Many scholars have argued that once “basic needs” have been met, higher income is no longer associated with higher in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of “basic needs” and multiple questions about well-being, we find no support for this claim. The relationship between well-being and income is roughly linear-log and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it.

Introduction

In 1974 Richard Easterlin famously posited that increasing average income did not raise average well-being, a claim that became known as the Easterlin Paradox. However, in recent years new and more comprehensive data has allowed for greater testing of Easterlin’s claim. Studies by us and others have pointed to a robust positive relationship between well-being and income across countries and over time (Deaton, 2008; Stevenson and Wolfers, 2008; Sacks, Stevenson, and Wolfers, 2013). Yet, some researchers have argued for a modified version of Easterlin’s hypothesis, acknowledging the existence of a link between income and well-being among those whose basic needs have not been met, but claiming that beyond a certain income threshold, further income is unrelated to well-being.

The existence of such a satiation point is claimed widely, although there has been no formal statistical evidence presented to support this view. For example Diener and Seligman (2004, p. 5) state that “there are only small increases in well-being” above some threshold. While Clark, Frijters and Shields (2008, p. 123) state more starkly that “greater economic prosperity at some point ceases to buy more happiness,” a similar claim is made by Di Tella and MacCulloch (2008, p. 17): “once basic needs have been satisfied, there is full adaptation to further economic growth.” The income level beyond which further income no longer yields greater well-being is typically said to be somewhere between $8,000 and $25,000. Layard (2003, p. 17) argues that “once a country has over $15,000 per head, its level of happiness appears to be independent of its income;” while in subsequent work he argued for a $20,000 threshold (Layard, 2005 p. 32-33). Frey and Stutzer (2002, p. 416) claim that “income provides happiness at low levels of development but once a threshold (around $10,000) is reached, the average income level in a country has little effect on average subjective well-being.”

Many of these claims, of a critical level of GDP beyond which happiness and GDP are no longer linked, come from cursorily examining plots of well-being against the level of per capita GDP. Such graphs show clearly that increasing income yields diminishing marginal gains in subjective well-being. However this relationship need not reach a point of nirvana beyond which further gains in well-being are absent. For instance Deaton (2008) and Stevenson and Wolfers (2008) find that the well-being–income relationship is roughly a linear-log relationship, such that, while each additional dollar of income yields a greater increment to measured happiness for the poor than for the rich, there is no satiation point.

In this paper we provide a sustained examination of whether there is a critical income level beyond which the well-being–income relationship is qualitatively different, a claim referred to as the modified-Easterlin hypothesis. As a statistical claim, we shall test two versions of the hypothesis. The first, a stronger version, is that beyond some level of basic needs, income is uncorrelated with subjective well-being; the second, a weaker version, is that the well-being–income link estimated among the poor differs from that found among the rich.

Claims of satiation have been made for comparisons between rich and poor people within a country, comparisons between rich and poor countries, and comparisons of average well-being in countries over time, as they grow. The time series analysis is complicated by the challenges of compiling comparable data over time and thus we focus in this short paper on the cross-sectional relationships seen within and between countries. Recent work by Sacks, Stevenson, and Wolfers (2013) provide evidence on the time series relationship that is consistent with the findings presented here.

To preview, we find no evidence of a satiation point. The income–well-being link that one finds when examining only the poor, is similar to that found when examining only the rich. We show that this finding is robust across a variety of datasets, for various measures of subjective well-being, at various thresholds, and that it holds in roughly equal measure when making cross-national comparisons between rich and poor countries as when making comparisons between rich and poor people within a country.

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One of global society’s foremost structural changes underway is its rapid aggregate shift from farmbased to city-based economies. More than half of humanity now lives in urban areas, and more than two-thirds of the world’s economies have a majority of their population living in urban settings. Much of the gradual movement from rural to urban areas is driven by long-term forces of economic progress. But one corresponding downside is that city-based societies become increasingly disconnected—certainly physically, and likely psychologically—from the practicalities of rural livelihoods, especially agriculture, the crucial economic sector that provides food to fuel humanity.

The nature of agriculture is especially important when considering the tantalizingly imminent prospect of eliminating extreme poverty within a generation. The majority of the world’s extremely poor people still live in rural areas, where farming is likely to play a central role in boosting average incomes. Agriculture is similarly important when considering environmental challenges like protecting biodiversity and tackling climate change. For example, agriculture and shifts in land use are responsible for roughly a quarter of greenhouse gas emissions.

As a single word, the concept of “agriculture” encompasses a remarkably diverse set of circumstances. It can be defined very simply, as at dictionary.com, as “the science or occupation of cultivating land and rearing crops and livestock.” But underneath that definition lies a vast array of landscape ecologies and climates in which different types of plant and animal species can grow. Focusing solely on crop species, each plant grows within a particular set of respective conditions. Some plants provide food—such as grains, fruits, or vegetables—that people or livestock can consume directly for metabolic energy. Other plants provide stimulants or medication that humans consume—such as coffee or Artemisia—but have no caloric value. Still others provide physical materials—like cotton or rubber—that provide valuable inputs to physical manufacturing.

One of the primary reasons why agriculture’s diversity is so important to understand is that it defines the possibilities, and limits, for the diffusion of relevant technologies. Some crops, like wheat, grow only in temperate areas, so relevant advances in breeding or plant productivity might be relatively easy to diffuse across similar agro-ecological environments but will not naturally transfer to tropical environments, where most of the world’s poor reside. Conversely, for example, rice originates in lowland tropical areas and it has historically been relatively easy to adopt farming technologies from one rice-growing region to another. But, again, its diffusion is limited by geography and climate. Meanwhile maize can grow in both temperate and tropical areas, but its unique germinating properties render it difficult to transfer seed technologies across geographies.

Given the centrality of agriculture in many crucial global challenges, including the internationally agreed Sustainable Development Goals recently established for 2030, it is worth unpacking the topic empirically to describe what the term actually means today. This short paper does so with a focus on developing country crops, answering five basic questions: 

1. What types of crops does each country grow? 

2. Which cereals are most prominent in each country? 

3. Which non-cereal crops are most prominent in each country? 

4. How common are “cash crops” in each country? 

5. How has area harvested been changing recently? 

Readers should note that the following assessments of crop prominence are measured by area harvested, and therefore do not capture each crop’s underlying level of productivity or overarching importance within an economy. For example, a local cereal crop might be worth only $200 per ton of output in a country, but average yields might vary across a spectrum from around 1 to 6 tons per hectare (or even higher). Meanwhile, an export-oriented cash crop like coffee might be worth $2,000 per ton, with potential yields ranging from roughly half a ton to 3 or more tons per hectare. Thus the extent of area harvested forms only one of many variables required for a thorough understanding of local agricultural systems. 

The underlying analysis for this paper was originally conducted for a related book chapter on “Agriculture’s role in ending extreme poverty” (McArthur, 2015). That chapter addresses similar questions for a subset of 61 countries still estimated to be struggling with extreme poverty challenges as of 2011. Here we present data for a broader set of 140 developing countries. All tables are also available online for download.

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• Cropshares_tables_clean
• Crop_Shares_metadata
• FAO crop codes
• FAO_cropshares
• WB income class
• WBcodescountrycode

One of global society’s foremost structural changes underway is its rapid aggregate shift from farmbased to city-based economies. More than half of humanity now lives in urban areas, and more than two-thirds of the world’s economies have a majority of their population living in urban settings. Much of the gradual movement from rural to urban areas is driven by long-term forces of economic progress. But one corresponding downside is that city-based societies become increasingly disconnected—certainly physically, and likely psychologically—from the practicalities of rural livelihoods, especially agriculture, the crucial economic sector that provides food to fuel humanity.

The nature of agriculture is especially important when considering the tantalizingly imminent prospect of eliminating extreme poverty within a generation. The majority of the world’s extremely poor people still live in rural areas, where farming is likely to play a central role in boosting average incomes. Agriculture is similarly important when considering environmental challenges like protecting biodiversity and tackling climate change. For example, agriculture and shifts in land use are responsible for roughly a quarter of greenhouse gas emissions.

As a single word, the concept of “agriculture” encompasses a remarkably diverse set of circumstances. It can be defined very simply, as at dictionary.com, as “the science or occupation of cultivating land and rearing crops and livestock.” But underneath that definition lies a vast array of landscape ecologies and climates in which different types of plant and animal species can grow. Focusing solely on crop species, each plant grows within a particular set of respective conditions. Some plants provide food—such as grains, fruits, or vegetables—that people or livestock can consume directly for metabolic energy. Other plants provide stimulants or medication that humans consume—such as coffee or Artemisia—but have no caloric value. Still others provide physical materials—like cotton or rubber—that provide valuable inputs to physical manufacturing.

One of the primary reasons why agriculture’s diversity is so important to understand is that it defines the possibilities, and limits, for the diffusion of relevant technologies. Some crops, like wheat, grow only in temperate areas, so relevant advances in breeding or plant productivity might be relatively easy to diffuse across similar agro-ecological environments but will not naturally transfer to tropical environments, where most of the world’s poor reside. Conversely, for example, rice originates in lowland tropical areas and it has historically been relatively easy to adopt farming technologies from one rice-growing region to another. But, again, its diffusion is limited by geography and climate. Meanwhile maize can grow in both temperate and tropical areas, but its unique germinating properties render it difficult to transfer seed technologies across geographies.

Given the centrality of agriculture in many crucial global challenges, including the internationally agreed Sustainable Development Goals recently established for 2030, it is worth unpacking the topic empirically to describe what the term actually means today. This short paper does so with a focus on developing country crops, answering five basic questions: 

1. What types of crops does each country grow? 

2. Which cereals are most prominent in each country? 

3. Which non-cereal crops are most prominent in each country? 

4. How common are “cash crops” in each country? 

5. How has area harvested been changing recently? 

Readers should note that the following assessments of crop prominence are measured by area harvested, and therefore do not capture each crop’s underlying level of productivity or overarching importance within an economy. For example, a local cereal crop might be worth only $200 per ton of output in a country, but average yields might vary across a spectrum from around 1 to 6 tons per hectare (or even higher). Meanwhile, an export-oriented cash crop like coffee might be worth $2,000 per ton, with potential yields ranging from roughly half a ton to 3 or more tons per hectare. Thus the extent of area harvested forms only one of many variables required for a thorough understanding of local agricultural systems. 

The underlying analysis for this paper was originally conducted for a related book chapter on “Agriculture’s role in ending extreme poverty” (McArthur, 2015). That chapter addresses similar questions for a subset of 61 countries still estimated to be struggling with extreme poverty challenges as of 2011. Here we present data for a broader set of 140 developing countries. All tables are also available online for download.

Downloads

• Download the full paper (PDF)
• Cropshares_tables_clean
• Crop_Shares_metadata
• FAO crop codes
• FAO_cropshares
• WB income class
• WBcodescountrycode

There is a lot of hope around the potential of Big Data—massive volumes of data (such as cell phone GPS signals, social media posts, online digital pictures and videos, and transaction records of online purchases) that are large and difficult to process with traditional database and software techniques—to help achieve the sustainable development goals. The United Nations even calls for using the ongoing Data Revolution –the explosion in quantity and diversity of Big Data—to make more and better data usable to inform development analysis, monitoring and policymaking: In fact, the United Nations believes that that “Data are the lifeblood of decision-making and the raw material for accountability. Without high-quality data providing the right information on the right things at the right time; designing, monitoring and evaluating effective policies becomes almost impossible.” The U.N. even held a “Data Innovation for Policy Makers” conference in Jakarta, Indonesia in November 2014 to promote use of Big Data in solving development challenges.

Big Data has already played a role in development: Early uses of it include the detection of influenza epidemics using search engine query data or the estimation of a country’s GDP by using satellite data on night lights. Work is also under way by the World Bank to use Big Data for transport planning in Brazil.

During the Data for Development session at the recent NetMob conference at MIT, we presented a paper in which we jump on the Big Data bandwagon. In the paper, we use mobile phone data to assess how the opening of a new toll highway in Dakar, Senegal is changing how people commute to work (human mobility) in this metropolitan area. The new toll road is one of the largest investments by the government of Senegal and expectations for its developmental impact are high. In particular, the new infrastructure is expected to increase the flow of goods and people into and out of Dakar, spur urban and rural development outside congested areas, and boost land valuation outside Dakar. Our study is a first step in helping policymakers and other stakeholders benchmark the impact of the toll road against many of these objectives.

Assessing how the impact of the new toll highway differs by area and how it changes over time can help policymakers benchmark the performance of their investment and better plan the development of urban areas.

The Dakar Diamniadio Toll Highway (in red in Figure 1), inaugurated on August 1, 2013 is the first section (32 km or 20 miles) of a broader project to connect the capital, Dakar, through a double three-lane highway to a new airport (Aeroport International Blaise Diagne, AIBD) and a special economic zone, the Dakar Integrated Special Economic Zone (DISEZ) and the rest of the country.

Note: The numbers indicate the incidence of increased inter cell mobility and were used to calculate the percentage increase in mobility.

The cost of this large project is estimated to be about $696 million (FCFA 380.2 billion or 22.7 percent of 2014 fiscal revenues, excluding grants) with the government of Senegal having already disbursed $353 million. The project is one of the first toll roads in sub-Saharan Africa (excluding South Africa) structured as a public-private partnership (PPP) and includes multilateral partners such as the World Bank, the French Development Agency, and the African Development Bank.

In our study, we ask whether the new toll road led to an increase in human mobility and, if so, whether particular geographical areas experienced higher or lower mobility relative to others following its opening.

Using mobile phone usage data (Big Data), we use statistical analysis in our paper to approximate where people live and where they work. We then estimate how the reduction in travel time following the opening of the toll road changes the way they commute to work.

As illustrated in the map of Figure 1, we find some interesting trends:

• Human mobility in the metropolitan Dakar area increased on average by 1.34 percent after the opening of the Dakar Diamniadio Toll Highway. However, this increase masks important disparities across the different sub-areas of the Dakar metropolitan areas. Areas in blue in Figure 1 are those for which mobility increased after the opening of the new road toll while those in red experienced decreased mobility.
• In particular, the Parcelles Assainies suburban area benefited the most from the toll road with an increase in mobility of 26 percent. The Centre Ville (downtown) area experienced a decrease in mobility of about 20 percent.

These trends are important and would have been difficult to discover without Big Data. Now, though, researchers need to parse through the various reasons these trends might have occurred. For instance, the Parcelles Assainies area may have benefited the most because of its closer location to the toll road whereas the feeder roads in the downtown area may not have been able to absorb the increase in traffic from the toll road. Or people may have moved from the downtown area to less expensive areas in the suburbs now that the new toll road makes commuting faster.

From these preliminary results (our study is work in progress, and we will be improving its methodology), we are encouraged by the fact that our method and use of Big Data has three areas of application for a project such as this:

Benchmarking: Our method can be used to track how the impact of the Dakar Diamniadio Toll Highway changes over time and for different areas of the Dakar metropolitan areas. This process could be used to study other highways in the future and inform highway development overall.

Zooming in: Our analysis is a first step towards a more granular study of the different geographic areas within the Dakar suburban metropolitan area, and perhaps inspire similar studies around the continent. In particular, it would be useful to study the socio-economic context within each area to better appreciate the impact of new infrastructure on people’s lives. For instance, in order to move from estimates of human mobility (traffic) to measures of “accessibility,” it will be useful to complement the current analysis with an analysis of land use, a study of job accessibility, and other labor markets information for specific areas. Regarding accessibility, questions of interest include: Who lives in the areas most/least affected? What kind of jobs do they have access to? What type of infrastructure do they have access to? What is their income level? Answers to these questions can be obtained using satellite information for land prices, survey data (including through mobile phones) and data available from the authorities. Regarding urban planning, questions include: Is the toll diverting the traffic to other areas? What happens in those areas? Do they have the appropriate infrastructure to absorb the increase in traffic?

Zooming out: So far, our analysis is focused on the Dakar metropolitan area, and it would be useful to assess the impact of new infrastructure on mobility between the rest of the country and Dakar. For instance, the analysis can help assess whether the benefits of the toll road spill over to the rest of the country and even differentiate the impact of the toll road on the different regions of the country.

This experience tells us that there are major opportunities in converting Big Data into actionable information, but the impact of Big Data still remains limited. In our case, the use of mobile phone data helped generate timely and relatively inexpensive information on the impact of a large transport infrastructure on human mobility. On the other hand, it is clear that more analysis using socioeconomic data is needed to get to concrete and impactful policy actions. Thus, we think that making such information available to all stakeholders has the potential not only to guide policy action but also to spur it. 

References

Atkin, D. and D. Donaldson (2014). Who ’ s Getting Globalized ? The Size and Implications of Intranational Trade Costs . (February).

Clark, X., D. Dollar, and A. Micco (2004). Port efficiency, maritime transport costs, and bilateral trade. Journal of Development Economics 75(2), 417–450, December.

Donaldson, D. (2013). Railroads of the Raj: Estimating the Impact of Transportation Infrastructure. forthcoming, American Economic Review.

Fetzer Thiemo (2014) “Urban Road Construction and Human Commuting: Evidence from Dakar, Senegal.” Mimeo

Ji, Y. (2011). Understanding Human Mobility Patterns Through Mobile Phone Records : A cross-cultural Study.

Simini, F., M. C. Gonzalez, A. Maritan, and A.-L. Barab´asi (2012). A universal model for mobility and migration patterns. Nature 484(7392), 96–100, April.

Tinbergen, J. (1962). Shaping the World Economy; Suggestions for an International Economic Policy.

Yuan, Y. and M. Raubal (2013). Extracting dynamic urban mobility patterns from mobile phone data.

Many proposals to accelerate and improve medical product innovation and regulation focus on reforming the product development and regulatory review processes that occur before drugs and devices get to market. While important, such proposals alone do not fully recognize the broader opportunities that exist to learn more about the safety and effectiveness of drugs and devices after approval. As drugs and devices begin to be used in larger and more diverse populations and in more personalized clinical combinations, evidence from real-world use during routine patient care is increasingly important for accelerating innovation and improving regulation.

First, further evidence development from medical product use in large populations can allow providers to better target and treat individuals, precisely matching the right drug or device to the right patients. As genomic sequencing and other diagnostic technologies continue to improve, postmarket evidence development is critical to assessing the full range of genomic subtypes, comorbidities, patient characteristics and preferences, and other factors that may significantly affect the safety and effectiveness of drugs and devices. This information is often not available or population sizes are inadequate to characterize such subgroup differences in premarket randomized controlled trials.

Second, improved processes for generating postmarket data on medical products are necessary for fully realizing the intended effect of premarket reforms that expedite regulatory approval. The absence of a reliable postmarket system to follow up on potential safety or effectiveness issues means that potential signals or concerns must instead be addressed through additional premarket studies or through one-off postmarket evaluations that are more costly, slower, and likely to be less definitive than would be possible through a better-established infrastructure. As a result, the absence of better systems for generating postmarket evidence creates a barrier to more extensive use of premarket reforms to promote innovation.

These issues can be addressed through initiatives that combine targeted premarket reforms with postmarket steps to enhance innovation and improve evidence on safety and effectiveness throughout the life cycle of a drug or device. The ability to routinely capture clinically relevant electronic health data within our health care ecosystem is improving, increasingly allowing electronic health records, payer claims data, patient-reported data, and other relevant data to be leveraged for further research and innovation in care. Recent legislative proposals released by the House of Representatives’ 21st Century Cures effort acknowledge and seek to build on this progress in order to improve medical product research, development, and use. The initial Cures discussion draft included provisions for better, more systematic reporting of and access to clinical trials data; for increased access to Medicare claims data for research; and for FDA to promulgate guidance on the sources, analysis, and potential use of so-called Real World Evidence. These are potentially useful proposals that could contribute valuable data and methods to advancing the development of better treatments.

What remains a gap in the Cures proposals, however, is a more systematic approach to improving the availability of postmarket evidence. Such a systematic approach is possible now. Biomedical researchers and health care plans and providers are doing more to collect and analyze clinical and outcomes data. Multiple independent efforts – including the U.S. Food and Drug Administration’s Sentinel Initiative for active postmarket drug safety surveillance, the Patient-Centered Outcomes Research Institute’s PCORnet for clinical effectiveness studies, the Medical Device Epidemiology Network (MDEpiNet) for developing better methods and medical device registries for medical device surveillance and a number of dedicated, product-specific outcomes registries – have demonstrated the potential for large-scale, systematic postmarket data collection. Building on these efforts could provide unprecedented evidence on how medical products perform in the real-world and on the course of underlying diseases that they are designed to treat, while still protecting patient privacy and confidentiality.

These and other postmarket data systems now hold the potential to contribute to public-private collaboration for improved population-based evidence on medical products on a wider scale. Action in the Cures initiative to unlock this potential will enable the legislation to achieve its intended effect of promoting quicker, more efficient development of effective, personalized treatments and cures.

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Over the past few years, more than 600 Accountable Care Organizations (ACOs) have formed across the country, charged with the dual goals of improving health while also reducing health care costs. Increasingly, evidence on how public and private ACOs are progressing toward these goals is beginning to emerge. Based on these results, major regulatory changes are anticipated in the months ahead that will impact accountable care programs in Medicare, as well as future uptake within the private sector.

On October 20, the Engelberg Center for Health Care Reform hosted a half day forum to assess the latest evidence on accountable care, discuss strategies to overcome unique ACO challenges, and provide an overview of accountable care reforms. Sean Cavanaugh of the Centers for Medicare and Medicaid Services (CMS) provided keynote remarks on the latest Medicare ACO results and potential changes to the Medicare Shared Savings Program (MSSP). Panel sessions featured leading experts in ACO research, implementation and health care policy.

 Join the conversation on Twitter using #ACOFuture or follow @BrookingsMed

Video

• The Medicare Shared Savings Program (MSSP): Looking Ahead
• Big Issues for ACOs Going Forward
• ACO Policy Issues and Solutions on the Horizon

Audio

• The State of Accountable Care: Evidence to Date and Next Steps

Transcript

• Uncorrected Transcript (.pdf)

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• ACO Public Event full deck FINAL
• 102014_Accountable Care_Transcript

Ron Haskins and Isabel Sawhill were just awarded the 2016 Daniel Patrick Moynihan Prize by the American Academy of Political and Social Science. The honor is presented to “a leading policymaker, social scientist, or public intellectual whose career demonstrates the value of using social science evidence to advance the public good.” In this case, however, for the first time the award was awarded jointly.

Here at Brookings, Belle and Ron have forged a powerful and unique intellectual partnership, founding and elevating the Center on Children and Families and producing world-class work on families, poverty, opportunity, evidence, parenting, work and education, and much more besides.

The Association highlighted Belle and Ron’s bipartisanship. This was appropriate, given that the two have different political backgrounds, and work with people across the political spectrum. The skills and attributes they display in order to work in this way are:

1. Deep respect for the views of others regardless of their politics.
2. Reverence for the evidence and for the facts.
3. A willingness to adapt their views to the facts, rather than (as so often in this town), the other way around. This has been true even when it has made their life more difficult with people on “their” side of the political spectrum.
4. A desire to work hard to bring ideas to bear on public policy. The point is to do good work, but also to have real impact.
5. An insatiable intellectual curiosity to find out more, push new boundaries, and to keep learning. (Both of them have new books out, of course.)

These attributes, when you think about it, are those every decent scholar should aspire to. Belle and Ron have shown us that the skills for bipartisanship turn out to be essentially the same skills as those required for good scholarship.

In his remarks at the Prize lecture, Ron focused on the rise, importance, and prospects for evidence-based policy. Ron has tackled this subject at book length in Show Me the Evidence. Here is part of what Ron had to say:

“Perhaps the most important social function of social science is to find and test programs that will reduce the nation’s social problems. The exploding movement of evidence-based policy and the many roots the movement is now planting, offer the best chance of fulfilling this vital mission of social science, of achieving, in other words, exactly the outcomes Moynihan had hoped for. Today, evidence-based policy rests on the mighty oak of program evaluation in general and the random assignment study in particular.”

Ron highlighted the growth of Pay for Success programs, the Obama administration’s emphasis on evidence-based initiatives, and the creation of the Ryan/Murray Commission on Evidence-Based Policy.

Ron argued that it was right to be skeptical about the likely impact of any particular intervention. But this is not to say that policy doesn’t work—just that some policies work, others don’t, and it good to know the difference. In his slides, Ron lists some programs that have been shown to have demonstrable, sustainable impact—what he described as “his entry in the evidence-based policy sweepstakes.”

But there are plenty of challenges ahead, including the need to improve our understanding of implementation; and the following critical question: “When a program fails, what’s next?” Ron argued that the answer should not be to simply pull the funding, but to work on improving performance.

Belle highlighted the work captured in her latest book, Generation Unbound, on how to reduce the damaging rise of unintended pregnancies and births in the U.S. Over 40 percent of children are born outside of marriage, and 60 percent of births to single women under age 30 are unplanned. In the spirit of being faithful to the facts, and focused on what works, Belle showed the costs of unintended pregnancies for poverty, family stability, and opportunity. Child poverty rates have increased, Belle estimates, by about 25 percent since 1970 because of changes in family structure.

So what are the solutions? In the spirit of following the evidence, Belle argued that the goal must be to help people plan for rather than drift into pregnancy, by broadening access to and use of long-acting reversible contraception. The best example is the intrauterine device, or IUD. The risks of pregnancy for women using this method of contraception are very much lower than for condoms or the pill: 

A fact-based analysis of a problem, followed by an evidence-based approach to solutions: Belle’s work on contraception (sometimes alongside Ron) is a perfect example of bipartisanship, impact-oriented scholarship and a commitment to evidence.

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Many scholars have argued that once “basic needs” have been met, higher income is no longer associated with higher in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of “basic needs” and multiple questions about well-being, we find no support for this claim. The relationship between well-being and income is roughly linear-log and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it.

Introduction

In 1974 Richard Easterlin famously posited that increasing average income did not raise average well-being, a claim that became known as the Easterlin Paradox. However, in recent years new and more comprehensive data has allowed for greater testing of Easterlin’s claim. Studies by us and others have pointed to a robust positive relationship between well-being and income across countries and over time (Deaton, 2008; Stevenson and Wolfers, 2008; Sacks, Stevenson, and Wolfers, 2013). Yet, some researchers have argued for a modified version of Easterlin’s hypothesis, acknowledging the existence of a link between income and well-being among those whose basic needs have not been met, but claiming that beyond a certain income threshold, further income is unrelated to well-being.

The existence of such a satiation point is claimed widely, although there has been no formal statistical evidence presented to support this view. For example Diener and Seligman (2004, p. 5) state that “there are only small increases in well-being” above some threshold. While Clark, Frijters and Shields (2008, p. 123) state more starkly that “greater economic prosperity at some point ceases to buy more happiness,” a similar claim is made by Di Tella and MacCulloch (2008, p. 17): “once basic needs have been satisfied, there is full adaptation to further economic growth.” The income level beyond which further income no longer yields greater well-being is typically said to be somewhere between $8,000 and $25,000. Layard (2003, p. 17) argues that “once a country has over $15,000 per head, its level of happiness appears to be independent of its income;” while in subsequent work he argued for a $20,000 threshold (Layard, 2005 p. 32-33). Frey and Stutzer (2002, p. 416) claim that “income provides happiness at low levels of development but once a threshold (around $10,000) is reached, the average income level in a country has little effect on average subjective well-being.”

Many of these claims, of a critical level of GDP beyond which happiness and GDP are no longer linked, come from cursorily examining plots of well-being against the level of per capita GDP. Such graphs show clearly that increasing income yields diminishing marginal gains in subjective well-being. However this relationship need not reach a point of nirvana beyond which further gains in well-being are absent. For instance Deaton (2008) and Stevenson and Wolfers (2008) find that the well-being–income relationship is roughly a linear-log relationship, such that, while each additional dollar of income yields a greater increment to measured happiness for the poor than for the rich, there is no satiation point.

In this paper we provide a sustained examination of whether there is a critical income level beyond which the well-being–income relationship is qualitatively different, a claim referred to as the modified-Easterlin hypothesis. As a statistical claim, we shall test two versions of the hypothesis. The first, a stronger version, is that beyond some level of basic needs, income is uncorrelated with subjective well-being; the second, a weaker version, is that the well-being–income link estimated among the poor differs from that found among the rich.

Claims of satiation have been made for comparisons between rich and poor people within a country, comparisons between rich and poor countries, and comparisons of average well-being in countries over time, as they grow. The time series analysis is complicated by the challenges of compiling comparable data over time and thus we focus in this short paper on the cross-sectional relationships seen within and between countries. Recent work by Sacks, Stevenson, and Wolfers (2013) provide evidence on the time series relationship that is consistent with the findings presented here.

To preview, we find no evidence of a satiation point. The income–well-being link that one finds when examining only the poor, is similar to that found when examining only the rich. We show that this finding is robust across a variety of datasets, for various measures of subjective well-being, at various thresholds, and that it holds in roughly equal measure when making cross-national comparisons between rich and poor countries as when making comparisons between rich and poor people within a country.

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Senior Fellows Ron Haskins and Isabel Sawhill are the first joint recipients of the Daniel Patrick Moynihan Prize from the American Academy of Political and Social Science (AAPSS). The prize is awarded each year to a leading policymaker, social scientist, or public intellectual whose career focuses on advancing the public good through social science. It was named after the late senator from New York and renowned sociologist Daniel Patrick Moynihan. The pair accepted the award May 12 at a ceremony in Washington, DC. 

In their joint lecture delivered at the ceremony, Haskins and Sawhill emphasized the importance of evidence-based public policy, highlighting Sawhill’s latest work in her book, Generation Unbound (Brookings, 2014). Watch their entire speech here:

“Marriage is disappearing and more and more babies are born outside marriage,” Sawhill said during the lecture. “Right now, the proportion born outside of marriage is about 40 percent. It’s higher than that among African Americans and lower than that among the well-educated. But it’s no longer an issue that just affects the poor or minority groups.”

Download Sawhill's slides » | Download Ron Haskins' slides »

The power of evidence-based policy is finally being recognized, Haskins added. “One of the prime motivating factors of the current evidence-based movement,” he said, “is the understanding, now widespread, that most social programs either have not been well evaluated or they don’t work.” Haskins continued:

Perhaps the most important social function of social science is to find and test programs that will reduce the nation’s social problems. The exploding movement of evidence-based policy and the many roots the movement is now planting, offer the best chance of fulfilling this vital mission of social science, of achieving, in other words, exactly the outcomes Moynihan had hoped for.

He pointed toward the executive branch, state governments, and non-profits implementing policies that could make substantial progress against the nation’s social problems.

Richard Reeves, a senior fellow at Brookings and co-director, with Haskins, of the Center on Children and Families (CCF), acknowledged Haskins and Sawhill’s “powerful and unique intellectual partnership” and their world-class work on families, poverty, opportunity, evidence, parenting, work, and education.

Haskins and Sawhill were the first to be awarded jointly by the AAPSS, which recognizes their 15-year collaboration at Brookings and the Center on Children and Families, which they established. In addition to their work at CCF, the two co-wrote Creating an Opportunity Society (Brookings 2009) and serve as co-editors of The Future of Children, a policy journal that tackles issues that have an impact on children and families.

Haskins and Sawhill join the ranks of both current and past Brookings scholars who have received the Moynihan Prize, including Alice Rivlin (recipient of the inaugural prize), Rebecca Blank, and William Julius Wilson along with other distinguished scholars and public servants.

Want to learn more about the award’s namesake? Read Governance Studies Senior Fellow and historian Steve Hess’s account of Daniel Patrick Moynihan’s time in the Nixon White House in his book The Professor and the President (Brookings, 2014).