Editing Behavioral economics (section)

== Related fields ==
=== Experimental economics ===
{{Main|Experimental economics}}
Experimental economics is the application of [[Experiment|experimental methods]], including [[statistical]], [[econometric]], and [[computational economics|computational]],<ref>{{cite journal | last1 = Roth | first1 = Alvin E. | author-link = Alvin E. Roth | year = 2002 | title = The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics | url = http://kuznets.fas.harvard.edu/~aroth/papers/engineer.pdf | journal = Econometrica | volume = 70 | issue = 4 | pages = 1341–1378 | doi = 10.1111/1468-0262.00335 | access-date = 2018-05-11 | archive-url = https://web.archive.org/web/20120112073316/http://kuznets.fas.harvard.edu/~aroth/papers/engineer.pdf | archive-date = 2012-01-12 | url-status = dead }}</ref> to study economic questions. [[Economic data|Data]] collected in experiments are used to estimate [[effect size]], test the validity of economic theories, and illuminate market mechanisms. Economic experiments usually use cash to motivate subjects, in order to mimic real-world incentives. Experiments are used to help understand how and why markets and other exchange systems function as they do. Experimental economics have also expanded to understand institutions and the law (experimental law and economics).<ref>{{cite journal | last1 = See | author-link2 = Kristoffel Grechenig | last2 = Grechenig | first2 = K. | last3 = Nicklisch | first3 = A. | last4 = Thöni | first4 = C. | year = 2010 | title = Punishment despite reasonable doubt—a public goods experiment with sanctions under uncertainty | url = https://ssrn.com/abstract=1586775 | journal = Journal of Empirical Legal Studies | volume = 7 | issue = 4| pages = 847–867 | doi = 10.1111/j.1740-1461.2010.01197.x| s2cid = 41945226 }}</ref>

A fundamental aspect of the subject is [[design of experiments]]. Experiments may be conducted in the [[Field experiments|field]] or in laboratory settings, whether of [[Experimental psychology|individual]] or [[Social psychology|group]] behavior.<ref>{{Multiref2
|1=[[Vernon L. Smith]], 2008a. "experimental methods in economics," ''[[The New Palgrave Dictionary of Economics]]'', 2nd Edition, [http://www.dictionaryofeconomics.com/article?id=pde2008_E000186&q=Experimental%20economics&topicid=&result_number=2 Abstract.]
|2=_____, 2008b. "experimental economics," ''The New Palgrave Dictionary of Economics'', 2nd Edition. [http://www.dictionaryofeconomics.com/article?id=pde2008_E000277&q=experimental%20&topicid=&result_number=2 Abstract.]
|3=Relevant subcategories are found at the ''Journal of Economic Literature'' classification codes at [[JEL classification codes#Mathematical and quantitative methods JEL: C Subcategories|JEL: C9]].
}}</ref>

Variants of the subject outside such formal confines include [[natural experiment|natural]] and [[quasi-natural experiment]]s.<ref>J. DiNardo,  2008.  "natural experiments and quasi-natural experiments," ''The New Palgrave Dictionary of Economics'', 2nd Edition. [http://www.dictionaryofeconomics.com/article?id=pde2008_N000142&edition=current&q Abstract.]</ref>

=== Neuroeconomics ===
{{Main|Neuroeconomics}}
Neuroeconomics is an [[Interdisciplinarity|interdisciplinary]] field that seeks to explain human [[decision making]], the ability to process multiple alternatives and to follow a course of action. It studies how economic behavior can shape our understanding of the [[brain]], and how neuroscientific discoveries can constrain and guide models of economics.<ref name="neuroeconomics.duke.edu">{{cite web|url = https://dibs.duke.edu/centers/d-cides/about/research|title = Research|website = Duke Institute for Brain Sciences|access-date = 2019-05-21|archive-url = https://web.archive.org/web/20190525112312/https://dibs.duke.edu/centers/d-cides/about/research|archive-date = 2019-05-25|url-status = dead}}</ref> It combines research methods from [[neuroscience]], [[Experimental economics|experimental]] and behavioral economics, and [[Cognitive psychology|cognitive]] and [[Social psychology|social]] psychology.<ref name="LevalloisClithero2012">{{cite journal|last1=Levallois|first1=Clement|last2=Clithero|first2=John A.|last3=Wouters|first3=Paul|last4=Smidts|first4=Ale|last5=Huettel|first5=Scott A.|title=Translating upwards: linking the neural and social sciences via neuroeconomics|journal=Nature Reviews Neuroscience|volume=13|issue=11|year=2012|pages=789–797|issn=1471-003X|doi=10.1038/nrn3354|pmid=23034481|s2cid=436025|url=https://resolver.caltech.edu/CaltechAUTHORS:20121129-131311721 }}</ref> As research into decision-making behavior becomes increasingly computational, it has also incorporated new approaches from [[theoretical biology]], [[computer science]], and [[mathematics]].

Neuroeconomics studies decision making by using a combination of tools from these fields so as to avoid the shortcomings that arise from a single-perspective approach. In [[mainstream economics]], [[Expected utility hypothesis|expected utility]] (EU) and the concept of [[rational agents]] are still being used. Many economic behaviors are not fully explained by these models, such as [[heuristics]] and [[Framing (social sciences)|framing]].<ref name="annualreviews.org">{{cite journal | last1 = Loewenstein | first1 = G. | last2 = Rick | first2 = S. | last3 = Cohen | first3 = J. | year = 2008 | title =  Neuroeconomics| journal = Annual Review of Psychology  | volume = 59 | pages = 647–672 | doi = 10.1146/annurev.psych.59.103006.093710 | pmid = 17883335 }}</ref> Behavioral economics emerged to account for these anomalies by integrating social, cognitive, and emotional factors in understanding economic decisions. Neuroeconomics adds another layer by using neuroscientific methods in understanding the interplay between economic behavior and neural mechanisms. By using tools from various fields, some scholars claim that neuroeconomics offers a more integrative way of understanding decision making.<ref name="neuroeconomics.duke.edu" />

=== Evolutionary psychology ===
{{Main|Evolutionary psychology}}
{{Further|Evolutionary economics}}
An [[evolutionary psychology]] perspective states that many of the perceived limitations in rational choice can be explained as being rational in the context of maximizing biological [[Fitness (biology)|fitness]] in the ancestral environment, but not necessarily in the current one. Thus, when living at subsistence level where a reduction of resources may result in death, it may have been rational to place a greater value on preventing losses than on obtaining gains. It may also explain behavioral differences between groups, such as males being less risk-averse than females since males have more variable [[reproductive success]] than females. While unsuccessful risk-seeking may limit reproductive success for both sexes, males may potentially increase their reproductive success from successful risk-seeking much more than females can.<ref name="AEP">Paul H. Rubin and C. Monica Capra. The evolutionary psychology of economics. In {{Cite book|last1=Roberts|first1=S. C.|url={{google books |plainurl=y |id=I20uPfEjsNQC}}|title=Applied Evolutionary Psychology|publisher=Oxford University Press|year=2011|isbn=9780199586073|editor1-last=Roberts|editor1-first=S. Craig|doi=10.1093/acprof:oso/9780199586073.001.0001}}</ref>

=== Behavioral Development Economics===

'''Behavioral Development Economics''' brings together the disciplines of behavioral economics and [[Development Economics|development economics]] to study economic decision-making in low-income countries under the influence of cognitive biases and social norms. This is important because while non-standard economic behavior exists globally, it is more pronounced in the Global South due to interactions with higher poverty rates, financial instability, informal labor markets and weaker formal institutions. The understanding of these regional differences is vital for effective policy-making in the Global South.<ref>Kremer, M., Rao, G., & Schilbach, F. (2019). Behavioral development economics. In Handbook of behavioral economics: applications and foundations 1 (Vol. 2, pp. 345-458). North-Holland.</ref>

====The psychology of poverty====
Recent research in behavioral development economics highlights how the experience of poverty itself can impair cognitive function and economic decision-making, potentially perpetuating poverty through behavioral channels. Scarcity captures attention, narrowing focus on immediate financial concerns while reducing mental “bandwidth” for other tasks.<ref>Mullainathan, S., & Shafir, E. (2013). Scarcity: Why having too little means so much. Times Books.</ref> While this focused attention may improve decision-making in some contexts—such as heightened price awareness <ref>Goldin, J., & Homonoff, T. A. (2013). Smoke gets in your eyes: Cigarette tax salience and regressivity. American Economic Journal: Economic Policy, 5(1), 302–336.</ref>—the overall effect of constant financial stress is detrimental. A study provides experimental evidence of this cognitive burden: when exposed to scenarios involving large financial stakes, low-income individuals in the U.S. exhibited reduced cognitive performance, while high-income individuals did not.<ref>Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty impedes cognitive function. Science, 341(6149), 976–980.</ref> Complementary field evidence from Indian sugarcane farmers showed significantly lower cognitive performance before harvest (when finances are tight) compared to post-harvest, suggesting that poverty-induced cognitive load, rather than fixed individual traits, drives decision-making quality.

==== Applications ====

===== Financial inclusion =====
[[Financial inclusion]] remains low in the Global South despite several attempts to improve the situation through financial literacy campaigns. This is unlikely to suffice in low-income setting because of behavioral barriers. Trust in financial institutions is much lower in the Global South as a result of historical experiences with inflation, bank failures and corruption. Accordingly, economic agents exhibit stronger [[status quo bias]] relying mostly on informal financial institutions through their social networks such as [[Rotating savings and credit association|rotating savings and credit associations (ROSCAs)]]. 
Accordingly, interventions that aimed at strengthening these informal institutions such as mobile money platforms like [[M-Pesa]] in Kenya and village savings and loans associations (VSLAs) have performed much better than financial literacy programs.<ref>Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya's mobile money revolution. American Economic Review, 104(1), 183-223.</ref> These interventions leveraged the existing trust structures and social networks to encourage adoption.

===== Health =====
Behavioral challenges in health decision-making also differ in developing contexts. While '''procrastination in preventive health care''' is universal, it is exacerbated in low-income countries where immediate financial costs often outweigh perceived future benefits. For example, while [[vaccine hesitancy]] exists globally, in the Global South, uptake is also hindered by limited access, misinformation, and lack of trust in public health institutions.<ref>Kremer, M., & Miguel, E. (2007). The illusion of sustainability. The Quarterly journal of economics, 122(3), 1007-1065.</ref> Compared to developed countries, where reminder systems or behavioral nudges can improve compliance, in the Global South, additional interventions, such as financial incentives or subsidized transportation, are often necessary to achieve significant behavioral change.

Historical experiences with colonial medicine have also created persistent mistrust in the health sector. A study shows that regions in Central Africa that were more exposed to cruel French colonial medical campaigns between 1921 and 1956 exhibit today lower vaccination rates and willingness to undertake non-invasive blood tests. Moreover, World Bank health projects in those regions have shown lower success rates. It is important here to note that within the same culture, different behavioral barriers exist because of different historical experiences.<ref>Lowes, S., & Montero, E. (2021). The legacy of colonial medicine in Central Africa. American Economic Review, 111(4), 1284-1314.</ref>

=====Labor markets=====

Labor markets differ substantially in the Global South compared to high-income countries because of higher rates of informal employment, self-employment and casual labor. For instance, an experiment that randomly assigned workers to industrial jobs in Ethiopia found that workers quickly quit or move to different sectors.<ref>Blattman, Christopher and Stefan Dercon. The impacts of industrial and entrepreneurial work on income and health: Experimental evidence from Ethiopia. American Economic Journal: Applied Economics, l0(3):l-38, 2018.</ref> This low preference for full-time jobs is driven by unpredictable demands on one’s time where workers prefer flexibility to be able to meet family demands. On the other hand, US workers show little valuation of work hours flexibility.<ref>Mas, Alexandre and Amanda Pallais. Valuing alternative work arrangements. American Economic Review, l07(l2):3722-59, 2017.</ref> Given less formal contracts and fixed schedules, workers are more prone to behavioral biases. Self-control problems are especially relevant in labor markets. For instance, many low-income workers in India are inebriated on the job because they are self-employed.<ref>Schilbach, F. (2019). Alcohol and self-control: A field experiment in India. American economic review, 109(4), 1290-1322.</ref> Without external monitoring or standardized hours, self-employed individuals must rely on internal discipline, which is often limited. Evidence from Indian data-entry workers shows that many voluntarily opt into contracts that penalize them for underperformance—known as dominated contracts—in order to commit themselves to working harder.<ref>Kaur, Supreet, Michael Kremer, and Sendhil Mullainathan. Self-control at work. Journal of Political Economy, l23(6):l227-l277, 2015.</ref> This suggests that workers are aware of their own time-inconsistency and use commitment mechanisms to increase productivity. Additionally, many workers engage in income-targeting behavior, adjusting labor supply based on daily financial needs rather than wage levels. For example, bicycle-taxi drivers in Kenya work longer when they have specific cash needs but do not reduce labor supply when given cash in the morning, indicating reference-dependent preferences focused on earned income.<ref>Pascaline Dupas, Jonathan Robinson, Santiago Saavedra,
The daily grind: Cash needs and labor supply,
Journal of Economic Behavior & Organization,
Volume 177,
2020,
Pages 399-414,
ISSN 0167-2681,
https://doi.org/10.1016/j.jebo.2020.06.017.</ref>

Behavioral frictions also extend to the workplace environment. While noise and heat are common in urban areas of the Global South, workers often underestimate how these factors affect their productivity, reflecting [[bounded rationality]]. In Kenya, exposure to moderate increases in noise significantly reduced textile output, yet workers were unaware of the impact and unwilling to pay for quieter conditions even when earnings depended on output.<ref>Dean, Joshua T. 2024. "Noise, Cognitive Function, and Worker Productivity." ''American Economic Journal: Applied Economics'' 16 (4): 322–60.
DOI: 10.1257/app.20220532</ref> Similar results are found for heat exposure in Indian factories, where temperature reductions improved output but were only implemented to save energy, not to boost productivity.<ref>Achyuta Adhvaryu, Namrata Kala, Anant Nyshadham; The Light and the Heat: Productivity Co-Benefits of Energy-Saving Technology. ''The Review of Economics and Statistics'' 2020; 102 (4): 779–792. doi: https://doi.org/10.1162/rest_a_00886</ref> These findings show that misperceptions about environmental effects on work performance can lead to suboptimal decisions by both workers and firms.

Wage-setting also reflects behavioral elements. In informal labor markets, wage rigidity persists despite the absence of formal institutions like unions. In Indian agriculture, wages rise with positive productivity shocks but are rarely reduced when shocks are negative, due to fairness concerns and social norms.<ref>Kaur, Supreet. (2019) Nominal Wage Rigidity in Village Labor Markets. ''American Economic Review'',  109 (10): 3585–3616. https://doi.org/10.1257/aer.20141625</ref> Similarly, low-wage offers are accepted more often when made privately rather than publicly, as workers face social pressure to reject “unfair” wages in public settings.<ref>Breza, Emily, Supreet Kaur, and Nandita Krishnaswamy. 
 Coordination without Organization: Collective Labor Supply in Decentralized Spot Markets. Working Paper. 2018.</ref> Furthermore, even small wage differences within work teams can reduce attendance and productivity, not only among lower-paid workers but also among those paid more, indicating that inequality itself demotivates workers through social comparison.<ref>Breza, Emily, Supreet Kaur, and Yogita Shamdasani. The morale effects of pay inequality. ''Quarterly Journal of Economics'', l33(2):6ll-663, 2018.</ref>

Behavioral constraints are also relevant for female labor-force participation (FLFP), which remains low in many developing countries. Psychological factors such as low [[self-efficacy]] and misperceptions about social norms contribute to this. In India, an intervention to raise self-efficacy significantly increased women’s employment,<ref>McKelway, Madeline. Women’s self-efficacy and women’s employment: Experimental evidence from India. Working Paper. 2018.</ref> while in Saudi Arabia, correcting men’s beliefs about their peers’ support for women working outside the home led to greater spousal support for job-seeking.<ref>Bursztyn, L., González, A. L., & Yanagizawa-Drott, D. (October 2020). Misperceived social norms: Female labor force participation in Saudi Arabia. ''American Economic Review''. 110 (10): 2997–3029. https://doi.org/10.1257/aer.20180975</ref> These findings show how behavioral interventions can shift labor supply decisions, particularly for marginalized groups.