estimated effects. To facilitate comparison of effect sizes across social outcomes, in some cases confidence intervals were permitted to extend beyond the bounds of the plots. The OLS estimates were estimated allowing AR(1) serial correlation in the error terms; IV standard errors were obtained via the bootstrap.

References

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Baumer, Eric P., Janet Lauritsen, Richard Rosenfeld, and Richard Wright. 1998. "The Influence of Crack Cocaine on Robbery, Burglary, and Homicide Rates: A Cross- City, Longitudinal Analysis." Journal of Research in Crime and Delinquency 33:316-340.

Bayer, Linda. 2000. Crack and Cocaine. New York: Chelsea House Publishers.

Beckett, Katherine, Kris Nyrop, and Lori Pfingst, 2006, “Race, Drugs, and Policing: Understanding Disparities in Drug Delivery Arrests.” Criminology 44:105-137.

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Venkatesh, Sudhir, and Steven Levitt, 2000, “Are We a Family or a Business?' History and Disjuncture in the Urban American Street Gang,” Theory and Society, 29 (Volume 3): 427- 462.

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“Crack Kids” Revisited.” The Journal of the American Medical Association, 287 (15), 1990- 1991.

_______________

Notes:

1 Homicide rates for young white males (with Hispanics included as whites) followed a similar pattern, although the fluctuations were far more muted. Homicide rates for white males over age 25 have steadily fallen since 1985.

2 We describe the data sources, definitions, sample availability, and precise construction of these variables and others used in the paper in the data appendix.

3 Neal (2004) provides further evidence of a downturn in black educational outcomes.

4 Over the past thirty years, for instance, the black-white ratio of median earnings for male full-time workers increased from .5 to .73 (Welch 2003), the black infant mortality rate fell by two-thirds (Almond, Chay, and Greenstone 2003), the fraction of blacks between the ages of 25-29 with four-year college degrees has increased nearly 3 fold (Blank 2001), and Black academic achievement (as measured by NAEP scores) has increased 0.6 standard deviations relative to whites (Grissmer, Flanagan, and Williamson 1998). The number of black entrepreneurs has more than doubled (Boston 2001). The number of blacks in Congress has increased five-fold.4 Similar advances have been made among high-level executives, professors and administrators at elite colleges and universities, and the fraction of blacks living in middle class neighborhoods.

5 Other competing explanations have also been proposed. Ferguson (2001) argues that the rise in popularity of hiphop music is to blame for the divergence in black-white test score gaps in 1988. McWhorter (2003) makes a similar argument.

6 Data can be found on Roland Fryer’s web page [http://post.economics.harvard.edu/faculty/fryer/fryer.html] , or Steven Levitt’s web page [http://pricetheory.uchicago.edu/levitt/home.html].

7 Victimization data are used instead of offender data because the identity of some offenders is unknown. Among homicides with a known offender, there is a high correlation between the race and age of the victim and offender.

8 All of these conclusions regarding the relationship between crack and social outcomes must be qualified with the important caveat that we are describing correlations in the data, rather than clear causal links.

9 A gram weighs about as much as a dime.

10 Crack differs from freebase cocaine because the creation procedure lacks the final step of removing the base from the mixture.

11 Note, however, that the consensus in the recent medical literature is that there are few long term effects of crack exposure in utero after controlling for the mother’s alcohol and tobacco consumption. (Frank et. al. 2001, Zuckerman, Frank, and Mayes 2002).

12 The exact data sources, definitions, and construction of each of the variables are described in greater detail in the appendix.

13 In an earlier version of this paper, we presented estimates in which we did attempt to differentiate between powder and crack cocaine, obtaining similar results.

14 Arrests for powder versus crack cocaine are not reported separately in the FBI data.

15 The data set includes separate drug codes for cocaine, cocaine hydrochloride, and a variety of cocaine salts. There does appear to be a pattern of classifying crack cocaine, which has the hydrocholoride portion of the molecule removed, as cocaine. It seems, however, that powder cocaine is also sometimes classified under the code “cocaine.”

16 For instance, a newspaper story that reported a cocaine addict, while in the process of committing a crime, got cracked over the head by a police baton, would erroneously register as a crack mention using our methodology.

17 This is similar to how psychometricians measure “intelligence” (g) – taking the results of several individual aptitude tests and determining the single factor that best explains the covariance in these related proxies. Mathematically, factor analysis identifies the eigenvectors (the scores) and corresponding eigenvalues (the loadings) of the variance-covariance matrix of the Y variables.

18 We follow standard practice of normalizing proxy measures included on the left-hand side to have mean zero and variance one whenever we do factor analysis.

19 If measurement error is positively correlated across proxies, then the improvement in the signal-to-noise ratio is less pronounced. The opposite is true if the measurement error is negatively correlated across proxies.

20 Note, however, that this instrumental variables strategy is a possible solution to the measurement error problem, but not to any possible endogeneity in the crack measure, as we discuss later.

21 The existence of a pre-crack period also allows us the opportunity to estimate our factor analysis on the pre-period data only, isolating any common factors that are moving the crack proxies before crack arrives. One can then remove these pre-existing factors in constructing a crack index. In practice, this has little impact on our results. An earlier version of this paper, available from the authors, describes this exercise and the underlying assumptions in detail.

22 The scaling of the loadings is arbitrary; we follow the standard normalization which is to make the square of the loadings sum to one. In constructing a crack index that is a weighted average of the underlying proxies, the weights one would use are the square root of the loadings we report in the table. After taking the square root of these values, the sum of the weights would add to one.

23The patterns observed in Figures 5-7 continue to hold if we simultaneously control for racial composition and the other factors like region and city size.

24 Results for the full set of cities and states in the sample, for the years 1985, 1989, 1993, 1997, and 2000 are reported in Appendix Table 1.

25 Recent work by Beckett et. al. (2006) in fact argues that Seattle has an acute crack problem relative to other cities.

26 Imposing the further assumption that the ratio of crack’s incidence across Whites and Blacks is constant across areas in a given year, we have also constructed an index of the crack intensity in a city controlling for racial composition. This measure of intensity more closely corresponds to the crack variable that one would use as a right-hand-side variable when using individual-level data, and is available for download at our website. The results we obtain are quite similar.

27 The crack index is close to zero and exhibits little variation prior to 1985, leading to unstable, imprecisely estimated coefficients in the early part of our sample. Thus, we do not report results for 1980-1984.

28 One could also instrument with each of the remaining crack proxies individually, but because of missing data in some of our proxies, the index approach is preferable in our setting. The results are not sensitive to using a different one of the crack proxies as the right-hand side variable. 29 More specifically, only a portion of the observed variation in cocaine arrests is attributable to variation in crack usage if there is measurement error in cocaine arrests. Assume that cocaine arrests are determined by a latent variable crack and noise, and that our estimated crack index using all of the proxies except cocaine arrests also reflects a combination of movements in crack and noise. Further assume that the error terms in these two measures are uncorrelated, and that these errors are also uncorrelated with the error term in equation (3). Then a consistent estimate of the impact of cocaine arrests can be obtained from two-stage least squares using the index as an instrument. In the standard application, one would then compute the overall impact of a change in cocaine arrests as the product of the change in cocaine arrests multiplied by the estimated coefficient from two-stage least squares. In computing the impact of crack (as opposed to cocaine arrests per se) in equation (3), however, we only wants to use the portion of the variation in cocaine arrests driven by crack. It can be shown that algebraically this equates to scaling down the full variation in cocaine arrests by the signal to signal- plus-noise ratio in cocaine arrests. This signal to signal-plus-noise ratio can be computed from the variance-covariance matrix of the crack proxies, combined with a scaling adjustment across proxies that can be inferred from the ratio of the IV estimates when one proxy is used as the instrument and the other variable is instrumented versus when the roles of the two variables are reversed.

30 Although the paper reports estimates of a single factor model, for verification purposes we also estimated models allowing for multiple factors (i.e. i and Zst are vectors). With multiple factors the estimated loadings and scores are unique only up to a rotation. The algorithm described will not converge unless a specific rotation is imposed at each stage of estimation.
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23 cents an hour: The perfectly legal slavery happening in modern-day America
In skyrocketing prison populations, corporations and the government have a pool of powerless, exploitable workers

by Terrell Jermaine Starr
Alternet
July 7, 2015

NOTICE: THIS WORK MAY BE PROTECTED BY COPYRIGHT

YOU ARE REQUIRED TO READ THE COPYRIGHT NOTICE AT THIS LINK BEFORE YOU READ THE FOLLOWING WORK, THAT IS AVAILABLE SOLELY FOR PRIVATE STUDY, SCHOLARSHIP OR RESEARCH PURSUANT TO 17 U.S.C. SECTION 107 AND 108. IN THE EVENT THAT THE LIBRARY DETERMINES THAT UNLAWFUL COPYING OF THIS WORK HAS OCCURRED, THE LIBRARY HAS THE RIGHT TO BLOCK THE I.P. ADDRESS AT WHICH THE UNLAWFUL COPYING APPEARED TO HAVE OCCURRED. THANK YOU FOR RESPECTING THE RIGHTS OF COPYRIGHT OWNERS.




If you thought slavery was outlawed in America, you would be wrong. The 13th amendment to the Constitution states that “neither slavery nor involuntary servitude, except as a punishment for crime whereof the party shall have been duly convicted, shall exist within the United States, or any place subject to their jurisdiction.”

In plain language, that means slavery in America can still exist for those who are in prison, where you basically lose all of your rights. (You don’t gain a lot of your rights back when you get out of prison, either, but that is a different story.) So, given the country’s penchant for rapacious capitalism, it may not come as a surprise that there is much of the American prison system that exploits American prisoners much like slaves.

In fact there is large-scale exploitation in American prisons benefiting American corporations and the military-industrial complex. UNICOR, better known as Federal Prison Industries, or FPI, is a government-owned corporation that employs inmates for as little as 23 cents per hour, to provide a wide range of products and services under the guise of a “jobs training program.” In theory, this is supposed to give inmates skills that will prepare them for the workforce upon release.

Critics of FPI have long claimed it exploits prisoners who don’t have the right to organize for representation to protect their rights and it unfairly competes with small businesses that can’t provide goods and services for the average pay of 92 cents an hour FPI workers make. The program employs around 13,000 prisoners per year. In 2013, it reported gross revenue of $609.7 million.

According to FPI’s website, inmates employed in the program carry out a wide range of services that include making house and office furniture, mattresses, flags, traffic signs and military items. These items are usually made for other federal agencies, but private companies can contract workers through FPI as well.

It is no surprise that the inmate/slave labor force has grown along with mass incarceration in America. The Prison Policy Initiative counts 2.3 million people in prison, according to the 2010 census, by far the highest rate of incarceration in the developed world.

Many more are ensnared in the criminal justice system’s other branches. At the end of 2013, nearly 5 million adults were either on probation or parole, according to Bureau of Justice Statistics. All of these populations and even those not even convicted of a crime are vulnerable to exploitative fees and byzantine rules seemingly designed to catch people and get them back into the grips of the prison system. Basically, there is a trifecta of exploitation in the American criminal justice system. As reported on AlterNet, the bail system in America keeps many people in jail in a massive form of pretrial detention one has to buy one’s way out of. And police departments are increasingly funding themselves by charging poor people exorbitant fees for minor infractions.

In terms of prison labor, one of its controversial services is the production of solar panels. Reuters reports that Suniva Inc, a Georgia-based solar cell and panel maker, uses prison labor for 10 percent of its manufacturing needs to keep its costs low so it can, in part, keep up with producers from China. The company is also backed by Goldman Sachs Group Inc.

Over the last 18 months, Suniva moved all of its solar panel assembly to the United States from Asia. Suniva’s deal with FPI helps it to avoid U.S. government tariffs on Chinese-made panels and capture lucrative government contracts. Roughly 200 inmates make solar panels in factories at prisons in Sheridan, Oregon and Otisville, New York, according to Reuters.

Solar panels made in America are more efficient in generating electricity from the sun, allowing companies like Suniva to sell them at premium rates. As for the inmates, Suniva’s vice president of global sales and manufacturing Mike Card says he doesn’t know how much they are paid.

Manufacturing solar panels is actually a good skill to have, but according to Alex Friedman, managing editor of Prison Legal News, FPI has no job placement program for inmates once they are released.

“You can have lots of skills, but it doesn’t necessarily mean you’re going to get a good job when you get out,” Friedman told AlterNet. “You can be really skilled at whatever it is, diesel engines even, but you also have a felony record. You’re getting out from prison after five or two years or whatever it is and starting from scratch.”

Another field where FPI inmates are providing labor is through military contracts. In 2013, federal inmates stitched more than $100 million worth of military uniforms for the Department of Defense, according to the New York Times. The federal inmates who make these garments earn no more than $2 per hour, something that puts competing small businesses that have to pay at least minimum wage at a major disadvantage.

Cathy Griffiths, operations manager for clothing maker American Power Source of Fayette, Ala., complained in 2012 that she had to let 50 of her 300 employees go after FPI won a lucrative contract with the U.S. Army. During the same year, American Apparel, Inc., an Alabama company that makes military uniforms, said it had to close down a plant and lay off 175 workers because it was forced to compete with FPI for federal contracts.

“We pay employees $9 on average,” Kurt Wilson, an executive with American Apparel, told Prison Legal News at the time. “They get full medical insurance, 401(k) plans and paid vacation. Yet we’re competing against a federal program that doesn’t pay any of that.”

With prisoners lacking even a modicum of labor protection, it is very hard for any company to compete with companies that use this source of dirt-cheap prison labor.

“Prisoners currently don’t fall under any fair labor standard practices or umbrellas,” Christopher Petrella, a researcher at UC Berkeley who studies labor abuses in prisons, told AlterNet. “So, often times, prisoners will get paid but they aren’t afforded the same protections as a worker outside of prison.

No one is complaining about prisoners having the right to work and learn skills that will help them once they are released from prison. But that is not the issue. In reality, FPI is paying far below minimum wage rates. Prison labor takes advantage of a vulnerable workforce that can’t advocate for itself, form a union, fight for its labor rights or seek legal protections for potential workplace abuses.

Prison workers have no political support, either. “There is virtually no constituency that really cares,” Petrella said. “That’s a very sobering and tragic thing to say, but I think it’s actually true. Prisoners are often times disenfranchised. They can’t even vote. So, if they can’t even vote, then what kind of constituency exists that politicians can then lean on to make these sorts of decisions about how they want to move forward with reforming the system?”

The number of inmates working under FPI make up just a small number of the 2.2 million prisoners behind bars in a wide range of state and federal work programs, so it’s just a small part of the larger issue of exploited labor. But it is important to note that the federal government finances and operates FPI, a corporation that outsources labor in exploitive ways.

Friedman says FPI and other work programs in general must undergo major reforms that include giving inmate workers the right to protect themselves from exploitation, paying them as much as a worker who is not locked up would make, and training them for jobs that will actually lead to employment once they leave prison. Without such changes, Friedman says prison labor is nothing more than slave labor.
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Re: CNN Tonight with Don Lemon on Bill Clinton & BLM

Postby admin » Sun Apr 24, 2016 11:51 pm

Here's the Latest Evidence of How Private Prisons Are Exploiting Inmates for Profit
by Gabrielle Canon
Jun. 17, 2015

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The for-profit prison industry sells itself as a cost-effective option for cash-strapped states, but according to a new study from the University of Wisconsin, privatized prisons are keeping inmates locked up longer in order to boost profits.

Researcher Anita Mukherjee studied eight years* of data from Mississippi, which has one of the highest incarceration rates in the country, and found that private prisons there doled out twice the amount of infractions against inmates, lengthening their sentences by an average of two or three months. The extra time, Mukherjee found, adds up to an increase of about $3,000 in additional costs per prisoner. Mukherjee also noted that inmates housed in private prisons were more likely to wind up back in the system after being released—despite industry claims of lower recidivism rates.

The study, which compares length of stays in private and public prisons, is not the first to highlight strategies undertaken by the private prison industry to raise returns for stockholders. Last year, Christopher Petrella, a researcher at the University of California-Berkeley, accused the Corrections Corporation of America of including provisions in its contracts with governments to keep the most costly inmates—those with health issues—from being transferred to its prisons. Through open records requests, Petrella found there were 14 different exclusion criteria, including disabled or elderly inmates, those who were HIV-positive, or anyone with "sensitive medical conditions and/or high risk diagnoses."

Today, the $5 billion private prison industry houses close to 20 percent of federal inmates.


Looking specifically at California prisons, Petrella highlights how health expenditures are among the largest costs, second only to security, and account for 31 percent of the overall budget. But, private prisons set up contracts that say they only will house the youngest, healthiest—and cheapest—prisoners.

These details, Petrella writes, failed to make their way into a Temple University cost analysis often cited by the industry as proof that privatization produces cost savings. The economists behind the study were funded by three of the largest prison companies in the United States—a fact they failed to disclose when the study was first published.

Other studies have had similar findings: An analysis by the Arizona Department of Corrections in 2011 found that most prisoners were no less costly when housed in private prisons, and that some cost up to $1,600 more each year than those in public prisons. A cost analysis by the University of Utah in 2007 showed cost savings from private prisons were minimal at best.

Between 2000 and 2010 the number of inmates serving sentences in private prisons doubled. Today, the $5 billion industry houses close to 20 percent of federal prisoners and about 7 percent of state prisoners, and private prisons are increasingly being used as immigration detention centers.

*The time period of the data has been corrected.
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