preference driven intra-household conflict and commitment ...tunidades conditional cash transfer...

csae CENTRE FOR THE STUDY OF

AFRICAN ECONOMIES

CENTRE FOR THE STUDY OF AFRICAN ECONOMIESDepartment of Economics . University of Oxford . Manor Road Building . Oxford OX1 3UQT: +44 (0)1865 271084 . F: +44 (0)1865 281447 . E: [email protected] . W: www.csae.ox.ac.uk

Reseach funded by the ESRC, DfID, UNIDO and the World Bank

Centre for the Study of African EconomiesDepartment of Economics . University of Oxford . Manor Road Building . Oxford OX1 3UQT: +44 (0)1865 271084 . F: +44 (0)1865 281447 . E: [email protected] . W: www.csae.ox.ac.uk

CSAE Working Paper WPS/2018-03

Preference Driven Intra-household Conflict andCommitment Savings Strategies∗

Pavel Luengas-Sierra†

4th April 2018

Abstract

I analyze whether intra-household conflict induces females to usecommitment savings strategies. A model of participation in Rotat-ing Savings and Credit Associations, an informal commitment sav-ings strategy, predicts females with mid-bargaining power levelswill participate to protect their savings from partner’s claims. Inthe model, preference heterogeneity for an indivisible good drivesconflict and the couple’s decision making is efficient by followingthe collective framework. I use the 2005 and 2009 waves of a na-tionally representative panel survey from Mexico to test the model.I exploit the difference-in-difference effect the 2007 Great Recessionhad on couples in which females but not males worked in manufac-tures prior the shock. It instruments a labor-earnings based femalebargaining power measure. I find in instrumented first-differencesestimations that, compared to other females in couple, females withmid-bargaining power levels are more likely to participate. Res-ults are robust to accounting for couple’s heterogeneity in discountfactors as additional conflict source. But when females are eithermore or less patient than their partner, female participation doubles.Results are robust to whether female’s partners participate. Butwhen they do, female participation increases fourfold. Results holdfor old couples, those that had been together longer, but not foryoung couples; suggesting old but not young couples reach efficientallocations.

∗I thank Simon Quinn, Climent Quintana-Domeque, and Michael Koelle for their extensive feedback. I am also grateful for helpunderstanding and using the IMSS data from Karina Torres-Cuevas and Samuel Ricardo Trujillo-Gonzalez. All errors and omissions aremy own.†DPhil Candidate in Economics. University of Oxford. [email protected]

1 INTRODUCTION

1 IntroductionCommitment savings strategies increase individual’s welfare by protecting savings frommany claims—claims from family or friends, claims from themselves, claims from part-ners. I focus on whether claims from partners induce females to use commitment sav-ings strategies and find that females with equal footing to males in the couple’s de-cision making are more likely to use them. Savings help individuals to purchase assets,make profitable investments, and smooth consumption. Evidence shows commitmentsavings strategies can help savings accumulation (Karlan et al., 2014). Females, in par-ticular in developing countries, have less bargaining power relative to their partners.I show that, on average, males in Mexico have six times the female bargaining powerin the couple’s decision making. Female bargaining power increases will increase theneed for commitment savings strategies that in turn can help savings accumulation andincrease female’s welfare. The positive welfare effects compound if females use thesesavings towards productive assets or towards investment on their children.

I empirically test a model in which females but not their partners derive utility froma good requiring savings to purchase. After pruning identification threats by instru-menting first-differences estimations, consistent with the model predictions, I find fe-males with the same bargaining power as their partner are more likely to use stra-tegic behavior compared to females with either less or more bargaining power. Withincouples, those that had been together longer show this behavior but those that had beentogether fewer years do not. Strategic behavior might arise differently. The results holdto whether couple members show heterogeneity in the way they discount utility overtime. I find females are twice as likely to use strategic behavior when they are eithermore or less patient than their partner. Whether her partner uses the same commit-ment savings strategy has the largest effect I find. When they do, female participationincreases fourfold.

Anderson and Baland (2002) develop an intra-household conflict model in which fe-males participate in Rotating Savings and Credit Associations (ROSCA) to protect theirsavings and tilt the couple decision making towards their preferences. The key featureof this model is the assumption of couple’s preference heterogeneity for an indivisiblegood. Couple’s preference heterogeneity for the good induces females to use strategicsavings behavior to tilt the couple’s decision making towards her preferences. Thecouple will purchase the good only if females have a similar bargaining power as theirpartner in the couple’s decision making. Decision making in the model follows the col-lective framework, resource allocation to whether purchase or not the good is efficient.ROSCA are an informal commitment savings strategy prevalent around the world. Themodel’s motivation is that females, particularly those in couple and earning income,are more likely to participate in a ROSCA. As I show on Section 2.1.1, the model’s im-plications apply to all commitment savings strategies because ROSCA participation inthe model is just a costly strategy that renders per period savings illiquid.

Empirical evidence for the model is limited. Gugerty (2007) and Dagnelie andLemay-Boucher (2012) focus on ROSCA participation and find no empirical evidencesupporting the model. They do not rigorously test it. Both studies focus on self-controlproblems as ROSCA participation rationale. Both note the intra-household conflict re-lated analysis is for completeness only. In the same way, most studies on commitmentsavings focus on the self-control problem rationale. And for completeness only; for ex-

1

1 INTRODUCTION

ample, check whether married females are more likely to participate.1 Whether marriedfemales participate more than other demographic groups is not the adequate way toempirically test this model. The adequate way to empirically test this model is to assesswhether participation and female bargaining power resembles the model predictions.The model predicts that females with middle bargaining power are more likely to par-ticipate compared with other females in couple. Anderson and Baland (2002) test theirmodel using information from a sample of ROSCA participants in a slum in Kenya anduse the female share of couple income as bargaining power measure. Although theyfind evidence for the model, they note their empirical specification suffers from omit-ted variable and reverse causality threats. Both threats bias any labor-earnings basedfemale bargaining power measure. The omitted variable bias is that the higher (un-observed) female preference for the indivisible good is positively correlated with bothhigher ROSCA participation and higher labor supply. The reverse causality bias is thatROSCA participation may increase individual income by facilitating beneficial socialconnections. I note another threat. Females in couple can participate in a ROSCA asa commitment savings strategy to cope with self-control problems. Estimates will alsobe biased if, and how, this participation rationale correlates with individual income orwith bargaining power.

Couple’s resource allocation in the collective framework is efficient. This alternat-ive decision-making framework to the unitary framework has ample evidence in itsfavor (Browning et al., 2014). A fledging literature strand focuses instead on whethercouples, and which, behave as if following the collective framework. Angelucci andGarlick (2016) present evidence on this research question. They divide couples intoold or young according to the household’s head age using data from Mexico’s Opor-tunidades Conditional Cash Transfer program. They use the household’s head age toproxy for the couple’s age and find that relative old couples but not young behave asif following the collective framework. They investigate whether cohort or life-cycle ef-fects drive this finding. They attribute their findings to time-invariant cohort effects.In essence, traditions prevalent in older couples enforce agreements reached in the col-lective’s framework bargaining process that leads to efficient outcomes.

In the Anderson and Baland (2002) model, preference heterogeneity driven conflictinduces females to use costly savings strategies to tilt the couple’s decision towardstheir preferences. In their model, couple decision-making follows the collective frame-work, resource allocation is efficient. Schaner (2015) proposes a model in which hetero-geneity in discount factors induce individuals to use costly savings strategies. She testsher model using a field experiment randomly offering different saving accounts to asample of married couples in Kenya. She finds this heterogeneity does lead individualsto engage in strategic savings behavior. In her model, this behavior is evidence of inef-ficient couple behavior. Why the same behavior might be efficient under one approachand inefficient under another is worth exploring.

I use the sample of non-migrant couples from the Mexican Family Life Survey(MxFLS), a large nationally representative individual panel survey to test the model.To deal with identification threats, first I provide a simple extension to the Andersonand Baland (2002) model to allow for self-control problems. I have no way to empir-ically account for this participation rationale. Participation rationales are not mutually

1In Ashraf et al. (2006) seminal contribution, the interaction term between married and female in the commitment savings strategytake-up regression is not significant.

2

1 INTRODUCTION

exclusive. Females might not only need to protect their savings from partners but alsoneed to protect them from themselves. The extension considers the Ambec and Treich(2007) model. This model predicts individuals will join a ROSCA to commit fixed andinflexible contributions to avoid spending money in superfluous goods. It also pre-dicts average income individuals, compared with poor or rich, are more likely to join.I provide empirical evidence that female’s income positively correlates with their bar-gaining power. Using this evidence, I extend the model and show that additional par-ticipation probability owing to self-control problems is over-represented at the right ofthe female bargaining power distribution. Participation will be at its highest at middle-high bargaining power and will decrease until only the female commands the couple’sdecision making. At this bargaining power level, females need not to protect their sav-ings from their partners, they need to protect them from themselves. Participation willnot be zero. It will be positive and will reflect the proportion of present-biased sophist-icated females in the population of females that commands the couple’s decision mak-ing. Labor-earnings based female bargaining power measures will capture this patterninsofar I account for the noted omitted variable and reverse causality threats. Second, Iaccount for these threats by using as female bargaining power measure a relative meas-ure of equivalent wage rates. I calculate individual labor earnings, wage earning or not,and express them in equivalent earnings per hour to construct a female to couple relat-ive measure. By using this measure, I mitigate the omitted variable bias threat but notin its entirety. Potential female earnings per hour are what matter to female bargainingpower whether females work or not (Pollak, 2005). Observed earnings per hour mightbe a good proxy for potential earnings but a large proportion of females in couple reportno labor earnings. Moreover, this measure does not account for the reverse causalitythreat.

To further deal with these identification threats, I instrument this measure with thedifference-in-difference effect the 2007 Great Recession had on couples in which femalesbut not males worked in manufactures prior the shock. This strategy is similar in spiritto the one by Autor et al. (2014). They show that China’s competition in manufacturinghad a large negative effect on U.S. workers employed in manufactures at the beginningof the 1991-2007 period, period before the Great Recession in which China’s exportmanufactures increased substantially. Other studies focus on Mexico and manufacturesowing to the U.S. demand for manufacture imports and the countries’ trade integration(Atkin, 2016; Koelle, 2017; Majlesi, 2016). The MxFLS individual panel waves I use,2005/06 and 2009/12, reflect pre- and post-recession periods.

I use a census of formal employment and wages by Mexico’s Social Security Insti-tute (IMSS) to show the Great Recession disproportionally affected females. When therecession reached its trough, female jobs in the export manufacture sector decreased20 percent and male jobs 15 percent compared to their recession’s onset levels. Femaleemployment recovered to its recession’s onset levels by the second quarter of 2012.Male employment recovered five quarters faster, by the first quarter of 2011. Afterthe recession ended, however, female real wages relative to male increased, in partic-ular in export manufactures. The female-to-male wage ratio in export manufacturesis low compared to other sectors but also improved more. Using the MxFLS, I showthat couples in which females but not males worked in manufactures prior the shockhad equal footing in the couple’s decision making. After the shock, female bargainingpower in these couples decreased by half. The decrease, I find, concentrated on more

3

1 INTRODUCTION

educated females. In the remaining couples, female bargaining power shows, from lowlevels, a minor increase.

The Great Recession was a sudden shock. Mexico’s firms and labor force had notime to prepare for it. Moreover, the shock was not caused by a productivity shock any-where; or by female self-control problems, or by their preference for indivisible goods,or by how beneficial ROSCA participation is to income, the three main identificationthreats. To be valid, the instrument must enter the model only through its effect on thelabor-earnings based female bargaining power measure. The Great Recession also hadnegative but muted effects on employment and wages in Mexico’s non-export man-ufacture sectors. Further, in the MxFLS, due to data limitations, I can narrow downindividual employment to manufacture employment, whether export oriented or not,to create the instrument. To aid the instrument’s exclusion validity restriction, I accountin the estimating equation for municipality time-varying formal employment and wagelevels by gender and by three industry groups.2 To focus only on the plausibly exo-genous labor demand component; for wages, I follow the same methodology used byAizer (2010) and Bertrand et al. (2015). For employment, I follow a traditional Bartikmethodology (Bartik, 1991). As robustness check, I show results hold when includingmunicipality level variables that capture other plausible effects of the Great Recessionon local marriage markets and female bargaining power through education attainmentand population composition changes; or to effects on local credit availability, or to ef-fects on local poverty levels. I also show results hold when including variables thatcapture the need to protect savings from family claims or capture a rationale exclus-ive to ROSCA participation: credit constrained individuals will use them to expeditepurchasing indivisible durable goods (Besley et al., 1993). I explore the instrument’sexclusion restriction validity and robustness to alternative participation rationales indetail on Section 5. The section also shows that sample reductions driven by attritionand other sample restrictions have no effect on the results.

I find support for the model in first-differences estimations instrumented via theControl Function Approach. I express female bargaining power through a variable zranging from zero, when females have no labor-earnings per hour; to one, when theyhave all couple’s labor-earnings per hour—couple’s decision making is on their hands.Females and their partners have equal footing in the couple’s decision making whenz “ 0.50. This measure is 0.14 for females in the sample analyzed, male bargainingpower is six times as large. To capture a concave relation between commitment savingsstrategies use and female bargaining power, I include in the estimating equation vari-able z and its quadratic term. Local-linear regressions show the data support a concaverelation reaching its maximum at middle bargaining power levels. In all parametric es-timations, I use the estimated coefficients’ signs and magnitudes of z and its quadraticterm in: a) hypothesis tests to assess whether they suggest a concave function; b) the es-timation of zmax, point at which the suggested concave function reaches the maximumy; c) and in hypothesis tests to assess whether the point zmax is below z “ 1 and thus par-ticipation decreases towards higher bargain power levels. The estimated coefficients’signs and magnitudes are difficult to interpret. To ease interpretation, through model’spredicted probabilities, I depict commitment savings strategies according variable z.

The parametric estimating equation is non-linear in endogenous variable z. The in-strument is a dummy variable. Traditional instrumental variables techniques cannot

2Export manufactures, non-export manufactures, and non-manufactures.

4

1 INTRODUCTION

be used to instrument two endogenous variables with a dummy variable. The ControlFunction Approach, under stringent assumptions, can. I make use of this instrumentalvariables technique. Before instrumenting, I create a more informative instrument bymultiplying the instrument by the years of education females had in 2005 because moreeducated females where more negatively affected. Estimates using either the originalinstrument or this version are similar, suggesting both work through the same popula-tion and identify the same local average effect. The continuous instrument has a higherrelation with endogenous variable z. Instrumented first-differences estimates using itare more precise, thus I prefer it.3 The F Statistic of the excluded instrument increasesfrom 11 to 14.

First-differences estimations of the preferred specification instrumented via the Con-trol Function Approach show female use of commitment savings strategies is 6 percentwhen females have no labor-earnings per hour, increases to 15 percent when z “ 0.51and decreases to 7 percent when z “ 1 and couple’s decision making is on females’hands. I can formally reject that the point estimate zmax, 0.51, equals 1 against thealternative (p-value=0.03).4 Instrumented estimates, however, are very imprecise. Iattribute the imprecision to sample power issues. In non-instrument first-differences,predicted probabilities towards higher female bargaining power levels show wide con-fidence intervals (they do show strategic behavior decrease, albeit at middle-high bar-gaining power levels). Two reasons explain the imprecision towards higher bargainingpower levels. First, an inherent nature of the population analyzed: around 70 percentof females in couple in the sample report not working. Second, data limitations: alarge proportion of the sample who report working does not report their earnings. Anyinstrumental variables technique exacerbates the efficiency loss I attribute to samplepower issues.

Results only hold for old couples, but not for young couples, in non-instrument first-differences estimations. I explore Angelucci and Garlick (2016) finding that old but notyoung couples work as efficient collective units by splitting the sample according to twovariables’ medians. First, a proxy variable similar to the one they use: partner’s age in2005. Second, the actual couple’s age: the number of years the couple had been togetherin 2005. Results using either variable are practically identical. Through extensive useof hypothesis testing, I show that variable z has no relation with commitment savingsstrategies in females in couples below the median and that couples above the medianbehave differently. The concave relation between commitment savings strategies useand bargaining power increases substantially for females in couples above the median.This relation is statistically different compared to its relation considering the wholesample or the one below the median.

The preferred specification adds two variables to identify whether females are eitherless or more patient than their partner. Heterogeneity in discount factors does lead tostrategic savings behavior and this behavior is efficient. Including these variables hasno effect on estimates for variable z and its quadratic term, results do hold.5 Resultsremain consistent with the Anderson and Baland (2002) model, and I also show onSection 2.3 that heterogeneity in discount factors can also lead to strategic savings be-havior in this model. Schaner (2015) notes whether strategic behavior is efficient or not

3The original instrument and its continuous version, including polynomials, were not informative enough in a traditional 2SLS.4In all hypothesis tests, the alternative is whether the value is below one. This result holds if the alternative is whether the value is

different from one.5I show on Section 4.1 that these variables are uncorrelated with the instrument.

5

1 INTRODUCTION

hinges on the assumption of what the savings are used for: it will be inefficient whenno private good consumption is involved and can be efficient otherwise. The Schaner(2015) model explicitly assumes that individuals only derive utility from public con-sumption. In her model, strategic savings behavior reduces the amount of consump-tion possible by reducing couple’s resources. Money is left on the table. The Andersonand Baland (2002) model explicitly assumes that females, besides consumption, alsoderive utility from a private indivisible good while males do not. I show on Section2.3 that, whether the couple purchases the good or not, the couple’s resource alloca-tion is efficient. No money is left on the table. Estimation results support the privategood consumption assumption. The main results hold and also show that female useof commitment savings strategies is 7 percent when they have similar time-preferencesrelative to their partners and increases to 13 and 12 when they are less or more patient.

I explore whether male’s use of commitment savings strategies affects female’s use.I show on Section 3.1.1 that males do participate in a ROSCA, in particular males incouple. They might also derive utility from an indivisible private good. I make noassumptions on the mechanisms behind why male’s use enters a female’s use model. Ijust add to the estimating equation a dummy variable. The dummy variable of whethermale’s participate is not related with the instrument and has a minor effect on female’srelative patience estimates. After including this variable, all results are similar, the onesfor z and its quadratic term slightly change towards this paper’s hypothesis. Estim-ates’ precision increases slightly because the variable has a precisely estimated effect,reducing the equation’s sum of squared residuals. The effect on female commitmentsavings strategies use is dramatically large. Use for females with partners not usingcommitment savings strategies is 6 percent. For females with partners also using them,participation increases fourfold to 25 percent.

I make four contributions to the literature. First, I strive for causality and provideevidence that females can use commitment savings strategies to protect own savingsfrom their partner’s claims. Second, my results join the large literature finding thatcouples do behave as collective efficient units but also contribute to the fledging literat-ure finding that old but not young couples behave efficiently. In contrast to Angelucciand Garlick (2016), my results support that life-cycle effects and not time-invariant co-hort effects like traditions drive this finding. I remove time-invariant effects throughfirst-differencing and the measure I use, unlike the age of a household member, needsnot to correlate with cohorts. Life-cycle effects, whether couples reach efficiency throughrepetition or couples who do not do not survive, are more consistent with the results.Third, I provide evidence supporting that heterogeneity in discount factors does leadto strategic savings behavior. In contrast to Schaner (2015), I show this behavior can beefficient. Fourth, I explicitly account for partner’s use. Participation increases fourfoldand all results hold. The Anderson and Baland (2002) model implicitly assumes hid-den information, males are uncertain about female’s income and do not know whetherfemales join a ROSCA. With perfect information, a male would prevent his partner’sparticipation to keep the couple’s decision making aligned to his preferences. Thatfemale participation probability increases from 6 to 25 percent when her partner par-ticipates makes hidden information unlikely. The couple might even be participatingin the same ROSCA or cooperate to participate in different ones. This suggest maleparticipation has a large un-modelled role. Finally, and not trivially, unlike these threestudies, by using a nationally representative sample of couples, my results have greater

6

2 INTRA-HOUSEHOLD CONFLICT MODEL AND BARGAININGPOWER MEASUREMENT

external validityThe paper proceeds as follows. Section 2 presents the model, its extension, and how

distribution factors shift female bargaining power. Section 3 presents the identificationstrategy. Section 3.1 describes data sources, provides ROSCA participation descriptivestatistics, and details sample reductions leading to the final sample for analysis. Section3.2 provides evidence of a differential effect by gender of the Great Recession and Sec-tion 3.3 links this effect to female bargaining power. Section 3.4 presents the benchmarkestimating equating and Section 3.5 how the Control Function Approach instrumentsit. Section 4 presents the results. Section 5 presents all robustness checks. Section 6concludes.

2 Intra-household Conflict Model and BargainingPower MeasurementBefore describing the model, I describe ROSCA participation characteristics and whyparticipation can be a commitment savings strategy. ROSCA participation can be acommitment savings strategy because participation renders savings illiquid, but howand when participants receive their savings back has important implications in theor-etical models. In a ROSCA, a group of individuals contribute the same fixed amountof money on a timely basis to a common pot that is distributed on each turn to one ofthem. For example, four participants meet weekly to contribute per session 10 USDto a common pot that one participant takes. After four meetings, every participant re-ceived the common 40 USD pot and the process ends. Participants’ savings receive nointerest payments or compensation for inflation. Participation involves no formal con-tracting, instead social sanctions, screening, and peer pressure—characteristics difficultto mimic by other commitment saving strategies—prevent participants from droppingout after receiving the pot. By saving in this way, participants render their savings il-liquid until it is their turn to receive the pot. Participants might receive the pot in thefirst round and receive in one week a month’s worth of savings or they might receiveit in the last round and render for a month their savings illiquid. Three ways determ-ine when participants will receive the pot: a fixed schedule, a random schedule, or bybidding. In a fixed schedule the ROSCA organizer decides the pot allocation order andthis order remains when the ROSCA process starts anew. In a random schedule the potallocation order is decided randomly and on each new process, instead of remainingfixed, the order changes. In a bidding ROSCA participants bid for their place in the potallocation order. Klonner (2003) develops a model to explain participation in a biddingROSCA. In his model, risk-averse participants experience a privately observed incomeshock and bidding eases risk sharing amongst participants given the presence of theseinformation asymmetries. Bidding is not prevalent in Mexico6, thus I focus only on theimplications of random or fixed pot allocations in theoretical models.

Besley et al. (1993) consider random pot allocation to provide the first theoreticalmodel of ROSCA participation. In their model, socially connected individuals withoutaccess to credit markets participate to expedite purchasing indivisible durable goodsthat require savings accumulation—for example, livestock or other household assets—because, compared to saving on their own, all but the last individual to receive the pot

6See for example Velez-Ibañez (1982)

7


can purchase the indivisible good faster. Besley and Levenson (1996) provide empir-ical evidence using national representative surveys in Taiwan. They show a positiverelation between ROSCA participation and indivisible goods acquisition. This model,however, does not explain participation in a fixed ROSCA. According to the model’srationale, individuals prefer receiving the pot sooner rather than later but individualsparticipate in a fixed ROSCA knowing ex-ante they will be the last to receive the pot orparticipate in a random ROSCA hoping to be the last (Dagnelie and Lemay-Boucher,2012). Anderson and Baland (2002) consider fixed pot allocation to develop their RO-SCA participation model. The model applies to all commitment savings strategies be-cause ROSCA participation in the model is just a costly strategy that renders per periodsavings illiquid. Fixed pot allocation renders savings illiquid satisfying the need forcommitment.

I describe next the model, its predictions, and how female bargaining power can bemeasured. I close the section with an alternative source of intra-household conflict andhow it relates to participation.

2.1 Conflict Driven by Indivisible Good Preference Heterogen-eity2.1.1 Model Set-up

The Anderson and Baland (2002) model stylizes a couple’s decision-making process topurchase or not an indivisible good. It consists of two individuals, male (m) and female( f ) and two periods. Individual utilities over the two periods are additive without dis-counting. Individual utility functions are well-behaved and a function of consumption(c) and an indivisible good with price normalized to be 1, and D “ 1 if the couple pur-chases it. Preference heterogeneity for the good drives intra-household conflict, femalesderive utility pδq from purchasing the good while utility for males is zero:

u f “ upc1q ` upc2q ` δD. um “ upc1q ` upc2q. (1)

The couple’s decision process follows the collective framework (Chiappori, 1988). Inthis framework, the pareto weight (µ) summarizes the couple’s bargaining process. Inthis particular model the pareto weight is expressed relative to females. The weight isbounded between µ “ 0 and females have no influence in the couple decision making,ergo the couple maximizes the male utility function, to µ “ 1 and the opposite applies.Individuals decide on savings and consumption per period to maximize the weightedcouple’s utility function U:

U “ µˆ u f ` p1´ µq ˆ um, (2)

subject to the following constrains, where Y represents the couple’s income on eachperiod:

s.t : s ě 0, Y ě c1 ` s, Y` s ě c2 `D, upY´ 1q ` upYq ` δ ď upYq ` upYq. (3)

The couple can only use savings psq to purchase the indivisible good, no credit isavailable.The last constraint models an incentive to save. According to the constraint,buying the indivisible good in the first period is not optimal.

8


Given no discounting and a price normalized to be 1, the optimal savings rate perperiod required to purchase the indivisible good is s “ 1{2. The model assumes femaleswant to purchase the indivisible good:

u f : upYq ` upYq ă upY´ 1{2q ` upY´ 1{2q ` δ, (4)

but the couple purchases it only when the following condition is satisfied:

U : upYq ` upYq ă upY´ 1{2q ` upY´ 1{2q ` pµˆ δq. (5)

By condition 5, couples with females with low bargaining power, denoted by a lowvalue of µ, will not purchase the indivisible good even if under the optimal savingsrate females would always prefer to do so.

In the model, ROSCA participation equates a simple commitment savings strategythat per period renders savings illiquid. By this strategy, the optimal savings rate perperiod is still s “ 1{2 but female savings are now hidden and illiquid. The model im-plicitly assumes hidden information: females can join a ROSCA even if this decisiontilts the couple’s decision towards her preferences. It also implicitly assumes uncer-tainty about individual income that allows some of it to be hidden. Males with perfectinformation would stop females from joining a ROSCA.

Participation is no panacea. Participation involves costs: the time opportunity costof attending meetings, transportation costs, etc. The model summarizes them with afixed cost T, low enough to not discourage participation at the optimal savings rate:

u f : upYq ` upYq ă upY´ 1{2q ` upY´ 1{2q ` δ´ T. (6)

Given a large number of ROSCA, females can decide a specific contribution perperiod, sR, by joining the ROSCA with the required number of members and contri-butions. In a sub-game perfect equilibrium solution, females decide to participate in aROSCA at t “ 0 if at t “ 2 the following condition is satisfied:

U : upY` 1{2q ´ T ď upY´ 1{2q ` pµˆ δq ´ T ; t “ 2. (7)

Anderson and Baland (2002) show females will participate at intermediate values oftheir relative bargaining power in the couple decision making. At high values, they donot need participation to purchase the indivisible good. At low values, the model con-ditions induce them to prefer consumption instead. To link female bargaining powerto ROSCA participation, they prove whether females participate or not within paretoweight ranges set apart by three pareto weight thresholds. The first threshold, µ1, sat-isfies Equation 5 with equality. Pareto weights within the range µ P rµ1, 1s induce thecouple to purchase the indivisible good whether females participate in a ROSCA or not.Females do not need to render the per-period s “ 1{2 illiquid; they can, for example,save these amounts at the dwelling. Females do not need to pay the cost T associatedwith ROSCA participation. Assuming no other participation rationales exist, femalesare strictly better-off not joining a ROSCA. The second threshold, µ2, is lower than µ1and satisfies Equation 7 with equality. Females foresee on t “ 0 the need to participatewhen pareto weights are within the range µ P rµ2, µ1q. They will participate to forcethe couple decision towards their preferences. The third threshold, µ3, is lower thanµ2. When pareto weights are lower than µ2, saving in a ROSCA at the optimal rate

9

/ /

/ /

/ /

/ /

/ /

/ /

/ /

/ /

/ /


sR “ 1{2 is not enough to ensure the couple will purchase the indivisible good. But thecouple will purchase it when the third threshold satisfies two conditions. First, savingsper period need to increase enough from the optimal rate7 to ensure the couple willpurchase the indivisible good:

U : upY` sRq ´ T ď upY` sR ´ 1q ` pµˆ δq ´ T ; t “ 2. (8)

Second, that the increase is low enough that females still would prefer to purchase thegood:

u f : upYq ` upYq ď upY´ sRq ` upY` sR ´ 1q ` δ´ T. (9)

When pareto weights are in the range µ P rµ3, µ2q, females will participate in the RO-SCA with the smallest contribution sR ą 1{2 that satisfies both conditions. Finally,when pareto weights are in the range µ P r0, µ3q the savings required are high enoughthat females prefer consumption instead to participating in a ROSCA. Figure 1 sum-marizes the model predictions and embeds a continuous function approximation aswell.

Figure 1: Female Bargaining Power and ROSCA Participation

µ∈[µ1,1]µ∈[µ2,µ1)

sr = 1/2

µ∈[µ3,µ2)

sr > 1/2

µ∈[0,µ3)

No

Yes

Rosca P

art

icip

ation, Y

es o

r N

o

0 µ3 µ2 µ1 1Pareto Weight

Females with either low or high bargaining power will not join a ROSCA to copewith intra-household conflict but might join one owing to other participation rationales.They might join a ROSCA to protect their savings against others or against future-selves. They might also join a ROSCA to expedite purchasing the indivisible good.Participation rationales are not mutually exclusive, individuals can protect their sav-ings for all these reasons at once.

7Higher savings rates than optimal allow couples to also increase consumption, from which males derive utility in the same way asfemales. For example, with sR “ 3{4 the couple can either consume an additional 6/4 units or consume an additional 1/2 units and buythe indivisible good. If Equation 8 is satisfied, the couple will buy the indivisible good and increase consumption.

10


2.1.2 Model Extension To Allow For Self Control Problems

Self-control problems are the commitment savings literature primary focus (see Bryanet al., 2010 for a survey). The consensus is that they induce individuals to use commit-ment savings strategies. For the most part, the literature equates self-control problemsto individuals exhibiting present-biased time-inconsistent preferences and behaving asif maximizing a quasi-hyperbolic utility function (Laibson, 1997). Commitment savingsstrategies allow present-biased individuals to force their future-selves to accomplishtheir current-selves savings goals. But only sophisticated individuals, aware of theirtime inconsistency, will use them (O’Donoghue and Rabin, 1999). For example, Ashrafet al. (2006) show that individuals they classify as present-biased, particularly females,are more likely to use a savings product that renders savings illiquid by restricting with-drawals without providing other benefits. They attribute take-up to sophistication. Selfcontrol problems can induce individuals to use commitment savings strategies. Howand if they relate to bargaining power confounds intra-household conflict as participa-tion rationale, specially if they relate to income and relative income measures accountfor bargaining power. For this reason, I extend the model to show on its prediction theeffect of allowing for self control problems.

To extend the model, I use the predictions of a model explaining ROSCA participa-tion as a commitment strategy to cope with self-control problems. The model predictsincreases in participation according to income, but only up to high income levels. First,I assume bargaining power weakly increases with income to link it with the paretoweight. Then, I deduce the additional participation owing to self-control within paretoweight ranges. To the left and right of the range µ P rµ3, µ1q, ROSCA participation tocope with intra-household conflict is zero but females might participate to cope withself-control problems. Additional participation, I show, will be higher in the rangeµ P rµ1, 1s compared to the range µ P r0, µ3q. Ambec and Treich (2007) develop a modelexplaining ROSCA participation as a commitment strategy to cope with self-controlproblems. In their model individuals want to avoid spending money in future pur-chases of superfluous goods. The model predicts individuals participate as a financialagreement to contribute fixed and inflexible amounts at regular dates that make thempoor enough to avoid spending money in superfluous goods. Participation increaseswith income but average income individuals, compared with poor and rich, are morelikely to participate. A simplification of their utility function to consider no discountingand two periods for females ( f ) is:

u f : upY´ It ˆmq ` Itφ` upY´ It`1 ˆmq ` βSrIt`1φs, (10)

where m is the cost of an indivisible superfluous good that provides utility φ and Itindicates whether females purchase it. Preferences for the superfluous good are time-inconsistent present-biased through the parameter βS. Females are partially or fullysophisticated; their perceived time-inconsistent problem—the belief, βS, about theirdecision-making process—is close to the parameter βS: βS » βS ă 1.

Purchasing the superfluous good on period 1 when upY ´ It ˆ mq ` φ ą upYq isoptimal. But from the perspective of females’ present self at t “ 0 it might not beoptimal to do so. For example, in the extreme case when βS “ 0 is never optimal

11


because upY´ It ˆmq ă upYq:

u f :upY´ It`1 ˆmq ` βSrIt`1φs ` upY´ It`2 ˆmq ` βSrIt`2φs, (11)

with βS » 0. Partially or fully sophisticated present-biased females will foresee theneed to avoid spending money in future purchases of superfluous goods and will seeka commitment saving strategy.

Now start with the simple case, females have all the bargaining power and µ equalsone. The utility maximized is the female utility function (Equation 4) and she is alreadysaving s “ 1{2 per-period without a ROSCA. By condition 12, she will join the ROSCAwith cost T in which the fixed contribution equals the cost m (sr “ m) of the superfluousgood:

u f :upY´ It`1 ˆmq ` βSrIt`1φs ` upY´ It`2 ˆmq ` βSrIt`2φs ă (12)upY´ sq ` upY´ sq ` δ´ T,

where s ď 1{2` sR and sR “ m. In this simple case, participation probability reflectsthe proportion of sophisticated present-biased females at bargaining power level µ “1. Females either time-consistent or present-biased and naïve will not participate, butsophisticated females with self-control problems will do so to avoid spending moneyin future purchases of superfluous goods.

Females with high pareto weight, I assume, have on average a higher or similarincome compared to females with low pareto weight but no lower. Consider indi-vidual income of each couple member before they match into a couple, the femaleshare, Yf {pYf ` Ymq, can be a proxy for the pareto weight µ. Female income is not un-ambiguously increasing on this pareto weight proxy. Bertrand et al. (2015) remind thatassortative matching concerns individuals’ ranks in the distribution of gender-specificattributes; in their example, a female in the 30th percentile of the female income dis-tribution matches the male in the 30th percentile of the male income distribution. Thefemale and male ranked in the 30th percentile could have incomes of 100 and 200 andthose in the 50th percentile incomes of 200 and 1800, leading to a negative relation of fe-male income and the pareto weight proxy. A negative relation is more likely the largerthe income gap is between top-ranked matched individuals.

I base the assumption of a weakly positive relation between female pareto weightand her income on empirical grounds. Consider the following illuminating exercise. Itake the MxFLS 2009/12 survey and match single individuals between 15 and 30 yearsof age. To simplify, I assume away that marriage markets are local and match themaccording to their national individual income rank. This simple exercise provides evid-ence that my assumption is reasonable. The mean female pareto weight proxy in 2351matched couples is 0.23 (std.dev. “ 0.19, median “ 0.28), the correlation between theproxy and reported female income is r “ 0.41. The positive correlation holds whencouples with females reporting having no income are excluded (r “ 0.32). Meanmonthly income for females reporting income and with pareto weight proxy below0.23 is 390 Mexican pesos (around 32 USD in 2010, the median is 400 pesos). The cor-responding figure for females with pareto weight proxy above 0.23 is 4186 pesos (339USD, the median is 3040).

Given the assumption’s plausibility, consider first the lowest pareto weight range.In the range µ P r0, µ3q, females prefer consumption to ROSCA participation. Females

12

•


could use participation to avoid purchasing the superfluous good but the probabilitywill be low if, consistent with the Ambec and Treich (2007) model and my assumption,females in this pareto weight range have a low income. In the next range, µ P rµ3, µ1q,no additional participation exists because females already joined a ROSCA.8 I assumefemales join only one ROSCA and will join the one in which the contribution satisfiesboth participation rationales. In the last range, µ P rµ1, 1q, females will only join aROSCA to cope with self-control problems. The participation probability in the rangeµ P rµ1, 1q will be increasing if female income is weakly increasing with her relativebargaining power but could be decreasing if the correlation between income and paretoweight is strong. For the specific case of Mexico in 2009, I show the correlation, althoughpositive, might not be strong. Participation in the range µ P rµ1, 1q should be highercompared to participation in the range µ P r0, µ3q.

Figure 2: ROSCA Participation Considering Intra-household Conflictand Self-control Problems

Females in Couple

0

1

Pa

rtic

ipa

tio

n P

rob

ab

ility


Pareto weightincreasing with income

Participation when µ=1

A. Self−Control Problems.Additional participation probability

0

1

Pa

rtic

ipa

tio

n P

rob

ab

ility


Intra−household Conflict

Adding Self−Control Problems

B. Rosca Participation

Figure 2 summarizes these deducations. Panel A shows the exposition on addi-tional ROSCA participation probability for females in couple considering self-controlproblems only. Panel B shows what ROSCA participation probability of females incouple would be considering both self-control problems and intra-household conflict.The key insight is that additional participation probability should be over-representedat the right of the pareto weight distribution. Participation will be at its highest at

8Within this range, in the range µ P rµ3, µ2q, females join a ROSCA with contribution sR ą 1{2. When the condition in Equation 12holds, the contribution could fully cover the cost m of the superfluous good. If not, she will chose one with a higher contribution so thatsR “ sR:Intra household `m. The same applies in the range µ P rµ2, µ1q where sR:Intra household “ 1{2. When the condition in Equation 12does not hold, she will buy the superfluous good but she still participates in a ROSCA to cope with intra-household conflict. For this tohold, having to choose between two participation rationales does not induce females to not join a ROSCA and that avoiding acquiringthe superfluous comes second in the pecking order.

13


middle-high bargaining power and will decrease until µ “ 1. At this bargaining powerlevel, participation will be positive and will reflect the proportion of present-biasedsophisticated females in the population of females that have all the couple’s bargainingpower.

Besides coping with self-control problems and intra-household conflict, femalescould use commitment savings strategies to protect own savings from others’ claims;for example, from family members. Females with large family networks could facea high pressure to share their savings, increasing their need for a commitment savingsstrategy. On the other hand, as showed by Angelucci et al. (2016), large family networkscould ease credit constraints of the poorer individuals in the network, decreasing thisneed. Given my assumptions, females with high bargaining power are, on average,better-off. If that is the case, and if they have large family networks, they need not tocope with intra-household conflict but instead need to protect their savings from fam-ily claims. This situation also leads to the dotted shape on Figure 2, confounding themodel’s extension implications.

Another confounder not related to commitment and specific to ROSCA participa-tion is credit constraints (Besley et al., 1993). Females without access to credit mar-kets could use ROSCA participation to expedite purchasing indivisible durable goods.Given my assumptions, the correlation of whether females are credit constrained andtheir pareto weight could be negative. Females with high bargaining power mighthave access to credit and will be less likely to participate join a ROSCA. I do not ana-lyze individual formal credit access but I do have precise information of formal creditavailability at the municipality in which they reside, and use the information in theanalysis.

On Section 5.1 I test whether results hold after controlling in the empirical specific-ation for family network size and formal credit local availability. Controlling for theseconfounders has no effect in the results.

2.2 Bargaining Power Measurement: Distribution FactorsAfter detailing the mechanism relating bargaining power, embodied by pareto weights,and commitment savings strategies, the next step is to figure out how to empirically as-sess the relation. In empirical applications, the literature focuses on distribution factorsas pareto weight shifters. Distribution factors influence the couple’s decision makingbut have no affect on individual preferences or in the couple’s budget constraint. Atypical example are social programs targeting one household member, typically fe-male. After accounting for the effect of the program in the budget constraint, studiesshow that household behavior changes towards reflecting a higher weight of recipientpreferences. For example, pensions in South Africa (Duflo, 2003) or Conditional Cashtransfers (CCT) in Mexico (Bobonis, 2009; Attanasio and Lechene, 2014; Angelucci andGarlick, 2016).

Given random allocation of the program in a sample of villages, whether couplesfollow the collective model framework has been widely studied using Mexico’s Opor-tunidades CCT. For example, consider the following two studies. They test the propor-tionality property of the collective model, a formal test to provide evidence against theunitary model that requires two distribution factors. Both studies use the random al-location of the program as the first distribution factor. Attanasio and Lechene (2014)

14


use a measure of relative family network size as the second distribution factor and findevidence against the unitary model and in favor of the collective model. Angelucciand Garlick (2016) perform the same test using the same data but use the village sexratio. Instead of focusing on finding evidence against the unitary model, they focus onwhether some households behave as if following the collective framework and its im-plications. They find that efficiency cannot be rejected for households with old headsbut can be rejected for households with young heads. On Section 4.2 I also explore thisaspect. I uses a different individual data source.9 I could use a relative family networksize measure or the sex ratio as distribution factors but relative family network sizemeasures are of limited use in my identification strategy; they confound a participationrationale—the need to protect own savings against family members. The municipal-ity sex ratio confounds no other participation rationales but the variable exhibits lowwithin variation in the panel sample I use. I focus instead on different distributionfactors.

To empirically assess the relation between pareto weights and ROSCA participa-tion, I follow an income pooling test approach. In this approach, holding total coupleincome fixed, evidence against the unitary model and in favor of the collective model isthat when the female share of the couple’s income increases, the couple’s optimizationsolution changes towards higher female utility (Browning et al., 2014). In the unitaryframework only total couple income matters to define the budget constraint and solvethe optimization problem. The income share of each couple member is irrelevant. An-derson and Baland (2002) follow this approach to empirically test their model. They usethe share of couple income as a distribution factor. This is valid distribution factor onlywhen individual income is exogenous in the specific empirical identification strategyfollowed.

I only use the share of couple income for reference purposes. Using this measure inthe context of saving to purchase an indivisible good plausibly leads to reverse caus-ality and omitted variable bias identification threats. Anderson and Baland (2002) ac-knowledge the reverse causality threat: ROSCA participation may increase individualincome by facilitating beneficial social connections. They also acknowledge the omittedvariable bias threat: a higher (unobserved) female preference for an indivisible good ispositively correlated with both a higher ROSCA participation and a higher labor supply(Browning et al., 2014 posit this argument as well.) To deal with both threats, I constructa relative measure of equivalent wage rates by calculating individual labor earnings,wage earning or not, and expressing them in equivalent earnings per hour. By using ameasure of observed earnings per hour, I mitigate the omitted variable bias threat. Butthe potential earnings per hour of females in couple not working is unobserved andthey represent a large proportion of the sample. Further, whether females work or not,changes in their potential wage rate determine their bargaining power (Browning et al.,2014).

Throughout the document, I denote this as “relative equivalent wage rate” (REWR).Below I present its estimation and how it relates to female share of couple income (SC).For its estimation, first I remove non-labor income for both females and males. ThenI divide total labor income in all occupations, whether they pay wages or not, by totalhours worked per month using the declared weeks worked per year and the declared

9The Oportunidades census in the experimental sample of villages captured limited savings information and no ROSCA participationor other commitment savings strategy information.

15


hours worked per week. I replace the equivalent wage rate to zero when individualsdo not work (e.g. when hours worked equal zero):

SC “Labor f ` NonLabor f

pLabor f ` NonLabor f q ` pLaborm ` NonLabormq(13)

REWR “

Labor fHrsWorked f

pLabor f

HrsWorked fq ` p

LabormHrsWorkedm

q(14)

As a first empirical approach, I estimate how this distribution factor relates to RO-SCA participation without imposing a functional form or accounting for any other vari-able. I estimate a local linear participation regression. For comparison purposes, I alsoestimate local linear regressions using instead the female share of couple income, themunicipality sex ratio, and a measure of relative family network size.

Figure 3 presents the results using the sample of females in couple in the MxFLS2009/12 survey. The figures also include histograms of each distribution factor. PanelsA and B present estimations using the relative equivalent wage rate and the femaleshare of couple income. Panel C uses the relative family network size constructed asRelFamNet “ pNonResidentFam f q{pNonResidentFam f ` NonResidentFamm, where foreach couple member I count the number of parents and adult siblings (18`) not livingwith the couple.10 Finally, Panel D uses the male-to-female municipality sex ratio.11

The estimated relation of participation with the sex ratio and relative family net-work size resembles an inverted-U shape. The sex ratio (Panel D) resembles this shapeexcept for a municipality in which participation and the ratio are both unusually large.The relative family network size (Panel C) also resembles this shape except at the dis-tribution tails. At the tails all the couple’s family members either belong to the femaleor to her partner. Participation could be used as a commitment strategy to protect ownsavings from family claims. The local linear regression results, in particular the increaseat the left extreme when all family members of the couple belong to the male, providesome evidence for this participation rationale because females with a large number offamily members are more likely to participate.

Consistent with the model extension, the non-parametric relation of ROSCA parti-cipation and the relative equivalent wage rate (Panel A) resembles an inverted-U shapewith higher participation for females with high bargaining power relative to femaleswith low bargaining power. This pattern is less clear when using the female share ofcouple income. Histograms in both panels show that a high proportion of females re-port having no labor income (Panel A) or any income (Panel B). Around 74 percent offemales report having no labor income and 72 percent report having no individual in-come. The large proportion of females without income does not drive the main resultsbecause first-differences estimations do not use variation from the sample reportingthe same measure in both waves—for example, not having individual income. It doeshinder the sample’s power through a large sample reduction. Survey limitations ex-acerbate this problem. The high proportion of females without labor income is consist-

10When both couple members have no family members (non-resident parents and adult siblings), I impute a value of 0.5 to bothcouple members.

11Angelucci and Garlick (2016) note that when separation is not a credible threat, the sex ratio at the time when couples marriedshould have a higher relation with bargaining power relative to the contemporaneous sex ratio. To better capture this idea, I use the sexratio from the 2005 Census instead of the using the one from the 2010 Census.

16

M M

_ i

/ _

/ 7


Figure 3: Non-parametric Estimations of Female ROSCA ParticipationAccording to Distribution Factors, MxFLS 2009/12

0

.05

.1

.15

.2

.25

.3

.35

RO

SC

A p

art

icip

atio

n=

1

0

20

40

60

80

100

His

tog

ram

of

Re

lative

Ba

rga

inin

g P

ow

er

Me

asu

reP

erc

en

t

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Relative Bargaining Power Measure [0,1]

Histogram Local Linear Regression

95% CI

Sample = 3457Local Linear Regresion: Bandwidth=0.10, Kernel=Epanechnikov

A. Relative Equivalent Wage Rate

0

.05

.1

.15

.2

.25

.3

.35

RO

SC

A p

art

icip

atio

n=

1

0

20

40

60

80

100

His

tog

ram

of

Re

lative

Ba

rga

inin

g P

ow

er

Me

asu

reP

erc

en

t



95% CI


B. Share of Couple Income

0

.05

.1

.15

.2

.25

.3

.35

RO

SC

A p

art

icip

atio

n=

1

0

5

10

15

His

tog

ram

of

Re

lative

Ba

rga

inin

g P

ow

er

Me

asu

reP

erc

en

t



95% CI


C. Relative Family Network

0

.05

.1

.15

.2

.25

.3

.35

RO

SC

A p

art

icip

atio

n=

1

0

5

10

15

His

tog

ram

of

Re

lative

Ba

rga

inin

g P

ow

er

Me

asu

reP

erc

en

t

.75 .8 .85 .9 .95 1 1.05 1.1 1.15 1.2 1.25Relative Bargaining Power Measure


95% CI


D. Sex Ratio, 2005Males 15+ / Females 15+

ent with a high proportion of inactive females in Mexico (around 40 percent accordingto data from the ENOE survey) but could also reflect data quality problems. The highproportion of females without individual income reflects that the individual incomecaptured by the MxFLS does not include the Oportunidades Conditional Cash Transfer.Particular to the distribution factor I focus on, a large proportion of individuals work-ing do not report their earnings. I discuss these and other individual level data issues inthe next section. Their consequence are imprecise estimates, specially towards the rightside of the distribution factor distribution (e.g. females with high bargaining power.)

2.3 Conflict Driven by Heterogeneity in Discount FactorsConflict arising from preference heterogeneity induce females to take costly strategicdecisions to tilt couple’s resources towards her preferences. Intra-household conflictarises from individual’s utility function heterogeneity: females prefer an indivisiblegood but males do not. The couple’s decision making follows the collective frame-work. On this framework the couple maximizes jointly a well-behaved utility function

17


under a budget constraint. Aware of the decision making process, and of the differ-ence in utility functions, females use ROSCA participation, a costly savings strategy,to tilt the couple’s decision making towards her preferences. The outcome is efficientwhether females participate in a ROSCA or not. If they participate, the couple exhaustsits budget in consumption and in purchasing the indivisible good. If they do not be-cause it is not optimal under the model conditions—for example, when µ P r0, µ3q—thecouple exhausts its budget in consumption. No money is left on the table.

Conflict might arise differently. Schaner (2015) proposes a model in which hetero-geneity in discount factors induce couples to engage in costly savings strategies. Shetests her model using a field experiment. She offered to couples individual or joint sav-ings accounts with randomly assigned interest rates. She designed individual accountsto be costly in terms of forgone interest earnings relative to joint savings accounts. Sheelicited individual time preferences using a large number of choices, a random set ofthem incentivized, that allowed her to estimate discount factors. She then used the es-timates to split the sample in two equal parts: Well matched couples, those in which theestimated individual discount factors differ from each other more than half a standarddeviation; and poorly matched, the remaining sample of couples. She finds that poorlymatched couples are twice as likely to use costly individual accounts compared to well-matched couples. According to her model, her findings are consistent with inefficientcouple behavior.

Why is strategic saving behavior efficient in one model and inefficient in another?The key, she notes, hinges on the assumption of what are the savings used for. In thefirst model, females prefer an indivisible good while males do not. Thus it explicitlyassumes a good private to the female. Schaner explicitly assumes public consumption.In her model, by foregoing interest gains, strategic behavior reduces the amount ofconsumption possible by reducing couple’s resources. Money is left on the table.12

Conflict arising from heterogeneity in discount factors would induce females to par-ticipate in a ROSCA, keeping the assumption of a female private good, for two reasons.The first applies to all commitment savings strategies: females more patient than theirpartners would want to defer today’s consumption to accumulate savings and purchasethe good while males prefer today’s consumption. The second is particular to ROSCAparticipation: participation expedites the acquisition of indivisible durable goods (Be-sley et al., 1993). Females more impatient than their partners would want to purchasethe good as soon as possible while males prefer to defer today’s consumption toward’stomorrow consumption. Males could also want to save towards acquiring their ownprivate goods. In Section 4.1 I extend the empirical specification to test whether fe-males who are either more or less patient than their partner are also more likely toparticipate. I find this to be the case.

12As she acknowledges, not a lot of money though. She estimates that the foregone interest rate is about 3 percent, equivalent to 24Ksh for those who save more than the median (75th percentile). The amount represents 2 percent of the 1177 Ksh weekly income ofpoorly matched couples.

18

3 IDENTIFICATION STRATEGY

3 Identification Strategy

3.1 Data3.1.1 Individual level data

The individual level data source is the Mexican Family Life Survey (MxFLS). This na-tionally representative longitudinal survey consists of three waves. Each wave capturesdetailed labor, savings, credit, migration, and marital status information. The latter in-cludes the identification of all individuals in couples within a household including whois their partner in the household roster. The information allows estimating for any in-dicator the partner’s corresponding one. I use only the last two waves, 2005/06 and2009/12, because they capture periods before and after the Great Recession. They lasttwo waves also have modules to elicit time preferences. In both waves, I link the sampleby interview month with the monthly national price index series to express all monet-ary amounts in constant December of 2010 Mexican pesos. I use the prevalent exchangerate in December 2010 (12.35 pesos per USD) to convert pesos to USD.

Individual income information captured by the surveys has two limitations, the firstone major. The first limitation relates to labor earnings: a large proportion of individu-als who report working and earning income do not report it. The consequence of thislimitation is a large sample reduction. The sample of individuals 15 years of age orolder in the wave 2009/12 is around 23, 000. Of them, around 12, 000 individuals re-port working and earning income but 20 percent do not report their earnings. The datathey do report suggest their earnings are similar, albeit somewhat lower, to those whodo report payment. The 80 percent who report earnings report working 43.1 hours perweek compared to 40.7 hours of the 20 percent who do not (t-stat“ ´5.9). Females whodo not report earnings report working 1.9 fewer hours per week compared to femaleswho do so (t-stat: ´2.5). The corresponding difference for males is ´2.9 hours (t-stat:´6.1). Bargaining power is a relative concept and I focus on relative labor earnings. Forthis reason, I drop couples from the analysis when at least one couple member reportsworking and earning income but does not report it. This limitation leads to a largesample reduction.

The second limitation explains the large proportion of the sample of females incouple reporting no individual income. Its effect on the identification strategy is minor.The publicly available datasets remove the variables that capture the amount the house-hold or individual receives from the Oportunidades Conditional Cash Transfer.13 TheMexican government reports that more than 7 million of the 32 million households inthe country received the transfer by the end of the 2014 fiscal year. The governmenttargets the transfer to females, most of them in couple. The consequence of this limit-ation is that individual income estimation for females in couple who are Oportunidadesrecipients is systematically lower.14 I focus on relative labor earnings, the consequenceof this limitation is minor.

To construct individual income, and in turn female bargaining power variables, I es-timate on each of the two waves individual labor and non-labor income and winsorize

13Only the first wave datasets include the variables, but questionnaires in all waves capture the information. The variable that shouldcapture the individual level information in all waves is iin01a1_2 in dataset iiia_iin.

14The program’s bi-monthly transfer is 335 pesos (28 USD in 2010) for food and between 175 and 1350 pesos (15 and 113 USD) for thechildren education component. The per-capita expenditure in households with females living with their partner is around 1035 pesos(86 USD). This expenditure reflects the income received by the beneficiaries but not reported in the datasets.

19


(99%) top outliers on each of the four series without distinguish individuals by gender.Taking the reported data as they are show implausible outliers, in particular for malesand for the wave 2005/06. Table A1 in the Appendix presents descriptive statisticscontrasting individual’s and couple’s income with and without winsorizing. Withoutwinsorizing, the maximum partner’s individual income is an implausible 14,000 USDper month. After winsorizing, the maximum drops to 2800 USD. The Table also showsthat winsorizing has a limited effect on female bargaining power indicators and onSection 5.2 I show it has no effect on the results.

The survey captures whether individuals participated in any ROSCA during the last12 months and some contribution information. Using participation information, I findparticipation in Mexico is mostly an urban phenomenon.15 Females in couple indeedare more likely to participate, but males also participate, albeit in a lower proportion.Panel A of Figure 4 presents participation estimations using the wave 2009/12. Par-ticipation in the sample of individuals 15 years of age or older is 11 percent. In citieswith population more than 100,000 is 14 percent and in rural areas16 is 7 percent (t-stat:´4.2). Participation of females in couple living with their partner, at 13 percent, is signi-ficantly higher compared with other females (10%, t-stat: ´4.6), males living with theirpartner (10%, t-stat: ´7.1), and other males (8%, t-stat: ´8.3).

Consistent with the Ambec and Treich (2007) model, participation increases withincome. Panel B of Figure 4 presents local linear regressions of participation accordingto individual income for these four demographic groups.17 Participation patterns forfemales living with their partner provide evidence that individual income and bargain-ing power are positively correlated. They show that females living with their partnerlocated between percentiles 0-50 are more likely to participate compared with femalesnot living with a partner in the same income group. The difference is not statisticallysignificant at conventional levels between percentiles 50-75 and is non-existent betweenpercentiles 75-100. At high relative bargaining power, denoted by high individual in-come, participation for females living with their partner should be similar to that ofsingle females (or those not living with their partner.) Neither needs to cope with intra-household conflict and participation reflects other rationales. At low relative bargain-ing power, denoted by low individual income, participation for females living withtheir partner is higher because they need to cope with intra-household conflict.

15Basu (2011) proposes a model predicting that fixed ROSCA are more likely in urban settings.16Rural areas are defined as villages with population less than 2,500.17I exclude from the sample individuals declaring not having income. The right side in Panel A presents estimations for individuals

declaring not having income. I also exclude individuals working and not reporting labor earnings.

20

URB RUR COU SIN COU SIN COU SIN COU SIN

A. Participation by Location, Gender, and Couple Status

i I1 T 1

INCOME =01

i

,i .1 FEMALES j MALES FEMALES 1 MALES

• National, urban, or rural • Living with Partner 0 Single or not living with partner

.... 1 ......

100 I I !Ili

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 National Per Capita Individual Income Centiles

.35

7 .25 -

0 +7, .2 ra

•0 .15 -

ca 0

B. Local Linear Regressions by Individual Income (Excludes if income is zero or if labor earnings are not reported)

........... ..... ........ .... ,` •

.05 -

0 1 5 10 15

(1) Females-Living with Partner — — — — - (2) Females-Other

— • — • — • - (3) Males-Living with Partner (4) Males-Other

0 NAT

.16

.14

.12

1 P

arti

cipa

tio

n=1

.08

.06

.04

.02


Figure 4: ROSCA Participation in Mexico, MxFLS 2009/12

Panel A: Sample standard errors clustered at the municipality level.Panel B: Local linear regressions using a bandwidth of 10 and the Epanechnikov

kernel. For clarity, the 95% confidence interval is only presented for female re-lated estimations. Individual income excludes Oportunidades Conditional CashTransfer. The information is not reported in the survey.

Consistent with the Schaner (2015) model, participation in the sample increaseswhen couples show heterogeneity in discount factors. In the MxFLS modules to eli-cit time preferences, individuals are asked in hypothetical questions to choose betweena payment of 1,000 pesos today—around 81 USD in 2010, or 12% of the sample’s coupleincome—or increasingly higher amounts in one month. All individuals 15 years of ageand older were eligible to receive the hypothetical questions. The questions, however,are not strictly comparable between waves and the number of choices is limited; itis not possible to estimate and bound discount factors using regression analysis. Theavailable information does allow to identify whether individuals, by differing in theirchoices, might have different discount factors. In the following descriptive statistics,I use the information in the MxFLS 2009/12 module to construct six individual re-sponse categories according to decreasing impatience order: [=1] Respondent prefers1000 pesos today against all other offered choices, [=2] Respondent prefers 3000 pesosin a month instead of 1000 pesos today, [=3] Prefers 2000 pesos in a month, [=4] 1500in a month, [=5] 1200 in a month, and [=6] 1000 in a month. Figure 5 presents ROSCA

21


participation estimations according to the difference in the categorical response (indi-vidual response minus partner response). Both males and females are more likely toparticipate if they are more or less patient relative to their partner. Directly related withthe few choices individuals were given, a large proportion of them (40%) prefer 1000pesos today against all few offered choices a month from today.

Figure 5: ROSCA Participation According toPatience Relative to the Partner, MxFLS 2009/12

More patient than partnerLess patient than partner

0

.05

.1

.15

.2

.25

.3

RO

SC

A P

art

icip

ation=

1

−5[3.8]

−4[4.0]

−3[5.1]

−2[9.1]

−1[7.0]

0[41.1]

1[6.8]

2[9.4]

3[5.6]

4[4.9]

5[3.2]

Respondent patience category minus Partner category[Category proportion, %]

Estimation 95% Conf. Int

Sample=5760. Sample standard errors clustered at the couple level

A. Females

More patient than partnerLess patient than partner

0

.05

.1

.15

.2

.25

.3

RO

SC

A P

art

icip

ation=

1

−5[3.3]

−4[4.9]

−3[5.6]

−2[9.5]

−1[6.8]

0[40.8]

1[7.1]

2[9.2]

3[5.1]

4[4.0]

5[3.8]

Respondent patience category minus Partner category[Category proportion, %]

Estimation 95% Conf. Int

Sample=5766. Sample standard errors clustered at the couple level

B. Males

Sample used in First-differences Analysis

Table 1 presents the sample of females used in first-differences regression analysis.Sample attrition is large, 34 percent of females from the first wave drop.18 ROSCAparticipants are less likely to drop from the sample. Individuals, or their partners, whoare more educated and with fewer non-resident family members are more likely todrop. Municipality characteristics are also correlated with attrition. Individuals livingin municipalities with higher poverty, higher outstanding municipality formal credit,and lower savings in banks are less likely to drop (refer to the attrition probit usingall individual 15 years of age, Table A4 in the Appendix). I show on Section 5.2 that,

18The second wave incorporates additional individuals and households that grew out from the first wave to ensure that cross-sectionsamples remain nationally representative (Rubalcava and Teruel, 2013).

22


besides the loss in power, the impact of sample attrition in the results is minor.

Table 1: Sample of Females in Couple for AnalysisMxFLS 2005/06–2009/12

Females in Couple 15 years of age or older 2005/06 % 2009/12 Pooled %

Total 5,041 100% 5,768Only in first wave 1,693 34%Only in second wave 2,420Present in both waves 3,348 66% 3,348 6,696 100%

Sample restrictions(1) Same municipality and same partner 3,211 3,211 6,422 96%(2) Female and partner between 15-64 in both waves 2,630 2,630 5,260 79%(3) Couples with labor-income 1,329 1,329 2,658 40%(4) Municipalities with Export Manufactures 1,032 1,032 2,064 31%

I make four restrictions to the balanced sample of around 3, 350 females to narrow itto the one used in the analysis. Section 5.2 shows that the effect on results of all but thefourth restriction is minor. First, I prune selective migration confounders by restrict-ing the sample to non-migrant couples—females living with the same partner and inthe same municipality in both waves. The impact on sample size of this restriction isminor; its impact on the results is minor too. Second, I only consider couples in whichboth members are between 15-64 years of age in both waves.19 The impact on samplesize of this restriction is not minor but older individuals are unlikely to be in the laborforce and I focus on relative labor earnings; its impact on the results, however, is minor.The third restriction keeps only couples with labor income. The impact on sample sizeof this restriction is large and I cannot assess its impact on the results. The sample afterthe first two restrictions is 2, 630 females. The third restriction reduces the sample to1, 329 females, a 54 percent reduction. The sample lost consists of 982 females whodrop from the sample because they or their partners in at least one wave report work-ing and earning income but do not report it; a further 319 females drop because bothcouple members report in at least one wave not working. The fourth sample restric-tion limits it to municipalities with female and male export manufacture employmentin 2005 and leads to a final sample of 1, 032 females. The rationale for this restriction isthat in these municipalities female employment and wages (relative to those for males)could be affected by the Great Recession, the external variation source I exploit in theidentification strategy. Moreover, Bartik type measures for the time period covered byboth waves can only be estimated if the municipality had formal employment in thebase year, 2005. On Section 5.2 I show that the effect of this sample restriction is againstthe model tested.

3.1.2 Municipality level data

I use municipality level information to account for local effects of the Great Recession onformal employment and wages, factors not necessarily related to ROSCA participationbut that might aid the instrument’s exclusion restriction validity. I consider also, asrobustness checks, local formal credit availability, local education and population levels

19For example, a male 62 years old in the first wave causes the couple to drop from the analysis regardless of the female’s age.

23


by gender, and municipalities’ poverty levels. All sources consist of administrativerecords or individual surveys (Census 2005 and 2010) covering all formal employment,or all savings and credit institutions, or all the population. I link municipality leveldata to the MxFLS waves using the municipality of residence, same in both waves fornon-migrant couples, and quarter and year of interview.

The source of formal employment and wages is a census by Mexico’s Social Se-curity Institute (IMSS). The census consists of information from labor contracts sub-mitted by employers. Employers submit these contracts because the IMSS provideshealth services and pensions to all employees enrolled. The publicly available dataset(http://datos.imss.gob.mx) does not provide individual level data. Instead, it providesemployment and daily payment levels through combinations of employer and em-ployee characteristics; for example, economic sector, municipality, gender, and age.20

The dataset provides the number of employees and the total amount earned by themper day for each combination. The large number of possible combinations allows theprecise estimation of wage rates.21 In early 2018, the publicly available dataset reportsmonthly information from 2000 to 2016, and December(12) information for 1997, 1998,and 1999. I use the months of March(3), June(6), September(9), and December(12) toexpress the data quarterly. The information for Mexico City cannot be dissagregatedin its 16 boroughs, each considered a municipality by the National Statistics Institute. Idefine the whole Mexico City as one municipality in all data sources. Excluding Mexicocity from the analysis has a limited effect in the results, estimates are more precise buttheir interpretation remains the same.

The source of formal savings and credit information is Mexico’s Municipalities Sav-ings and Intermediation dataset (MSI). The datasets comprises indicators based onadministrative records that banks and other financial institutions report to Mexico’sbanking and securities regulator (CNBV). These records provide accurate saving andcredit indicators from commercial and development financial institutions monthly orquarterly from 2000 to 2011. I use the last available data point of an indicator, usuallythe first quarter of 2011, as the municipality level indicator for individuals interviewedafter the last available data point.22

Finally, education and population levels by gender, and municipalities’ povertylevels are from official Census results. I use official information from the Census 2005and the Census 2010. Indicators from the Census 2005 are linked to individuals in theMxFLS 2002/05 regardless of when they were interviewed. The same applies for theCensus 2010 and the MxFLS 2009/12. The marginality index measures municipalitypoverty levels. This government reported index uses principal components on censusvariables and is similar to the one used to target poor municipalities by Oportunidades.

20The characteristics from the employer side are municipality in which the firm is located (around 2,500), industry at four digits (276industries), and firm size (7 categories). From the employee side, the categories are gender (2), age (14 categories), and wage rate relativeto the official minimum wage rate (25 categories).

21The number of combinations is large; for example, in the second quarter of 2012 there are 1,082,253 combinations with informationfor females and 1,806,339 for males. The wage rate calculated in the second quarter of 2012 is the mean daily payment of about twomales in 1.8 million observations and one female in 1.1.

22Four percent of the interviews in the MxFLS 2009/12 concluded in 2011 and only one percent in 2012.

24


3.2 Effects of the Great Recession on Formal Employment andWagesI focus on the effect the Great Recession had on Mexico’s export manufacture sectoras external source of variation in the identification strategy. The Great Recession star-ted in December 2007 and ended in June 2009.23 Both dates serve as reference pointsfor descriptive statistics. Foreign labor demand may cause variations in Mexico’s ex-port manufacture employment and wages likely independent of Mexico’s labor supplycharacteristics, such as individual’s motivations to use a commitment savings strategy.Further, this recession, fair to assume, was an unanticipated shock for which local laborsupply had no time to adjust. On this section I link the Great Recession to a decreasein the relative number of female jobs in the export manufacture sector. Relative to maleemployment, female employment decreased more and took five quarters longer to re-cover to pre-recession levels. In the recovery period, however, relative to male, femalereal wages increased, in particular in export manufactures (with the exception of exportmanufactures, male real wages decreased in all sectors.)

Other studies also exploit employment variation in Mexico’s export manufactures,or manufactures, as well. Atkin (2016) analyses a period, 1986 to 2000, in which tradeties between Mexico and the U.S. strengthened. He instruments local expansions ofexport manufacture employment with large single-firm expansions or contractions. Hefinds that for every 25 jobs created one student drops-out after finishing grade 9 ratherthan continuing studying. Koelle (2017) analyses a recent period in which the GreatRecession caused the temporal destruction and quick recovery of export manufacturejobs. He predicts job creation using a Bartik type measure that interacts the initial levelof local employment per industry with the growth rates of bilateral exports to the U.S.He finds that small-firm owners reduce their firms’ size in anticipation of local expan-sions of export manufactures. Majlesi (2016) focuses on the impact of female bargainingpower on the number of household decisions under their responsibility. He exploits,using Bartik type measures too, variation in the labor demand component of manu-factures. He finds that increases in these relative opportunities increase the numberof household decisions under females’ responsibility. He shows the increase leads tochildren health improvements.

To start, I assign manufacture industries as export oriented or not. Atkin (2016)defines as export manufacture oriented industries that exported more than 50 percentof their output in at least half of the years between 1986 and 2000. Koelle (2017) followsa similar approach but considers only exports to the U.S. and defines as export man-ufacture oriented industries that exported more than 50 percent of their output to theU.S. in all months between 2005 and 2007. Similar to Atkin (2016), I use the export andoutput information from the Nicita and Olarreaga (2007) dataset24 and define as exportmanufacture oriented industries that exported more than 50 percent of their output inall years between 1994 and 2000. I use the period 1994–2000 for practical reasons. It iscloser to the period I analyze, 2005–2012, and data for two manufacture industries inthe Nicita and Olarreaga (2007) dataset only exist in the period 1994–2000. Table A2 inthe Appendix presents per industry and year the export to output ratios in the manu-facture sector. The table also compares the industries I define as export manufactures

23National Bureau of Economic Research, http://www.nber.org/cycles.html24To classify industries, the Nicita and Olarreaga (2007) dataset uses ISIC codes and the IMSS dataset proprietary codes. I match the

IMSS codes to ISIC codes using industry names. The process is straightforward.

25

f-- --

IP po'

9 6 9'

';1'1' pfd

1 1 5:559

P. .0d di

6 It °A oq I

P 6 1

0/

/ I

/


to those defined by Atkin (2016). The group of industries with large export-to-outputratios is evident, and it is similar to the groups defined by Atkin (2016) and Koelle(2017).25

Figure 6: Formal Employment in Mexico, 1997–2016

2007q4

2009q2

MxFLS−1 MxFLS−2 MxFLS−3

7

8

9

10

11

12

13

14

All

oth

er

secto

rs (

Mill

ions)

1.75

2

2.25

2.5

2.75

Manufa

ctu

res (

Mill

ions)

97q4 00q4 02q4 05q4 07q4 09q4 11q4 13q4 16q4

2012q2

80

90

100

110

120

130

140

80

90

100

110

120

130

140

20

07

q4

=1

00

97q4 00q4 02q4 05q4 07q4 09q4 11q4 13q4 16q4

Female Employment in ManufacturesLevels in 2007q4=100

2011q1

80

90

100

110

120

130

140

80

90

100

110

120

130

140

20

07

q4

=1

00

97q4 00q4 02q4 05q4 07q4 09q4 11q4 13q4 16q4

Male Employment in ManufacturesLevels in 2007q4=100

2007q4

2009q2

8

9

10

11

12

13

14

All

oth

er

se

cto

rs (

Mill

ion

s)

.75

1

1.25

1.5

1.75

2

2.25

2.5

Ma

nu

factu

res (

Mill

ion

s)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

MxFLS−Waves Export Manufactures

Other Manufactures Other Sectors (right)

Notes: The red vertical lines denote the start and end of the Great Recession. The solid line marks the fourthquarter of 2007 and the dashed line the second quarter of 2009. Data for 1997, 1998, and 1999 only available forDecember.

I divide formal employment in Mexico per industry in three groups: export man-ufactures, other manufactures, and all other sectors (e.g. non-manufacture industries,

25Compared to Atkin (2016) definition, my definition excludes Footwear except rubber or plastic (ISIC 3 Rev 2 code 324) and addsPottery china earthenware (ISIC 3 Rev 2 code 361) and Furniture except metal (ISIC 3 Rev 2 code 332). Koelle (2017) includes in his defin-ition the following ten industries: Textile product mills, apparel manufacturing, leather and allied products, fabricated metal products,machinery and equipment, computer and electronic products, electrical equipment, appliance, and related components, transportationequipment, furniture and related products, and miscellaneous manufacture products.

26


services, agriculture, etc.). Figure 6 presents for the three groups employment trendsfrom 1997 to 2016. Of the three, export manufacture levels are the most volatile. Thefirst large drop on this series, starting around the end of 2000, could be attributed toChina’s entry to the World Trade Organization in December, 2001. This was an anti-cipated shock and firms had time to adjust to it. I attribute the second large drop tothe unanticipated Great Recession. Employment in export manufactures shows a largedecrease between the recession’s start in the fourth quarter of 2007 and the recession’send in the second quarter of 2009. To ease interpretation, the figure also presents fe-male and male employment levels in export and non-export manufactures normalizedto their level at the recession’s onset. Female employment decreased more. Both femaleand male employment levels recovery was swift. The annual growth rate in the thirdquarter of 2010 was 15 percent for female employment and 13 for male employment.But compared to male employment, female employment levels in export manufacturestook five quarters longer to recover to pre-recession levels. Male employment levelsin export manufactures recovered to the level in the fourth quarter of 2007 by the firstquarter of 2011. Female employment levels recovered only until the second quarter of2012.

The recession’s effect on female relative employment is negative but its effect onreal relative wages is not. Female real wages in the manufacture sector, in particularin export manufactures, are considerably lower than male wages but increased afterthe recession while male wages stagnated. I calculate by gender and sector empiricaldistribution functions of employment according to constant daily wage rates.26 Figure7 presents the empirical distribution functions for each of the three industries group.Each panel presents four empirical distribution functions: One for the second quarterof 2009, the end of the Great Recession; one for two years before it ended; one fortwo years after; and one for three years after. By the second quarter of 2012, threeyears after the recession ended, employment levels in export manufactures for bothmales and females recovered to pre-recession levels. The distributions are truncatedup to 5 minimum wages (22 USD) for clarity.27 Around 90 percent of females in exportmanufactures earn per day the equivalent of 5 minimum wages or less compared with70 percent of males. Female wages, however, increased after the recession, in particularin the range 3 to 5 minimum wages, while male wages stagnated. A similar patternexists in other manufactures. Around 80 percent of females in export manufacturesearn per day the equivalent of 5 minimum wages or less compared with 65 percent ofmales. Female wages also improved in the range 3 to 5 minimum wages while malewages decreased, in particular in the range 2 to 3 minimum wages. Finally, in non-manufactures28 real earnings decreased for both males and females, in particular in therange 2 to 3 minimum wages, but female earnings show a lower decrease.

26I use December of 2010 prices as deflactor and express total daily wages as multiples of the prevalent minimum wage at the time.At the time, the minimum wage was around 54 Mexican pesos per day or around 4 USD. The official minimum daily wage rate in pesosfrom January to December of 2010 was 57.46 in geographic zone A, 55.84 in geographic zone B, and 54.47 in geographic zone C. I use thevalue for geographic zone C. The exchange rate in December 2010 was 12.35 Mexican pesos per USD. The following is an example of thecalculation process: in the second quarter of 2012 there are 1,082,253 combinations of employer and employee attributes with numberof employed females. I divide total earnings reported per combination by the corresponding number of employed females. The femaleemployment empirical distribution function is the distribution of this variable weighted by the number of females on each combination.

27Around 70 to 75 percent of formally employed workers earn up to 5 minimum wages.28Most of formal employment in Mexico, around 75 percent, is in non-manufactures.

27

OTHER MANUFACTURES NON-MANUFACTURES

5 1 2 3 4 Constant Daily Wage Rate (Dec 2010)

EDF weighted by workers per cell. Cells per year: 2007: 141524, 2009: 141155, 2011: 152223, 2012: 154473



EXPORT MANUFACTURES OTHER MANUFACTURES

EXPORT MANUFACTURES

.8-

- - 2007q2 ft-21 — 2009q2 [t]

2011q2 [t+2] — 2012q2 [t+3]



.7

5

.4F7 .4 ,

.3

.2

0

NON-MANUFACTURES

5 0

1 2 3 4 Constant Daily Wage Rate (Dec 2010)


- - 2007q2 ft-21 — 2009q2 [t]

2011q2 [t+2] — 2012q2

.2

.9

8

1 7 .

.6

.5

.4

U .3


EDF weighted by workers per cell. Cells per year:

2007q2 [t-2] — 2009q2 [t]

2011q2 [t+2] — 2012q2 [t+3]

•7-

.2

1

0

2007: 299599, 2009: 294046, 2011: 303899, 2012: 310736

5 0

1 2 3 4 Constant Daily Wage Rate (Dec 2010)


- - 2007q2 [t-2] — 2009q2 [t] - 2011q2 [t+2] — 2012q2 [t-F3]

.3-U

- - 2007q2 [t-2] — 2009q2 [t] - 2011q2 [t1-2] — 2012q2 [t+3]

1

-.- 2007q2 [t-2] — 2009q2 [t]

2011q2 [t+2] — 2012q2 [t+3]

9-

8

.7 _


Figure 7: Empirical Distribution Function of Employment By Contract Wage Rate

A. Female Employment

B. Male Employment

Female and male wage patterns reflect on their relative measure, the female-to-malewage ratio. Compare to other sectors, in particular non-manufactures, in export manu-facture employment, this ratio proportionally increased more. Figure 8 presents the realdaily wage rate by gender and sector and the corresponding wage ratios. The female-to-male wage ratio in all formal employment was around 0.83 in the period coveredby the second wave of the MxFLS, and before the recession. It increased to around0.86 in the period covered by the third wave.. The corresponding wage ratios in non-manufacture employment are 0.91 and 0.92. The corresponding wage ratios in exportmanufacture employment are 0.63 and 0.66; and 0.70 and 0.72 in other manufacturesemployment.

How the differential effects captured by the formal employment census reflect oncouple’s bargaining power, proxied by the share of couple income or the relative meas-

28

GO

...A.A

LLototir51.6:".

--e-

cb %F.11....1.4. ILV,,Eptcr. 6i

1 per

,,


ure of earnings I construct, is the next section focus.

Figure 8: Real Daily Wage by Gender and Sector, 2002–2015

150

162 [3]

216 [4]

270 [5]

324 [6]

Da

ily W

ag

e R

ate

(C

on

sta

nt

MX

N,

De

c−

20

10

)

2002 2004 2006 2008 2010 2012 2014 2016

A. Females

150

162 [3]

216 [4]

270 [5]

324 [6]

Da

ily W

ag

e R

ate

(C

on

sta

nt

MX

N,

De

c−

20

10

)

2002 2004 2006 2008 2010 2012 2014 2016

B. Males

0.63

0.66

0.70

0.72

.6

.65

.7

.75

Fe

ma

le W

ag

e R

ate

/ M

ale

Wa

ge

Ra

te

2002 2004 2006 2008 2010 2012 2014 2016

Export Manufactures Other Manufactures

C. Wage Ratio

0.83

0.86

0.91

0.92

.75

.8

.85

.9

.95

Fe

ma

le W

ag

e R

ate

/ M

ale

Wa

ge

Ra

te

2002 2004 2006 2008 2010 2012 2014 2016

All Sectors Non−manufactures

D. Wage Ratio

Note: The red vertical lines denote the start and end of the Great Recession. The solid line marks the fourth quarterof 2007 and the dashed line the second quarter of 2009.The equivalent minimum daily wage rate is presented in brackets. The following are the equivalent USD amounts

per minimum daily wage: [1] 4, [2] 9, [3] 13, [4] 17, [5] 22, and [6] 26.

3.3 Impact on Bargaining Power of the Great RecessionTo capture the effect on bargaining power of changes in employment and wages I at-tribute to the Great Recession, I divide couples according to female and male employ-ment in the wave 2005/06, a period before the recession. The survey’s informationdistinguishes manufacture employment but the available categories are not enough tonarrow export oriented industries.29 Based on the available information, and using thesample of couples for analysis, I create four groups: 1) Couples in which the female but

29I define individuals as working in manufactures if their main occupations are: [51] Chiefs, supervisors, and other control workers incraft and industrial manufacture and in maintenance and repairing activities. [52] Craftsmen and manufacturers in the transformationindustry and workers of maintenance and repairing activities. [53] Operators of fixed machinery of continuous movement and equip-ment in the process of industrial production. [54] Assistants, laborers and similar in the process of artisan and industrial manufactureand in repairing and maintenance activities.

29


not the male worked on manufactures (e.g. the male was working on other activities,looking for work, or out of the labor force); 2) both worked on manufactures; 3) the malebut not the female; and 4) no couple member worked on manufactures. Then I calculatethe female share of couple income mean and the relative equivalent wage rate mean foreach group and for the wave 2005/06, before the recession, and the wave 2009/12, afterthe recession. Table 2 presents the results. The first columns show the couple groupsand their relative size, the next columns the mean share of couple income in both wavesand its difference across waves, the last columns focus on the relative equivalent wagerate. Few couples (3%) had females working in manufactures in 2005.

Table 2: Balanced Sample of CouplesAccording to Employment in Manufactures in 2005

Working on Manufactures Share of couple income (0-100) Relative Equivalent Wage Rate (0-100)in 2005 (N=2064) 2005/06 2009/12 ∆ 2005/06 2009/12 ∆Male Female (%) Mean (SD) Mean (SD) (p-value) Mean (SD) Mean (SD) (p-value)

(IV) No Yes (2%) 44.0 (29.9) 26.6 (33.0) -17.4 (0.12) 55.8 (32.6) 26.9 (34.7) -29.0** (0.02)

By female education:ď 8 years (46%) 39.8 (32.6) 32.5 (39.8) -7.4 (0.42) 43.1 (35.7) 34.2 (44.2) -8.9 (0.54)ě 9 years (54%) 47.7 (28.7) 21.9 (27.7) -25.8 (0.13) 67.2 (26.6) 21.0 (25.9) -46.1*** (0.00)

(2) Yes Yes (1%) 31.7 (17.6) 33.2 (47.1) 1.5 (0.94) 43.7 (34.8) 36.7 (48.0) -7.0 (0.82)(3) Yes No (39%) 7.1 (16.7) 11.9 (22.9) 4.9*** (0.00) 8.5 (21.3) 15.0 (28.1) 6.5*** (0.00)(4) No No (61%) 11.5 (23.7) 14.9 (26.6) 3.3** (0.01) 12.6 (26.4) 17.5 (30.7) 4.9*** (0.00)

All 10.5 (21.9) 14.1 (25.6) 3.6*** (0.00) 11.9 (25.6) 16.8 (30.0) 4.9*** (0.00)

Standard errors clustered at the municipality level. *** pă0.01, ** pă0.05, * pă0.10.

Overall, female bargaining power measured by the share of couple income is low,but it is higher for females on the first group. In 2005, the female share of couple incomein the whole sample was just 11 percent. Females in couples in which the female butnot the male worked on manufactures where on equal footing on the household de-cision making by providing close to half the couple income (44%). When both workedon manufactures, the female share of couple income is lower—reflecting their lowerrelative payment in the sector—at 32 percent. For the remaining two groups, it is closeto the sample’s mean. From 2005 to 2009, the female share of couple income increasedfor the whole sample from 11 to 14 percent. Critically, it increased for all couples exceptfor the ones in which females worked on manufactures but their partners did not. Inthese couples the female share of couple income decreased from 44 to 27 percent. Theseestimates, however, are imprecise owing to the small number of couples with femaleswho worked on manufactures. I find similar but more precise patterns using the rel-ative equivalent wage rate, the variable I focus on. The decrease for couples in whichfemales worked on manufactures but their partners did not, from 56 to 27 percent, ishigher. These results suggest that the negative effect on employment out-weights thepositive one on wages. I also find that more educated females where disproportion-ally affected. Females with 9 years of education or more comprise half of the femalesin couples in which they but not the male worked on manufactures. For this group,their share of couple income decreased from 48 to 22 percent compared with 40 to 33of females with 8 years of education. I use this finding to convert a dummy variableinstrument to a more informative continuous one.

30


The difference in difference effect of the Great Recession according to couple groupsconstitutes my instrument for female bargaining power measures. The recession is the“treatment;” the 2005/06 and 2009/12 the pre- and post-treatment periods, the couplesin which the female but not the male worked on manufactures constitute the treatmentgroup; and the remaining couples the control. Note that the parallel trends assump-tion might not hold. Figure 6 shows that in the (recent) pre-treatment period formalemployment levels in manufactures were stable whereas they were slowly increasingin all other sectors. On the other hand, Figure 8 shows in the same period that femalerelative wages in manufactures were increasing and they were decreasing in all othersectors. The employment effect is likely to dominate. In the absence of “treatment,”female bargaining power in control couples might have been closing its gap relativeto the treatment couples. I cannot reach a firm conclusion because I lack informal em-ployment and wages trend information. But in the absence of “treatment,” female bar-gaining power in the treatment group would likely hover around 40-50 percent and inthe control group it would likely slowly increase from 10-15 to 15-20 percent. Femalebargaining power would not drastically halve in treatment group from 40-50 percent to20-25 percent.

3.4 Estimating equationI use the following linear probability model as the benchmark specification:

yimt “ β1zimt ` β2z2imt ` β3cincimt

`

j“3ÿ

j“1

ÿ

BPtw,lu

´

β j,B, f emBFemale,sector: jmt ` β j,B,mal B

Male,sector: jmt

¯

` λYQt ` µm ` ηi ` δw2009{12 ` ε imt (15)

where yimt is a dummy variable for ROSCA participation of female i, in municipalitym, at year-quarter of interview t. The vector YQt consists of year-quarter time fixedeffects, w2009{12 is a dummy variable for whether the sample is from wave 2009/12,µm are municipality fixed effects, and ηi individual fixed effects. The variable z standsfor the relative equivalent wage rate and cincimt for the couple income. For compar-ison purposes, I also use the share of couple income as variable z. To parametricallycapture a concave relation, I include the variable’s z quadratic term and test two hy-potheses: Ho β1 “ 0, Ha β1 ą 0; Ho β2 “ 0, Ha β2 ă 0. Given the quadratic functionalform I impose, this is a necessary but not sufficient condition to establish that parti-cipation decreases from middle or middle-high female bargaining power levels. Theparameters, although of correct sign, might suggest a function that does not decrease.Thus I estimate the point zmax at which the function y “ β1z ` β1z2 reaches its max-imum and present its confidence interval. I formally test whether this point is below1, Ho zmax “ 1, Ha zmax ă 1, to establish whether participation decreases from middleor high female bargaining power. Finally, I rely on the model’s predicted participationprobabilities to show the shape the parameters suggest and its confidence interval.

The shape the parameters suggest will not resemble an inverted U shape like theone presented in Figure 1 for two reasons. The first, as I show on Section 2.1.2, self-control problems induce higher participation for females with high bargaining power

31


levels. I cannot account for this participation rationale in the model, and I show it canrelate to female bargaining power through its relation with individual income. Thesecond, as I note on Section 2.2, are omitted variable and reverse causality threats. Inote that a critical identification threat is the omitted variable bias arising from a posit-ive relation between (unobserved) female preference for an indivisible good and laborsupply that leads to both higher bargaining power when measured by an income-basedvariable z and higher ROSCA participation y. First differences in a balanced sampleof non-migrant couples deal with individual (ηi) and municipality (µm) fixed effectsand address the omitted variable bias inasmuch it is time-invariant. Using the relat-ive equivalent wage rate as variable z ameliorates this threat, but does not eliminate it.Neither first-differencing nor defining z in this way address the reverse causality threatin which participation y leads to a higher z through higher income from beneficial so-cial connections. Instrumental variables techniques can deal with both reasons. In avalid instrumental variables strategy, the estimated parameter will suggest an invertedU shape like the one presented in Figure 1

The remaining variables in the benchmark estimating equation aid on the instru-ment’s exclusion restriction. By this restriction, whether couples belong to the group inwhich the female and not the male worked in manufactures in 2005 should affect parti-cipation y through bargaining power z. On Section 3.2, I show the Great Recession hada disproportionally large effect on females and on the export manufacture sector. OnSection 3.3, I show that female bargaining power proxied by the variable z decreasedonly for the treatment group; further, in the control group I cannot reject that femalebargaining power in couples in which males but not females were employed in man-ufactures was affected differently compared with couples in which neither worked inmanufactures or with couples in which both did (the latter owing to large standard er-rors.) But the instrument only narrows down manufactures, whether export orientedor not. Further, although muted, the recession had impacts on non-export orientedemployment and wages. Not only female employment and wages were affected, butalso those corresponding to males. For these reasons, I include separately municipal-ity wage and employment levels by gender and by the three industry groups I define.These variables at the municipality level account for the time-varying differential ef-fect in wages or employment the recession could had, thus aiding on the instrument’sexclusion restriction.

Before their inclusion on the estimating equation, I partial-out the labor-supply com-ponent of the equilibrium employment and wages levels. Changes in employment orwages could respond to local labor supply changes—for example, female education orfemale possibility or willingness to supply labor in the municipality—that are in turnrelated with participation y determinants. To use only variations in labor demand, Iconstruct municipality Bartik type employment (Bartik, 1991) indicators as follows:

{growthgsmt “

ÿ

jPs

γgsj,2005 ˆ

lg´m,j,yq ´ lg

´m,j,2005q

lg´m,j,2005q

(16)

where {growthgsmt is the predicted municipality employment growth rate; s denotes the

group of industries j (n=276) that comprise export manufactures (s “ 1), other manufac-tures (s “ 2), or other sectors (s “ 3); t represents a year-quarter and g indexes femaleor male employment. γ

gsj,2005 is the proportion of male or female employment per in-

32


dustry j in sector s in all four quarters that constitute 2005.30 lg´m,j,2005q is the female or

male employment level per industry j at the national level in quarter q of 2005 whenmunicipality m is excluded from the calculation. lg

´m,j,yq is the same measure at anyyear-quarter. I add to each individual in the sample of analysis predicted municipalityemployment levels plgs

mt using the year and quarter of interview and the municipality ofresidence. The process to convert the growth rates {growth

gsmt to predicted employment

level plgsmt is straightforward.31

To prune its labor supply component, I construct municipality Bartik type wagesusing the same methodology as Bertrand et al. (2015), who based it on Aizer (2010):

pwgsmt “

ÿ

jPs

γgsj,2005 ˆwg

´m,j,yq (17)

where pwgsmt is the predicted municipality wage rate. The wage wg

´m,j,yq is the nationalwage rate per gender in industry j in sector s at year-quarter yq when municipalitym is excluded. The proportion γ

gsj,2005 is identical to the one used to create Bartik type

employment growth rates. According to recent developments on Bartik type meas-ures, their validity as instruments comes from the exogeneity of this location specificindustry proportion (Goldsmith-Pinkham et al., 2016).

The constructed Bartik type measures are correlated with actual wage and employ-ment levels. Table A3 in the Appendix presents correlations between actual and pre-dicted levels for the quarters corresponding to the MxFLS waves 2005/06 and 2009/12.All correlations are above r “ 0.30, and in most cases between r “ 0.40 and r “ 0.60. Ingeneral, the lowest correlations are for the export manufacture sector and the highestfor the non-export manufacture sector (around r “ 0.90).

These additional variables in the estimating equation need not to be enough. TheGreat Recession could also impacted credit availability, female or male education levels,or other non-labor factors that might influence participation y. On Section 5.2, I exploreas robustness check the inclusion in the estimating equation of municipality level creditavailability, poverty rate, and female or male population or education levels and showthat including does not impact the results. I also show that excluding either the group ofemployment or wages variables, in particular employment, does not impact the resultsbut excluding both does.

After defining the variables in the benchmark estimating equation and its role, thenext step is to define an instrumental variables technique for non-linear endogenousvariables and a single binary instrument.

3.5 Instrumental Variables via Control Function ApproachThe estimating equation is non-linear on variable z, assumed to be endogenous evenafter controlling for individual and municipality fixed effects. I make two assump-tions. First, that the self-control problem rationale, and both the unobserved female

30I pooled the four quarters to avoid seasonal effects. The proportion for males or females per sector adds to 1.31For the wave 2005/06, first I estimate the annual growth rate {growth

smg from each quarter in 2005 to the corresponding quarter in

2006 using equation 16.Then I multiply these growth rates by the average per quarter municipality employment level per industry j in2005. The process for the wave 2009/12 is similar. The only difference is that first I calculate the growth rate for each quarter in 2005 tothe corresponding quarter in 2009, then I calculate the growth rate for each quarter in 2009 to the corresponding quarter in 2010, 2011,and 2012.

33


preference for indivisible goods and the effect of beneficial social connections on in-come can be summarized in a single positive linear relation between them and variablez. Second, that by including the variable’s quadratic term, the equations’ functionalform is correctly specified. The first is a reasonable assumption and the second is sup-ported by non-parametric estimations (Section 2.2). The Control Function Approach(CFA) allows the consistent estimation of models non-linear in endogenous variablesbut is more restrictive than standard instrumental variables methods. Its key advantageis that instrumenting two endogenous variables with a single binary instrument is notpossible using standard methods. For notation’s clarity, I re-state the relative equival-ent wage rate (or the share of couple income) as variable y2, ROSCA participation as y1,the vector of exogenous variables z consists of the remaining variables in the estimatingequation (zother) and a single binary instrument (zexc). I follow a similar notation to theone used by Imbens and Wooldridge.32Under this notation, the structural and reducedform equations after differencing are:

∆y1imt “ β1∆y2imt ` β2∆y22imt ` ∆zotherθ1 ` ∆ε1imt

∆y2imt “ ∆zπ2 ` ∆v2

where the critical assumption is Epε1imt|z, µm, ηiq “ 0. This assumption is stronger thanthe assumption of zero covariance of exogenous variables and the error and impliesthat the functional form is correctly specified. The assumption Epε1imt v2|µm, ηiq ą 0denotes the positive endogeneity bias. A linear projection of ∆ε1imt on ∆v2 would be∆ε1imt “ ρ1∆v2 ` ∆ξ1. Substituting this linear projection into the structural equationleads to

∆y1imt “ β1∆y2imt ` β2∆y22imt ` ∆zotherθ1 ` ρ1∆v2 ` ∆ξ1

The CFA consists of adding the residuals ∆v2, which can be consistently estimatedusing the reduced form, to the structural equation. Their addition will control for theendogeneity of ∆y2imt and ∆y2

2imt. Imbens and Wooldridge note the approach mightbe more efficient than standard instrumental variables techniques but relies on morestringent assumptions. Due to the non-linearity in endogenous variables, they alsonote, a linear projection of the error in the structural equation on the one in the reducedform is not enough. The following assumptions are required: Epε1imt|z, y2imt, µm, ηiq “

Epε1imt|z, v2, µm, ηiq “ Epε1imt|v2, µm, ηiq “ ρ1v2. The last two equalities are stringentassumptions, they impose, conditional to fixed effects, independence of z with ε1imtand v2 and linearity in the conditional expectation of Epε1imt|v2, µm, ηiq.

Without the CFA ostensibly I would need two instruments, one for variable z andone for variable z2. But I would need three instruments inasmuch as the omitted vari-able and reverse causality threats operate through the couple’s income. I find thatthe couple’s income is positively correlated with variable z in the reduced form equa-tion. But the variable is postive and significant in pooled OLS estimations and be-comes negative and not significant in all first difference specifications, suggesting thatfirst-differencing dealt with a good part of the endogeneity problem operating throughcouple’s income, Epε1imtcincimt|µm, ηiq “ 0. It is not a functional form misspecificationissue. This result holds when the couple’s income is expressed in logarithms or using aquadratic polynomial.

32See for example: http://www.nber.org/WNE/lect_6_controlfuncs.pdf

34

4 RESULTS

4 ResultsTable 3 presents the benchmark results using relative equivalent wage rate as femalebargaining power variable z. I cluster standard errors at the municipality level in theseand all regressions and present the number of clusters used in the estimations (tipicallyaround 90). The first column presents a pooled OLS and the second the correspond-ing first-differences estimation. Estimates for variables z and couple’s income differ,suggesting that omitting individual or municipality fixed effects bias the estimation. Iincorporate in the table two sets of estimates and hypothesis tests. The first one showswhether estimated parameters β1 and β2 signs and magnitudes are consistent with aconcave function. The second shows at what level of female bargaining power z parti-cipation y reaches its maximum, what the maximum is, and tests whether the estimatedlevel for z is below a value of 1, and thus whether participation decreases at high femalebargaining power levels. For both pooled OLS estimates and first-difference estima-tions, estimated parameter signs suggest concavity, participation reaches its maximumat middle-high bargaining power at similar levels (0.71 and 0.68). Also for both, I canreject the hypothesis that participation reaches its maximum at a value of 1 against thealternative that it reaches its maximum below this value, but just barely for pooled OLSestimates. Overall, both estimation methods are consistent with the extended intra-household conflict model, more so first-difference estimates.

The fourth and fifth columns present instrumental variable results using the con-trol function approach. Column four uses as excluded instrument the difference-in-difference effect of the Great Recession by couple groups where couples in which fe-males but not males worked on manufactures in 2005 constitute the “treatment” groupand the remaining couples the control. The post-treatment period corresponds to thewave 2009/12. The Table includes from the first stage, the linear projection of variable zon all excluded regressors after differencing, the excluded instrument’s estimated para-meter. The magnitude and statistical significance of the excluded instrument are high.Variable z, bounded from 0 to 1, decreases 0.39 (SE=0.12) for the “treated” couples. Thesystem is identified according to Kleibergen and Paap’s version of the full matrix rankcondition test for non i.i.d errors, the case of this linear probability model with hetero-skedastic robust standard errors clustered at the municipality level. The F statistic ofthe excluded instrument is 10.9. The estimated parameter β1 magnitude is between thepooled OLS and first-differences estimates and β2 magnitude is similar across the threeestimation methods. Critically, participation reaches its maximum at seemingly lowerbargaining power level (0.55) and I can reject the hypothesis that participation reachesits maximum at a value of 1 against the alternative that it reaches its maximum belowthis value. The estimation for parameter β1 , however, is very imprecise.

To increase precision, I create a more informative instrument based on the find-ing that for couples in which females but not males worked on manufactures in 2005,female bargaining power decreased more for more educated females. Column five re-places the instrument with its interaction with female years of education in 2005. In the-ory, with a continuous instrument, I could now implement a traditional instrumentalvariable strategy. But in practice, levels and polynomials of this instrument—includingthe level of its original version—where not informative enough in the system of firststage equations. The new instrument does increase the Control Function Approachprecision. First stage results show that the instrument is more informative with a FStatistic of 14.4 compared to 10.9 of its original version. Reassuring, the more precisely

35

4 RESULTS

estimated parameters using either instrument are similar, suggesting the local averageeffect both identify is the same. The estimates, however, are still imprecise and usingseemingly unrelated regression I cannot reject that the control approach estimates for β1and β2 are statistically different to first-difference estimates (p-values: 0.683 and 0.957).

For comparison purposes, Table 4 presents results using instead the share of coupleincome as variable z. Reassuring, the results are very similar. They are also more im-precise because the overlap of the instruments, who operate through labor conditions,with female bargaining power z is smaller because variable z now considers non-laborincome.

36

4 RESULTS

Table 3: Linear Probability Model of Female ROSCA Participationz=Relative Equivalent Wage Rate

POLS FD FD-IV FD-IV(1) (2) (3) (4)

z 0.325*** 0.446*** 0.364 0.377*(0.122) (0.154) (0.230) (0.216)

z2 -0.228 -0.330* -0.330* -0.330*(0.143) (0.168) (0.169) (0.169)

Couple income 0.0438** -0.0436 -0.0381 -0.0390(0.0173) (0.0369) (0.0389) (0.0388)

Predicted residuals 0.0848 0.0714(0.221) (0.177)

Bartik Wage and Employment Yes Yes Yes Yes

First Stage. Dep. var: zExcluded instrument

(a) Worked in Manufactures (2005) -0.394***Couple: Fem=1 Male=0 ˆwave=2009/12 (0.119)

(b) Excluded instrument interacted -0.0487***with female years educ. in 2005 (0.0128)

Underidentification testKleibergen-Paap statistic 4.82 3.94p-value 0.028** 0.047**

F Statistic of excluded instrument 10.92 14.40

Observations 2,056 1,029 1,029 1,029Clusters 90 90 90 90

Ho : β1 “ 0 Ha : β1 ą 0 0.005*** 0.002*** 0.059* 0.042**Ho : β2 “ 0 Ha : β2 ă 0 0.058* 0.027** 0.027** 0.027**

zmax : β1 ` 2β2 “ 0zmax 0.71 0.68 0.55 0.57

[95% CI] [0.31 1.12] [0.39 0.97] [-0.13 1.23] [0.02 1.13]Ho : zmax “ 1 Ha : zmax ă 1 0.081* 0.015** 0.099* 0.065*

Participation at zmax

y 0.24 0.21 0.16 0.17[95% CI] [0.18 0.29] [0.12 0.30] [-0.05 0.38] [-0.02 0.36]

Heteroskedasticity robust errors clustered at the municipality level in brackets. *** pă0.01, ** pă0.05, *pă0.10.Balanced sample of individuals in which both couple members in both waves are: living in the same muni-

cipality, with the same partner, and between 15-64 years of age.All equations include time-fixed effects (quarterly from 2005 to 2012) and a dummy for whether the sample

is from the wave 2009/12.The hypothesis test for zmax uses the non-linear combination of estimators β1 and β2 in which a concave

function of the form y “ β1z ` β1z2 reaches its maximum. The estimate y is the predicted participationprobability at zmax and the confidence interval uses standard errors estimated using the delta method.

37

4 RESULTS

Table 4: Linear Probability Model of Female ROSCA Participationz=Share of Couple Income

POLS FD FD-IV FD-IV(1) (2) (3) (4)

z 0.376*** 0.474*** 0.355 0.376(0.121) (0.146) (0.359) (0.321)

z2 -0.317** -0.305* -0.307* -0.306*(0.148) (0.161) (0.161) (0.160)

Couple income 0.0444** -0.0408 -0.0354 -0.0364(0.0171) (0.0366) (0.0398) (0.0396)


Bartik Wage and Employment Yes Yes Yes Yes

First Stage. Dep. var: zExcluded instrument

(a) Worked in Manufactures (2005) -0.260**Couple: Fem=1 Male=0 ˆwave=2009/12 (0.110)

(b) Excluded instrument interacted -0.0324**with female years educ. in 2005 (0.0136)

Underidentification testKleibergen-Paap statistic 2.86 2.27p-value 0.091* 0.132


Observations 2,056 1,029 1,029 1,029Clusters 90 90 90 90

Ho : β1 “ 0 Ha : β1 ą 0 0.001*** 0.001*** 0.163 0.122Ho : β2 “ 0 Ha : β2 ă 0 0.018** 0.030** 0.030** 0.030**

zmax : β1 ` 2β2 “ 0zmax 0.59 0.78 0.58 0.61

[95% CI] [0.38 0.81] [0.37 1.18] [-0.48 1.64] [-0.25 1.47]Ho : zmax “ 1 Ha : zmax ă 1 0.000*** 0.138 0.218 0.189


y 0.23 0.24 0.17 0.18[95% CI] [0.18 0.29] [0.15 0.33] [-0.20 0.53] [-0.15 0.51]

Heteroskedasticity robust errors clustered at the municipality level in brackets. *** pă0.01, ** pă0.05, *pă0.10.Balanced sample of individuals in which both couple members in both waves are: living in the same muni-

cipality, with the same partner, and between 15-64 years of age.All equations include time-fixed effects (quarterly from 2005 to 2012) and a dummy variable for whether the

sample is from the wave 2009/12.The hypothesis test for zmax uses the non-linear combination of estimators β1 and β2 in which a concave

function of the form y “ β1z ` β1z2 reaches its maximum. The estimate y is the predicted participationprobability at zmax and the confidence interval uses standard errors estimated using the delta method.

38

4 RESULTS

4.1 Effect of Patience Relative to Partner and Partner’s ROSCAParticipationTo test whether heterogeneity in discount factors increases female participation, I aug-ment the benchmark specification with two variables. The first one is a dummy variablethat captures whether the couple’s answers in the module eliciting discount factorssuggest females are less patient than their partner. The second one captures insteadwhether they are more patient. These simple variables are strictly comparable betweenwaves. I also include whether females’ partners participate in a ROSCA or not. De-scriptive statistics in Section 3.1.1 suggest that male participation in the nationally rep-resentative sample of couples, although lower than female participation, is also large.The intra-household conflict model based on preference heterogeneity for a indivisiblegood, besides an assumed lack of preference for the good, assigns no role for the hus-band. By adding this variable, I empirically test whether the model predictions hold topartners’ participation.33 These additions lead to my preferred specification:

yimt “ β1zimt ` β2z2imt ` β3cincimt

` β4LessPatThanPartnerimt ` β5MorePatThanPartnerimt ` β6ParnerInRoscaimt

`

j“3ÿ

j“1

ÿ

BPtw,lu

´

β j,B, f emBFemale,sector: jmt ` β j,B,mal B

Male,sector: jmt

¯

` λYQt ` µm ` ηi ` δw2009{12 ` ε imt (18)

The newly added variables are not correlated with the excluded instrument. I use thespecification presented in Equation 18 but exclude variables z, its square, couple in-come, and the vector of Bartik type employment and wages. In the first-differencedspecification of the equation using the instrument interacted with years of educationas dependent variable, all three variables show a minute relation with the instrumentand are far from being statistically significant. The estimated parameter for whetherfemales are more patient than their partner is 0.0005 (SE“ 0.0081), for whether theyare more patient is ´0.0095 (SE=0.0088), and for whether their partner participates in aROSCA is ´0.0051 (SE“ 0.0094).

33The information in the survey does not distinguish whether females and their partners participate in the same ROSCA.

39

4 RESULTS

Table 5: Effect on Participation of Patience Relative to Husband andHusband ROSCA Participation

Linear Probability Model of Female ROSCA Participationz=Relative Equivalent Wage Rate

POLS FD FD-IV POLS FD FD-IV(1) (2) (3) (4) (5) (6)

z 0.325*** 0.458*** 0.360 0.337*** 0.449*** 0.357*(0.120) (0.156) (0.218) (0.115) (0.153) (0.211)

z2 -0.230 -0.349** -0.349** -0.240* -0.352** -0.352**(0.143) (0.174) (0.174) (0.136) (0.169) (0.169)

Less patient=1 0.0371** 0.0747*** 0.0752*** 0.0298* 0.0634** 0.0637**(0.0164) (0.0265) (0.0267) (0.0161) (0.0260) (0.0261)

More patient=1 0.0428** 0.0515** 0.0550** 0.0362* 0.0484* 0.0517*(0.0185) (0.0251) (0.0275) (0.0189) (0.0261) (0.0282)

Partner in Rosca=1 0.265*** 0.186*** 0.189***(0.0364) (0.0405) (0.0415)

Couple income 0.0426** -0.0484 -0.0417 0.0328* -0.0578 -0.0518(0.0172) (0.0365) (0.0381) (0.0168) (0.0348) (0.0360)


Bartik Wage and Employment Yes Yes Yes Yes Yes Yes

First Stage. Dep. var: zExcluded instrument interacted -0.0475*** -0.0474***

with female years educ. in 2005 (0.0128) (0.0129)Underidentification test

Kleibergen-Paap statistic 3.94 3.95p-value 0.047** 0.047**


Observations 2,056 1,029 1,029 2,056 1,029 1,029Clusters 90 90 90 90 90 90

Ho : β1 “ 0 Ha : β1 ą 0 0.004*** 0.002*** 0.051* 0.002*** 0.002*** 0.047**Ho : β2 “ 0 Ha : β2 ă 0 0.055* 0.024** 0.024** 0.041** 0.020** 0.020**

zmax : β1 ` 2β2 “ 0zmax 0.71 0.66 0.52 0.70 0.64 0.51

[95% CI] [0.31 1.10] [0.39 0.92] [0.01 1.02] [0.34 1.06] [0.40 0.88] [0.01 1.00]Ho : zmax “ 1 Ha : zmax ă 1 0.072* 0.006*** 0.031** 0.051* 0.002*** 0.026**


y 0.23 0.21 0.16 0.24 0.2 0.15[95% CI] [0.18 0.29] [0.12 0.29] [-0.02 0.33] [0.18 0.29] [0.12 0.29] [-0.01 0.32]

Heteroskedasticity robust errors clustered at the municipality level in brackets. *** pă0.01, ** pă0.05, * pă0.10.Balanced sample of individuals in which both couple members in both waves are: living in the same municipality, with the

same partner, and between 15-64 years of age.All equations include time-fixed effects (quarterly from 2005 to 2012) and a dummy variable for whether the sample is from

the wave 2009/12.The hypothesis test for zmax uses the non-linear combination of estimators β1 and β2 in which a concave function of the form

y “ β1z` β1z2 reaches its maximum. The estimate y is the predicted participation probability at zmax and the confidenceinterval uses standard errors estimated using the delta method.

40

4 RESULTS

Table 5 presents the preferred specification results. The first three columns add topooled OLS, first-differences, and instrumented first-differences estimations using thebenchmark specification the variables that capture female’s patience relative to theirpartner. The last three columns further add whether female’s partners participate ina ROSCA. The inclusion of whether female’s partners participate in a ROSCA has aminor effect on the parameters that capture patience relative to partner, thus I concen-trate on the last three columns. Column (4) presents pooled OLS and column (5) first-difference estimates. Estimates for all five individual level variables’ parameters aredifferent between both specifications, once more suggesting that omitting individual ormunicipality fixed effects bias the estimation. Column (6) presents instrumental vari-able results using the control function approach and the instrument interacted withfemale years of education in 2005. Compared with column (5), except for z and z2, theparameter estimates for all individual variables are almost identical, suggesting thatthe control function approach affects only the female bargaining power related vari-ables. Compared with its equivalent estimation in column (4) in Table 3, in spite of theadded variables, how informative is the instrument is similar (the instrument’s effecton variable z is almost identical and its F Statistic is 13.5 vs 14.4). The estimates showa minor precision increase owing to a decrease of the equation’s sum of squared resid-uals from adding these variables. More important, parameter estimates from column(6) suggest that participation reaches its maximum at middle bargaining power levels(0.51) and I can reject the hypothesis that participation reaches its maximum at a valueof 1 against the alternative that it reaches its maximum before. Estimates, however, arestill imprecise.

Predicted participation probabilities are a better way to interpret the results. I es-timate predicted probability participation by female bargaining power z from 0 to 1in 0.05 steps. Figure 9 depicts the predictions. Results suggest, in particular for thefirst-differences instrumented specification, that participation increases with femalebargaining power until middle or middle-high bargaining power levels and decreasesat higher levels. According to first-differences estimates, participation increases from5.9 percent when females have no bargaining power (z=0) to 20.0 percent when bar-gaining power reaches 0.64, and from this point decreases towards 15.7 percent whenfemales have all the bargaining power in the couple decision making (z=1). Not onlyinstrumenting deals with the omitted variable bias from the unobserved female prefer-ence for indivisible goods and reverse causality from the participation’s beneficial socialconnections effect on income but also deals with the additional participation probab-ility owing to self-control problems. After instrumenting using the control functionapproach, the results suggest an inverted U-shape as the one on Figure 1. Participationincreases from 6.4 percent when females have no bargaining power (z=0) to 15.0 per-cent when bargaining power reaches 0.51, and from this point decreases to 6.9 percentwhen females have all bargaining power (z=1). Participation’s reaches its maximum ata point estimate of z “ 0.57 in the benchmark specification and decreases to 0.52´ 0.51in the preferred specification (all measures, however, with large standard errors). Sofar, the variables aiding the instrument’s exclusion restriction validity are municipal-ity employment and wage level variables affecting all individuals, regardless of theirgender. Any effect the Great Recession had on a model commitment savings strategiesuse for females applies to a similar model for males. By including whether female’spartners participate, or their patience relative to females, I seem to capture directly any

41

Patience Relative to Partner Partner in a Rosca

— POLS

u< .15 - to 0

FD

.1

.35 -

/

I .1 .2 .3 .4 .5 .6 .7 .8 .9

Z=FEMALE EQUIVALENT WAGE RATE / (EWR„,4,LE + EWIRk,,, E)

a

3

II z 0 2

o_

F 2

o_

FD-IV

.05 -

0

.4

.35

.3

.25

.2

.15

.4

.35

.3

7 25

.2

'61 .15

1

.05

0

.05

0

Ho: 9(1) = 9sAmE

Ha: S)LESS * 95AME 0.096* Ha: 9LEss > y ',SAME 0.048** Ha: 9MORE * 95AmE 0.177 Ha: s/MORE > 5,'SAME 0.089*

0.130 0.118

1 0.066

LESS SAME MORE

I 0.253

0.063

NO YES

Predicted Participation Probability -- 95% CI 0 Pred. Part. Prob. (9) 95% CI 90% CI

4 RESULTS

effects the Great Recession had on male’s participation not captured by the Bartik typemeasures I use.

But the effect of a large proportion of females not earning labor income and the largesample loss owing to unreported labor income reflects, without instrumenting, on wideconfidence intervals at high female bargaining power levels. This identification strategyoriginal sin reflects on dramatically large confidence intervals in the instrumented es-timation. I can formally reject that participation reaches its maximum at z “ 1 againstthe alternative that participation reaches its maximum before this value. The z “ 0.51point estimate at which participation reaches its maximum is consistent with middlebargaining power levels but its confidence interval overlaps the range z P r0 1s almostin its entirety and participation at this point is statistically different from zero only atthe 10 percent confidence level. The 90 percent confidence interval for the 15.0 percentparticipation prediction when bargaining power reaches a value z “ 0.51 is [0.01 0.30]whereas the 95 percent confidence interval is [-0.01 0.32].

Figure 9: Parameter InterpretationPredicted Participation Probabilities

Note: Predicted participation probabilities correspond to the estimations presented in Table 5. Pooled OLS predictions arefrom column (4), first-differences from column (5), and instrumented first-differences from column (6). Parameter estimatesfrom column (6) are used to evaluate the effect of patience relative to partner and partner’s participation.Dots in predicted participation curves by female bargaining power denote where the function reaches its maximum.

Discount factors heterogeneity and partner’s ROSCA participation, on the otherhand, have a clear-cut effect. Figure 9 also depicts predicted probabilities accordingto these variables. Discount factors heterogeneity has a large effect on female particip-ation. Participation for females showing a similar patience relative to their partners is6.6 percent and it is twice as high, at 12-13 percent, for females more or less patient thantheir partners. Partner’s participation has an even larger effect. Participation for fe-males with partners not participating in a ROSCA is 6.3 percent and increases fourfold,

42

4 RESULTS

to 25.3 percent, for females with partners who do participate. The intra-household con-flict model based on preference for an indivisible good heterogeneity assumes hiddeninformation that allows females to tilt the couple decision making towards her pref-erences. These results cast doubt on the hidden information assumption. The couplemight jointly decide to participate in a ROSCA, and might even be participating in thesame one.

4.2 Heterogeneity According to Partner Age or Years in CoupleOn this section I test whether some couples, and which ones, behave as if followingthe collective model. The collective model assumes efficiency due to a stable decisionprocess in which the couple acts cooperatively by reaching binding agreements. Ina static setting, it is assumed that social norms, tradition, or other considerations, al-low enforcing these agreements. In a dynamic setting, enforcement is assumed bya repeated-game argument (Browning et al., 2014). Under the repeated-game argu-ment, couples learn through repetition how reach these agreements; and those whodo not, might be less likely to survive. As noted in Section 2.2, Angelucci and Gar-lick (2016) provide evidence on this question by dividing couples in the Oportunidadesexperimental sample according to median household head age and testing in the sub-samples the proportionality property of the collective model. They find that youngercouples do not behave as if following the collective model. They in turn investigatewhether cohort characteristics; for example, older cohorts traditions or customs, or life-cycle effects drive their findings. They attribute their findings to cohort effects. This hasan important policy implication: the positive welfare effect of policies like ConditionalCash Transfers will decrease over time.

I use as measure the number of years individuals have been a couple by being mar-ried or living together. This measure, unlike the household head age, needs not tooverlap with cohorts. By using it, and by first-differencing, I focus on the life-cycle ef-fects. I test whether the main results of this paper hold in sub-samples according to themedian number years the couple has been together or the median males’s age in thewave 2005/06. Throughout this paper, evidence of the collective model is an increas-ing relation of distribution factors and ROSCA participation until middle distributionfactor levels, point at which participation decreases.

43

4 RESULTS

Table 6: Heterogeneity According to Partner Age or Years in CoupleLinear Probability Model of Female ROSCA Participation

First-Differencesz=Relative Equivalent Wage Rate

All Partner Age (2005) Years in Couple (2005)ă“ 38 39` ă“ 14 15`

(1) (2) (3) (4) (5)

z 0.449*** 0.137 0.746*** 0.145 0.761***(0.153) (0.215) (0.213) (0.225) (0.214)

z2 -0.352** -0.0754 -0.611*** -0.0485 -0.668***(0.169) (0.238) (0.230) (0.243) (0.235)

Less patient=1 0.0634** 0.0871** 0.0374 0.0581 0.0496(0.0260) (0.0371) (0.0375) (0.0377) (0.0364)

More patient=1 0.0484* 0.0622 0.0395 0.0473 0.0373(0.0261) (0.0442) (0.0311) (0.0472) (0.0328)

Partner in Rosca=1 0.186*** 0.224*** 0.0869 0.216*** 0.134*(0.0405) (0.0649) (0.0752) (0.0678) (0.0758)

Couple income -0.0578 0.0274 -0.109** -0.0192 -0.0817*(0.0348) (0.0554) (0.0418) (0.0540) (0.0450)

Bartik type Wage and Employment Yes Yes Yes Yes Yes

Observations 1,029 520 509 502 495Clusters 90 85 88 86 88

Ho : β1 “ 0 Ha : β1 ą 0 0.002*** 0.263 0.000*** 0.261 0.000***Ho : β2 “ 0 Ha : β2 ă 0 0.020** 0.376 0.005*** 0.421 0.003***

Ho : βp1q1 “ β

pjq1 Ha :‰ 0.058* 0.059* 0.062* 0.078*

Ho : βp1q2 “ β

pjq2 Ha :‰ 0.159 0.108 0.087* 0.099*

Ho : βBelow1 “ βAbove

1 Ha :‰ 0.045** 0.057*Ho : βBelow

2 “ βAbove2 Ha :‰ 0.114 0.078*

zmax : β1 ` 2β2 “ 0zmax 0.64 0.61 0.57

[95% CI] [0.40 0.88] [0.45 0.77] [0.42 0.72]Ho : zmax “ 1 Ha : zmax ă 1 0.002*** 0.000*** 0.000***


y 0.20 0.26 0.25[95% CI] [0.12 0.29] [0.14 0.37] [0.13 0.37]

Heteroskedasticity robust errors clustered at the municipality level in brackets. *** pă0.01, ** pă0.05, * pă0.10.Balanced sample of individuals in which both couple members in both waves are: living in the same municipality,

with the same partner, and between 15-64 years of age.All equations include time-fixed effects (quarterly from 2005 to 2012) and dummy for whether the sample is from

the wave 2009/12.Owing to missing information, the number of observations in columns (4) and (5) add up to 997 and not to 1029.

Table 6 presents the results. The first column presents first difference estimationsusing my preferred specification and the whole sample of females in couple (it is alsocolumn (5) on Table 5). The second and third columns use sub-samples according thepartner’s median age in 2005; 39 years of age, the same median Angelucci and Garlick

44

A. PARTNER AGE IN 2005 B. YEARS IN COUPLE IN 2005

.4

.35

.3

.25

.2

RO

SC

A P

AR

TIC

IPA

TIO

N=1

.15

.1

.05

0 1 0 .1 .5 .6 .8 .2 .4 .7 .3

Above median

All

Below median

Above median

„z.

„---

All

Below median

RO

SC

A P

AR

TIC

IPA

TIO

N=1

4 RESULTS

(2016) find in their sample. The fourth and fifth columns instead use the median num-ber of years the couple had been together in 2005; 15 years. I find the similar parameterestimates for z and z2 using either measure. I find that the distribution factor does notenter the model in a quadratic form in younger couples and that the concave relationof the distribution factor and participation increases in the older couple subsample re-lative to the whole sample. I can formally reject that the estimated parameters for zand z2 are the same between sub-samples above or below the median, and those belowthe median are not statistically different from zero. I can also formally reject that theseestimated parameters are the same relative to those estimated using the whole sample.Figure 10 depicts the predicted participation probabilities and clearly shows that oldercouples behave as if following the collective model while younger couples do not.

Besides parameter estimates for z and z2, all magnitudes, and some signs, differbetween the sub-samples below or above the median using partner’s age as the coupleage. Using the number of years the couple had been together points towards moresimilar parameter estimates between sub-samples. This suggest the measure I prefer,in contrast to partner’s age, is a better measure.

Figure 10: Heterogeneity According to Partner Age or Years in CoupleParameter Interpretation

Predicted Participation Probabilities

Participation predictions using estimations in Table 6.

45

5 ROBUSTNESS CHECKS

5 Robustness Checks

5.1 Alternative Participation RationalesControlling in the specification for the need to protect savings from family membersand for formal credit availability at the municipality level has no effect in the results. Toaccount for the need to protect savings from family members, I include the number ofnon-resident parents and adult (18`) siblings females declare to have and the partner’scorresponding number. To account for formal credit availability, I include the numberof commercial or development bank branches and ATMs in the municipality per 1km2

square using the municipality surface area and per capita using its population (15`)in 2005. These indicators are time-variant but their within variation is relatively low.Using within variation only, the coefficient of variation of ROSCA participation in theregression sample is 160, compared to 24 or 26 of female or partner’s number of adultnon-resident family members, and with 36 of the number of bank branches and ATMs.

Table 7 presents the results. The first three columns present the preferred specifica-tion pooled OLS, first-differences, and instrumented first-differences estimates. In nextthree columns, estimations incorporate the variables. All estimated parameters remainunchanged to the variables’ inclusion. For example, I cannot reject that estimated para-meters β1 or β2 statistically differ within estimation methods after the variables’ in-clusion. Consistent with the theory, the need to protect savings from family membersincreases ROSCA participation and participation is lower in municipalities with higherformal credit availability. Pooled OLS and first-difference estimates for these variablesare similar but no longer statistically significant in the latter. I attribute this to their lowwithin variation. Each additional female family member is related to a participationincrease of 0.5 percentage points (the mean number of family members in the sample is6). The number of partner’s family members has no effect in female participation. Theproviso noted in Section 2.2 still applies. It is difficult to interpret these results becausethe relative number of family members could also be a female bargaining power de-terminant. Further, including whether the municipality has banks goes a long way inaccounting for formal credit availability but it is still a crude measure. In the next sec-tion, I explore the effect of adding to the specification better formal credit and savingsusage indicators.

46

5 ROBUSTNESS CHECKS

Table 7: Robustness to Alternative Participation RationalesLinear Probability Model of Female ROSCA Participation

z=Relative Equivalent Wage Rate

POLS FD FD-IV POLS FD FD-IV(1) (2) (3) (4) (5) (6)

z 0.337*** 0.449*** 0.357* 0.337*** 0.450*** 0.377*(0.115) (0.153) (0.211) (0.113) (0.155) (0.211)

z2 -0.240* -0.352** -0.352** -0.240* -0.353** -0.353**(0.136) (0.169) (0.169) (0.134) (0.171) (0.171)

Less patient=1 0.0298* 0.0634** 0.0637** 0.0247 0.0630** 0.0632**(0.0161) (0.0260) (0.0261) (0.0162) (0.0262) (0.0262)

More patient=1 0.0362* 0.0484* 0.0517* 0.0322* 0.0480* 0.0506*(0.0189) (0.0261) (0.0282) (0.0191) (0.0262) (0.0281)

Partner in Rosca=1 0.265*** 0.186*** 0.189*** 0.259*** 0.185*** 0.188***(0.0364) (0.0405) (0.0415) (0.0369) (0.0404) (0.0414)

Couple income 0.0328* -0.0578 -0.0518 0.0323* -0.0584* -0.0536(0.0168) (0.0348) (0.0360) (0.0169) (0.0344) (0.0353)

Non-resident parents and adult siblings 0.00497* 0.00558 0.00560(0.00262) (0.00488) (0.00489)

Partner’s parents and adult siblings 0.00208 -0.00530 -0.00536(0.00260) (0.00473) (0.00474)

Branches per capita (2005) per 1km sq. -0.0337*** -0.0554 -0.0555(0.0117) (0.0504) (0.0504)


Bartik type Wage and Employment Yes Yes Yes Yes Yes Yes

First Stage. Dep. var: zExcluded instrument interacted -0.0474*** -0.0476***

with female years educ. in 2005 (0.0129) (0.0129)Underidentification test

Kleibergen-Paap rk 3.95 3.95p-value 0.047** 0.047**

F Statistic (rk) 13.54 13.64

Observations 2,056 1,029 1,029 2,056 1,029 1,029Clusters 90 90 90 90 90 90

Ho : β1 “ 0 Ha : β1 ą 0 0.002*** 0.002*** 0.047** 0.002*** 0.002*** 0.039**Ho : β2 “ 0 Ha : β2 ă 0 0.041** 0.020** 0.020** 0.038** 0.021** 0.021**

Ho : βpiq1 “ β

pi`3q1 Ha :‰ 1.000 0.940 0.283

Ho : βpiq2 “ β

pi`3q2 Ha :‰ 1.000 0.905 0.904

Heteroskedasticity robust errors clustered at the municipality level in brackets. *** pă0.01, ** pă0.05, * pă0.10.Balanced sample of individuals in which both couple members in both waves are: living in the same municipality, with

the same partner, and between 15-64 years of age.All equations include time-fixed effects (quarterly from 2005 to 2012) and a dummy variable for whether the sample is

from the wave 2009/12.

47

5 ROBUSTNESS CHECKS

5.2 Exclusion Restriction Validity and Sample Composition Ef-fectsResults show the instrument is informative. Empirical evidence of whether it is alsovalid is difficult to come by with a non-linear endogenous variable and with only oneinstrument, or with two that have the same rationale and also are highly correlated. Ifind that the instrument is not a regressor in a consistently estimated equation, provid-ing some empirical evidence of its validity. Murray (2006) notes that it can be possibleto test the validity of the exclusion restriction by adding the candidate instrument to theestimating equation if and only if the equation is consistently estimated. By includingthe predicted residuals, I assume that estimates in column (3) on Table 3 are consistent.The control function approach in this column uses the instrument not interacted withyears of education. To test the validity of the exclusion restriction, I added the differ-enced instrument interacted with years of education as regressor in the control functionapproach estimation presented in the column. The estimated parameter βivˆyearsedu is´.0022 (SE“ .0413), representing a non economically significant 0.2 percent reductionin participation that is far away from statistically significant as well.34 In my applica-tion, this approach is inherently flawed owing to multicollinearity.35 In any traditionalinstrumental variables approach, the two instruments should have different rationalesand the one used for the consistent estimation must be valid. Thus I argue for theinstrument’s validity.

The instruments’ rationale is that the Great Recession was a sudden shock unrelatedto self-control problems and to omitted variable and reverse causality bias. The criticalassumption is that the Great recession enters a model of ROSCA participation onlythrough its difference-in-difference effect in the relative labor income measure I use,which pursues capturing female bargaining power. In my setup, the treatment groupconsists of couples in which females worked in manufactures while their partners didnot prior the shock. The control group consists of all other couples. Although the effecton female export manufacture employment was large, the recession had also negativebut muted effects on male employment or employment in other sectors. It also affectedrelative wages. The inclusion in the estimating equation of Bartik type wage and em-ployment variables by sector and gender pursues capturing all other labor demanddriven effects the Great Recession directly had on formal employment and wages. OnSection 4.1, I show including patience relative to partner and partner’s participationhelps as well. These variables, however, are not essential. In this section I show thatacross 9 specifications that add variables to the preferred specification, the original pre-ferred specification, and the benchmark specification, the largest difference I found isbetween the benchmark and the preferred specification. The latter agreeing more withthe model. But the difference is small and both specifications do provide evidence forthe model. Removing Bartik type wage and employment variables, in particular em-ployment variables, does lead to large differences. It follows, I argue, that these are

34For comparison purposes, the estimate is 0.0045 (SE“ .0083) in the non-instrumented first-differences estimation. This estimation,however, is inconsistent.

35Note that had I included the instrument not interacted with years of education leads to perfect multicollinearity by including both zand all the regressors and the error of its linear projection. The correlation in the regression sample of the differenced instrument and itsdifferenced interaction with female years of education is r “ 0.93. The estimated parameters for β1 and β2 in column (4) are 0.364 (0.230)and´0.330* (0.169). Adding the instrument and its interaction with female years of education as regressor has no effect on β2 (´0.330* ,SE“ 0.171) but β1 decreases to 0.322 (SE“ 0.888). I attribute the change to multicollinearity given the large increase in the already largestandard errors.

48

Table 8: Robustness Checks: Exclusion Restriction Validity, IEffect on Estimates of Adding Municipality Level Variables, First-Differences Estimations

z z2 Excluded instrument Maximum y at z: Ho : zmax “ 1Coef. SE Coef. SE Coef. SE F Stat. zmax 95% CI Ha : zmax ă 1

Benchmark regression model 0.446*** (0.154) -0.330* (0.168) 0.68 [0.39 0.97] 0.015**0.377* (0.216) -0.330* (0.169) -0.0487*** (0.0128) 14.40 0.57 [0.02 1.13] 0.065*

Preferred specification 0.449*** (0.153) -0.352** (0.169) 0.64 [0.40 0.88] 0.002***0.357* (0.211) -0.352** (0.169) -0.0474*** (0.0129) 13.55 0.51 [0.01 1.00] 0.026**

Preferred specification (Non-winzorised variables) 0.461*** (0.152) -0.362** (0.168) 0.64 [0.41 0.87] 0.001***0.366* (0.210) -0.362** (0.168) -0.0475*** (0.0128) 13.71 0.50 [0.03 0.98] 0.021**

Preferred specification adding municipality variablesa) [1] Poverty [Marginality Index] 0.446*** (0.152) -0.350** (0.168) 0.64 [0.40 0.88] 0.002***

0.368* (0.210) -0.350** (0.168) -0.0487*** (0.0129) 14.17 0.53 [0.03 1.02] 0.031**b) [2] Branches per capita (15`, 2005) per 1km2 0.445*** (0.154) -0.347** (0.170) 0.64 [0.40 0.89] 0.002***

0.359* (0.211) -0.347** (0.170) -0.0475*** (0.0129) 13.49 0.52 [0.01 1.03] 0.032**c) [2] and [3] Formal Credit per capita (15`, 2005) 0.444*** (0.155) -0.346** (0.171) 0.64 [0.39 0.89] 0.002***

0.346 (0.213) -0.346** (0.171) -0.0474*** (0.0130) 13.40 0.50 [-0.01 1.01] 0.028**d) [2], [3], and [4] Formal Savings per capita (15`, 2005) 0.444*** (0.155) -0.346** (0.171) 0.64 [0.39 0.89] 0.002***

0.347 (0.214) -0.346** (0.171) -0.0472*** (0.0129) 13.27 0.50 [-0.02 1.02] 0.030**e) [5] Male and [6] female population (15`; 2005, 2010) 0.451*** (0.154) -0.355** (0.169) 0.64 [0.40 0.87] 0.001***

0.364* (0.213) -0.355** (0.170) -0.0477*** (0.0129) 13.65 0.51 [0.02 1.00] 0.026**f) [7] Male and [8] female years of education (15`; 2005, 2010) 0.451*** (0.152) -0.352** (0.167) 0.64 [0.40 0.88] 0.002***

0.381* (0.214) -0.352** (0.167) -0.0472*** (0.0131) 13.05 0.54 [0.03 1.05] 0.040**g) [1], [2], [3], [4] 0.441*** (0.153) -0.344** (0.170) 0.64 [0.39 0.89] 0.002***

0.360* (0.214) -0.344** (0.170) -0.0486*** (0.0130) 14.04 0.52 [0.00 1.05] 0.037**h) [1], [2], [3], [4], [5], [6] 0.445*** (0.155) -0.349** (0.171) 0.64 [0.40 0.88] 0.002***

0.366* (0.215) -0.349** (0.171) -0.0486*** (0.0130) 14.08 0.52 [0.01 1.04] 0.036**i) [1], [2], [3], [4], [5], [6], [7], [8] 0.448*** (0.153) -0.349** (0.170) 0.64 [0.40 0.89] 0.002***

0.377* (0.217) -0.349** (0.170) -0.0486*** (0.0132) 13.58 0.54 [0.02 1.07] 0.043**

Definitions: 1) Marginality index is a principal component index estimated by the Mexican government using census indicators. 2) Savings and credit amounts in banks at each municipalityin constant pesos (Dec-2001) at the end of the quarter. 3) Savings: Balance of all deposits in commercial or development banks. 4) Credit: Outstanding commercial credit balance of micro-firms or entrepreneurs. 5) Microfirms/Entrepreneurs: Firms are non-financial private sector entities, micro-firms are firms up to 30 employees if working in the industry sector; up to 5 ifworking in commerce; or up to 20 if working in services. Entrepreneurs are individuals with entrepreneurial activities with a Mexican address.Source: Population and education levels are from the Population Count 2005 and the Census 2010. Savings and credit indicators are from MexicoâAZs Municipalities Savings and

Intermediation (MSI dataset).

49

Table 9: Robustness Checks: Exclusion Restriction Validity, IIEffect on Estimates of Removing Variables

First-Differences Estimations

z z2 Excluded instrument Maximum y at z: Ho : zmax “ 1Coef. SE Coef. SE Coef. SE F Stat. zmax 95% CI Ha : zmax ă 1

Preferred specification 0.449*** (0.153) -0.352** (0.169) 0.64 [0.40 0.88] 0.002***0.357* (0.211) -0.352** (0.169) -0.0474*** (0.0129) 13.55 0.51 [0.01 1.00] 0.026**

Exclude from the specification:

j)řj“3

j“1

´

wFemale,sector: jmt ` wMale,sector: j

mt

¯

0.444*** (0.155) -0.348** (0.170) 0.64 [0.40 0.88] 0.002***

0.362 (0.219) -0.348** (0.171) -0.0463*** (0.0131) 12.52 0.52 [-0.02 1.06] 0.040**

k)řj“3

j“1

´

lFemale,sector: jmt ` lMale,sector: j

mt

¯

0.426*** (0.159) -0.324* (0.173) 0.66 [0.38 0.93] 0.007***

0.263 (0.211) -0.324* (0.174) -0.0473*** (0.0126) 14.19 0.41 [-0.10 0.92] 0.011**

l)ř

BPw,lřj“3

j“1

´

BFemale,sector: jmt ` BMale,sector: j

mt

¯

0.420** (0.162) -0.320* (0.175) 0.66 [0.38 0.94] 0.008***

0.290 (0.222) -0.320* (0.176) -0.0459*** (0.0130) 12.41 0.45 [-0.13 1.04] 0.033**m) cincimt 0.358** (0.140) -0.255 (0.157) 0.70 [0.31 1.09] 0.067*

0.284 (0.212) -0.255 (0.157) -0.0468*** (0.0124) 14.31 0.56 [-0.18 1.29] 0.120

n) cincimt `ř

BPw,lřj“3

j“1

´

BFemale,sector: jmt ` BMale,sector: j

mt

¯

0.336** (0.149) -0.231 (0.165) 0.73 [0.26 1.20] 0.128

0.288 (0.223) -0.320* (0.176) -0.0453*** (0.0125) 13.04 0.45 [-0.14 1.04] 0.034**

50

5 ROBUSTNESS CHECKS

essential for identification.There could be other factors related to the instrument not modelled in the estimat-

ing equation. Besides its impact on formal employment and wages, the Great recessioncould have lowered local credit availability and ROSCA participation would in turnincrease. It could also have effects on bargaining power not operating through rel-ative income. By focusing on non-migrant couples I partial out individual selectivemigration but not all migration effects. For example, the Great recession could haveled to male or female out-migration in the municipalities affected by the shock, alteringlocal marriage markets for the couples that stayed and thus female bargaining powerthrough the credibility or not of separation. It could also have effects on education.Atkin (2016) shows that a positive export manufacture shock leads to school drop outincreases and that individuals are more responsive to gender specific shocks, with alarger response from males. Extended to my analysis, and assuming the size of theshock to female employment outweighs females’ lower relative response, his resultssuggests female education in affected municipalities could have increased with plaus-ible positive effects on female bargaining power. I empirically test for the validity ofthe exclusion restriction by adding variables that capture indirect effects through localcredit availability, migration, and education.

Table 8 presents the results and shows that accounting for these effects at the mu-nicipality level has no impact on the results. The table has three components acrosstwelve specifications. The first one shows the estimated β1 and β2 parameters for boththe first-differences and its instrumented version via the control function approach.The second one shows the instrument’s reduced form coefficient and its F Statistic. Thethird one shows the estimated bargaining power level at which participation reachesits maximum, its 95% confidence interval, and a test of whether this value is less thanone. The first three specifications are the benchmark regression model (Equation 15),the preferred specification (Equation 18), and the preferred specification based on non-winsorized variables. Adding patience relative to partner, and in particular whetherthe partner participates in a ROSCA, reduces the estimated bargaining power level atwhich participation reaches its maximum from 0.68 to 0.64 in the first difference spe-cification and from, 0.57 to 0.51 in its instrumented version, with the proviso that theseestimations are subject to large standard errors. Winsorizing individual income vari-ables has no effect in the results.36 The remaining nine specifications add municipalitylevel variables to the (winzorised) preferred specification. The first specification addsthe municipality poverty level to capture any effect on aggregate income not captureby formal employment related measures. The second controls for formal credit mar-ket availability using the number of bank and ATMs in the municipality (see Section5.1), the third one and fourth add the municipality outstanding formal credit balanceor formal savings balance, the fifth controls for the male and female population to cap-ture migration effects, the sixth controls for female and male years of education, andthe last three specifications incorporate combinations of these variables until the ninthconsists of all variables considered.

The bargaining power level at which participation reaches its maximum in the pre-ferred specification is 0.51 and ranges from 0.50 to 0.54 across the nine specificationstesting the exclusion restriction’s validity. This low effect on the results provides some

36That the coefficients differ belies that because both differ the predicted probabilities can be similar. The concave functions y “0.357x´ 0.352x2 and y “ 0.366x´ 0.362x2 are almost identical in the range [0 1].

51

5 ROBUSTNESS CHECKS

evidence of its validity. In reduced form equations, the variables show a low correla-tion with the instrument. The instrument’s point estimate in the reduced form of thepreferred specification is ´0.0474 and ranges from ´0.0472 to ´0.0487 across the ninespecifications. This suggests that the instrument has a low relation with other plausibleeffects of the recession not modelled in the estimating equation. Non-instrumented firstdifference results show that the added variables have almost no effect on ROSCA parti-cipation. Two plausible reasons come to mind, the first one is that these variables fail tocapture plausible indirect effects through local credit availability, migration, and edu-cation or that there are other effects not considered. First, all these variables come fromadministrative records or subjects covering all firms, financial institutions, or peoplein the municipality. Further, on Section 5.1 I provide evidence that this is not the caseby showing that the number of banks and ATMs is related to ROSCA participation.The second is that indirect effects on participation are low relative to the direct effectthrough formal employment and wages.

Adding variables to the specification has no effect but removing variables does. Tak-ing the preferred specification and removing the set of Bartik type variables related toemployment has a large effect in the results. I present the results in Table 9. Its struc-ture is the same compared with the previous one presented, but focuses in five dif-ferent specifications. The first removes only the Bartik type wage related variables, thesecond removes only the employment ones, the third removes both, the fourth removesonly the couple’s income, and the fifth removes the couple’s income and all Bartik typemeasures. Removing only Bartik type wages leads to a similar bargaining power levelat which participation reaches its maximum, 0.52, compared with the preferred spe-cification’s 0.51 but the estimate’s imprecision increases. On the other hand, removingBartik type employment levels but not wages reduces this level to 0.41, a level notconsistent with middle bargaining power levels. Note that removing Bartik type em-ployment levels but not wages does not have this large effect on the non-instrumentedfirst-differences specification. In the non-instrumented specification this removal in-creases the level from 0.64 to 0.66. The third specification removes both wages andemployment levels, which leads to a reduction from 0.51 to 0.45, suggesting that thevariables, as showed on Section 3.2, work on opposite directions. The fourth specifica-tion removes the couple’s income, removing it increases the level to 0.56 and I cannotlonger reject that participation does not decrease at high bargaining power levels. Thefifth specification removes in addition all Bartik type measures, removing the couple’sincome and these variables reduces the level to 0.45 and I still can reject that particip-ation does not decrease at high bargaining power levels. These results support that atleast the Bartik type employment measures are essential for identification because byremoving them the instrument is no longer valid.

Effect on Estimates owing to Sample Composition Changes

Attrition has no effect on the results but focusing the sample to municipalities withfemale and male formal employment in export manufactures in 2005 does; its effect,however, is against the hypothesis tested. I test the effect on estimates of attrition orsample composition decisions using the wave 2005/06. In this cross-section, I comparepreferred specification estimates across four samples: (1) The whole sample withoutrestrictions; (2) the balanced sample to account for the effect of attrition; (3) non-migrant

52

6 CONCLUDING REMARKS

couples—the balanced sample in which couples are in the same municipality and withthe same partner in both waves; and (4) sample (3) but further restricting the sampleto couples in which both members are between 15-64 years of age in both wages. Thelast two samples are consequence of identification strategy decisions. Another decisionis to limit the sample to municipalities with female and male formal employment inexport manufactures in 2005 because Bartik type wage or employment measures canonly be constructed for these municipalities and because these were the municipalitiesaffected by the shock. I estimate four additional specifications adding this restriction toeach of the first four samples.

Figure 11 presents the results. Panel A depicts predicted participation probabilityfor each of the first four samples whereas Panel B depicts those corresponding to thelast four. The figures also include the estimated paramters β1 and β2. Predicted par-ticipation probabilities in Panel A are very similar. On the other hand, restricting thesample to municipalities with female and male formal employment in export manufac-tures in 2005 increases participation at high bargaining power levels and thus decreasesthe probability that participation at high bargaining power levels decreases. Finally,Panel C depicts the predicted participation probability for some of the nine specific-ations presented in Table 8 whereas Panel D presens those corresponding to the fivespecifications presented in Table 9. Comparing both panels makes clear that addingvariables to the specification has no effect but removing variables does.

6 Concluding RemarksCommitment savings strategies help savings accumulation and can be particularly use-ful for females in couple. This paper focuses on whether claims from partners inducefemales to use commitment savings strategies. I address the identification threat relatedto self-control problems as participation rationale, and the omitted variable and reversecausality bias owing to the use of labor-income based female bargaining power meas-ure. I extensively test the validity of the instrument’s exclusion restriction. I also showresults are robust to sample attrition or to other sample reductions. Results suggestfemales with equal footing to males in the couple’s decision making indeed seem touse them more compared to other females in couple. But females typically have lowerbargaining power than males in the couple’s decision making, in particular in develop-ing countries. Policies such as Conditional Cash Transfers often target females underthe assumption that, relative to males, female preferences are towards children needsor towards household needs. These programs improve female’s bargaining power, andevidence of their positive welfare effects is ample. Owing to their effect on female bar-gaining power, their positive welfare effects might compound through efficient com-mitment savings strategies use.

Partner’s have a much more active role on female’s commitment savings strategiesuse than the one assumed in the intra-household conflict model driven by preferenceheterogeneity for an indivisible good. When male and female time discounting differs,female’s use doubles. I attribute this strategic behavior to efficient couple decision mak-ing. Further, when males use commitment savings strategies, female’s use increasesfourfold. This finding cast doubts on hidden information driving strategic behavior.Couples might coordinate to use commitment savings strategies and might be usingthe same one.

53

- B =0.360***, 0,=-0.285** (5) No restrictions

- a)=0.444***, 92=-0.355- (6) Balanced sample

k=0-427***, 62=-0 .333** (7) + same mun /partner

- 61=0.426***, 0,=-0.336* (8) + 15-64 in both waves

- (1) 0,=0.353***, 0,=-0.300***

.4

- 0,=0.353***, 0.,=-0.300*" (1) No restrictions

- 0,=0.378***, 02=-0.315** (2) Balanced sample

- - 0,=0.333***, '0,0.260* (3) + same munipartner

- - 0,1=0.341** ,Viz= 0.277* (4) + 15-64 in both waves

.05 -

0 I I I I I I 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1

Z=FEMALE EQUIVALENT WAGE RATE / (EWIRF0,,„E + EViIR,,AALE)

.05 -

0 I I I I I I I I I 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1

Z=FEMALE EQUIVALENT WAGE RATE / (EWRF„4„L, + EVVRt„A„)

(1)

.3

II 0 .25 - P < a_

rz • 2 rz o_

N .15- u) 0 (1)

C. Adding variables B. Removing variables

[B]enchmark

Preferred

Preferred, non-winsorized (0)

Add poverty and formal credit/savings (g)

(g) plus female/male population (h)

(h) plus female/male years of education (i)

Benchmark

Preferred

Exclude bartik wages (j) or employment (k)

Exclude bartik wages and employment (I)

Exclude couple income (m)

- - (m) less bartik wages and employment (n)

RO

SC

A P

AR

TIC

IPA

TIO

N1

RO

SC

A PA

RT

ICIP

AT

ION

=1

0 .1 .2 .3 .4 .5 0

.6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1

Z=FEMALE EQUIVALENT WAGE RATE / (EWR,e,„,„Le + EWRuur Z=FEMALE EQUIVALENT WAGE RATE / (EWRFE,j,LE +

.35 -

3-

II z 0 .25-

.2-

u .15 - v., 0 ce

A. All Municipalities B. Municipalities with Bartik Export Manufacture Employment (Female and Male)

Figure 11: Robustness ChecksPredicted Participation Probabilities

Effect on Estimates owing to Sample Composition Changes, POLS using MxFLS 2005/06

Effect on Estimates of Specification Changes, First-Differences instrumented via CFA

54

6 CONCLUDING REMARKS

I contribute as well to the fledging literature strand finding old but not youngcouples behave efficiently. My findings, however, support that life-cycle effects andnot cohort effects drive this result. Conditional Cash Transfers indeed have a limitedeffect on young couples, those that have been together fewer years. If cohort effectssuch as tradition drive the results, the positive effect of these policies will decrease overtime. My results favor time-variant life-cycle effects. Conditional Cash Transfers posit-ive welfare effects need not to decrease over time.

55

REFERENCES

ReferencesAizer, Anna, “The Gender Wage Gap and Domestic Violence.,” The American Economic

Review, 2010, 100 (4), 1847–1859.

Ambec, Stefan and Nicolas Treich, “Roscas as financial agreements to cope with self-control problems,” Journal of Development Economics, 2007, 82 (1), 120–137.

Anderson, Siwan and Jean marie Baland, “The Economics of Roscas and Intrahouse-hold Resource Allocation*,” Quarterly Journal of Economics, 2002, 117 (3), 963–995.

Angelucci, Manuela and Robert Garlick, “Heterogeneity and Aggregation : Testingfor Efficiency in Intra-Household Allocation,” 2016, pp. 1–26.

, Giacomo De Giorgi, and Imran Rasul, “Consumption and investment in resourcepooling family networks,” 2016.

Ashraf, Nava, Dean S Karlan, and W. Yin, “Tying Odysseus to the Mast: EvidenceFrom a Commitment Savings Product in the Philippines,” The Quarterly Journal ofEconomics, 2006, 121 (2), 635–672.

Atkin, David, “Endogenous Skill Acquisition and Export Manufacturing in Mexico,”American Economic Review, aug 2016, 106 (8), 2046–2085.

Attanasio, Orazio P. and Valï£¡rie Lechene, “Efficient Responses to Targeted CashTransfers,” Journal of Political Economy, 2014, 122 (1), 178–222.

Autor, David H, David Dorn, Gordon H Hanson, and Jae Song, “Trade Adjustment:Worker-Level Evidence *,” The Quarterly Journal of Economics, 2014, 129 (4), 1799–1860.

Bartik, Timothy J, “The Effects of Metropolitan Job Growth on the Size Distributionof Family Income,” Upjohn Working Papers and Journal Articles 91-06, W.E. UpjohnInstitute for Employment Research mar 1991.

Basu, Karna, “Hyperbolic discounting and the sustainability of rotational savings ar-rangements,” American Economic Journal: Microeconomics, 2011, 3 (4), 143–171.

Bertrand, Marianne, Emir Kamenica, and Jessica Pan, “Gender Identity and RelativeIncome within Households,” The Quarterly Journal of Economics, 2015, 130 (2), 571–614.

Besley, T, S Coate, and G Loury, “The Economics of Rotating Savings and Credit As-sociation,” The American Economic Review, 1993, 83.

Besley, Timothy and Alec Levenson, “The anatomy of an informal financial market :Roscas in Taiwan,” Journal of development economics, 1996, 51 (1), 45–68.

Bobonis, Gustavo J, “Is the Allocation of Resources within the Household Efficient?New Evidence from a Randomized Experiment,” Journal of Political Economy, 2009,117 (3), 453–503.

56

REFERENCES

Browning, Martin, Pierre-André Chiappori, and Yoram Weiss, Economics of the Familynumber 9780521791595. In ‘Cambridge Books.’, Cambridge University Press, 2014.

Bryan, Gharad, Dean Karlan, and Scott Nelson, “Commitment devices,” Annual Re-view of Economics, 2010, 2, 671–698.

Chiappori, Pierre-Andre, “Rational Household Labor Supply,” Econometrica, 1988, 56(1), 63–90.

Dagnelie, Olivier and Philippe Lemay-Boucher, “Rosca Participation in Benin: ACommitment Issue,” Oxford Bulletin of Economics and Statistics, 2012, 74 (2), 235–252.

Duflo, Esther, “Grandmothers and Granddaughters: Old-Age Pensions and Intra-household Allocation in South Africa,” The World Bank Economic Review, 2003, 17(1), 1–25.

Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift, “Notes on Bartik Instru-ments,” 2016.

Gugerty, Mary Kay, “You Can’t Save Alone: Commitment in Rotating Savings andCredit Associations in Kenya,” Economic Development and Cultural Change, 2007, 55(2), 251–282.

Karlan, Dean, Aishwarya Lakshmi Ratan, and Jonathan Zinman, “Savings by and forthe Poor: A research review and agenda,” Review of Income and Wealth, 2014, 60 (1),36–78.

Klonner, Stefan, “Rotating savings and credit associations when participants are riskaverse,” International Economic Review, 2003, 44 (3), 979–1005.

Koelle, Michael, “Microenterprises and the Lure of Wage Work: Theory and Evidencefrom Mexican Export Manufacturing,” 2017.

Laibson, David, “Golden eggs and hyperbolic discounting,” The Quarterly Journal ofEconomics, 1997, 112 (2), 443–478.

Majlesi, Kaveh, “Labor market opportunities and women’s decision making powerwithin households,” Journal of Development Economics, 2016, 119, 34–47.

Murray, Michael P, “Avoiding Invalid Instruments and Coping with Weak Instru-ments,” Journal of Economic Perspectives, 2006, 20 (4), 111–132.

Nicita, Alessandro and Marcelo Olarreaga, “Trade, production, and protection data-base, 1976-2004,” World Bank Economic Review, 2007, 21 (1), 165–171.

O’Donoghue, Ted and Matthew Rabin, “Doing it now or later,” American EconomicReview, 1999, 89 (1), 103–124.

Pollak, R. A., “Bargaining Power in Marriage: Earnings, Wage Rates and HouseholdProduction.,” 2005.

Rubalcava, Luis and Graciela Teruel, “Mexican Family Life Survey,” 2013.

57

REFERENCES

Schaner, Simone, “Do opposites detract? Intrahousehold preference heterogeneity andinefficient strategic savings,” American Economic Journal: Applied Economics, 2015, 7(2), 135–174.

Velez-Ibañez, Carlos G., “Social Diversity, Commercialization, and OrganizationalComplexity of Urban Mexican/Chicano Rotating Credit Associations: Theoreticaland Empirical Issues of Adaptation.,” Human Organization, 1982, 41 (2), 107–120.

58

A APPENDICES

A Appendices

59

Table A1: Descriptive StatisticsFemale Sample for Analysis

MxFLS waves 2005/06 and 2009/12

N Mean Std.Dev. Min Max

Rosca participation=1 2,061 0.14 0.35 0.00 1.00

z=Bargaining power indicatorsUsing winsorised individual labor and non-labor income

Share of couple income 2,064 0.12 0.24 0.00 1.00Relative equivalent wage rate 2,064 0.14 0.28 0.00 1.00

Not using winsorized incomeShare of couple income 2,064 0.12 0.24 0.00 1.00Relative equivalent wage rate 2,064 0.14 0.28 0.00 1.00

Income (Constant USD per month)Using winsorised individual labor and non-labor income

Couple income (1000s) 2,064 0.67 0.57 9.68E-05 5.47Labor income (winsorized 99% per wave) 2,064 117.55 299.79 0.00 2650.84Non-labor income (winsorized 99% per wave) 2,064 2.72 20.00 0.00 209.67Partner’s income (Labor + Non-Labor) 2,064 553.78 435.84 0.00 2817.47

Not using winsorized incomeCouple income (1000s) 2,064 0.71 0.80 9.68E-05 14.93Labor income 2,064 120.22 322.47 0.00 4446.68Non-labor income 2,064 2.91 52.86 0.00 1466.89Partner’s income (Labor + Non-Labor) 2,064 589.35 690.58 0.00 14087.35

Municipality Bartik EmploymentExport manufacture (Per capita 2005, 15+ Males) 2,064 0.04 0.04 1.97E-04 0.17Export manufacture (Per capita 2005, 15+ Females) 2,064 0.02 0.02 1.18E-05 0.09Other manufacture (Per capita 2005, 15+) 2,064 0.15 0.10 1.23E-03 0.47Other sector (Per capita 2005, 15+) 2,064 0.02 0.03 7.72E-05 0.20

Municipality Bartik Wage (Constant USD)Export manufacture (Males) 2,064 17.77 4.08 5.54 36.58Export manufacture (Females) 2,064 12.84 2.11 0.36 20.77Non-export manufacture 2,064 19.18 3.08 9.41 34.39Other sector 2,064 18.05 2.43 8.07 24.46

All monetary values are expressed in constant terms using the national price index (Dec-2010=1). Constant mexicanpesos are expressed in USD using the December of 2010 exchange rate of 12.35 pesos per USD.Missing values on ROSCA participation where not imputed.Sample for analysis: 1) Females in couples that in both waves reside in the same municipality and with the same

partner, 2) Both female and partner are between 15-64 in both waves, 3) Couples in which both members have validlabor-income, 4) the couple resides in municipalities that had male and female export manufacture employment in both2005 and 2009.Valid labor income is reporting no labor earnings or reporting the amount earned. When at least one couple member

reports having labor earnings but does not report the amount, the couple is excluded from the analysis. Other missingvalues are when both couple members report having no labor income (e.g. both are not working.)Both the individual labor and non-labor income series of individuals in couple are winsorized at the top 99% on each

wave without distinguishing gender and before making any sample restriction.All regressions distinguish other manufacture and other sector employment and wages by gender.

60

AA

PP

EN

DIC

ES

Table A2: Export to Output Ratio, Manufactures 1986–2000

isic3rev2 Description 1986 1987 1998 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Mean (1986-00) Atkin (2016) Own

314 Tobacco 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.03 0.04 0.05 0.05 0.04 0.03 0.02311 Food products 0.07 0.10 0.11 0.08 0.07 0.07 0.07 0.08 0.04 0.08 0.08 0.08 0.09 0.08 0.07 0.08313 Beverages 0.07 0.11 0.08 0.07 0.06 0.05 0.05 0.05 0.05 0.10 0.10 0.11 0.12 0.11 0.13 0.08352 Other chemicals 0.07 0.07 0.07 0.07 0.07 0.08 0.09 0.10 0.08 0.16 0.15 0.15 0.17 0.16 0.17 0.11341 Paper and products 0.12 0.10 0.12 0.10 0.07 0.05 0.21 0.19 0.08 0.14 0.13 0.14 0.14 0.14 0.12 0.12369 Other non-metallic mineral products 0.13 0.14 0.13 0.15 0.10 0.08 0.08 0.09 0.06 0.17 0.18 0.18 0.15 0.14 0.12 0.13354 Miscellaneous petroleum and coal products 0.01 0.01 0.02 0.02 0.01 0.03 0.01 0.02 0.05 0.20 0.28 0.34 0.40 0.45 0.34 0.15355 Rubber products 0.05 0.01 0.05 0.08 0.04 0.03 0.07 0.12 0.13 0.28 0.26 0.26 0.26 0.33 0.38 0.16371 Iron and steel 0.11 0.10 0.11 0.14 0.11 0.11 0.12 0.15 0.18 0.36 0.27 0.27 0.25 0.19 0.18 0.18342 Printing and publishing 0.19 0.14 0.11 0.11 0.12 0.18 0.39 0.48 0.08 0.19 0.25 0.27 0.28 0.22 0.22 0.22356 Plastic products 0.21 0.08 0.09 0.16 0.09 0.12 0.56 0.74 0.27 0.37 0.39 0.39 0.41 0.43 0.46 0.32362 Glass and products 0.30 0.28 0.26 0.24 0.23 0.24 0.33 0.31 0.32 0.42 0.39 0.39 0.42 0.46 0.52 0.34351 Industrial chemicals 0.23 0.27 0.29 0.29 0.33 0.34 0.39 0.43 0.39 0.52 0.43 0.42 0.43 0.40 0.42 0.37324 Footwear except rubber or plastic 0.09 0.21 0.19 0.17 0.19 0.27 0.55 0.54 0.20 0.45 0.55 0.60 0.62 0.50 0.45 0.37 1321 Textiles 0.24 0.20 0.20 0.20 0.19 0.19 0.49 0.56 0.27 0.52 0.51 0.51 0.54 0.53 0.55 0.38372 Non-ferrous metals 0.37 0.37 0.41 0.46 0.45 0.41 0.53 0.66 0.39 0.68 0.53 0.45 0.49 0.41 0.41 0.47

381 Fabricated metal products 0.25 0.15 0.17 0.22 0.17 0.22 0.51 0.74 0.56 0.83 0.96 1.04 1.13 1.03 1.13 0.61 1 1384 Transport equipment 0.47 0.57 0.43 0.39 0.41 0.41 0.42 0.56 0.54 0.96 0.88 0.79 0.83 0.82 0.77 0.62 1 1361 Pottery china earthenware 0.30 0.27 0.23 0.22 0.18 0.19 0.37 0.41 0.84 1.30 1.14 1.09 1.16 1.20 1.12 0.67 1353 Petroleum refineries 0.60 0.91 0.89 0.98 0.80 1.02 1.25 0.92 1 1323 Leather products 0.63 1.33 1.25 1.29 1.34 1.31 1.48 1.23 1 1382 Machinery except electrical 0.99 0.82 0.83 0.84 0.94 1.04 1.84 2.19 1.00 1.57 1.40 1.27 1.40 1.55 1.65 1.29 1 1331 Wood products except furniture 0.72 0.71 0.92 1.17 0.94 1.14 2.62 3.22 0.77 1.29 1.36 1.27 1.26 1.18 1.19 1.32 1 1332 Furniture except metal 0.62 0.28 0.29 0.22 0.18 0.28 1.88 2.43 0.83 1.67 2.22 2.14 2.14 2.27 3.13 1.37 1322 Wearing apparel except footwear 0.49 0.34 0.25 0.13 0.17 0.19 1.38 1.82 0.54 3.20 3.64 4.82 5.61 5.32 5.18 2.21 1 1383 Machinery electric 1.03 0.17 0.18 0.26 0.21 0.24 3.26 4.30 3.93 6.83 6.59 6.60 6.75 7.08 7.72 3.68 1 1385 Professional and scientific equipment 1.70 1.22 0.75 0.46 0.63 0.47 3.40 2.58 4.65 7.16 6.87 6.50 7.02 7.25 6.28 3.80 1 1

390 Other manufactured products 2.17 1.07 1.30 1.25 1.64 1.44 4.60 5.08 3.55 6.77 6.20 5.57 5.86 4.68 4.04 3.68

Source: Own calculations based on Nicita and Olarreaga (2007) data on export and outputs. Output represents the value of goods produced in a year, whether sold or stocked. Exportrepresents the value of exports of the reporting country. Export and Output values are reported in thousand dollars.Export manufacture definitions:

– Atkin (2016). Exports represent more than 50% of output in at least half the sample years from 1986 to 2000.

– Own definition. Exports represent more than 50% of output in all years from 1994 to 2000.

61

AA

PP

EN

DIC

ES

Table A3: Correlations between Wages and Employment Levels and their Bartik MeasuresMunicipalities with Formal Employment, 2005q2–2012q1

Export Manufactures Other Manufactures Other SectorsAll Males Females All Males Females All Males Females

correl n correl n correl n correl n correl n correl n correl n correl n correl n

Wages 2005q2 0.57 1048 0.58 1020 0.51 859 0.53 1225 0.50 1197 0.41 1077 0.63 1484 0.68 1466 0.51 14142005q3 0.54 1050 0.57 1023 0.43 863 0.55 1220 0.51 1189 0.50 1075 0.63 1491 0.66 1480 0.51 14192005q4 0.55 1046 0.57 1021 0.43 852 0.55 1223 0.51 1187 0.49 1079 0.61 1506 0.67 1497 0.52 14202006q1 0.54 1037 0.57 1013 0.39 849 0.58 1211 0.55 1176 0.51 1072 0.59 1496 0.65 1484 0.50 14152006q2 0.54 1030 0.57 1006 0.41 840 0.57 1207 0.53 1171 0.48 1062 0.61 1496 0.66 1481 0.50 14102006q3 0.53 1026 0.56 1002 0.40 827 0.55 1200 0.51 1163 0.48 1058 0.63 1487 0.66 1472 0.51 1408

2009q3 0.47 962 0.50 940 0.33 763 0.53 1156 0.51 1124 0.46 1019 0.58 1476 0.60 1464 0.49 13922009q4 0.48 954 0.50 933 0.33 765 0.53 1155 0.51 1121 0.44 1019 0.60 1474 0.61 1463 0.52 13912010q1 0.47 946 0.49 923 0.33 754 0.54 1154 0.52 1121 0.45 1015 0.59 1475 0.60 1464 0.51 13922010q2 0.46 948 0.48 926 0.31 756 0.53 1153 0.50 1119 0.46 1012 0.61 1470 0.61 1459 0.54 13882010q3 0.45 949 0.46 926 0.31 754 0.52 1150 0.50 1116 0.45 1013 0.59 1464 0.60 1454 0.51 13882010q4 0.45 939 0.47 916 0.32 747 0.51 1145 0.49 1117 0.47 1014 0.60 1467 0.61 1456 0.53 13882011q1 0.46 937 0.47 914 0.32 750 0.51 1143 0.49 1115 0.45 1013 0.58 1463 0.60 1452 0.51 13852011q2 0.45 934 0.46 912 0.32 747 0.51 1142 0.49 1114 0.45 1008 0.60 1461 0.61 1449 0.54 13852011q3 0.46 926 0.47 904 0.31 740 0.50 1135 0.48 1110 0.45 999 0.58 1462 0.59 1451 0.51 13902011q4 0.47 916 0.48 895 0.31 738 0.50 1131 0.48 1102 0.44 995 0.60 1464 0.61 1452 0.51 13912012q1 0.46 910 0.48 889 0.32 730 0.51 1132 0.49 1102 0.44 997 0.58 1464 0.59 1451 0.51 1387

Employment 2005q2 0.68 1048 0.70 1048 0.65 1048 0.95 1225 0.95 1225 0.95 1225 0.59 1484 0.62 1484 0.53 14842005q3 0.67 1050 0.69 1050 0.63 1050 0.95 1220 0.95 1220 0.95 1220 0.59 1491 0.62 1491 0.53 14912005q4 0.66 1047 0.69 1047 0.62 1047 0.95 1223 0.95 1223 0.95 1223 0.59 1506 0.62 1506 0.53 15062006q1 0.66 1037 0.68 1037 0.62 1037 0.95 1211 0.94 1211 0.95 1211 0.58 1496 0.62 1496 0.51 14962006q2 0.65 1030 0.68 1030 0.62 1030 0.95 1207 0.94 1207 0.95 1207 0.57 1496 0.61 1496 0.50 14962006q3 0.66 1026 0.68 1026 0.62 1026 0.94 1200 0.94 1200 0.94 1200 0.58 1487 0.62 1487 0.51 1487

2009q3 0.69 963 0.72 963 0.65 963 0.93 1156 0.93 1156 0.94 1156 0.57 1476 0.61 1476 0.49 14762009q4 0.69 955 0.71 955 0.65 955 0.93 1155 0.93 1155 0.94 1155 0.57 1474 0.61 1474 0.49 14742010q1 0.68 947 0.70 947 0.64 947 0.93 1154 0.92 1154 0.93 1154 0.56 1475 0.61 1475 0.47 14752010q2 0.68 949 0.70 949 0.64 949 0.93 1153 0.92 1153 0.93 1153 0.55 1470 0.60 1470 0.47 14702010q3 0.67 950 0.70 950 0.63 950 0.92 1150 0.92 1150 0.93 1150 0.56 1464 0.60 1464 0.47 14642010q4 0.67 940 0.69 940 0.63 940 0.93 1145 0.92 1145 0.93 1145 0.56 1467 0.60 1467 0.48 14672011q1 0.67 938 0.69 938 0.63 938 0.93 1143 0.92 1143 0.93 1143 0.55 1463 0.60 1463 0.46 14632011q2 0.67 935 0.69 935 0.63 935 0.93 1142 0.92 1142 0.93 1142 0.55 1461 0.60 1461 0.46 14612011q3 0.67 926 0.69 926 0.63 926 0.92 1135 0.92 1135 0.93 1135 0.55 1462 0.60 1462 0.47 14622011q4 0.67 917 0.69 917 0.63 917 0.92 1131 0.92 1131 0.93 1131 0.55 1464 0.60 1464 0.47 14642012q1 0.65 911 0.67 911 0.61 911 0.92 1132 0.92 1132 0.93 1132 0.55 1464 0.60 1464 0.46 1464

62

A APPENDICES

Table A4: Attrition Probit. Marginal EffectsSample of Couples in MxFLS 2005/06

coef se

Rosca participation=1 -0.0302* (0.0182)Female=1 0.00285** (0.00126)Thought of migrating from village or city=1 0.0338** (0.0143)(partner) Thought of migrating from village or city=1 0.0345** (0.0139)

Couple’s income 0.000838* (0.000446)Age -0.0130*** (0.00174)Age squared 0.000147*** (1.92e-05)Partner’s age -0.0140*** (0.00157)Partner’s age squared 0.000155*** (1.82e-05)Years of education 0.00424*** (0.00134)Partner’s years of education 0.00444*** (0.00133)Household members (0-5) -0.0131 (0.0106)Household members (6-12) -0.00472 (0.00719)Household members (13-18) 9.55e-05 (0.00824)Household members (19-64) 0.00987 (0.00630)Household members (65+) -0.00610 (0.0155)Number of non-resident parents and adult siblings -0.00343** (0.00155)(Partner) non-resident parents and adult siblings -0.00358** (0.00154)Municipality level measuresMarginality Index -0.0496** (0.0233)Expenditure per population 15+ (2005) -9.126** (3.649)Female population (1000s, 15+) -0.00208 (0.00174)Male population (1000s, 15+) 0.00240 (0.00197)Branches per population (2005) per 1km sq. -0.0106 (0.0163)Savings (millions) per population 15+ (2005) 0.00140* (0.000770)Commercial to Micro-firms/Entrepreneurs (millions) per population 15+ (2005) -0.0120*** (0.00451)Number of credits 1.90e-08 (1.15e-07)Share of credits in default 0.0519 (0.0498)Observations 10,074Clusters 147Log likelihood -6189Pseudo R2 0.0369

Heteroskedasticity robust errors clustered at the municipality level in brackets. *** pă0.01, ** pă0.05, * pă0.10. Marginaleffects reported.

63

preference driven intra-household conflict and commitment ...tunidades conditional cash transfer...

Documents