essay about cause and effect of alcoholism

Alcoholism: Causes, Risk Factors, and Symptoms

Introduction, causes of alcoholism, effects of alcoholism, works cited.

The term alcoholism may be used to refer to a wide range of issues associated with alcohol. Simply put, it is a situation whereby an individual cannot stay without alcohol. An alcoholic usually drinks alcohol uncontrollably and persistently.

Alcoholism usually leads to ill health, and it affects relationships between the individual and the people around him. It may also be considered as a disease and may be referred to as an alcohol use disorder. Alcohol abuse may also cause damage to vital organs in the human body, including the brain and heart (Mukamal et al. 1965). Therefore, it may lead to psychiatric and medical issues. Psychiatric disorders, such as depression and schizophrenia, may occur.

Others include phobias, dysthymia, mania, and depression. Alcoholism may also cause neurologic deficits. These deficits may be manifested through certain impairments such as brain damage and memory loss. Such individuals also have difficulty executing certain functions and may experience issues with body balance and gait.

The brain might be affected as certain changes occur in its structure and chemistry. With time, a person develops physical dependence and tolerance. This causes the inability to stop drinking and causes complications as one tries to stop the habit.

This is particularly referred to as alcohol withdrawal syndrome. Identifying alcoholics for the purpose of treatment may be difficult since such individuals may avoid seeking help due to stigmatization. However, certain factors influence the risk for the condition.

These factors include mental health, depression, age, gender, ethnicity, and family history (Mukamal et al. 1965). This paper will discuss the genetic and environmental factors that cause alcoholism and highlight the complications, conditions, and diseases associated with the disorder.

The main causes of this disorder may be categorized into two. These include genetic and environmental factors. The genetic material that determines the metabolism of the drink also influences the risk of the disorder. Persons with a family history of the disorder may also develop it.

A particular study argued that the expression of genes was influenced if an individual started using alcoholic beverages at an early stage in life (Agrawal et al. 69). This increased the risk of alcohol dependence among such individuals.

Persons with a genetic disposition to the disorder would most probably start drinking at an early stage. Individuals who start drinking at an early stage are also more likely to develop alcoholism. It is also argued that 40% of alcoholics misuse alcohol by the time they are in their late adolescent stage. However, certain researchers disagree with this idea (Schwandt et al., 74).

Individuals who do not receive support from family and friends are highly likely to develop alcoholism. Therefore, some social and emotional factors may cause an ex-drinker to start drinking again. For example, mental and emotional stress can contribute to alcoholism.

An individual under the influence of alcohol may not be able to feel the pain associated with stress. With the normal alcohol intake, an individual’s brain might be at some equilibrium. When the individual tries to quit, the brain responds. This response may come in terms of stress, anxiety, and depression.

These feelings cause chemical imbalances that force an alcoholic to go back to drinking in order to feel better. Social and cultural pressures from media and other sources may also affect the drinking habits of an individual. The media’s portrayal of alcohol as a pleasurable and beneficial drink may encourage individuals to start drinking or cause ex-drinkers to return to their old habits (Bierut et al. 237).

The damaging effect of alcohol on the nervous system is more profound among adolescents and those with a genetic disposition to the disorder. These effects may cause the degeneration of the cerebral cortex. Consequently, this increases impulse behavior that may lead to alcoholism.

Despite the severe damages to the central nervous system due to alcoholism, it is possible to reverse some of the damages through withdrawal from the drug. Another risk factor is the availability of alcohol. This drug is most commonly abused. In terms of popularity, beer may come next after water and tea.

The difference in genetic characteristics also determines the risk of developing the disorder. This is mainly because different races have certain different genetic characteristics.

Therefore, they differ in terms of alcohol metabolism. The difference in genetic makeup may explain the difference in the rate of alcohol dependence among the different races.

The genetic component that determines the rate at which alcohol metabolizes is referred to as the alcohol dehydrogenase allele. The Native Americans and African Americans are said to have an allele that is not highly associated with alcohol dependence. The Native Americans, on the other hand, are more likely to develop alcohol dependence.

The effects of alcohol abuse are diverse. Consumption of excess alcohol may lead to several diseases and complications. For example, it may lead to the inflammation of the pancreas, liver disease, and cancers. Alcohol-related cancers are believed to form as the elements in the alcoholic drink are converted into acetaldehyde. This is a potent carcinogen.

Different parts of the body may host the cancerous cells. These areas include the liver, breast, and mouth. The larynx and the throat are also likely to be affected. Alcoholics who take tobacco have an increased risk of cancers (Bierut et al. 237).

Liver cirrhosis is another condition that may occur as a result of excessive drinking of alcohol. This is manifested through the scarring of the organ to such an extent that it cannot perform its functions. However, some individuals who drink moderately have also been shown to suffer from the disease (Mukamal et al. 1965).

Pregnant alcoholic mothers may also cause problems for the unborn. Fetal alcohol syndrome may result from such habits. Excessive use of alcohol may cause impaired brain development and brain shrinkage. Although the brain normally shrinks in old age, excessive use of alcohol increases this rate. With the increased rate, such individuals are likely to develop dementia and have memory issues.

Alcoholism also increases the risk of cognitive and neuropsychiatric disorders. Excessive use of the beverage may cause an increase in the level of toxic amino acid in the plasma. This may be the reason why some individuals suffer from withdrawal seizures.

Alcohol abuse may also cause issues with memory and may impair learning. Alcoholism may also greatly affect the brain. For example, brain lesions are likely to occur. Alcohol-related brain damage comes about due to a combination of several factors.

Alcoholism may also cause heart attacks and strokes. Abuse of alcohol increases the risks of a heart attack. Some studies have shown that drinking alcohol in moderation may offer some level of protection to individuals against heart attack (Mukamal et al. 1965).

This applies specifically to individuals who had suffered a heart attack before. Prolonged use of alcohol in large quantities also causes alcohol cardiomyopathy. This disease affects the muscles of the heart. As the heart muscles fail, this may lead to heart failure.

Alcoholism is also associated with alcohol-related death. Many deaths worldwide have been attributed to the excessive use of alcohol (Doll et al. 199). Individuals who use alcohol excessively are at a higher risk of death than those who take alcohol moderately. Individuals with diseases that may be augmented by the excessive use of alcohol are also at great risk of alcohol-related death. Such diseases include oral cancers and liver disease.

Another effect of alcoholism is anemia. Excessive use of alcoholic beverages causes a reduction in the number of erythrocytes. This condition is referred to as anemia. Since red blood cells are used to transport oxygen around the body, the low level of oxygen due to low numbers of the cells leads to fatigue, shortness of breath, and dizziness.

Alcoholism may either be caused by genetic or environmental factors. Persons with a genetic disposition to the disorder are likely to start drinking and become alcoholics. Genetic variations may determine the difference in alcohol metabolism.

The environmental factors that may cause alcoholism include the availability of alcohol and sociocultural pressures. Certain environmental factors lead to depression that may encourage alcoholism. The effects of alcoholism are diverse. Alcoholism may lead to diseases such as liver disease, heart disease, and cancers. Excessive use of alcohol may affect almost all vital organs of the body and may eventually lead to death.

Agrawal, Arpana, et al. “Evidence for an interaction between age at 1 st drink and genetic influences on DSM-IV alcohol dependence symptoms.” Alcoholism Clinical & Experimental Research 33.12 (2010): 67-80. Print.

Bierut, Laura, et al. “Co-occurring risk factors for alcohol dependence and habitual smoking.” Alcohol Research & Health 24.4 (2000): 233-241. Print.

Doll, Richard, et al . “Mortality in relation to alcohol consumption: A prospective study among male British doctors.” International Journal of Epidemiology 34.1 (2005): 199-204. Print.

Mukamal, Kenneth, et al. “Prior alcohol consumption and mortality following acute myocardial infarction.” JAMA 285.15 (2001): 1965-1970. Print.

Schwandt, Melanie, et al. “Alcohol response and consumption in adolescent rhesus macaques: Life history and genetic influences.” International Biomedical Journal 44.1 (2010): 67-80. Print.

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StudyCorgi. (2020, January 12). Alcoholism: Causes, Risk Factors, and Symptoms. https://studycorgi.com/alcoholism-causes-and-effects/

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The Cause and Effects of Drinking Alcohol, Essay Example

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Alcohol is widely abused across the world and the effects of excessive alcohol intake can be devastating. When not taken in moderation, alcohol can be harmful to the health and social life of the individual, as well as harmful to the individual’s family. These devastating effects can result in alcohol related disease, outcast from social circles and loss of friends and a breakdown in the family.

Excessive alcohol consumption is extremely harmful to the health of a person. Alcohol abuse can lead to heart disease, liver failure and rapid aging. Many deaths are caused by alcoholism. Excessive drinking also increases the decision to take risky actions, including drinking and driving. Drinking and driving causes many deaths every year and is a major problem among those of all ages. Excessive alcohol consumption can also cause memory loss and other psychological issues.

Consuming alcohol at a high rate can also cause social problems. This can include loss of friends and being outcast from your community. This happens because of the bad choices that come with alcoholism and most people do not want to associate with those who drink excessively. It is not just harmful to the drinker, but harmful to their friends and family.

In relation to loss of friends, it is likely that excessive drinking can cause a breakdown in family. Alcoholism is devastating for families. Many times an excessive drinker will not keep up with their responsibilities within the family. This means that they are likely to not finish school or college, lose their jobs and not participate in taking care of their children. This can lead to financial problems and the inability to take care of the themselves of the family.

There are many reasons why excessively consuming alcohol is dangerous and harmful. Drinking in moderation or abstaining from alcohol altogether is important to live a happy and healthy life. Among the effects of drinking alcohol, the most obvious are major health problems, loss of friends and social status and a breakdown in the family unit.

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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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Alcoholism cause and effect essay

Table of Contents

Introduction

There are several terms that have been developed to define alcoholism, including alcohol dependence and alcohol abuse. As Roh (2015) demonstrates, today alcoholism is referred to as alcohol use disorder that occurs when a person consumers so much alcohol that the body becomes addicted and dependent on alcohol. Alcoholism manifests through addition, which makes alcohol the most important thing in the life of the alcoholic. Inasmuch as the negative consequences of alcohol use disorder are well defined, people with the disorder continue to consume alcohol, a factor that leads to negative effects on their overall livelihoods.

This is a research paper whose theme is alcoholism. This paper aims at answering the following research questions: What are the statistical facts related to alcoholism? Which body systems are affected by alcoholism? Based on the statistics on the prevalence and information on the body systems affected by alcoholism, what are the interventions that can be implemented to reduce the effects and prevalence of alcoholism? By answering these questions, this research aims at proving that there is a relationship between multidisciplinary approaches to alcohol abuse prevention and successful interventions to minimize alcoholism.

There have been several studies that have been conducted to determine the exact causes of alcoholism. Across these studies, Powers, Berger, Fuhrmann and Fendrich (2017) highlighted that there are no specific emergent causes of alcoholism, though the risk factors of alcoholism continue to be defined. According to Roh (2015), there is a thin line between moderate alcohol consumption and excessive consumption, with the latter being attributed to addiction. In an attempt to ascertain this, Allamani (2012) classified males who consume more than 15 drinks weekly and women who take more than 12 drinks weekly as highly at risk of alcoholism. However, Opačić, Oreb and Radat (2017) explained that alcoholism can purely be linked to social and psychological issues. Peer pressure is among the leading social issues associated with alcoholism, while depression, anxiety and mental abuse are the psychological issues that are attributed to alcoholism.

As of 2015 in the US, alcohol use disorder was prevalent across 6.2% of the population aged above 18 years, based on the report by the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The prevalence of this disorder was higher among men (8.4%) compared to women (5.3%). On the other hand, 2.5% of youths aged between 12 and 17 were reported to have alcohol use disorder (niaaa.nih.gov, 2018). However, the disorder was prevalent among the females in this age group (2.7%) compared to the 2.3% of the males. The NIAAA further reports that approximately 88,000 deaths occur annually due to alcoholism. Men constitute the larger proportion of these deaths. On the global scale, there are 3.3 million deaths annually that accrue from alcoholism, translating to nearly 6% of the total deaths globally.

The effects of alcoholism on the body have been well documented by Bernstein (2012), who writes that inculcated behavior of excessive alcohol consumption leads to disorders of the digestive system, the circulatory system, the central nervous system and the endocrine system. The most common body part affected by alcoholism is the liver. In 2015, nearly 47% of the liver disease deaths in the USA were attributed to alcoholism (niaaa.nih.gov, 2018). Besides, alcohol has been identified as a leading cause of cirrhosis, a chronic disease of the liver that manifests through inflammation and cell degeneration. Alcoholism was identified by the NIAAA as the major cube of the cirrhosis deaths, as it contributed 48% of these deaths in 2015.

The statistics of alcoholism prevalence and the effects of alcoholism on the body paint a picture of the need to develop appropriate interventions to prevent it. According to Opačić, Oreb and Radat (2017), the prevention measures of alcoholism are aimed at reducing the abuse of alcohol as well as its consequences. As such, legislators, healthcare providers, the community and the potential addicts should all be included in the efforts directed towards preventing alcoholism. Legislators play a critical role in reducing alcohol consumption through increasing the taxes levied on alcohol and increasing the minimum age that is legally accepted for drinking. Besides, legislators also create laws that oblige alcohol manufacturers to inform and warn the drinkers of the effects of alcohol abuse.

Health care providers play the role of promoting the recovery efforts of those diagnosed with alcohol use disorder. Doctors and nurses are influential in enabling the addicts overcome the effects of withdrawal, while psychological health experts enable the addicts to develop a positive attitude towards life. The communities are pivotal in developing programs and educational interventions for saving the lives of the addicts. The communities include the citizen advocacy groups, media, business enterprises, the police and schools. In all these interventions, the input of addicts should be embedded, as Roh (2015) demonstrates that such inclusion increases the suitability of the programs among the populations at high risk of alcoholism.

In this discussion, the statistics of alcoholism prevalence have been provided, indicating that alcoholism is highly prevalent among men than women. The study also establishes that the liver is the body organ that is largely affected by alcoholism, an argument that is supported from the data detailing the number of liver disease deaths associated with alcoholism. The solutions to alcoholism listed in this paper include policy interventions, healthcare prevention approach and community based programs. In conclusion, there is a relationship between multidisciplinary approaches to alcohol abuse prevention and successful interventions to minimize alcoholism.

• Allamani, A. (2012). Alcohol Consumption Policies and the Prevention of Alcohol Consumption-Related Problems: Needs, Duties, and Responsibilities*.  Substance Use & Misuse ,  47 (12), 1252-1259. http://dx.doi.org/10.3109/10826084.2012.716483
• Bernstein, D. (2012). Alcoholic Liver Disease.  Clinics In Liver Disease ,  16 (4), xiii-xiv. http://dx.doi.org/10.1016/j.cld.2012.09.009
• niaaa.nih.gov. (2018).  Alcohol Facts and Statistics | National Institute on Alcohol Abuse and Alcoholism (NIAAA) .  Niaaa.nih.gov . Retrieved 29 January 2018, from https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/alcohol-facts-and-statistics
• Opačić, A., Oreb, T., & Radat, K. (2017). Characteristics and Significance of Professional-Led Support Groups in the Treatment of Alcoholism.  Alcoholism Treatment Quarterly ,  35 (4), 359-371. http://dx.doi.org/10.1080/07347324.2017.1350541
• Powers, G., Berger, L., Fuhrmann, D., & Fendrich, M. (2017). Family history density of substance use problems among undergraduate college students: Associations with heavy alcohol use and alcohol use disorder.  Addictive Behaviors ,  71 (9), 1-6. http://dx.doi.org/10.1016/j.addbeh.2017.02.015
• Roh, S. (2015). New Directions in Healthcare for Alcohol Use Disorder.  Health Care : Current Reviews ,  03 (02). http://dx.doi.org/10.4172/2375-4273.1000144
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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.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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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Problem of Excess Alcohol Drinking in Society Essay

Introduction, causes of alcohol consumption, consequences, works cited.

For generations, alcohol has held an important place in the spiritual, emotional and social experience of people. For this reason, people drink as a form of relaxation, to mark important cultural events, and as a way of celebrating with friends (Heron 7). Taken in moderation, alcohol does not have any drastic effects on the drinker.

Problems only arise when alcohol is consumed in excess. Each year, nearly 80,000 lives are lost in the United States due to excessive use of alcohol (Centers for Disease Control and Prevention n.p.). It is estimated that in 2006, some $ 223.5 billion was lost due to excessive consumption of alcohol.

There are a number of reasons why people consume alcohol. People drink alcohol as a way of reducing associations in their minds. Alcohol weakens molecules separating neurons in the brains, thereby impairing communication. Consequently, an individual finds it hard to associate ideas. Psychologists also say that we drink as a way of escaping the self. Most people say that they drink alcohol in order to drown their sorrows. Since alcohol impairs communication, people momentarily forget their troubles.

Like other things in life, there are other underlying reasons that shape our drinking habits. For example, some people drink because they feel sad, angry, or lonely.

Others drink as a way of bonding with their friends and loved ones (Heron 8). Therefore, our drinking habits, whether in excess or in moderation, are shaped by hidden motivations. In the case of heavy drinkers, this behavior could be due to the need to address underlying problems, such as difficulty in dealing with low self-esteem, inability to handle strong emotions, and problems with relationships.

Peer pressure is yet another reason why people may start drinking alcohol (Centers for Disease Control and Prevention n.p.). For example, a teenager may start experimenting with alcohol while in college because his friends are also doing it. In this case, he feels compelled to experiment with alcohol so that he can belong with his peers. We also drink since alcohol has become culturally normalized.

The media aggressively promote alcohol consumption to an extent that it has now become culturally normalized. Alcohol is also readily available in supermarkets, bars, and discount stores. In fact, children under the age of 18 years can buy alcohol without some seller requesting to see their IDs first. For these reasons, consumption of alcohol has become normal and socially acceptable.

Effects of alcohol use

The effects of alcohol on the drinker are dependent on a number of factors. First, it depends on the body chemistry. This means that some people can get tipsy quite easily while others need larger quantities of alcohol to get drunk.

The effects of alcohol are also dependent on one’s weight, gender, and age (Masters 21). For example, women tend to get drunk by smaller quantities of alcohol compared with men. Effects of alcohol also depend on one’s weight. Blood alcohol concentration (BAC) is the level of alcohol in the blood that causes one to get intoxicated, and is weight-dependent.

Thus, a man who weighs say, 200 pounds, may be less intoxicated than one who weighs 150 pounds even after both men have consumed the same quantity of alcohol. Among the elderly, the rate at which the liver metabolizes alcohol is slower in comparison with younger people. Other important factors to consider include quantity and type of alcohol consumed, drinking experience, and whether one had eaten or not, before taking alcohol.

The effects of alcohol use on one’s behavior also vary, depending on the amount of alcohol consumed. As one gets drunk, they are talkative, and more confident. As they become more intoxicated with alcohol, their speech is slurred, while their balance and coordination gets impaired. Their reflexes also slows down, and their exhibit unstable emotions.

Excessive consumption of alcohol is associated with immediate health risk that if not addressed, can lead to long-term health risks.

Immediate health risks

Excessive consumption of alcohol is linked violent behavior. Masters (23) reports that nearly 35% of the violent crimes are caused by individuals under the influence of alcohol. Moreover, excessive alcohol use also leads to cases of child neglect and maltreatment (The National Center on Addition and Substance Abuse 4).

Excessive use of alcohol also causes unintentional injuries such as falls, burns, traffic injuries, and drawings (Rehm et al. 41). Risky sexual behaviors such as sexual assault and engaging in unprotected sex are also some of the other immediate health risks of excessive alcohol use (Naimi et al. 1139).

Long-term health risks

If the immediate health risks of excessive alcohol use are not addressed, the victim could suffer neurological impairments, in addition to suffering from various social problems. They are also likely to develop chronic illnesses. Some of the neurological problems attributed to long-term excessive alcohol use include stroke, dementia, and neuropathy (Corrao et al. 615).

Over time, too much of alcohol can also cause psychiatric problems like anxiety, depression, and suicidal thoughts (Booth and Feng 162). Excessive alcohol use is also linked to liver diseases such as cirrhosis, which is today one of the leading causes of lifestyle-related deaths in the United States (Heron (8).

There are various reasons why people consume alcohol, including peer pressure, to drown sorrows, and to bond with families and friends, among others. Excessive consumption of alcohol causes both immediate and long-term health effects, including violence, involvement in risky sexual behaviors, and neurological and psychiatric problems.

Centers for Disease Control and Prevention. Alcohol-Related Disease Impact (ARDI) , Atlanta, GA: CDC, 2012. Print.

Corrao, Giovanni, Vincenzo, Bagnardi and Antonella, Zambon. “A meta-analysis of alcohol consumption and the risk of 15 diseases.” Prev Med , 38(2004):613-619.

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the preconception period and the risk of unintended pregnancy: Implications for women and their children.” Pediatrics , 11.5(2003):1136-1141.

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Solar eclipse 2024: Follow the path of totality

Solar eclipse myths and rumors bubble up, from radiation to food poisoning.

Bill Chappell

People visit a NASA information booth to grab solar eclipse glasses in Russellville, Arkansas. The space agency has debunked a number of myths about the total solar eclipse — including ideas about food going bad, or unborn babies being harmed. Mario Tama/Getty Images hide caption

People visit a NASA information booth to grab solar eclipse glasses in Russellville, Arkansas. The space agency has debunked a number of myths about the total solar eclipse — including ideas about food going bad, or unborn babies being harmed.

Will a solar eclipse harm a pregnant woman's baby if she looks at it? Does an eclipse emit special radiation that can instantly blind you?

Those are some of the ideas people have been asking about — and that experts have been pooh-poohing — as people in North America anticipated seeing a total eclipse, from Mazatlán to Montreal.

Here's what time the eclipse will be visible in your region

Monday's total solar eclipse begins over Mexico's Pacific Coast at around 11:07 a.m. PT, moving east through Texas and up to Maine, finally leaving the continent on Newfoundland's Atlantic coast.

Solar eclipses have long triggered fanciful explanations and warnings, from religious mythology to modern-day superstition. In recent days, for instance, a message circulated online warning people to turn off their cellphones and other devices before midnight ahead of the eclipse, warning of powerful radiation and cosmic rays.

In reality, a solar eclipse brings a temporary sharp drop in solar radiation — an event that ham radio operators have been eagerly anticipating for months, with competitions and experiments looking to fill the Earth's suddenly radiation-free ionosphere with radio signals.

Persistent but unfounded beliefs even prompted NASA to devote a special page to debunking misconceptions about a solar eclipse.

Total eclipses don't produce rays that cause blindness, NASA says

During totality, electromagnetic radiation from the sun's corona will not harm you. In fact, the only time it's safe to look at the sun without eye protection, as the sun's brightness is fully obscured by the moon and its corona is visible.

But outside of totality, your eyes can be harmed during an eclipse. If the sun is only partially obscured, looking at it will damage your retina. You can look if you have special solar glasses, but don't count on those to protect you if you want to use a telescope or camera lens that doesn't have a solar filter.

As NASA says , "the concentrated solar rays will burn through the filter and cause serious eye injury."

Another thing to remember: Take breaks if you're using a special filter to look at the sun before or after totality. As the space agency says , the sun's infrared radiation can make you uncomfortable, "as it literally warms the eye."

You should look away from the sun periodically, or use an indirect viewer like a pinhole projector to track the eclipse.​

More things NASA says are NOT true about a total solar eclipse

Myth: if you are pregnant you should not watch an eclipse because it can harm your baby..

Another notion that seems to be rooted in concern about radiation. To put people's mind at ease, NASA employs a sort of "you're already soaking in it" example, citing the neutrino particles produced by the sun's nuclear fusion:

"Every second, your body is pelted by trillions of these neutrinos no matter if the sun is above or below the horizon. The only consequence is that every few minutes a few atoms in your body are transmuted into a different isotope by absorbing a neutrino. This is an entirely harmless effect and would not harm you, or if you are pregnant, the developing fetus."

MYTH: Eclipses will poison any food that is prepared during the event.

NASA gives a hypothetical: What if some bad potato salad makes people sick during an eclipse? Food poisoning is very common — and it shouldn't be blamed on a rare celestial event, the agency notes.

"The basic idea is that total solar eclipses are terrifying and their ghostly green coronae look frightening, so it is natural to want to make up fearful stories about them and look for coincidences among events around you."

Other myths have to do with omens and major events

Here are four that NASA singles out for debunking:

MYTH: Eclipses are harbingers of something very bad about to happen.

Myth: solar eclipses foretell major life changes and events about to happen., myth: solar eclipses are a sign of an exceptional celestial event taking place in time and space., myth: solar eclipses six months after your birthday, or on your birthday, are a sign of impending bad health..

NASA ascribes many of these ideas to astrological forecasts being propped up by confirmation bias.

As the agency says, "We tend to remember all the occasions when two things happened together, but forget all of the other times when they did not."

Other myths — such as the idea that the moon turns black during an eclipse, or that the Earth's two poles don't see eclipses — are simply false, the agency says.

Eclipses have deep spiritual meanings

Ideas about an eclipse's potentially powerful effects aren't new. In fact, solar eclipses do also cause some unusual things to happen .

Want to see how a solar eclipse alters colors? Wear red and green on Monday

People in totality can expect to feel a sudden drop in temperature, for instance. Stars and planets become visible in the middle of the day, and humans can experience a range of odd visual effects — from the sharpness of shadows to the movement of "shadow bands" and a change in how we perceive color.

Then there's the eerie effect of the eclipse moving from west to east, adding to the perception that time isn't moving in its normal path.

Many cultures and religions link eclipses to energy, seeing them as events of renewal and promise — or in some cases, of vital energy being drained away.

For the Ojibwe and other Indigenous peoples in the Great Lakes region, a story about a solar eclipse centers on a boy and his sister who trap the sun after it burns him.

In many folktales, magical animals try to eat the sun or the moon. In Hindu mythology, a serpent god, Rahu Ketu, wanted to eat the sun — but then his head was cut off. That created two new entities, Rahu and Ketu, according to the Folklife Today blog from the Library of Congress.

"These are the deities of eclipses and comets. Rahu is fixated on eating the sun and the moon, and will try to catch them and gobble them up," the blog notes. "Fortunately he only succeeds once in a while. Since his head was cut off, the sun or moon just falls out the hole where his neck used to be. This is an eclipse."

As Folklife Today notes, in many cultures, humans take up the duty of ending an eclipse, often by making noise and beating on drums or gongs to dispel the spirit that's attempting to take the sun.