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\begin{document}

\title{Coupling Labor Supply Decisions: An Experiment in India}

\author{
    \begin{tabular}{c@{\hspace{2cm}}c}
        Matt Lowe & Madeline McKelway \\
        \small \textit{University of British Columbia} & \small \textit{Dartmouth College}
    \end{tabular}
}

\date{\textit{Journal of the European Economic Association}, 2026}

\begin{frame}[noframenumbering, plain]
    \titlepage
\end{frame}



\begin{frame}{Couples Make More Joint Decisions in More Developed Countries}
	\begin{center}
		\includegraphics[width=0.32\textwidth]{n_alone_gdp.pdf}
		\includegraphics[width=0.32\textwidth]{n_hus_gdp.pdf}
		\includegraphics[width=0.32\textwidth]{n_joint_gdp.pdf}
	\end{center}
\begin{itemize}
	\item Latest Demographic and Health Survey for each country: married women report who usually makes three household decisions (own healthcare, major purchases, family visits)
	\item Wife-alone and joint decision-making rise with GDP per capita; husband-alone falls
	\pause
	\item[] $\Rightarrow$ \textbf{Can external intervention advance the transition toward joint decision-making?}
\end{itemize}
\end{frame}

\begin{frame}{Joint Household Interventions Seen as Best Practice}
\begin{wideitemize}
	\item Researchers and policymakers often view it as best practice for interventions to involve couples jointly
	\begin{itemize}
		\item 26\% of intra-household experiments in the AEA RCT Registry include joint interventions
	\end{itemize}
	\pause
	\item Backed by evidence that spouses' differing incentives can produce inefficient household decisions
	\begin{itemize}
		\item Spouses withhold information from each other \textcolor{gray}{(Anderson \& Baland 2002, Ashraf 2009, Castilla \& Walker 2013, Ashraf et al. 2014, Fiala 2018, Zhang 2024a,b)}
		\item Face bargaining costs \textcolor{gray}{(Coase 1960, Riedl 1995, Anderlini \& Felli 2001, Coase 2005)}
	\end{itemize}
	\item Joint interventions could mitigate these frictions -- and promote family unity \textcolor{gray}{(Ashraf 2024)}
	\pause
	\item \textbf{This paper: do interventions toward joint decision-making work as predicted?}
\end{wideitemize}
\end{frame}

\begin{frame}{Our Context: A New Job for Women in India}
\begin{wideitemize}
	\item Female LFP low in India; raising it is a policy priority \textcolor{gray}{(Fletcher et al. 2017)}
	\item Household frictions likely to influence take-up of female work
	\begin{enumerate}
		\item Women's work not concealable; must come to joint decision if woman is to work
		\item Women in India voice more support for women's employment than men\\ \textcolor{gray}{(Bernhardt et al. 2018, Field et al. 2021, Bursztyn et al. 2023)}
		\item Marriages in India often arranged; spouses in young couples may not know each other well and may face especially high bargaining costs
	\end{enumerate}
	\item Husbands might withhold information, and bargaining costs might prevent joint decision-making -- joint interventions could make information symmetric or nudge spouses to discuss female employment 
\end{wideitemize}
\end{frame}

\begin{frame}{Field Experiment and Predicted Effects}
\begin{wideitemize}
	\item Partner with large carpet manufacturer, offering women paid training and employment in weaving
	\item Randomized how opportunity presented to married couples, using 2-by-3 design
	\begin{itemize}
		\item Randomized whether ticket enabling enrollment in program given to (i) husband or (ii) wife
		\item Cross-randomized whether non-ticketed: (i) not informed about ticket, (ii) informed, or (iii) informed with spouse and encouraged to discuss
	\end{itemize}
	\pause
	\item Household model with bargaining frictions: information and discussion should both raise enrollment
	\item Academic experts (N = 70) and local experts (N = 241 weavers) made similar predictions 
\end{wideitemize}
\end{frame}

\begin{frame}{Preview of Results}
\begin{wideitemize}
	\item No effect of giving the ticket to the wife rather than the husband
	\item Information had no effect on enrollment (if anything, negative)
	\begin{itemize}
		\item Direct evidence of information diffusion: over 70\% of uninformed spouses knew about ticket by endline
	\end{itemize}
	\item Discussion kick-started decision-making ($+$1 discussion), yet \textbf{reduced enrollment by $\sim$50\%}	
	\pause
	\item Why? Some suggestive evidence that involving both spouses made both feel entitled to a veto
	\begin{itemize}
		\item Negative effects driven by couples in which ticketed spouse was more supportive of women weaving
	\end{itemize}
	\item External validity: couples in IHDS that discuss work-related topics more have lower FLFP
	\pause
	\item Take-aways:
	\begin{itemize}
		\item Nudges toward joint decision-making can backfire, producing a status quo bias against take-up
		\item Much remains to be learned about intervention into the household
	\end{itemize}
\end{wideitemize}
\end{frame}

\begin{frame}{Contribution to Literature}
\begin{wideitemize}
	\item Main contribution: evidence that prompting HH discussion about an opportunity can lower take-up
	\begin{itemize}
		\item Our treatment narrowly designed to prompt discussion; more intensive interventions that also change the nature of discussion find favorable results, at least from policymaker's perspective \textcolor{gray}{(Ashraf et al. 2020, Boyer et al. 2022, Donald et al. 2022, Bjorkman Nyqvist et al. 2023, Kala \& McKelway 2025)}
		\item Negative effects of joint intervention not without precedent \textcolor{gray}{(Anderberg et al. 2024, Dean \& Jayachandran 2019, Gazeaud et al. 2023)}
	\end{itemize}
	\item Evidence on information withholding in the HH: in contrast to existing literature, we find no strategic withholding, and direct evidence of information diffusion \textcolor{gray}{(Anderson \& Baland 2002, Ashraf 2009, Ashraf et al. 2014, Castilla \& Walker 2013, Fiala 2018, Zhang 2024a,b)}
	\item Household constraints to female labor supply \textcolor{gray}{(Bursztyn et al. 2020, Dean \& Jayachandran 2019, Heath \& Tan 2020, Field et al. 2021, Kala \& McKelway 2025, McKelway 2025a,b)}
	\item Introduce expert and local prediction \textcolor{gray}{(DellaVigna \& Pope 2018, DellaVigna et al. 2019, Thomas et al. 2020)} to work on household intervention
\end{wideitemize}
\end{frame}

{\setbeamertemplate{footline}{}
\begin{frame}{Outline of Talk}
\begin{wideitemize}
	\item[1.] \textbf{Background and setting}
	\item[2.] Experimental design
	\item[3.] Predictions: model and expert surveys
	\item[4.] Results
	\item[5.] Mechanisms
	\item[6.] External validity
	\item[7.] Conclusion
\end{wideitemize}
\end{frame}
}
\addtocounter{framenumber}{-1}

\begin{frame}{Setting: Rural Uttar Pradesh, India}
\begin{columns}
   \begin{column}{.4\textwidth}
    \begin{center}
	    \includegraphics[scale=0.13]{MapUP.png}
	\end{center}
	\end{column}
    \begin{column}{.6\textwidth}
	\begin{wideitemize}
		\item One of India's poorest, most gender unequal states \\ \textcolor{gray}{NITI Aayog 2018}
		\item At baseline, 82\% of husbands employed vs. 13\% of wives
		\pause
		\item Partner: Obeetee, one of India's largest carpet producers
		\begin{itemize}
			\item Area known as ``carpet belt" of India
			\item Carpet weaving common occupation for men in the area
		\end{itemize}
	\end{wideitemize}
    \end{column}
\end{columns}
\end{frame}

\begin{frame}{Weaving Training and Employment Program for Women}
\begin{columns}
    \begin{column}{.62\textwidth}
	\begin{wideitemize}
		\item Firm offers weaving training and employment for women
		\begin{itemize}
			\item 4 months paid training in weaving
			\item Followed by long-term work as weaver
			\item All-female weaving centers
			\item Centers in women's villages
			\item Aged 18 to 30
		\end{itemize}
		\item Experiment done in conjunction with recruitment for 6 new female weaving centers (120 positions)
	\end{wideitemize}
    \end{column}
    \begin{column}{.38\textwidth}
    \begin{center}
	    \includegraphics[scale=0.4]{WomanWeaver.png}
	\end{center}
	\end{column}
\end{columns}
\end{frame}

{
\setbeamerfont{frametitle}{size=\large}

\begin{frame}{Fact 1: Wives Are More Supportive of Women's Employment than Husbands}
    \begin{center}
        \input{genderprefdiff.tex}
    \end{center}

    \begin{itemize}
        \item Asked husbands and wives, separately, six questions: 
        \textit{How appropriate would it be for men / women in your household 
        to hold a job as a construction laborer / weaver / teacher?}
        
        \item Wives also more interested in the specific job opportunity we 
        advertised (column 7)
    \end{itemize}
\end{frame}
}

\begin{frame}{Fact 2: Spouses Often Disagree About Whether Women Should Weave}
\begin{columns}
    \begin{column}{.55\textwidth}
    \begin{center}
	    \includegraphics[width=\textwidth]{heatmap_hist_weaver.pdf}
	\end{center}
    \end{column}
    \begin{column}{.45\textwidth}
    \begin{wideitemize}
    	\item 58\% of couples give different answers on the appropriateness of women weaving
    	\item Disagreement usually (67\%) takes the form of the wife being more supportive
    	\item But a sizeable share (33\%) takes the opposite form
    \end{wideitemize}
	\end{column}
\end{columns}
\end{frame}


\begin{frame}{Fact 3: Husbands' Opinions More Predictive of Program Take-Up than Wives'}
\begin{columns}
    \begin{column}{.6\textwidth}
    \begin{center}
	    \input{prefpredict.tex}
	\end{center}
    \end{column}
    \begin{column}{.4\textwidth}
    \begin{wideitemize}
    	\item Both spouses' opinions predict take-up
    	\item But husbands' opinions significantly more predictive, consistent with high male bargaining power
    	\pause
    	\item[] $\Rightarrow$ Facts 1-3 motivate our experimental design
    \end{wideitemize}
	\end{column}
\end{columns}
\end{frame}

{\setbeamertemplate{footline}{}
\begin{frame}{Outline of Talk}
\begin{wideitemize}
	\item[1.] Background and setting
	\item[2.] \textbf{Experimental design}
	\item[3.] Predictions: model and expert surveys
	\item[4.] Results
	\item[5.] Mechanisms
	\item[6.] External validity
	\item[7.] Conclusion
\end{wideitemize}
\end{frame}
}
\addtocounter{framenumber}{-1}

\begin{frame}{Timeline of Experiment}
	\begin{center}
		\includegraphics[scale=0.63]{Timeline.png}
	\end{center}
\end{frame}

\addtocounter{framenumber}{-1}
\begin{frame}{Timeline of Experiment}
	\begin{center}
		\includegraphics[scale=0.63]{Census.png}
	\end{center}
\end{frame}

\addtocounter{framenumber}{-1}
\begin{frame}{Timeline of Experiment}
	\begin{center}
		\includegraphics[scale=0.63]{Rnd.png}
	\end{center}
\end{frame}

\addtocounter{framenumber}{-1}
\begin{frame}{Timeline of Experiment}
	\begin{center}
		\includegraphics[scale=0.63]{Baseline.png}
	\end{center}
\end{frame}

\addtocounter{framenumber}{-1}
\begin{frame}{Timeline of Experiment}
	\begin{center}
		\includegraphics[scale=0.63]{Enrollment.png}
	\end{center}
\end{frame}

\addtocounter{framenumber}{-1}
\begin{frame}{Timeline of Experiment}
	\begin{center}
		\includegraphics[scale=0.63]{Endline.png}
	\end{center}
\end{frame}

\begin{frame}{Randomization Step \#1: Couples with Enrollment Tickets}
	\vspace{8pt}
	\begin{center}
		\includegraphics[scale=0.06]{Ticket Example (without top cut off, names blurred).jpg}
	\end{center}
	\vspace{8pt}
	\begin{itemize}
		\item Unique ticket required to enroll; couples had to attend enrollment day together
		\pause
		\item Printed tickets for only 90\% of couples, chosen randomly (common knowledge)
		\item Ensures plausible deniability (ticket recipient can tell spouse they did not receive a ticket)
		\item Remaining 10\% got baseline survey but excluded from analysis
	\end{itemize}
\end{frame}

\begin{frame}{Randomization Step \#2: Ticketed Spouse}
	\begin{center}
		\includegraphics[scale=0.6]{TreatTicket.png}
	\end{center}
\end{frame}

\begin{frame}{Randomization Step \#3: Information to Non-Ticketed Spouse}
	\begin{center}
		\includegraphics[scale=0.35]{TreatInfo.png}
	\end{center}
	\begin{itemize}
		\item[]
		\item Ticketed spouse always informed of what information would be given to non-ticketed spouse
	\end{itemize}
\end{frame}
\addtocounter{framenumber}{-1}

\begin{frame}{Randomization Step \#3: Information to Non-Ticketed Spouse}
	\begin{center}
		\includegraphics[scale=0.6]{TreatInfoA.png}
	\end{center}
\end{frame}
\addtocounter{framenumber}{-1}

\begin{frame}{Randomization Step \#3: Information to Non-Ticketed Spouse}
	\begin{center}
		\includegraphics[scale=0.6]{TreatInfoBC.png}
	\end{center}
\end{frame}
\addtocounter{framenumber}{-1}

\begin{frame}{Randomization Step \#3: Information to Non-Ticketed Spouse}
	\begin{center}
		\includegraphics[scale=0.6]{TreatInfoD.png}
	\end{center}
\end{frame}

\begin{frame}{Analysis Sample}
\begin{columns}
    \begin{column}{.41\textwidth}
	\begin{wideitemize}
		\item Analyze couples in which:
		\begin{enumerate}
			\item Ticket assigned
			\item Both spouses completed baseline
		\end{enumerate}
		\item N = 495 couples
		\begin{itemize}
			\item 732 couples had tickets; 32\% attrited (spouse unavailable, out of time, or no consent)
			\item At most 24\% of attritors could have known their treatment
			\item Attrition unrelated to treatment; balance among non-attritors close to chance levels
		\end{itemize}
	\end{wideitemize}
    \end{column}
    \begin{column}{.59\textwidth}
    \begin{center}
	    \includegraphics[scale=0.43]{NPerTreatCell.png}
	\end{center}
	\end{column}
\end{columns}
\end{frame}

{\setbeamertemplate{footline}{}
\begin{frame}{Outline of Talk}
\begin{wideitemize}
	\item[1.] Background and setting
	\item[2.] Experimental design
	\item[3.] \textbf{Predictions: model and expert surveys}
	\item[4.] Results
	\item[5.] Mechanisms
	\item[6.] External validity
	\item[7.] Conclusion
\end{wideitemize}
\end{frame}
}
\addtocounter{framenumber}{-1}

\begin{frame}{Ex Ante Model and Predictions}
\begin{wideitemize}
	\item HH only enrolls if couple bargains and decides jointly, but two frictions may prevent bargaining:
	\begin{itemize}
		\item Information withholding: informed spouse can choose not to tell uninformed about job
		\item Bargaining costs: if spouses disagree about enrollment, one must pay cost to start bargaining process
	\end{itemize}
	\pause
	\item Prediction 1: \textit{Info} should raise enrollment relative to \textit{NoInfo}
	\begin{itemize}
		\item Effect should be larger when ticketed spouse less supportive of enrollment than non-ticketed
	\end{itemize}
	\item Prediction 2: \textit{Discuss} should raise enrollment relative to \textit{Info}
	\begin{itemize}
		\item Effect should be larger when couples disagree (no cost when couples agree)
	\end{itemize}
	\item Prediction 3: effect of \textit{WifeTicket} ambiguous
	\begin{itemize}
		\item Husbands more likely to \textit{want} to withhold; wives more likely to \textit{need} to
		\item But effect should be larger when wife more supportive than husband
	\end{itemize}
\end{wideitemize}
\end{frame}

\begin{frame}[label=ExpertSurvey]{Eliciting Predictions of Researchers}
\pause
\begin{wideitemize}
	\item Compiled list of authors of intra-household papers, focusing on those done in developing countries
	\begin{itemize}
		\item Papers published in 7 leading econ journals (5 general interest, 2 field), presented at NEUDC, cited in earlier draft, or included on PhD development syllabi for household lectures
	\end{itemize}
	\pause
	\item Emailed survey to 361 experts
	\begin{itemize}
		\item Paper had been circulated -- survey screened out those who had seen and could remember some results
	\end{itemize}
	\item 70 experts (19\% of contacted) passed screening questions and gave predictions
	\begin{itemize}
		\item Skew more junior than full sample, but experienced: average of $\sim$3,500 Google Scholar citations
	\end{itemize}
	\pause 
	\item Given actual enrollment rate in Husband Ticket \& $NoInfo$ cell, asked to predict enrollment rates in other cells 
\end{wideitemize}
\end{frame}

\begin{frame}{Eliciting Predictions of Local Experts}
\begin{wideitemize}
	\item Do locals, with knowledge of local culture, predict better than researchers? \textcolor{gray}{(Thomas et al. 2020)}
	\item Surveyed 241 male and female weavers (49\% female) working in Obeetee loom centers
	\pause
	\item Asked to imagine Obeetee recruiting women for its female weaving program in a village nearby, with a surveyor going door-to-door to inform married couples in the week before enrollment
	\item Rank three strategies by likelihood of getting women to apply:
	\begin{enumerate}
		\item Inform the husband alone
		\item Inform the wife alone
		\item Inform the two together and encourage them to discuss during the meeting
	\end{enumerate}
\end{wideitemize}
\end{frame}

\begin{frame}{Researchers Expect Information and Discussion to Raise Enrollment}
\begin{columns}
    \begin{column}{.65\textwidth}
    \begin{center}
	    \includegraphics[scale=0.68]{summary_predictions_only.pdf}
	\end{center}
    \end{column}
    \begin{column}{.35\textwidth}
	\begin{wideitemize}
		\item Predict $+$5.5 pp from \textit{Info}: 90\% predict a positive effect
		\item Predict $+$6.1 pp from \textit{Discuss} vs. \textit{Info}: 86\% predict a positive effect
		\item Predict small \textit{WifeTicket} effect ($+$1.7 pp), with little consensus (56\% positive)
	\end{wideitemize}
	\end{column}
\end{columns}
\end{frame}

\begin{frame}{Local Experts Agree: Discussion Ranked Most Effective}
\begin{columns}
    \begin{column}{.55\textwidth}
    \begin{center}
	    \includegraphics[scale=0.6]{local_expert_predictions.pdf}
	\end{center}
    \end{column}
    \begin{column}{.45\textwidth}
	\begin{wideitemize}
		\item 68\% of locals rank \textit{Discuss} best; only 8\% rank it worst
		\begin{itemize}
			\item Less positive than researchers: corresponding stats for researchers are 76\% and 1\% 
		\end{itemize}
		\item Unlike researchers, locals expect informing the husband to beat informing the wife -- consistent with norms of male authority
	\end{wideitemize}
	\end{column}
\end{columns}
\end{frame}

{\setbeamertemplate{footline}{}
\begin{frame}{Outline of Talk}
\begin{wideitemize}
	\item[1.] Background and setting
	\item[2.] Experimental design
	\item[3.] Predictions: model and expert surveys
	\item[4.] \textbf{Results}
	\item[5.] Mechanisms
	\item[6.] External validity
	\item[7.] Conclusion
\end{wideitemize}
\end{frame}
}
\addtocounter{framenumber}{-1}

\begin{frame}{Actual Findings Unexpected}
\begin{columns}
    \begin{column}{.65\textwidth}
    \begin{center}
	    \includegraphics[scale=0.68]{summary_predictions_results.pdf}
	\end{center}
    \end{column}
    \begin{column}{.35\textwidth}
	\begin{wideitemize}
		\item $p_z$ = p-value for comparison with control mean
		\item $p_e$ = p-value for comparison with avg expert prediction
		\item Enrollment \textit{declines} as the non-ticketed spouse is brought into the decision, whoever holds the ticket
		\item Reject 4 of 5 expert-predicted effects ($p < 0.10$)
	\end{wideitemize}
	\end{column}
\end{columns}
\end{frame}

\begin{frame}{Main Results: No Effect of Information}
\begin{columns}
    \begin{column}{.5\textwidth}
	\begin{wideitemize}
		\item $Enroll_i = \beta_1 Info_i + \beta_2 Discuss_i + \beta_3 WifeTicket_i + \alpha \textbf{MS}_i + \gamma \textbf{X}_i + \varepsilon_i$
		\pause
		\item \textit{Info}: $-$3 to $-$4 pp, insignificant, off a \textit{NoInfo} base of 18\%
		\item Reject mean expert prediction of $+$5.5 pp ($p \leq 0.03$)
	\end{wideitemize}
	\end{column}
    \begin{column}{.5\textwidth}
    \begin{center}
	    \input{mainresults_slide1.tex}
	\end{center}
    \end{column}
\end{columns}
\end{frame}

\begin{frame}{Main Results: Discussion Reduces Enrollment}
\begin{columns}
    \begin{column}{.5\textwidth}
	\begin{wideitemize}
		\item \textit{Discuss}: $-$8 to $-$10 pp vs. \textit{NoInfo} ($p<0.01$ with controls)
		\begin{itemize}
			\item A 44--56\% drop
		\end{itemize}
		\item $-$5 to $-$6 pp vs. \textit{Info} ($p = 0.06$ with controls)
		\begin{itemize}
			\item A 33--43\% drop
		\end{itemize}
		\item Easily reject mean expert predictions ($p<0.001$)
		\item Effects persist: similar impacts on program participation one month in
	\end{wideitemize}
	\end{column}
    \begin{column}{.5\textwidth}
    \begin{center}
	    \input{mainresults_slide2.tex}
	\end{center}
    \end{column}
\end{columns}
\end{frame}

\begin{frame}{Main Results: No Effect of Which Spouse Gets the Ticket}
\begin{columns}
    \begin{column}{.5\textwidth}
	\begin{wideitemize}
		\item Enrollment 15\% under \textit{HusbTicket} vs. 16\% under \textit{WifeTicket}, insignificant ($p = 0.83$ with controls)
		\item Cannot reject the (small) mean expert prediction of $+$1.7 pp
		\item Saturated specification: effects of \textit{Info} and \textit{Discuss} do not differ by \textit{WifeTicket}
	\end{wideitemize}
	\end{column}
    \begin{column}{.5\textwidth}
    \begin{center}
	    \input{mainresults_slide3.tex}
	\end{center}
    \end{column}
\end{columns}
\end{frame}

\begin{frame}{Why No Effect of Information? It Diffused Anyway}
\begin{columns}
    \begin{column}{.5\textwidth}
	\begin{wideitemize}
		\item Endline: asked non-ticketed spouses whether their spouse had received a ticket
		\item In \textit{NoInfo}, 72\% knew -- despite plausible deniability
		\item \textit{Info} raised awareness by 10 pp, but for couples where sharing wouldn't lead to enrollment (given null effects on enrollment)
	\end{wideitemize}
	\end{column}
    \begin{column}{.5\textwidth}
    \begin{center}
	    \input{firststage_knows.tex}
	\end{center}
    \end{column}
\end{columns}
\end{frame}

\begin{frame}{Discussion Treatment Kick-Started a Decision Process}
\begin{columns}
    \begin{column}{.5\textwidth}
	\begin{wideitemize}
		\item 80\% of \textit{Discuss} couples discussed the job in the designated three minutes (surveyor reports)
		\item \textit{Discuss} raised the total number of discussions couples reported by $\sim$1, from a base of $\sim$3
		\item So no crowd-out of organic discussion: the treatment prompted \textit{more} deliberation...
		\item ...yet enrollment fell
	\end{wideitemize}
	\end{column}
    \begin{column}{.5\textwidth}
    \begin{center}
	    {\footnotesize \input{firststage_ndisc.tex}}
	\end{center}
    \end{column}
\end{columns}
\end{frame}

\begin{frame}{Heterogeneity Predicted by Ex Ante Model Also Rejected}
\begin{columns}
    \begin{column}{.5\textwidth}
	\begin{wideitemize}
		\item Use baseline question on female weaver appropriateness
		\item Model: \textit{Info} effect larger when ticketed spouse less supportive of female weavers
		\begin{itemize}
			\item No: interaction $\approx$ 0
		\end{itemize}
		\item Model: \textit{Discuss} effect larger when couples disagree
		\begin{itemize}
			\item No: if anything more \textit{negative}
		\end{itemize}
		\item Bargaining-frictions model rejected. What \textit{does} explain the results?
	\end{wideitemize}
	\end{column}
    \begin{column}{.5\textwidth}
    \begin{center}
	    {\scriptsize \input{exantehet_slide.tex}}
	\end{center}
    \end{column}
\end{columns}
\end{frame}

{\setbeamertemplate{footline}{}
\begin{frame}{Outline of Talk}
\begin{wideitemize}
	\item[1.] Background and setting
	\item[2.] Experimental design
	\item[3.] Predictions: model and expert surveys
	\item[4.] Results
	\item[5.] \textbf{Mechanisms}
	\item[6.] External validity
	\item[7.] Conclusion
\end{wideitemize}
\end{frame}
}
\addtocounter{framenumber}{-1}

\begin{frame}{An Ex Post Explanation: Veto Decision-Making}
\pause
\begin{wideitemize}
	\item Suppose couples enroll only if \textit{neither} spouse vetoes it
	\begin{itemize}
		\item All couples had de jure veto power: enrollment required both spouses to attend
	\end{itemize}
	\item Spouses incur private utility cost if they exercise veto
	\begin{itemize}
		\item Captures internal or external pressure to be agreeable, can vary by gender
		\item Could make it preferable to go along with enrollment even if opposed
	\end{itemize}
	\item Treatments vary the cost by determining who felt a \textit{right} to make the decision
	\begin{itemize}		
		\item Ticketed spouses always feel entitled
		\item \textit{Discuss}, and to some extent \textit{Info}, make non-ticketed spouses feel entitled too
	\end{itemize}
	\pause
	\item Rationalizes main results:
	\begin{itemize}
		\item No withholding: opposed ticket-holders are comfortable vetoing, so lose nothing by sharing
		\item Negative effect of discussion: enrollment less likely when two spouses can veto rather than one
	\end{itemize}
\end{wideitemize}
\end{frame}

\begin{frame}{Veto Model: Three Additional Predictions}
\begin{wideitemize}
	\item[1.] \textbf{Asymmetric negative effects}: \textit{Info} and \textit{Discuss} should reduce enrollment \textit{only} when the ticketed spouse is more supportive of enrollment than the non-ticketed one
	\begin{itemize}
		\item Near opposite of ex ante model
	\end{itemize}
	\item[2.] \textbf{\textit{WifeTicket} effect in \textit{NoInfo}}: among disagreeing couples, assigning the ticket to the more supportive spouse should raise enrollment in \textit{NoInfo} -- but less so in \textit{Info} or \textit{Discuss}, which strengthen the non-ticketed spouse's veto
	\item[3.] \textbf{Decision-making power}: the non-ticketed spouse should be reported as more influential over the enrollment decision in \textit{Info} and \textit{Discuss}
\end{wideitemize}
\end{frame}

{
\setbeamerfont{frametitle}{size=\large}

\begin{frame}{Prediction 1: Interventions Reduce Enrollment Only If Ticketed More Supportive}
\begin{columns}
\begin{column}{0.35\textwidth}
\begin{wideitemize}
	\item \textit{Info} and \textit{Discuss} effects null when ticketed spouse equally or less supportive
	\item Both significantly reduce enrollment when ticketed spouse \textit{more} supportive
	\item As veto model predicts; near opposite of ex ante model
\end{wideitemize}
\end{column}
\begin{column}{0.65\textwidth}
	\begin{center}
	    \includegraphics[scale=0.62]{er_enrolled_6bar_NonTickOpposedHet_noCI.pdf}
	\end{center}
\end{column}
\end{columns}
\end{frame}
}

\begin{frame}[label=VetoEvidence]{Predictions 2 and 3: Mixed Evidence}
\begin{wideitemize}
	\item Prediction 2 supported: among disagreeing couples, assigning the ticket to the more supportive spouse raises enrollment by 8--12 pp in \textit{NoInfo}
	\begin{itemize}
		\item No such effect in \textit{Info} or \textit{Discuss} -- consistent with these treatments equalizing de facto veto power
	\end{itemize}
	\item Prediction 3 not supported: nulls on reports of which spouse most influenced the decision
	\pause
	\item Independent support: we asked local experts why discussion might \textit{reduce} enrollment, grouping options into six categories 
	\begin{itemize}
		\item 51\% could not think of any reason (consistent with overall prediction of positive effects) 
		\item Among those who could: explanations reflecting veto mechanism most common (47\%), followed by awkwardness or argument-provoking explanations (36\%) and wrong interference into the household (16\%)		
	\end{itemize}
\end{wideitemize}
\end{frame}

\begin{frame}{Alternative Mechanisms for No Effect of Information}
\pause 
\begin{wideitemize}
	\item Internalized norms of honesty
	\begin{itemize}
		\item 84\% of husbands and 69\% of wives say their spouse ``never" keeps secrets
		\item People lie surprisingly little even with economic motives to do so \textcolor{gray}{(Abeler et al. 2019)}
	\end{itemize}
	\item Spouses may not have believed they had plausible deniability
	\begin{itemize}
		\item Though comprehension checks passed (99\% knew spouse would not be told about ticket), and the share of women without tickets plausibly perceived as larger than 10\%
	\end{itemize}
\end{wideitemize}
\end{frame}

\begin{frame}{Alternative Mechanisms for Negative Effect of Discussion}
Test alternatives as possible (caveat that tests often under-powered)
\vspace{8pt}
\begin{wideitemize}
	\item Stories we do \textbf{not} rule out (due to lack of evidence ruling them out or some evidence ruling them in):
	\begin{itemize}
		\item No time to prepare arguments 
		\item Forcing discussion prevented supportive spouse from raising topic at optimal time
		\item Surveyors' presence increased salience of norm that women not work
		\item Treatment made (positively) misrepresenting information about the job harder
	\end{itemize}
	\pause
	\item Stories we rule out:
	\begin{itemize}
		\item Discussion was awkward, forcing discussion led to argument, surveyors' presence increased adherence to conversational norms, deeper deliberation (if taking job the wrong decision), fewer days to decide, second surveyor visit, experimenter demand
	\end{itemize}
\end{wideitemize}
\end{frame}

\begin{frame}{Mechanisms: Concluding Remarks}
\begin{wideitemize}
	\item Find some evidence consistent with veto power decision-making as an explanation for our effects
	\item This evidence only suggestive, and mixed
	\begin{itemize}
		\item Experiment was not designed to test for veto decision-making directly
		\item Heterogeneity results only indirect evidence
		\item No effects on reports of who had most influence over the decision
	\end{itemize}
	\item Several alternative mechanisms remain plausible
	\item Future research needed to more carefully test for veto decision-making and explore other explanations
\end{wideitemize}
\end{frame}

{\setbeamertemplate{footline}{}
\begin{frame}{Outline of Talk}
\begin{wideitemize}
	\item[1.] Background and setting
	\item[2.] Experimental design
	\item[3.] Predictions: model and expert surveys
	\item[4.] Results
	\item[5.] Mechanisms
	\item[6.] \textbf{External validity}
	\item[7.] Conclusion
\end{wideitemize}
\end{frame}
}
\addtocounter{framenumber}{-1}

\begin{frame}{Do Negative Effects of Discussion Generalize? Evidence from the IHDS}
\begin{columns}
    \begin{column}{.41\textwidth}
	\begin{wideitemize}
		\item Indian Human Development Survey (2011/12): married women report how often they discuss work-related topics with their husbands
		\item Wives who discuss work topics more are \textit{less} likely to be employed
		\begin{itemize}
			\item Control for amount of discussion about other topics
			\item Robust to demographic controls, geographic FE, household FE, husband's work hours
		\end{itemize}
		\item Identification far weaker than in the experiment -- at best suggestive of external validity
	\end{wideitemize}
	\end{column}
    \begin{column}{.59\textwidth}
    \begin{center}
	    {\scriptsize \input{ihds_slide.tex}}
	\end{center}
    \end{column}
\end{columns}
\end{frame}

{\setbeamertemplate{footline}{}
\begin{frame}{Outline of Talk}
\begin{wideitemize}
	\item[1.] Background and setting
	\item[2.] Experimental design
	\item[3.] Predictions: model and expert surveys
	\item[4.] Results
	\item[5.] Mechanisms
	\item[6.] External validity
	\item[7.] \textbf{Conclusion}
\end{wideitemize}
\end{frame}
}
\addtocounter{framenumber}{-1}

\begin{frame}{Implications for Policy}
\begin{wideitemize}
	\item Key contribution of our paper: prompting household discussion about opportunity can lower take-up 
	\item Ambiguous whether \textit{Discuss} made households worse off, but for policymaker seeking to raise female employment, it clearly backfired
	\pause 
	\item Many anti-poverty policies introduce new opportunities to households, hoping they will take up
	\begin{itemize}
		\item E.g. immunizations, agricultural technologies, workfare programs
	\end{itemize}
	\item Nudges toward joint decision-making can produce a \textbf{status quo bias} against take-up
	\item More intensive interventions may be needed \textcolor{gray}{(Ashraf et al. 2020, Bjorkman Nyqvist et al. 2023, Boyer et al. 2022, Donald et al. 2022, Kala \& McKelway 2025)}
\end{wideitemize}
\end{frame}

\begin{frame}{Implications for Research}
\begin{wideitemize}
	\item Our findings are not what experts (or we) predicted
	\item Researchers were uncertain -- and still overconfident
	\begin{itemize}
		\item Expected 2.7 of their 7 predictions to fall within 5 pp of the truth; only 1.1 did
		\item More experienced experts gave \textit{less} accurate predictions
	\end{itemize}
	\item Locals also mispredicted (and with confidence: 92\% were strongly certain of their rankings) 
	\begin{itemize}
		\item Suggests a failure of introspection into HH decision-making, not just researchers missing local knowledge
	\end{itemize}
	\pause
	\item Why do we care? Models can have opposite predictions for effects of intervention:
	\begin{itemize}
		\item Bargaining frictions: intervention toward joint decision-making moves decisions away from the status quo
		\item Veto: intervention toward joint decision-making produces status quo bias
	\end{itemize}
	\item Deeper understanding of household decision-making processes important for research and policy
\end{wideitemize}
\end{frame}

\end{document}
