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• How to Write a Thesis Statement | 4 Steps & Examples

How to Write a Thesis Statement | 4 Steps & Examples

Published on January 11, 2019 by Shona McCombes . Revised on August 15, 2023 by Eoghan Ryan.

A thesis statement is a sentence that sums up the central point of your paper or essay . It usually comes near the end of your introduction .

Your thesis will look a bit different depending on the type of essay you’re writing. But the thesis statement should always clearly state the main idea you want to get across. Everything else in your essay should relate back to this idea.

You can write your thesis statement by following four simple steps:

• Start with a question
• Write your initial answer
• Develop your answer
• Refine your thesis statement

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Table of contents

What is a thesis statement, placement of the thesis statement, step 1: start with a question, step 2: write your initial answer, step 3: develop your answer, step 4: refine your thesis statement, types of thesis statements, other interesting articles, frequently asked questions about thesis statements.

A thesis statement summarizes the central points of your essay. It is a signpost telling the reader what the essay will argue and why.

The best thesis statements are:

• Concise: A good thesis statement is short and sweet—don’t use more words than necessary. State your point clearly and directly in one or two sentences.
• Contentious: Your thesis shouldn’t be a simple statement of fact that everyone already knows. A good thesis statement is a claim that requires further evidence or analysis to back it up.
• Coherent: Everything mentioned in your thesis statement must be supported and explained in the rest of your paper.

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The thesis statement generally appears at the end of your essay introduction or research paper introduction .

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts and among young people more generally is hotly debated. For many who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its many benefits for education: the internet facilitates easier access to information, exposure to different perspectives, and a flexible learning environment for both students and teachers.

You should come up with an initial thesis, sometimes called a working thesis , early in the writing process . As soon as you’ve decided on your essay topic , you need to work out what you want to say about it—a clear thesis will give your essay direction and structure.

You might already have a question in your assignment, but if not, try to come up with your own. What would you like to find out or decide about your topic?

For example, you might ask:

After some initial research, you can formulate a tentative answer to this question. At this stage it can be simple, and it should guide the research process and writing process .

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Now you need to consider why this is your answer and how you will convince your reader to agree with you. As you read more about your topic and begin writing, your answer should get more detailed.

In your essay about the internet and education, the thesis states your position and sketches out the key arguments you’ll use to support it.

The negatives of internet use are outweighed by its many benefits for education because it facilitates easier access to information.

In your essay about braille, the thesis statement summarizes the key historical development that you’ll explain.

The invention of braille in the 19th century transformed the lives of blind people, allowing them to participate more actively in public life.

A strong thesis statement should tell the reader:

• Why you hold this position
• What they’ll learn from your essay
• The key points of your argument or narrative

The final thesis statement doesn’t just state your position, but summarizes your overall argument or the entire topic you’re going to explain. To strengthen a weak thesis statement, it can help to consider the broader context of your topic.

These examples are more specific and show that you’ll explore your topic in depth.

Your thesis statement should match the goals of your essay, which vary depending on the type of essay you’re writing:

• In an argumentative essay , your thesis statement should take a strong position. Your aim in the essay is to convince your reader of this thesis based on evidence and logical reasoning.
• In an expository essay , you’ll aim to explain the facts of a topic or process. Your thesis statement doesn’t have to include a strong opinion in this case, but it should clearly state the central point you want to make, and mention the key elements you’ll explain.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

Follow these four steps to come up with a thesis statement :

• Ask a question about your topic .
• Write your initial answer.
• Develop your answer by including reasons.
• Refine your answer, adding more detail and nuance.

The thesis statement should be placed at the end of your essay introduction .

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‘Poverty Is The Parent Of Revolution And Crime’ – Aristotle

• September 15, 2020
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Poor countries are disproportionately affected by domestic war and other forms of conflict. The World Bank acknowledges that “on average, a country that experiences major violence has a poverty rate significantly higher than a country that had no violence.” The ten poorest countries, as reported by the World Bank in 2019, have a GDP per capita of less than USD 600. The list includes war-ridden countries such as Sudan, Afghanistan, the Central African Republic, and the Democratic Republic of Congo. Of course, there are outliers – Malawi and Sierra Leone, for example, ranked 59th and 46th, respectively, on the Global Peace Index. The index weighs factors including a country’s level of violent crime, political terror, conflicts fought, and percentage of displaced people. Of the top ten richest countries represented on the Global Peace Index, all but one ranked in the top 27th. Four ranked within the top ten, with Iceland placing 1st. This stark contrast paints a harsh picture of the relationship between wealth and peace.

The first of the United Nations Sustainable Development Goals is to “end poverty in all its forms everywhere.” The World Bank reported a decrease in global poverty of approximately 26% between 1990 and 2015. These years saw nearly 1.1 billion people leave extreme poverty (began earning more than USD 1.90 a day). Increased productivity and the expansion of the middle class are largely responsible for this reduction in global poverty.

However, COVID-19 raises serious concerns that this progress may be reversed. It is likely that those who can least afford to weather it will feel the global recession the most. The United Nations University World Institute for Development Economics Research predicts that the pandemic is likely to increase global poverty by 500 million people – 8% of the global population. Further, the World Bank estimates that between 40-60 million people may fall into the category of extreme poverty.

Not enough is being done to eradicate poverty and create a more equitable distribution of resources. According to the United Nations, nearly one in every ten employed workers lived in extreme poverty in 2018. Approximately 20,000 people die every day from malnutrition when the earth has enough resources to feed the world one and a half times over. In 2017, Oxfam, a charitable organization, revealed that the “world’s richest 1% get 82% of the [world’s] wealth.” Oxfam also claimed that the 22 wealthiest men on earth have more wealth than all the women in Africa – of which there are over 500 million.

The UN Millennium Project found that poor countries are “more likely to have weak governments.” Weak government institutions positively reinforce poverty because state institutions rely on public funding to maintain public goods, such as education, trade regulations, the justice system, police forces, and healthcare. When state institutions are insufficiently funded, the goods they can provide to the public are limited. In 2000, the World Health Organization ranked 191 countries’ health system performance. In that same year, the ten countries with the lowest GDP per capita all ranked lower than 143 rd place. Myanmar and Sierra Leonne ranked 190 th and 191 st , respectively. Without the resources to enable taxpayers to prosper, public institutions further diminish their source of income, trapping poor countries in a vicious cycle.

“Poor and hungry societies are much more likely than high-income societies to fall into conflict over scarce vital resources, such as watering holes and arable land,” says the UN Millennium Project. In 1997, the Democratic Republic of Congo (DRC) hurdled into a civil war. In that same year, DRC also experienced its lowest GDP per capita (USD 140) since it gained independence in 1960. Militia groups and the country’s military fought for control over the country’s east, and although the war technically ended in 2004, internal conflict continues. Many militia groups extract the county’s natural resources, particularly coltan, which is plentiful in DRC. Tantalum, which is extracted from coltan, is a common – and, therefore, highly sought-after – component in electrical devices like mobile phones. Guerillas have exploited these resources to fund weapon purchases. In 2012, the United States implemented the Dodd-Frank Act to ban companies from using “conflict materials” like tantalum. However, this had the unintended consequence of increasing unemployment in the industry. As their poverty worsened, many of the newly unemployed workers were driven to join militia groups instead.

Poverty is not the only cause of war. A country’s socio-political environment, its history, or its geography may all be factors. Wars also often have a religious or ethnic component. Whether poverty is a cause of war or merely a symptom, it reinforces the likelihood of internal conflict.

The cost of poverty is too high. Poverty is intolerable on its own. Paired with an increased likelihood of war, it is beyond unjustifiable. It is relatively easier for developed countries to look inward and prioritize inequalities at home. However, it is a global responsibility to eliminate all forms of poverty, everywhere. To eradicate poverty, world leaders must first let go of their nationalistic ideologies. Barack Obama once said, “As the wealthiest nation on Earth, I believe the United States has a moral obligation to lead the fight against hunger and malnutrition, and to partner with others.” This “we are all in this together” philosophy will be essential if the world has any hope of achieving the United Nation’s target of eliminating extreme poverty by 2030.

Changing the world’s wealth distribution will rely heavily on rich countries. The developed world holds an inequitable amount of wealth. Free from political or religious motives, privileged countries should work collaboratively to raise the standard of living in the poorest countries. This will mean less money to spend on domestic issues. However, in perspective, it makes sense for the world to focus its resources on those who need them most. This is not to say developed countries should not spend public money at home – they should. It is to say that where first world countries can go without, they should, to help their neighbors. The biggest barrier to this approach is the peoples’ capacity for compassion. Compassion will dictate the sacrifices people are willing to endure. In turn, this will influence the countries’ political will to substantially help those in need.

Billionaires are a first world problem, but they affect us all. A large proportion of the world’s wealth is heavily concentrated on a small percentage of the upper class. According to an analysis by Forbes, the top 10 richest men hold more wealth than many countries, the likes of Saudi Arabia, Switzerland, Turkey, and Belgium included. One person should not legally be allowed to have more money than they can spend, while another does not have enough to eat. Ending the era of billionaires would drastically narrow the income inequality gap.

Beyond all else, people must believe they can lift the world out of poverty. The world has already achieved more than it thought possible. International newspaper The Economist reported in 2017 that “someone escapes extreme poverty every 1.2 seconds.” In 2015, 1.1 billion fewer people were living in extreme poverty than there were in 1950. That should be evidence enough that change is possible.

Eradicating global poverty is not insurmountable. It will be hard. But it is necessary.

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Poverty and Its Effects on Women Research Paper

Thesis statement, introduction, reasons of poverty, positive effects of poverty, negative effects, effects on the governmental level, avoiding poverty.

Poverty is regarded to be one of the most serious problems in the world. It is argued that the difference between the Golden Billiard and the poorest population has 250% increased for the latest decade (Berrick, 2007, p. 68). As for the poverty among women, it should be stated that women experience more disasters from poverty then men, and the reason of this disparity is covered not only in discrimination, but also in the fact that poor women often have to raise children alone. This paper is aimed to analyze the effect of poverty on women, define whether there are positive effects of poverty, and explain the effects on the governmental level: how the troubles of poverty are solved, and how they may be avoided.

Originally, poverty is regarded to be the deprivation of the essential needs and requirements, which is caused by the decreased general welfare, unemployment or inflation. Sometimes these three factors are intermixed, and the situation appears to be the worst, from the viewpoint of the economic success. As for the poverty among women, it should be emphasized that the reasons of poverty among women differ from those among men, and the issues of poverty itself are more severe. It is also stated that the poverty among women seriously touches the welfare of their children.

The corresponding social researches revealed that women are at greater risk of poverty in comparison with men. The women’s testimonies in the surveys also revealed the fact that the policies of the governments often do not entail the necessary flexibilities, and tools for narrowing the poverty gap among between men and women. According to the latest statistics, the amount of poor women increased from 11,3 percent (in 2007) till 13.8 (in 2008). (Connolly, 2008, p.32).

As it has been already emphasized, the reasons of poverty may be different. However, poverty among women has its own, unique reasons. The fact is that, each poor women possesses unique personal features and financial reserves. Independently on the marital status, the challenge is great, as living in poverty and raising children are often the issues that come jointly.

According to the surveys, African American and Hispanic women are the poorest, and, mainly, it is closely linked with the matters of racial discrimination. More than 25% of black women and nearly 25% of Hispanic women are poor. As it may be seen, the rates of poverty among white women is twice lower (11.6%).

The fact is that, women are often paid less than men, even if they have the same jobs, work equal hours and have similar qualifications. Women, working full time get only 77 percent of the salary that men earn, also working full time. Thus, this 23 percent gap makes women poorer. This gap is reasoned not by the lack of training or qualification, but the banal discrimination. The researches reveal that in 2007, women with bachelor qualifications were paid 14% less than men with similar qualifications (Buvinic, 2007, p. 45).

Arguing on the matter of salary gap, it is emphasized that women are highly segregated, as they are paid less, and if the job is dominated by women, it is not originally highly paid. Women are tracked into such called Pink-Collar jobs: these are teaching, child care, nursing, cleaning, and waitressing, which are originally paid less, than the male-dominated jobs.

Taking into account the factor of marital status, it is well known that divorced women bear the childcare costs more often than the divorced men do. Women are more often subjected to unpaid care giving (children, disabled close people, sometimes animals etc.). Consequently that gender wage gap increases.

Luckily, this gap is not inevitable, and it has decreased for the recent 30 years. Currently, more women have an opportunity to get the desired job, and increase the welfare of their families. Nevertheless, this gap is still serious, and discrimination is high (Connolly, 2008, p. 67).

In spite of the fact that poverty touches women more severely, it is necessary to highlight that women often overcome the troubles and the consequences of poverty braver than men do. Originally, women’s mind is more powerful, as they often have more reasons to live, and often have someone to take care of. Thus, women realize that someone else depends on them, and women often have more motivations to overcome the consequences and disasters of poverty.

Women who were not always poor struggle for the re-achievement of the welfare even more severely, as they feel the necessity and obligation to restore the wellbeing of their families. Those, who lost their jobs, and appeared at the poverty line are ready for almost everything in order to save the family life and the wellbeing of the family. They agree for physical and low paid jobs, if there are no other alternatives, they get employed for two or even three jobs, work over time and night shifts.

The stresses and constant loads (moral or physical) may have positive effects, as women may lose some weight, and achieve the perfect physical condition. Thus, when the disaster finishes, a women may have an improved figure, that she has always dreamt for. Poverty changes the view of the surrounding world, and often causes the changes in the attitude to other people. Anyway, overcoming difficulties is the life experience, that is the most precious thing in this life, as basing on the life experience people plan their further life, and try not to drop the same brick the next time.

Another fact, that may be attributed to positive effects of poverty among women, is that there are more homeless men, than women. Taking into consideration, that the percentage of poverty among women is higher than among men, this indicator is rather impressive. Consequently, it only confirms the statement, that women overcome the consequences and disaster of poverty more bravely.

Poverty may have the opposite effect, and quite different reasons, if a women does not have anyone to give care to. These women are more subjected to drug and alcohol addiction, they ma commit suicide, or participate in street gangs. This effect changes to opposite depending on a factor of care giving, as the very nature of women is based on care giving.

The stories, that are represented in novels, movies, either real or fictional represent the women, who struggle for their own, and their families’ wellbeing. Surely, if there is a family – there is strong motivation for taking care of it, and these women do everything possible to overcome poverty. Surely, there are also the exceptions, however these women are deprived the parents’ rights, and, finally, they do not have anyone to take care of, except pets.

Poverty among women frequently causes high rates of poverty among children. This mainly relates the latter examples, as children of the parents, whose parental rights were deprived, often do not wish to stay in the children’s houses, and escape to live in streets. This is the reason of children’s prostitution, high rates of death among children and, surely, crime among children (Dujon, 2006, p. 15).

As for the effects, which are not associated with family and care giving, there may be the effects, closely linked with health, fitness, appearance and others. Originally, poor people eat worse food than the others, as they can not afford the balanced healthy diet. Consequently, these people (not only women) gain or lose weight, acquire deceases such as tuberculosis, ulcer, skin deceases and others, closely linked with nutrition and living conditions. Most people move to cheaper apartments in order to be able to pay for them. It causes the psychological stress, which may give impulse for the development of some concealed decease. The new environment may contain offending allergens, parasites or other decease transmitting insects. The increased stress, long-lasting hunger, unfavorable environment may cause the stopping of the women’s reproductive function, and cause other serious damages to health.

Poverty, as the national disaster requires the governmental solution, in order to have the essential effect. These solutions are often reasoned in the law projects, that include the child, unemployment allowances, allowances to mothers of large families and others. These are the significant attempts to overcome the poverty, however, they are often reasoned by the increased rates of poverty, as there is generally no need to pay allowances, if the rates are not too high. When the rates increase, the people become more unsatisfied, and government is forced to restore the financial balance by paying allowances.

These issues always reason the appearance of the corresponding legislative basement and the necessary experience for creating the further laws associated with the increase of welfare of the peoples (Dujon, 2006, p. 24).

The high rates of poor women reason the appearing of special hospitals, where the women may be granted qualified and free of charge medical assistance, especially if a woman has a suckling baby. Poor women are treated as the people entitled to special benefits, and have the advantages in employment, healthcare, legislative maintenance and other spheres of life. These women may buy the goods for the reduced prices, especially if they need to feed a kid, or several children.

The fact is that, poverty may be avoided, if both sides (citizens and government) are strongly intended to avoid it. Citizens should reasonably follow their finances and follow the financial situation in the country. Shortly, it is necessary not to spend money flippantly. The government, in its turn, should steadily develop the economy of the state, and do everything in order to increase the welfare of the majority of the citizens. Thus, the mutually supported actions may help to avoid poverty. However, there are still countries in the world, where the governments are not interested in the welfare of their peoples. Citizens of these States are obliged to everything in order to keep their families, and sometimes they commit crimes, or sell their organs for illegal transplantations. Poor women in these countries often do not have an opportunity to mind the rules of hygiene, and their children are often born with the inborn deficiencies, or serious deceases.

The effect of poverty on women may be either positive or negative. Actually, the positive impact is often indirect, and is often associated with the health or some benefits offered at the governmental level. As for the negative, they are also mainly indirect, and relate the issues of health, parental rights, living environment and stresses. Originally, all the consequences may be overcome, and the statistics show, that women are more powerful in overcoming the difficulties of poverty than men, and, it is necessary to mention that women often have more motivations to go on living, as they have someone to live for. Care giving is the women’s nature, and this nature helps them to minimize the negative effects of poverty and survive during the disasters.

Finally, it is necessary to emphasize, that minimizing of the effects, that poverty may have, is the task of both sides – citizens, and government, as only joint actions and strong determination may solve this serious problem.

• Berrick, Jill Duerr. Faces of Poverty: Portraits of Women and Children on Welfare. New York: Oxford University Press, 2007.
• Buvinic, Mayra. “Women in Poverty: A New Global Underclass.” Foreign Policy 2007: 38
• Connolly, Deborah R. Homeless Mothers: Face to Face with Women and Poverty. Minneapolis: University of Minnesota Press, 2008.
• Dujon, Diane, and Ann Withorn, eds. For Crying out Loud: Women’s Poverty in the United States. Boston: South End Press, 2006.
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Impacts of COVID‐19 on global poverty, food security, and diets: Insights from global model scenario analysis

David laborde.

1 Markets, Trade and Institutions Division at IFPRI, International Food Policy Research Institute, Washington District of Columbia, USA

Will Martin

Associated data.

Table A2: Coverage of household surveys in POVANA database

Table A.5. Estimated Impacts of COVID‐19 on GDP and on Poverty

This study assesses the impact of coronavirus disease 2019 (COVID‐19) on poverty, food insecurity, and diets, accounting for the complex links between the crisis and the incomes and living costs of vulnerable households. Key elements are impacts on labor supply, effects of social distancing, shifts in demand from services involving close contact, increases in the cost of logistics in food and other supply chains, and reductions in savings and investment. These are examined using IFPRI's global general equilibrium model linked to epidemiological and household models. The simulations suggest that the global recession caused by COVID‐19 will be much deeper than that of the 2008–2009 financial crisis. The increases in poverty are concentrated in South Asia and sub‐Saharan Africa with impacts harder in urban areas than in rural. The COVID‐19‐related lockdown measures explain most of the fall in output, whereas declines in savings soften the adverse impacts on food consumption. Almost 150 million people are projected to fall into extreme poverty and food insecurity. Decomposition of the results shows that approaches assuming uniform income shocks would underestimate the impact by as much as one‐third, emphasizing the need for the more refined approach of this study.

1. INTRODUCTION

Global cases of coronavirus disease 2019 (COVID‐19) worldwide have grown exponentially since February 2020, despite progress on managing this pandemic in some countries, with worldwide daily reported new cases rising from around 500 in late February to almost 600,000 by November, with the threat of further increases during the northern‐hemisphere winter. The epicenter of the pandemic shifted from China to Europe and then to the United States and Latin America, with the disease resurgent in the northern autumn. COVID‐19 is now also spreading rapidly in low‐ and middle‐income countries in Africa and Asia, many of which lack robust health systems or strong social safety nets that can soften the pandemic's public health and economic impacts.

More than half of the world population has been, still is, or is again under some form of social distancing regime designed to contain the health crisis. Business activity has fallen sharply because of a combination of policy action and personal responses designed to reduce risk of contracting the virus, with personal action probably more important than policy in reducing economic activity (Goolsbee & Syverson, 2020 ). The International Labour Organization estimates that during the first three quarters of 2020, the number of working hours worldwide declined by 17% relative to that in the last quarter of 2019; a drop equivalent to a loss of almost 500 million full‐time jobs (ILO, 2020a ). Governments in Europe, the United States, and other high‐income countries have taken unprecedented fiscal and monetary stimulus measures to compensate for the income losses of businesses and workers and contain an inevitable economic crisis. But the relief responses of low‐ and middle‐income countries have been more limited.

COVID‐19 poses a serious threat to global food security through various transmission mechanisms (Laborde, Martin, Swinnen, & Vos, 2020 ). From what is currently known, the worst of these threats is the global economic recession causing many to lose income and leaving many vulnerable people unable to afford the food they need. Income declines not only reduce demand for food but also induce shifts in the mix of products consumed, notably resulting in less consumption of more nutrient‐rich foods (like fruits, vegetables, and animal‐sourced foods) and relatively more of calorie‐rich foods (like basic grains and sugar). Other threats arise from disruptions in agricultural input markets, farm production, marketing, and distribution of food caused by the need for social distancing to combat the global health crisis.

As COVID‐19 and its economic fallout spread in the poorest parts of the world, more people have become poor and food insecure. Although some context‐specific estimates of the impacts of these shocks on poverty and food insecurity are available, it will be years before comprehensive and comparative survey‐based information on these impacts become available. A key contribution of this paper is to assess these impacts using an integrated global modeling framework that includes national and household models. In a new scenario analysis, presented in this study, we estimate that globally, absent adequate responses in poorer nations, close to 150 million more people could fall into extreme poverty (measured against the PPP$1.90 poverty line) in 2020—an increase of 20% from prepandemic levels. This, in turn, would drive up food insecurity.

Assessing the poverty impact of COVID‐19 is no trivial matter, however. This is so not only because the crisis is still unfolding and available information of its precise socioeconomic consequences is incomplete, but also because the channels of influence are multiple and interconnected globally. Although several analyses of the poverty impacts have used simple tools provided by the World Bank's PovcalNet website 1 and assumed uniform shifts in the distribution of income per country to provide estimates of the impacts on poverty (see, for example, the studies by the World Bank in Mahler, Lakner, Aguilar, & Wu, 2020 and World Bank, 2020b ; and that of UN‐WIDER by Sumner, Hoy, & Ortiz‐Juarez, 2020 ), we are concerned that this assumption fails to account for the complexity of the channels of effect and may substantially underestimate the impacts of the pandemic. Our methodology allows to account for the disproportionate impact of the pandemic on the poor (Swinnen, 2020 ), something neglected in analyses using uniform shifts in all incomes. Results from a range of studies examining the impacts of COVID‐19 on GDP and on poverty are presented in Online Appendix A.5 . This shows that estimates of the severity of the impact increased dramatically after March 2020. The results of this study fall within the range of other estimates.

In this paper, we use information on the nature of the shocks to income, the structure of the global economy, and linked household models to provide more detailed estimates of the likely implications for income distribution, poverty, and the food security of vulnerable families. The next section of the paper looks at the transmission channels from COVID‐19 to poverty and food security. The third examines our modeling framework, including the MIRAGRODEP global computable general equilibrium (CGE) model and the POVANA framework. The fourth section presents the key assumptions of the COVID‐19 scenario used in the analysis, whereas the fifth presents key results from the analysis and identifies the main transmission channels of the global macroeconomic and poverty impacts. A sixth section provides an update of the reference scenario to illustrate the sensitivity of the results to changes in key assumptions and to validate those assumptions against the most recent available evidence about observed impacts of the pandemic. The final section concludes.

2. TRANSMISSION CHANNELS OF COVID‐19′S IMPACT ON POVERTY, FOOD SECURITY, AND NUTRITION

COVID‐19 has smaller direct impacts on agricultural production than many other pandemics. The 1918 “Spanish Flu” pandemic, for example, caused substantial losses in farm output because of high morbidity and mortality among working‐age males (Schultz, 1964 ). Some other pandemics, such as Swine flu and Avian flu, have directly reduced agricultural production. By contrast, COVID‐19 involves a relatively short period of sickness for most of its victims, has its highest mortality rates among older people, many of whom have left the formal workforce, and does not directly affect crops or livestock. However, it does have substantial impacts on agriculture and food security, generally through less direct channels of influence. Therefore, it is useful to begin the discussion by laying out the channels through which COVID‐19 affects food markets and food security. We then turn to the modeling framework that we use to evaluate these impacts.

The main channels of effect between the COVID‐19 pandemic and food security are:

• income losses and demand shocks;
• food supply chain disruptions;
• consumer responses, such as hoarding, food waste, and dietary shifts;
• policy responses: hoarding at country level (food export bans) and fiscal stimulus.

Income losses play an important role in reducing food security during the COVID‐19 pandemic. We know from the work of Amartya Sen ( 1981 ) that food insecurity and even famines frequently are not associated with physical shortages of food. What matters more is people's ability to access food. Some of the current income declines are direct consequences of the disease, such as working time lost due to the disease; whereas others are policy responses designed to reduce the rate of disease transmission. It appears that the most important are individual responses as people try to avoid situations where they are likely to catch (or transmit) the disease (Goolsbee & Syverson, 2020 ). Because individuals consider primarily their own risk of infection, some degree of coordinated distancing is appropriate to reduce the externalities imposed on others and particularly the loss of life associated with the pandemic. These social distancing policies range from simple measures such as encouraging wearing of masks and frequent handwashing, through more intrusive policies such as restricting activities with high transmission risk, to strict lockdown requirements.

The income losses resulting from these actions are primarily outside the food system as food‐related activities have generally been designated “essential” activities exempt from being locked down, except for some restaurants and other food‐away from home outlets. Hence, most of the direct income losses are outside the agri‐food system. Unskilled workers in nonessential activities are at greatest risk of falling into unemployment because they generally do not have the telecommuting options that have greatly reduced the impact of this pandemic on overall economic activity and employment.

Food supply chain disruptions caused by COVID‐19 are also affecting food security. Staple food production in high‐income countries has been relatively little affected, whereas labor‐intensive activities in some markets and processing activities have been strongly affected by disease outbreaks. Another key point of breakdown has been in processing of some agricultural products—and particularly production of meat—where low temperatures and proximity of workers can result in very high rates of disease transmission. Other disruptions to food supply chains have come from restriction on the movement of workers, the dramatic reduction in international air travel, and slowdowns in the administrative approvals for food trade. At the consumer end, restaurant services have been particularly hard hit both by lockdown policies and by consumer risk aversion.

Most consumer responses have been consequences of the COVID shocks, but some have injected additional volatility into the system. Uncertainty about the impact of the pandemic on availability of some foods has added volatility to food demand as consumers have sought to stockpile food items, such as meat and dairy products. Another early feature of adjustment to the pandemic was increased food loss as suppliers struggled to adjust their product mix in response to shifts in final sales away from food services to consumption at home. A third feature of adjustment appears to have been a run down in financial assets as affected households seek to reduce the impact of income losses on their access to food. In one carefully studied case, Abate, de Brauw, and Hirvonen ( 2020 ) found that only a small fraction of Ethiopian households appear to have enough savings to cover more than a month's food needs. The same study tracking households during the COVID‐19 outbreak also finds that income losses and food price changes appear to have changed demand for food, with declines in consumption of nutrient‐rich products like legumes, vegetables, and dairy.

Policy responses to the pandemic also play a major role in the outcome. Although economies would likely have had substantial reductions in economic activity as people sought to avoid catching (and/or transmitting) the disease, lockdown policies appear to have increased the adverse short‐run impact on output, while—where properly implemented—reducing the rate of transmission and potentially allowing a swifter recovery. In some cases, this has had a high payoff, by sharply reducing the impact of the disease, while, in other cases, such as the United States, the opportunity to reduce the incidence of the disease to low levels in the first round was missed. Even when containment policies were initially successful, frequent resurgences of the disease suggest that the economic impacts are likely to last until effective treatments and/or vaccines are widely available.

Fiscal and monetary stimulus appears to have had a substantial impact on output levels in many of the higher income countries, with initial fiscal stimulus of around 11% of GDP in the United States and substantial stimulus packages in many other high‐income countries. 2 Although fiscal stimulus packages have been announced in many developing countries, these generally appear to be much smaller as a share of GDP than those in the higher income countries. Expansion of social protection programs has been an important element in the response with 212 countries, mostly in the developing world, introducing almost 1200 measures by September 2020. 3 About half of the social assistance measures were cash based, with most being short term in duration. In developing countries, the size and duration of such responses seems to be highly variable. As little is known so far about the precise allocation of those resources across households, we do not account for the social protection measures taken by developing countries in the scenario analysis presented below. Our focus is rather on assessing the direct impact of the crisis on poverty in the absence of such social protection measures.

Many countries implemented restrictions on food exports early in the crisis designed to avoid increases in domestic food prices (Martin & Glauber, 2020 ). Fortunately, however, these restrictions did not set off an upward price spiral of the type seen in 2007–2008 (Anderson, Ivanic, & Martin, 2014 ). Although 22 countries had announced or imposed food export restrictions, affecting around 5% of calories embedded in traded food, early in the crisis, all but one had been eliminated by the end of September. 4

3. THE MODELING FRAMEWORK

We use a global modeling framework to assess the potential impacts of the COVID‐19 crisis on global poverty and food security. Specifically, we combine two economic modeling frameworks: IFPRI's global CGE model, MIRAGRODEP, 5 and the POVANA household dataset and model. This framework has been used previously to study the impact of a macroeconomic slowdown on global poverty in Laborde and Martin ( 2018 ). The main differences between the current work and the previous study are twofold. First, the Laborde–Martin study looks at a change in economic growth projections for 2015–2030 and compared poverty outcomes in 2030, using the dynamic version of the CGE and projecting household surveys until 2030.

In the current exercise, we focus on single‐year (2020) scenario results under a range of assumptions about short‐term impacts of COVID‐19, as explained further below. Second, in Laborde and Martin ( 2018 ), alternative IMF projections for global growth are regenerated by imposing commensurate changes in total factor productivity on the corresponding MIRAGRODEP parameter values. In contrast, in the current exercise, the factors underlying the socioeconomic impacts of COVID‐19, such as health impacts, social distancing, restrictions on (labor) mobility, international transport, and the closure of some business activities, are translated into MIRAGRODEP's model terms to simulate endogenously the impacts on economic growth, incomes, employment, consumption, prices, trade, and ultimately, poverty.

The two modeling frameworks are linked in top‐down fashion; that is, the relevant results of the CGE model‐based scenario analysis are introduced, along with the direct impacts of the pandemic on households, as shocks to the household survey model to assess poverty outcomes. In addition, the health impacts of the disease on labor supply and productivity are linked to outcomes from epidemiological models. This process is summarized in Figure  1 .

Implementation of the Covid‐19 scenarios [Color figure can be viewed at wileyonlinelibrary.com ]

Source : Authors’ depiction.

The main technical features of the MIRAGRODEP and POVANA models and their linkages are summarized in Online Appendix A.1 . For the present analysis, we assume in the MIRAGRODEP model that unskilled workers are harder hit than skilled workers by social distancing measures, as skilled workers are more likely able to continue work from home. We assume further that producers have very little ability to change the capital–labor utilization ratio within a single year. Governments in high‐income countries are assumed to have put in place economic stimulus measures (see below under scenario assumptions), while—for the present analysis—those of poorer countries are assumed to have limited ability to borrow to provide such substantial stimulus, and so maintain the public deficit/surplus to GDP constant.

The POVANA household model uses data on the full income distribution for around 300,000 households. 6 Having this detail avoids having to make ex‐ante or ad‐hoc assumptions about how the economic shocks caused by COVID‐19 change the distribution of income in any given country. In our approach, real incomes of households change endogenously with the simulated changes in the full vector of changes in employment; changes in prices of goods, services, and factors (including wages); and other income determinants (productivity). Changes in poverty levels are calculated by comparing the poverty rates before and after the changes in household incomes.

Finally, the POVANA database provides information about household consumption patterns. This also allows identification of the impacts of economic shocks (like the consequences of COVID‐19) on the costs of goods consumed by the household, and particularly on the costs of food consumed. Income losses and food price shocks will disproportionately hurt poor people's food security, because they spend most of their income on food: as much as 70%. Rich people spend only a small share—perhaps around 15%—of their incomes on food (Figure  2 ). The most immediate threat of COVID‐19 to food security arises from reductions in the incomes of poor and vulnerable people. Some of these losses arise from income losses in agriculture, but a much larger share of these income losses arises from disruption to nonagricultural income sources.

Engel's law: Declining food expenditure shares with rising incomes [Color figure can be viewed at wileyonlinelibrary.com ]

Source : POVANA database. Authors’ computation. Note : The blue line represents the estimated share of food consumption in total expenditures estimated through a polynomial of degree 3 on the log of individual income household, normalized by their own country's poverty line.

4. THE COVID‐19 SCENARIO

We model a range of impacts of the COVID‐19 pandemic. Beyond the direct effects of the disease on the ability to work, income losses arise from people's desire to avoid catching the disease and their altruistic concerns to avoid infecting other people, and from policy responses designed to reduce the adverse externalities associated with an unmitigated pandemic. No global economy‐wide model incorporating these features is available to fully assess these potential impacts and behavioral changes. Many of the changes in behavior and in the functioning of economies are not yet fully understood and their impacts on economic activity were still not fully known when preparing this scenario analysis. It is also difficult to rely on experience from past events, because no events like the COVID‐19 pandemic have occurred on this scale in today's globalized world. Therefore, we have had to make several assumptions about the responses of economic agents to this unprecedented situation.

In crafting the scenarios used here, we have based our choices on earlier work, such as the analysis we undertook in March 2020, 7 when we looked at the differential impacts on productivity and trade costs for a 1% global economic slowdown during 2020. Before looking at the specific scenario assumptions, it is important to keep in mind that the model operates on an annual time step and the impacts of any shock are calculated as the average impact for the year. Therefore, a disruption lasting 10 days is associated with a 10/365 impact and a price shock, for example, such as the decline in oil prices, must be calibrated on the shift in annual average prices and not on the “peak” value.

We distinguish four drivers of COVID‐19 impacts: domestic supply disruptions, global market disruptions, household behavioral responses, and policy responses.

4.1. Domestic supply disruptions

4.1.1. disruptions in labor markets.

We consider two broad impacts on labor markets. The first is the direct impact of mortality and morbidity on labor supply. The second is the impacts on labor supply of social distancing actions undertaken to reduce transmission of the disease. The first impact is linked to the direct impact of the disease. For our reference scenario, we use estimates provided by Imperial College London for each country (Walker et al., 2020 ). 8 Specifically, we use the “Social distancing of the whole population” scenario for all countries. As their online materials do not provide results by age cohorts, we reestimated those, following a procedure explained in Online Appendix A.3 . We note that this direct effect is generally quite small due compared to the next type of disruption.

Social distancing results in some willing workers become unable to sell their labor. In our reference scenario, we use the “social‐distancing” parameter from the Imperial College estimates as a base value, and assume that 12 weeks of confinement is imposed in each country, except in African countries, for which we limit it to 8 weeks, due to the more limited ability of poor populations to manage long periods of economic disruption; lower population densities than in South Asian countries; the younger average age of people in the region and the consequent more relaxed implementation of confinement policies. These assumptions result in reductions in labor supply of 23% in most countries or 15% in Africa. We consider that one‐third of skilled workers impacted by social distancing can continue working through telecommuting. This crude estimate is based on the ILO's early review of the impact of COVID‐19 on jobs of April 2020 (ILO, 2020b ) and Dingel and Neiman ( 2020 ). 9

4.1.2. Disruptions in specific value chains

Although agriculture and food sectors have been identified as essential in most countries, we also assume some supply disruption caused by reduced labor mobility (e.g., for seasonal migrant labor) and further, that perishable farm products suffer greater postharvest losses due to logistics problems and demand fallout. An increase in postharvest losses of perishable products (fruits, vegetables, meat, and dairy) of five points is included. Although this estimate is conjectural, anecdotal evidence suggests that losses have been substantial in some cases and minimal in others making an average loss of 5% seem a reasonable guesstimate for the present purpose of analysis

Total factor productivity in transportation is assumed to decline by 5% to capture losses of logistical efficiency. This number is extrapolated based on anecdotal evidence ranging from monitoring of GPS tracking devices on truck fleets in the United States (see the work of ATRI) 10 and from recent surveys conducted in West Africa. 11 While crude, this estimate provides at least a reasoned estimate of the extent of disruption to transportation sectors, especially in developing countries.

Because both autonomous social distancing (driven by fear of catching the disease) and lockdown policies designed to reduce externalities tend to reduce activity in high‐contact services such as restaurants, travel, bars, and gyms, we introduce a “shadow tax” 12 of 25% for both final and intermediate consumption of these services. This reduces the demand for these services, ceteris paribus , by about one‐third on average.

4.2. Global market disruptions

To capture the effects of the “oil war” between Saudi Arabia and Russia in late 2019 and early 2020 but predating COVID‐19, we introduce an exogenous expansion of the supply of oil. The combined effect of this larger supply of oil and the lower demand caused by the COVID‐19 crisis induces a drop in global real energy prices by 25% for crude oil and natural gas and 17% for refined oil and gas products. 13

The containment measures cause bottlenecks and delays in international freight and transport. In terms of the model parameters, this assumption has been translated into an increase in the average cost of international freight by 3%, not considering any feedback on energy prices. We calibrate these numbers to capture the increased time required to trade, because of logistical delays in harbors and at airports caused by new regulations, lack of inspectors, and other frictions associated with the pandemic. These lost days are converted into ad‐valorem equivalents using a procedure developed by Hummels and Schaur ( 2013 ).

4.3. Household and business responses

We assume that private sector agents and businesses reduce their savings as a coping mechanism to compensate for the adverse impact of the pandemic on current incomes. In the global CGE model, the savings reduction is defined for each country/region subject to two constraints: first, to the extent they can, private sector agents try to limit their welfare loss to 5% of initial income, but, second, they cannot cut their savings rates by more than 6% of initial income and cannot let their savings become negative. These boundaries were chosen based on changes in gross saving rates observed in previous crises. For instance, in the United States, between 2006 and 2009, the gross savings rate fell from 18.0% to 15.1%, whereas the world average declined from 26.6% to 24.1%. 14

It should be noted that MIRAGRODEP cannot fully capture the differences in savings behavior across economic agents. Typically, in contrast to the above, household savings tend to increase during recessions, which Keynes characterized as the “paradox of thrift” (Keynes, 1936 ). Although poor households may be unable to save and may even need to dispose of assets to survive, more affluent households try to save more in uncertain times, reducing consumption and thereby deepening the recession. In the United States, for instance, COVID‐19 substantially limited consumption spending, leading the personal savings rate (as a share of disposable income) to increase from around 7% in early 2020 to 32% in April to taper off to 23% in May of the same year. 15 Overall savings appear to be down, however, with the fall in corporate savings being larger than the increase in household savings, as happened during the Great Recession of 2008–2009, 16 and, as a result, investment decline as well. In MIRAGRODEP, the corporate sector is included with the household sector, so we assume that the expected impact of COVID‐19 on corporate savings predominates the aggregate impact, with overall savings declining.

The composition of food demand will also change during the recession. Households are expected to reduce demand for fresh products (such as fruits, vegetables, meats, and fish). This food demand shift is endogenous to income and price shifts in the model. The simulated impacts shown further below could underestimate the true effects, because we do not account for changes in consumer perceptions. Some recent survey‐based evidence suggests that consumers perceive fresh products as less safe in association with COVID‐19, as apparent in the study by Tamru, Hirvonen, and Minten ( 2020 ) for Ethiopia. In Europe and the United States, such perceptions plus awareness that better nourishment makes people less vulnerable to the virus have led to shifts in food demand from animal‐sourced toward plant‐based food products. 17 However, the evidence is too scarce as yet to be able to make proper assumptions about such shifts in consumer preferences, and hence, they are not accounted for in the scenario analysis.

4.4. Policy responses

Due to their limited actual role, we did not include specific export restriction measures regarding food products (see Section  2 and the IFPRI Food Trade Policy Tracker). The present scenario does account for the substantial economic stimulus packages being implemented by most high‐income countries, including significant income transfers to households. For the OECD countries, except Mexico, Chile, Israel, and Turkey, we assume a stimulus package of, on average, 3.2% of GDP. The fiscal stimulus is introduced in the form of higher net income transfers (or lower income taxes) from the government to the representative household.

Because of the paucity of information about stimulus packages in the rest of the world, and a concern that some of what is reported may be an exaggeration of the extent of new stimulus provided, we have omitted the impacts of fiscal stimulus in the rest of the world. We are thus measuring the unmitigated impact of the shock to help calibrate policy responses, rather than an assessment of the consequences after mitigation policies have been implemented.

5. SCENARIO RESULTS

5.1. global macroeconomic impacts.

Under the given assumptions, we conclude that COVID‐19 will result in a severe global recession with global GDP falling by 5% 18 in 2020. This COVID‐19 recession looks likely to be much deeper than that seen during the global financial crisis of 2008–2009. The economic fallout in the initial epicenters of the pandemic (China, Europe, and the United States) is also severely hurting net commodity‐exporting developing countries through declines in trade and other commodity prices, restrictions on international travel and freight, compounding the economic costs of poorer nations’ own COVID‐19‐related restrictions on movements of people and economic activity. We consider first the macroeconomic impacts and then the effects on poverty.

For developing countries as a group, we project the economic fallout to lead to a decline of aggregate GDP of 3.6% relative to 2019, but economies in Central Asia, Africa, Southeast Asia, and Latin America would be hit much harder due to their relatively high dependence on remittances, trade, and/or primary commodity exports. The recession is expected to be less severe in China and the rest of East Asia, where—with the present scenario assumptions—we expect the economic recovery to start sooner with the earlier lifting of containment measures.

We expect harsh economy‐wide impacts in sub‐Saharan Africa with GDP falling on average by almost 9% from the previous year, although agri‐food sectors may be spared and could even expand, as the collapse in export earnings and remittance incomes, 19 with domestic production rising in light of reduced ability to import food push. Lower labor demand in urban service sectors may push workers to return to agriculture, also contributing to greater domestic food production. With more workers in the sector, however, individual incomes would remain low.

5.2. Poverty impacts

Without social and economic mitigation measures such as fiscal stimulus and expansion of social safety nets in the global South (scenario assumption), the impact on extreme poverty (measured against the PPP$1.90 per person per day international poverty line) is devastating as shown in Figure  3 . The number of poor increases by 20% (almost 150 million people) with respect to the situation in the absence of COVID‐19, affecting urban and rural populations in Africa south of the Sahara the most, as 80 million more people join the ranks of the poor, a 23% increase. The poverty increase in rural areas is expected to be smaller than that in urban areas, partly because of the lower rate of transmission of the disease and partly because of the robustness of demand and supply for food relative to many other, more vulnerable sectors. Accordingly, we estimate that, in sub‐Saharan Africa, the number of poor people could increase by 15% in rural areas, but as much as 44% in urban areas. In this scenario, the number of poor people in South Asia is projected to increase by 15% or 42 million people.

Global and Regional Poverty Impacts of MIRAGRODEP‐COVID 19 scenario (April 2020) by selected regions (Absolute and percentage change from 2020 baseline values) [Color figure can be viewed at wileyonlinelibrary.com ]

Source : MIRAGRODEP and POVANA Simulations.

In both cases, the impacts on rural populations are smaller because the direct impact of COVID‐19 on agriculture is less severe than on other sectors. As these estimates refer to the numbers of extremely poor people, that is, those who typically lack the means to buy enough food, we expect a commensurate rise in the number of food‐insecure people. The ability to distinguish the reduced sensitivity of rural households to COVID‐19 is an important advantage of the more complex framework used in this study. Applying uniform income declines to the initial distribution of income will almost always result in larger poverty increases for rural people because their initial incomes are so much lower than those of urban residents in developing countries.

The estimated income declines due to COVID‐19 are much larger than seen in many earlier studies such as in Vos, Martin, and Laborde ( 2020 ), Mahler et al. ( 2020 ), and World Bank ( 2020a ) and in most of the scenarios considered in McKibbin and Fernando ( 2020 ). However, they are substantially below the (uniform) income declines of 20% considered as an upper bound in Sumner et al. ( 2020 ). The estimates in this study fall within the range of studies surveyed in Appendix Table A.5 .

5.3. Changes in diets and impacts on nutrition

The income and price changes associated with the pandemic are likely to result in some quite substantial changes in patterns of food consumption, with adverse nutritional consequences. The declines in income and supply disruptions are likely to cause quite substantial shifts in demand away from nutrient‐dense foods such as fruits and vegetables, dairy products, and meats, and toward basic staple foods such as rice, maize, and other basic grains. Figure  4 confirms this as a global pattern. The dietary shift is (on average) similar in both developed and developing regions. The changes in consumption can be considerably sharper at the country level, as shown in Figure A.4 online.

COVID‐19 impacts on diets (average effect for world) (percentage change in average global household consumption by product) [Color figure can be viewed at wileyonlinelibrary.com ]

Source : MIRAGRODEP Simulation (April 2020 scenario).

Note : Global average based on weighted changes at the estimated at the country or regional levels. Weights are based on base value of consumption, while changes are computed on the evolution of the volume of consumption for each national representative household.

5.4. Decomposition of impacts by main drivers

Given the multiple shocks used for these simulations, it is useful to understand which shocks influence the simulated outcomes the most. Not only does this provide insights into the driving forces behind both the macroeconomic and poverty outcomes, but also it allows a comparison of our approach relative to the much simpler approach of simply reducing consumption uniformly in line with the decline in GDP at constant prices used by Sumner et al. ( 2020 ), Mahler et al. ( 2020 ), and World Bank ( 2020b ). The decomposition was done by deleting one shock at a time from the full simulation and assessing the impact of that shock. Adding up these effects provides a good estimate of the total impact and allows a decomposition of the total effect into its sources.

The first three bars in Figure  5 show that the dominant influence on the loss of aggregate GDP due to the pandemic is the reductions in labor supply, both from individual health‐related responses and from social‐distancing policies. Disruptions in logistics and the savings adjustment play small to negligible roles in the declines in GDP. The second group of bars shows the decomposition for the impacts on agri‐food sector GDP. Again, reductions in supply are primarily driven by reductions in labor availability, although these are less important than for the whole economy because a large share of agricultural value‐added is treated as essential. The savings adjustment mitigates the impact on food consumption and hence also on agri‐food production.

Decomposition of the simulated macroeconomic impacts by main transmission channel [Color figure can be viewed at wileyonlinelibrary.com ]

Source : MIRAGRODEP simulations results (April 2020 scenario). Note : Each bar in the graph represents 100% of the change in each variable in the COVID‐19 scenario and shows for each driver's positive or negative contribution (in percentage shares) to the overall change.

Income losses owing to the pandemic's direct impact on people's ability to work and that of the social distancing measures also explain most of the reduction in total food consumption, compounded by supply disruptions raising the logistical costs embedded in food prices. The savings adjustment is a mitigating factor. The increases in logistical costs affect demand for fruits and vegetables most strongly, outweighing income losses through social distancing; most notably in developing countries.

The estimates in this study fall within the range of studies surveyed in Appendix Table A.5 .

Figure  5 further shows that the adjustment rule regarding private savings mitigates the macroeconomic impact of the recession on overall household consumption. 20 The mitigating effect on consumption is generally stronger developed than in developing countries whose, on average, much poorer economic actors have less capacity to absorb the shock by drawing on own savings.

These results show that different shocks have different impacts on the different outcomes, with the direct reductions in labor having the largest impacts on GDP, whereas reductions in saving have important impacts on consumption, and increases in the cost of logistics in food supply chains having the greatest impact on consumption of fruits and vegetables.

Figure  6 provides a decomposition for the total poverty impacts parallel to that for the macroeconomic impacts presented in Figure  5 . Not surprisingly, it shows that the reductions in employment and in labor supply and social distancing have the largest impacts on poverty. Logistical costs have the second largest impacts, while other influences, such as oil price changes and changes in savings and investment, reduce the total increase in poverty in several regions.

Simulated changes in extreme poverty by cause (shares of total impact) [Color figure can be viewed at wileyonlinelibrary.com ]

Source : MIRAGRODEP simulations results (April 2020 scenario).

Note : Each bar in the graph represents 100% of the change in each variable in the COVID‐19 scenario and shows for each driver's positive or negative contribution (in percentage shares) to the overall change.

To illustrate the difference between our approach and other studies assessing the poverty impact of the pandemic, we decompose in Figure  7 the change in the poverty rate into three components. The first, shown in the blue bar, is the impacts of average changes in incomes and in the cost of living on household real incomes. The second incorporates the nonneutral impacts of the COVID‐19 shocks on the cost of living to each household and the consequent impact on household incomes. The third considers, in addition, the nonneutral impact of the shocks on households’ individual incomes. It takes into account, for instance, the fact that many workers supplying unskilled labor—which is assumed to be the situation of the poorest—are unable to work remotely, and hence generally suffer greater income losses than higher income workers, both through the quantity of labor they can supply and the wage rates they receive.

Decomposing the simulated changes in extreme poverty owing to COVID‐19 by average income and distributional shock (shares of total impact) [Color figure can be viewed at wileyonlinelibrary.com ]

It is clear from Figure  7 that the traditional estimate of the poverty impact of the pandemic—the observed changes in real incomes resulting from changes in average nominal incomes and consumer costs—explain most of the changes in poverty. At the global level, these uniform changes explain just over 110 million of the nearly 150 million increase in poverty. In sub‐Saharan Africa, both the uniform income effect and the differential impact on the incomes of the poor raise poverty, but this is substantially offset by many poor people being lifted out of poverty by declines in their idiosyncratic costs of living. This benefit, likely largely driven by declines in farm prices, explains why the increase in poverty observed in Figure  3 is so much smaller in Africa than in South Asia. The pattern for changes in rural poverty follows closely that observed for overall poverty.

6. A SCENARIO UPDATE

In previous sections, we discussed at length the analytical framework used to assess the macroeconomic and poverty impacts of the COVID‐19 crisis and described the contributions of the different drivers to the outcomes for poverty and food insecurity. That reference scenario was elaborated in April 2020, based on our observations and interpretations of the world economy, the health crisis, and the mitigation options taken up to that point in time. Although our basic methodology has not changed, new information available by the final quarter of 2020 about COVID‐19 effects on social distancing, labor supply, and policy responses differs in a number of respects from used the underpin the assumption of the original reference scenario.

To illustrate the changes in information and approach over that time, we provide an updated scenario, based on new information available for the period up to September 2020, using updated assumptions as summarized in Table  2 . For health effects, we shifted from the estimates in the epidemiological model of Imperial College (Walker et al., 2020 ) to that of the London School of Hygiene and Tropical Medicine (Pearson et al., 2020 ) that provides greater detail on pandemic mitigation options adopted by countries around the world. We further rely on Google Mobility reports (Google, 2020 ) to track the evolution of social distancing intensity and the changes in face‐to‐face services (e.g., mobility to recreation location). Also, more recent macroeconomic assessments, such as the ADB Economic Outlook (ADB, 2020 ), allow us to update the assumptions about changes in consumption behavior and participation to labor markets, and the value of some specific parameters (e.g., number of workday losses) under varying mitigation strategies adopted by countries.

Comparison of key assumptions for April and September 2020 MIRAGRODEP‐COVID 19 scenarios

Macroeconomic impacts of MIRAGRODEP‐COVID 19 scenario (April 2020) by country and country group, 2020

Source : MIRAGRODEP Simulation.

Note : Regions in bold aggregated results computed postsimulations, weighted by the relevant country‐level variable. Details for rich countries are omitted. Real consumption is limited to household private consumption and defined as the equivalent variation (welfare).

Note : Regions in bold aggregated results computed postsimulations, weighted by the relevant country‐level variable. Real household consumption is measured as the “equivalent variation” of welfare. Real GDP is computed following national accounting principles. Fisher price indices between base prices and simulation prices are used. Exports of goods and services are measured FOB at constant international dollars but final export prices.

The changes in results for macroeconomic outcomes, agri‐food value‐added, and poverty are shown in Table  3 . Although the scenarios are broadly similar in terms of the nature of the drivers, the magnitudes of the shocks have been updated and made more country‐specific. The broad upshot is that the global recession is expected to be even deeper in 2020 (a 7.1% decline in global GDP instead of a 5.1% decline). The revised assumptions do not change the earlier expectation that the agri‐food sector has held up relatively well, showing resilience compared to the rest of the economy. Globally, the agri‐food sector could even expand as agricultural production has remained relatively stable, whereas costs are down with the drop in prices for manufacturing and services.

Poverty and macroeconomic impacts of MIRAGRODEP‐COVID 19 scenarios for 2020 (April and September 2020 scenarios)

Source : MIRAGRODEP and POVANA simulations (April and September 2020 scenarios).

The aggregate findings of the updated scenario for global poverty are practically unchanged, with the number of poor expected to rise by just under 150 million. However, the regional distribution of poverty increases differs substantially from that presented in the previous sections. In the new scenario, the economic crisis is expected to be deeper than previously anticipated in South Asia, particularly in India, and milder in Africa. As a result, this simulation projects a smaller, though still significant increase in poverty sub‐Saharan Africa (50 million instead of near 80 million) and the larger increase affecting people in South Asia (72 million instead of 42 million).

7. CONCLUSIONS

The key goal of this paper was to provide a rigorous framework to assess the risks pandemics like COVID‐19 pose to global poverty and food security. Accordingly, we first considered the nature of the relationships between the COVID‐19 pandemic and the overall economy. This made clear that the major impacts of the pandemic on poverty and food security are more likely to come from shocks to household incomes, and hence to food access, than from impacts on food markets directly. However, we recognize that there are important direct impacts of the disease on food markets, particularly in the more labor‐intensive parts of the food chain, and in areas such as food services, where the need for social distancing is sharply reducing the operation of restaurants.

Given the multiplicity of links between the pandemic, household incomes, and food security, we concluded that a framework linking economy‐wide modeling with household models was needed to capture the impacts of the shock on poverty. We used the MIRAGRODEP global CGE model linked to epidemiological models to capture the impacts on the global economy, and the POVANA household models to capture the impacts at the household level.

The simulation experiments were designed to capture the impacts of the crisis begin with the direct, thus far seemingly minor, impacts of the disease on labor supply resulting from increases in morbidity and mortality. The next key shock was the impacts of social distancing, whether undertaken out of concern about catching the disease or as part of a concerted policy of suppressing the disease—a very important channel of effect with highly specific impacts by sector and type of labor. In addition, we considered the impacts of increases in logistical costs associated with the disease.

Our initial results suggested that COVID‐19 would cause a decline in global GDP of about 5% in 2020, with a similar decline in South Asia and a larger decline (−9%) in Africa South of the Sahara, and much larger declines in global trade because of both increases in logistical costs and declines in investment as consumers and governments seek to reduce the adverse impacts of the crisis on living standards by reducing private and government savings. Consumers are also expected to have shifted their food purchases, buying less nutrient dense, but more expensive, products such as fruits and vegetables, meat, and dairy products, and buying more calorie‐rich and cheaper cereals and processed foods. In an updated scenario, however, using new information about— inter alia —the spread of COVID‐19 and related social distancing measures, particularly taking into account the reduced estimates of the spread of the disease in Africa, we expect that the global recession could be steeper than previously anticipated, driven in part by a much stronger economic decline in South Asia.

To better understand these results, we decomposed them by major drivers. The economic consequences of reduced labor supply and social distancing drive most of the impacts on GDP worldwide. Fiscal stimulus in high‐income countries and declines in private savings mitigate some, but far from all, the adverse impact on total and food consumption.

The analysis concludes that the pandemic will likely increase the number of people in poverty by about 150 million people, or 20% of current poverty levels. In our reference scenario, most of this increase in extreme poverty was expected to occur in Africa South of the Sahara and South Asia, where many people are currently close to this poverty line. An updated analysis suggests that the increase in poverty may be smaller than originally anticipated in Africa and larger in South Asia, with the global total impact remaining very similar at just under 150 million.

The analytical framework that we use captures many important nonneutralities in the effects of the crisis that are ignored in simpler analyses assuming that all incomes change equally. For example, we find that poverty increases are likely to be smaller, both in absolute numbers and relative to current poverty rates, in rural areas that are likely less hard hit by the crisis. An analysis of these poverty results suggests that accounting for just the average changes in incomes and in consumer prices would capture only about three‐quarters of the total impact of the crisis on poverty rates. Many of the impacts are nonneutral between the poor and the rich and outcomes for the poor are, on average, substantially worse for higher income and more educated people, many of whom can continue to work productively at a distance.

The actual implications of COVID‐19 for poverty and food security will depend on a wide range factors, many of which are simply unknown at this point—such as resurgence of the disease during the northern winter and spring, and the efficacy and adoption of potential vaccines. Thus, the results in this paper should not be taken in any way as a precise forecast of the outcome. Rather, the paper provides an approach for evidence‐based “what‐if” scenario analysis of the impacts of broad‐based shocks such as COVID‐19 for poverty, food insecurity, and dietary change. As such it should help better understand the relative importance of the multiple channels of transmission and inform policymakers about the socioeconomic consequences of mitigation measures taken to reduce public health risks, and hence, the potential trade‐offs between efforts to safeguard lives and those to protect livelihoods.

Supporting information

Figure A4 COVID‐19 impacts on diets in China and Nigeria

ACKNOWLEDGMENTS

The authors gratefully acknowledge helpful comments received from three anonymous reviewers and from Jo Swinnen on previous drafts of this paper. The authors are further grateful to the CGIAR Research Program on Policies, Institutions and Markets (PIM), the Bill and Melinda Gates Foundation, and USAID for financial support to the research that formed the basis for this paper.

Laborde, D. , Martin, W. , & Vos, R. (2021). Impacts of COVID‐19 on global poverty, food security and diets: Insights from global model scenario analysis . Agricultural Economics . 2021; 52 :375–390. 10.1111/agec.12624 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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2 See https://www.imf.org/en/Topics/imf‐and‐covid19/Policy‐Responses‐to‐COVID‐19#I

3 See the World Bank's “living paper” at https://tinyurl.com/yd4g4z45

4 Up‐to‐date counts are available at IFPRI's food trade policy tracker.

5 Modelling International Relations under Applied General Equilibrium model enhanced for the AGRODEP modeling consortium ( http://www.agrodep.org/models/library ).

6 See Online Appendix A.2 for the coverage of the household survey data used for the present analysis.

7 https://www.ifpri.org/spotlight/ifpri‐resources‐and‐analyses‐covid‐19‐also‐known‐coronavirus

8 In the updated scenario (discussed in Section  6 ), we use alternative projections for the spread of COVID‐19 of the epidemiological model of the London School of Hygiene and Tropical Medicine (LSHTM) (Pearson et al., 2020 ).

9 See Online Appendix A.3 for the procedure for deriving this estimate.

10 American Transportation Research Institute; see, for instance, https://tinyurl.com/yxkr92g6

11 Reuters, ‘West African food trade under strain as COVID‐19 shuts borders’, May 27, 2020. https://tinyurl.com/yxzjt9fm

12 We use a shadow tax instead of a preference shifter in the model to avoid changing the utility function that would compromise the welfare analysis.

13 For comparison, oil prices for WTI Crude contracts declined by 33% between June 2019 and June 2020 (from US$53 to US$35 per barrel) and by 35% between the start of 2020 and November 10 of the same year (from US$62 to US$40), after showing a steep decline between January and the end of April and a slow recovery. Since the model combines natural gas and crude oil into one variable, the simulated decline in global energy prices is somewhat lower than the observed 35%, given that the impact of the supply and demand shifts on the price of natural gas has been smaller than that on oil.

14 World Bank, World Development Indicators, https://databank.worldbank.org/reports.aspx?source=world‐development‐indicators# , accessed 24 July 2020.

15 https://fred.stlouisfed.org/series/PSAVERT

16 https://fred.stlouisfed.org/series/B057RC1Q027SBEA

17 https://tinyurl.com/y4w97xee

18 This decline is relative to 2019 levels. Relative to the 2020 baseline (counterfactual without the COVID‐19 shock), this implies a 7% decline in global GDP. Only in Table  1 , do we present the macroeconomic impacts relative to the previous year (for ease of comparison with other estimates and projections). All other simulation results are with respect to the 2020 baseline (counterfactual without the COVID shock).

19 Remittance incomes make up more than 10% of gross foreign exchange earnings in sub‐Saharan Africa. In the model, we assume the region's earnings from remittances drop by 8%. Recent projections project a decline of 9% for 2020 (World Bank 2020c ).

20 It is important to point out that the impact on consumption is softened further in the model estimations because GDP is measured in real terms through a Fisher index, while the impact on consumption is measured through a welfare metric (equivalent variation) typically used in CGE models.

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How to write a thesis statement + examples

What is a thesis statement?

Is a thesis statement a question, how do you write a good thesis statement, how do i know if my thesis statement is good, examples of thesis statements, helpful resources on how to write a thesis statement, frequently asked questions about writing a thesis statement, related articles.

A thesis statement is the main argument of your paper or thesis.

The thesis statement is one of the most important elements of any piece of academic writing . It is a brief statement of your paper’s main argument. Essentially, you are stating what you will be writing about.

You can see your thesis statement as an answer to a question. While it also contains the question, it should really give an answer to the question with new information and not just restate or reiterate it.

Your thesis statement is part of your introduction. Learn more about how to write a good thesis introduction in our introduction guide .

A thesis statement is not a question. A statement must be arguable and provable through evidence and analysis. While your thesis might stem from a research question, it should be in the form of a statement.

Tip: A thesis statement is typically 1-2 sentences. For a longer project like a thesis, the statement may be several sentences or a paragraph.

A good thesis statement needs to do the following:

• Condense the main idea of your thesis into one or two sentences.
• Answer your project’s main research question.
• Clearly state your position in relation to the topic .
• Make an argument that requires support or evidence.

Once you have written down a thesis statement, check if it fulfills the following criteria:

• Your statement needs to be provable by evidence. As an argument, a thesis statement needs to be debatable.
• Your statement needs to be precise. Do not give away too much information in the thesis statement and do not load it with unnecessary information.
• Your statement cannot say that one solution is simply right or simply wrong as a matter of fact. You should draw upon verified facts to persuade the reader of your solution, but you cannot just declare something as right or wrong.

As previously mentioned, your thesis statement should answer a question.

If the question is:

What do you think the City of New York should do to reduce traffic congestion?

A good thesis statement restates the question and answers it:

In this paper, I will argue that the City of New York should focus on providing exclusive lanes for public transport and adaptive traffic signals to reduce traffic congestion by the year 2035.

Here is another example. If the question is:

How can we end poverty?

A good thesis statement should give more than one solution to the problem in question:

In this paper, I will argue that introducing universal basic income can help reduce poverty and positively impact the way we work.

• The Writing Center of the University of North Carolina has a list of questions to ask to see if your thesis is strong .

A thesis statement is part of the introduction of your paper. It is usually found in the first or second paragraph to let the reader know your research purpose from the beginning.

In general, a thesis statement should have one or two sentences. But the length really depends on the overall length of your project. Take a look at our guide about the length of thesis statements for more insight on this topic.

Here is a list of Thesis Statement Examples that will help you understand better how to write them.

Every good essay should include a thesis statement as part of its introduction, no matter the academic level. Of course, if you are a high school student you are not expected to have the same type of thesis as a PhD student.

Here is a great YouTube tutorial showing How To Write An Essay: Thesis Statements .

Rethinking the ‘Feminisation of Poverty’ Thesis

Gender, Generation and Poverty: Exploring the ‘Feminisation of Poverty’ in Africa, Asia and Latin America. Edited by Sylvia Chant. Cheltenham, UK, 2007, 428 pp. £89.95 ISBN 9781 84376 992 7 (cased), £29.95 ISBN 978 1 84376 993 4 (paperback).

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• Volume 61 , pages 293–296, ( 2009 )

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Last Updated: Apr 01, 2024 Views: 12

What is a thesis statement.

A thesis statement is a sentence that states the main idea of your paper. It is not just a statement of fact, but a statement of position. What argument are you making about your topic? Your thesis should answer that question.

How long should my thesis statement be?

Thesis statements are often just one sentence. Keep thesis statements concise, without extra words or information. If you are having trouble keeping your thesis statement to one sentence, consider the following:

• Is your thesis is specific enough?
• Does your thesis directly supports your paper?
• Does your thesis accurately describes your purpose or argue your claim?

Can I see some example thesis statements?

The following websites have examples of thesis statements:

• Thesis Statements This link opens in a new window (UNC)
• Tips and Examples for Writing Thesis Statements This link opens in a new window (OWL at Purdue)
• Writing an Effective Thesis Statement This link opens in a new window (Indiana River State College)

These web resources may be helpful if you are looking for examples. However, be sure to evaluate any sources you use! The Shapiro Library cannot vouch for the accuracy of information provided on external websites.

Where can I find more information?

Video tutorials.

• The Persuasive Thesis: How to Write an Argument This link opens in a new window (SNHU Academic Support)
• Research and Citation Playlist This link opens in a new window (SNHU Academic Support)
• Planning a Paper series: Drafting a Thesis Statement This link opens in a new window ( Infobase Learning Cloud - SNHU Login Required)

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• Build a Critical Analysis Thesis This link opens in a new window (SNHU Academic Support)
• Build a Compare & Contrast Thesis This link opens in a new window  (SNHU Academic Support)
• Build a History Thesis This link opens in a new window  (SNHU Academic Support)
• Build a Persuasive Thesis This link opens in a new window  (SNHU Academic Support)

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Nursing, dental students gain patient insights via poverty simulation

Published 04.01.2024

Photos by Jennifer Cline, writer/magazine editor

Sixty-six nursing and dental hygiene students took part in a “poverty simulation,” designed to put them in the shoes of families trying to meet obligations on a low income.

Through four 15-minute weeks, families – some newly unemployed, some recently deserted by the “breadwinner,” some homeless, some recipients of Temporary Assistance for Needy Families, and some senior citizens receiving disability or retirement or grandparents raising grandchildren – were tasked with keeping their homes secure and their families fed. That included obtaining and keeping a job; paying utilities, mortgages and rent; purchasing food; securing transportation; and sending children to school or providing daycare. It also included “luck of the draw” cards that could bring good news or throw a wrench into a family’s plans.

Throughout the course of the “month,” four families who entered the simulation as homeless secured housing, while six other families ended the simulation without homes. Others had utilities shut off, pawned appliances and other valuables, and landed in “jail” when their children were “neglected” or they were enticed to take part in illegal “side businesses.”

During a post-activity discussion, students reported feeling stressed, worried and hopeless.

“I saw you making decisions between buying prescriptions and paying for food,” said Melissa L. Furman, instructor of nursing, who played the dual roles of shopping center clerk and doctor. “That’s real life.”

Dental hygiene student Elizabeth L. Ortiz, of Elizabethtown, felt the importance of becoming aware what resources are available to the patients she will eventually see.

“You never know what someone else is going through,” many students noted.

Terri A. Stone, assistant professor of nursing, has led poverty simulations for students in the Community Health Nursing class for several years. This year, it became an interprofessional collaboration with students in the dental hygiene program, as well as students in nursing instructor Steve C. Sofopoulos’ Adult Health Nursing course.

The simulation is a product of the Missouri Association for Community Action.  

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Microsoft 365 Life Hacks > Writing > How AI can help you improve your thesis statement

How AI can help you improve your thesis statement

Creating a thesis statement can be a challenging undertaking. Thankfully, today’s writers can use AI to assist in the creation process. While writing with AI can feel intimidating, the right tools and knowing how to use them can enhance your thesis statement and guide you through the creation process. From generating ideas to polishing your final draft, here’s how to use AI to create a quality thesis.

Selecting a topic

AI-powered tools have access to vast databases of academic papers, journals, and other scholarly materials. If you’re trying to choose a thesis topic or questioning the viability of your current topic, AI can assist by brainstorming ideas and highlighting relevant research you can use as evidence for your claims.

Creating an initial draft

AI tools can help you create a preliminary draft of your thesis statement, which you can continue to build on as your argument and research evolve. You can request a fresh draft at any stage in the writing process, as AI only requires basic information about your topic and area of research to get started. Based on your input, the AI tool will utilize its database of knowledge to generate a thesis statement.

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Refining your thesis with AI feedback

Once you have a solid draft, utilize AI feedback to refine your writing. Ask for an analysis of your thesis statement for clarity, coherence, grammar, and more. By highlighting areas for improvement, AI can help refine your thesis statement so it accurately conveys your research focus and argument. There are a few ways this process not only improves the quality of your statement but also enhances your understanding of what makes an effective thesis:

• Efficiency. AI tools can significantly speed up the brainstorming and drafting phases, giving you more time to focus on researching and outlining your thesis. This is especially useful for tight deadlines.
• Objectivity. AI feedback is based on data and algorithms that can provide a largely unbiased perspective on the quality of your thesis statement. This objective analysis can help you improve your thesis and overall writing.
• Consistency. AI tools can help you align the rest of your paper with your initial thesis statement to ensure consistency throughout your work.

Choosing the right AI tool for academic writing

When seeking an AI assistant for thesis drafting, choose AI tools, including GPTs, designed for professional or academic writing . AI applications that are familiar with academia can offer feedback and suggestions tailored to fit the conventions of scholarly writing.

AI has revolutionized academic writing, offering powerful tools for creating and refining thesis statements. By leveraging AI tools, you can achieve a higher level of clarity and persuasiveness in your work, so try them out the next time you need to write an academic paper!

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