Kinship and Consumption: the Effect of Spouses'outside Options On

Kinship and Consumption: The Eﬀect of Spouses’Outside Options on Household Productivity

Selma Telalagi´c∗

January 2014

Abstract

This paper exploits the exogenous variation in land rights in Malawi to estimate the impact of spouses’outside options on household productivity. In Malawi, some individuals trace their descent through their mother and others through their father. Descent is exogenously determined by parentage. Where descent is traced through the mother (matriliny), women inherit household land. This gives them stronger rights to land and better outside options than their husbands. The opposite is true of patrilineal descent. The estimates show that matrilineal households consume over 10% more than patrilineal households on average, once a rich set of control variables is included. Matrilineal husbands allocate their labour more productively due to the incentives created by land rights. This is a new result to the household literature, which has focused on intra-household allocation with total resources taken as given. The results suggest that spouses’ outside options aﬀect household productivity and, thus, total resources available for sharing.

Keywords: Household productivity, Consumption, Land rights, Matriliny, Malawi

JEL Classiﬁcation: D12, D13, J12, J16

∗Department of Economics and Nuﬃ eld College, Oxford University, [email protected]. The author is grateful to Hamish Low for valuable guidance and to Toke Aidt, Wiji Arulampalam, Ian Crawford, Pramila Krishnan, Christine Valente, Ansgar Walther and seminar paticipants at the University of Cambridge, University of Oxford, Université Libre de Bruxelles, CSAE conference and EEA-ESEM Gothenburg conference for helpful com- ments. Financial support from the ESRC and Faculty of Economics, Cambridge is gratefully acknowledged.

1 1 Introduction

It has been recognised that spouses’ outside options affect how a household allocates its total resources (Lundberg, Pollak and Wales 1997, Duflo and Udry 2004, Doss 1996, Hoddinott and Haddad 1995). This paper asks a new question building on this literature: what is the impact of spouses’ outside options on the total resources available to the household?1 In other words, do outside options affect productivity? To answer this question, I examine the case of rural Malawi, where there is exogenous variation in the inheritance rights of land. Some households are matrilineal, where land belongs to the wife. Other households are patrilineal, where land belongs to the husband. Descent is predetermined for any individual in Malawi. This provides a useful laboratory for analysing the effect of outside options on productivity. Using consumption and labour allocation as indicators of productivity and land rights as an indicator of outside option, I find that consumption is significantly higher in households where land belongs to the wife. I show that men use their endowment of time more productively in these households to generate a larger household “pie”. The result makes use of the fact that Malawi has one of the highest divorce rates in Africa, which makes it likely that outside options will be exercised.2 In Malawi, matrilineal and patrilineal tribal groups have co-existed since the mid-19th century (Phiri 1988).3 Matriliny is considered to afford women greater autonomy than patriliny (Lam- phere 1974, Davison 1997, Johnson 1988, Johnson and Hendrix 1982, Dyson and Moore 1983). Historically, British colonialists, Christian missionaries and other groups believed that matriliny is detrimental to economic performance (Peters 1997). These beliefs resulted from the observation that the South is the poorest area in Malawi and is at the same time predominantly matrilineal. In addition to estimating the impact of outside options on productivity and consumption, this paper disentangles region and descent in Malawi to assess whether these historical criticisms of matriliny were justified. I show that the highest current consumption is observed for matrilineal households, where women have stronger outside options than their husbands. In the raw statistics (Table 3), I show that while the North is richer than the South, matrilineal households are richer, on average, in each region. The regression results demonstrate that matrilineal households consume 10% more on average in real terms than patrilineal households, once a rich set of control variables is included. I also show that this higher consumption is not explained by lower savings. The higher consumption of matrilineal households is confirmed for per capita expenditure and equivalent expenditure. The

1 In this paper, I define an individual’soutside option as his or her utility when divorced. 2 Lifetime divorce probabilities in Malawi are between 40 65% (Reniers 2003). Over 40% of women remarry within the first two years after a divorce. − 3 A matrilineal descent system is where inheritance passes through the female line. Family land is passed down from mother to daughter or, more traditionally, from brother to sister’s son. A patrilineal descent system is where inheritance passes through the male line, from father to son. This affects property division following divorce, where household land accrues to the wife in matriliny but to the husband in patriliny. Matrilocal residence is where a couple locates in the wife’s village after marriage. Patrilocal residence is where a couple locates in the husband’s village after marriage. In this chapter, I use the terms ’kinship’and ’descent’interchangeably to describe the system of land inheritance that a household follows.

2 consumption gap is attributed to differences in labour allocation: men use their time endowment more productively in matrilineal households. I develop a theoretical framework of husbands’labour allocation decisions in Malawi that explains why households may suffer when land rights accrue to men. Wage labour in Malawi is predominantly carried out by men, who face a decision between allocating time to agriculture and wage work. Where land belongs to men, husbands are residual claimants of agricultural income should the couple divorce; as a result, they allocate more time to agriculture at their optimal choice. As wages are higher than the average product of agricultural labour, this leads to lower household income and, consequently, lower consumption than for those households where land belongs to women. There is a mismatch between what is individually optimal and what is optimal for the household: patrilineal husbands are better off engaging in more agricultural labour, even though their spouse and children would benefit if they switched to wage labour. Support for this hypothesis is obtained in an analysis of labour allocation, which shows that patrilineal husbands allocate more time to agricultural labour and less to wage labour. They also have lower wage earnings. I also verify that wages are significantly higher than agricultural productivity, a key assumption of the framework. I also analyse how the intra-household allocation of resources to private goods depends on land rights. I find that a significantly higher share of expenditure is devoted to sons’ education and men’s clothing in patrilineal households, which is consistent with the idea that husbands have higher outside options in these households. This chapter makes an important contribution to the household economics literature: it assesses the effect of spouses’ outside options on the productivity of the household. To the best of the author’sknowledge, this is a new question to this literature. The chapter also makes a contribution to the property rights literature by showing that although strong property rights increase individual- level investment in Malawi, this may not be a desirable outcome at the household level. Where non-cooperative decision-making about labour allocation cannot be alleviated, weakening men’s land rights can help achieve a second-best outcome. The chapter relates to existing empirical work on the effects of asymmetric land rights of spouses. Udry (1996) examines agricultural production in households in Burkina Faso and demonstrates that the productivity of husbands’plots is significantly higher than that of their wives’plots, suggesting that a reallocation of resources could result in a better outcome for the household. One reason why household outcomes may be ineffi cient is tenure insecurity: Udry and Goldstein (2008) argue that women in Ghana do not make long-term investments in their land by leaving it fallow because fallow land is likely to be expropriated. Furthermore, my results are related to the literature on property rights. Besley (1995) argues that individuals may underinvest in their plots due to a risk of expropriation.4 Some authors argue that men’s weak land rights in matrilineal households in Malawi result in lower long-term

4 Empirical studies supporting this eﬀect include Ali et al. (2011) and Deininger and Jin (2006). In contrast, Brasselle et al. (2002) ﬁnd no impact of land rights on investment.

3 investment and, as a result, lower income (Place and Otsuka 2001, Kishindo 2010). In this chapter, I find that weak land rights do reduce investment: matrilineal men spend less time on agricultural work than patrilineal men. However, this is beneficial to the household because weak land rights help align private benefits more closely with household benefits. The chapter is structured as follows. In Section 2, I present a framework for husbands’income- earning decisions in Malawi. In Section 3, I describe the empirical strategy and in Section 4 I estimate the effect of descent on consumption. Section 5 examines labour allocation and Section 6 explores intra-household allocation. Section 7 concludes.

2 Household Decision-Making in Malawi

2.1 Marriage in Malawi

In this Subsection, I describe in more detail the rules governing land rights and other features of marriage in Malawi. Malawi is a poor country: 57% of rural households are at the poverty line.5 In rural Malawi, individuals belong to tribes, whose rules are particularly important for family life. Typically, tribes follow either matrilineal or patrilineal descent. Women are considered to be more autonomous in matrilineal than patrilineal communities because they have access to land. Land access is especially important in rural, horticultural societies such as Malawi, because it is a crucial source of livelihood. The other main asset is labour (Takane 2008). In Malawi, kinship is spatially correlated. Figure 1, a map of Malawi, depicts this dispersion by district.6 Darker shading represents districts where matriliny is more prevalent, relative to patriliny. In the Southern region, most districts are predominantly matrilineal. In the Central region, there is a more equal balance of matrilineal and patrilineal communities. The Northern region has a strong patrilineal presence. In matrilineal marriages, the woman receives land from her natal kin. The couple works on this land as long as they are married. The husband is expected to work for his wife’sfamily and show that he is hard-working and useful (Roberts 1964). Should the couple divorce, the wife keeps all of the family land that she has been given. She continues to work on it and does not have to remarry. The husband, on the other hand, has to return to his village. He does not have any claims to his wife’s land. He may be given a temporary plot of land owned by his family to work on, with the understanding that this is only until he can ﬁnd another woman to marry. Marriage is the crucial way that a man obtains access to land in a matrilineal setting (Kishindo 2010). Patrilineal marriages are in many ways the opposite of matrilineal marriages. Marriage is the primary way that a woman can obtain access to land in a patrilineal setting. The husband receives

5 In 2010. Figure from World Bank (http://data.worldbank.org/country/malawi). 6 The prevalence of matriliny and patriliny by district is calculated based on the Living Standards Measurement Study data used in the empirical section of this chapter. For the purposes of this map, in those villages where both types of descent are practised, half of the households are apportioned matrilineal descent while half are apportioned patrilineal descent. The ﬁgures are weighted based on the sampling strategy of the data (see footnote 19).

4 Figure 1: A map of Malawi depicting the prevalence of matriliny and patriliny by district.

5 land from his natal kin, which the couple use to earn their livelihoods.7 If the couple divorce, the wife has no claims to the household land and is forced to return to her natal village, where she may receive a temporary plot of land. She faces pressure to remarry, however. Her family may discourage divorce because of the risk that her bridewealth will need to be returned (Schatz 2002).8 Marital residence adds an interesting nuance to the outside options of spouses. Living in the wife’svillage (matrilocality) cements a woman’spower in the household because she is surrounded by her kin, while her husband is a stranger in the village; the wife’s brother plays a particularly strong role in the matrilineal-matrilocal household, with the wife often taking orders from him rather than her husband (Phiri 1983). In contrast, patrilocality can increase the husband’soutside option because he is surrounded by his own kin. A matrilineal man is only likely to reside patrilocally if he has no sisters or if the family is particularly wealthy (Peters 1997). However, if a matrilineal man is the eldest brother to several sisters, he may be expected to reside in his natal village so that he can look after his sisters and the family land. Although patrilineal couples almost always locate patrilocally, marital residence is aﬀected by circumstances.9 There may be an endogenous element to a matrilineal or patrilineal couple’s marital residence. This is in contrast to lineage, which is exogenous. These rules for land access imply that matrilineal men tend to own less land then patrilineal men, while matrilineal women tend to own more land than patrilineal women. Evidence of this is given in Appendix A. This is important because it determines the outside options of spouses. Matrilineal husbands have low outside options, as do patrilineal wives. This makes divorce more accessible for matrilineal women and patrilineal men. Although land rights follow a clear set of rules based on descent, the rules for other property are less clear-cut, especially in the case of divorce. Consumption goods tend to be shared equally on divorce. There is a strong degree of labour specialisation in rural Malawi. Almost all households derive a substantial amount of their income from agriculture. Women tend to engage in agricultural labour, performing many tasks on their own (Hirschmann and Vaughan 1983). Men usually work agriculturally and for wages. It is rare for women to work for wages, unless they are unmarried (Spring 1995). This implies that men are predominantly responsible for providing a household’s consumption goods (Schatz 2002). Domestic labour is predominantly carried out by women (Spring 1995). Matrilineal women have more control over household labour decisions than patrilineal women

7 The question of primogeniture, where inheritance passes to the oldest son at the expense of other sons, is important to address here. There is no deﬁnitive evidence on whether this takes place in Malawi. However, the important distinction is between the land access rights of a husband and his wife, rather than a husband and his brothers. Even if a man has older brothers and inherits less land than them in a patrilineal setting, he still inherits some land or at least has access to the family’sland should he need it. This is in contrast to his wife who, by virtue of being a patrilineal woman, has no land rights at all. 8 Bridewealth is when the husband-to-be or his kin pay an amount in money or kind to the woman’sfamily. Some researchers argue that the origins of bridewealth are in compensating the woman’sfamily for their economic loss due to the value of the woman’slabour. 9 Matrilocality is not in line with patrilineal kinship traditions and would indicate poor economic circumstances that forced the couple to move to the wife’svillage.

6 (Davison 1997). Historically, matriliny has been attacked on various economic grounds. In particular, matrilineal marriages have been said to deter husbands from investing resources to improve household land, since they have no rights to it should the couple divorce (Phiri 1983). Richard Kettlewell is a popular example of a colonialist who held a grudge against matriliny because of this hypothesised eﬀect of tenure insecurity (Peters 2002, Simpson 2000). Despite negative outside inﬂuences, matriliny has remained surprisingly prevalent in Malawi, with around 60% of rural households being matrilineal.

2.2 A Framework for Income-Generating Decisions

In this Subsection I present a framework for analysing the income-generating decisions made by husbands in Malawi, who choose their labour allocations non-cooperatively as in, for example, Ulph (1988).10 Husbands decide on how to split their time between wage work and agricultural work; each type of labour generates household income that is used for consumption. In addition, I assume that wives supply agricultural labour inelastically. This framework is justiﬁed by the labour patterns observed in rural Malawi, where men work both agriculturally and for wages whereas women tend to spend most of their time on agricultural and domestic work. There is a social barrier to women working for wages.11 The husband’s decision is aﬀected by an exogenous probability of divorce.12 The aim of this framework is to analyse husbands’ investment decisions under uncertainty; this relates to a rich literature on the impact of property rights on investment (e.g. Besley 1995). Husbands choose whether to invest in assets that may be expropriated at a later date. Intuitively, the higher the probability of expropriation, the less investment there is in equilibrium. A similar result is expected in the present framework: patrilineal men, who have stronger land rights than matrilineal men, should invest more in their land. This framework demonstrates under which assumptions this is the case. A household consists of a husband and wife who each live for two periods, t = 1, 2.13 They

10 This assumption lies in contrast to the collective model of household decision-making (Chiappori 1988), which assumes that outcomes are Pareto effi cient. I assume a non-cooperative framework because of the evidence against Pareto effi cient outcomes in households in developing countries, such as Udry (1996), Udry et al. (1995) and Dercon and Krishnan (2000). 11 Women may make a decision on a different dimension: descent may affect their choice between domestic labour and agricultural labour, if weak property rights mean that they would prefer to invest in their children. As a result, patrilineal women may spend less time on agriculture and more time on domestic labour than matrilineal women. However, Section 5 will show that this effect on wives’agricultural labour is not observed. 12 Since Malawi has one of the highest divorce rates on the continent (Reniers 2003), incorporating divorce is important. I discuss the effect of relaxing this assumption at the end of this Subsection. 13 There are no children in this model. If children consume but do not produce, a simple way of introducing children would be to include a proportional ’tax’on all consumption, reducing the amount that both the husband and wife receive. This would not change the predictions of the model. Assuming that children contribute economically is unlikely to change the predictions either, as children’slabour is controlled by parents; therefore, their optimal labour allocation would be similar to their parents’optimal labour allocation, reinforcing the predicted effects. Another way in which children could affect parents’behaviour is if they are a vehicle for investment. However, the ancestry of children is traced in the same way as that of land, so that children belong to the mother in a matrilineal household and to the father in a patrilineal household. Thus, investment in children should mimic investment in land.

7 marry with an exogenous amount of household land (L¯1) just before the start of the ﬁrst period. In the ﬁrst period, they derive utility from current consumption, where the husband’sconsumption is denoted by c1. Spouses also place value on the next period with discount factor β [0, 1]. The ∈ husband splits his time, 1, between two types of labour: agricultural and wage (h1, 1 h1). The − wife supplies 1 unit of agricultural labour inelastically; as a result, all production functions are written in terms of husband’s labour only. Wage labour allows the couple to enjoy consumption immediately through the wage rate w1. The husband receives an exogenous share α [0, 1] of the ∈ wage income he earns; the remainder is consumed by his wife. The original stock of land depreciates at rate D [0, 1]. The husband’slabour increases the value of the land through the function f(h1). ∈ Income from agricultural labour is enjoyed in the second period. In the second period, the couple may be divorced or married, which is determined by an exogenous probability (δ).14 Thus, there are two states of the world in the second period, single and married: j = S, M. As before, the spouses derive utility from their consumption, with the husband’s consumption denoted by c2. Each spouse has a new labour allowance; the husband makes a choice between agricultural and wage labour (h2, 1 h2) while the wife continues to supply agricultural − labour inelastically. The wage rate is w2 and wage income is consumed immediately. The spouses also reap consumption from the agricultural good they invested in in the previous period; this can be interpreted as consuming crops that have taken one period to grow. Any agricultural labour in the second period further increases the amount of consumption generated by this land good. The j j j production function is given by g(A2, h2), where A2 is the amount of land in state j. If the couple remain married, they each enjoy consumption from the full amount of land, L2; in this case, land M is a public good and A = L2. If they divorce, the husband receives a share λ [0, 1] of the 2 ∈ land and the wife receives the rest. This parameter measures the property allocation rule following divorce and captures the strength of the spouses’relative outside options, with λ 1 for patrilineal → households and λ 0 for matrilineal households. Divorce determines whether income is shared: → wage and agricultural income are split according to the sharing rule α if the couple remain married; otherwise, they enjoy agricultural income from the amount of land they receive on divorce and only the husband consumes wage income, as the wife does not engage in wage labour.15

2.2.1 The husband’soptimal labour allocation

The main focus of this analysis is the husband’s labour decision, which he makes optimally by maximising his lifetime utility. Let u(ct) denote the husband’s utility function from consumption in period t. I assume that the functions u( ), f( ) and g( ) are concave and twice differentiable. · · · 14 In a related paper (Telalagić 2012), I relax this assumption and allow women to divorce their husbands when they do not generate suffi cient consumption goods. 15 A crucial assumption here is that α is independent of λ: the share of consumption received in marriage does not depend on outside options. This may seem like a strong assumption, but it is justified in the Malawian context because consumption goods tend to be shared equally on divorce, rather than in line with land allocation. This implies that if the model were to include savings through durables, men would receive a share α of durables on divorce. I will examine how intra-household allocation of consumption depends on kinship in Section 6 to test the assumption of the independence of α and λ.

8 I also assume that the two inputs in g( ) are complementary as in, for example, a Cobb-Douglas · ¯ S production function. Using U1(L1) to denote the husband’svalue function in period 1 and U2 (L2) M and U2 (L2) to denote his value functions in the second period when single and married respectively, the husband solves the following problem:

¯ S M U1(L1) = max u(c1) + β(δU2 (L2) + (1 δ)U2 (L2)) c1,h1 − s.t. : c1 = αw1(1 h1) − L2 = (1 D)L¯1 + f(h1), −

where

S S U (L2) = max u(c ) 2 S S 2 c2 ,h2 S S S s.t. : c = w2(1 h ) + g(λL2, h ), 2 − 2 2 M M U (L2) = max u(c ) 2 M M 2 c2 ,h2 M M M s.t. : c = α(w2(1 h ) + g(L2, h )). 2 − 2 2

S M S M After substituting for c1, c2 and c2 , the husband has three choice variables: h1, h2 and h2 . The optimal choice of h1 is given by the following ﬁrst-order condition:

¯ S ∂U1(L1) S ∂g(λL2, h2 ) = αw1u0(c1) + βf 0(h1)(δλu0(c2 ) ∂h1 − ∂L2 M M ∂g(L2, h2 ) +(1 δ)αu0(c2 ) ) = 0. (1) − ∂L2

This shows that the husband sets his labour supply such that the marginal utility from wage labour is equal to the marginal utility from agricultural labour. These marginal utilities depend not only on the wage rate and marginal product of labour, but also on the sharing rule for consumption, the property rights regime, the divorce rate and the husband’spreferences.

In the second period, the husband chooses h2 depending on whether he is single or married, such that

9 S S ∂U2 (L2) S ∂g(λL2, h2 ) S = u0(c2 )( S w2) = 0, ∂h2 ∂h2 − M M ∂U (L2) M ∂g(L2, h2 ) M = u0(c2 )α( M w2) = 0. ∂h2 ∂h2 −

The husband sets the marginal product of wage labour equal to the marginal product of agricultural labour in each state.

2.2.2 The eﬃ cient labour allocation

To assess the extent to which the husband’soptimal labour allocation is ineffi cient, it is necessary to derive a benchmark. I define the effi cient labour allocation to be that which maximises household welfare. The household’s value function in period 1 is denoted by HW1(L¯1). I assume that household utility is a weighted average of the husband’sand wife’sutilities, where the husband has j weight κ [0, 1] and the wife has weight 1 κ. The wife’s utility function is denoted v(˜ct ) with ∈ j j − value function Vt , where c˜t is the consumption she receives in state j and period t. The wife’s consumption is fixed in the divorced state, as she does not have a labour allocation decision; it is S defined to be c˜ = s((1 λ)L2), where s( ) is a concave function of land input. While married, she 2 − · receives (1 α)et, where et is household consumption in period t. Note that αet = ct. − There are no externalities in the second period; the husband’sindividually optimal choice is also socially effi cient. To see this, consider the husband’smaximisation problem in the second period. In the married state, the husband chooses labour supply to maximise αe2, his share of second-period consumption. Since this is proportional to the wife’s share, his choice also maximises her second- period consumption, so that it is optimal from the household’sperspective. In the divorced state, the wife’s consumption is independent of the husband’s choice. Therefore, the husband’s choice is optimal as long as it maximises his consumption, which is the case by definition. Due to the absence of externalities in the second period, the effi cient labour allocation is equivalent to the choice of a ‘household planner’who dictates the first-period labour decision h1 and leaves the husband to make his own decision in the second period. Therefore, the effi cient labour allocation is defined by the solution to the following problem:

HW1(L¯1) = max κu(αe1) + (1 κ)v((1 α)e1) e1,h1 − − S S +β(δ(κU (L2) + (1 κ)V (L2)) 2 − 2 M M +(1 δ)(κU (L2) + (1 κ)V (L2))) − 2 − 2 s.t. : e1 = w1(1 h1), − L2 = (1 D)L¯1 + f(h1), − 10 where

S S V2 (L2) = v(˜c2 ), S c˜ = s((1 λ)L2), 2 − M V (L2) = max v((1 α)e2) 2 M e2,h2 − M M s.t. : e2 = w2(1 h ) + g(L2, h ). − 2 2

The optimal choice of h1 is given by the following condition:

∂HW1(L¯1) = w1(καu0(αe1) + (1 κ)(1 α)v0((1 α)e1)) ∂h1 − − − − S S ∂g(λL2, h2 ) +βf 0(h1)(δ(κλu0(c2 ) ∂L2 S ∂s((1 λ)L2) +(1 κ)(1 λ)v0(˜c2 ) − ) − − ∂L2 M ∂g(L2, h2 ) +(1 δ)(καu0(αe2) + (1 κ)(1 α)v0((1 α)e2)) ) = 0. − − − − ∂L2

If κ = 1, this condition is identical to condition (1). As long as the wife has some weight in the household welfare function (so κ = 1), the husband’s optimal choice of h1 is not eﬃ cient. Letting 6 E h1∗ denote the husband’s optimal choice of h1 and h1 the eﬃ cient choice of h1, the relationship between the two choices is explained in the following special case.

Special case 1 Assumption 1 The sharing rule in marriage is equal: α = 2 . Assumption 2 The utility functions describing the husband’sand wife’spreferences are the same: u( ) = v( ). · · E Suppose Assumptions 1 and 2 hold. Then there is overinvestment in land ( h1∗ > h1 ) when E E λ = 1 and underinvestment in land ( h1∗ < h1 ) when λ = 0. Moreover, there exists a λ (0, 1) E ∈ such that h1∗ = h1 .

Proof. In Appendix B.

The husband overinvests in land when he is the residual claimant of this asset on divorce and underinvests when he has no claim to it on divorce. His labour decision impacts the utility of the wife because she receives a share of the consumption he generates; however, the husband does not internalise this externality. As a result, his labour decision is ineﬃ cient.

11 2.2.3 The eﬀect of kinship on the husband’slabour allocation

Although the special case predicts the extent of overinvestment or underinvestment at the boundary levels of λ, it does not predict how the husband’sindividually optimal agricultural labour changes

dh1 with λ. This is captured by the comparative static dλ , whose sign is derived here. It is possible to derive this sign by totally differentiating the husband’s first-order condition for the first period with respect to λ. First, the following assumption needs to be made:

Assumption 3 g00 u00 λL2( + g0 ) < 1, − g0 u0

S 2 S ∂g(λL2,h2 ) ∂ g(λL2,h2 ) S S where g0 denotes , g00 denotes 2 , u0 denotes u0(c2 ) and u00 denotes u00(c2 ). ∂L2 ∂L2 This condition can be interpreted as a constraint on the curvature of the functions g( ) and u( ). In · · g00 particular, λL2 is the production function equivalent to the coeffi cient of relative risk aversion − g0 u00 for a utility function; the term λL2g0 has a similar but less direct interpretation. This assump- − u0 tion implies that both the production function and utility function should not be too concave; in other words, the rate of diminishing marginal product and diminishing marginal utility should not be too high. In the case of linear functions, for example, this is always satisfied as g00 and u00 are equal to zero. These assumptions appear reasonable in the context of Malawi. At the levels of production that households find themselves, it is unlikely that strong diseconomies of scale occur. The following proposition predicts the effect of λ on labour allocation.

Proposition 1 If Assumption 3 holds, an increase in the share of land accruing on divorce leads

dh1 to an increase in ﬁrst-period agricultural labour at the expense of ﬁrst-period wage labour: dλ > 0. S M dh2 dh2 Similarly, second-period agricultural labour is increasing in λ in both states: dλ , dλ > 0.

Proof. In Appendix B.

This proposition states that husbands with a high value of λ will have higher agricultural labour in both periods than husbands with a low value of λ, assuming that the functions g( ) and u( ) · · do not exhibit strong diseconomies of scale. Thus, matrilineal men will invest less labour time in household land than patrilineal men. In addition, if matrilineal households can be accurately defined by the case λ = 0 and patrilineal households can be accurately described as having λ = 1, then the special case implies that the effi cient first-period labour allocation lies somewhere between the labour allocations of matrilineal and patrilineal men. Since λ increases the amount of land available in the second period due to its positive effect on first-period agricultural labour, this also means that more time is spent on agricultural labour in the second period. The intuition for why patrilineal men spend more time investing in land than matrilineal men is that the marginal benefit of agricultural work is higher for the former than the latter.

12 2.2.4 The Eﬀect of kinship on household consumption

For empirical purposes, a household’sconsumption is only observed when the couple is still married. Therefore, the eﬀect of kinship on household consumption can be predicted from the theoretical framework by analysing the eﬀect of λ on e1 and e2. First, the following assumption is made:

Assumption 4

M M ∂g(L2, h2 ) 1 ∂g(L2, h2 ) w2 > + . M dhM ∂h2 2 ∂L2 dL2

This assumption provides a lower bound on the second-period wage. Increasing λ increases first-period agricultural labour, which benefits the husband in two ways: it increases the marginal product of second-period agricultural labour and increases the amount of land available in the second period. On the other hand, more land means that the husband is less inclined to work for wages; this results in a loss in wage income. Therefore, intuitively, Assumption 4 says that when λ increases and therefore first-period agricultural labour increases, the benefit of this through its effect on second-period land and agricultural labour is less than the cost through its effect on wage income. A necessary condition for this to be the case is that the wage is greater than the marginal product of agricultural labour, which I will test in Section 5. In the following proposition, I derive predictions on the effect of λ on the household’sconsumption in the first period and in the second-period married state.

de1 Proposition 2 If Assumption 3 holds, then dλ < 0. If, in addition, Assumption 4 holds, then de2 dλ < 0.

Proof. In Appendix B.

Proposition 2 states that a household’sconsumption is decreasing in the husband’sshare of land owned. A necessary condition for this to be the case is that the wage is larger than the marginal agricultural product. Studies have calculated these two productivity measures in developing countries and frequently concluded that wages far exceed the agricultural marginal product, termed a ‘productivity gap’. For example, Gollin et al. (2012) use household surveys to show that even after considering sectoral differences in hours worked and human capital, an agricultural productivity gap remains. Vollrath (2009) calculates the ratio of the marginal product of labour in industry to that in agriculture for many countries; Malawi has the second highest value in the sample, with a ratio of 13.7. This implies that the marginal product of labour in industry is 13.7 times higher than the marginal product of labour in agriculture. If this is correct, then two identical households in Malawi that only differ in their labour allocation decision between wage work and agriculture will have vastly different incomes. This is linked to early development economics, which argues that structural change, involving the movement of workers from the primary sector into the secondary

13 and tertiary sectors, is crucial for development (e.g. Rostow 1960). Together, these studies provide strong support for Assumption 4. The framework oﬀers predictions on the consumption and labour allocation of matrilineal and patrilineal households: in particular, households with higher values of λ should have lower consumption and higher agricultural labour by men than households with lower values of λ. A list of the assumptions and predictions that will be tested in this paper is in Table 1.

Table 1: Predictions and assumptions that will be tested Prediction/assumption Section where tested

de1 de2 dλ , dλ < 0 4. Consumption M ∂g(L2,h2 ) w > M 5. Labour ∂h2 M dh1 dh2 dλ , dλ > 0 5. Labour corr(α, λ) = 0 6. Intra-Household Allocation

λ is higher for patrilineal HHs Appendix A

2.2.5 Extensions to the model

There are several possible extensions to the model; I discuss the eﬀect of these extensions on Proposition 1. First, one can relax the assumption of exogenous probability of divorce. In Malawi, divorce rates tend to be higher in matrilineal areas where λ is low; allowing for this in the model by assuming a function δ(λ) would reinforce the eﬀect in Proposition 1 at high levels of λ but dampen it at low levels. This is because a higher divorce rate increases the probability of exercising one’s outside option. Men with low λ would allocate more time to agricultural labour but men with high λ would allocate even more time to agriculture. The overall prediction of Proposition 1 would be maintained. Including divorce as a choice variable would allow men to choose between the divorced and married states in the second period. In patrilineal communities, men hold the power to divorce their wives. Since wives do not make any labour choices, allowing men to choose whether to divorce

dh1 would have little effect on their behaviour. Therefore, the sign of dλ would not change. On the other hand, in matrilineal settings, women tend to hold the power to divorce their husbands. Women may use this power to affect men’sbehaviour. To allow for this, the divorce probability would have to depend on the first-period labour choice in a pre-defined way. One possible assumption is that women divorce men who do not help them suffi ciently with their land, which would drive men to spend more time on agriculture. This effect goes in the opposite direction to the predicted effect of Proposition 1. A second possible assumption is that men obtain power by working for wages because they have a source of income that women are unable to earn, which would drive them to spend less time in agriculture. This would reinforce the effect in Proposition 1. In fact, Kerr (2005a)

14 argues that increasing wage earnings increases men’s power in households in Malawi. In addition, Telalagić (2012) demonstrates that women in Malawi provide active incentives to encourage men to earn wage labour. Therefore, it is likely that the second assumption about behaviour is correct, reinforcing the effect in Proposition 1. Another possible extension is to introduce savings through assets such as durables, which requires wage income. Such savings would be lower for those men who focus on agricultural labour. However, the existence of savings would not affect the substitution between agricultural and wage labour unless the rate of return on assets or utility from assets is particularly high. A further way to extend the model is to include agricultural income in the first period. In this sense, agricultural labour could provide immediate consumption. The primary force that drives patrilineal men toward wage labour is that agricultural labour does not generate any first-period

dh1 consumption; this would no longer be the case, so the predicted sign of dλ at high levels of λ would be reinforced. For matrilineal men, the existence of ﬁrst-period agricultural income would make agricultural labour more attractive; however, this is assuming that agriculture is a productive way

dh1 of earning income, which is unlikely to be the case. Therefore, the overall predicted sign of dλ would not be aﬀected.

3 Empirical Strategy

The general relationship this paper aims to shed light on is the effect of spouses’outside options on household productivity. The specific relationship estimated is the effect of land rights on consumption and labour allocation. To measure land rights, I use kinship: whether the household is matrilineal or patrilineal. This is the best measure of land rights in Malawi, as kinship governs how land is shared following divorce. However, kinship may capture other factors too, such as the likelihood of divorce; I address these factors empirically. In this Section, I explain how the effect of land rights on consumption, labour allocation and intra-household allocation will be identified in the data. The sequence of the tests is as follows. In the next Section, I test Proposition 2, namely that matrilineal households have higher consumption than patrilineal households. For robustness, I examine alternative measures of consumption, sample restrictions, alternative measures of wealth, savings and tribal fixed effects. In Section 5 I test Proposition 1, which predicts that matrilineal men allocate more time to wage labour and less time to agricultural labour than patrilineal men. I also test the necessary component of Assumption 4, namely that the wage exceeds the marginal product of agricultural labour. For robustness, I examine income and husbands’wage earnings. In Section 6, I examine the effect of kinship on intra- household allocation to examine the consistency of the data with existing results on intra-houshold allocation and to test the assumption of the framework that α and λ are independent. Next, I explain each of the tests in more detail.

15 3.1 Proposition 2: The Eﬀect of Kinship on Consumption

In order to test Proposition 2 and analyse the effect of kinship on consumption, I take advantage of the fact that kinship is predetermined for any individual in Malawi. There is an exogenous assignation of kinship across individuals. However, due to the way that tribes settled in Malawi, kinship is not independent of geography. As geography is likely to affect consumption both directly and through other factors such as prices, covariates that are correlated with geography (G), kinship and consumption need to be controlled for. These variables capture the exogenous factors that enter the income generation function. I denote the vector of these covariates by Z. Let Di be a dummy variable equal to one if household i is patrilineal and zero otherwise. Then, a regression of consumption on kinship, geography and the covariates Z will give a causal effect of kinship on consumption as long as

C1i,C0i Di Gi, Zi i, { } ⊥ | ∀

where C1i is the potential consumption outcome of household i if it were patrilineal and C0i is its potential consumption outcome if it were matrilineal. In words, conditional on geography and other regional covariates, the potential consumption outcomes of households across the two kinship types are independent of their kinship (Angrist and Pischke 2008). If, further, I include in Z all variables relating to kinship that do not measure land rights, the regression will measure the causal eﬀect of land rights on consumption. These land rights are then interpreted as capturing spouses’ relative outside options. This framework suggests the following regression equation, which will be estimated using Ordinary Least Squares:

ln Ci = α + βDi + γGi + θZi + ωHi + ui, (2)

where Hi is a vector of household characteristics that are not correlated with descent but may improve the precision of the estimates. The coeffi cient of interest is β.16 As the dependent variable is the log of consumption, β is interpreted as the mean percentage difference between the consumption of matrilineal and patrilineal households. The framework of the previous Section suggests that β < 0. However, critics of matriliny would argue that β > 0. The value of β is an empirical question that is answered in the next Section. Relating to the theoretical framework, I assume that the sample consists of a mixture of households in their first and second periods.

de1 de2 Therefore, the empirical analysis tests for an average of dλ and dλ . For robustness, I also examine

16 The key assumption is that conditional on included covariates, Di is exogenous. If this is not the case, the estimate of β will be biased. Any omitted variables that aﬀect consumption outcomes are likely to be negatively correlated with Di, implying that economic conditions favour matrilineal households. This is because summary statistics (not reported) show that matrilineal villages are closer to urban areas and face lower constraints in soil quality, on average. If this is the case, there will be a downward bias on β: the true eﬀect of patriliny on consumption will be more positive than estimated.

16 savings, which are a measure of future consumption. By analysing both current consumption and savings, a good picture of lifetime consumption is obtained. I also examine per capita and equivalent expenditure as measures of Ci for robustness.

3.2 Proposition 1: The Eﬀect of Kinship on Labour Allocation

Using the same set of right-hand side variables as in (2), I test Proposition 1 by examining the eﬀect of kinship on labour allocation. I estimate the following set of equations with Ordinary Least Squares:17

w w hi = αw + βwDi + γwGi + θwZi + ωwHi + ui , (3) a a hi = αa + βaDi + γaGi + θaZi + ωaHi + ui , (4) a w a w hi − = αa w + βa wDi + γa wGi + θa wZi + ωa wHi + ui − , (5) − − − − − a+w a+w hi = αa+w + βa+wDi + γa+wGi + θa+wZi + ωa+wHi + ui , (6)

where hw denotes hours of wage labour by the husband, ha denotes his hours of agricultural a w a+w labour, h − denotes the diﬀerence between the hours of agricultural and wage labour and h denotes the sum of the hours of agricultural and wage labour. Proposition 1 implies that βw <

0, βa > 0, βa w > 0 and βa+w = 0. That is, patrilineal men spend less time on wage labour and − more time on agricultural labour compared to matrilineal men; in addition, the difference between the two types of labour is higher for patrilineal than matrilineal men, while the sum is no different between the two kinship groups, implying a substitution effect. I also test the necessary component of Assumption 4, namely that wages are higher than the average product of agricultural labour. The method is explained in more detail in Section 5.1.

3.3 The Independence of α and λ

In order to test the assumption that α and λ are independent, I estimate a series of Working-Leser expenditure share regressions:

g ei = αg + τ gEi + βgDi + γgGi + θgZi + ωgHi + πgPi + ui, (7)

g where g = 1, ..., n is a set of n categories of goods, ei is the share of total expenditure spent on good g, Ei is the total expenditure of household i, Pi is a vector of the log of prices of various goods and the remaining right-hand side variables are as in (2). Total expenditure is instrumented

17 I estimate these equations independently with the same right-hand side variables for all speciﬁcations.

17 with wealth, measured by house construction material and number of livestock owned, and a Two Stage Least Squares procedure is used. If α and λ are independent, then kinship will not aﬀect the intra-household allocation of consumption and we will observe βg = 0 for all goods in g. To verify that λ is higher for patrilineal households, I disaggregate land ownership by spouse and kinship in Appendix A.

4 Consumption

4.1 The Data and Summary Statistics

The source of the data is the Malawi Living Standards Measurement Study (LSMS), conducted by the World Bank and the Malawi National Statistical Offi ce (NSO). Households were interviewed between March 2010 and March 2011. In total, 12271 households were interviewed, of which 10038 resided in rural areas.18 I restrict the sample to rural households where the household head is married, which gives a sample of 7350 households. The final sample consists of 7136 households, due to some missing observations. Aggregate real consumption expenditure, both at the household level and per capita, is provided in the data. I use the household-level measure for most of the analysis. The consumption aggregate includes food purchased, produced for own consumption and received as a gift, various household items, the rental value of durables, the rental value of accommodation and expenditure on health and schooling. Consumption expenditure is deflated by a temporal and spatial price index.19 Summary statistics are presented in Table 2. I disaggregate the data based on descent.20 Details of variable definitions can be found in Appendix C. Matrilineal households own less land and have fewer members on average, although matrilineal and patrilineal households have approximately the same number of children on average. Mean education levels are similar across descent types. However, patrilineal husbands and wives are slightly older on average. On the whole, there are no major differences in basic characteristics across descent types. The regional dispersion of descent is clear from the table: while there are close to no matrilineal households in the Northern sample,

18 Villages were selected based on probability proportional to size. Households within these villages were randomly selected. All summary statistics are weighted based on the probability of being sampled and clustered at the village level. 19 The price index was calculated by the NSO. It consists of a spatial price index, calculated as a Laspeyres price index using prices for 29 food items and 13 non-food items with base period February/March 2010, and a temporal price index, calculated using the monthly CPI for the three regions. 20 Descent is measured based on the following question, which was asked to village informants: "Do individuals in this community trace their descent through their father, their mother, or are both kinds of descent traced?" I label the category where both kinds of descent are possible as ’dual descent,’even though strictly speaking, each household will practise one or the other. I report the results for this category but focus on the distinction between matrilineal and patrilineal households. It needs to be acknowledged that there could be a small element of endogeneity to this variable, because it measures the descent traced in the village where the couple are resident, which may not be the descent traced by the couple’s family. As a result, the choice of residence may aﬀect this. However, I assume that this is not a problem, primarily because individuals are likely to reside in the village of one of the spouses’families. As inter-marriage between matrilineal and patrilineal individuals is uncommon, the village is likely to have the same descent pattern as the household itself.

18 53% of the Southern sample is matrilineal. Divorce rates are highest in matrilineal communities, on average.21

Table 2: Summary statistics by descent Patrilineal Matrilineal Dual descent P-value P=M=D22 P-value P=M23

Land (total, acres) 2.13 2.06 2.34 0.45 0.42 (0.06) (0.07) (0.24)

HH size 5.12 5.05 5.34 0.12 0.38 (0.06) (0.04) (0.14)

# Own children 2.97 2.93 3.17 0.13 0.50 (0.05) (0.04) (0.11)

Age (husb) 41.51 40.13 42.02 0.00 0.00 (0.39) (0.25) (1.10)

Age (wife) 35.21 34.29 36.34 0.01 0.03 (0.34) (0.23) (0.90)

Any schooling (husb) 0.80 0.81 0.80 0.81 0.56

Any schooling (wife) 0.67 0.69 0.65 0.59 0.40

South 0.24 0.53 0.41 0.00 0.00

Centre 0.57 0.47 0.48 0.13 0.05

North 0.19 0.00 0.11 0.00 0.00

Divorce rate 0.08 0.12 0.11 0.00 0.00 (0.00) (0.00) (0.01)

Patrilocal 0.41 0.22 0.28 0.00 0.00

Matrilocal 0.08 0.17 0.14 0.00 0.00

N (number of obs.) 2448 4409 279 7136 6857 This table reports mean (standard error). Standard errors are not reported for dummy variables.

21 The divorce rate is measured at the district level. It is calculated from the entire LSMS sample and represents the proportion of household heads who reported being separated or divorced. The ﬁgures are consistent with those in Reniers (2003), calculated from the 2001 Demographic and Health Survey of Malawi.

19 Further, there is a clear correlation between lineage and marital residence: patrilineal households tend to be patrilocal, while matrilineal households are almost equally likely to be matrilocal or patrilocal. Other residence types are possible, such as when both spouses are from the same village.24

4.2 Expenditure Data

Summary statistics of real expenditure are in Table 3. The raw statistics in this table lie at the heart of this paper. Much of what has been discussed in the theoretical framework can already be seen at this level. While the South is indeed the poorest region, as observed by the colonialists and missionaries, the same cannot be said for matrilineal communities. In fact, matrilineal households consume more on average than patrilineal households in all regions, with a particularly significant difference in the Southern region. It appears that patriliny is driving the poverty in the South. The difference in mean expenditure between matrilineal and patrilineal households is a statistically significant 11%. In the regression results, I expect to observe lower consumption and more agricultural labour in the South. However, there should be a positive effect of matriliny on consumption and wage labour over and above this.

Table 3: Real household consumption expenditure (’000sMWK) by descent and region Patrilineal Matrilineal Dual Descent All P-value P=M=D P-value P=M North 206.78 209.43 263.17 210.26 0.45 N/A (8.57) (N/A) (42.83) (8.61) N 930 9 79 1018

Centre 220.82 252.91 331.89 244.21 0.02 0.01 (8.60) (8.73) (81.77) (7.01) N 915 1881 111 2907

South 141.66 198.77 173.58 188.00 0.00 0.00 (9.79) (7.81) (20.88) (6.57) N 603 2519 89 3211

All 199.03 224.08 259.85 217.50 0.01 0.00 (5.95) (5.95) (45.62) (4.65) N 2448 4409 279 7136 7136 6857 This table reports mean (standard error).

22 The column "P-value P=M=D" reports the p-value on the test where the null hypothesis is that the value for all three groups is the same. 23 The column "P-value P=M" reports the p-value on the test where the null hypothesis is that the value for matrilineal and patrilineal households is the same. Households in dual descent villages are excluded from this test. 24 Other residence patterns also include neolocality, where a couple sets up their home in a new village, separate from either spouse’sfamily. This includes couples from abroad.

20 4.3 Regression Results

In this Subsection, I test whether the difference in mean expenditure observed in the summary statistics persists when relevant variables are controlled for. This is a test of Proposition 2: do patrilineal households have lower consumption than matrilineal households? For robustness, I examine per capita and equivalent expenditure; I also use alternative measures of wealth, restrict the sample to the Southern and Central regions only, examine savings, include tribal fixed effects and consider village fixed effects in turn. I estimate Equation (2), where the primary coeffi cient of interest is that on the dummy variable capturing patrilineal descent; I also include a dummy variable for dual descent while matriliny is the base case.25 The results are presented in Table 4. Each regression includes a vector of basic characteristics and further controls are added with each specification.26 The key result is that matrilineal households consume significantly more than patrilineal households, on average, in all specifications. I discuss each specification in turn. The first specification includes basic controls only and no dummy variables for descent. Thus, households in the South have 7.8% higher consumption than households in the North, while households in the Central region have 20.8% higher consumption than households in the North. Speci- fication (2) adds dummy variables for patriliny and dual descent: matrilineal households consume 19.0% more than patrilineal households on average. The difference is much larger than the initial difference in means, suggesting that the control variables disadvantage matrilineal households with respect to their consumption levels. In addition, the dummy for the Southern region loses significance and the coeffi cient on the Central region falls in magnitude, suggesting that descent explains a large proportion of the regional variation. Adding geographical variables in specification (3) further controls for the spatial correlation of descent seen in Figure 1. I include a rich set of geographical controls, covering temperature, rainfall, soil quality, greenness and agro-ecological zones.27 These variables explain more than half of the gap between the average consumption levels of matrilineal and patrilineal households. This suggests that patrilineal households are located within the worst geographical areas in each region. Indeed, the regional dummy variables lose significance in regression (3), implying that the geographical variables explain the remaining regional effects that were not explained by descent. In some ways, including geographical controls accounts for Assumption 3, which addresses the productivity of the land. The effect of geography on expenditure is likely to work through agricultural productivity: geography affects the innate productivity of the land, which in turn affects income and thus expenditure. The coeffi cients and significance of the geographical variables

25 All standard errors are clustered at the village level; the sample is also weighted based on the sampling strategy, which selected villages based on probability proportional to size. 26 The basic covariates that are included in all regressions are the amount of rainy and dry season land owned, the age of the spouses, whether the spouses ever attended school, whether they can read English and/or Chichewa, the year the consumption expenditure refers to, the year of the rainy and dry season that agriculture is reported on, the month of the interview, household size and dummy variables for the Southern and Central regions. The Northern region is taken as the base case. 27 Details of these variables can be found in Appendix C.

21 Table 4: The eﬀect of descent on consumption (1) (2) +descent (3) +geo. (4) +econ.,demog. (5) +gender (6) +resid. Ln(real expenditure) Patrilineal -0.190∗∗∗ -0.063∗∗ -0.094∗∗∗ -0.108∗∗∗ -0.104∗∗∗ (0.036) (0.032) (0.032) (0.033) (0.033)

South 0.078∗ -0.070 -0.069 0.042 0.078 0.088 (0.041) (0.050) (0.082) (0.075) (0.077) (0.077)

Central 0.208∗∗∗ 0.097∗∗ -0.014 -0.006 -0.013 -0.016 (0.043) (0.047) (0.070) (0.067) (0.066) (0.066)

Immigration 0.105∗∗∗ 0.103∗∗∗ 0.101∗∗∗ (0.025) (0.025) (0.025)

Any business empl. 0.071∗∗ 0.081∗∗∗ 0.080∗∗∗ (0.029) (0.029) (0.029)

Any wage empl. 0.078∗∗∗ 0.070∗∗∗ 0.071∗∗∗ (0.025) (0.025) (0.025)

Divorce rate -0.010 -0.009 (0.006) (0.006)

Women’sgroup 0.040 0.040 (0.029) (0.029)

Matrilocal -0.088∗∗∗ (0.024)

Patrilocal 0.034∗ (0.019) Basic Y Y Y Y Y Y Geography N N Y Y Y Y Village economy N N N Y Y Y HH Composition N N N Y Y Y N 7136 7136 7136 7136 7136 7136 R2 0.262 0.277 0.333 0.365 0.366 0.369 Standard errors are reported in parentheses. ∗∗∗ denotes signiﬁcance at 1% level, ∗∗ at 5% level and ∗ at 10% level.

22 Table 5: The geographical variables in specification (3) Category Variable Coeffi cient Temperature Average daily range 0.016∗∗∗ Temperature seasonality 0−.000 Min. temp. of coldest month 0.008∗ Avg. temp. of wettest quarter 0−.007 Rainfall Avg. 12-month tot. rainfall in 2009, 2010 0.001 , 0.001 − ∗∗ ∗∗ Avg. rainfall in wettest quarter in 2009, 2010 0.001∗∗, 0.001∗ Avg. start of wettest quarter in 2009, 2010 0.010, 0−.014 − Greenness Total change in greenness in 2009,2010 0.005 , 0.007 ∗∗ − ∗∗∗ Onset of greenness increase in 2009,2010 0.006∗∗∗, 0.003 Onset of greenness decrease in 2009,2010 0.008 , 0.001 − ∗∗∗ Soil quality Nutrient availability F = 4.62∗∗∗ Rooting conditions F = 22.15∗∗∗ Excess salts F = 4.67∗∗∗ Agro-ecology Agro-ecological zones F = 1.32 Greenness is the emergence of vegetation at the beginning of the growing season. The four agro-ecological zones in Malawi are tropic-warm/semiarid, tropic-warm/subhumid, tropic-cool/semiarid and tropic-cool/subhumid. ∗∗∗ denotes significance at 1% level, ∗∗ at 5% level and ∗ at 10% level. are reported in Table 5. Particularly significant effects are observed for temperature variance and greenness, which is the onset of spring. Soil quality is especially significant, which is intuitive as this is a crucial factor that affects crop growth. Most of the geographical variables have p-values less than 1%, suggesting that they explain agricultural productivity well. Regression (4) includes economic and demographic variables: employment opportunities28, prop- erties of the village economy29 and household composition.30 These characteristics affect the

28 Employment opportunities are captured by three variables: Immigration, Any business employment and Any wage employment. Immigration is a dummy variable defined by the community-level response to the question "Do people come to this community during certain times of the year to look for work?" The employment sector variables are defined by the community-level response to the question "Which activities are the three most important sources of employment for individuals in this community?" Business employment includes beer-brewing, handicraft production, small-scale industry and any other responses suggesting business. Wage employment includes small-scale trade and service provision, large-scale commercial industry, professional occupations, civil service, and any other responses suggesting wage work, such as working on an estate. If any of these activities are listed as one of the three most important sources of employment for the village, a ‘1’is recorded for the relevant variable. Primary economic activities (farming, fishing and selling firewood) are excluded from the analysis because more than 99% of communities report engaging in those. 29 This category consists of eight variables: one controls for the distance from the household to the nearest road and one for the household’s distance to the nearest town with a population of at least 20 000, while the remaining six measure the proportion of surveyed households (excluding the respondent) in the respondent’svillage that farm maize, tobacco, rice, groundnut, cassava and mango. 30 This category consists of variables measuring the number of household members that are male children, female children, male adults, female adults, male elders, female elders and individuals whose gender or age is missing. It also includes a dummy variable measuring the gender of the household head and a further dummy variable indicating the presence of a brother-in-law in the household. When these variables are included, household size is omitted.

23 income-earning opportunity set and the trade-off between different types of labour. Economic and demographic characteristics do not explain the consumption gap, which is a statistically significant 9.4%. Employment opportunities are important, however: the presence of immigration, suggesting a strong village economy, raises average consumption by over 10%. The presence of business employment raises consumption by 7% on average and the presence of wage employment raises average consumption by almost 8%. When variables relating to gender are included in regression (5), the consumption gap does not change in a significant way. The divorce rate and women’sstatus do not explain the consumption gap.31 The summary statistics illustrated that marital residence is highly correlated with kinship. I add marital residence, measured at the household level, in regression (6). Relative to other residence patterns, matrilocality is damaging to consumption levels, while patrilocal households fare slightly better on average. This suggests that, on average across all kinship types, patrilocal households have the highest consumption. This is not inconsistent with the consumption gap observed between patrilineal and matrilineal households, as matrilocality is the least common residence choice for all kinship groups. A household’s location decision may not be entirely exogenous, even if it is highly correlated with descent. Patrilineal households have a limited ability to choose their residence. However, wealthier men in matrilineal communities are more likely to locate patrilocally because they are more likely to have access to their family’sland. Although I control for the amount of land owned, there may be a marriage market factor that I am unable to capture.32 Due to this fact, I do not include marital residence in the remaining regressions of this paper. I choose regression (5) as the preferred specification. Having controlled for basic characteristics, geography, economic characteristics, household composition and gender, a highly significant consumption gap of over 10% between matrilineal and patrilineal households persists. This gap is consistent with the 11% gap observed in means. There is strong evidence to support Proposition 2, namely that matrilineal households consume significantly more than patrilineal households.

4.4 Robustness Checks

In this Subsection, I carry out several robustness checks. First, I verify that the consumption gap is observed in alternative measures of consumption. I replace real household expenditure in regression (5) with per capita (pc) real expenditure and equivalent (eq) real expenditure. Equivalent expenditure is a more accurate measure of per capita expenditure: it gives children a lower weight than adults because the former consume less.33 These results are speciﬁcations (7) and (8) in Table

31 Gender includes the proportion of divorced household heads in the district and a dummy variable for the existence of a women’sgroup in the village, which acts as a proxy for women’sstatus. 32 Bargaining power may be an unobservable omitted variable that could bias the results. It will be related to the marriage market as well as to the choice of residence. However, there is little reason to believe that bargaining power should aﬀect total consumption except through channels such as fertility and labour choices, which are observable. In later sections I analyse labour and intra-household allocation, which may be more aﬀected by bargaining power. 33 The weights were chosen by NSO researchers and are as follows: 0.33 for children aged under 1, 0.47 for ages 1-2, 0.55 for ages 2-3, 0.63 for ages 3-5, 0.73 for ages 5-7, 0.79 for ages 7-10, 0.84 for ages 10-12, 0.91 for ages 12-14, 0.97

24 6. It is clear that the effect of kinship on expenditure holds across these alternative measures of expenditure and is not significantly different from the gap in regression (5).34 This confirms that the consumption gap between matrilineal and patrilineal households is robust to alternative measures of spending. Second, I address the issue that land may be an inadequate measure of wealth, insofar as it is inaccurate or has an endogenous element. In regression (9), I replace land with two alternative measures of wealth: the number of livestock owned and the type of construction material used for the house, with the best type (permanent) as the base case. Although the consumption gap is slightly smaller in this specification, patrilineal households still consume significantly less on average than matrilineal households. Third, there is the issue that only nine households in the Northern sample are matrilineal. As a result, there may be insuffi cient variation in kinship in the North to provide accurate results. In regression (10), I restrict the sample to the Southern and Central regions only. The coeffi cient of interest is a statistically significant 10.8%, suggesting that the inclusion of Northern households does not invalidate the results. Fourth, I discuss savings. It may be that matrilineal households are impatient, in which case they exhibit higher consumption today at the expense of future consumption. As a result, it would be misleading to conclude that matrilineal households are more productive. In addition, examining

de2 savings can shed some light on the sign of dλ . Rural households in Malawi have limited savings; their low income is associated with a high marginal propensity to consume. Direct data on the amount of savings are not available and less than 1% of households report non-zero values of interest earned on savings and pension income. Alternative measures of savings are the use value of durables and the number of livestock, which 85% and 53% of households report having non-zero values of respectively. Estimating specification (5) but replacing consumption with the use value of durables or the number of livestock does not yield a significant coeffi cient on patriliny in either estimate (results not reported). This implies that these measures of savings are not significantly different across matrilineal and patrilineal households. The survey also includes a question on the household head’s subjective assessment of whether household income is suffi cient for building household savings. Matrilineal households report that they are significantly better able to build their savings than patrilineal households, with a p-value of 0.00. The evidence suggests that either savings are no different between matrilineal and patrilineal households or the former have higher savings. Therefore, matrilineal households are likely to have higher consumption in the future, not just at present. Fifth, I consider the possibility of tribal fixed effects. There may be characteristics of tribes that correlate with descent and consumption but that do not affect outside options, such as work ethic. for ages 14-16 and 1 for ages 16 and up. 34 There is a possible issue of under-reported household size. There may be several households using the same cooking facilities for example, even though they refer to themselves as separate households. In this sense it would appear as though per capita consumption is higher than it actually is. Whether this is more likely to occur in matrilineal households is ambiguous, however, so this remains a caveat on the results.

25 These need to be taken into account to ensure the validity of interpreting kinship as capturing relative outside options. In Table 17 in Appendix D, I add a series of dummy variables measuring the most spoken language in the community to specification (5). As language is highly correlated with tribe, this is a good measure of tribal fixed effects. The coeffi cient on patriliny is still significant and negative, with a slightly smaller magnitude. It is interesting that among those communities speaking one of the three most common languages (Chewa, Yao and Tumbuka), those speaking the predominantly matrilineal languages of Chewa and Yao have significantly higher consumption than those speaking the predominantly patrilineal Tumbuka. In addition, the fact that there is a negative patrilineal effect over and above these tribal fixed effects further supports Proposition 2; for example, being in a tribe that speaks Chewa is beneficial but being patrilineal invalidates some of this positive effect. Comparing the 10.8% coeffi cient observed in specification (5) to the 8.6% coeffi cient observed here, it can be argued that there is a 2.2% consumption gap between matrilineal and patrilineal households due to tribal customs and a 8.6% gap due to the direct effect of land rights, which are interpreted as capturing spouses’relative outside options.35

Table 6: Robustness checks (7) (8) (9) + other wealth (10) S&C only Ln(pc real exp) Ln(eq real exp) Ln(real expenditure)

Patrilineal -0.108∗∗∗ -0.106∗∗∗ -0.085∗∗∗ -0.108∗∗∗ (0.033) (0.033) (0.032) (0.034)

Semi-permanent -0.273∗∗∗ (0.023)

Traditional -0.391∗∗∗ (0.021)

# Livestock 0.012∗∗∗ (0.001) N 7136 7136 7136 6118 R2 0.369 0.351 0.421 0.365 Controls included: Basic (land excluded in (9)), Regions, Geography, Village economy, Employment, HH Composition and Gender. Standard errors are reported in parentheses.

∗∗∗ denotes signiﬁcance at 1% level, ∗∗ at 5% level and ∗ at 10% level.

35 An alternative way of capturing geography and other economic variables is to include village-level fixed effects. However, since kinship is also measured at the village level, there is insuffi cient variation to estimate the effect of kinship when village-level fixed effects are included. In fact, when I estimate regression (5), omitting all village-level variables apart from kinship but including village-level fixed effects, the patriliny variable is forcibly dropped: Stata does not provide an estimate of the coeffi cient due to multicollinearity.

26 5 Labour

The driving force of the predicted effect of land rights on consumption in Section 2 is the men’s labour decision, which I analyse in this Section. Thus far, the results have shown that matrilineal households have higher consumption. According to the theoretical framework, this is because matrilineal men allocate a greater share of their labour to wage work. The necessary assumption for this to be a valid explanation of the results in Section 4 is that wage work is more productive than agricultural work.36 I test, in the first instance, whether this assumption holds. Second, I test for a difference in labour allocation by kinship.37 For robustness, I also examine income.

5.1 Wages and agricultural productivity

In order to give an indicative idea of whether there is support for Assumption 4, I calculate the rate of return to wage work and agricultural work for husbands in the sample. In line with the theoretical framework, I assume that while married, agricultural income can be treated as a public good. Also in line with the framework, I assume that wages are kept by husbands on divorce but agricultural income is allocated based on kinship: matrilineal husbands do not receive any agricultural income on divorce, while patrilineal men receive all of their agricultural income. In order to take these expectations into account, I calculate the annual probability of divorce by assuming a model where individuals move between the two states of marriage and divorce according to a Markov chain (see Appendix E for full calculations). The probability of divorce in each period can be calculated if the stationary proportions of married and divorce individuals are known, along with the probability of moving from the divorced to the married state (the remarriage probability). I assume that the current distribution of married and divorced individuals in the nationally representative LSMS sample is the stationary solution to this model. I calculate the remarriage probability using the Malawi Longitudinal Study of Families and Health (2010), which asks husbands about when their marriages started and ended. Using this information, I calculate the average proportion of divorced husbands who remarry within a year. This is the annual remarriage probability. I calculate this separately for patrilineal and matrilineal husbands. I feed this information into the model to yield the annual divorce probability. These ﬁgures are in Table 7. The return to wage work is calculated as the average hourly wage in the sample. Respondents engaged in wage work reported the amount of their last salary and the period of time it covered; from this, I calculate the hourly wage of each individual. I also calculate the average hourly wage paid by the Malawi Social Action Fund (MASAF) public works programme for comparison; this is a lower bound on wages as the programme oﬀers a social safety net in particularly poor villages.38

36 A caveat on this is that if labour and consumption are codetermined, the former will not explain the latter. However, in the theoretical framework, the assumption is that the husband treats the two decisions separately. M 37 dh1 dh2 The theoretical framework predicts that dλ > 0 and dλ > 0. Since I have assumed that the sample consists of a combination of households in their ﬁrst and second periods, I test for a weighted average of these two eﬀects. 38 The average hourly MASAF wage is calculated as the average of the reported male and female MASAF wages, divided by four. This is because the daily MASAF tasks have been estimated to take four hours (Chirwa, Mvula

27 The wage is purposefully set below the market wage (Dzimadzi and Chinsinga 2004); in this sense it operates differently to employment guarantee schemes elsewhere (such as the National Rural Employment Guarantee of India, which sets wages above the market clearing wage). The agricultural product is calculated as the estimated value of consumption from own production in the last year divided by the annualised number of hours of own-farm agricultural labour by all household members.39 However, this is not a completely accurate representation of the return to agricultural labour for matrilineal men, as their expected return depends on the probability that they will be divorced next year. Assuming they don’t receive any agricultural income on divorce, their expected average product of agricultural labour is, in its simplest form, the probability that the marriage will not end next year times the average product of agricultural labour. Since I assume that patrilineal men keep all their land on divorce, their average product of agricultural labour (APAL) is the same as their expected APAL. I take a weighted average of these three calculations for matrilineal and patrilineal households; this is in Table 7. There are four key points in this table. First, the estimated annual divorce probability is almost twice as high among matrilineal households than patrilineal households. This is consistent with the stylised fact that matrilineal communities tend to have more marriages and divorces than patrilineal communities, with a shorter average duration of marriage. Indeed, the remarriage rate is higher among matrilineal communities, suggesting that their turnover of marriages is higher. Second, the estimated wage is significantly higher than the estimated APAL for both groups. This provides strong evidence for Assumption 4. The difference is particularly pronounced in matrilineal communities when the divorce probability is taken into account in estimating the expected rate of return to agricultural work. The expected APAL is significantly lower than the wage for matrilineal husbands, which rationalises the decision of matrilineal husbands to engage in more wage work than patrilineal husbands. Third, the MASAF wage is not significantly different from the APAL. As the MASAF wage is set below market wages, this is consistent with the idea that wages are substantially higher than the APAL. Fourth, the estimated wage is not significantly different across matrilineal and patrilineal communities. This suggests that differences in consumption and labour allocation are not attributable to differences in returns. Therefore, these results provide convincing evidence that the rate of return to wage work is significantly higher than the rate of return to agricultural work. and Dulani 2004). Note that in the survey, the MASAF wage is only reported by those villages that have a MASAF program, which are likely to be particularly poor. 39 Assuming that production exhibits diminishing marginal returns, if the average agricultural product is lower than the wage, then the marginal agricultural product is also lower than the wage. I assume that the average agricultural product is the same for all household members. This is a simplifying assumption that ensures the identification of the agricultural product, because it is not possible to identify how much of consumption from own production came from the labour of each individual household member. A further important assumption for these calculations to have a valid interpretation is that the wage and agricultural product are constant. If they are not and people only accept wage work when the wage is high enough or only work for agriculture if the average product is high enough, then the calculated average wage and agricultural product will over-estimate their true values. Although the over-estimation itself is not a problem for testing Assumption 4, an issue arises if they are over-estimated by different amounts.

28 Table 7: Estimates of the wage and agricultural product Parameter Matrilineal Patrilineal Proportion married (pm) 72.4% 76.4% Proportion divorced (pd) 12.3% 8.7% Annual remarriage probability (β) 63.3% 45.5% Annual divorce probability (α) 10.7% 5.2% Average Wage 177.36 185.44 (21.16) (22.93) N 829 425

MASAF Wage 111.27 161.56 (24.31) (59.95) N 828 343

Average Product of Agricultural Labour 115.74 122.51 (10.72) (20.17) N 3506 1881 Expected Average Product of Agricultural Labour 103.31 122.51 H0: Average Wage=APAL 0.01∗∗∗ 0.04∗∗ H0: MASAF Wage=APAL 0.87 0.48 Units are MWK. This table reports mean (standard error). The expected average product of agricultural labour is calculated as ((1 α) + λα) AP AL, where α is the divorce − ∗ probability and λ is the share of land kept on divorce (which equals one in patriliny and zero in matriliny). Tests of null hypotheses report p-values and signiﬁcance: * indicates signiﬁcance at 10%, ** at 5% and *** at 1%.

29 5.2 Labour Allocation

In order to analyse the impact of kinship on labour allocation, I first present summary statistics of labour allocation in Table 8. This table shows the number of hours each spouse spends per week on each type of activity, disaggregated by lineage.40 Women tend to work harder when they are patrilineal, while men tend to work harder when they are matrilineal. This is consistent with the idea that women have more autonomy in matrilineal communities, while men have more autonomy in patrilineal communities. Men tend to split their time between different types of economic labour, while women tend to engage in agricultural labour and domestic work, devoting less than an hour per week on wage labour. This is in line with the division of labour assumed in the theoretical framework. Both spouses tend to allocate more labour to agricultural work when they are patrilineal, while both spouses tend to allocate more labour to wage work when they are matrilineal. Patrilineal spouses allocate more time to ganyu labour than matrilineal spouses. This makes sense, as ganyu labour is typically carried out by the poorest households (see footnote 39). I verify whether these labour allocation patterns are still present in a regression analysis when relevant variables are controlled for. I estimate Equations (3)-(6) specified in Section 3. The results for husbands’and wives’labour are in Table 9. Husbands’labour allocation is in line with the theoretical predictions. While there is no significant difference between the total labour time of patrilineal and matrilineal men, the former spend approximately 1 hour and 30 minutes more on agriculture and a similar amount of time less on wage work per week, on average. This substitution effect is confirmed in regressions (IV) and (V), as patrilineal men spend over three hours more on agriculture than wage work, while the sum of these two labour types is not significantly different between matrilineal and patrilineal men.41 The results for women’slabour show that patrilineal women work harder than matrilineal women on average. Most of this additional work takes the form of agricultural labour. They also appear to substitute a small amount of agricultural labour for wage labour. There is no a priori reason why this should be the case. One possible reason is the omission of an important unobservable variable:

40 Total labour includes own-farm agricultural labour, wage labour, business activities, ganyu labour and unpaid labour. Ganyu labour is agricultural labour on other people’sland, which is paid a very low wage either in cash or in kind. Engaging in ganyu labour is a sign of food insecurity as households are choosing to work on others’land for a low but immediate wage instead of working on their own land. Wage labour captures any work, excluding ganyu, carried out for a wage, salary or commission. Domestic labour is time spent fetching water and firewood. The questionnaire did not ask about more typical domestic tasks like cooking and cleaning. In addition, there is no data on leisure, which is why the total number of hours is not equal to the number of hours in a week. 41 Although the regressions control for employment opportunities, a more careful analysis of employers can verify that the observed difference in wage work hours is an active choice made by households rather than a result of employment opportunities. When examining the share of individuals working for different types of employers in the whole sample, it is observed that significantly more matrilineal than patrilineal men work for private individuals and companies and state-owned organisations. No difference is observed for government and religious employers. However, when the same analysis is conducted for the restricted sample of those husbands who engage in wage work, no difference in shares of matrilineal and patrilineal husbands working for different employers is observed apart from government employers. However, this is significant at the 9% level and the government only employs 4% of the whole sample. These findings suggest that any difference in employers observed for the whole sample is driven by the fact that matrilineal men are more likely to work for wages in the first place. Conditional on having decided to work for wages, almost no difference in employer is observed. This suggests that there is little difference in the employment opportunities available to matrilineal and patrilineal households.

30 Table 8: Labour hours per week Labour Matrilineal Patrilineal Dual descent P=M=D P=M Total Husb. 22.61 21.23 18.98 0.16 0.16 (0.62) (0.77) (2.32) Wife 13.70 15.39 11.74 0.06 0.04 (0.44) (0.69) (2.01) Agricultural (own-farm) H 11.73 12.26 10.51 0.55 0.46 (0.42) (0.57) (1.68) W 10.94 11.80 9.55 0.32 0.22 (0.40) (0.59) (1.81) Wage H 5.28 3.75 5.02 0.04 0.01 (0.49) (0.36) (2.08) W 0.47 0.26 0.40 0.17 0.06 (0.09) (0.07) (0.32) Ganyu H 2.66 2.88 1.66 0.02 0.48 (0.17) (0.26) (0.38) W 0.94 1.57 0.55 0.00 0.00 (0.08) (0.16) (0.16) Domestic H 0.84 0.62 0.60 0.12 0.05 (0.07) (0.08) (0.23) W 8.33 8.55 7.51 0.22 0.41 (0.18) (0.20) (0.59) N 4409 2448 279 7136 6857 This table reports mean (standard error). Columns 4 and 5 report p-values for the rejection of the null hypothesis.

31 Table 9: The eﬀect of descent on labour allocation (I) (II) (III) (IV) (V) Husband’sLabour Total Agric Wage Agric - Wage Agric + Wage Patrilineal -0.139 1.509∗∗ -1.644∗ 3.153∗∗∗ -0.136 (1.018) (0.701) (0.843) (1.199) (0.983) N 7136 7136 7136 7136 7136 R2 0.149 0.181 0.147 0.170 0.152

(VI) (VII) (VIII) (IX) (X) Wife’sLabour Total Agric Wage Agric - Wage Agric + Wage Patrilineal 1.788∗∗ 1.433∗∗ -0.332∗∗ 1.765∗∗ 1.101 (0.798) (0.664) (0.167) (0.691) (0.679) N 7136 7136 7136 7136 7136 R2 0.162 0.208 0.048 0.193 0.192 Controls included: Basic, Region, Geography, Village economy, HH Composition Gender and the price index. Standard errors are reported in parentheses. ∗∗∗denotes significance at 1% level, ∗∗ at 5% level and ∗ at 10% level. bargaining power. If bargaining power is omitted and if husbands with higher bargaining power do not allow their wives to engage in wage labour to safeguard their power, then a downward bias on the coeffi cient of patriliny will result: a negative effect which is mistakenly attributed to land rights, rather than low bargaining power. The results suggest that labour allocation may explain the consumption gap observed in Section 4. Matrilineal households consume more than patrilineal households. Simultaneously, matrilineal men spend more time on wage labour and less time on agricultural labour. Therefore, there is support for both propositions of the theoretical framework.

5.3 Robustness: Income

To verify the robustness of the labour allocation results, I examine the eﬀect of kinship on income: patrilineal households should earn less income and, in particular, less wage income by the husband than matrilineal households. Table 10 supports this prediction.42 Patrilineal households earn less income than matrilineal households and patrilineal husbands earn less wage income than matrilineal husbands, on average. The income gap represents approximately 40% of mean income for the entire

42 The construction of the income aggregate follows the method of Hoddinott and Haddad (1995). Income was calculated as the sum of crop sales, wages from employment, earnings from ganyu, proﬁt from business activities, remittances and other gifts, for all members of the household. I do not include income from livestock sales. Wage earnings represent the ’wages from employment’component of income earned by the husband and thus exclude any earnings from ganyu. The relative importance of these components in income is as follows: ganyu forms the largest share of income on average (29.9%), followed by crop sales (29.0%), wage employment (16.1%), proﬁt from business (13.6%) and remittances (11.4%). The share of wage earnings in income is lower for patrilineal than matrilineal households, while the share of ganyu earnings in income is higher for patrilineal than matrilineal households. This is consistent with the labour patterns observed in the previous Subsection.

32 Table 10: Income (XI) (XII) Income Wage earnings (H) Patrilineal -32.474∗∗ -15.817∗∗∗ (15.534) (6.111) N 7136 7136 R2 0.082 0.088 Controls included: Basic, Region, Geography, Village economy, HH Composition and Gender. Standard errors are reported in parentheses. ∗∗∗denotes significance at 1% level, ∗∗ at 5% level and ∗ at 10% level. sample. The wage earnings gap is 19.2% of mean income, which is not inconsistent with the gap observed for total expenditure.43 The evidence shows that matrilineal households consume significantly more than patrilineal households; at the same time, patrilineal men dedicate more of their labour to agriculture than wage work and earn less wage income. This suggests that while matrilineal men are deterred from investing in land, this is beneficial to the household. This is an example of ‘positive’hold-up. The husband’sweaker outside option increases the productivity of the household.

6 Intra-Household Allocation

The analysis in Section 4 focused on consumption at the household level and per capita. The analysis of per capita consumption may not reflect consumption at the individual level, as the benefits and costs of differences in household productivity may accrue asymmetrically. If this is the case, a welfare comparison of matriliny and patriliny is not clear-cut. To explore this issue, I examine the intra-household allocation of expenditure. Inferring individual consumption from household consumption data is diffi cult; however, one can look at goods that are private by definition. Two such goods are clothing and education. The former is important for adults whereas the latter is important for children. I examine spending on men’sand women’sclothing and sons’and daughters’ education, as well as total household education spending, total household clothing spending and food spending. First, I present mean expenditure shares of these goods, disaggregated by kinship (Table 11). Patrilineal households spend a significantly higher expenditure share on food, education

43 The gap in wage earnings is even closer to the gap in purchased consumption, which is obtained by excluding consumption from production and gifts from the consumption aggregate and estimating regression (5) with the log of purchased consumption as the dependent variable. The coeﬃ cient on patriliny is 16.3% with a p-value of 0.000 (N = 7135). This is a much larger gap than the gap in total expenditure and is almost the same as the wage earnings gap. This is consistent with the idea that matrilineal households consume more because of husbands’preference of wage labour over agricultural labour, which would make the gap particularly pronounced for that component of consumption that requires cash payment. The regressions are not reported fully because I calculate the purchased consumption aggregate from the raw consumption data using the LSMS/NSO guidelines, which do not give full details of unit conversions and prices. Therefore, the aggregate may not match the LSMS/NSO method completely. However, the correlation between the two measures, when comparing total expenditure, is high.

33 and sons’education. However, from summary statistics alone, it is not possible to ascertain whether this is due to the difference in total expenditure by kinship or whether kinship has an effect on intra-household allocation over and above its effect through total expenditure. In order to explore the direct effect of kinship on intra-household allocation, I estimate a series of Working-Leser expenditure functions as in Equation (7) in Section 3, examining the effect of kinship on the share of expenditure devoted to the various categories discussed.44 Although the literature tends to instrument expenditure with income (e.g. Attanasio and Lechene 2010), I do not do this because of the typically low savings of households in Malawi discussed in Section 4, which imply that income and expenditure are highly correlated. As a result, income will be just as endogenous as expenditure. Instead, I instrument expenditure with the number of livestock owned and the construction material of the dwelling, which together capture wealth. The right-hand side variables are as in specification (5); I also include the log of household size and the log of prices of various goods as additional controls.45 The results of these Two Stage Least Squares regressions are in Table 12. The key result is that descent has a weak positive effect on the expenditure share devoted to sons’education and men’sclothing and no effect on the remaining variables in Table 12. Patrilineal households allocate an additional 0.1% of their expenditure to sons’education and men’sclothing, compared to matrilineal households. This suggests the presence of gender bias and is consistent with the idea that husbands are more empowered in patrilineal households. However, the magnitude of the effect is small, which suggests that the assumption of the theoretical framework regarding the independence of α and λ is a reasonable one. The coeffi cients on other variables of interest indicate that the regressions are well-specified. For example, the share of expenditure allocated to food in regression (a) declines significantly with per capita expenditure, which is in line with Engel’slaw. In contrast, the share of expenditure accruing to clothing is significantly increasing with per capita real expenditure. The effect of household composition also indicates that the regressions are well-specified. While all demographic groups but one increase food expenditure, there are differential effects for other expenditure categories. For example, regression (b) shows that the share of expenditure allocated to education is increasing in the number of adults in the family but decreasing with the number of children. An additional adult is associated with an approximately 0.5% higher share of expenditure allocated to education, whereas an additional child is associated with an approximately 0.3% lower share of expenditure allocated to education. 44 This analysis could be improved on by estimating a full demand system, such as an Almost Ideal Demand System. However, the lack of suffi ciently detailed price data prevents this (see footnote 45). 45 The regressions include the log of the cost of milling maize and the cost of milling rice and the log of the prices of maize grain, maize flour, rice, bread, scones, beans, cabbage, tomatoes, banana, milk, egg, chicken, fish, beef, tea, salt, sugar, oil, chips, soap, a toothbrush, toothpaste, clothes soap, vaseline, chitenje cloth, trousers, coca cola, beer, cigarettes, a watch, firewood, charcoal, paraffi n, a bicycle, a mattress and a mosquito net. On average, the goods for which prices are available represent 55.2% of spending. 46 One observation was not included in the expenditure share analysis because the disaggregated expenditure calculated from the data did not match the value calculated by the NSO and the reason for the difference was not clear.

34 Table 11: Summary statistics of expenditure shares Patrilineal Matrilineal Dual Descent P=M=D P=M Food (%) 66.65 63.64 62.31 0.00 0.00 (0.46) (0.36) (1.57) Education (%) 1.12 0.94 1.45 0.05 0.03 (0.07) (0.05) (0.38) Daughters’education (%) 0.47 0.43 0.59 0.55 0.50 (0.04) (0.03) (0.16) Sons’education (%) 0.59 0.46 0.55 0.04 0.07 (0.04) (0.03) (0.11) Clothing (%) 2.60 2.86 2.61 0.24 0.11 (0.13) (0.10) (0.27) Women’sclothing (%) 0.93 1.02 0.88 0.22 0.13 (0.05) (0.04) (0.11) Men’sclothing (%) 0.62 0.60 0.56 0.85 0.73 (0.04) (0.03) (0.10) N 46 2448 4408 279 7136 6857 This table reports mean (standard error). Columns 4 and 5 report p-values for the rejection of the null hypothesis.

Regressions (c) and (d) show that there is preferential treatment in educating daughters and sons, depending on household composition. An additional female adult reduces the percentage share of expenditure devoted to sons’education by 0.3% and increases the share devoted to daughters’ education by 0.7%. Similarly, an additional male adult increases the share spent on sons’education by 0.6% and reduces the share spent on daughters’education by 0.2%. This pattern is also seen in the effect of elderly male and female members of the household. Together, these results suggest that there is strong preference for educating own-sex children in households in Malawi. Regressions (f) and (g) show that there is little effect of demographic composition on men’sand women’sclothing. There is evidence of increasing and decreasing economies of scale to household size: while there are increasing economies of scale to the purchase of food, there are decreasing economies of scale to education. The results demonstrate that patrilineal households spend a greater share of their expenditure on their sons’education and men’sclothing. However, the magnitudes are small and the coeffi cients are only significant at the 10% level. This suggests that the correlation between kinship and the intra-household allocation of private goods is weak. This supports the assumption of the framework that land rights (λ) and how consumption goods are shared in marriage (α) are independent. The results also demonstrate that there is strong gender bias in the intra-household allocation of children’seducation in Malawi.

35 Table 12: The eﬀect of descent on intra-household allocation (a) (b) (c) (d) (e) (f) (g) Food (%) Educ (%) Daughters’educ (%) Sons’educ (%) Clothing (%) Women’sclo (%) Men’sclo (%) Ln(pc real expend) -13.190∗∗∗ 0.332 0.133 0.320∗∗∗ 0.891∗∗ 0.150 0.261∗ (1.155) (0.232) (0.175) (0.118) (0.365) (0.128) (0.152)

Patrilineal -0.885 0.092 -0.007 0.110∗ -0.150 -0.101 0.091∗ (0.818) (0.094) (0.047) (0.063) (0.183) (0.075) (0.054)

Ln(HH size) -16.275∗∗∗ 1.909∗∗∗ 0.978∗∗∗ 1.323∗∗∗ 1.537∗∗∗ -0.664∗∗∗ -0.088 (2.351) (0.369) (0.241) (0.233) (0.579) (0.251) (0.231)

# children (m) 1.402∗∗∗ -0.274∗∗∗ -0.239∗∗∗ -0.118∗∗ -0.204∗ 0.041 -0.011 (0.461) (0.088) (0.059) (0.051) (0.105) (0.046) (0.039)

# children (f) 1.669∗∗∗ -0.249∗∗∗ -0.066 -0.254∗∗∗ -0.178∗ 0.069 -0.009 (0.480) (0.086) (0.059) (0.053) (0.107) (0.049) (0.039)

# adults (m) 1.000∗ 0.557∗∗∗ -0.195∗∗∗ 0.591∗∗∗ -0.292∗∗ 0.038 0.001 (0.551) (0.105) (0.055) (0.078) (0.116) (0.053) (0.042)

# adults (f) 1.723∗∗∗ 0.516∗∗∗ 0.720∗∗∗ -0.264∗∗∗ -0.186 0.166∗∗∗ 0.001 (0.579) (0.122) (0.093) (0.069) (0.136) (0.057) (0.051)

# elderly (m) 2.904∗∗∗ -0.002 -0.493∗∗∗ 0.330∗∗ -0.158 0.233∗∗ 0.023 (1.104) (0.228) (0.173) (0.134) (0.257) (0.111) (0.085)

# elderly (f) -1.174 -0.119 0.199∗ -0.453∗∗∗ 0.094 0.118 0.111 (1.024) (0.179) (0.106) (0.116) (0.239) (0.099) (0.086) N 7135 7135 7135 7135 7135 7135 7135 Ln(pc real expend) instrumented with number of livestock and construction material of dwelling. F-stat on excluded instrument: 140.6; partial R2: 0.10. Controls included: Basic, Region, Geography, Village economy, Employment, Gender, # uncategorised HH members, Ln(various prices) (see footnote 45). Standard errors are reported in parentheses. ∗∗∗ denotes signiﬁcance at 1% level, ∗∗ at 5% level and ∗ at 10% level. 7 Conclusion

The aim of this paper has been to estimate the impact of spouses’outside options on productivity. Matriliny and patriliny have a key distinction in that descent and therefore land rights pass through the female line in matriliny but through the male line in patriliny. This implies that women have stronger land rights than their husbands in matrilineal households, while men have stronger land rights than their wives in patrilineal households. Detailed household data on Malawi, where both kinship systems co-exist, have been used to provide a causal regression analysis of the impact of descent on consumption, where descent is interpreted as capturing spouses’relative outside options. Historically, matriliny has been criticised because the fact that men do not own land could lead to disincentives for long-term investment and, as a result, poor economic performance. This paper shows that while matrilineal men are disincentivised from agricultural labour, this leads to better economic outcomes. Matrilineal households have higher consumption, on average. More generally, this demonstrates that the size of the household pie is not invariant to spouses’outside options. The existing literature on intra-household allocation needs to take into account that productivity and thus total resources may change with bargaining power. I show that matrilineal households, where women have strong land rights, consume over 10% more than patrilineal households. These results are confirmed for per capita and equivalent expenditure. I present a two-stage framework of the husband’s labour allocation decision in rural Malawi, which explains under what assumptions patrilineal men allocate a greater share of their labour to agriculture than matrilineal men. Intuitively, the reason for the difference is the asym- metry between labour specialisation and property division following divorce: only men engage in wage labour; at the same time, they do not have rights to land following divorce under matriliny but have full rights under patriliny. This incentivises matrilineal men to spend more time on wage labour. However, because wages are higher than agricultural productivity, matrilineal households are better off. I provide evidence to support this framework by showing that wages are significantly higher than the average agricultural product, a key assumption of the framework. I show that patrilineal men spend significantly more time on agricultural labour than matrilineal men and that they earn significantly less wage income. Apart from being a goal in its own right, empowering women by increasing their outside options can have positive side effects, as this paper has demonstrated. In developing countries, providing men with incentives to move out of agricultural labour and into wage labour can help households raise their consumption levels. More generally, I have provided evidence to suggest that household productivity depends on spouses’outside options. This needs to be taken into account in studies of bargaining power and consumption.

37 Appendices

A Land Ownership

It is important to provide evidence for the idea that men and women have differential rights to land in patrilineal and matrilineal communities. This can be demonstrated with the LSMS data analysed in this chapter. I examine the plots of land that households use for farming, combining plots used for rainy and dry season cultivation. I calculate the total area of land owned solely by the husband or wife, as well as land owned jointly by the spouses and land owned by other members of the household. The figures are weighted based on the sampling strategy. Table 13 shows these figures, disaggregated by kinship type. The figures show that women own significantly more land and men own significantly less land on average in matrilineal than patrilineal communities. Since the total amount of land owned by households is not significantly different across these communities, this implies that women own a greater share of household land on average in matrilineal than patrilineal communities, while the opposite is true for men. Although the land comes from all sources, including inheritance, leases and purchases, these figures do reflect land entitlement following divorce. This is because most plots reported in the sample were inherited. Therefore, there is significant evidence that women have stronger rights to land following divorce when they are matrilineal, while men have stronger rights to land following divorce when they are patrilineal.

Table 13: Land ownership, disaggregated by owner and kinship Matrilineal Patrilineal Dual descent P-value P=M=D P-value P=M Husband’sland 0.79 1.07 1.16 0.00 0.00 (0.04) (0.07) (0.21) Wife’sland 0.56 0.32 0.37 0.00 0.00 (0.03) (0.04) (0.09) Jointly owned land 0.34 0.29 0.35 0.44 0.27 (0.04) (0.03) (0.07) Others’land 0.38 0.46 0.46 0.17 0.07 (0.02) (0.04) (0.12) Total land 2.06 2.13 2.34 0.45 0.42 (0.07) (0.06) (0.24) N 4409 2448 279 7136 6857 This table reports mean (standard error). The unit of measurement is acres. P-values are reported for the rejection of the null hypothesis.

38 B Proof of Special Case and Propositions

B.1 Special Case

Special Case Suppose Assumptions 1 and 2 hold. Then there is overinvestment in agriculture E E ( h1∗ > h1 ) when λ = 1 and underinvestment in agriculture ( h1∗ < h1 ) when λ = 0. Moreover, there E exists a λ∗ (0, 1) such that h = h . ∈ 1∗ 1 Proof. It is easy to show that the planner’s objective function HW is strictly concave in the

ﬁrst-period labour choice h1. Given Assumptions 1 and 2, the husband and wife’s value functions M M in the married state are identical, or V2 = U2 . Thus, the planner’sﬁrst-order condition is

∂HW 1 e1 S M = κ u0( )w1 + βf 0 (h1) δU 0 (L2) + (1 δ) U 0 (L2) ∂h −2 2 2 − 2 1 1 e1 S M + (1 κ) u0( )w1 + βf 0 (h1) δV 0 (L2) + (1 δ) U 0 (L2) . − −2 2 2 − 2 First, suppose λ = 1. In order to show that that the husband’s choice h1∗ is too high, it is suffi cient, due to the concavity of HW , to show that ∂HW < 0 at h . Note that when λ = 1, the ∂h1 1∗ S wife has no land in the divorced state, so that V2 0 (L2) = 0. Evaluating the derivative at h1∗, the first term is zero by the husband’sfirst-order condition. It is then suffi cient to show that the second term is strictly negative, or

1 e1 M u0( )w1 + βf 0 (h1) (1 δ) U 0 (L2) < 0. −2 2 − 2

This inequality is satified by the husband’sfirst-order condition. Therefore, ∂HW < 0 at h and ∂h1 1∗ E h1∗ > h1 . Second, suppose λ = 0. Note that in this case, the husband has no land in the divorced state, S so that U2 0 (L2) = 0. In order to show the choice of h1∗ is too low, it is suffi cient to show that ∂HW > 0 when the derivative is evaluated at h . Evaluating the derivative at h , the first term ∂h1 1∗ 1∗ is zero by the husband’sfirst-order condition. It is then suffi cient to show that the second term is strictly positive, or

1 e1 S M u0( )w1 + βf 0 (h1) δV 0 (L2) + (1 δ) U 0 (L2) > 0. −2 2 2 − 2 This inequality is satisfied by the husband’sfirst-order condition. Finally, since the planner’s first-order condition is a continuous function of λ, there exists a E ∂HW E λ (0, 1) such that = 0 when the derivative is evaluated at h∗. This implies that h∗ = h ∈ ∂h1 1 1 1 at λE.

39 B.2 Proposition 1

Proposition 1 If Assumption 3 holds, an increase in the share of land accruing on divorce leads

Proof. The ﬁrst-order condition for the choice of h1 is

S M S ∂g(λL2, h2 ) M ∂g(L2, h2 ) β(δλu0(c2 ) f 0(h1) + (1 δ)u0(c2 ) f 0(h1)) αw1u0(c1) = 0. ∂L2 − ∂L2 − Totally diﬀerentiating both sides with respect to λ yields

S S ∂g(λL2, h2 ) βδf 0(h1)(u0(c2 ) ∂L2 2 S S S ∂ g(λL2, h2 ) ∂g(λL2, h2 ) 2 S +λL2(u0(c2 ) 2 + ( ) u00(c2 ))) ∂L2 ∂L2 dh + 1 [α2w2u (c )f (h ) dλ 1 00 1 0 1 S S ∂g(λL2, h2 ) 2 2 +β(δλ(λu00(c2 )( ) (f 0(h1)) ∂L2 2 S S 2 S S 2 ∂ g(λL2, h2 ) dh2 ∂ g(λL2, h2 ) +λu0(c2 )(f 0(h1)) ( S + 2 ) ∂L2∂h2 dL2 ∂L2 S S ∂g(λL2, h2 ) +u0(c2 ) f 00(h1)) ∂L2 M M ∂g(L2, h2 ) 2 2 +(1 δ)((u00(c2 )( ) (f 0(h1)) − ∂L2 2 M M 2 M M 2 ∂ g(L2, h2 ) dh2 ∂ g(L2, h2 ) u0(c2 )(f 0(h1)) ( M + 2 ) ∂L2∂h2 dL2 ∂L2 M M ∂g(L2, h2 ) +u0(c2 ) f 00(h1))] ∂L2 = 0.

This total diﬀerential consists of a constant term, which is the ﬁrst two lines of the expression,

dh1 and the derivative dλ multiplied by several terms. The expression can be rewritten more generally as

dh C + A 1 = 0, dλ S 2 S S S ∂g(λL2,h2 ) S ∂ g(λL2,h2 ) ∂g(λL2,h2 ) 2 S where C = βδf 0(h1)(u0(c2 ) + λL2(u0(c2 ) 2 + ( ) u00(c2 ))) and A is ∂L2 ∂L2 ∂L2 dh1 the term in the square bracket. In order to derive the sign of dλ , the signs of C and A need to be derived. I begin with A. Since the functions u( ), f( ) and g( ) are concave, their ﬁrst · · · derivatives are positive but their second derivatives are negative. Therefore, all terms in A have

40 ∂2g(Aj ,hj ) dhj an unambiguously negative sign, apart from u (cj ) 2 2 (f (h ))2 = K , because the sign of 0 2 j dL2 0 1 j ∂L2∂h2 dhj 2 is unknown. The sign can be derived by totally differentiating the second-period first-order dL2 condition with respect to L2. I will focus on the condition in the divorce state; the following calculations also hold for the married state. The first-order condition is

S ∂g(λL2, h2 ) S = w2. ∂h2 The total derivative is

∂2g(λL , hS) dhS ∂2g(λL , hS) 2 2 2 + 2 2 = 0. S 2 S ∂(h2 ) dL2 ∂h2 ∂L2 Therefore,

dh ∂2g(λL , hS)/∂hS∂L 2 = 2 2 2 2 > 0. 2 S S 2 dL2 − ∂ g(λL2, h2 )/∂(h2 )

This implies that Kj has a positive sign. However, it can be shown that the sum of Kj and an- 2 S S ∂ g(λL2,h2 ) 2 other term in A is deﬁnitely negative. In particular, I take the sum of KS and u0(c2 ) 2 (f 0(h1)) : ∂L2

2 S 2 S S ∂ g(λL2, h2 ) dh2 2 S ∂ g(λL2, h2 ) 2 u0(c2 ) S (f 0(h1)) + u0(c2 ) 2 (f 0(h1)) ∂L2∂h2 dL2 ∂L2 2 S 2 S S 2 ∂ g(λL2, h2 ) dh2 ∂ g(λL2, h2 ) = u0(c2 )(f 0(h1)) ( S + 2 ) ∂L2∂h2 dL2 ∂L2 ∂2g(λL , hS) ∂2g(λL , hS)/∂hS∂L ∂2g(λL , hS) = u (cS)(f (h ))2( 2 2 2 2 2 2 + 2 2 ) 0 2 0 1 S 2 S S 2 2 − ∂L2∂h2 ∂ g(λL2, h2 )/∂(h2 ) ∂L2 2 S ∂ g(λL2,h ) 2 ( 2 ) 2 S ∂L ∂hS ∂ g(λL , h ) = u (cS)(f (h ))2( 2 2 + 2 2 ). 0 2 0 1 2 S S 2 2 −∂ g(λL2, h2 )/∂(h2 ) ∂L2

In order for this term to be negative, the expression inside the brackets needs to be negative: 2 S ∂ g(λL2,h2 ) 2 ( S ) 2 S ∂L2∂h2 ∂ g(λL2,h2 ) 2 S S 2 + 2 < 0. It can be shown that this is the case as the long as the Hessian ∂ g(λL2,h2 )/∂(h2 ) ∂L2 matrix of g( ) has a positive determinant, which is always true if g( ) is concave: · ·

2 S ∂ g(λL2,h ) 2 ( 2 ) 2 S ∂L ∂hS ∂ g(λL , h ) 2 2 + 2 2 < 0 2 S S 2 2 −∂ g(λL2, h2 )/∂(h2 ) ∂L2 2 g S L2h2 + gL2L2 < 0 ⇔ −g S S h2 h2 2 gL2L2 ghS hS gL hS > 0, ⇔ 2 2 − 2 2

where the sign changes in the last line because g S S is negative. The last line is the determinant h2 h2

41 of the Hessian matrix of g( ), which is always positive if g ( ) is concave, which I have assumed to · · be the case. Therefore, the expression is always negative and A is unambiguously negative. The constant term C is unambiguously positive if we assume that the rate of diminishing marginal product of g( ) and rate of diminishing marginal returns of u( ) are low. The term C is · · S 2 S S S ∂g(λL2, h2 ) S ∂ g(λL2, h2 ) ∂g(λL2, h2 ) 2 S βδf 0(h1)(u0(c2 ) + λL2(u0(c2 ) 2 + ( ) u00(c2 ))), ∂L2 ∂L2 ∂L2

whose sign is given by the expression inside the brackets, since βδf 0(h1) is always positive. The ﬁrst term inside the brackets is positive while the second and third are negative. In order for the sum of these to be positive, it is required that

g00 u00 λL2( + g0 ) < 1, (8) − g0 u0 S 2 S ∂g(λL2,h2 ) ∂ g(λL2,h2 ) S S where g0 denotes , g00 denotes 2 , u0 denotes u0(c2 ) and u00 denotes u00(c2 ). ∂L2 ∂L2 In order to understand the implications of the condition more fully, I assume a Cobb-Douglas 1 ρ S θ γ c2− production function and iso-elastic utility: g(λL2, h2 ) = (λL2) h2 , 0 < θ, γ < 1 and u(c2) = ρ . Condition (8) simpliﬁes to

c 2 > g(λL , hS) ρ 2 2 S S w2(1 h ) > (ρ 1)g(λL2, h ), ⇔ − 2 − 2 where ρ is the coeffi cient of relative risk aversion. The condition is likely to be satisfied when the intertemporal elasticity of substitution is high (ρ is low) or wage income is significantly larger than agricultural income. Relating this to Condition (8), at low levels of agricultural production the marginal product of agricultural labour is likely to be high and the condition on the shape of g( ) will be satisfied. A low ρ will satisfy the condition on the shape of u( ). · · dh1 Recalling that A is negative and that C + A dλ = 0, it must be unambiguously true that if the first derivatives of g( ) and u( ) do not diminish at too fast a rate, C is positive and · · dh 1 > 0. dλ S M The effect of λ on h2 and h2 is found by totally differentiating the first-order conditions that S define these optimal choices. For example, in the case of h2 :

∂2g(λL , hS) dhS ∂2g(λL , hS) dh ∂2g(λL , hS) 2 2 2 + 2 2 λf (h ) 1 + 2 2 L = 0. S 2 S 0 1 S 2 ∂(h2 ) dλ ∂h2 ∂L2 dλ ∂h2 ∂L2 Thus,

42 S 2 S 2 S dh2 1 ∂ g(λL2, h2 ) dh1 ∂ g(λL2, h2 ) = − ( λf 0(h1) + L2), ∂2g(λL ,hS ) S S dλ 2 2 ∂h ∂L2 dλ ∂h ∂L2 S 2 2 2 ∂(h2 )

dh1 which is positive as long as dλ > 0. Similarly, differentiating the first-order condition that M defines the optimal choice of h2 yields

M 2 M dh2 1 ∂ g(L2, h2 ) dh1 = f 0(h1) = 0, ∂2g(λL ,hM ) M dλ − 2 2 ∂h ∂L2 dλ M 2 2 ∂(h2 )

dh1 which again is positive as long as dλ > 0.

B.3 Proposition 2

de1 Proposition 2 If Assumption 3 holds, then dλ < 0. If, in addition, Assumption 4 holds, de2 then dλ < 0.

Proof. The derivative of ﬁrst-period consumption with respect to λ is

de1 dh1 = w1 , dλ − dλ

dh1 which is always negative as long as dλ is positive. The derivative of second-period consumption with respect to λ is

M M M de2 dh1 ∂g(L2, h2 ) dh2 ∂g(L2, h2 ) = f 0(h1) ( M + w2). dλ dλ ∂h2 dL2 ∂L2 −

dh1 Since f 0(h1) dλ is positive, a suﬃ cient condition for this to be negative is

M M M ∂g(L2, h2 ) dh2 ∂g(L2, h2 ) M + w2 < 0, ∂h2 dL2 ∂L2 − which is satisﬁed by Assumption 4.

C Variables

Tables 14 and 15 below provide details of the variables used in this chapter. Geographical variables are explained separately in Table 16.

43 Table 14: Variables Variable name Variable type Description Land Continuous Amount of land owned by household (HH), separated by type of cultivation (dry/rainy), in acres Age Continuous Individual’sage Any schooling Indicator = 1 if individual ever attended school, = 0 otherwise HH Size Continuous Number of members of HH South/Centre/North Indicator = 1 if HH is in the South/Centre/North, = 0 otherwise Patrilineal Indicator = 1 if HH resides in a patrilineal community, = 0 otherwise Matrilineal Indicator = 1 if HH resides in a matrilineal community, = 0 otherwise Dual Descent Indicator = 1 if HH resides in a community with both patrilineal and matrilineal descent, = 0 otherwise Patrilocal Indicator = 1 if HH lives in the husband’snatal village, = 0 otherwise Matrilocal Indicator = 1 if HH lives in the wife’snatal village, = 0 otherwise Other residence Indicator = 1 if HH is neither patrilocal nor matrilocal, = 0 otherwise Divorce rate Continuous % of household heads who report being divorced or separated in district Any business/wage empl. Indicator = 1 if any type of business/wage employment is listed as one of three main sources of village employment, = 0 otherwise 44 Crop prop Continuous Proportion of HHs interviewed in village that farm this crop, excluding respondent HH Immigration Indicator = 1 if individuals come to village at certain times of the year to work, = 0 otherwise Dist to nearest road Continuous Distance (km) from HH to nearest road Dist to nearest pop centre Continuous Distance (km) from HH to nearest town with population > 20 000 # Own Children/Children Continuous Number of own children/children, of any age/aged between 0-14 years, that are members of the HH # Adults Continuous Number of adults, aged between 15-59 years, that are members of the HH # Elderly Continuous Number of elderly, aged 60 years or over, that are members of the HH # Uncategorised Continuous Number of individuals whose age was unreported, that are members of the HH Pc/eq real exp Continuous Per capita/equivalent real expenditure Women’sgroup exists Indicator = 1 if a women’sgroup in the village exists, = 0 otherwise Semi-permanent/Traditional Indicator = 1 if house is made of semi-permanent/traditional material, = 0 otherwise. Excluded group: permanent. Language (e.g. Chewa) Indicator = 1 if Language is most spoken in community, = 0 otherwise Table 15: Variables cont. Variable name Variable type Description Total labour Continuous Total number of hours spent last week on agricultural, wage, ganyu, business and unpaid work Wage labour Continuous Total number of hours spent on wage work last week Agric labour Continuous Total number of hours spent on agricultural work last week

45 Ganyu labour Continuous Total number of hours spent on ganyu work last week Income Continuous Total earnings of HH in past 12 months, consisting of salaries, income from crop sales, profit from business and remittances from children and others Wage earnings Continuous Total earnings of husband from all wage work in past 12 months Food/Education/Clothing (%) Continuous Share of total HH expenditure spent on food/education/clothing Table 16: Geographical variables Category Variable type Reference period Description Temperature Continuous 1960-1990 Average daily range: mean of max. temp.- min. temp. Temperature Continuous 1960-1990 Temperature seasonality: standard deviation of monthly climatology Temperature Continuous 1960-1990 Minimum temperature of coldest month Temperature Continuous 1960-1990 Average temperature of wettest quarter Rainfall Continuous 2008-2009, 2009-2010 Average 12-month total rainfall, July-June Rainfall Continuous 2008-2009, 2009-2010 Average total rainfall in wettest quarter, July-June Rainfall Continuous 2008-2009, 2009-2010 Average start of wettest quarter in dekads, from July onwards Greenness Continuous 2008-2009, 2009-2010 Total change in greenness within primary growing season, 46 averaged by district Greenness Continuous 2008-2009, 2009-2010 Onset of greenness increase in day of year, starting July 1st, averaged by district Greenness Continuous 2008-2009, 2009-2010 Onset of greenness decrease in day of year, starting July 1st, averaged by district Soil quality Indicator N/A Nutrient availability: 7 categories defining how serious this is as a constraint Soil quality Indicator N/A Rooting conditions: 7 categories defining how serious this is as a constraint Soil quality Indicator N/A Excess salts: 7 categories defining how serious this is as a constraint Agro-ecology Indicator N/A Agro-ecological zones created from WorldClim climate data D Language

Table 17: Language (11) + language (11) cont. Ln(real expenditure) Ln(real expenditure)

Patrilineal 0.086∗∗ Tonga (M = 14.0%,N = 192) -0.036 − (0.034) (0.101)

Chewa (M = 76.6%,N = 3859) 0.139∗ Other (M = 3.2%,N = 165) -0.118 (0.081) (0.101)

Yao (M = 85.0%,N = 644) 0.210∗∗ Lambya (M = 12.8%,N = 70) -0.153 (0.091) (0.096)

Nyanja (M = 80.2%,N = 377) 0.102 Nkhonde (M = 4.5%,N = 67) -0.034 (0.084) (0.109)

Lomwe (M = 81.8%,N = 308) 0.148 Sukwa (M = 0%,N = 35) -0.055 (0.094) (0.104)

Sena (M = 7.7%,N = 307) -0.026 Nyakyusa (M = 0%,N = 20) -0.094 (0.121) (0.140)

Ngoni (M = 72.4%,N = 272 0.062 Senga (M = 3.2%,N = 7) 0.098 (0.104) (0.098) N 7136 R2 0.368 M indicates % of households speaking this language who are matrilineal. The excluded language is the predominantly patrilineal Tumbuka, N=797, M=4.6%. Controls included: Basic, Region, Geography, Village economy, Employment, HH Composition and Gender. Standard errors are

in parentheses. ∗∗∗ denotes signiﬁcance at 1% level, ∗∗ at 5% level and ∗ at 10% level.

47 E A Model of Divorce Probabilities

Let us consider a world with a continuum of measure 1 of individuals. The individuals are inﬁntely lived and are either married or divorced in any given period. At any time t = 1, 2, ..., the proportion of married individuals is Mt and the proportion of divorced individuals is Dt = 1 Mt. Married − individuals divorce with probability α per period and divorced individuals remarry with probability β per period. Divorces and remarriages are identical and independent across individuals and time periods.

By the law of large numbers, the evolution of the proportions Mt and Dt can be described by a Markov chain with the following transition matrix T :

Mt Dt

Mt+1 1 α β − Dt+1 α 1 β −

In the long run, the proportions of married and divorced individuals converge to the stationary distribution of this Markov chain. This is a vector p that satisﬁes the following condition,

T p = p,

or,

(T I)p = 0. −

In fact, p is an eigenvector of T with eigenvalue equal to 1, normalised so that the elements add up to 1. Using these facts gives the following solution for p:

α β p = [ , ] α + β α + β = [pm, pd],

where pm and pd are the equilibrium proportions of married and divorced individuals in the population respectively. This can be used to solve for α as a function of β, pm and pd:

p α = β m . (9) ∗ pd

48 Therefore, as long as β, pm and pd are known, the divorce probability α can be calculated. I assume that each time period t represents one year; this is consistent with the theoretical framework, where husbands engage in agricultural labour that yields produce at the end of the season. I use the weighted values of pm and pd observed in the LSMS sample. That is, I use the proportion of household heads who are either married or divorced/separated, calculated separately for the patrilineal and matrilineal groups. I exclude individuals who report being widowed or having never married; however, there is no need to normalise pm and pd. The values of pm and pd for patrilineal and matrilineal households are given in Table 7. To calculate β, I use the Malawi Longitudinal Study of Families and Health (MLSFH). This survey asks individuals about their marriages and divorces. As years are reported for each marriage and divorce, I am able to calculate the number of years each remarriage takes. I use the 2010 wave of the data as this matches the LSMS sample most closely; I restrict the sample to men only, as the theoretical framework focuses on their decisions. The annual remarriage probability is calculated as the proportion of men who remarry within one year of their divorce. This calculation is carried out separately for matrilineal and patrilineal men, where I identify lineage through the men’s tribal aﬃ liation. Together, these calculations yield the remarriage probabilities for matrilineal and patrilineal men in Table 7. Using this information in formula (9) along with pm and pd, I am able to calculate the value of α for patrilineal and matrilineal husbands.

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