research paper over effects of alcohol

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• April 01, 2024 | VOL. 181, NO. 4 CURRENT ISSUE pp.255-346
• March 01, 2024 | VOL. 181, NO. 3 pp.171-254
• February 01, 2024 | VOL. 181, NO. 2 pp.83-170
• January 01, 2024 | VOL. 181, NO. 1 pp.1-82

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Substance Use Disorders and Addiction: Mechanisms, Trends, and Treatment Implications

• Ned H. Kalin , M.D.

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The numbers for substance use disorders are large, and we need to pay attention to them. Data from the 2018 National Survey on Drug Use and Health ( 1 ) suggest that, over the preceding year, 20.3 million people age 12 or older had substance use disorders, and 14.8 million of these cases were attributed to alcohol. When considering other substances, the report estimated that 4.4 million individuals had a marijuana use disorder and that 2 million people suffered from an opiate use disorder. It is well known that stress is associated with an increase in the use of alcohol and other substances, and this is particularly relevant today in relation to the chronic uncertainty and distress associated with the COVID-19 pandemic along with the traumatic effects of racism and social injustice. In part related to stress, substance use disorders are highly comorbid with other psychiatric illnesses: 9.2 million adults were estimated to have a 1-year prevalence of both a mental illness and at least one substance use disorder. Although they may not necessarily meet criteria for a substance use disorder, it is well known that psychiatric patients have increased usage of alcohol, cigarettes, and other illicit substances. As an example, the survey estimated that over the preceding month, 37.2% of individuals with serious mental illnesses were cigarette smokers, compared with 16.3% of individuals without mental illnesses. Substance use frequently accompanies suicide and suicide attempts, and substance use disorders are associated with a long-term increased risk of suicide.

Addiction is the key process that underlies substance use disorders, and research using animal models and humans has revealed important insights into the neural circuits and molecules that mediate addiction. More specifically, research has shed light onto mechanisms underlying the critical components of addiction and relapse: reinforcement and reward, tolerance, withdrawal, negative affect, craving, and stress sensitization. In addition, clinical research has been instrumental in developing an evidence base for the use of pharmacological agents in the treatment of substance use disorders, which, in combination with psychosocial approaches, can provide effective treatments. However, despite the existence of therapeutic tools, relapse is common, and substance use disorders remain grossly undertreated. For example, whether at an inpatient hospital treatment facility or at a drug or alcohol rehabilitation program, it was estimated that only 11% of individuals needing treatment for substance use received appropriate care in 2018. Additionally, it is worth emphasizing that current practice frequently does not effectively integrate dual diagnosis treatment approaches, which is important because psychiatric and substance use disorders are highly comorbid. The barriers to receiving treatment are numerous and directly interact with existing health care inequities. It is imperative that as a field we overcome the obstacles to treatment, including the lack of resources at the individual level, a dearth of trained providers and appropriate treatment facilities, racial biases, and the marked stigmatization that is focused on individuals with addictions.

This issue of the Journal is focused on understanding factors contributing to substance use disorders and their comorbidity with psychiatric disorders, the effects of prenatal alcohol use on preadolescents, and brain mechanisms that are associated with addiction and relapse. An important theme that emerges from this issue is the necessity for understanding maladaptive substance use and its treatment in relation to health care inequities. This highlights the imperative to focus resources and treatment efforts on underprivileged and marginalized populations. The centerpiece of this issue is an overview on addiction written by Dr. George Koob, the director of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), and coauthors Drs. Patricia Powell (NIAAA deputy director) and Aaron White ( 2 ). This outstanding article will serve as a foundational knowledge base for those interested in understanding the complex factors that mediate drug addiction. Of particular interest to the practice of psychiatry is the emphasis on the negative affect state “hyperkatifeia” as a major driver of addictive behavior and relapse. This places the dysphoria and psychological distress that are associated with prolonged withdrawal at the heart of treatment and underscores the importance of treating not only maladaptive drug-related behaviors but also the prolonged dysphoria and negative affect associated with addiction. It also speaks to why it is crucial to concurrently treat psychiatric comorbidities that commonly accompany substance use disorders.

Insights Into Mechanisms Related to Cocaine Addiction Using a Novel Imaging Method for Dopamine Neurons

Cassidy et al. ( 3 ) introduce a relatively new imaging technique that allows for an estimation of dopamine integrity and function in the substantia nigra, the site of origin of dopamine neurons that project to the striatum. Capitalizing on the high levels of neuromelanin that are found in substantia nigra dopamine neurons and the interaction between neuromelanin and intracellular iron, this MRI technique, termed neuromelanin-sensitive MRI (NM-MRI), shows promise in studying the involvement of substantia nigra dopamine neurons in neurodegenerative diseases and psychiatric illnesses. The authors used this technique to assess dopamine function in active cocaine users with the aim of exploring the hypothesis that cocaine use disorder is associated with blunted presynaptic striatal dopamine function that would be reflected in decreased “integrity” of the substantia nigra dopamine system. Surprisingly, NM-MRI revealed evidence for increased dopamine in the substantia nigra of individuals using cocaine. The authors suggest that this finding, in conjunction with prior work suggesting a blunted dopamine response, points to the possibility that cocaine use is associated with an altered intracellular distribution of dopamine. Specifically, the idea is that dopamine is shifted from being concentrated in releasable, functional vesicles at the synapse to a nonreleasable cytosolic pool. In addition to providing an intriguing alternative hypothesis underlying the cocaine-related alterations observed in substantia nigra dopamine function, this article highlights an innovative imaging method that can be used in further investigations involving the role of substantia nigra dopamine systems in neuropsychiatric disorders. Dr. Charles Bradberry, chief of the Preclinical Pharmacology Section at the National Institute on Drug Abuse, contributes an editorial that further explains the use of NM-MRI and discusses the theoretical implications of these unexpected findings in relation to cocaine use ( 4 ).

Treatment Implications of Understanding Brain Function During Early Abstinence in Patients With Alcohol Use Disorder

Developing a better understanding of the neural processes that are associated with substance use disorders is critical for conceptualizing improved treatment approaches. Blaine et al. ( 5 ) present neuroimaging data collected during early abstinence in patients with alcohol use disorder and link these data to relapses occurring during treatment. Of note, the findings from this study dovetail with the neural circuit schema Koob et al. provide in this issue’s overview on addiction ( 2 ). The first study in the Blaine et al. article uses 44 patients and 43 control subjects to demonstrate that patients with alcohol use disorder have a blunted neural response to the presentation of stress- and alcohol-related cues. This blunting was observed mainly in the ventromedial prefrontal cortex, a key prefrontal regulatory region, as well as in subcortical regions associated with reward processing, specifically the ventral striatum. Importantly, this finding was replicated in a second study in which 69 patients were studied in relation to their length of abstinence prior to treatment and treatment outcomes. The results demonstrated that individuals with the shortest abstinence times had greater alterations in neural responses to stress and alcohol cues. The authors also found that an individual’s length of abstinence prior to treatment, independent of the number of days of abstinence, was a predictor of relapse and that the magnitude of an individual’s neural alterations predicted the amount of heavy drinking occurring early in treatment. Although relapse is an all too common outcome in patients with substance use disorders, this study highlights an approach that has the potential to refine and develop new treatments that are based on addiction- and abstinence-related brain changes. In her thoughtful editorial, Dr. Edith Sullivan from Stanford University comments on the details of the study, the value of studying patients during early abstinence, and the implications of these findings for new treatment development ( 6 ).

Relatively Low Amounts of Alcohol Intake During Pregnancy Are Associated With Subtle Neurodevelopmental Effects in Preadolescent Offspring

Excessive substance use not only affects the user and their immediate family but also has transgenerational effects that can be mediated in utero. Lees et al. ( 7 ) present data suggesting that even the consumption of relatively low amounts of alcohol by expectant mothers can affect brain development, cognition, and emotion in their offspring. The researchers used data from the Adolescent Brain Cognitive Development Study, a large national community-based study, which allowed them to assess brain structure and function as well as behavioral, cognitive, and psychological outcomes in 9,719 preadolescents. The mothers of 2,518 of the subjects in this study reported some alcohol use during pregnancy, albeit at relatively low levels (0 to 80 drinks throughout pregnancy). Interestingly, and opposite of that expected in relation to data from individuals with fetal alcohol spectrum disorders, increases in brain volume and surface area were found in offspring of mothers who consumed the relatively low amounts of alcohol. Notably, any prenatal alcohol exposure was associated with small but significant increases in psychological problems that included increases in separation anxiety disorder and oppositional defiant disorder. Additionally, a dose-response effect was found for internalizing psychopathology, somatic complaints, and attentional deficits. While subtle, these findings point to neurodevelopmental alterations that may be mediated by even small amounts of prenatal alcohol consumption. Drs. Clare McCormack and Catherine Monk from Columbia University contribute an editorial that provides an in-depth assessment of these findings in relation to other studies, including those assessing severe deficits in individuals with fetal alcohol syndrome ( 8 ). McCormack and Monk emphasize that the behavioral and psychological effects reported in the Lees et al. article would not be clinically meaningful. However, it is feasible that the influences of these low amounts of alcohol could interact with other predisposing factors that might lead to more substantial negative outcomes.

Increased Comorbidity Between Substance Use and Psychiatric Disorders in Sexual Identity Minorities

There is no question that victims of societal marginalization experience disproportionate adversity and stress. Evans-Polce et al. ( 9 ) focus on this concern in relation to individuals who identify as sexual minorities by comparing their incidence of comorbid substance use and psychiatric disorders with that of individuals who identify as heterosexual. By using 2012−2013 data from 36,309 participants in the National Epidemiologic Study on Alcohol and Related Conditions–III, the authors examine the incidence of comorbid alcohol and tobacco use disorders with anxiety, mood disorders, and posttraumatic stress disorder (PTSD). The findings demonstrate increased incidences of substance use and psychiatric disorders in individuals who identified as bisexual or as gay or lesbian compared with those who identified as heterosexual. For example, a fourfold increase in the prevalence of PTSD was found in bisexual individuals compared with heterosexual individuals. In addition, the authors found an increased prevalence of substance use and psychiatric comorbidities in individuals who identified as bisexual and as gay or lesbian compared with individuals who identified as heterosexual. This was most prominent in women who identified as bisexual. For example, of the bisexual women who had an alcohol use disorder, 60.5% also had a psychiatric comorbidity, compared with 44.6% of heterosexual women. Additionally, the amount of reported sexual orientation discrimination and number of lifetime stressful events were associated with a greater likelihood of having comorbid substance use and psychiatric disorders. These findings are important but not surprising, as sexual minority individuals have a history of increased early-life trauma and throughout their lives may experience the painful and unwarranted consequences of bias and denigration. Nonetheless, these findings underscore the strong negative societal impacts experienced by minority groups and should sensitize providers to the additional needs of these individuals.

Trends in Nicotine Use and Dependence From 2001–2002 to 2012–2013

Although considerable efforts over earlier years have curbed the use of tobacco and nicotine, the use of these substances continues to be a significant public health problem. As noted above, individuals with psychiatric disorders are particularly vulnerable. Grant et al. ( 10 ) use data from the National Epidemiologic Survey on Alcohol and Related Conditions collected from a very large cohort to characterize trends in nicotine use and dependence over time. Results from their analysis support the so-called hardening hypothesis, which posits that although intervention-related reductions in nicotine use may have occurred over time, the impact of these interventions is less potent in individuals with more severe addictive behavior (i.e., nicotine dependence). When adjusted for sociodemographic factors, the results demonstrated a small but significant increase in nicotine use from 2001–2002 to 2012–2013. However, a much greater increase in nicotine dependence (46.1% to 52%) was observed over this time frame in individuals who had used nicotine during the preceding 12 months. The increases in nicotine use and dependence were associated with factors related to socioeconomic status, such as lower income and lower educational attainment. The authors interpret these findings as evidence for the hardening hypothesis, suggesting that despite the impression that nicotine use has plateaued, there is a growing number of highly dependent nicotine users who would benefit from nicotine dependence intervention programs. Dr. Kathleen Brady, from the Medical University of South Carolina, provides an editorial ( 11 ) that reviews the consequences of tobacco use and the history of the public measures that were initially taken to combat its use. Importantly, her editorial emphasizes the need to address health care inequity issues that affect individuals of lower socioeconomic status by devoting resources to develop and deploy effective smoking cessation interventions for at-risk and underresourced populations.

Conclusions

Maladaptive substance use and substance use disorders are highly prevalent and are among the most significant public health problems. Substance use is commonly comorbid with psychiatric disorders, and treatment efforts need to concurrently address both. The papers in this issue highlight new findings that are directly relevant to understanding, treating, and developing policies to better serve those afflicted with addictions. While treatments exist, the need for more effective treatments is clear, especially those focused on decreasing relapse rates. The negative affective state, hyperkatifeia, that accompanies longer-term abstinence is an important treatment target that should be emphasized in current practice as well as in new treatment development. In addition to developing a better understanding of the neurobiology of addictions and abstinence, it is necessary to ensure that there is equitable access to currently available treatments and treatment programs. Additional resources must be allocated to this cause. This depends on the recognition that health care inequities and societal barriers are major contributors to the continued high prevalence of substance use disorders, the individual suffering they inflict, and the huge toll that they incur at a societal level.

Disclosures of Editors’ financial relationships appear in the April 2020 issue of the Journal .

1 US Department of Health and Human Services: Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality: National Survey on Drug Use and Health 2018. Rockville, Md, SAMHSA, 2019 ( https://www.samhsa.gov/data/nsduh/reports-detailed-tables-2018-NSDUH ) Google Scholar

2 Koob GF, Powell P, White A : Addiction as a coping response: hyperkatifeia, deaths of despair, and COVID-19 . Am J Psychiatry 2020 ; 177:1031–1037 Link ,  Google Scholar

3 Cassidy CM, Carpenter KM, Konova AB, et al. : Evidence for dopamine abnormalities in the substantia nigra in cocaine addiction revealed by neuromelanin-sensitive MRI . Am J Psychiatry 2020 ; 177:1038–1047 Link ,  Google Scholar

4 Bradberry CW : Neuromelanin MRI: dark substance shines a light on dopamine dysfunction and cocaine use (editorial). Am J Psychiatry 2020 ; 177:1019–1021 Abstract ,  Google Scholar

5 Blaine SK, Wemm S, Fogelman N, et al. : Association of prefrontal-striatal functional pathology with alcohol abstinence days at treatment initiation and heavy drinking after treatment initiation . Am J Psychiatry 2020 ; 177:1048–1059 Abstract ,  Google Scholar

6 Sullivan EV : Why timing matters in alcohol use disorder recovery (editorial). Am J Psychiatry 2020 ; 177:1022–1024 Abstract ,  Google Scholar

7 Lees B, Mewton L, Jacobus J, et al. : Association of prenatal alcohol exposure with psychological, behavioral, and neurodevelopmental outcomes in children from the Adolescent Brain Cognitive Development Study . Am J Psychiatry 2020 ; 177:1060–1072 Link ,  Google Scholar

8 McCormack C, Monk C : Considering prenatal alcohol exposure in a developmental origins of health and disease framework (editorial). Am J Psychiatry 2020 ; 177:1025–1028 Abstract ,  Google Scholar

9 Evans-Polce RJ, Kcomt L, Veliz PT, et al. : Alcohol, tobacco, and comorbid psychiatric disorders and associations with sexual identity and stress-related correlates . Am J Psychiatry 2020 ; 177:1073–1081 Abstract ,  Google Scholar

10 Grant BF, Shmulewitz D, Compton WM : Nicotine use and DSM-IV nicotine dependence in the United States, 2001–2002 and 2012–2013 . Am J Psychiatry 2020 ; 177:1082–1090 Link ,  Google Scholar

11 Brady KT : Social determinants of health and smoking cessation: a challenge (editorial). Am J Psychiatry 2020 ; 177:1029–1030 Abstract ,  Google Scholar

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Alcohol and Your Brain: The Latest Scientific Insights

Want to protect your brain here's what you need to know about alcohol consumption..

Posted March 18, 2024 | Reviewed by Devon Frye

• What Is Alcoholism?
• Find a therapist to overcome addiction
• Transient memory loss, “blackouts,” and hangovers related to alcohol consumption are brain health risks.
• Alcohol use disorder (alcoholism) is a risk factor for developing dementia.
• Heavy or excessive alcohol consumption is dangerous to the brain for a number of reasons.
• The impact of mild to moderate alcohol consumption (1-3 drinks a day) on brain function is less clear.

Depending on who you ask, you might be told to drink a few glasses of red wine a day or to avoid alcohol altogether. The reasons for such recommendations are many, but, by and large, they tend to stem from a study someone read about or saw reported in the news.

So why is it so hard to know whether alcohol is good or bad for us—especially for our brains? In this post, we’ll explore the current science and some practical ideas on how to approach the topic.

What Is Alcohol Anyway?

When people talk about drinking “alcohol,” they’re almost always referring to the consumption of ethanol. Ethanol is a natural product that is formed from the fermentation of grains, fruits, and other sources of sugar. It’s found in a wide range of alcoholic beverages including beer, wine, and spirits like vodka, whiskey, rum, and gin.

Evidence for human consumption of alcohol dates back over 10,000 years. Consumption of alcohol has and continues to serve major roles in religious and cultural ceremonies around the world. But unlike most food products, in the last century, alcohol has been wrapped up in nearly perpetual controversy over its moral effects and health implications.

How Does Alcohol Impact the Brain?

As anyone who’s consumed alcohol knows, ethanol can directly influence brain function. Ethanol is classified as a “depressant” because it has a generally slowing effect on brain activity through activation of γ-aminobutyric acid (GABA) pathways.

In an acute sense, consumption of alcohol can lead to uninhibited behavior, sedation, lapses in judgment, and impairments in motor function. At higher levels, the effects can progress to coma and even death.

The Known Brain-Damaging Effects of Excess Alcohol

There is no debate here: Excessively high levels of alcohol consumption over short periods of time are toxic and potentially deadly, specifically because of its effects on the brain.

One critical fact to understand about the overall and brain-specific effects of alcohol is that the entirety of the debate around the risk/benefit ratio concerns mild to moderate alcohol consumption. As it relates to the effects of high amounts of alcohol on the body and brain, the research is consistent: It’s a very bad choice.

High amounts of alcohol use are causal risk factors in the development of disease in the heart, liver, pancreas, and brain (including the brains of children in utero). In fact, 1 in 8 deaths in Americans aged 20-64 is attributable to alcohol use. When it comes to adults, excessive alcohol use can cause multiple well-defined brain issues ranging from short-term confusion to dementia .

What Is “Excessive” or “High” Alcohol Use?

Key to the nuance in the conversation about alcohol use are definitions. Across the board, “excessive” or “high” alcohol use is linked to worse overall and brain health outcomes. So what does that mean?

While definitions can be variable, one way to look at this is the consumption of 4 or more drinks on an occasion (for women) and 5 or more for men. Additionally, excess alcohol is defined as drinking more than 8 drinks a week (women) and 15 a week (men), or consuming alcohol if you are pregnant or younger than age 21.

Beyond this, by definition, consuming enough alcohol to cause a “brownout,” “blackout,” hangover, or other overt brain symptomatology is evidence that the alcohol you’ve consumed is creating problems in your brain. Alcohol use disorder (or alcoholism ) is also a clear issue for the brain. It has been linked to a higher risk for dementia, especially early-onset dementia in a study of 262,000 adults, as well as to smaller brain size .

Is There a “Safe” Amount of Alcohol for the Brain?

In a highly publicized article from Nature Communications , researchers looked at brain imaging data from nearly 37,000 middle-aged to older adults and cross-referenced their brain scans with their reported alcohol consumption. The findings were profound: People who drank more alcohol had smaller brains, even in people drinking only one or two alcoholic beverages a day.

Conversely, other recent data suggest a lower risk for dementia in people consuming a few alcoholic beverages a day. This includes a 2022 study showing that in around 27,000 people, consuming up to 40 grams of alcohol (around 2.5 drinks) a day was linked to a lower risk for dementia versus abstinence in adults over age 60. A much larger study of almost 4 million people in Korea noted that mild to moderate alcohol consumption was linked to a lower risk for dementia compared to non-drinking.

How Do We Make Sense of This Data?

When it comes to the bottom line as it relates to alcohol consumption and brain health, the data are rather solid on some fronts, and a bit less so on others. There’s also the potential for confounding variables, including the fact that many people like to drink alcohol to enjoy and enhance social bonds (which we know are beneficial for the brain). Here’s a summary of what the most recent research is telling us.

• Experiencing transient memory loss, “blackouts,” or hangovers related to alcohol consumption is overt evidence of threats to brain health.
• The impact of mild to moderate alcohol consumption (1-3 drinks a day) on brain function is less clear, but it seems unreasonable to start alcohol use for brain health.

Austin Perlmutter, M.D. , is a board-certified internal medicine physician and the co-author of Brain Wash .

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Alcohol's Effects on Health

Research-based information on drinking and its impact.

National Institute on Alcohol Abuse and Alcoholism (NIAAA)

Alcohol's effects on the body.

Drinking too much – on a single occasion or over time – can take a serious toll on your health.  Here’s how alcohol can affect your body:

Brain: Alcohol interferes with the brain’s communication pathways, and can affect the way the brain looks and works. These disruptions can change mood and behavior, and make it harder to think clearly and move with coordination .  

Heart: Drinking a lot over a long time or too much on a single occasion can damage the heart, causing problems including:

• Cardiomyopathy – Stretching and drooping of heart muscle
• Arrhythmias – Irregular heart beat
• High blood pressure  

Liver: Heavy drinking takes a toll on the liver, and can lead to a variety of problems and liver inflammations including:

• Steatosis, or fatty liver
• Alcoholic hepatitis

Pancreas: Alcohol causes the pancreas to produce toxic substances that can eventually lead to pancreatitis , a dangerous inflammation in the pancreas that causes its swelling and pain (which may spread) and impairs its ability to make enzymes and hormones for proper digestion . 

Cancer: According to the National Cancer Institute: "There is a strong scientific consensus that alcohol drinking can cause several types of cancer. In its Report on Carcinogens, the National Toxicology Program of the US Department of Health and Human Services lists consumption of alcoholic beverages as a known human carcinogen.

"The evidence indicates that the more alcohol a person drinks–particularly the more alcohol a person drinks regularly over time–the higher his or her risk of developing an alcohol-associated cancer. Even those who have no more than one drink per day and people who binge drink (those who consume 4 or more drinks for women and 5 or more drinks for men in one sitting) have a modestly increased risk of some cancers. Based on data from 2009, an estimated 3.5% of cancer deaths in the United States (about 19,500 deaths were alcohol related."

Clear patterns have emerged between alcohol consumption and increased risks of certain types of cancer:

• Head and neck cancer, including oral cavity, pharynx, and larynx cancers.
• Esophageal cancer, particularly esophageal squamous cell carcinoma. In addition, people who inherit a deficiency in an enzyme that metabolizes alcohol have been found to have substantially increased risks of esophageal squamous cell carcinoma if they consume alcohol.
• Liver cancer.
• Breast cancer: Studies have consistently found an increased risk of breast cancer in women with increasing alcohol intake. Women who consume about 1 drink per day have a 5 to 9 percent higher chance of developing breast cancer than women who do not drink at all.
• Colorectal cancer.

For more information about alcohol and cancer, please visit the National Cancer Institute's webpage " Alcohol and Cancer Risk " (last accessed October 21, 2021).

Immune System: Drinking too much can weaken your immune system, making your body a much easier target for disease.  Chronic drinkers are more liable to contract diseases like pneumonia and tuberculosis than people who do not drink too much.  Drinking a lot on a single occasion slows your body’s ability to ward off infections – even up to 24 hours after getting drunk.

For more information about alcohol's effects on the body, please visit the  Interactive Body feature  on NIAAA's  College Drinking Prevention website .

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Even a Little Alcohol Can Harm Your Health

Recent research makes it clear that any amount of drinking can be detrimental. Here’s why you may want to cut down on your consumption beyond Dry January.

By Dana G. Smith

Sorry to be a buzz-kill, but that nightly glass or two of wine is not improving your health.

After decades of confusing and sometimes contradictory research (too much alcohol is bad for you but a little bit is good; some types of alcohol are better for you than others; just kidding, it’s all bad), the picture is becoming clearer: Even small amounts of alcohol can have health consequences.

Research published in November revealed that between 2015 and 2019, excessive alcohol use resulted in roughly 140,000 deaths per year in the United States. About 40 percent of those deaths had acute causes, like car crashes, poisonings and homicides. But the majority were caused by chronic conditions attributed to alcohol, such as liver disease, cancer and heart disease.

When experts talk about the dire health consequences linked to excessive alcohol use, people often assume that it’s directed at individuals who have an alcohol use disorder. But the health risks from drinking can come from moderate consumption as well.

“Risk starts to go up well below levels where people would think, ‘Oh, that person has an alcohol problem,’” said Dr. Tim Naimi, director of the University of Victoria’s Canadian Institute for Substance Use Research. “Alcohol is harmful to the health starting at very low levels.”

If you’re wondering whether you should cut back on your drinking, here’s what to know about when and how alcohol impacts your health.

How do I know if I’m drinking too much?

“Excessive alcohol use” technically means anything above the U.S. Dietary Guidelines ’ recommended daily limits. That’s more than two drinks a day for men and more than one drink a day for women.

There is also emerging evidence “that there are risks even within these levels, especially for certain types of cancer and some forms of cardiovascular disease,” said Marissa Esser, who leads the alcohol program at the Centers for Disease Control and Prevention.

The recommended daily limits are not meant to be averaged over a week, either. In other words, if you abstain Monday through Thursday and have two or three drinks a night on the weekend, those weekend drinks count as excessive consumption. It’s both the cumulative drinks over time and the amount of alcohol in your system on any one occasion that can cause damage.

Why is alcohol so harmful?

Scientists think that the main way alcohol causes health problems is by damaging DNA. When you drink alcohol, your body metabolizes it into acetaldehyde, a chemical that is toxic to cells. Acetaldehyde both “damages your DNA and prevents your body from repairing the damage,” Dr. Esser explained. “Once your DNA is damaged, then a cell can grow out of control and create a cancer tumor.”

Alcohol also creates oxidative stress, another form of DNA damage that can be particularly harmful to the cells that line blood vessels. Oxidative stress can lead to stiffened arteries, resulting in higher blood pressure and coronary artery disease.

“It fundamentally affects DNA, and that’s why it affects so many organ systems,” Dr. Naimi said. Over the course of a lifetime, chronic consumption “damages tissues over time.”

Isn’t alcohol supposed to be good for your heart?

Alcohol’s effect on the heart is confusing because some studies have claimed that small amounts of alcohol, particularly red wine, can be beneficial. Past research suggested that alcohol raises HDL, the “good” cholesterol, and that resveratrol, an antioxidant found in grapes (and red wine), has heart-protective properties.

However, said Mariann Piano, a professor of nursing at Vanderbilt University, “There’s been a lot of recent evidence that has really challenged the notion of any kind of what we call a cardio-protective or healthy effect of alcohol.”

The idea that a low dose of alcohol was heart healthy likely arose from the fact that people who drink small amounts tend to have other healthy habits, such as exercising, eating plenty of fruits and vegetables and not smoking. In observational studies, the heart benefits of those behaviors might have been erroneously attributed to alcohol, Dr. Piano said.

More recent research has found that even low levels of drinking slightly increase the risk of high blood pressure and heart disease, and the risk goes up dramatically for people who drink excessively. The good news is that when people stop drinking or just cut back, their blood pressure goes down . Alcohol is also linked to an abnormal heart rhythm, known as atrial fibrillation , which raises the risk of blood clots and stroke.

What types of cancer does alcohol increase the risk for?

Almost everyone knows about the link between cigarette smoking and cancer, but few people realize that alcohol is also a potent carcinogen. According to research by the American Cancer Society, alcohol contributes to more than 75,000 cases of cancer per year and nearly 19,000 cancer deaths.

Alcohol is known to be a direct cause of seven different cancers : head and neck cancers (oral cavity, pharynx and larynx), esophageal cancer, liver cancer, breast cancer and colorectal cancer. Research suggests there may be a link between alcohol and other cancers as well, including prostate and pancreatic cancer, although the evidence is less clear-cut.

For some cancers, such as liver and colorectal, the risk starts only when people drink excessively. But for breast and esophageal cancer, the risk increases, albeit slightly, with any alcohol consumption. The risks go up the more a person drinks.

“If somebody drinks less, they are at a lower risk compared to that person who is a heavy drinker,” said Dr. Farhad Islami, a senior scientific director at the American Cancer Society. “Even two drinks per day, one drink per day, may be associated with a small risk of cancer compared to non-drinkers.”

Which condition poses the greatest risk?

The most common individual cause of alcohol-related death in the United States is alcoholic liver disease, killing about 22,000 people a year . While the risk rises as people age and alcohol exposure accumulates, more than 5,000 Americans in their 20s, 30s and 40s die from alcoholic liver disease annually.

Alcoholic liver disease has three stages: alcoholic fatty liver, when fat accumulates in the organ; alcoholic hepatitis, when inflammation starts to occur; and alcoholic cirrhosis, or scarring of the tissue. The first two stages are reversible if you stop drinking entirely; the third stage is not.

Symptoms of alcoholic liver disease include nausea, vomiting, abdominal pain and jaundice — a yellow tinge to the eyes or skin. However, symptoms rarely emerge until the liver has been severely damaged.

The risk of developing alcoholic liver disease is greatest in heavy drinkers, but one report stated that five years of drinking just two alcoholic beverages a day can damage the liver. Ninety percent of people who have four drinks a day show signs of alcoholic fatty liver.

How do I gauge my personal risk for alcohol-related health issues?

Not everyone who drinks will develop these conditions. Lifestyle factors such as diet, exercise and smoking all combine to raise or lower your risk. Also, some of these conditions, such as esophageal cancer, are pretty rare, so increasing your risk slightly won’t have a huge impact.

“Every risk factor matters,” Dr. Esser said. “We know in public health that the number of risk factors that one has would go together into an increased risk for a condition.”

A pre-existing condition could also interact with alcohol to affect your health. For example, “people who have hypertension probably should not drink or definitely drink at very, very low levels ,” Dr. Piano said.

Genes play a role, too. For instance, two genetic variants, both of which are more common in people of Asian descent, affect how alcohol and acetaldehyde are metabolized. One gene variant causes alcohol to break down into acetaldehyde faster, flooding the body with the toxin. The other variant slows down acetaldehyde metabolism, meaning the chemical hangs around in the body longer, prolonging the damage.

So should I cut back — or stop drinking altogether?

You don’t need to go cold turkey to help your health. Even reducing a little bit can be beneficial, especially if you currently drink over the recommended limits. The risk “really accelerates once you’re over a couple of drinks a day,” Dr. Naimi said. “So people who are drinking five or six drinks a day, if they can cut back to three or four, they’re going to do themselves a lot of good.”

Light daily drinkers would likely benefit by cutting back a bit, too. Try going a few nights without alcohol: “If you feel better, your body is trying to tell you something,” said George Koob, director of the National Institute on Alcohol Abuse and Alcoholism.

Notably, none of the experts we spoke to called for abstaining completely, unless you have an alcohol use disorder or are pregnant. “I’m not going to advocate that people completely stop drinking,” Dr. Koob said. “We did prohibition, it didn’t work.”

Generally, though, their advice is, “Drink less, live longer,” Dr. Naimi said. “That’s basically what it boils down to.”

A More Mindful Approach to Drinking

If you consume alcohol, but are looking for a healthier approach to drinking, here are some tips..

  Consider the Dangers : Heart disease risk increases along with   our  alcohol consumption . Drinking can also lead to cancer and liver and kidney disease.

  ‘Go Dry’ for a Month: If you tend to overindulge, one month off from drinking can be an opportunity to examine your alcohol use .

 Cut Back:  You don’t need to abstain to rein in your alcohol consumption. Here are some tips to develop healthier drinking habits.

 Try Meditation: Mindfulness and strategies from cognitive behavioral therapy can also help you be more intentional about your relationship to drinking .

 Enjoy Your Drink: Learn to savor that glass of wine the way a connoisseur would — it starts with a shift in perspective  and a few best practices .

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• 25 March 2024
• Correction 27 March 2024

Weird new electron behaviour in stacked graphene thrills physicists

• Dan Garisto

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Electrons in stacked sheets of staggered graphene collectively act as though they have fractional charges at ultralow temperatures. Credit: Ramon Andrade 3DCiencia/Science Photo Library

Minneapolis, Minnesota

Last May, a team led by physicists at the University of Washington in Seattle observed something peculiar. When the scientists ran an electrical current across two atom-thin sheets of molybdenum ditelluride (MoTe 2 ), the electrons acted in concert, like particles with fractional charges. Resistance measurements showed that, rather than the usual charge of –1, the electrons behaved similar to particles with charges of –2/3 or –3/5, for instance. What was truly odd was that the electrons did this entirely because of the innate properties of the material, without any external magnetic field coaxing them. The researchers published the results a few months later, in August 1 .

Strange topological materials are popping up everywhere physicists look

The same month, this phenomenon, known as the fractional quantum anomalous Hall effect (FQAHE), was also observed in a completely different material. Researchers led by Long Ju, a condensed-matter physicist at the Massachusetts Institute of Technology (MIT) in Cambridge, saw the effect when they sandwiched five layers of graphene between sheets of boron nitride. They published their results in February this year 2 — and physicists are still buzzing about it.

At the American Physical Society (APS) March Meeting, held in Minneapolis, Minnesota, from 3 to 8 March, Ju presented the team’s findings, which haven’t yet been replicated by other researchers. Attendees, including Raquel Queiroz, a theoretical physicist at Columbia University in New York City, said that they thought the results were convincing, but were scratching their heads over the discovery. “There is a lot we don’t understand,” Queiroz says. Figuring out the exact mechanism of the FQAHE in the layered graphene will be “a lot of work ahead of theorists”, she adds.

Although the FQAHE might have practical applications down the line — fractionally charged particles are a key requirement for a certain type of quantum computer — the findings are capturing physicists’ imagination because they are fundamentally new discoveries about how electrons behave.

“I don’t know anyone who’s not excited about this,” says Pablo Jarillo-Herrero, a condensed-matter physicist at MIT who was not involved with the studies. “I think the question is whether you’re so excited that you switch all your research and start working on it, or if you’re just very excited.”

Strange maths

Strange behaviour by electrons isn’t new.

In some materials, usually at temperatures near absolute zero, electrical resistance becomes quantized. Specifically, it’s the material’s transverse resistance that does this. (An electrical current encounters opposition to its flow in both the same direction as the current — called longitudinal resistance — and in the perpendicular direction — what’s called transverse resistance.)

Quantized ‘steps’ in the transverse resistance occur at integer multiples of electron charge: 1, 2, 3 and so on. These plateaus are the result of a strange phenomenon: the electrons maintain the same transverse resistance even as charge density increases. That’s a little like vehicles on a road moving at the same speed, even with more traffic. This is known as the quantum Hall effect.

In a different set of materials, with less disorder, the transverse resistance can even display plateaus at fractions of electron charge: 2/5, 3/7 and 4/9, for example. The plateaus take these values because the electrons collectively act like particles with fractional charges — hence the fractional quantum Hall effect (FQHE).

Key to both phenomena is a strong external magnetic field, which prevents electrons from crashing into each other and enables them to interact.

(Left to right) Long Ju, Zhengguang Lu, Yuxuan Yao and Tonghang Han are all part of the team at MIT that demonstrated the fractional quantum anomalous Hall effect in layered graphene. Credit: Jixiang Yang

The FQHE, discovered in 1982, revealed the richness of electron behaviour. No longer could physicists think of electrons as single particles; in delicate quantum arrangements, the electrons could lose their individuality and act together to create fractionally charged particles. “I think people don’t appreciate how different [the fractional] is from the integer quantum Hall effect,” says Ashvin Vishwanath, a theoretical physicist at Harvard University in Cambridge. “It’s a new world.”

Over the next few decades, theoretical physicists came up with models to explain the FQHE and predict its effects. During their exploration, a tantalizing possibility appeared: perhaps a material could exhibit resistance plateaus without any external magnetic field. The effect, now dubbed the quantum anomalous Hall effect — ‘anomalous’, for the lack of a magnetic field — was finally observed in thin ferromagnetic films by a team at Tsinghua University in Beijing, in 2012 3 .

Carbon copy

Roughly a decade later, the University of Washington team reported the FQAHE for the first time 1 , in a specially designed 2D material: two sheets of MoTe 2 stacked on top of one another and offset by a twist.

This arrangement of MoTe 2 is known as a moiré material. Originally used to refer to a patterned textile, the term has been appropriated by physicists to describe the patterns in 2D materials created from atom-thin lattices when they are stacked and then twisted, or staggered atop one another. The slight offset between atoms in different layers of the material shifts the hills and valleys of its electric potential. And it effectively acts like a powerful magnetic field, taking the place of the one needed in the quantum Hall effect and the FQHE.

Xiaodong Xu, a condensed-matter physicist at the University of Washington, talked about the MoTe 2 discovery at the APS meeting. Theory hinted that the FQAHE would appear in the material at about a 1.4º twist angle. “We spent a year on it, and we didn’t see anything,” Xu told Nature .

Source: Adapted from Ref. 2.

Then, the researchers tried a larger angle — a twist of about 4º. Immediately, they began seeing signs of the effect. Eventually, they measured the electrical resistance and spotted the signature plateaus of the FQAHE. Soon afterwards, a team led by researchers at Shanghai Jiao Tong University in China replicated the results 4 .

Meanwhile, at MIT, Ju was perfecting his technique, sandwiching graphene between layers of boron nitride. Similar to graphene, the sheets of boron nitride that Ju’s team used were a mesh of atoms linked together in a hexagonal pattern. The material’s lattice has a slightly different size from graphene’s; the mismatch creates a moiré pattern (see ‘Anomalous behaviour’).

Last month, Ju published a report 2 about seeing the characteristic plateaus. “It is a really amazing result,” Xu says. “I'm very happy to see there’s a second system.” Since then, Ju says, he’s also seen the effect when using four and six layers of graphene.

Both moiré systems have their pros and cons. MoTe 2 exhibited the effect at a few kelvin, as opposed to 0.1 kelvin for the layered graphene sandwich. (Low temperatures are required to minimize disorder in the systems.) But graphene is a cleaner and higher-quality material that is easier to measure. Experimentalists are now trying to replicate the results in graphene and find other materials that behave similarly.

Moiré than bargained for

Theorists are relatively comfortable with the MoTe 2 results, for which the FQAHE was partly predicted. But Ju’s layered graphene moiré was a shock to the community, and researchers are still struggling to explain how the effect happens. “There’s no universal consensus on what the correct theory is,” Vishwanath says. “But they all agree that it’s not the standard mechanism.” Vishwanath and his colleagues posted a preprint proposing a theory that the moiré pattern might not be that important to the FQAHE 5 .

Welcome anyons! Physicists find best evidence yet for long-sought 2D structures

One reason to doubt the importance of the moiré is the location of the electrons in the material: most of the activity is in the topmost layer of graphene, far away from the moiré pattern between the graphene and boron nitride at the bottom of the sandwich that is supposed to most strongly influence the electrons. But B. Andrei Bernevig, a theoretical physicist at Princeton University in New Jersey, and a co-author of another preprint proposing a mechanism for the FQAHE in the layered graphene 6 , urges caution about theory-based calculations, because they rely on currently unverified assumptions. He says that the moiré pattern probably matters, but less than it does in MoTe 2 .

For theorists, the uncertainty is exciting. “There are people who would say that everything has been seen in the quantum Hall effect,” Vishwanath says. But these experiments, especially the one using the layered graphene moiré, show that there are still more mysteries to uncover.

Nature 628 , 16-17 (2024)

doi: https://doi.org/10.1038/d41586-024-00832-z

Updates & Corrections

Correction 27 March 2024 : An earlier version of this story spelled researcher Tonghang Han’s name incorrectly in the photo caption.

Park, H. et al. Nature 622 , 74–79 (2023).

Article   PubMed   Google Scholar  

Lu, Z. et al. Nature 626 , 759–764 (2024).

Chang, C.-Z. et al. Science 340 , 167–170 (2013).

Xu, F. et al. Phys. Rev. X 13 , 031037 (2023).

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Dong, J. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2311.05568 (2023).

Kwan, Y. H. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2312.11617 (2023).

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The effects of alcohol use on academic achievement in high school

Ana i. balsa.

a Research Professor, Center for Applied Research on Poverty, Family, and Education, Department of Economics, Universidad de Montevideo; Prudencio de Pena 2440, Montevideo, 11600, Uruguay; Phone: (+598 2) 707 4461 ext 300; Fax: (+598 2) 707 4461 ext 325; yu.ude.mu@aslaba

Laura M. Giuliano

b Assistant Professor, Department of Economics, University of Miami, Coral Gables, FL 33124, United States; [email protected]

Michael T. French

c Professor of Health Economics, Health Economics Research Group, Department of Sociology, Department of Economics, and Department of Epidemiology and Public Health, University of Miami, Coral Gables, FL 33124, United States; ude.imaim@hcnerfm

This paper examines the effects of alcohol use on high school students’ quality of learning. We estimate fixed-effects models using data from the National Longitudinal Study of Adolescent Health. Our primary measure of academic achievement is the student’s GPA abstracted from official school transcripts. We find that increases in alcohol consumption result in small yet statistically significant reductions in GPA for male students and in statistically non-significant changes for females. For females, however, higher levels of drinking result in self-reported academic difficulty. The fixed-effects results are substantially smaller than OLS estimates, underscoring the importance of addressing unobserved individual heterogeneity.

1. Introduction

In the United States, one in four individuals between the ages of 12 and 20 drinks alcohol on a monthly basis, and a similar proportion of 12 th graders consumes five or more drinks in a row at least once every two weeks ( Newes-Adeyi, Chen, Williams, & Faden, 2007 ). Several studies have reported that alcohol use during adolescence affects educational attainment by decreasing the number of years of schooling and the likelihood of completing school ( Chatterji & De Simone, 2005 ; Cook & Moore, 1993 ; Gil-Lacruz & Molina, 2007 ; Koch & McGeary, 2005 ; McCluskey, Krohn, Lizotte, & Rodriguez, 2002 ; NIDA, 1998 ; Renna, 2007 ; Yamada, Kendrix, & Yamada, 1996 ) Other research using alternative estimation techniques suggests that the effects of teen drinking on years of education and schooling completion are very small and/or non-significant ( Chatterji, 2006 ; Dee & Evans, 2003 ; Koch & Ribar, 2001 ).

Despite a growing literature in this area, no study has convincingly answered the question of whether alcohol consumption inhibits high school students’ learning. Alcohol consumption could be an important determinant of how much a high school student learns without having a strong impact on his or her decision to stay in school or attend college. This question is fundamental and timely, given recent research showing that underage drinkers are susceptible to the immediate consequences of alcohol use, including blackouts, hangovers, and alcohol poisoning, and are at elevated risk of neurodegeneration (particularly in regions of the brain responsible for learning and memory), impairments in functional brain activity, and neurocognitive defects ( Zeigler et al., 2004 ).

A common and comprehensive measure of high school students’ learning is Grade Point Average (GPA). GPA is an important outcome because it is a key determinant of college admissions decisions and of job quality for those who do not attend college. Only a few studies have explored the association between alcohol use and GPA. Wolaver (2002) and Williams, Powell, and Wechsler (2003) have studied this association among college students, while DeSimone and Wolaver (2005) have investigated the effects of underage drinking on GPA during high school. The latter study found a negative association between high school drinking and grades, although it is not clear whether the effects are causal or the result of unobserved heterogeneity.

Understanding the relationship between teenage drinking and high school grades is pertinent given the high prevalence of alcohol use among this age cohort and recent research on adolescent brain development suggesting that early heavy alcohol use may have negative effects on the physical development of brain structure ( Brown, Tapert, Granholm, & Delis, 2000 ; Tapert & Brown, 1999 ). By affecting the quality of learning, underage drinking could have an impact on both college admissions and job quality independent of its effects on years of schooling or school completion.

In this paper, we estimate the effects of drinking in high school on the quality of learning as captured by high school GPA. The analysis employs data from Waves 1 and 2 of the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative study that captures health-related behaviors of adolescents in grades 7 through 12 and their outcomes in young adulthood. Our analysis contributes to the literature in several ways. First, we focus on the effect of drinking on academic achievement during high school. To date, and to the best of our knowledge, only one other study in the literature has analyzed the consequences of underage drinking on high school GPA. Second, rather than rely on self-reported GPA, we use objective GPA data from academic transcripts, reducing the potential for systematic biases in the estimation results. Third, we take advantage of the longitudinal nature of the Add Health data and use fixed-effects models to purge the analysis of time invariant unobserved heterogeneity. Fixed-effects techniques are superior to instrumental variables (IV) estimation when the strength and reliability of the instruments are suspect ( French & Popovici, 2009 ). Finally, we explore a variety of mechanisms that could underlie a detrimental effect of alcohol use on grades. In addition to analyzing mediators related to exposure to education (days of school skipped), we investigate the effect of drinking on students’ ability to focus on and adhere to academic objectives.

2. Background and significance

Behavioral research has found that educational performance is highly correlated with substance abuse (e.g., Bukstein, Cornelius, Trunzo, Kelly, & Wood, 2005 ; Hawkins, Catalano, & Miller, 1992 ). Economic studies that look at the link between alcohol use and educational outcomes have customarily focused on measures of educational attainment such as graduation (from high school or college), college matriculation, and years of school completed (e.g., Bray, Zarkin, Ringwalt, & Qi, 2000 ; Chatterji, 2006 ; Cook & Moore, 1993 ; Dee & Evans 2003 ; Koch & Ribar, 2001 ; Mullahy & Sindelar, 1994 ; Renna, 2008 ; Yamada et al., 1996 ). Consistent with the behavioral research, early economic studies found that drinking reduced educational attainment. But the most rigorous behavioral studies and the early economic studies of attainment both faced the same limitation: they were cross-sectional and subject to potential omitted variables bias. Some of these cross-sectional economic studies attempted to improve estimation by using instrumental variables (IV). Cook and Moore (1993) and Yamada et al. (1996) found that heavy or frequent drinking in high school adversely affects high school and college completion. Nevertheless, the validity and reliability of the instruments in these studies are open to debate ( Chatterji, 2006 ; Dee & Evans, 2003 ; French & Popovici, 2009 ).

By contrast, more recent economic studies that arguably use better estimation methods have found that drinking has modest or negligible effects on educational attainment. Dee and Evans (2003) studied the effects of teen drinking on high school completion, college entrance, and college persistence. Employing changes in the legal drinking age across states over time as an instrument, they found no significant effect of teen drinking on educational attainment. Koch and Ribar (2001) reached a similar conclusion applying family fixed effects and instrumental variables to NLSY data. Though they found that drinking had a significant negative effect on the amount of schooling completed among men, the effect was small. Finally, Chatterji (2006) used a bivariate probit model of alcohol use and educational attainment to gauge the sensitivity of the estimates to various assumptions about the correlation of unobservable determinants of these variables. She concluded that there is no evidence of a causal relationship between alcohol use and educational attainment when the correlation coefficient is fixed at plausible levels.

Alcohol use could conceivably affect a student’s quality of learning and academic performance regardless of its impact on school completion. This possibility is suggested by Renna (2008) , who uses a research design similar to that used by Dee and Evans (2003) and finds that although binge drinking does not affect high school completion rates, it does significantly increase the probability that a student graduates with a GED rather than a high school diploma. Drinking could affect learning through a variety of mechanisms. Recent neurological research suggests that underage drinking can impair learning directly by causing alterations in the structure and function of the developing brain with consequences reaching far beyond adolescence ( Brown et al., 2000 ; White & Swartzwelder, 2004 ). Negative effects of alcohol use can emerge in areas such as planning and executive functioning, memory, spatial operations, and attention ( Brown et al., 2000 ; Giancola & Mezzich, 2000 ; Tapert & Brown, 1999 ). Alcohol use could also affect performance by reducing the number of hours committed to studying, completing homework assignments, and attending school.

We are aware of five economic studies that have examined whether drinking affects learning per se. Bray (2005) analyzed this issue indirectly by studying the effect of high school students’ drinking on subsequent wages, as mediated through human capital accumulation. He found that moderate high school drinking had a positive effect on returns to education and therefore on human capital accumulation. Heavier drinking reduced this gain slightly, but net effects were still positive. The other four studies approached the question directly by focusing on the association between drinking and GPA. Three of the GPA studies used data from the Harvard College Alcohol Study. Analyzing data from the study’s 1993 wave, both Wolaver (2002) and Williams et al. (2003) estimated the impact of college drinking on the quality of human capital acquisition as captured by study hours and GPA. Both studies found that drinking had a direct negative effect on GPA and an indirect negative effect through reduced study hours. Wolaver (2007) used data from the 1993 and 1997 waves and found that both high school and college binge drinking were associated with lower college GPA for males and females. For females, however, study time in college was negatively correlated with high school drinking but positively associated with college drinking.

To our knowledge, only one study has looked specifically at adolescent drinking and high school GPA. Analyzing data from the Youth Risk Behavior Survey, DeSimone and Wolaver (2005) used standard regression analysis to estimate whether drinking affected high school GPA. Even after controlling for many covariates, they found that drinking had a significant negative effect. Their results showed that the GPAs of binge drinkers were 0.4 points lower on average for both males and females. They also found that the effect of drinking on GPA peaked for ninth graders and declined thereafter and that drinking affected GPA more by reducing the likelihood of high grades than by increasing the likelihood of low grades.

All four GPA studies found that drinking has negative effects on GPA, but they each faced two limitations. First, they relied on self-reported GPA, which can produce biased results due to recall mistakes and intentional misreporting ( Zimmerman, Caldwell, & Bernat, 2006 ). Second, they used cross-sectional data. Despite these studies’ serious efforts to address unobserved individual heterogeneity, it remains questionable whether they identified a causal link between drinking and GPA.

In sum, early cross-sectional studies of educational attainment and GPA suggest that drinking can have a sizeable negative effect on both outcomes. By contrast, more recent studies of educational attainment that use improved estimation methods to address the endogeneity of alcohol use have found that drinking has negligible effects. The present paper is the first study of GPA that controls for individual heterogeneity in a fixed-effects framework, and our findings are consistent with the more recent studies of attainment that find small or negligible effects of alcohol consumption.

Add Health is a nationally representative study that catalogues health-related behaviors of adolescents in grades 7 through 12 and associated outcomes in young adulthood. An initial in-school survey was administered to 90,118 students attending 175 schools during the 1994/1995 school year. From the initial in-school sample, 20,745 students (and their parents) were administered an additional in-home interview in 1994–1995 and were re-interviewed one year later. In 2001–2002, Add Health respondents (aged 18 to 26) were re-interviewed in a third wave to investigate the influence of health-related behaviors during adolescence on individuals when they are young adults. During the Wave 3 data collection, Add Health respondents were asked to sign a Transcript Release Form (TRF) that authorized Add Health to identify schools last attended by study participants and request official transcripts from the schools. TRFs were signed by approximately 92% of Wave 3 respondents (about 70% of Wave 1 respondents).

The main outcome of interest, GPA, was abstracted from school transcripts and linked to respondents at each wave. Because most of the in-home interviews during Waves 1 and 2 were conducted during the Spring or Summer (at the end of the school year) and alcohol use questions referred to the past 12 months, we linked the in-home questionnaires with GPA data corresponding to the school year in which the respondent was enrolled or had just completed at the time of the interview.

The in-home questionnaires in Waves 1 and 2 offer extensive information on the student’s background, risk-taking behaviors, and other personal and family characteristics. These instruments were administered by computer assisted personal interview (CAPI) and computer assisted self-interview (CASI) techniques for more sensitive questions such as those on alcohol, drug, and tobacco use. Studies show that the mode of data collection can affect the level of reporting of sensitive behaviors. Both traditional self-administration and computer assisted self-administered interviews have been shown to increase reports of substance use or other risky behaviors relative to interviewer-administered approaches ( Azevedo, Bastos, Moreira, Lynch, & Metzger, 2006 ; Tourangeau & Smith, 1996 ; Wright, Aquilino, & Supple, 1998 ). Several measures of alcohol use were constructed on the basis of the CAPI/CASI questions: (1) whether the student drank alcohol at least once per week in the past 12 months, (2) whether the student binged (drank five or more drinks in a row) at least once per month in the past 12 months, (3) the average number of days per month on which the student drank in the past 12 months, (4) the average number of drinks consumed on any drinking day in the past 12 months, and (5) the total number of drinks per month consumed by the student in the past year.

Individual characteristics obtained from the in-home interviews included age, race, gender, grade in school, interview date, body mass index, religious beliefs and practices, employment status, health status, tobacco use, and illegal drug use. To capture environmental changes for respondents who changed schools, we constructed indicators for whether the respondent attended an Add Health sample school or sister school (e.g. the high school’s main feeder school) in each wave. We also considered family characteristics such as family structure, whether English was spoken at home, the number of children in the household, whether the resident mother and resident father worked, whether parents worked in blue- or white-collar jobs, and whether the family was on welfare. Finally, we took into account a number of variables describing interview and household characteristics as assessed by the interviewer: whether a parent(s) or other adults were present during the interview; whether the home was poorly kept; whether the home was in a rural, suburban, or commercial area; whether the home environment raised any safety concerns; and whether there was evidence of alcohol use in the household.

Respondents to the in-home surveys were also asked several questions about how they were doing in school. We constructed measures of how often the respondents skipped school, whether they had been suspended, and whether they were having difficulties paying attention in school, getting along with teachers, or doing their homework. We analyzed these secondary outcomes as possible mediators of an effect of alcohol use on GPA.

Our fixed-effects methodology required high school GPA data for Waves 1 and 2. For this reason, we restricted the sample to students in grades 9, 10, or 11 in Wave 1 (N=22,792) who were re-interviewed in Waves 2 and 3 (N=14,390), not mentally disabled (N=13,632), and for whom transcript data were available at Wave 3 (N=10,430). In addition, we excluded 1,846 observations that had missing values on at least one of the explanatory or control variables. 1 The final sample had 8,584 observations, which corresponded to Wave 1 and Wave 2 responses for 4,292 students with no missing information on high school GPA or other covariates across both waves. Male respondents accounted for 48% of the sample.

Table 1 shows summary statistics for the analysis sample by wave and gender. Abstracted GPA averages 2.5 for male students and 2.8 for female students, 2 with similar values in Waves 1 and 2. Approximately 9% of males and 6% of females reported drinking alcohol at least one time per week in Wave 1. The prevalence of binge drinking (consuming five or more drinks in a single episode) at least once a month is slightly higher: 11% among males and 7% among females. On average, the frequency of drinking in Wave 1 is 1.34 days per month for male respondents and 0.94 days per month for female respondents, while drinking intensity averages 2.8 drinks per episode for males and 2.2 drinks per episode for females. By Wave 2, alcohol consumption increases in all areas for both males and females. The increases for males are larger, ranging from an 18% increase in the average number of drinks per episode to a 55% increase in the fraction who binge monthly.

Summary Statistics

Note : Based on responses to survey questions regarding most recently completed school year.

Of the Wave 1 respondents, 87% of males and 90% of females had skipped school at least once in the past year, with males averaging 1.47 days skipped and females averaging 1.37 days. Further, 11% of males and 7% of females had been suspended at least once. Regarding the school difficulty measures, 50% of male respondents in Wave 1 reported at least one type of regular difficulty with school: 32% had difficulty paying attention, 15% did not get along with their teachers, and 35% had problems doing their homework. Among females, 40% had at least one difficulty: 25% with paying attention, 11% with teachers, and 26% with homework.

Table 2 tabulates changes in dichotomous measures of problem drinking by gender. Among males, 82.6% did not drink weekly in either wave; 8.1% became weekly drinkers in Wave 2; 4.8% stopped drinking weekly in Wave 2; and the remaining 4.5% drank weekly in both waves. Among females, 88.5% did not drink weekly in either wave; 5.3% became weekly drinkers in Wave 2; 3.7% stopped drinking weekly in Wave 2; and 2.5% drank weekly in both waves. The trends in monthly binging were similar, with the number of students who became monthly bingers exceeding that of students who stopped bingeing monthly in Wave 2. The proportion of respondents reporting binge-drinking monthly in both waves (6.6% and 3.4% for men and women, respectively) was higher than the fraction of students who reported drinking weekly in both waves.

Tabulation of Changes in Dichotomous Measures of Alcohol Use By Gender

4. Empirical methods and estimation issues

We examined the impact of adolescent drinking on GPA using fixed-effects estimation techniques. The following equation captures the relationship of interest:

where GPA it is grade point average of individual i during the Wave t school year, A it is a measure of alcohol consumption, X it is a set of other explanatory variables, c i are unobserved individual effects that are constant over time, ε it is an error term uncorrelated with A it and X it , and α, β a , and β x are parameters to estimate.

The coefficient of interest is β a , the effect of alcohol consumption on GPA. The key statistical problem in the estimation of β a is that alcohol consumption is likely to be correlated with individual-specific unobservable characteristics that also affect GPA. For instance, an adolescent with a difficult family background may react by shirking responsibilities at school and may, at the same time, be more likely to participate in risky activities. For this reason, OLS estimation of Equation (1) used with cross-sectional or pooled longitudinal data is likely to produce biased estimates of β a . In this paper, we took advantage of the two high school-administered waves in Add Health and estimated β a using fixed-effects techniques. Because Waves 1 and 2 were only one year apart, it is likely that most unobserved individual characteristics that are correlated with both GPA and alcohol use are constant over this short period. Subtracting the mean values of each variable over time, Equation (1) can be rewritten as:

Equation (2) eliminates time invariant individual heterogeneity ( c i ) and the corresponding bias associated with OLS estimation of Equation (1) .

We estimated Equation (2) using different sets of time-varying controls ( X it ). 3 We began by controlling only for unambiguously exogenous variables and progressively added variables that were increasingly likely to be affected by alcohol consumption. The first set of controls included only the respondent’s grade level, indicators for attending the sample school or sister school, and the date of the interview. In a second specification, we added household characteristics and interviewer remarks about the household and the interview. This specification includes indicators for the presence of parents and others during the interview and thus controls for a potentially important source of measurement error in the alcohol consumption variables. 4 The third specification added to the second specification those variables more likely to be endogenous such as BMI, religious beliefs/practices, employment, and health status. A fourth specification included tobacco and illegal drug use. By adding these behavioral controls, which could either be mediators or independent correlates of the drinking-GPA association, we examined whether the fixed-effects estimates were influenced by unmeasured time variant individual characteristics.

The fifth and sixth specifications were aimed at assessing possible mechanisms flowing from changes in alcohol use to changes in GPA. Previous research has found that part of the association between alcohol consumption and grades can be explained by a reduction in study hours. Add Health did not directly ask respondents about study effort. It did, however, ask about suspensions and days skipped from school. These school attendance variables were added to the set of controls to test whether an effect of alcohol use on human capital accumulation worked extensively through the quantity of, or exposure to, schooling. Alternatively, an effect of alcohol use on grades could be explained by temporary or permanent alterations in the structure and functioning of an adolescent’s developing brain with resulting changes in levels of concentration and understanding (an intensive mechanism). To test for the mediating role of this pathway, we added a set of dichotomous variables measuring whether the student reported having trouble at least once a week with each of the following: (i) paying attention in school, (ii) getting along with teachers, and (iii) doing homework.

Finally, we considered the number of days the student skipped school and the likelihood of having difficulties with school as two alternative outcomes and estimated the association between these variables and alcohol use, applying the same fixed-effects methodology as in Equation (2) . To analyze difficulties with school as an outcome, we constructed a dichotomous variable that is equal to one if the student faced at least one of the three difficulties listed above. We estimated the effect of alcohol use on this variable using a fixed-effects logit technique.

Separate regressions were run for male and female respondents. The literature shows that males and females behave differently both in terms of alcohol use ( Ham & Hope, 2003 ; Johnston, O’Malley, Bachman, & Schulenberg, 2007 ; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996 ; Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994 ) and school achievement ( Dwyer & Johnson, 1997 ; Jacob, 2002 ; Kleinfeld, 1998 ). These gender differences are clearly evident in the summary statistics presented in Table 1 . Furthermore, the medical literature suggests that there may be gender differences in the impact of alcohol consumption on cognitive abilities (e.g. Hommer, 2003 ).

In addition to examining differential effects by gender, we tested for differential effects of alcohol use along three other dimensions: age, the direction of change in alcohol use (increases vs. decreases), and initial GPA. These tests, as well as other extensions and robustness checks, are described in Section 6.

Table 3 shows the fixed-effects estimates for β a from Equation (2) . Each cell depicts a different model specification defined by a particular measure of alcohol use and a distinctive set of control variables. Rows (a)-(d) denote the alcohol use variable(s) in each specification, and Columns (1)-(6) correspond to the different sets of covariates. Control variables are added hierarchically from (1) to (3). We first adjusted only by grade level, sample school and sister school indicators, and interview date (Column (1)). We then added time-varying household characteristics and interviewer assessments (Column (2)), followed by other individual time-varying controls (Column (3)). Column (4) adds controls for the use of other substances, which could either be correlates or consequences of alcohol use. Columns (5) and (6) consider other potential mediators of the effects found in (1)-(3) such as days skipped, suspensions from school, and academic difficulties.

Fixed effects Estimates; Dependent Variable = GPA

Notes : See Table 1 for list of control variables in each model specification. Robust standard errors in parentheses;

The results for males provide evidence of a negative yet small effect of alcohol use on GPA. No major changes were observed in the estimates across the different specifications that incrementally added more controls, suggesting that the results are probably robust to unmeasured time-varying characteristics. In what follows, therefore, we describe the results in Column (3), which controls for the greatest number of individual time-varying factors (with the exception of tobacco and illicit drug use). Weekly drinking and monthly binge drinking are both negatively associated with GPA, but neither of these coefficients is statistically significant (Rows (a) and (b)). The continuous measure of alcohol consumption has a statistically significant coefficient (Row (c)), suggesting that increasing one’s alcohol intake by 100 drinks per month reduces GPA by 0.07 points, or 2.8% relative to the mean. The results in Row (d) suggest that variation in both the frequency and the intensity of alcohol use contributes to the estimated effect on grades. An increase of one day per month in drinking frequency reduces GPA by 0.005 points, and consumption of one additional drink per episode reduces GPA by 0.004 points.

Columns (4)-(6) report the estimates of interest after controlling for use of other substances, days skipped or suspended from school, and difficulties with school. Relative to the effects identified in Column (3), controlling for tobacco and illegal drug use reduces the negative effect of total number of drinks on GPA by 9% or 0.006 GPA points (see row (c), Column (4)). Adding the school attendance variables to the set of controls in Column (3) results in a point estimate of −0.06 or 0.01 GPA points below the coefficient in Column (3) (see Column (5)). Adding the school difficulty variables results in a reduction in GPA of 0.007 GPA points or a 10% decrease relative to the estimate in Column (3). While not shown in the table, the inclusion of both school difficulty and attendance variables as controls explains approximately 20% of the effect of alcohol use on grades, with the alcohol use estimates remaining statistically significant at the 10% level.

For females, the estimated coefficients are much smaller than those for males, and for two measures (binge-drinking and drinking frequency), the estimates are actually positive. However, none of the coefficients are statistically significant at conventional levels. 5 Interestingly, after controlling for substance use, difficulties with school, and school attendance, the estimates become less negative or more positive. But they remain statistically non significant.

Table 4 shows the effect of alcohol use on the number of school days skipped during the past year. These results are qualitatively similar to the findings for GPA, suggesting some small and statistically significant effects for males but no significant effects for females. For males, increasing the number of drinks per month by 100 leads to an additional 0.72 days skipped (p<0.10) when controlling for household features, interviewer comments, and individual characteristics such as body mass index, religiosity, employment, and health status (see Column (3), Row (c)). Controlling for tobacco and illegal drug use reduces the coefficient slightly to 0.69 days. The results in Row (d) suggest that this effect is driven mainly by variation in drinking intensity, with an additional drink per episode resulting in an increase of 0.06 days skipped.

Fixed-effects Estimates; Dependent Variable = School Days Skipped

Notes : Robust standard errors in parentheses;

Table 5 contains estimates of the relationship between alcohol use and our dichotomous measure of having difficulty in school. For males, we found one small but statistically significant effect: consumption of an additional 100 drinks per month is associated with a 4% increase in the probability of having trouble in school. For females, the estimated coefficients are all positive and larger than those found for males, and four out of five are statistically significant. The probability of having trouble in school is roughly 11% higher for females who drink weekly relative to those who do not, and there is a similar effect for monthly binge drinking (Rows (a) and (b)). Furthermore, the likelihood of difficulties increases by 7% with an additional 100 drinks per month (Row (c)). These findings suggest that female students suffer adverse consequences from alcohol consumption, even if these effects do not translate into lower grades. Finally, in Row (d), we see that these adverse effects are driven by increases in drinking frequency rather than drinking intensity.

Fixed-effects Logit Estimates; Dependent Variable = Difficulty with School

Notes : Dependent variable is a dummy variable equal to one if respondent had trouble at least once a week with one or more of the following: (1) paying attention in school, (2) getting along with teachers, or (3) doing homework. Robust standard errors in parentheses;

Our main results thus far point to two basic conclusions. After controlling for individual fixed effects, alcohol use in high school has a relatively minor influence on GPA. But there are also some interesting gender differences in these effects. For males, we find small negative effects on GPA that are partially mediated by increased school absences and difficulties with school-related tasks. For females, on the other hand, we find that alcohol use does not significantly affect GPA, but female drinkers encounter a higher probability of having difficulties at school.

Our basic estimates of the effects of drinking on GPA complement those of Koch and Ribar (2001) , who find small effects of drinking on school completion for males and non-significant effects for females. However, our analysis of school-related difficulties suggests that females are not immune to the consequences of drinking. Namely, females are able to compensate for the negative effects of drinking (e.g., by working harder or studying more) so that their grades are unaffected. This interpretation is consistent with Wolaver’s (2007) finding that binge drinking in college is associated with increased study hours for women but with reduced study hours for men. It is also reminiscent of findings in the educational psychology and sociology literatures that girls get better grades than boys, and some of this difference can be explained by gender differences in classroom behavior ( Downey & Vogt Yuan, 2005 ) or by greater levels of self-discipline among girls ( Duckworth & Seligman, 2006 ).

When interpreting our results, there are some important caveats to keep in mind. First, we must emphasize that they reflect the contemporaneous effects of alcohol use. As such, they say nothing about the possible cumulative effects that several years of drinking might have on academic performance. Second, we can only examine the effect of alcohol use on GPA for those students who remain in school. Unfortunately, we cannot address potential selection bias due to high school dropouts because of the high rate of missing GPA data for those students who dropped out after Wave 1. 6 Third, we acknowledge that our fixed-effects results could still be biased if we failed to account for important time-varying individual characteristics that are associated with GPA differentials across waves. It is reassuring, however, that our results are generally insensitive to the subsequent inclusion of additional time-varying (and likely endogenous) characteristics, such as health status, employment, religiosity, tobacco use, and illicit drug use. Finally, we cannot rule out possible reverse causality whereby academic achievement affects alcohol use. Future research using new waves of the data may provide further insight on this issue. In the next section, we discuss some additional issues that we are able to explore via robustness checks and extensions.

6. Robustness checks and extensions

6.1. ols versus fixed effects.

In addition to running fixed-effects models, we estimated β a using OLS. Separate regressions were run by gender and by wave. We first regressed GPA on measures of alcohol use and the full set of time-varying controls used in the fixed-effects estimation (see Column (3), Table 3 ). Next, we added other time-invariant measures such as demographics, household characteristics, and school characteristics. Finally, we controlled for tobacco and illegal drug use. The comparison between fixed-effects and OLS estimates (Appendix Table A1 ) sheds light on the extent of the bias in β ^ a OLS . For males, OLS estimates for Wave 1 were 3 to 6 times larger (more negative) than fixed-effects estimates (depending on the measure of alcohol use), and OLS estimates in Wave 2 were 3 to 4 times larger than those from the fixed-effects estimation. The bias was even more pronounced for females. Contrary to the results in Table 3 , OLS estimates for females were statistically significant, quantitatively large, and usually more negative than the estimates for males.

OLS Cross-sectional Estimates; Dependent Variable = GPA

6.2. Outlier analysis

Concerns about misreporting at the extreme tails of the alcohol use distributions led us to re-estimate the fixed-effects model after addressing these outliers. A common method for addressing extreme outliers without deleting observations is to “winsorize” ( Dixon, 1960 ). This technique reassigns all outlier values to the closest value at the beginning of the user-defined tail (e.g., 1%, 5%, or 10% tails). For the present analysis, we used both 1% and 5% tails. As a more conventional outlier approach, we also re-estimated the models after dropping those observations in the 1% tails. In both cases we winsorized or dropped the tails using the full Wave 1 and Wave 2 distribution (in levels) and then estimated differential effects.

After making these outlier corrections, the estimates for males became larger in absolute value and more significant, but the estimates for females remained statistically non-significant with no consistent pattern of change. 7 For males, dropping the 1% tails increased the effect of 100 drinks per month on GPA to −0.15 points (from −0.07 points when analyzing the full sample). Winsorizing the 5% tails further increased the estimated effect size to −0.31 points.

We offer two possible interpretations of these results for males. First, measurement error is probably more substantial among heavier drinkers and among respondents with the biggest changes in alcohol consumption across waves, which could cause attenuation bias at the top end. 8 Second, the effect of drinks per month on GPA could be smaller among male heavier drinkers, suggesting non-linear effects. Interestingly, neither of these concerns appears to be important for the analysis of females.

6.3. Differential effects

Thus far we have reported the differential effects of alcohol use on GPA for males and females. Here, we consider differential effects along three other dimensions: age, direction of change in alcohol use (increases vs. decreases), and initial GPA. To examine the first two of these effects, we added to Equation (2) interactions of the alcohol use measure with dichotomous variables indicating (i) that the student was 16 or older, and (ii) that alcohol use had decreased between Waves 1 and 2. 9 For males, the negative effects of drinking on GPA were consistently larger among respondents who were younger than 16 years old. None of the interaction terms, however, were statistically significant. We found no consistent or significant differences in the effect of alcohol consumption between respondents whose consumption increased and those whose consumption decreased between Waves 1 and 2. All results were non-significant and smaller in magnitude for females. It should be noted, however, that the lack of significant effects could be attributed, at least in part, to low statistical power as some of the disaggregated groups had less than 450 observations per wave.

To examine whether drinking is more likely to affect low achievers (those with initial low GPA) than high achievers (higher initial GPA), we estimated two fixed-effects linear probability regressions. The first regression estimated the impact of alcohol use on the likelihood of having an average GPA of C or less, and the second regression explored the effect of drinking on the likelihood of having a GPA of B- or better. For males, we found that monthly binging was negatively associated with the probability of obtaining a B- or higher average and that increases in number of drinks per month led to a higher likelihood of having a GPA of C or worse. Frequency of drinking, rather than intensity, was the trigger for having a GPA of C or worse. For females, most coefficient estimates were not significant, although the frequency of drinking was negatively associated with the probability of having a GPA of C or worse.

6.4 Self-reported versus abstracted GPA

One of the key advantages of using Add Health data is the availability of abstracted high school grades. Because most educational studies do not have such objective data, we repeated the fixed-effects estimation of Equation (2) using self-reported GPA rather than transcript-abstracted GPA. To facilitate comparison, the estimation sample was restricted to observations with both abstracted and self-reported GPA (N=2,164 for males and 2,418 for females).

The results reveal another interesting contrast between males and females. For males, the results based on self-reported grades were fairly consistent with the results based on abstracted grades, although the estimated effects of binging and drinking intensity were somewhat larger (i.e., more negative) when based on self-reported grades. But for females, the results based on self-reported grades showed positive effects of alcohol consumption that were statistically significant at the 10% level for three out of five consumption measures (monthly binging, total drinks per month, and drinks per episode). Furthermore, with the exception of the frequency measure (drinking days per month), the estimated effects were all substantially larger (i.e., more positive) when based on self-reported GPA. This suggests that females who drink more intensively tend to inflate their academic performance in school, even though their actual performance is not significantly different from that of those who drink less. Males who drink more intensely, on the other hand, may tend to deflate their academic accomplishments.

6.5. Analysis of dropouts

In Table 3 , we estimated the effects of alcohol consumption on GPA conditional on being enrolled in school during the two observation years. While increased drinking could lead an adolescent to drop out of school, reduced drinking could lead a dropout to re-enroll. Our GPA results do not address either of these possible effects. Of those who were in 9 th grade in Wave 1, roughly 2.3% dropped out before Wave 2. Of those who were in 10 th and 11 th grades in Wave 1, the dropout rates were 3.7% and 5.0%, respectively. Our core estimates would be biased if the effect of alcohol use on GPA for non-dropouts differed systematically from the unobserved effect of alcohol use on GPA for dropouts and re-enrollers in the event that these students had stayed in school continuously.

To determine whether dropouts differed significantly from non-dropouts, we compared GPA and drinking patterns across the two groups. Unfortunately, dropouts were much more likely to have missing GPA data for the years they were in school, 10 so the comparison itself has some inherent bias. Nevertheless, for those who were not missing Wave 1 GPA data, we found that mean GPA was significantly lower for dropouts (1.11) than for those students who stayed in school at least another year (2.66). Dropouts were also older in Wave 1 (16.9 vs. 15.9 years old) and more likely to be male (54% vs. 48%). They also consumed alcohol more often and with greater intensity in the first wave. While there is evidence of differences across the two groups in Wave 1, it is unclear whether dropouts would have differed systematically with respect to changes in GPA and in drinking behavior over time if they had stayed in school. Due to the small number of dropout observations with Wave 1 GPA data, we could not reliably estimate a selection correction model.

6.6. Attrition and missing data

As described in the data section, a large fraction of the Add Health respondents who were in 9th, 10th, or 11th grade in Wave 1 were excluded from our analysis either because they did not participate in Waves 2 or 3, did not have transcript data, or had missing data for one or more variables used in the analysis. (The excluded sample consisted of 7,104 individuals out of a total of 11,396 potentially eligible.) Mean characteristics were compared for individuals in the sample under analysis (N=4,292) and excluded respondents (N=7,104) in Wave 1. Those in the analysis sample had higher GPAs (both self-reported and abstracted, when available) and were less likely to have difficulties at school, to have been suspended from school, or to have skipped school. They were less likely to drink or to drink intensively if they drank. They were more likely to be female and White, speak English at home, have highly educated parents, have a resident mother or father at home, and be in good health. They were less likely to have parents on welfare, live in commercial areas or poorly kept buildings, and smoke and use drugs.

The above comparisons suggest that our estimates are representative of the sample of adolescents who participated in Waves 2 and 3 but not necessarily of the full 9 th , 10 th , and 11 th grade sample interviewed at baseline. To assess the magnitude and sign of the potential attrition bias in our estimates, we considered comparing fixed-effects estimates for these two samples using self-reported GPA as the dependent variable. But self-reported GPA also presented a considerable number of missing values, especially for those in the excluded sample at Wave 2. Complete measures of self-reported GPA in Waves 1 and 2 were available for 60% of the individuals in the analysis sample and for less than 30% of individuals in the excluded sample.

As an alternative check, we used OLS to estimate the effects of alcohol use on self-reported GPA in Wave 1 for the excluded sample, and compared these to OLS coefficients for our analysis sample in Wave 1. The effects of alcohol use on self-reported grades were smaller for individuals excluded from our core analysis. Because the excluded individuals tend to consume more alcohol, the finding of smaller effects for these individuals is consistent with either of the two explanations discussed in Section 6.2 above. First, the effect of consuming alcohol on GPA could be smaller for those who drink more. And second, measurement error is probably more serious among heavier drinkers, potentially causing more attenuation bias in this sample.

To summarize, the analysis described above suggests that some caution should be exercised when extrapolating the results in this paper to other populations. Due to missing data, our analysis excludes many of the more extreme cases (in terms of grades, substance use, and socioeconomic status). However, our analysis suggests that the effects of alcohol use on grades are, if anything, smaller for these excluded individuals. It therefore supports our main conclusions that the effects of alcohol use on GPA tend to be small and that failure to account for unobserved individual heterogeneity is responsible for some of the large negative estimates identified in previous research.

7. Conclusion

Though a number of investigations have studied the associations between alcohol use and years of schooling, less is known about the impact of adolescent drinking on the process and quality of learning for those who remain in school. Moreover, studies that have examined the impact of drinking on learning have faced two important limitations. First, they have relied on self-reported grades as the key measure of learning and are therefore subject to potential biases that result from self-reporting. Second, they have relied on cross-sectional data and suffer from potential biases due either to unobserved individual heterogeneity or to weak or questionable instrumental variables.

In the present study, we contribute to the existing literature by exploiting several unique features of the nationally representative Add Health survey. First, we measure learning with grade point averages obtained from the respondents’ official school transcripts. Second, we exploit Add Health’s longitudinal design to estimate models with individual fixed effects. This technique eliminates the bias that results from time-invariant unobserved individual heterogeneity in the determinants of alcohol use and GPA. Finally, we explore a variety of pathways that could explain the association between alcohol use and grades. In particular, we examine the effects of alcohol consumption on both the quantity of schooling—as measured by days of school skipped—and the quality—as measured by difficulties with concentrating in school, getting along with teachers, or completing homework.

The main results show that, in general, increases in alcohol consumption result in statistically significant but quantitatively small reductions in GPA for male students and in statistically non-significant changes for females. For both males and females, comparisons of the fixed-effects models with standard cross-sectional models suggest that large biases can result from the failure to adequately control for unobserved individual heterogeneity. Our findings are thus closely aligned with those of Koch and Ribar (2001) and Dee and Evans (2003) , who reach a similar conclusion regarding the effects of drinking on school completion.

Our analysis also reveals some interesting gender differences in how alcohol consumption affects learning in high school. Our results suggest that for males, alcohol consumption has a small negative effect on GPA and this effect is partially mediated by increased school absences and by difficulties with school-related tasks. For females, however, we find that alcohol use does not significantly affect GPA, even though it significantly increases the probability of encountering difficulties at school. Gender differences in high school performance are well documented in the educational psychology and sociology literatures, yet no previous studies have estimated gender differences in high school learning that are directly associated with alcohol use. Our study is therefore unique in that regard.

Finally, our study also highlights the potential pitfalls of using self-reported grades to measure academic performance. Not only do we find evidence that use of self-reports leads to bias; we also find that the bias differs by gender, as drinking is associated with grade inflation among females and grade deflation among males. Hence, the conceptual discoveries uncovered in this research may be as important for future investigations as the empirical results are for current educational programs and policies.

Acknowledgements

Financial assistance for this study was provided by research grants from the National Institute on Alcohol Abuse and Alcoholism (R01 AA15695, R01 AA13167, and R03 AA016371) and the National Institute on Drug Abuse (RO1 DA018645). This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( http://www.cpc.unc.edu/addhealth ). No direct support was received from grant P01-HD31921 for this analysis. We gratefully acknowledge the input of several colleagues at the University of Miami. We are also indebted to Allison Johnson, William Russell, and Carmen Martinez for editorial and administrative assistance. The authors are entirely responsible for the research and results reported in this paper, and their position or opinions do not necessarily represent those of the University of Miami, the National Institute on Alcohol Abuse and Alcoholism, or the National Institute on Drug Abuse.

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1 Due to a significant fraction of missing responses, we imputed household income and household welfare status using both predicted values on the basis of other covariates and the sample mean for households that were also missing some of the predicting covariates. We added dummy variables to indicate when an observation was imputed.

2 Grades and numerical grade-point equivalents have been established for varying levels of a student’s academic performance. These grade-point equivalents are used to determine a student’s grade-point average. Grades of A, A-, and B+ with respective grade-point equivalents of 4.00, 3.67, and 3.33 represent an “excellent” quality of performance. Grades of B, B−, and C+ with grade-point equivalents of 3.00, 2.67, and 2.33 represent a “good” quality of performance. A grade of C with grade-point equivalent of 2.00 represents a “satisfactory” level of performance, a grade of D with grade-point equivalent of 1.00 represents a “poor” quality of performance, and a grade of F with grade-point equivalent of 0.00 represents failure.

3 Note that some demographics (e.g., race, ethnicity) and other variables that are constant over time do not appear in Equation (2) because they present no variation across waves.

4 Of particular concern is the possibility that measurement error due to misreporting varies across waves—either because of random recall errors or because of changes in the interview conditions. (For example, the proportion of interviews in which others were present declined from roughly 42% to 25% between Wave 1 and Wave 2.) Such measurement error could lead to attenuation bias in our fixed-effects model. On the other hand, reporting biases that are similar and stable over time are eliminated by the fixed-effects specification.

5 We tested the significance of these differences by pooling males and females and including an interaction of a gender dummy with the alcohol consumption measure in each model. We found statistically significant differences in the effects of monthly bingeing, drinks per month, and drinking days per month.

6 If alcohol use has small or negligible effects on school completion - as found by Chatterji (2006) , Dee and Evans (2003) , and Koch and Ribar (2001) - then such selection bias will also be small.

7 These results are not presented in the tables but are available from the authors upon request.

8 Examination of the outliers showed that only 15% of those who reported a total number of drinks above the 95th percentile of the distribution did so in both waves.

9 These fixed-effects regressions were adjusted by the same set of controls as in Table (3) , Column (3).

10 More than two-thirds of those who dropped out between Waves 1 and 2 were missing Wave 1 GPA data

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