Demographers’ interest in fertility trends and determinants in developed countries: Is it warranted?

Studies of fertility trends and determinants in developed countries are high on demographers’ research agenda. The interest in this subject is probably, to a large extent, motivated by a notion about low fertility being problematic, but demographers have not been much engaged in efforts to find out whether that is actually the case, at least as judged from the contents of the major demography journals. In this paper, the possibility of various individual- and societal-level effects of low fertility is briefly reviewed. Some of the harmful effects may be foreseen and considered an acceptable disadvantage by couples making fertility decisions, while others more rightly can be considered social problems. It is argued that knowledge about fertility trends and determinants may help us learn more about the consequences of low fertility and see clearer whether interventions may be justified and what specific steps one might take. Further efforts to expand this knowledge should therefore be welcome, and it is possible that demographers can make an important contribution by applying this knowledge themselves in studies of consequences of fertility. A higher priority to forecasting might also be worthwhile.consequences, determinants, low fertility, research priorities, social problem

The problematic estimation of "imitation effects" in multilevel models

It seems plausible that a person’s demographic behaviour may be influenced by that among other people in the community, for example because of an inclination to imitate. When estimating multilevel models from clustered individual data, some investigators might perhaps feel tempted to try to capture this effect by simply including on the right-hand side the average of the dependent variable, constructed by aggregation within the clusters. However, such modelling must be avoided. According to simulation experiments based on real fertility data from India, the estimated effect of this obviously endogenous variable can be very different from the true effect. Also the other community effect estimates can be strongly biased. An "imitation effect" can only be estimated under very special assumptions that in practice will be hard to defend.bias, endogenous, estimation, imitation effect, models, multilevel, simulation, survey

The High Fertility of College Educated Women in Norway

College education has a positive impact on birth rates, net of age and duration since previous birth, according to models estimated separately for second and third births. There are also indications of such effects on first-birth rates, in the upper 20s and 30s. Whereas a high fertility among the better-educated perhaps could be explained by socioeconomic or ideational factors, it might just as well be a result of selection. When all three parity transitions are modelled jointly, with a common unobserved factor included, negative effects of educational level appear. On the whole, the effects are less clearly negative for women born in the 1950s than for those born in the 1940s or late 1930s. The cohorts from the 1950s show educational differentials in completed fertility that are quite small and to a large extent stem from a higher proportion of childlessness among the better-educated. Second-birth progression ratios are just as high for the college educated as for women with only compulsory education, and the third-birth progression ratios differ very little. This reflects weakly negative net effects of education after first birth and spill-over effects from the higher age at first birth, counterbalanced by differential selectivity of earlier parity transitions.education, fertility, hazard models, parity-specific, unobserved heterogeneity

The impact of individual and aggregate unemployment on fertility in Norway

Continuous-time hazard models are estimated from register-based birth, migration, education and unemployment histories for the complete Norwegian population, linked with aggregate data for municipalities. The analysis covers the period 1992-98. First-birth rates are slightly higher among women who had been unemployed twelve months before than among others, whereas higher-order birth rates are slightly lower. Although men’s unemployment has a more pronounced negative effect, according to paternity rate models, the overall conclusion is that unemployment in Norway has had a negligible impact on fertility through individual-level effects. Aggregate-level effects are more important. Higher-order birth rates are lower in municipalities where men’s or women’s unemployment is high than elsewhere. All in all, the peak unemployment level of 6% experienced in 1993 is found to be associated with a reduction of about 0.08 in total fertility. The results accord well with economic theories for first and higher-order births that are based on the assumption that women are still the primary caretakers.birth rate, fertility, multilevel, parity-specific, register data, unemployment

A Fixed-Effects Analysis of How Income Inequality in a Municipality Affects Individual Mortality in Norway

There is still much uncertainty about the impact of income inequality on health and mortality. Some studies have supported the original hypothesis about adverse effects, while others have shown no effects, and a few even indicated beneficial effects. In this investigation, register data covering the entire Norwegian population were used to estimate how income inequality in the municipality of residence, measured by the Gini coefficient, affected mortality in men and women aged 30-89 in the years 1980- 2002, net of their individual incomes. The total exposure time was about 55 million person years, and there were about 850000 deaths. Adverse effects were estimated when individual and average income and some other commonly used control variables were included in the models. However, because there are annual measurements in each municipality, the data provide a rare opportunity to include also municipality fixed-effects, to pick up time-invariant unobserved factors at that level. When this was done, there was actually more evidence for beneficial than for adverse effects. In addition to illustrating the potential importance of the fixed-effects approach, these findings should add to the scepticism about the existence of harmful health effects of income inequality, and especially in a Nordic context.Fixed-effects; Gini; Income; Inequality; Mortality; Municipality; Norway; Register

A cancer survival model that takes sociodemographic variations in 'normal' mortality into account: comparison with other models

Study objectives - Sociodemographic differentials in cancer survival have occasionally been studied by using a relative-survival approach, where all-cause mortality among persons with a cancer diagnosis is compared with that among similar persons without such a diagnosis (’normal’ mortality). One should ideally take into account that this ’normal’ mortality not only depends on age, sex and period, but also various other sociodemographic variables. However, this has very rarely been done. A method that allows such variations to be considered is presented here, as an alternative to an existing technique, and is compared with a relative-survival model where these variations are disregarded and two other methods that have often been used. Design, setting and participants – The focus is on how education and marital status affect the survival from twelve common cancer types among men and women aged 40-80. Four different types of hazard models are estimated, and differences between effects are compared. The data are from registers and censuses and cover the entire Norwegian population for the years 1960- 1991. There are more than 100 000 deaths to cancer patients in this material. Main results and conclusions - A model for registered cancer mortality among cancer patients gives results that for most, but not all, sites are very similar to those from a relative-survival approach where educational or marital variations in ’normal’ mortality are taken into account. A relative-survival approach without consideration of these sociodemographic variations in ’normal’ mortality gives more different results, the most extreme example being the doubling of the marital differentials in survival from prostate cancer. When neither sufficient data on cause of death nor on variations in ’normal’ mortality are available, one may well choose the simplest method, which is to model all-cause mortality among cancer patients. There is little reason to bother with the estimation of a relative-survival model that does not allow sociodemographic variations in ’normal’ mortality beyond those related to age, sex and period. Fortunately, both these less data demanding models perform well for the most aggressive cancers.Cancer survival models; education; marriage

Does income inequality really influence individual mortality?

There is still much uncertainty about the impact of income inequality on health and mortality. Some studies have supported the original hypothesis about adverse effects, while others have shown no effects. One problem in these investigations is that there are many factors that may affect both income inequality and individual mortality but that cannot be adequately controlled for. The longitudinal Norwegian register data available for this study allowed municipality dummies to be included in the models to pick up time-invariant unobserved factors at that level. The results were compared with those from similar models without such dummies. The focus was on mortality in men and women aged 30-79 in the years 1980-2002, and the data included about 500000 deaths within 50 million person-years of exposure. While the models without municipality dummies suggested that income inequality in the municipality of residence, as measured by the Gini coefficient, had an adverse effect on mortality net of individual income, the results from the models that included such dummies were more mixed. Adverse effects appeared among the youngest, while among older men, there even seemed to be beneficial effects. In addition to illustrating the potential importance of controlling for unobserved factors by adding community dummies (doing a ‘fixed-effects analysis’ according to common terminology in econometrics), the findings should add to the scepticism about the existence of harmful health effects of income inequality, at least in the Nordic context.fixed-effects, Gini Index, income, inequality, mortality, multilevel, municipality, Norway, registers

A simulation-based assessment of the bias produced when using averages from small DHS clusters as contextual variables in multilevel models

There is much interest these days in the importance of community institutions and resources for individual mortality and fertility. DHS data may seem to be a valuable source for such multilevel analysis. For example, researchers may consider including in their models the average education within the sample (cluster) of approximately 25 women interviewed in each primary sampling unit (PSU). However, this is only a proxy for the theoretically more interesting average among all women in the PSU, and, in principle, the estimated effect of the sample mean may differ markedly from the effect of the latter variable. Fortunately, simulation experiments show that the bias actually is fairly small - less than 14% - when education effects on first birth timing are estimated from DHS surveys in sub-Saharan Africa. If other data are used, or if the focus is turned to other independent variables than education, the bias may, of course, be very different. In some situations, it may be even smaller; in others, it may be unacceptably large. That depends on the size of the clusters, and on how the independent variables are distributed within and across communities. Some general advice is provided.average, bias, clustering, contextual, DHS, measurement error, multilevel, simulation, size, small

Children, family and cancer survival in Norway

Models for all-cause mortality among 45000 men and women with cancer in 12 different sites were estimated, using register and census data for complete Norwegian birth cohorts. This observed-survival method seemed to be an adequate approach. The results support the idea that women who were pregnant shortly before a breast cancer diagnosis may have a poorer prognosis than others. In principle, such an effect may also reflect that these women have a young child during the follow-up period, and are burdened by that. However, this social explanation can hardly be very important, given the absence of a corresponding significant effect in men and for other cancer sites in women. Breast cancer is different from other malignancies also with respect to the effect of parenthood more generally, regardless of the timing of the pregnancies. On the whole, male and female cancer patients with children experience a lower mortality than the childless, although without a special advantage associated with adult children. This suggests a social effect, perhaps operating through a link between parenthood, life style and general health. No parity effect was seen for breast cancer, however, which may signal that the social effect is set off against an adverse physiological effect of motherhood for this particular cancer. Among men, both marriage and parenthood were associated with a good prognosis. Married male cancer patients with children had a mortality 1/3 lower than that among the childless and never-married. Women who had never married did not have the same disadvantage.Cancer; Census; Children; Family; Marriage; Register; Social; Survival

The Importance of Municipality Characteristics for Cancer Survival in Norway: A Multilevel Analysis

Discrete-time hazard models for cancer mortality in cancer patients were estimated from register and census data to find out whether various socio-economic, ideational and institutional community factors had an impact on cancer survival in Norway in the 1990s, also beyond that of the corresponding individual-level variables. Such a multilevel approach has not been employed in previous analyses of cancer survival. In addition to confirming the better prognosis for patients with high education, it was found that, among patients at the same educational level, mortality was lowest for those who lived in a municipality where the average education was relatively high. The impact of economic resources was less pronounced. While a low unemployment rate in the municipality and high individual income reduced mortality among cancer patients, a high average income had no effect. Also those who lived in municipalities where a large proportion voted with the Christian Democratic Party had an advantage, which suggests a beneficial impact of affiliation with religious communities or support for the central Christian ideas. Moreover, there was an excess mortality among patients who lived in municipalities served by a relatively small hospital that did not have any responsibility beyond the county level. These patients may have got somewhat inadequate treatment at a low level in the hospital structure, or they have perhaps not wanted, or been able to fully comply with, the recommended follow-up treatment at the highest level. Even with such factors included in the model, there was significant regional variation. Cancer survival was relatively poor, net of differences in the stage distribution, in the capital, the central parts of Southern and Western Norway, and the peripheral parts of Southern Norway.Cancer; Hospital; Multilevel; Region; Socio-economic; Survival; Education; Income; Religion; Unemployment