The first step in gaining contact with a sample person, when selected from a list of persons, is locating that person.(1) If the sample person has not changed address or telephone number from the time the list was prepared, this is a trivial issue. The difficulty arises when persons or households change addresses. The propensity of locating units is driven by factors related to whether or not the unit moves and the quality of contact information provided at the time of initial data collection.
A number of survey design features may affect the likelihood of locating sample units. For example, the quality of the contact information decays as time between the initial data collection (or creation of the list) and the followup survey increases. Similarly, tracking rules affect location propensity. For cost reasons, a survey organization may track people only within a limited geographic area, such as a county or within a country. The amount and quality of information collected by the survey organization specifically for tracking movers also is driven by cost considerations. The more reliable and valid data available for tracking purposes can reduce tracking effort, and make more resources available for those units that are proving to be particularly difficult to locate.
Household characteristics also affect the likelihood of moving, and thus the propensity to locate the household or household members. Geographic mobility is related to the household or individual life stage, as well as cohort effects. For example, younger people are typically much more mobile than older persons. The number of years that a household or individual has lived at a residence, the nature of household tenure (i.e., whether the household members own or rent the dwelling), and community attachments through family and friends also determine the likelihood of moving.
Household income is strongly related to residential mobility. Using data from the Current Population Survey, we find that 19.6 percent of those with household incomes under $10,000 had moved between March 1996 and March 1997, compared to 10 percent of those with incomes above $75,000. Similarly, 25.9 percent unemployed persons age 16 or older had moved in this period, compared to 16.8 percent of those employed, and 11.1 percent not in the labor force.
Life events also are known to be related to moving likelihood. A birth in a household, a death of a significant individual, marriage, job change, crime victimization, and other events are associated with increased likelihood of moving. Furthermore, these life events may increase the difficulty of locating individuals. For example, a name change in marriage or following divorce can make it more difficult to track and locate someone who has moved. This is particularly relevant for welfare leaver studies, as this population is likely to be undergoing these very types of changes.
An important factor that can reduce the likelihood of moving, or provide more data on units that do move, is the social aspect of community attachment or connectedness. Individuals who are engaged in the civic aspects of their community or participate socially are posited to be more stable and less likely to move. Furthermore, those linked into their current community life are likely to leave many traces to their new address, and likely to be politically, socially, and economically engaged in their new community. Their lives are more public and accessible through multiple databases such as telephone directories, credit records, voter registration, library registration, membership in churches or religious organizations, or children in schools. Again, we expect that sample units in welfare leaver studies are not particularly rich in these sources of tracking information.
To the extent that the survey variables of interest are related to mobility, lifestyle changes, social isolation, or willingness to be found, nonresponse through nonlocation can lead to bias. Because these studies are primarily about changes in individual lives, failure to obtain complete data on the more mobile or those subject to lifestyle changes will underrepresent individuals with these particular characteristics in such surveys. Furthermore, the effects of disproportionate representation in the sample due to mobility or lifestyle changes may not be simply additive. For example, we expect that those who do not have a telephone and those who refuse to provide a telephone number both would be difficult to locate in subsequent waves of a survey, but for different reasons.
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