A defining characteristic of persistent viral infections is the loss and functional inactivation of antiviral effector T cells, which prevents viral clearance. Interleukin-10 (IL-10) suppresses cellular immune responses by modulating the function of T cells and antigen-presenting cells. In this paper, we report that IL-10 production is drastically increased in mice persistently infected with lymphocytic choriomeningitis virus. In vivo blockade of the IL-10 receptor (IL-10R) with a neutralizing antibody resulted in rapid resolution of the persistent infection. IL-10 secretion was diminished and interferon γ production by antiviral CD8+ T cells was enhanced. In persistently infected mice, CD8α+ dendritic cell (DC) numbers declined early after infection, whereas CD8α− DC numbers were not affected. CD8α− DCs supported IL-10 production and subsequent dampening of antiviral T cell responses. Therapeutic IL-10R blockade broke the cycle of IL-10–mediated immune suppression, preventing IL-10 priming by CD8α− DCs and enhancing antiviral responses and thereby resolving infection without causing immunopathology.

Autoimmune diabetes is caused by selective loss of insulin-producing pancreatic ␤-cells. The main factors directly implicated in ␤-cell death are autoreactive, cytotoxic (islet-antigen specific) T-lymphocytes (CTL), and inflammatory cytokines. In this study, we have used an antigen-specific model of virally induced autoimmune diabetes to demonstrate that even high numbers of autoreactive CTL are unable to lyse ␤-cells by perforin unless major histocompatibility complex class I is upregulated on islets. This requires the presence of inflammatory cytokines induced by viral infection of the exocrine pancreas but not of the ␤-cells. Unexpectedly, we found that the resulting perforin-mediated killing of ␤-cells by autoreactive CTL is not sufficient to lead to clinically overt diabetes in vivo, and it is not an absolute prerequisite for the development of insulitis, as shown by studies in perforin-deficient transgenic mice. In turn, destruction of ␤-cells also requires a direct effect of ␥-interferon (IFN-␥), which is likely to be in synergy with other cytokines, as shown in double transgenic mice that express a mutated IFN-␥ receptor on their ␤-cells in addition to the viral (target) antigen and do not develop diabetes. Thus, destruction of most ␤-cells occurs as cytokine-mediated death and requires IFN-␥ in addition to perforin. Understanding these kinetics could be of high conceptual importance for the design of suitable interventions in prediabetic individuals at risk to develop type 1 diabetes. Diabetes

We model earnings processes allowing for lots of heterogeneity across agents. We also introduce an extension to the linear ARMA model which allows the initial convergence in the long run to be different from that implied by the conventional ARMA model. This is particularly important for unit root tests, which are actually tests of a composite of two independent hypotheses. We fit to a variety of statistics including most of those considered by previous investigators. We use a sample drawn from the Panel Study of Income Dynamics (PSID), and focus on white males with a high‐school degree. Despite this observable homogeneity, we find more latent heterogeneity than previous investigators. We show that allowance for heterogeneity makes substantial differences to estimates of model parameters and to outcomes of interest. Additionally, we find strong evidence against the hypothesis that any worker has a unit root.

Consumption by couples rises sharply in the beginning and falls later in life; the causes of the early rise are hotly contested. Among the suggestions are rule of thumb behavior, demographics, liquidity constraints, the precautionary motive, and nonseparabilities between consumption and labor supply. We develop two tests of the extreme hypothesis that only changes in family structure matter. We estimate effects of the numbers and ages of children on consumption. These estimates allow us to rationalize all of the increase in consumption without recourse to any of the causal mechanisms. Our estimates can be interpreted either as giving upper bounds on the effects of children or as evidence that the other causes are not important. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Finn Tarp, two anonymous referees and seminar participants at University of Colorado and George Washington University for helpful suggestions and comments. We would also like to thank Robert E. Evenson for use of the data set and for answering numerous questions about it. AbstractA potential determinant of intrahousehold distribution is the birth order of children. While a number of studies have analysed birth order effects in developed countries there are still only a few dealing with developing countries. This paper develops a model of intrahousehold allocation with endogenous fertility, which captures the relation between birth order and investment in children and shows that a birth order effect in intrahousehold allocation can arise even without assumptions about parental preferences for specific birth order children or genetic endowments varying by birth order. The important contribution is that fertility is treated as endogenous, something which other models of intrahousehold allocation have ignored despite the large literature on determinants of fertility. The implications of the model are that children with higher birth orders have an advantage over siblings with lower birth orders and that parents who are inequality averse will not have more than one child. The model furthermore shows that not taking account of the endogeneity of fertility when analysing intrahousehold allocation may seriously bias the results. The effects of a child's birth order on its human capital accumulation are analysed using a longitudinal data set from the Philippines. Contrary to most longitudinal data sets this data set covers a very long period. We are, therefore, able to examine the effects of birth order on both number of hours in school during education and completed education. The results for both are consistent with the predictions of the model. JEL Classification Numbers: D1, I2, J2, O12.