Poverty dynamics in Nairobi's slums; Testing for state dependence and heterogeneity effects

In this paper we develop a general class of dynamic brand choice models, called Lightning Bolt (LB) models, which are consistent with the theory of random utility maximization of consumer choice behavior. The underlying random utility process is Markov, and the inter-temporal evolution of the (utility-maximizing) brand choice process is also Markov. The models permit parsimonious parameterizations of the random utility process in brand choices with the resulting switching probabilities being functionally related to explanatory variables. The model allows for structural state dependence (feedback), habit persistence (inertia), and unobserved heterogeneity. The theoretical development shows that several well known stochastic brand choice models can be deduced from random utility maximization theory. From a managerial perspective, the usefulness of the proposed model stems from its ability to separate out the effects of habits, state dependence and heterogeneity. Strong state dependence effects imply incentives for inducing trial of a brand (e.g., product sampling). In contrast with state dependence, a strong habit persistence effect may be indicative of buyer behavior where inducement of trial of a different brand may not be sufficient to maintain a (sustained) defection of the consumer from the habitually purchased brand to the trial brand. Failure to distinguish between these two effects has important implications. For example, a model that only accounts for state dependence effects would, in the presence of only habit persistence, incorrectly attribute it to state dependence. Based on this the manager could decide to embark on an expensive sampling program that might prove ineffective due to the absence of state dependence. It is also important to distinguish between the effects of unobserved heterogeneity and state dependence. In the absence of true state dependence, failing to account for unobservable variations across households (such as differences in price sensitivities), results in the temporally persistent unobservable elements showing up as state dependence in the model. Hence, a manager may incorrectly opt for sampling as the appropriate marketing action, whereas, a couponing or price promotion strategy should have been preferred. We estimate the model parameters using the AC Nielsen household scanner panel data set on catsup purchases. Further, we investigate empirical techniques for overcoming the “initial conditions” problem that affects many dynamic models of brand choice. Through simulation analysis using the estimated parameters, we show that the calculated profitability of a promotion must take into account the multi-period impact due to state dependence. Further, we demonstrate how ignoring heterogeneity can result in spurious state dependence, thereby making marketing tactics such as product sampling appear far more attractive than they actually are. Specifically, the model that accounts only for habit persistence and state dependence effects predicts a share increase of 2.4% points through sampling for one of the brands in the empirical analysis. Once the effects of unobserved heterogeneity are accounted for, however, this number drops precipitously to 0.3%. While even this low number might fulfill the objectives of the brand manager, it ensures that expectations are not eight times that number. It is important to note that this paper is a first attempt at analyzing the three fundamental dimensions of dynamic brand choice behavior. Our formulation represents a “reduced form” approach as opposed to a “structural” approach. Other limitations of the model include our focus on time-invariant choice sets. Extensions of the model to situations where choice sets vary over time are possible, but are difficult. Another limitation is the dependence on multivariate extreme value distributions. An alternative would be to use multivariate normal distributions, i.e., a probit-like structure which has certain useful properties. A comparison of the two approaches would be useful. Empirical extensions of the model to allow for higher-order processes and nested logit type model structures could also be fruitful. Explicitly incorporating variety-seeking behavior in the model would further enrich the theoretical framework proposed in this paper.

This paper examines the role of state dependence in explaining the retirement status of older men. Following from the work of James Heckman and others, apparent and true state dependence are distinguished in a dynamic retirement model. Using a two year longitudinal sample of men aged 58 to 62 in 1969, apparent and true state dependence are controlled for through estimation of an error- components model and a retirement-transition model. The results indicate substantial evidence of state dependence. Copyright 1987 by University of Chicago Press.

This study analyzes TV program content for viewers with respect to the following three properties: primary utility (intrinsic value by genre, cast, and so on), macro externality, such as the effect of others' past behavior (audience ratings), and state dependence, or the viewer's past actions that determine whether to watch the program. Focusing on the effects of macro externality and state-dependence on viewer behavior, the authors construct a hierarchical Bayesian model and derive an individual utility function. Applying their method to the choice of TV programs in Japan, they estimate the effects of macro externality and state dependence on an individual's viewing behavior, and illustrate the distribution of these effects. The authors find that macro externality has a strong overall effect on the total number of viewers and the state-dependence effect has a slightly positive effect. Moreover, comparing the attributes revealed that male viewers tend to show a lower effect of ratings and higher state dependence than females. They find that the older the viewers are, the higher the effect of state dependence. Further, using utility function data obtained by our estimation, they conduct a simulation to measure the dynamic effects of macro externality and state dependence.

Rats with cannulas implanted in the septal area were conditioned, tested, or both conditioned and tested shortly after intracerebral injection of local anesthetic via the cannulas. A 2 X 2 factorial design was used to determine whether the presumed state of temporary septal area dysfunction, previously shown to produce amnesia, has state-dependent properties. A state-dependent learning effect was observed in the rats both conditioned and tested in the dysfunctional stat remembered the aversive conditioning better than those conditioned in the dysfunctional state but tested in the normal state. Since rats conditioned in the normal state but tested during septal dysfunction did appear to exhibit conditioned fear when tested, the state-dependent effect was asymmetrical. Performance effects of the procaine injection were observed and accounted for in determining the state-dependent nature of temporary septal area dysfunction.

No prior investigation has considered the effects of state dependence and unobserved heterogeneity on the relationship between regular physical activity (RPA) and latent health stock (LHS). Accounting for state dependence corrects the possible overestimation of the impact of socioeconomic factors. We estimated the degree of the state dependence of RPA and LHS among middle-aged Japanese workers. The 5 years’ longitudinal data used in this study were taken from the Longitudinal Survey of Middle and Elderly Persons. Individual heterogeneity was found for both RPA and LHS, and the dynamic random-effects probit model provided the best specification. A smoking habit, low educational attainment, longer work hours, and longer commuting time had negative effects on RPA participation. RPA had positive effects on LHS, taking into consideration the possibility of confounding with other lifestyle variables. The degree of state dependence of LHS was positive and significant. Increasing the intensity of RPA had positive effects on LHS and caused individuals with RPA to exhibit greater persistence of LHS compared to individuals without RPA. This result implies that policy interventions that promote RPA, such as smoking cessation, have lasting consequences. We concluded that smoking cessation is an important health policy to increase both the participation in RPA and LHS.

Since FY 2013, as a part of the Affordable Care Act (ACA) program, the Hospital Value-Based Purchasing (HVBP) program has adjusted Medicare’s payments to hospitals based on the total performance score of the hospital. First, the program reduces a portion of the hospital’s Medicare payments in a specific fiscal year, and then, by the end of the same fiscal year, the amount of the payment reductions will be awarded to the hospitals based on the total performance score; thus, the hospitals that do not receive the reward will lose the portion of money reduced by Medicare. In this research, we apply the theory of state dependence and use the dynamic random effect probit model to estimate this effect. The results show that the hospital payment adjustment dynamics have a very significant state dependence effect (0.341); this means that hospitals that received a reward in the previous year are 34.1% more likely to receive a reward this year than the ones that received a penalty in the previous year. Meanwhile, we also find that the state dependence effect varies significantly across hospitals with different ownership (proprietary/government owned/voluntary nonprofit), and the results show that voluntary nonprofit hospitals exhibit the largest effect of state dependence (0.370), while government-owned hospitals exhibit the lowest effect of state dependence (0.293), and proprietary hospitals are in the middle. Among the factors that influence the likelihood that a hospital receives a reward, we find that teaching hospitals with a large number of beds (>400) are less likely be rewarded; in terms of ownership, we find that voluntary nonprofit hospitals are more likely be rewarded; in terms of demographic factors, hospitals where the average household income are higher within the region are more likely be rewarded.

This paper analyzes the persistence of firms’ exporting behavior in a panel of German manufacturing firms using dynamic binary choice models. We distinguish between true and spurious state dependence in exports and apply fixed effects methods that allow us to verify the robustness of our results to critical assumptions on firms’ initial export status. We find robust evidence of state dependence in the current export status of firms. We also document an important role of unobserved permanent firm heterogeneity (spurious state dependence) and quantify the relative importance of different export determinants. Our results, which are consistent with the findings of previous studies on firms in developing countries and in the United States, show the presence of important sunk costs in export market entry and a depreciation of knowledge and experience in export markets.

This study investigates discrepancies in offset choices between real-world and hypothetical scenarios by comparing stated preference (SP) and self-reported revealed preference (RP) data. Using multinomial logit (MNL) and mixed logit (MXL) models, key findings are: (1) preferences for offset contributions and prices are consistent across SP and RP, but differ in project types, locations, and certifications. (2) partial data enrichment harmonizes SP and RP within MNL models, outperforming full enrichment. (3) positive state dependence shows previous choices influence stated ones, with individual heterogeneity in MXL models. (4) incorporating unobserved heterogeneity and state-dependence effects is crucial when merging SP and RP.

Using longitudinal data from the Nairobi Urban and Demographic Surveillance System (NUHDSS), we examined the seasonal pattern of pneumonia mortality among under-five children living in Nairobi's slums. We included 17,787 under-five children resident in the NUHDSS from January 1, 2003 to December 31, 2005 in the analysis. Four hundred thirty-six deaths were observed and cause of death was ascertained by verbal autopsy for 377 of these deaths. Using Poisson regression, we modeled the quarterly mortality risk for pneumonia. The overall person-years (PYs) were 21,804 giving a mortality rate of 20.1 per 1,000 PYs in the study population. Pneumonia was the leading cause of death contributing 25.7% of the total deaths. Pneumonia mortality was highest in the second quarter (risk ratio [RR] = 2.3, confidence interval [CI]: 1.2-4.2 compared with the fourth quarter). The study provides evidence that pneumonia-related mortality among under-fives in Nairobi's slums is higher from April to June corresponding to the rainy season and the beginning of the cold season.

Probabilistic versus random-utility models of state dependence: an empirical comparison

BackgroundCurrent estimates of maternal mortality ratios in Kenya are at least as high as 560 deaths per 100,000 live births. Given the pervasive poverty and lack of quality health services in slum areas, the maternal mortality situation in this setting can only be expected to be worse. With a functioning health care system, most maternal deaths are avoidable if complications are identified early. A major challenge to effective monitoring of maternal mortality in developing countries is the lack of reliable data since vital registration systems are either non-existent or under-utilized. In this paper, we estimated the burden and identified causes of maternal mortality in two slums of Nairobi City, Kenya.MethodsWe used data from verbal autopsy interviews conducted on nearly all female deaths aged 15–49 years between January 2003 and December 2005 in two slum communities covered by the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). In describing the distribution of maternal deaths by cause, we examined maternal and late maternal deaths according to the ICD-10 classification. Additionally we used data from a survey of health care facilities that serve residents living in the surveillance areas for 2004–2005 to examine causes of maternal death.ResultsThe maternal mortality ratio for the two Nairobi slums, for the period January 2003 to December 2005, was 706 maternal deaths per 100,000 live births. The major causes of maternal death were: abortion complications, hemorrhage, sepsis, eclampsia, and ruptured uterus. Only 21% of the 29 maternal deaths delivered or aborted with assistance of a health professional. The verbal autopsy tool seems to capture more abortion related deaths compared to health care facility records. Additionally, there were 22 late maternal deaths (maternal deaths between 42 days and one year of pregnancy termination) most of which were due to HIV/AIDS and anemia.ConclusionMaternal mortality ratio is high in the slum population of Nairobi City. The Demographic Surveillance System and verbal autopsy tool may provide the much needed data on maternal mortality and its causes in developing countries. There is urgent need to address the burden of unwanted pregnancies and unsafe abortions among the urban poor. There is also need to strengthen access to HIV services alongside maternal health services since HIV/AIDS is becoming a major indirect cause of maternal deaths.

Emerging experimental evidence suggests that both networks and their component neurons respond to similar inputs differently depending on the state of network activity. The network state is determined by the intrinsic dynamical structure of the network and may change as a function of neuromodulation, the balance or stochasticity of synaptic inputs to the network and the history of network activity. Much of the knowledge on state-dependent effects comes from comparisons of awake and sleep states of the mammalian brain. Yet, the mechanisms underlying these states are difficult to unravel. Several vertebrate and invertebrate studies have elucidated cellular and synaptic mechanisms of state-dependence resulting from neuromodulation, sensory input, and experience. Recent studies have combined modeling and experiments to examine the computational principles that emerge when network state is taken into account; these studies are highlighted in this article. We discuss these principles in a variety of systems (mammalian, crustacean, and mollusk) to demonstrate the unifying theme of state-dependence of network output.

This article uses multilevel recurrent discrete-time hazard analysis to simultaneously model the impact of life cycle events and structural processes on poverty entry and exit across European Regions. Research questions are, (1) what is the importance of life cycle events on the road to entry into and exit from poverty, (2) are there any differences in poverty dynamics between European Regions and if so, how can we explain these differences? The analysis is based on individual and household panel data of the European Community Household Panel linked with a regional time series database. Main findings are that men's poverty dynamics is dominated by employment-related events, while for women demographic events also play a role. Regional structural factors only have a slight or no influence on poverty transitions, but the welfare regime turns out to be highly significant for poverty entry.

Life cycle employment and fertility across institutional environments

Does Solo Self-Employment Serve as a 'Stepping Stone' to Employership?