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on Experimental Economics |
| By: | Zhang, Wei; Meinzen-Dick, Ruth Suseela; Valappanandi, Sanoop; Balakrishna, Raksha; Reddy, Hemalatha; Janssen, Marco A.; Thomas, Liya; Priyadarshini, Pratiti; Kandikuppa, Sandeep; Chaturvedi, Rahul; Ghate, Rucha |
| Abstract: | This paper presents results from a framed field experiment in which participants make decisions about extraction of a common-pool resource, a community forest. The experiment was designed and piloted as both a research activity and an experiential learning intervention during 2017-2018 with 120 groups of resource users (split by gender) from 60 habitations in two Indian states, Andhra Pradesh and Rajasthan. We examine whether local beliefs and norms about community forest, gender of participants, within-experiment treatments (non-communication, communication, and optional election of institutional arrangements (rules)) and remuneration methods affect harvest behaviour and groups’ tendency to cooperate. Furthermore, we explore whether the experiment and subsequent community debriefing had learning effects. Results reveal a “weak†Nash Equilibrium in which participants harvested substantially less than the Nash prediction even in the absence of communication, a phenomenon stronger for male than female participants in both states. For male groups in both states, both communication and optional rule election are associated with lower group harvest per round, as compared to the reference non-communication game. For female groups in both states, however, communication itself did not significantly slow down resource depletion; but the introduction of optional rule election did reduce harvest amounts. For both men and women in Andhra Pradesh and men in Rajasthan, incentivized payments to individual participants significantly lowered group harvest, relative to community flat payment, suggesting a possible “crowding-in†effect on pro-social norms. Despite the generally positive memory of the activity, reported actual changes are limited. This may be due to the lack of follow-up with the communities between the experiment and the revisit. The fact that many of the communities already have a good understanding of the importance of the relationships between (not) cutting trees and the ecosystem services from forests, with rules and strong internal norms against cutting that go beyond the felling of trees in the game, may have also meant that the game did not have as much to add. Findings have methodological and practical implications for designing behavioral intervention programs to improve common-pool resource governance. |
| Keywords: | INDIA; SOUTH ASIA; ASIA; gender; extraction; community forestry; collective ownership; field experimentation; forests; game; experiential learning; payment methods; common-pool resource; framed field experiments; |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:fpr:ifprid:2091&r= |
| By: | Ebers, Axel; Thomsen, Stephan L. |
| Abstract: | Objective: Previous social-psychological research has demonstrated the positive effects of online bystander programs on various crime-related outcomes, while information systems research has demonstrated the ability of gamification to improve motivation, engagement, and learning. This study bridges the gap between social psychology and information systems research by evaluating a bystander program that combines the simulation of a dangerous situation in a virtual environment with the application of game principles and game design elements. Method: We developed three research hypotheses and tested them using two randomized online field experiments (RCTs). During the first experiment, we collected data from 4,188 users on Facebook and randomly assigned them to four treatment arms, including three different configurations of the treatment and one control group. During the second experiment, we collected data from a representative sample of the population and observed them across three waves. Results: The results from the first experiment support the hypotheses that the bystander program motivates people to intervene in violent situations and that gamification enhances the motivational effect. The results from the second experiment support the hypothesis that the program makes people feel more capable of intervening. They also show that the treatment effects persist over a long period of time and hold for the overall population. Conclusions: We conclude that the gamification approach offers great potential for bystander education and that social media are well suited for the dissemination and upscaling of bystander programs. Policymakers can use these findings to improve the effectiveness and efficiency of future bystander programs or similar prevention measures. |
| Keywords: | Bystander intervention; gamification; program evaluation; field experiments; social media; Facebook |
| JEL: | C93 D91 K42 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-692&r= |
| By: | Peter Andre |
| Abstract: | Meritocracies aspire to reward effort and hard work but promise not to judge individuals by the circumstances they were born into. The choice to work hard is, however, often shaped by circumstances. This study investigates whether people’s merit judgments are sensitive to this endogeneity of choice. In a series of incentivized experiments with a large, representative US sample, study participants judge how much money two workers deserve for the effort they exerted. In the treatment condition, unequal circumstances strongly discourage one of the workers from working hard. Nonetheless, I find that individuals hold the disadvantaged worker fully responsible for his choice. They do so, even though they understand that choices are strongly influenced by circumstances. Additional experiments identify the cause of this neglect. In light of an uncertain counterfactual state – what would have happened on a level playing field – participants base their merit judgments on the only reliable evidence they possess - observed effort levels. I confirm these patterns in a structural model of merit views and a vignette study with real-world scenarios. |
| Keywords: | Meritocracy, attitudes toward inequality, redistribution, fairness, responsibility, social preferences, inference, uncertain counterfactual |
| JEL: | C91 D63 D91 H23 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2021_318v1&r= |
| By: | Cerrone, Claudia; Hermstrüwer, Yoan; Kesten, Onur |
| Abstract: | Public school choice often yields student placements that are neither fair nor efficient. Kesten (2010) proposed an efficiency-adjusted deferred acceptance algorithm (EADAM) that allows students to consent to waive priorities that have no effect on their assignment. In this article, we provide first experimental evidence on the performance of EADAM. We compare EADAM with the deferred acceptance mechanism (DA) and with two variants of EADAM. In the first variant, we vary the default option: students can object – rather than consent – to the priority waiver. In the second variant, the priority waiver is enforced. We find that both efficiency and truth-telling rates are substantially higher under EADAM than under DA, even though EADAM is not strategy-proof. When the priority waiver is enforced, we observe that efficiency further increases, while truth-telling rates decrease relative to the EADAM variants where students can decide to eschew the waiver. Our results challenge the importance of strategy-proofness as a condition of truth-telling and point at a trade-off between efficiency and vulnerability to preference manipulation. |
| Keywords: | efficiency-adjusted deferred acceptance algorithm; school choice; consent; default rules; law |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:syd:wpaper:2021-09&r= |
| By: | Peter Andre |
| Abstract: | Meritocracies aspire to reward hard work but promise not to judge individuals by the circumstances into which they were born. However, the choice to work hard is often shaped by circumstances. I show that people’s merit judgments are insensitive to circumstances’ effect on choice. In an experiment, US participants judge how much money workers deserve for the effort they exert. Unequal circumstances discourage some workers from working hard. Nonetheless, participants hold disadvantaged workers responsible for their choices. Participants reward the effort of disadvantaged and advantaged workers identically, regardless of the circumstances under which choices are made. Additional experiments identify an important underlying mechanism. Individuals understand that choices are influenced by circumstances. But, in light of an uncertain counterfactual state – what exactly would have happened on a level playing field – individuals base their merit judgments on the only reliable evidence they possess: observed effort levels. I confirm these patterns in a structural model of fairness views. Finally, a vignette study shows that merit judgments can be similarly “shallow” when choices are shaped by racism or poverty. |
| Keywords: | Meritocracy, fairness, responsibility, attitudes toward inequality, redistribution, social preferences, inference, uncertainty, counterfactual thinking |
| JEL: | C91 D63 D91 H23 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2022_318v2&r= |
| By: | Evans, George; Gibbs, Christopher; McGough, Bruce |
| Abstract: | We propose and experimentally test a model of boundedly rational and heterogeneous expectations that unifies adaptive learning, k-level reasoning, and replicator dynamics. Level-0 forecasts evolve over time via adaptive learning. Agents revise over time their depth of reasoning in response to forecast errors, observed and counterfactual. The unified model makes sharp predictions for when and how fast markets converge in Learning-to-Forecast Experiments, including novel predictions for individual and market behavior in response to announced events. The experimental results support these predictions. Our unified model is developed in a simple framework, but can clearly be extended to more general macroeconomic environments. |
| Keywords: | expectations; adaptive learning; level-k reasoning; behavioral macroeconomics; experiments |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:syd:wpaper:2021-10&r= |
| By: | Ahmed, Akhter; Hamadani, Jena Derakhshani; Hassan, Md. Zahidul; Hidrobo, Melissa; Hoddinott, John F.; Koch, Bastien; Raghunathan, Kalyani; Roy, Shalini |
| Abstract: | Evidence shows transfer programs can improve early childhood development (ECD). However, knowledge gaps remain on how short-term impacts on ECD evolve as children grow older, how program design features and context affect child development impacts over time, and through what pathways such impacts occur. We study the Transfer Modality Research Initiative (TMRI), a 2-year randomized controlled trial in two regions of Bangladesh that provided cash or food transfers, with or without complementary nutrition programming, to mothers of children aged 0-2 years at baseline. Drawing on data collected at 6 months post-program (when children were about 2-4 years old) and at 4 years post-program (when children were about 6-8 years old), we assess post-program impacts of TMRI on children’s home environment and development. We find strong post-program impacts on the home environment from cash transfers in the Northern region, particularly when combined with complementary programming, however limited post-program effects on child development outcomes. Improvements found in child development tend to be concentrated in boys. We find few post-program improvements in home environment or child development from food transfers in the Southern region, with or without complementary programming. |
| Keywords: | BANGLADESH; SOUTH ASIA; ASIA; early childhood development; children; child development; social protection; cash transfers; poverty; food transfers; behavior change communication |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:fpr:ifprid:2090&r= |
| By: | Jie Bai (Harvard Kennedy School); Maggie X. Chen (George Washington University); Jin Liu (New York University); Xiaosheng Mu (Princeton University); Daniel Yi Xu (Duke University) |
| Abstract: | We study how search and information frictions shape market dynamics in global e-commerce. Observational data and self-collected quality measures from AliExpress establish the existence of search and information frictions. A randomized experiment that offers new exporters exogenous demand and information shocks demonstrates the potential role of sales accumulation in enhancing seller visibility and overcoming these demand frictions. However, we show theoretically and quantitatively that this demand-reinforcement mechanism is undermined by the large number of online exporters. Our structural model rationalizes the experimental findings and quantifies efficiency gains from reducing the number of inactive sellers. |
| Keywords: | global e-commerce, exporter dynamics, product quality, information frictions, search frictions |
| JEL: | F14 L11 O12 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:pri:econom:2021-11&r= |
| By: | Hughes, Karl; Kulomo, Decolius; Nyoka, Bestari |
| Abstract: | While dairy production has the potential to diversify smallholder agriculture and increase incomes, there are multiple constraints. One is the consistent provision of quality feed. High protein, leguminous fodder shrubs—also referred to as Fodder Tree Technology (FTT)—can help address this constraint, yet adoption levels are generally low. Implemented in Kenya and Malawi, the Shrubs for Change (S4C) project is employing several approaches to address this situation, including those informed by behavioural science. Given that approximately 500 shrubs per cow are needed to generate enough leaf matter to bolster milk production, promoting FTT at scale necessitates the production, distribution, and successful planting of large numbers of shrub seedlings. We implemented a field experiment in Malawi’s Southern Region in late 2021 to test the effectiveness of a social learning intervention intended to motivate dairy farmers to significantly scale up the production of FTT seedlings. This intervention involved meeting with dairy farmers in 39 randomly selected milk production zones to review the numbers of seedlings being produced vis-à-vis local demand, coupled with the development of action plans to address identified production gaps. While we find that this intervention increased the setting up of private nurseries by 10% (p<0.05), it only increased overall seedling production by an average of 20 additional seedlings per dairy farmer (p>0.1). We offer several explanations for this lower than expected and statistically insignificant result, which point to the need for iterative rounds of engagement with farmers when supporting them to take up FTT and other complex agronomic and sustainable land management innovations. |
| Date: | 2021–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:kdyf7&r= |
| By: | Augustine Denteh (Tulane University); Helge Liebert (University of Zurich) |
| Abstract: | We provide new insights into the finding that Medicaid increased emergency department (ED) use from the Oregon experiment. Using nonparametric causal machine learning methods, we find economically meaningful treatment effect heterogeneity in the impact of Medicaid coverage on ED use. The effect distribution is widely dispersed, with significant positive effects concentrated among high-use individuals. A small group—about 14% of participants—in the right tail with significant increases in ED use drives the overall effect. The remainder of the individualized treatment effects is either indistinguishable from zero or negative. The average treatment effect is not representative of the individualized treatment effect for most people. We identify four priority groups with large and statistically significant increases in ED use—men, prior SNAP participants, adults less than 50 years old, and those with pre-lottery ED use classified as primary care treatable. Our results point to an essential role of intensive margin effects— Medicaid increases utilization among those already accustomed to ED use and who use the emergency department for all types of care. We leverage the heterogeneous effects to estimate optimal assignment rules to prioritize insurance applications in similar expansions. |
| Keywords: | Medicaid, ED visit, effect heterogeneity, machine learning, efficient policy learning |
| JEL: | H75 I13 I38 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:tul:wpaper:2201&r= |
| By: | Marie Claire Villeval (GATE - Groupe d'analyse et de théorie économique - UL2 - Université Lumière - Lyon 2 - ENS LSH - Ecole Normale Supérieure Lettres et Sciences Humaines - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Beyond a summary of the paper, this review of "Group Identity and Social Preferences" by Yan Chen and Sherry X. Li highlights its exceptional impact on our understanding of group-contingent social preferences. This paper has made an important theoretical contribution by introducing group identity in the Charness and Rabin (2002)'s model of social preferences. The core of the contribution is to show experimentally that social identity influences distributional preferences, reciprocity and welfare-maximizing behavior. In particular, charity increases and envy decreases when people are matched with an in-group compared to an out-group, and people are more likely to reward and less likely to punish an ingroup than an out-group match. This paper has also contributed to the methodological debates about the use of minimal group identity in laboratory experiments. It has inspired many research programs on the role of group-contingent preferences in various dimensions of decision-making in society. It is also important to emphasize its policy implications regarding how groupcontingent social preferences could be activated to improve efficiency and the quality of social interactions in our segmented societies. This research agenda is more relevant than ever. |
| Date: | 2021–08–27 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03504316&r= |
| By: | Albert Menkveld (VU - Vrije Universiteit Amsterdam [Amsterdam]); Anna Dreber (Stockholm School of Economics - Department of Economics); Felix Holzmeister (University of Innsbruck - Department of Economics); Juergen Huber (University of Innsbruck); Magnus Johannesson (Stockholm School of Economics - Department of Economics); Michael Kirchler (University of Innsbruck); Sebastian Neusüss (Aalto University); Michael Razen (University of Innsbruck); Utz Weitzel (VU - Vrije Universiteit Amsterdam [Amsterdam]); Gunther Capelle-Blancard (UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); David Abad-Díaz; Menachem Abudy; Tobias Adrian; Yacine Ait-Sahalia; Olivier Akmansoy; Jamie Alcock; Vitali Alexeev; Arash Aloosh; Livia Amato; Diego Amaya; James Angel; Alejandro Avetikian; Amadeus Bach; Edwin Baidoo; Gaetan Bakalli; Bao Li; Andrea Barbon; Oksana Bashchenko; Parampreet Bindra; Geir Bjønnes; Jeffrey Black; Bernard Black; Dimitar Bogoev; Santiago Bohorquez Correa; Oleg Bondarenko; Charles Bos; Ciril Bosch-Rosa; Elie Bouri; Christian Brownlees; Anna Calamia; Nga Cao; Laura Capera Romero; Massimiliano Caporin; Allen Carrion; Tolga Caskurlu; Bidisha Chakrabarty; Jian Chen; Mikhail Chernov; William Cheung; Ludwig Chincarini; Tarun Chordia; Sheung Chow; Benjamin Clapham; Jean-Edouard Colliard; Carole Comerton-Forde; Edward Curran; Thong Dao; Wale Dare; Ryan Davies; Riccardo de Blasis; Gianluca de Nard; Fany Declerck; Oleg Deev; Hans Degryse; Solomon Deku; Christophe Desagre; Mathijs van Dijk; Chukwuma Dim; Thomas Dimpfl; Yun Dong; Philip Drummond; Tom Dudda; Teodor Duevski; Ariadna Dumitrescu; Teodor Dyakov; Anne Dyhrberg; Michał Dzieliński; Asli Eksi; Izidin El Kalak; Saskia ter Ellen; Nicolas Eugster; Martin Evans; Michael Farrell; Ester Felez-Vinas; Gerardo Ferrara; El Ferrouhi; Andrea Flori; Jonathan Fluharty; Sean Foley; Kingsley Fong; Thierry Foucault; Tatiana Franus; Francesco Franzoni; Bart Frijns; Michael Frömmel; Servanna Fu; Sascha Füllbrunn; Baoqing Gan; Ge Gao; Thomas Gehrig; Roland Gemayel; Dirk Gerritsen; Javier Gil-Bazo; Dudley Gilder; Lawrence Glosten |
| Abstract: | In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants. |
| Keywords: | non-standard errors,multi-analyst approach,liquidity |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03500882&r= |
| By: | Laura D. Quinby; Gal Wettstein |
| Abstract: | This study explores how workers respond to reports about Social Security’s finances, using an online experiment in which participants are shown identical articles with different headlines. The headline for the control group reports that Social Security has a “long-term financing shortfall,†but does not directly reference the trust fund. The headlines for the three treatment groups highlight the depletion of the trust fund. Two treatment groups saw headlines emphasizing the trust fund’s 2034 reserve depletion date – using increasingly sensationalist language – while a third treatment group saw a headline explaining that ongoing program revenues will cover three-quarters of scheduled benefits after 2034. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:crr:crrwps:wp2021-10&r= |
| By: | Chapkovski, Philipp |
| Abstract: | The popularity of online behavioral experiments grew steadily even before the COVID-19 pandemic. With the start of lockdowns, online studies were often the only available option for the behavioral economists, sociologists and political scientists. The usage of most well-known platforms such as mTurk was so intensive that it harmed the quality of data. But even before the pandemics-induced quality crisis, online studies were limited in scope, since real-time interactions between participants were hard to achieve due to the large proportion of drop-outs and issues with creating stable groups. Using the crowdsourcing platform Toloka, we successfully ran several multi-round interactive experiments. Toloka’s large online audience, relatively low exposure of participants to sociological surveys and behavioral studies, and a convenient application programming interface makes it a perfect tool to run behavioral studies that require real-time interactions of participants. |
| Keywords: | Crowdsourcing,survey research,MTurk,online research |
| JEL: | C90 C92 C81 C88 B41 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:249771&r= |
| By: | Ndashimye, Felix; Hebie, Oumarou; Tjaden, Jasper |
| Abstract: | Phone surveys have increasingly become important data collection tools in developing countries, particularly in the context of sudden contact restrictions due to the COVID-19 pandemic. Phone surveys offer particular potential for migration scholars aiming to study cross-border migration behavior. Geographic change of location over time complicates the logistics of face-to-face surveys and heavily increases costs. There is, however, limited evidence of the effectiveness of the phone survey modes in different geographic settings more generally, and in migration research more specifically. In this field experiment, we compared the response rates between WhatsApp—a relatively new but increasingly important survey mode—and interactive voice response (IVR) modes, using a sample of 8446 contacts in Senegal and Guinea. At 12%, WhatsApp survey response rates were nearly eight percentage points lower than IVR survey response rates. However, WhatsApp offers higher survey completion rates, substantially lower costs and does not introduce more sample selection bias compared to IVR. We discuss the potential of WhatsApp surveys in low-income contexts and provide practical recommendations for field implementation. |
| Date: | 2021–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:khd32&r= |
| By: | Arnaud Tognetti (Karolinska Institutet [Stockholm], IAST - Institute for Advanced Study in Toulouse); Valerie Durand (UMR ISEM - Institut des Sciences de l'Evolution de Montpellier - EPHE - École pratique des hautes études - PSL - Université Paris sciences et lettres - UM - Université de Montpellier - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - CNRS - Centre National de la Recherche Scientifique - Institut de recherche pour le développement [IRD] : UR226); Dimitri Dubois (CEE-M - Centre d'Economie de l'Environnement - Montpellier - UMR 5211 - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique - Montpellier SupAgro - Institut national d’études supérieures agronomiques de Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Melissa Barkat‐defradas (UMR ISEM - Institut des Sciences de l'Evolution de Montpellier - EPHE - École pratique des hautes études - PSL - Université Paris sciences et lettres - UM - Université de Montpellier - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - CNRS - Centre National de la Recherche Scientifique - Institut de recherche pour le développement [IRD] : UR226); Astrid Hopfensitz (GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - CNRS - Centre National de la Recherche Scientifique - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UL2 - Université Lumière - Lyon 2 - ENS Lyon - École normale supérieure - Lyon); Camille Ferdenzi (CRNL - Centre de recherche en neurosciences de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - Université de Lyon - INSERM - Institut National de la Santé et de la Recherche Médicale - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Several physical features influence the perception of how cooperative a potential partner is. While previous work focused on face and voice, it remains unknown whether body odours influence judgements of cooperativeness and if odour-based judgements are accurate. Here, we first collected axillary odours of cooperative and uncooperative male donors through a public good game and used them as olfactory stimuli in a series of tasks examining whether and how they influence cooperative decision-making in an incentivized economic game and ratings of cooperativeness. Our results show that having access to the donor's body odours provided a strategic advantage to women during economic decisions (but not to men): with age, women were more likely to cooperate with cooperative men and to avoid interacting with uncooperative men. Ratings of cooperativeness were nonetheless unrelated to the donors' actual cooperativeness. Finally, while men with masculine and intense body odours were judged less cooperative, we found no evidence that donors' actual cooperativeness was associated with less masculine or less intense body odour. Overall, our findings suggest that, as faces and voices, body odours influence perceived cooperativeness and might be used accurately and in a non-aware manner as olfactory cues of cooperativeness, at least by women.. |
| Date: | 2021–12–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03477414&r= |
| By: | Kramer, Berber; Pattnaik, Subhransu; Ward, Patrick S. |
| Abstract: | This paper analyzes the potential linkages between innovations in agricultural credit and women’s empowerment. We provide survey evidence of lower baseline demand for agricultural credit among women than men. When asked to imagine that their financial institution would use data on past cultivation through observations of smartphone and satellite imagery to review loan applications and insure loans, women reported significantly more often than men that this would increase (and not decrease) the likelihood that they would apply for loans, and their desired loan amounts increased significantly more than those of men. Moreover, we find that the gender gap in demand for agricultural credit is explained, in part, by differences in empowerment between women and men, suggesting that increasing women’s empowerment could help bridge gender gaps in credit access and utilization. Using a cluster randomized trial, we assess whether gender sensitization has an effect on women’s empowerment and demand for credit, but we do not find that gender trainings help shift women’s empowerment or demand for credit. We conclude that improving access to digital credit is not going to be sufficient to empower women. Instead, gender responsive or gender transformative programming is required to improve demand and create an enabling environment in which norms are changed and make it easier for women to take out agricultural credit. |
| Keywords: | INDIA; SOUTH ASIA; ASIA; gender; demand; agricultural credit; credit; digital technology; surveys; microfinance; women's empowerment; digital credit |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:fpr:ifprid:2093&r= |
| By: | Jia Wang; Hongwei Zhu; Jiancheng Shen; Yu Cao; Benyuan Liu |
| Abstract: | It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. We evaluate the performance of our approach with backtesting on historical trading data of SPDR SP 500 Trust ETF over eight years. The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.03158&r= |
| By: | Paul Goldsmith-Pinkham (Yale University); Peter Hull (University of Chicago); Michal Kolesár (Princeton University) |
| Abstract: | We study the causal interpretation of regressions on multiple dependent treatments and flexible controls. Such regressions are often used to analyze randomized control trials with multiple intervention arms, and to estimate institutional quality (e.g. teacher value-added) with observational data. We show that, unlike with a single binary treatment, these regressions do not generally estimate convex averages of causal effects—even when the treatments are conditionally randomly assigned and the controls fully address omitted variables bias. We discuss different solutions to this issue, and propose as a solution a new class of efficient estimators of weighted average treatment effects. |
| Keywords: | regressions, treatment effect |
| JEL: | C30 |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:pri:econom:2021-41&r= |