Academic journal article Demographic Research

Forgotten Marriages? Measuring the Reliability of Marriage Histories

Academic journal article Demographic Research

Forgotten Marriages? Measuring the Reliability of Marriage Histories

Article excerpt

1. Introduction

Retrospective marriage histories collected in surveys are a valuable source of information on nuptiality. They usually contain information on respondents' reported marriages, including marriage dates and how unions ended. Researchers have previously used survey-based marriage histories to calculate the probabilities of divorce and remarriage, examine the sociodemographic factors associated with these events, and identify respondents who divorced and/or remarried between survey waves (Amoateng and Heaton 1989; Anglewicz and Reniers 2014; Boileau et al. 2009; Brandon 1990; Fedor, Kohler, and Behrman 2015; Gage-Brandon 1992; Grant and Yeatman 2014; Hampshire and Randall 2000; Locoh and Thiriat 1995; Reniers 2003, 2008; Reniers and Tfaily 2008; Takyi and Gyimah 2007; Tilson and Larsen 2000). Despite their value, retrospective marriage histories, like other forms of survey data, may be incomplete or contain incorrect information. While researchers typically acknowledge problems with retrospective marriage histories, such as respondents omitting unsuccessful or short unions and misreporting dates (Boileau et al. 2009; Reniers 2008), it is unknown to what extent these problems occur and, more importantly, how marriage analyses are affected. Ideally, the validity of marriage histories would be measured by comparing them against public records: however, this is not feasible in many parts of Africa because civil marriages are not the norm (Enel, Pison and Lefebvre 1994; van de Walle and Meekers 1994).

An alternative method is to measure their reliability by comparing marriage histories reported by the same respondent at two or more points in time. Using data from the Malawi Longitudinal Study of Families and Health (MLSFH), this study investigates whether respondents consistently report their spouses (by name), status of marriage, and dates of marriage across two survey waves. This study also investigates the characteristics associated with marriage omission and marriage date inconsistencies and examines whether misreporting biases marriage indicators. Results indicate that a considerable amount of misreporting exists and that misreporting does not appear to be random. Several marriage, individual, survey, and interviewer characteristics are associated with misreporting and marriage indicators are shown to be affected by misreporting.

1.1 Theories of misreporting

Two types of misreporting are common in surveys where respondents are asked to provide autobiographical information. The first type of misreporting relates to the reporting of the event itself. A large body of literature has shown that some respondents underreport and/or overreport events such as unemployment, migration, births, pregnancy, cohabitation, and sexual behavior (Courgeau 1992; Dare and Cleland 1994; Hayford and Morgan 2008; Hertrich 1998; Mathiowetz and Duncan 1988; Ratcliffe et al. 2002; Smith and Thomas 2003). Overreporting or underreporting events can lead to calculated rates, such as birth rates and divorce rates, being over- or underestimated. It can also result in biased population-level indicators. Furthermore, regression estimates can be biased if individuals are incorrectly coded as having experienced certain events, such as sexual debut.

The second type of misreporting occurs when respondents misreport event dates such as migration, marriage, and divorce, as well as the ages at which events occur, including age at first sex or marriage (Auriat 1993; Hertrich 1998; Mitchell 2010; Smith and Thomas 2003; Wringe et al. 2009; Zaba et al. 2009). Misreporting event dates can affect calculated rates by simultaneously increasing and decreasing the number of events occurring in two adjacent time periods, leading to both over- and underestimates of rates during a particular time period. By changing the temporal ordering of events, misreporting event dates can also affect analyses attempting to assign causality. Lastly, misreporting event dates can lead to the misrepresentation of trends, such as age at first sex or marriage. …

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