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What influence do empowered women have? Land and the reality of women’s relative power in Peru

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Abstract

Using primary data from rural Peru and a novel econometric framework, this paper evaluates the effects of gendered land inheritance on women’s relative power. We find limited evidence that increasing a woman’s landholdings increases her power; while an increase in landholdings of men in her household decreases her power. A coincident increase in land for both has significant empowering effects. Thus, gender policies need to go beyond interventions that exclusively target women—empowering women also requires empowering men in specific ways. We also provide fresh insight into using women’s empowerment for economic development by distinguishing power from the influence that women possess over specific household decisions because of their power. We characterize each influence (e.g., household expenditures) by two statistics—threshold (the level of empowerment required to awaken the influence) and sensitivity (the response of the influence to marginal increases in power). We find that different thresholds of power are required for women to influence different household decisions. Credit and land-rental require much greater power and are less sensitive than livestock, household-goods, or management of agricultural land. Distinguishing power from influence adds great value to the survey questions now used to create indices of women’s power.

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Notes

  1. Assuming women have greater preferences for the outcome than men.

  2. Only two studies (Pitt et al. 2006; Chakrabarti and Biswas 2012) use multidimensional measures of empowerment using a two stage structural equation model (SEM) which is different than the GSEM we utilize.

  3. For example, expenditures on a good that increases productivity of the household or the household farm.

  4. It is possible there is a systematic mechanism through women trade their influence over their less preferred goods for influence over their more preferred goods. Say a woman has high preference for children’s education but low influence over the purchase of books (e.g., she can’t walk to the market alone). When she is empowered she may have an arrangement with her spouse to get the book for her in return for doing something he prefers but has low influence over. In this case, the wedge between power/preference and household outcomes will depend on the success of such arrangements. It may be possible that such arrangements neutralize influence effects on household outcomes—but that is an empirical question.

  5. The levels k = 1…K, as described earlier, range from man as the sole decision-maker to woman as the sole decision-maker. The model we apply allows the number of levels to vary across the thirteen types of decisions.

  6. More generally, the τjk can be interpreted as the amount of power required for a woman to have the kth or higher level of influence over a decision. We focus on the highest level, K, and consider a woman to have influence over a decision if she is likely to be a sole decision-maker. Note that in Eq. (2) when a woman’s power λn = τjK she is likely to become the sole decision-maker. That is, when a woman’s power is equal to τjK, she will have a 50% or greater chance of being the sole decision-maker over decision j. Although our discussion focuses on sole decision-making by women (the highest level of woman’s influence), in our empirical work we calculate threshold and sensitivities of an influence for each of the k levels of influence. Selected results on all levels are presented in the next section.

  7. The ordered logit yields estimates of probabilities that a respondent has influence level k (e.g., the probability that a woman is the sole decision-maker) by breaking up the domain of a continuous scale into K categories using cutpoints τjk to separate adjacent levels of influence (Greene and Hensher 2010).

  8. IRT assumes local independence, that is, a respondent’s responses, conditional on the latent trait, are uncorrelated.

  9. More generally, DIF arises when survey questions that measure a latent trait are responded to differently by different groups of people with the same level of the latent trait (Millsap and Everson (1993)). Although DIF arises naturally in ordered choice models it is not controlled in economic studies due to methodological limitations of ordered logit and probit models (Greene and Hensher 2010).

  10. Results are available upon request.

  11. We also ran all the models with a different empowerment ordering where joint and women’s sole-decision making was the highest category of empowerment. This result held in all of our models. Results are available upon request.

  12. The full sample model (model 1 Table 3) includes both male and female respondents from the same households. This raises the possibility that the standard errors could be clustered at the household level. We accounted for clustering using the Jackknife cluster estimator. The approach resamples the data by systematically leaving one cluster from the sample and calculating the expected value of the resulting standard error estimates (Cameron and Miller 2015). The results regarding the main land variables remain qualitatively the same after clustering.

  13. A skewness and kurtosis test for normality (D’Agostino et al., 1990) cannot reject the hypothesis that the density function of power estimated using IRT is normally distributed. Results are available upon request.

  14. PCA can be thought of as a multiple regression framework where the right hand side variables are unobserved factor variables. Values of the coefficients or “factor loadings” are backed out of the model under a minimization criteria.

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Acknowledgements

This research was supported by a grant from the Climate Change, Agriculture and Food Security research program of the CGIAR. We thank the staff of the International Potato Center (CIP), especially Cecilia Turin, for help and support with fieldwork. The usual disclaimer applies.

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Appendix 1

Appendix 1

We consider two decision-makers, a man (m) and a woman (w), who have different preferences over two goods qj, for j = 1, 2. For the sake of exposition, we assume q1 is a good whose consumption is directly aligned with economic development. For instance, q1 can be expenditures on children’s education which increases the human capital of the household. Alternatively, q1 can be thought of directly as money secured through a loan or savings to buy a new technology for future production on the household farm (farm production and consumption decisions are assumed to be separable, for simplicity). In contrast, q2 denotes a household durable (e.g., a vehicle) whose consumption is assumed to be less aligned with development outcomes. Decision-making in the household is assumed to be cooperative and results in pareto efficient outcomes. Thus, the household utility function can be written as a weighted sum of the individual utilities. The household’s optimization problem is:

$$\begin{array}{*{20}{c}} {} \\ {Max\,U} \\ {q^1,q^2} \end{array} = \lambda (z)\left[ {u_f(q^1,q^2)} \right] + (1 - \lambda (z))\left[ {u_m(q^1,q^2)} \right]{,}$$
$$s.t:q^1 + q^2 = I{,}$$

where I is the household budget. The function λ(.) ∈ 0, 1], assumed continuously differentiable in its arguments, represents the woman’s weight in the household utility function. In the literature it is customary to interpret λ as the woman’s bargaining power or empowerment (e.g., Reggio 2011; Doss 2013). λ(z) is identified by specifying the “distribution” factors, z, to include variables that affect household utility through bargaining power but not directly. Based on this framework, scholars have argued that women’s landholdings can increase women’s bargaining power and, thereby, their involvement in household economic decisions which, in turn, can lead to increases in outcomes such as q1 consumption that help economic development.

Totally differentiating the FOCs for the household’s problem above, we obtain an expression for the effect of a land transfer on q1:

$$\begin{array}{l}\left[ {\begin{array}{*{20}{c}} {\lambda (z)u_{11}^f + (1 - \lambda (z))u_{11}^m} & {\lambda (z)u_{12}^f + (1 - \lambda (z))u_{12}^m} & { - 1} \\ {\lambda (z)u_{21}^f + (1 - \lambda (z))u_{21}^m} & {\lambda (z)u_{22}^f + (1 - \lambda (z))u_{22}^m} & { - 1} \\ { - 1} & { - 1} & 0 \end{array}} \right]\left[ {\begin{array}{*{20}{c}} {\frac{{dq^1}}{{dz}}} \\ {\frac{{dq^2}}{{dz}}} \\ {\frac{{d\mu }}{{dz}}} \end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}} { - \left( {u_1^f - u_1^m} \right)\lambda \prime (z)} \\ { - \left( {u_2^f - u_2^m} \right)\lambda \prime (z)} \\ 0 \end{array}} \right]\end{array}{,}$$

where μ is the marginal utility of household income. Using Cramer’s rule, the derivative dq1/dz is the ratio of two determinants \(\frac{{|Ac|}}{{|A|}}\) where Ac is the bordered Hessian with the first column replaced by the vector on the RHS of (1). The numerator |Ac| is can be simplified as:

$$dq^1/dz = \frac{1}{{|A|}}\lambda \prime (z)\left[ {\Delta U} \right]{,}$$

where \(\Delta U = (u_1^f - u_1^m) - (u_2^f - u_2^m)\) denotes the difference in the woman’s marginal utilities, relative to the man, corresponding to the consumption of q1 and q2, respectively. The sign of dq1/dz depends on the sign of the numerator: dq1/dz > 0 if λ′(z) > 0 and ΔU > 0. In words, dq1/dz > 0 if z empowers women and if women, relative to men, have greater preferences for consuming q1 and if their relative preference for q1 is greater than that for q2. Note that by the second order conditions for a maximum, the bordered Hessian |A| > 0.

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Guerra, M.J.M., Mohapatra, S. & Swallow, B. What influence do empowered women have? Land and the reality of women’s relative power in Peru. Rev Econ Household 17, 1225–1255 (2019). https://doi.org/10.1007/s11150-019-09461-2

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