Data
Given the scale of the emigration phenomenon from Georgia coupled with the growing share of elderly within the total population, Georgia provides an excellent case study through which the relationship between migration and the well-being of the elderly “left behind” can be explored. Analysing migration-related trends in Georgia has been a problem in the past due to limited data on migration and a lack of nationally-representative data. This analysis benefits from data collected via a nationally-representative household survey implemented within the European Commission-financed study “the Effects of Migration on Children and the Elderly Left Behind in Moldova and Georgia”. The survey was explicitly designed to explore the possible consequences of familial separation through migration on the well-being of children and the elderly. In the absence of a nationally-representative sampling frame, one was elaborated on the basis of recently-updated election registration lists. Households were then selected via the random route method, and the survey was implemented among those households that contained one or more children or elderly persons. The survey collected information on the demographic features of household members, household living conditions, members’ migration histories, and the experiences and conditions of elderly household members. Migrants were identified as “any individual who lived abroad for three or more months consecutively at the time of the survey”. To retain elderly individuals as the unit of analysis, information was collected directly from individuals over the age 60 about work history, time allocation, physical health and nutrition, mental health, mobility, and relationships with household and non-household members.
Data was collected among 4010 households across all regions of Georgia between March and December 2012,Footnote 1 nearly two-thirds of which contained one or more individuals aged 60 or above. The total survey sample included 3407 elderly individuals, 2202 of which were included in the final analytical sample. As this analysis is concerned with differences in well-being outcomes of individuals with and without adult migrant children, individuals without adult children were excluded from the sample. Only those individuals who provided information on all indicators used in the analysis were retained for the final sample as well, which reduced the sample size to 2202 observations.
Key characteristics of the analytical sample are provided in Table 2. As can be seen from the table, a slightly larger proportion of elderly persons belong to the older age cohort, and women far outnumber men. In terms of household composition, almost half of elderly individuals live with other adults, followed by more than 35% of the elderly sample who live in households with at least one child below 18 years old, indicating a high incidence of multigenerational households. Approximately 10% of older individuals live with their partner, and the remaining 7.8% live alone.
Table 2 Key Characteristics of Elderly Sample
Given the focus of this analysis on the connections between migration and elderly well-being, the sample of elderly individuals was split in two groups according to the migration status of the elderly person’s adult children. According to the sample, almost 38% of elderly individuals have at least one child living outside Georgia; the proportion is slightly higher for the youngest cohort (40.5% against 35.6%). Finally, a slightly larger percent of the elderly population live in an urban area, although this result is driven by the youngest cohort as a slightly higher number of elderly above 70 years old live in rural areas.
Indicators
The focus of this multidimensional well-being analysis necessitates the construction of an elderly-specific index comprised of indicators representing possible attainments within four domains of well-being. In line with the definition of well-being provided above, such an index was chosen for its multidimensional structure, inherently comparative nature, and replicability. While such an index allows for identification of the proportion of the population that can be considered well or deprived, it more importantly allows for comparison of well-being attainment across population groups and per dimension and indicator.
The present elderly well-being index (EWB) contains four dimensions: physical, social, emotional, and housing well-being. Within this index, indicators of well-being were selected based on their appropriateness in capturing well-being in the Georgian context; indeed, single-country studies advantageously allow for the selection of indicators and thresholds to be tailored to local norms and values (Roelen et al. 2009). The indicators also reflect the possibilities and constraints of the survey data. Most indicators retained the elderly individual as the unit of analysis, as information on opinions and achievements was collected from elderly respondents directly. Indicators reflecting material living conditions, however, reflect the situation of the entire household; each individual within the household is assumed to experience the same conditions. Table 3 summarises the indicators selected per domain to represent the well-being of elderly individuals.
Table 3 Well-being Indicators per Dimension
Physical well-being is comprised of two indicators. The first measures the elderly individual’s ability to perform activities of daily living (basic mobility functions) such as bathing, dressing, walking, and going to the bathroom without assistance. The mobility indicator is a composite measure created through factor analysis, which was conducted to determine the underlying factors that explain rates of mobility. For the factor analysis, we created several binary variables measuring elderly individual’s ability to perform essential daily functions, all of which were correlated with each other. The second physical well-being indicator measures an individual’s ability to take medication without aid, which is used as a proxy of functional independence. The ability to self-administer is correlated with other activities measuring independence given the levels of mental cognisance required (Kaneda et al. 2011).
Housing well-being is the domain that captures material living standards. Elderly individuals who live in homes with appropriate flooring (e.g., not unpolished wood, dirt, or clay), with electricity, and with access to safe drinking water (e.g., not from surface water or rainwater collection) are considered well-off in this dimension. Housing conditions rather than more traditional indicators of material well-being such as income or expenditures were included for two reasons. The first is that reporting of income/expenditure data is often unreliable, and the second is that incomes/expenditures are likely to shape the attainment of well-being in other domains and should thus be included as a control in multivariate analyses.
The dimension of social well-being encompasses relationships with family and community members, as both types of social ties are important in shaping well-being outcomes. Extensive literature supports the idea that a good relationship with family and people in the community helps improve overall elderly well-being (Ward et al. 2012; Kaneda et al. 2011; Fillenbaum 1984). Care support from family and friends–or the lack thereof as a consequence of living far away from each other—has been identified as an important component of social functioning.
While many instruments exist for measuring the dimension of emotional well-being, there is limited consensus on the best tool to use, on standards of measurement, and on thresholds for defining deprivation or health, particularly across disciplines. Based on previous studies and on the available data, the indicators chosen to measure emotional health were self-reported depression and self-reported current life satisfaction. These two indicators indicate level of self-perceived wellness. Depression and life satisfaction were measured using a set of questions designed for the mental health inventory (MHI-38), an instrument designed to measure mental health among the elderly (Department of Health and Ageing 2003). The choice to measure depression using self-reported questions reflects the view that self-reported measures are usually better than clinical diagnostic tools, as they measure causes of late-life depression, such as coping with chronic illnesses, disability, feeling of loneliness, etc. (Kaneda et al. 2011). The indicator of life satisfaction was measured using a ten-point Likert scale in which respondents rated satisfaction with their current life. Based on the Cantril Self-Anchoring Striving Scale,Footnote 2 a score of seven or higher indicates that an individual is “thriving” or satisfied with his/her own life.
Methodology
The purpose of the empirical analysis is to assess elderly well-being and compare the well-being of elderly persons with and without a migrant child. A step-wise approach was adopted for the analysis. First, well-being with respect to each indicator was analysed separately. An elderly individual can be considered not deprived if s/he meets the established well-being threshold set for a given indicator. Indicator well-being rates (IWB) are calculated by counting the number of elderly persons who meet the requirement and are expressed as a share of all the elderly (Roelen et al. 2011; Roelen and Gassmann 2012)Footnote 3:
$$ { I WB}_x=\frac{1}{n}\sum_{i=1}^n{I}_{i x} $$
where n is the number of elderly for which the indicator is observable and I
ix
is a binary variable taking the value 1 if the elderly person i has reached the threshold and 0 if the elderly person has not with respect to indicator x. The denominator, n, differs across indicators depending on the number of actual observations. Indicators observed at household level, such as for monetary well-being or housing, are translated to all elderly persons living in the respective household, assuming equal access and intra-household distribution.
A second step involved building a multidimensional well-being index inspired by the methodology developed by Alkire and Foster (2011) for the measurement of multidimensional poverty. This is a well-established methodology in the field of poverty and wellbeing analysis and it underlies the global multidimensional poverty index annually published in the Human Development Report since 2010 by UNDP. An elderly person is considered to be multidimensionally well if the weighted combination of indicators is equal to or exceeds 70% of the total. The decision to set the cut-off at 70% of the aggregated indicators follows the cut-off used for multidimensional child well-being indices (e.g. Roelen and Gassmann 2012) and reflects the opposite of the 30% threshold used when measuring levels of deprivation (see, e.g. Alkire and Foster 2011). Each domain is assigned equal weight and each indicator within a domain is also equally weighted (see Table 4 below). This facilitates the interpretation of results (Atkinson 2003) but also asserts that each dimension is considered of equal importance. Weights can be determined in various ways, such as through participatory processes, based on expert opinion, or derived from survey data. Calibration of weights depends on the information available; in the absence of information on the relative value or importance of specific dimensions (or indicators), equal weights are chosen. The lower the well-being cut-off, the higher is the share of elderly doing well and the lower is the average intensity of well-being.
Table 4 Indicator Well-being Rates: Dimensions & Weights
In establishing the multidimensional well-being index, all elderly individuals who are well in any indicator are identified and subsequently assigned the indicator weight, or zero if they have failed to attain wellness. An elderly person is considered well if the sum of the weighted indicators is equal to or higher than the cut-off value. Elderly individuals with positive outcomes are then assigned a value of one; all others are assigned a value of zero. The incidence (or headcount rate) of multidimensional well-being is the percentage of elderly individuals considered well as a proportion of all elderly individuals.
The following section describes the results of the multidimensional index. Descriptive statistics for indicator and multidimensional well-being are presented, and multivariate analysis is subsequently applied in order to test for group differences and to identify other correlates that determine elderly well-being, such as personal characteristics of the elderly person and household characteristics. Separate binary outcome models are estimated for selected indicators using standard probit models:
$$ \Pr \left({y}_i=1|{x}_i\right)=\Phi \left({x}_i\beta \right),\kern0.5em \mathrm{with}\ \mathrm{i}=1,\dots, \mathrm{N} $$
Where y
i
is the binary outcome variable, Φ is the standard normal distribution function, x
i
is a vector of explanatory variables, and β is a vector of coefficients to be estimated. In this case the dependent variable is the probability that an individual is vulnerable with respect to a specific indicator. The models are estimated with robust standard errors and results are presented as average marginal effects.