New User Special Price Expires in

Let's log you in.

Sign in with Facebook


Don't have a StudySoup account? Create one here!


Create a StudySoup account

Be part of our community, it's free to join!

Sign up with Facebook


Create your account
By creating an account you agree to StudySoup's terms and conditions and privacy policy

Already have a StudySoup account? Login here

Senior Seminar in Economics

by: Destinee Auer

Senior Seminar in Economics ECON 4990

Destinee Auer
GPA 3.67


Almost Ready


These notes were just uploaded, and will be ready to view shortly.

Purchase these notes here, or revisit this page.

Either way, we'll remind you when they're ready :)

Preview These Notes for FREE

Get a free preview of these Notes, just enter your email below.

Unlock Preview
Unlock Preview

Preview these materials now for free

Why put in your email? Get access to more of this material and other relevant free materials for your school

View Preview

About this Document

Class Notes
25 ?




Popular in Course

Popular in Economcs

This 17 page Class Notes was uploaded by Destinee Auer on Monday October 12, 2015. The Class Notes belongs to ECON 4990 at Georgia College & State University taught by Staff in Fall. Since its upload, it has received 27 views. For similar materials see /class/221951/econ-4990-georgia-college-state-university in Economcs at Georgia College & State University.


Reviews for Senior Seminar in Economics


Report this Material


What is Karma?


Karma is the currency of StudySoup.

You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 10/12/15
The Economic Impact of Malaria A Focus on Africa ECON 4990 Senior Seminar The Economic Impact of Malaria A Focus on Africa I Introduction The word malaria is perhaps mentioned almost as frequently as a family member s name in households in African countries Unfortunately it is more than just a word Malaria is a disease which is caused by protozoan parasites of the genus plasmodium and is widespread in tropical and subtropical regions of the world a disproportional share being in the continent of Africa Malaria parasites are transmitted by the female anopheles mosquito to its victims who become ill for days and in the severe cases may die of the illness It is one of the major health problems especially in regions where it is prevalent According to the World Health Organization WHO and the World Bank WB malaria is a disease that is both preventable and curable but yet it kills about 27 million people around the world each year Approximately 90 percent of these deaths occur in subSaharan Africal where a child s life is lost to malaria every 30 seconds Almost everyone that lives in subSaharan Africa contracts the disease at least once a year Sachs 2005 p 197 Other than the impact ofthe disease on the health ofpeople in sub Saharan Africa malaria also poses a threat to the economic growth and standard of living of most countries on the continent It is estimated that malaria costs Africa 12 billion yearly and between 1965 and 1990 malarial nations worldwide have had an annual economic growth of less than onefifth of the annual economic growth of malariafree nations in the world World Bank The economic growth and standard of living is impaired by malaria as it causes an increased cost 1 Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Angola Comoros Democratic Republic of Congo Republic of Congo Cote d lvoire Equatorial Guinea Eritrea Ethiopia Gabon The Gambia Ghana GuineaBissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mayotte Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Sudan Swaziland Tanzania Togo Uganda Zambia and Zimbabwe of healthcare working days and days at school lost due to malaria and loss of investment and tourism in some countries It is perhaps undeniable that malaria parasite chooses where it thrives due to geographic and climatic factors However with underlying evidence showing that the richest African countries are not in the highrisk malaria zones Gallup and Sachs 2001 and the poorest ones are by the use of macroeconomic variables and Ordinary Least Square OLS regression this paper intends to show more evidence that malaria has a signi cant economic effect on the standard of living of these countries This paper is more interested in a causal relationship between malaria and poverty with the former as the cause and the latter as the effect although studies such as Gallup and Sachs 2001 that would be discussed in more detail in the literature review section show evidence of causal relationship going in both directions It is important to note that poverty and standard of living will be used interchangeably in this paper In essence the data collected and the model introduced in this paper intends to provide more evidence to answer the question Does malaria have a signi cant effect on the standard of living of the countries in subSaharan Africa The following section section II will provide a review of the literature relating to the economic relationship between malaria and poverty In section 111 an empirical model will be introduced and the variables and data used will be explained in detail Section IV presents the results obtained from running the regression equation introduced in section III and will provide analysis of the results The last section Section V concludes the paper and briefly addresses policy initiatives under consideration by governments and organizations towards the control of malaria 11 Literature Review Several studies have been done on the relationship between malaria and poverty It is important to note that most if not all highlight subSaharan Africa as bearing a disproportionate amount of the severe cases Gallup and Sachs 2001 argue that malaria and poverty are connected Malaney Spielman and Sachs 2004 pose an interesting question as to whether Africa would still be poor if freed of malaria These two papers present general views very similar to the purpose of this paper while there are others that would also be discussed that present speci c aspects of the relationship between malaria and poverty The content of the study done by Gallup and Sachs 2001 is very closely related to the study done in this paper In their paper they address the question as to if the dramatic correlations they find between growth of income per capita and malarial countries mean that malaria causes poverty and low growth in three ways First they consider the correlation between malaria and income levels after controlling for other factors that are likely to affect the world distribution of income such as geography history and policy Secondly they discuss the determinants of malaria risks which they argue that unlike other diseases in poor countries caused by defrcient living conditions such as diarrhea and tuberculosis malaria is not primarily a consequence of poverty but rather largely determined by climate and ecology Lastly they explore the impact of malaria on subsequent economic growth thereby providing the most direct evidence of the importance of malaria as a cause of poverty The first way they address the question as to if malaria causes poverty and low growth is of more importance to the content of this paper than the other two The study uses an index of malaria prevalence derived from historical maps of the geographical extent of high malaria risk digitized from maps by Pampana and Russell and the WHO It also uses as an index of malaria intensity the fraction of the populations at risk of malaria multiplied by the fraction of cases of malaria that are falciparum malaria as most malaria mortality and severe morbidity is due to the malignant Plasmodium falciparum one of the four malaria species To encompass their belief that there exist strong patterns between geography and income levels around the world they include geographic variables in their model These geographic variables include a country s accessibility to the coast measured by the share of population within 100 km of the coast which they argue is an important indicator of success in foreign trade and integration into the global economy and hence is related to high income levels Other geographic measures Gallup and Sachs 2001 use are minimum distance to the core world markets like New York Rotterdam and Tokyo resource deposits proxied by the log of hydrocarbon reserves per person and percentage of a country s land area in the geographical tropics When the regression is run with the logarithm of the Gross Domestic Product GDP per capita as the dependent variable and the geographic variables and falciparum malaria index explained above as the independent variables both in two different years 1950 and 1995 the coefficient on malaria is negative and significant at 1 level in both cases When indicators for former colonies and socialist countries in the postWorld War II era and trade openness in the 1965 1990 period are added as more independent variables the coefficient on malaria still remains negative and significant at the 1 level They point out that geography history and policy all have clear correlations with income levels but taking them into account does not alter pattern of lower incomes in malarial countries thus the association of malaria with poverty seems to be more than just a facade for other possible causes of low income In addition Malaney Spielman and Sachs 2004 present an interesting economic methodology for evaluating the burden of malaria They state that the standard method of calculating the CostofIllness C01 is COI Private Medical Costs NonPrivate Medical Costs Forgone Income Pain and Suffering From a case study performed using COI analysis in some African countries the studies indicated that a case of malaria in Africa cost 984 in 1987 of which 183 was direct and 801 was indirect due to forgone income as a result of malaria morbidity and mortality An important point about the COI analyses that supports the claim of a relationship between malaria and poverty is that it shows the poor bear proportionately more of the cost as the figure represents a higher percentage of their income The COI approach is also criticized for its ability to omit costs that are not easily estimated numerically and its general difficulty in assessment Another approach 7 WillingnesstoPay WTP is presented whereby through household surveys they try to determine the value a household places on trying to avoid the disease Such an approach was also criticized under the context of eXistence values and the kind of inaccurate answers such survey may produce They also support the suggestion that causation runs in both directions between malaria and poverty and the effect of malaria on trade and vice versa The study of this paper is more closely related to that of Malaney et al 2004 in the result which further supports the suspected causal relationship between malaria and poverty than in the approaches used While their paper makes use of the C01 and WTP approaches that involve medical costs and household surveys this paper makes us of a very different model that involves macroeconomic variables Furthermore Sachs Mellinger and Gallup 2000 provide a convincing argument on the effect of physical geography on economic performance Their study shows evidence which supports the argument that it is more than a coincidence that most of the countries with lower economic growth than the rest of the world have a highrisk of malaria These same countries with lower economic growth happen to be in similar geographic areas that are different from the geographic areas where countries with higher economic growth are found Together with the other results they found such as cost of trade and agricultural productivity and their effect on the economy they mention that the prevalence of diseases such as malaria have a lasting foothold in certain geographic zones Sachs Mellinger Gallup 2000 On another hand Laximinarayan 2004 focuses on the households His study points out that government is yet to give adequate attention to reducing the incidence of malaria and blame the situation on poor understanding of the economic impact of the disease It is mentioned that both the members who suffer from an episode of malaria and those that do not in households are adversely affected by malaria The effect is neither always apparent nor measurable because of the long history of adaptive coexistence with the disease The paper argues that even without being direct victims of the disease households suffer because they have less access to various economic opportunities Macroeconomists speculate that economic opportunities that are affected include foreign direct investment tourism and limitations on internal movement of the population In agreement to the issue of ecology the study done in Vietnam showed that an increase in government expenditure on malaria control and treatment lacked uniform effect in diverse places as a result of the difference in ecological characteristics of each place In conclusion Laxminarayan points out that without clear evidence that there will be improvement in household living standards by a measure greater than the cost of investing in malaria control malaria may be unable to attract sustained commitment from policymakers to eradicate the disease Laxminarayan 2004 Finally in a review of the evidence on the link between malaria and poverty Worrall Basu and Hanson 2004 found mixed evidence on malaria incidence as some studies show a correlation between socioeconomic status and malaria and a few did not They do however support that important socioeconomic differentials exist in access to malaria interventions which increases the vulnerability of the poorest They also criticize the different methods used to measure socioeconomic status in different studies that have been done on the topic Worrall Basu Hanson 2005 Savigny and Binka 2004 argue that monitoring the future impact of malaria burden in subSaharan Africa will require investment in information systems and their ability to monitor change of impact indications They mention an important point that given the inadequate resources in order to attain successful malaria eradication strategies plans and decisions of how to utilize these resources will need to be smart On the other hand Hanson 2004 discusses the role of public and private expenditures in malaria control using economic analysis to determine the optimal forms of malaria control policies III Data and the Model After careful analysis of the literature relating malaria to poverty it seems appropriate to introduce the model The empirical work done in most of the reviewed literature relating malaria to poverty is similar to that of this paper However one variable present in most studies that is omitted from this one is on geography It seems logical that they would include geographical variables as most of the other studies have been done on the economic effect of malaria globally This paper focuses on only countries in subSaharan Africa and follows an underlying assumption that the geographic differences are not as adverse between the countries within sub Saharan Africa as they are between countries globally As a result of unavailability of data in some African countries this paper uses data collected from fortytwo countries2 in the subSaharan African region These fortytwo countries 2 Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Angola Comoros Democratic Republic of Congo Republic of Congo Cote d lvoire Eritrea Ethiopia Gabon The Gambia Ghana GuineaBissau Kenya Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger may sometimes be referred to as observations Unlike Gallup and Sachs 2001 that use the malaria index derived from malaria maps and falciparum prevalence as a malaria variable rather than the cases of malaria reported by the WHO because they feel the national reporting systems are systematically different between countries with high or low levels of malaria this paper does use the cases of malaria reported by the WHO Other than the fact that the data from WHO is more readily available just as with the geography variables there is an underlying assumption that if any the systematic difference that exists in the national reporting systems within the forty two countries does not significantly affect the model However the cases reported in these countries are for the most recent years reported and they fall between 1998 and 2003 Before going into further details about the variables in the model it is perhaps appropriate to now present the model The model is as follows InGDP per capita in rimalarm Cases 2 lnPhysical Capital 1 3Labor Growth 4 Trade Openness 5 lnCorruption I ndex error term The dependent variable in this model is the logarithm log of Gross Domestic Product GDP per capita which is used as a measure of the standing of living in the various countries in this paper It is taken from The World Development Indicators 1999 CD ROM and is measured as GDP at market prices in constant 2000 US dollars divided by the population Because of the differences in years of the malaria cases reported and in order to capture if any the effect of the malaria cases on standard of living it seemed inappropriate to use the GDP per capita of just any given base year Therefore the GDP per capita for each observation was taken from the year that the malaria cases were reported for that particular observation For example the malaria cases reported in Nigeria is for the year 2003 and so the GDP per capita used for that observation was Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone South Africa Sudan Swaziland Tanzania Togo Uganda Zambia and Zimbabwe the GDP per capita reported for 2003 This is done with the belief that occurrences of malaria in a given year would affect the standard of living in that year It may be argued and perhaps proven that malaria occurrences affect the standard of living in a given country for years after but for the intent of this paper the model is focusing on the effect in a given year It could be referred to as a shortterm effect Other than the malaria variable there are four other explanatory variables that are intended to represent variables that would usually explain economic growth as presented by Farr Lord and Wolfenbarger 2000 Although in their paper the variables are intended to explain economic growth this paper assumes that because GDP per capita is also a macroeconomic variable these variables may also be used to explain it With the exception of the measure of trade openness and corruption index the other variables are calculated using data from The World Development Indicators 1999 CD ROM and are explained below The first of these explanatory variables is the fraction of income invested in physical capital It is measured by gross domestic fixed investment as a percentage of GDP and is presented for each country for various years In the regression it is used in its logarithmic form In order to capture an effect of this variable in the model and as observed in papers that use data from developing countries the figures used in this model are an average of the annual figures given from 1999 to 2004 Farr et al 2000 employ the same averaging technique in their study stating that it helps eliminate noise that is common in annual data The second of the nonmalaria variables is a measure of the rate of labor growth It is proxied by taking the percentage changes in the total labor force for the various observations To maintain conformity to the averaging of annual figures the percentage change is calculated by using 1999 as the base year and 2004 as the current year Between those years three countries have a negative labor growth rate As a result the variable on labor growth is not put in its logarithmic form as the logarithm of a negative number does not exist Another nonmalaria variable is a measurement of trade openness Trade openness here is proxied using net exports3 Data to calculate this measure is acquired from the Central Intelligence Agency CIA World Fact Book The gross exports give the total US dollar amount of merchandise exports on a free on board fob basis and are calculated on an exchange rate basis rather than in purchasing power parity PPP terms The gross imports give the total US dollar amount of merchandise imports on a cost insurance and freight cif or free on board fob basis and are calculated on an exchange rate basis rather than in purchasing power parity PPP terms The two components exports and imports are the given 2006 estimates There was little surprise that the net exports for most of the observations were negative as it is observed that developing countries tend to import more goods than is exported That however is beyond the scope of this paper As a result of the negative gures again the logarithm cannot be taken for reasons previously explained It is therefore put in the model as is some figures being negative others positive It is important to take note of this as when analyzing the results in the next section the interpretation of the coefficients for the explanatory variables in the logarithmic form will be different than that which is not in the logarithmic form The last explanatory variable is a measure of economic freedom which is proxied using data that measures corruption perception index in 2006 in the various observations The score relates to perceptions of the degree of corruption as seen by business people and country analysts and originally is given in a range of 010 with 10 being highly clean and 0 being highly corrupt Before being ran in the model presented in this paper the figures were subtracted from 10 thereby switching the interpretation of the end points of the range It thus became that 10 3Gross Exports 7 Gross Imports indicated highly corrupt and 0 highly clean The reason for doing this will be explained in more details in the next section IV Results After careful explanation of the data and the variables used in the model the regression results will now be presented The model was run using Ordinary Least Squares OLS regression analysis Table 1 shows the regression results of the model equation 1 presented in section 111 Table 1 Regression Results Dependent variable log GDP per capita Variable Coef cient t statistic Constant 1319852 6175643 Log Malaria Cases 0124487 3029237 Log Physical Capital 0445680 1404416 Labor Growth 0126183 1993900 Trade Openness 355E11 2317939 Log nmmtinn Index 3375347 3787602 signi cant at the 001 level or better signi cant at the 005 level or better Looking at the results beginning with the malaria variable the negative and statistically signi cant coefficient of the variable supports the suspected relationship between the malaria cases and GDP per capita It is important to note that although earlier mentioned that the paper is interested in the causal relationship between malaria and standard of living with the cause being the former and the latter the effect regression does not necessarily imply causation Causality must be justified or inferred from the theory that underlies the observable fact that is tested empirically Gujarati 2006 p 134 Because the logarithm of malaria cases is run on the logarithm of GDP per capita the coefficient on the logarithm of malaria cases gives a proportional change in GDP per capita given a proportional change in malaria cases holding all other variables constant Put in another way it measures the partial elasticity of GDP per capita with respect to malaria cases holding all other variables constant The negative relationship therefore implies that in the fortytwo observations included in the model one percent increase in malaria cases will result to a 0124487 percent decrease in GDP per capita holding other variables constant Although the purpose of this paper is to analyze the economic impact of malaria and the other variables were added to prevent underspeci cation of the model they each have results that are worth discussing Firstly the coef cient of the logarithm of physical capital is positive although only slightly statistically signi cant This shows a positive proportional relationship between investment on physical capital and GDP per capita This relationship is expected as the observations are based on developing countries that have more allowance for capital development The coef cient on the rate of labor growth variable is negative and statistically signi cant at the 10 level of con dence Again this is not surprising as it is expected that an increase in labor growth may indicate an increase in population thereby leading to a proportional decrease in GDP per capita The coef cient on the trade openness variable is positive and statistically signi cant This goes along with the Gallup and Sachs 2001 results that a positive relationship exists between GDP per capita and the openness to the economy Put in another way greater openness of the economy will result in increased economic growth The measure of economic freedom variable which is the corruption index is also negative and statistically signi cant This is expected as a large amount of empirical evidence now exists that show the impact of economic freedoms on economic growth and the standard of living including that shown in the paper by Farr Lord and Wolfenbarger 2000 In section 111 it had been mentioned that the corruption index data was put in a form whereby the higher the gure the more the corruption The original data acquired from the Transparency International organization indicated a higher gure meant less corruption This resulted in a positive coef cient in the regression results that may create confusion at rst glance Thus the form was changed So with the regression result presented it is now clear to see a negative relationship occurring between corruption and GDP per capita while barely affecting the coef cients and the signi cance of the coefficients of the other variables Furthermore the R2 value of 063 indicates that the model explains approximately 63 of the variation in GDP per capita Since the standard errors from the regression are not large it is safe to assume that there is not a signi cant level of multicollinearity To further support this assumption there is also not a case of a large R2 value with few signi cant t ratios in the results As a result the paper does not delve into trying to reduce multicollinearity as if it does exist it more than likely would be in a relatively small degree As the model uses cross sectional data rather than a time series data there is little or no need to be concerned with autocorrelation IV Conclusion Does malaria have a signi cant effect on the standard of living of the countries in sub Saharan Africa This is the question asked in section Ithat this paper was interested in providing evidence to answer After review of previous literature on the subject a new model presented and its results analyzed there is more evidence to support a possible answer to the question From the regression results there is a negative relationship between malaria cases and per capita GDP which further adds to evidence that malaria has a signi cant effect on the standard of living of countries in subSaharan Africa More evidence to support this argument may be gathered from the numerous empirical evidence that exist showing the negative relationship between malaria and GDP per capita poverty or standard of living and economic growth A criticism of the model used in this paper is that the observations were only slightly large In future better research methods may be used to collect more data for the countries that were not included in the model for lack of data in order to increase the sample size In addition a panel data set which uses data from each country over a period may be more helpful in catching the effect of the malaria occurrences in these countries over time Suffice to say the more evidence presented creates relevance to debates concerning governmental policy In recent years more countries within and outside the subSaharan African region have started paying more attention to the prevalence of malaria The majority of policies focus on malaria prevention more than on treatment The commonly known efficient prevention tools are treated bed nets indoor spraying of insecticides and the use of dichlorodiphenyltrichloroethane DDT All three have proven effective and fairly inexpensive although not to the poor victims of malaria in these subSaharan African countries As a result funding from national and international levels are required Unfortunately funding from these sources only started to come through recently The most controversial of these treatments has been DDT Although there is evidence as discussed by Bates and Lorenzo 2007 that the use of DDT eradicated malaria in Europe and Us in 1940s and 1950s without any known harm to humans DDT was banned in 1972 as a result of pressure from environmentalists who argued that the chemical damaged the environment and posed a threat to human health According to The Herald 2006 opponents of DDT argue that potential effects of indoor spraying include reproductive health neurological effects effect on breast milk and increased risk of breast cancer and the r quot quotquot of 39 of quot to DDT Some African countries had previously 1 banned DDT following pressure by western countries that feared the contamination of beef and vegetables exported from countries that used DDT Fortunately the WHO and the US Agency for International Development USAID are now endorsing the use of DDT and other insecticides for indoor spraying and encouraging world powers to get more involved Organizations such as WHO argue that policies that support the lack of DDT will endanger more lives than the concentration of the chemical needed Finally with continuing research and empirical studies done on this issue more attention may be brought to policy makers so that in time subSaharan Africa may experience drastic reduction in malaria cases and subsequently reduction in loss of lives to malaria In future research more advanced research tools may have to be developed to better capture the effect of malaria on productivity economic growth and standard of living Ultimately with more funding directed towards the prevention and elimination of malaria keeping other factors constant there should be an observable increase in the economic growth of subSaharan African countries and subsequently an improved standard of living Bibliography Bate R De Lorenzo M 2007 Rwanda ReConsider DDT against malaria The New Times January 10 Accessed on April 23 2007 from Galileo Virtual Library Farr KW Lord RA Wolfenbarger JL 2004 Additional evidence of the linkages between economic growth and the institutions of economic freedom political rights and civil liberties Working paper Gallup JL Sachs JD 2001 The Economic Burden of Malaria The American Journal of TropicalMedicine andHygiene 6412 8596 Gujarati N 2006 Essentials of Econometrics 3e New York NY McGrawHillIrwin Hanson K 2004 Public and private roles in malaria control the contributions of economic analysis The American Journal ofTropical Medicine and Hygiene 712 168173 Laxminarayan R 2004 Does reducing malaria improve household living standards Tropical Medicine and International Health 92 267272 Malaney P Spielman A Sachs J 2004 The malaria gap The American Journal of Tropical Medicine andHygiene 712 141146 Sachs JD Mellinger AD Gallup JL 2000 The geography of poverty and wealth Scienti c America 16 September Sachs JD 2005 The End ofPoverty led New York NY The Penguin Press Savigny D Binka F 2004 Monitoring future impact on malaria burden in subSaharan Africa The American Journal ofTropical Medicine and Hygiene 712 224231 The Herald 2006 Malaria DDT Panacea to malaria scourge The Herald October 12 Accessed on January 26 2007 from Galileo Virtual Library The World Development Indicators 1999 International Bank for Reconstruction and Development CDRom Win Stars version 42 The World Factbook wwwciagov Accessed March 15 2007 World Malaria Report 2005 wwwrbmwhointwmr2005 Accessed February 18 2007 Worrall E Basu S Hanson K 2005 Is malaria a disease ofpoverty A review ofthe literature Tropical Medicine and International Health 1010 10471059 wwwwhoorg Accessed February 18 2007


Buy Material

Are you sure you want to buy this material for

25 Karma

Buy Material

BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.


You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

Why people love StudySoup

Bentley McCaw University of Florida

"I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

Jennifer McGill UCSF Med School

"Selling my MCAT study guides and notes has been a great source of side revenue while I'm in school. Some months I'm making over $500! Plus, it makes me happy knowing that I'm helping future med students with their MCAT."

Steve Martinelli UC Los Angeles

"There's no way I would have passed my Organic Chemistry class this semester without the notes and study guides I got from StudySoup."

Parker Thompson 500 Startups

"It's a great way for students to improve their educational experience and it seemed like a product that everybody wants, so all the people participating are winning."

Become an Elite Notetaker and start selling your notes online!

Refund Policy


All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email


StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here:

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.