Uninsured Risks, Loan Contracts and the Declining Equity Premium

Using a two period model with moral hazard and uninsured risk, we argue that the decline in equity premium from its historically high level is due to a gradual elimination of barriers to universal banking. The loan contracts set up by financial intermediaries became more complete in nature with the advent of universal banking in the 90s following the Gramm-Leach-Billy Act. Hence, it is the nature of the loan contracts, not just the borrowing constraint and uninsured risks that is more fundamental in explaining the size of the equity premium.

Six concerns about the data in aid debates: applying an epidemiological perspective to the analysis of aid effectiveness in health and development.

Is aid helping, hindering, or having no effect on development and health? The answer to this question is highly contested, with proponents on all sides adhering strongly to their competing interpretations. We ask how it is possible for those who are often using the same data to hold such divergent views. Here, we employ an epidemiological perspective and find that, in many cases, the arguments are characterised by methodological weaknesses. There may be selective citation of results and failure to account for bias and confounding, such as where an extraneous factor influencing the outcome is correlated with increased aid or, in confounding by indication, where increased aid is a consequence of a country being in an especially adverse situation. Studies may also lack external validity, whereby lack of data (a widespread problem) or similar considerations mean that analyses are undertaken on an unrepresentative subset of countries. Multiple outcome measures can also be problematic, where the main outcome of interest is not specified in advance. Many studies fail to account for differential time lags between changes in aid and the outcomes being studied. Some studies may also be underpowered to detect an association where one exists. Although, ideally, this debate should be informed by large scale randomised controlled trials, this will often be unfeasible. Given this limitation, it is essential that those engaged in it are cognisant of the many methodological issues that face any observational study

Relationship Banking, State Co-Ordination and Long-Term Debt: Reinterpreting the Big Push

We develop a lending game in which relationship-specific investments by firms benefit banks and vice versa. We show that even if all firms and banks prefer high-tech relationship loans under the first-best, asymmetric information and investment non-contractibility make them choose low-tech transaction loans. However, governments with intermediate risk ratings can use Groves subsidies for a concerted switch to long-term relationship loans. To avoid premature liquidation, they finance the scheme with long-term foreign debt. Thus, we try to explain the positive correlation between subsidies and long-term domestic and foreign debt, which was a salient feature of the East Asian development experience.Relationship Banking; Groves Subsidies; Intermediate Rating; Long-term Debt

Nutritional determinants of worldwide diabetes: an econometric study of food markets and diabetes prevalence in 173 countries.

OBJECTIVE: Ageing and urbanization leading to sedentary lifestyles have been the major explanations proposed for a dramatic rise in diabetes worldwide and have been the variables used to predict future diabetes rates. However, a transition to Western diets has been suggested as an alternative driver. We sought to determine what socio-economic and dietary factors are the most significant population-level contributors to diabetes prevalence rates internationally. DESIGN: Multivariate regression models were used to study how market sizes of major food products (sugars, cereals, vegetable oils, meats, total joules) corresponded to diabetes prevalence, incorporating lagged and cumulative effects. The underlying social determinants of food market sizes and diabetes prevalence rates were also studied, including ageing, income, urbanization, overweight prevalence and imports of foodstuffs. SETTING: Data were obtained from 173 countries. SUBJECTS: Population-based survey recipients were the basis for diabetes prevalence and food market data. RESULTS: We found that increased income tends to increase overall food market size among low- and middle-income countries, but the level of food importation significantly shifts the content of markets such that a greater proportion of available joules is composed of sugar and related sweeteners. Sugar exposure statistically explained why urbanization and income have been correlated with diabetes rates. CONCLUSIONS: Current diabetes projection methods may estimate future diabetes rates poorly if they fail to incorporate the impact of nutritional factors. Imported sugars deserve further investigation as a potential population-level driver of global diabetes

Complexity in Mathematical Models of Public Health Policies: A Guide for Consumers of Models

Sanjay Basu and colleagues explain how models are increasingly used to inform public health policy yet readers may struggle to evaluate the quality of models. All models require simplifying assumptions, and there are tradeoffs between creating models that are more “realistic” versus those that are grounded in more solid data. Indeed, complex models are not necessarily more accurate or reliable simply because they can more easily fit real-world data than simpler models can. Please see later in the article for the Editors' Summar

Adaptive TTL-Based Caching for Content Delivery

Content Delivery Networks (CDNs) deliver a majority of the user-requested content on the Internet, including web pages, videos, and software downloads. A CDN server caches and serves the content requested by users. Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for production CDN traffic that is non-stationary. We propose two TTL-based caching algorithms and provide provable guarantees for content request traffic that is bursty and non-stationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using a stochastic approximation approach. Given a feasible target hit rate, we show that the hit rate of d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We implement d-TTL and f-TTL and evaluate both algorithms using an extensive nine-day trace consisting of 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out the non-stationary traffic for rarely-accessed objects

State-Level and County-Level Estimates of Health Care Costs Associated with Food Insecurity.

IntroductionFood insecurity, or uncertain access to food because of limited financial resources, is associated with higher health care expenditures. However, both food insecurity prevalence and health care spending vary widely in the United States. To inform public policy, we estimated state-level and county-level health care expenditures associated with food insecurity.MethodsWe used linked 2011-2013 National Health Interview Survey/Medical Expenditure Panel Survey data (NHIS/MEPS) data to estimate average health care costs associated with food insecurity, Map the Meal Gap data to estimate state-level and county-level food insecurity prevalence (current though 2016), and Dartmouth Atlas of Health Care data to account for local variation in health care prices and intensity of use. We used targeted maximum likelihood estimation to estimate health care costs associated with food insecurity, separately for adults and children, adjusting for sociodemographic characteristics.ResultsAmong NHIS/MEPS participants, 10,054 adults and 3,871 children met inclusion criteria. Model estimates indicated that food insecure adults had annual health care expenditures that were $1,834 (95$1,073-2,595, P < .001) higher than food secure adults. For children, estimates were 80 higher, but this finding was not significant (95% CI, -$171 to$329, P = .53). The median annual health care cost associated with food insecurity was $687,041,000 (25th percentile,$239,675,000; 75th percentile, $1,140,291,000). The median annual county-level health care cost associated with food insecurity was$4,433,000 (25th percentile, $1,774,000; 75th percentile,$11,267,000). Cost variability was related primarily to food insecurity prevalence.ConclusionsHealth care expenditures associated with food insecurity vary substantially across states and counties. Food insecurity policies may be important mechanisms to contain health care expenditures

Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challenge for aid delivery organizations. Accurate forecasting of IDP migration would empower humanitarian aid groups to more effectively allocate resources during conflicts. We show that monthly flow of IDPs from province to province in both Syria and Yemen can be accurately forecasted one month in advance, using publicly available data. We model monthly IDP flow using data on food price, fuel price, wage, geospatial, and news data. We find that machine learning approaches can more accurately forecast migration trends than baseline persistence models. Our findings thus potentially enable proactive aid allocation for IDPs in anticipation of forecasted arrivals