Chapter 4. Horizontal inequalities

Carmen Diana Deere
Ravi Kanbur
Frances Stewart

This chapter discusses the importance of horizontal inequalities, i.e. inequalities in both the income and non-income dimensions among groups of people with shared characteristics; of intra-household inequality; and of gender inequalities in the distribution of wealth (the gender wealth gap). Measurement of horizontal inequalities raises the question of which group classification to adopt, whether to weight measures for each group by their population size in order to obtain an aggregate measure, and how to take into account intra-group distribution. The chapter then considers how estimates of overall inequality might be impacted by the neglect of intra-household inequality, highlighting the difference between household and individual welfare, and how to obtain better estimates of the gender wealth gap.

    

Carmen Diana Deere is Distinguished Professor Emerita of Latin American Studies and Food & Resource Economics, University of Florida; Ravi Kanbur is T.H. Lee Professor of World Affairs, International Professor of Applied Economics and Management, and Professor of Economics, Cornell University; Frances Stewart is Emeritus Professor of Development Economics, University of Oxford. Although the three themes have been integrated in a single chapter because of their substantive inter-relatedness, Frances Stewart is the author of the section on Horizonal Inequalities, Ravi Kanbur on Intra-household Inequality, and Carmen Diana Deere on the Gender Wealth Gap. The authors wish to thank Nora Lustig for bringing them together for this exercise, and Marco Mira D'Ercole and Joe Stiglitz for their comments. Section 4.2 was prepared by Frances Stewart and draws on Stewart (2016). Section 4.3 was prepared by Ravi Kanbur and extracted from Kanbur (2018). Section 4.4 was prepared by Carmen Diana Deere.

The opinions expressed and arguments employed in the contributions below are those of the author(s) and do not necessarily reflect the official views of the OECD or of the governments of its member countries.

4.1. Introduction

A major concern of this report is inequality of income, consumption and wealth among individuals. This type of inequality (also called vertical inequality), while important in many context, ignores systematic inequities among population groups, is often restricted to the ‘economic’ dimensions of inequality, and assumes that each individual in a household receives the mean income of that household. This chapter discusses the importance of horizontal inequalities (i.e. inequalities among groups of people with shared characteristics), both in the income and non-income dimensions, of intra-household inequality, and of gender inequalities in the distribution of wealth (i.e. the gender wealth gap). The three sections of this chapter, while covering topics which are important in their own right, also link with each other in important ways. For example, a key aspect of intra-household inequality is inequality between women and men within the household, and this relates to the broader question of horizontal inequality in society; in turn, gender inequalities are especially important in the case of wealth inequality, an issue that this chapter explores based on a specific measurement initiative.

While, as argued below, these inequalities are of great importance and policy relevance, there are no systematic efforts to collect the necessary data and publish the appropriate indicators. This is due, in part, to the conceptual and practical challenges that their measurement entails. However, as explained below, much more could be done to standardise the practice of collecting the relevant information and broadening the diagnostic indicators used for social progress assessments.

4.2. Horizontal inequalities

4.2.1. Why horizontal inequalities matter

Horizontal inequalities constitute one of the most important types of inequality, notably because of their implications for justice and social stability. Relevant group categories include race, ethnicity, religion, gender and age. Despite their importance, much more attention is normally given to vertical inequalities (or inequalities among individuals generally, whatever groups they belong to) in analysis and policy.

Most people are members of many groups and, in assessing horizontal inequalities within any society, the first issue to address is which group classification to adopt. The appropriate classification(s) will reflect felt identity distinctions, not only in relation to people’s own perceived identity but also to how they perceive others. Some group categories may be transient or unimportant – for example, membership of a particular club. But other categorisations shape the way people see themselves and how they are treated and behave. Age and gender distinctions are universally important, but societies differ as to what the other salient identities are, and there can be changes in their importance over time. For example, race has been an important identity distinction in South Africa, yet it is possibly of lesser importance today than previously. Ethnicity is a highly relevant category within many Latin American and African countries, associated with discrimination, grievance and sometimes mass violence. Religion constitutes a critical dividing line between people the world over today, but in Europe it no longer leads to the wars it once did.

Group categorisations are fluid and may be blurred at the edges but nonetheless are keenly felt, are often a source of discrimination, and are typically associated with low levels of inter-group trust and weak social interactions. Identity differences are particularly significant in relation to social and political stability when categories overlap – e.g. when members of different ethnic groups also adhere to different religions.

Distributional issues are most often considered along a single dimension – notably in the income space – although the need for multi-dimensional measures has been strongly advocated (Sen, 1980). Multi-dimensionality is an essential feature of horizontal inequality. Three prime dimensions are socio-economic, political and cultural recognition. For each of these there is an array of elements. For example, socio-economic inequalities include inequalities in access to basic services – e.g. education, health care, water – and inequalities in economic resources, including income, assets, employment and so on. In the political dimension, relevant inequalities include those in representation in government, the upper levels of the bureaucracy, the military and the police, and in local administrations. On the cultural side, relevant inequalities include those in recognition, use and respect for language, religion and cultural practices.

There are many causal connections across various dimensions and elements. For example, educational inequalities may be responsible for a range of economic inequalities, with reverse causality present such that low incomes tend to be associated with low education of children. Inequalities in cultural recognition can lead to educational and economic inequalities if, for example, a group’s language is not used in government business or the education system. The tighter the causal connections, the more consequential these inequalities are. As with group classification, the relevant dimensions vary across societies. While land inequalities are of major significance in agrarian societies, for example, they matter little in economies where agriculture is relatively insignificant and where inequalities in financial asset ownership and skills determine life chances.

Horizontal inequalities are important both in themselves and instrumentally, since they affect other objectives (Loury, 1988). Above all, any significant horizontal inequality is unjust since there is no reason why people should receive unequal rewards or have unequal political power merely because they are black rather than white, women rather than men, or of one ethnicity rather than another. Anti-discrimination law is justified on this principle. Another intrinsic reason for concern with horizontal inequalities is that they can have a direct impact on well-being. Individual well-being can be affected not only by a person’s own circumstances, but also by how well their group is doing, since membership of certain groups can form an integral part of a person's identity. Likewise, relative group poverty contributes to the perception that an individual may be trapped permanently in a poor position. Psychologists have shown, for example, that psychological ills of African-Americans are sometimes associated with the position of their group (Broman, 1997). Hence, it has been argued that the relative position of the group should enter into an individual’s welfare function (Akerlof and Kranton, 2000).

Besides these intrinsic reasons for concern, horizontal inequality affects the achievement of other objectives. The most powerful instrumental reason is that horizontal inequalities have been shown to raise the risk of violent conflict significantly (Stewart, 2008; Cederman, Weidmann and Gleditsch, 2011). Group inequalities provide powerful grievances which leaders can use to mobilise political protest, by calling on cultural markers (e.g. a common history or language or religion) and pointing to group exploitation. This type of mobilisation is especially likely to occur where there is political as well as economic inequality, such that the leaders of the more deprived groups are excluded from political power and therefore have a motive for mobilising. Examples where group inequalities have been a factor in provoking conflicts include Côte d'Ivoire, Rwanda, Northern Ireland, Chiapas and Sudan (Gurr, 1993; Langer, 2005; Stewart, 2002; Murshed and Gates, 2005). Sharp horizontal inequalities within countries (and between them) are an important source of grievance and of political instability, independently of the extent of vertical inequality. Indeed, most econometric investigations have shown little connection between vertical inequality and conflict (Fearon and Laitin, 2000; Collier and Hoeffler, 2004).

Another instrumental reason for concern with horizontal inequalities is that they are often an outcome of historic and current discrimination against people because of their identity, which is likely to lead to inefficiency when talented people fail to realise their potential. For example, most studies show that affirmative action for African-Americans in the United States had a positive impact on economic efficiency (Badgett and Hartmann, 1995).

Finally, it may be difficult to attain certain targets, such as poverty elimination or universal education, without tackling horizontal inequality and the overall position of a deprived group, because deprived groups often find it particularly difficult to access state services.

4.2.2. Measuring horizontal inequalities

Given their significance, there is a need for systematic measurement and monitoring of horizontal inequalities. There is a lack of systematic data by group, though economic data by group is increasingly collected by national governments as well as through some global surveys such as Demographic and Health Surveys (DHS) and Living Standard Measurement Surveys (LSMS). The collection of data on inequalities in political power or cultural recognition is very rare, undertaken only by some individual scholars (e.g. Gurr, 1993; Langer, 2005; Wimmer, Cederman and Min, 2009).

The measurement of horizontal inequalities raises particular issues, beyond those involved in measuring vertical inequalities (Mancini, Stewart and Brown, 2008). First, there is the question of which group classification to adopt. Second, group size varies, and hence it may be desirable to weight any aggregate measure by the population of each group. Third, it may also be important to take into account intra-group distribution, since the political and policy implications of inequalities between groups can differ according to whether the differences arise at the top of the distribution of each group, or at the bottom, or because of uniform differences throughout the distribution of each group. A common measure of aggregate horizontal inequality in a country is a population-weighted coefficient of variation of average group performance on any indicator. Foster’s general-means approach shows how group differences vary along the distribution (Foster, Lopez-Calva and Szekely, 2003). This involves estimating parametric means for each group at different points in the group distribution. An aggregate measure of horizontal inequalities for a country as a whole is helpful for comparisons across countries and over time, but for domestic policy purposes simple comparisons of each group with the country average are often sufficient.

4.2.3. What to do?

Goal 10 of the Sustainable Development Goals calls for the reduction of inequalities between and within countries, and makes explicit reference to inequalities based on “age, sex, disability, race, ethnicity, origin, religion or economic or other status”. It is emphasised that no goal should be considered as attained by a country if it is not met for significant groups. This has clear relevance for measurement, monitoring and policy. This is an issue which applies world-wide. In the European Union, for example, there has been a long process aimed at defining a set of “core social variables”, to be included in all official surveys, which would allow common breakdowns of the population across various well-being dimensions.1 For monitoring, there is a need to develop a common set of group categories and dimensions of opportunities and outcomes across countries, with common standards and definitions, particularly for some horizontal breakdowns such as disability, race and ethnicity. Given the differences in salient groups and dimensions of deprivation across countries, however, only a minimal or core set of indicators are likely to be applicable world-wide. There is a need for flexibility in monitoring so as to fit the particular context.

A wide range of policies for tackling horizontal inequalities have been adopted in different countries (Stewart, Brown and Langer, 2008). The first requirement is to identify which groups are particularly deprived and which dimensions of deprivation are most prevalent. Policies can be universal or targeted. Universal policies provide benefits or impose taxes according to universal categories, applicable equally to everyone in society. Generally, these benefits, such as universal access to healthcare services, are likely to benefit deprived groups most, and consequently to reduce horizontal inequalities. Targeted policies identify particular groups and grant their members particular favours, such as access to government employment or educational scholarships. Such targeted policies are often known as “affirmative action”. Affirmative action can be effective, but the policies can also have undesirable side-effects, in some circumstances changing behaviour, encouraging strong identification with the favoured identity (“ethnicisation”) and provoking opposition among the non-favoured groups (Hoff and Stiglitz, 1974; Harrison et al., 2006; Brown, Langer and Stewart, 2012). Yet, in some situations, the visibility and rapidity of affirmative action is desirable to reduce resentment among deprived groups. Anti-discrimination law can be an effective policy when discrimination is at the root of inequalities, but it needs to be enforced and backed up by universal legal access. However, many horizontal inequalities arise from historic reasons, and consequently anti-discrimination law will only be effective in reducing such inequalities if discrimination is interpreted very broadly, recognising the historic origin. The most effective approach is to combine universal and targeted policies, as was successfully adopted in Northern Ireland and Malaysia (Faaland, Parkinson and Saniman, 2003; Todd and Ruane, 2012). But in both cases, while horizontal inequalities were greatly reduced, societal cohesion remained fragile, pointing to the need for complementary policies to promote societal integration.

As noted earlier, horizontal inequalities affecting people's well-being go well beyond the strictly economic and include cultural discrimination, official and non-official behaviour (e.g. by the police or the media), and political discrimination, all of which can affect economic opportunities as well as well-being. Consequently, the policy arena needs to be correspondingly extensive.

4.3. Intra-household inequality and the measurement of money-metric inequality

4.3.1. Why intra-household inequality matters

Consider any indicator of economic or social well-being, such as consumption, education or health. Our normative frameworks are typically built on realisations of such indicators for each individual. When the value of an indicator falls below a normatively determined critical value, that individual is identified as being in deprivation. This critical value can be the poverty line for consumption, or other similar lines such as an adequate level of nutrition. The variation in the indicator across individuals in the population under consideration is the basis of inequality measurement. An important strand of the literature then begins with accounting for this variation along different dimensions. For example, how much of this variation is due to variations by caste, race or ethnicity is often the starting point for a deeper investigation of the role of these factors in inequality. Similarly, variation accounted for by gender is a key element of discussion of gender inequality in a society. Indeed, as discussed in the previous section, inequality across groups with shared characteristics is the basis for analysis of horizontal inequality.

Gender inequality raises a troubling question: could it be that boys and girls, and men and women, are treated so differently within the household that their well-being differs from each other? In other words, is there intra-household inequality? Intra-household inequality would lead us to question many normative frameworks where the household is meant to be an institution for cooperation and equity. If intra-household inequality exists, it contributes to overall inequality, and its patterns can in turn shed light on inequality across genders, and across age groups, in the population as a whole.

4.3.2. Measuring intra-household inequality

The standard instrument for measuring individual well-being is the household survey, which collects a mixture of individual and household-level information. A key piece of information collected at the household level is data on household income and consumption (or, more accurately, on consumption expenditure). This is the central data source for generating headline poverty and inequality measures in a large number of countries. In the case of consumption data, leaving to one side a number of well-discussed issues such as the length of the recall period for expenditures, allowing for home produced consumption, housing services and price variations, the question arises as to how to go from household level consumption to information on individual level consumption, which is needed to generate inequality and poverty measures.

The answer for official figures for most countries is straightforward and somewhat disconcerting. Total household expenditure is typically divided by the number of members of the household, and each individual is allocated the per capita consumption of the household. In other words, it is assumed that there is no intra-household inequality. This is also the implicit assumption when adult equivalent scales are used to allow for different consumption needs by demographic characteristics. There is assumed to be no inequality across equivalised individuals. Put another way, our standard method of generating headline inequality and poverty measures systematically suppresses intra-household inequality. It therefore understates overall inequality, focusing only on inequality in household per capita consumption.

Before turning to empirical studies which try to establish the magnitude of intra-household inequality, it is as well to take up the argument that an understatement of inequality levels is not necessarily important when the focus is on changes in inequality over time, as a constant understatement will not affect the trend as such. This is of course true, but the following points should also be considered. First, if we are interested in overall inequality, surely the level matters as well – at the very least, a constant understatement may matter very differently at different levels of inequality. Second, how do we know that the understatement is constant? We will not know this unless we explore the matter empirically, and allow at least for the possibility of understatement.

How much understatement of inequality is there as the result of the neglect of intra-household inequality? The question is not easy to answer given the nature of standard data sources. If we had true individual-level consumption, which we do not measure in standard household surveys, the question would be irrelevant since we could observe the true overall inequality. There are two possible strategies we can follow.

The first is to use structural econometrics. In this approach, you start with a model of intra-household allocation, with a free parameter from which intra-household inequality can be inferred; then you estimate this parameter from observed patterns of household level consumption. This is the approach followed by Lise and Seitz (2011) who conclude that “previous work underestimates the level of individual consumption inequality by between 25% and 50%” (p. 352).

The second approach is to use indicators for which we do have individual-level data, either in the standard household surveys or in especially collected datasets. Since in these cases we do indeed have the “true” distribution of the indicator across all individuals, we can construct the hypothetical distribution where each individual in a household is allocated the household’s per capita value of that indicator. The difference between inequality in the true distribution and the synthetic distribution gives us an estimate of how far wrong we would have gone had we not had individual level data on the indicator.

For two-earner households in a large number of surveys in the Luxembourg Income Study, Malghan and Swaminathan (2016) find that within-household inequality accounts for 30% or more or total inequality. Ponthieux (2015) uses a question in the EU-SILC 2010 thematic module (“What proportion of your personal income do you keep separate from the common household budget?”) to construct a “modified equivalised income” measure. The author finds that “departing from the assumption of full income pooling within couples results in increased levels of various indicators of inequality”. For calorie intake, in one of the first studies to quantify intra-household inequality, Haddad and Kanbur (1990) use a specially designed survey of a small number of households in the Philippines which collected information on nutritional intake of each individual. Using calorie adequacy as the well-being indicator, they find that possible errors in inequality could be of the order of 30%.

These are all, of necessity, indirect approaches to estimating the understatement of inequality when intra-household inequality is suppressed as in our standard headline measures. But they all indicate significant scaling up of standard measures of overall inequality which neglect intra-household inequality.

4.3.3. Intra-household inequality and the growth elasticity of poverty reduction

Clearly, the estimated level of overall inequality is significantly impacted by the neglect of intra-household inequality. This understatement must surely affect the assessment of well-being in a society for any given level of per capita income. Empirical work is not sufficiently advanced to test if the understatement is constant or not but, in terms of changes over time, a constant understatement will obviously not affect trends. But are there other aspects of the development discourse, and indeed the discourse in developed countries, which are affected by the understatement of true inequality?

A key concept introduced in development economics in the last quarter century is that of the “growth elasticity of poverty reduction”. The basic idea behind this notion stems from the argument that the reduction of absolute poverty between two periods can be broken down into a “growth component” and an “inequality change component”. To derive the first component, analysts construct a distribution where all incomes grow at the growth rate of per capita income between the two periods. Then, by construction, inequality is unchanged since each income has grown in the same proportion. You can compute the poverty in the synthetic distribution, and label the change in poverty the “growth component” of poverty change, since it is the result of this “distribution neutral” growth. The remaining part of the actual poverty change can then be attributed to inequality change.

The percentage change in the “growth component” of poverty divided by the growth rate of the economy (which is, of course, the percentage change in per capita income) is designated the “growth elasticity of poverty reduction”, measuring the responsiveness of poverty to distribution-neutral economic growth. However, the “growth elasticity of poverty reduction” is itself a function of the level of inequality. While the general case is technically ambiguous, Bourguignon (2003) has shown that, for specified functional forms and empirical simulations, the growth elasticity is lower the higher is the level of income inequality. This finding has been interpreted as implying that reducing inequality could not only have a direct level effect on poverty, for a given per capita income, but also have an indirect effect by increasing the responsiveness of poverty reduction to economic growth. For his specific parametrisations Bourguignon (2003) finds that when the Gini coefficient rises by a third, the elasticity falls by a third.

One implication of the above discussion is that the true level of inequality is understated because standard methods suppress intra-household inequality. This must mean, by the Bourguignon (2003) argument, that the true growth elasticity of poverty reduction is overstated in standard calculations, since they rely on measures that understate true inequality. And the quantitative magnitudes are significant.

4.3.4. Estimating the “true” levels of inequality

Quantifying intra-household inequality is a first step towards getting a more accurate measure of the level of inequality, and of the responsiveness of poverty reduction to economic growth. It can also provide a platform for investigating inequality across gender and age groups, both of which are aspects of horizontal inequality. But, as we have seen, so far as the headline money-metric measures of inequality are concerned, standard national household surveys collect consumption information only at the household level, so that understatement of inequality is endemic to official statistics.

It is unlikely that official national household surveys can be turned to collecting individual-level consumption information, especially in developing countries. But there are alternatives, following the small empirical literature that exists. First, structural econometrics methods can be used to estimate intra-household inequality parameters. Second, systematic investigation of other indicators available at the individual level in standard household surveys can be analysed to develop a sense of the understatement in these cases if individual information is not available. Thus information on personal income streams and questions on the extent of income pooling can be used creatively by researchers to explore and estimate intra household inequality. Third, small specialised surveys, like the one in Haddad and Kanbur (1990), can be mounted. As more data is collected we will get a sharper sense of the understatement of inequality as the result of suppressing intra-household inequality.

4.4. The gender wealth gap

4.4.1. Why the gender wealth gap matters

As seen in the previous section, a growing literature has demonstrated that household and individual welfare are not necessarily the same, and that intra-household inequality may condition economic outcomes. Specifically, how a woman’s fall-back position (those resources she controls should the household dissolve) conditions her bargaining power within the household has been of much interest (Deere and Doss, 2006). To test this proposition, much of the bargaining power literature has focused either on non-labour income (data on which is readily available in household income surveys and can be derived from either asset ownership or public or private transfers) or on the ownership of particular assets, such as land or financial assets.

While much is known about the gender wage gap,2 comparatively little is known about the gender asset or wealth gap, whether among couples (i.e. the intra-household distribution of wealth) or for the population as a whole. This is largely because data on asset ownership collected through household surveys – including in large-scale wealth surveys – have tended to be at the household rather than the individual level, constraining gender analysis. Analyses concerned with gender inequality have been limited to the study of household types, i.e. male or female sole-headed households in comparison to married couples.3 Gender analyses of households composed of couples are sometimes attempted by focusing on the sex of the respondent, who is typically the best informed on financial matters;4 but since wealth data is collected at the household rather than the individual level, such analyses do not shed light on the intra-household distribution of assets. The assumption that, in married couples, all assets are pooled and the benefits shared among all household members, i.e. the assumption of a unitary household, has prevailed for too long. However, in most legal systems, property rights are ceded to individuals, not households. As Doss, Grown and Deere (2008) argue, analyses of “household wealth” ignore institutional frameworks governing individual property rights, as defined by marital regimes, inheritance laws and social norms.

Whether asset ownership is in fact pooled in marriage (and consensual unions) largely depends on a country’s default marital regime – the rules governing how property acquired prior to and during the marriage and how inheritances are treated should the union be dissolved (Deere and Doss, 2006). For example, under the separation of property regime, the prevailing regime in many African, Middle Eastern and South Asian countries,5 all property acquired by individuals prior to or after marriage, including any inheritances received, are considered to be their own individual property, i.e. should a union dissolve each person leaves with only their own personal property. In some countries which have traditionally had this default marital regime, such as the United Kingdom, divorce legislation reform has subsequently modified this outcome, so that property acquired during the marriage with the earnings of either spouse is pooled and divided equally. In this case, the outcome resembles partial community property, under which property acquired prior to marriage and any inheritances are considered individual property, while property acquired during the marriage is split equally among the spouses upon its dissolution.

The main point is that institutional parameters shape the accumulation of wealth by individuals, and must be duly accounted for in data collection efforts and economic analysis. As an illustration, in Ecuador – where partial community property prevails and inheritance norms and practices are equitable – married women own 44% of couple wealth; conversely, in both Ghana and Karnataka, India – characterised by the separation of property marital regime as well as by male bias in inheritance – married women own only 19% and 9%, respectively, of couple wealth (Deere et al., 2013).

4.4.2. Measuring the gender wealth gap

As mentioned above, when data on asset ownership is collected in household surveys, it has tended to be at the household rather than the individual level, constraining gender analysis. Among the large-scale wealth surveys included in the Luxembourg Wealth Study, for example, only the German Socio Economic Panel collects data on individual ownership of a broad range of physical and financial assets, allowing analysis of the intra-household distribution of wealth (Grabka, Marcus and Sierminska, 2015). Two other surveys collect partial data on what belongs to individuals: the United Kingdom Wealth and Assets Survey (on financial assets and liabilities, pension wealth and real estate) and the Italian Survey of Household Income and Wealth (on real estate).6

The multi-purpose surveys most frequently carried out in developing countries are the Living Standard Measurement Survey (LSMS) and the Demographic and Health Survey (DHS). An analysis of a sample of 72 LSMS questionnaires across six world regions for the mid-2000s revealed that the great majority of countries collected data on household ownership of housing, land, livestock and major consumer durables. Only 21% of these, however, collected data on who in the household owned the residence, 17% on who owned the land, and 14%, on who owned non-agricultural businesses (Doss, Grown and Deere, 2008). A subsequent analysis of 167 household survey questionnaires for 23 Latin American and Caribbean countries found that only 23 questionnaires, for 11 countries, collected gender-disaggregated ownership information on at least one asset, most frequently for the main residence (Deere, Alvarado and Twyman, 2012). Since 2009, the DHS has included questions asking surveyed individuals whether they are owners or co-owners of the main residence and land.7 Thus, while it is increasingly possible to measure gender gaps with regard to specific assets, large lacunas remain in terms of being able to estimate total individual wealth and the gender wealth gap.

One of the reasons why progress on measuring individual level wealth has been slow has been because of methodological concerns, such as whether reliable data on the valuation of assets can be elicited from respondents. Other issues include who should be interviewed in an asset survey, how ownership should be defined, how the value of assets should be measured, and whether all assets need be included in wealth estimates.8 The Gender Asset Gap Project was launched in 2009 to explore whether it was feasible to collect detailed, gender disaggregated wealth data in developing countries, and to study the potential gender biases in the methods employed to do so. For this purpose, national-level household surveys were carried out in 2010 in Ecuador and Ghana and at the state level in India (Doss et al., 2011 and 2014). Two other projects are currently investigating some of these questions: the Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA),9 and Evidence and Data for Gender Equality (EDGE).10

The issue of who should be interviewed in a household wealth survey aiming to collect individual-level data has also been raised with respect to household income surveys. There is growing consensus that direct reporting is superior to reporting by proxy (where one household member reports on the income or assets of all other household members rather than just on their own resources).11 The MEXA report, for example, recommends that household surveys move beyond their reliance on asking a single respondent (whether the household head or “the most knowledgeable” person in the household) to include multiple respondents, beginning with the members of the main couple, if not all adults (Kilic and Moylan, 2016).

The issue of how ownership should be defined has been raised primarily in the context of asset information,12 since there are various ways that it can be measured: reported ownership, documented ownership, or one or several of the components of the bundle of property rights. Documented ownership (having a deed or other form of documentation) tends to be the most secure form of ownership. However, housing and land titles are not always widely available in developing countries. To mitigate this problem, many recent wealth surveys first ask about reported ownership and then ask about documentation and, if available, whose names are on the document. In contexts where private property rights are not well defined it may be useful to ask about the full range of rights separately (i.e. to use, to lease, to use as collateral, to sell or bequeath) to explore “effective rights”.13

The valuation of assets is commonly measured by asking respondents what an asset could be sold for today in its present condition (potential sales price or realisation value) and/or its replacement cost. Household income surveys often ask about the rental value of immovable property whereby the present value of the asset can be estimated. All of these measures assume the existence of rental or sales markets for assets, although in developing countries some of these may be particularly thin. The Gender Asset Project, nonetheless, found that the incidence of non-reporting on these different value measures was relatively low (Doss et al., 2013). Another concern is whether knowledge about asset markets and hence values is gender biased, leading to over or under-reporting depending on who is interviewed. Nevertheless, this is difficult to determine in the absence of a benchmark such as administrative data on immovable property, which is rarely available in most developing countries (Doss et al., 2013; Deere and Catanzarite, 2016).

4.4.3. What can be done to obtain better estimates of the gender wealth gap?

Collecting data on the ownership and value of all assets is a time-consuming process, leading to the question of whether there are any short cuts, particularly if an asset module is to be added to a multi-purpose household survey. The Gender Asset Gap Project, which collected data on ownership and value down to the last chicken in three developing countries, suggests that, as a minimum, data should be collected on all immovable property (i.e. the main residence, agricultural land and other real estate), businesses and financial assets. In the three countries covered by the project, immovable property and businesses ranged from 82% (Ghana) to 93% (India) of total household physical wealth.14 Nonetheless, the composition of wealth may vary across the wealth distribution, with consumer durables making up a large share of wealth among the poorest quintile. Thus, the range and number of assets that need to be included in a wealth survey depend on its specific objectives.

Finally, for comparative purposes it is important for household wealth surveys to collect data on the marital regime – i.e. whether couples were married under civil, religious or community law; and if the former, under what particular option if various are available. Moreover, to enrich gender analysis, it is important to collect data on how assets were acquired, who decides on their use, and – for potential use as an instrumental variable – on whether a respondent’s parents owned immovable property.

Besides allowing analysis of the intra-household distribution of resources, the questions that gender disaggregated wealth data could answer are many. Examples of the types of questions that could be analysed include: How large is the gender wealth gap? Does it vary by countries’ level of economic development or across the distribution in any systematic way? To what extent is the gender wealth gap conditioned by the institutional framework of each country, specifically marital and inheritance regimes? Are there differences in magnitude between the gender wealth gap among couples and the population as a whole, and how does this relate to increases in the divorce rate and specific divorce legislation? Does the composition of assets owned by men and women differ? What are the sources of the gender wealth gap and how much of it is explained by the observable characteristics of men and women?

4.5. Conclusions

The different aspects of inequality discussed in this chapter have clear implications for measurement and statistics, and these have been highlighted in each of the sections. But they also raise important policy questions. For example, in arguing for the need to have measures of intra-household inequality with respect to income, consumption and wealth, one might mention that in many countries social assistance is based on various kinds of household means tests, excluding from support those members of non-poor households who are individually poor and get a small share of the household income and wealth.

At the same time, an exclusive focus on vertical inequality, to the exclusion of inequalities across broadly defined groups based on, for example, ethnicity could mislead policy-makers in situations where vertical inequality is falling but horizontal inequality is rising, thus stoking social instability.

As a final example (linking the gender wealth gap with intra-household allocation, since wealth affects bargaining power within the household), neglect of gender specific wealth inequalities will mislead policy-makers on the final beneficiaries of transfer and other schemes targeted at the household level. Particularly in developing countries, but also in developed countries, a focus on horizontal inequality, intra-household inequality and the gender wealth gap will pay policy dividends.

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Notes

← 1. http://ec.europa.eu/eurostat/documents/3859598/5901513/KS-RA-07-006-EN.PDF/71481ffb-771a-489b-a749-1a055c0247d4.

← 2. See Weichselbaumer and Winter-Ebmer (2005) for a meta-analysis of the gender wage gap internationally; World Bank (2012) for a good summary of findings for developing countries; and the discussion in Chapter 5.

← 3. See Schmidt and Sevak (2006) for such an analysis with the US Panel Study of Income Dynamics and Yamokoski and Keister (2006) for an analysis utilising the US National Longitudinal Survey of Youth.

← 4. See Neelakantan and Chang (2010) for such an analysis with the US Health and Retirement Survey and Ruel and Hauser (2013) for a similar study utilising the Wisconsin Longitudinal Study.

← 5. World Bank (2012, p. 162) provides a summary of the default marital regimes in many developed and developing countries.

← 6. The content of these wealth surveys is described in www.lisdatacenter.org/frontend#/home.

← 7. See www.measuredhs.com.

← 8. OECD (2013) discusses some of the general issues. See Doss, Grown and Deere (2008) and Doss et al. (2011) for some of the initial discussions of these issues from a gender perspective.

← 9. MEXA was implemented in Uganda by the Development Data Group of the World Bank with support from EDGE and the World Bank Living Standard Measurement Study-Integrated Surveys on Agriculture. See Kilic and Moylan (2016) for preliminary results on the experiment with five survey treatments over thirteen asset groups.

← 10. EDGE is a project of the UN Statistical Division and UN Women in collaboration with the African Development Bank, the Asian Development Bank, FAO, the OECD, and the World Bank. It aims to develop guidelines for the collection of individual-level data on asset ownership and entrepreneurship, and is piloting data collection in seven countries.

← 11. See Fisher, Reimer and Carr (2010) on how men tend to understate the income of their wives, compared to wives’ reports, hence potentially underestimating household income.

← 12. In household income or employment surveys it is usually assumed that the person who earns the income “owns” it in the sense of controlling its use. However, there is growing evidence from developing countries that women, in particular, may not always control the income they earn. See World Bank (2012), Fig. 2.9.

← 13. On measuring land ownership in Africa, see Doss et al. (2015). An alternative, pursued in the MEXA experiment in Uganda, is to focus on economic ownership, defined as who keeps the proceeds of a sale should an asset be sold.

← 14. The remaining share corresponds to livestock, agricultural equipment and a broad range of consumer durables, including vehicles. Financial assets range from 2% (Ecuador) to 5% (Ghana) of gross household wealth (Doss et al., 2013).

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