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We explore the US states' evolving policy responses to the COVID-19 pandemic by examining governors' decisions to begin easing five types of social distancing policies after the initial case surge in March-April 2020. Applying event history models to original data on state COVID-19 policies, we test the relative influence of health, economic, and political considerations on their decisions. We find no evidence that differences in state economic conditions influenced when governors began easing. Governors of states with larger recent declines in COVID-19 deaths per capita and improving trends in new confirmed cases and test positivity were quicker to ease. However, politics played as powerful a role as epidemiological conditions, driven primarily by governors' party affiliation. Republican governors made the policy U-turn from imposing social distancing measures toward easing those measures a week earlier than Democratic governors, all else equal. Most troubling of all, we find that states with larger Black populations eased their social distancing policies more quickly, despite Black Americans' higher exposure to infection from SARS-CoV-2 and subsequent death from COVID-19. © The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association.

ObjectiveThis study aims to identify the individual and contextual factors consistently associated with utilisation of essential maternal and child health services in Nigeria across time and household geolocation.Design, setting and participantsSecondary data from five nationally representative household surveys conducted in Nigeria from 2003 to 2018 were used in this study. The study participants are women and children depending on essential maternal and child health (MCH) services.Outcome measuresThe outcome measures were indicators of whether participants used each of the following essential MCH services: antenatal care, facility-based delivery, modern contraceptive use, childhood immunisations (BCG, diphtheria, tetanus, pertussis/Pentavalent and measles) and treatments of childhood illnesses (fever, cough and diarrhoea).MethodsWe estimated generalised additive models with logit links and smoothing terms for households’ geolocation and survey years.ResultsHigher maternal education and households’ wealth were significantly associated with utilisation of all types of essential MCH services (pConclusionThis study adds to the evidence that maternal education and household wealth status are consistently associated with utilisation of essential MCH services across time and space. To increase utilisation of essential MCH services across different geolocations, interventions targeting poor communities and households with more children under 5 years of age should be appropriately designed. Moreover, additional interventions should prioritise to reduce inequities of essential MCH service utilisation between the wealth quantiles and between education status.

ImportanceWhile much of the attention on the COVID-19 pandemic was directed at the daily counts of cases and those with serious disease overwhelming health services, increasingly, reports have appeared of people who experience debilitating symptoms after the initial infection. This is popularly known as long COVID.ObjectiveTo estimate by country and territory of the number of patients affected by long COVID in 2020 and 2021, the severity of their symptoms and expected pattern of recoveryDesignWe jointly analyzed ten ongoing cohort studies in ten countries for the occurrence of three major symptom clusters of long COVID among representative COVID cases. The defining symptoms of the three clusters (fatigue, cognitive problems, and shortness of breath) are explicitly mentioned in the WHO clinical case definition. For incidence of long COVID, we adopted the minimum duration after infection of three months from the WHO case definition. We pooled data from the contributing studies, two large medical record databases in the United States, and findings from 44 published studies using a Bayesian meta-regression tool. We separately estimated occurrence and pattern of recovery in patients with milder acute infections and those hospitalized. We estimated the incidence and prevalence of long COVID globally and by country in 2020 and 2021 as well as the severity-weighted prevalence using disability weights from the Global Burden of Disease study.ResultsAnalyses are based on detailed information for 1906 community infections and 10526 hospitalized patients from the ten collaborating cohorts, three of which included children. We added published data on 37262 community infections and 9540 hospitalized patients as well as ICD-coded medical record data concerning 1.3 million infections. Globally, in 2020 and 2021, 144.7 million (95% uncertainty interval [UI] 54.8–312.9) people suffered from any of the three symptom clusters of long COVID. This corresponds to 3.69% (1.38–7.96) of all infections. The fatigue, respiratory, and cognitive clusters occurred in 51.0% (16.9–92.4), 60.4% (18.9–89.1), and 35.4% (9.4–75.1) of long COVID cases, respectively. Those with milder acute COVID-19 cases had a quicker estimated recovery (median duration 3.99 months [IQR 3.84–4.20]) than those admitted for the acute infection (median duration 8.84 months [IQR 8.10–9.78]). At twelve months, 15.1% (10.3–21.1) continued to experience long COVID symptoms.Conclusions and relevanceThe occurrence of debilitating ongoing symptoms of COVID-19 is common. Knowing how many people are affected, and for how long, is important to plan for rehabilitative services and support to return to social activities, places of learning, and the workplace when symptoms start to wane.Key PointsQuestionWhat are the extent and nature of the most common long COVID symptoms by country in 2020 and 2021?FindingsGlobally, 144.7 million people experienced one or more of three symptom clusters (fatigue; cognitive problems; and ongoing respiratory problems) of long COVID three months after infection, in 2020 and 2021. Most cases arose from milder infections. At 12 months after infection, 15.1% of these cases had not yet recovered.MeaningThe substantial number of people with long COVID are in need of rehabilitative care and support to transition back into the workplace or education when symptoms start to wane.

Laws restricting foreign funding to domestically operating nongovernmental organizations (NGOs) have proliferated in developing countries. This is puzzling because Western powers support the norm that NGOs are critical for democracy and development, recommend governments partner with NGOs, and sometimes use trade sanctions to encourage adherence to this norm. We examine whether rising trade with China influences the onset of NGO restrictions. China, which has emerged as an important export destination, articulates a different norm of state sovereignty over NGOs and does not sanction developing countries that enact restrictive NGO laws. Analysis of 153 developing countries from 2000 to 2015 finds that increasing exports to China may double the risk of NGO crackdown, but only when accompanied by declining exports to Western democracies. NGO scholars should recognize there are multiple norms about state-NGO relationship and that norm acceptance is influenced by the economic clout of the power that espouses a particular norm.

We use COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 and a deterministic SEIR (susceptible, exposed, infectious and recovered) compartmental framework to model possible trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the effects of non-pharmaceutical interventions in the United States at the state level from 22 September 2020 through 28 February 2021. Using this SEIR model, and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates and mask use per capita), we assessed scenarios of social distancing mandates and levels of mask use. Projections of current non-pharmaceutical intervention strategies by state—with social distancing mandates reinstated when a threshold of 8 deaths per million population is exceeded (reference scenario)—suggest that, cumulatively, 511,373 (469,578–578,347) lives could be lost to COVID-19 across the United States by 28 February 2021. We find that achieving universal mask use (95% mask use in public) could be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Universal mask use could save an additional 129,574 (85,284–170,867) lives from September 22, 2020 through the end of February 2021, or an additional 95,814 (60,731–133,077) lives assuming a lesser adoption of mask wearing (85%), when compared to the reference scenario., A modeling study using case and mortality data from the first 8 months of the COVID-19 pandemic in the United States explores five potential future scenarios of social distancing mandates and mask use at the state level, with projections of the course of the epidemic through winter 2021.

Scholars have debated whether changes in digital environments have led to greater concentration or dispersal of scientific citations, but this debate has paid little attention to how other changes in the publication environment may impact the commonly used measures of inequality. Using Monte Carlo experiments, we demonstrate that a variety of inequality measures—including the Gini coefficient, the Herfindahl-Hirschman index, and the percentage of articles ever cited—are substantially biased downward by increases in the total number of articles and citations. We propose and validate a resampling-based correction for this “marginals bias” and apply this correction to empirical data on scholarly citation distributions using Web of Science data covering four broad scientific fields (health, humanities, mathematics and the computer sciences, and the social sciences) from 1996 to 2014. We find that in each field the bulk of the apparent decline in citation inequality in recent years is an artifact of marginals bias, as are most apparent interfield differences in citation inequality. Researchers using inequality measures to compare citation distributions and other distributions with many cases at or near the zero-bound should interpret these metrics carefully and account for the influence of changing marginals.

Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021.All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification.Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1-19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1-129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71-4·36]), the USA (1·13 million [1·08-1·18]), Russia (1·07 million [1·06-1·08]), Mexico (798 000 [741 000-867 000]), Brazil (792 000 [730 000-847 000]), Indonesia (736 000 [594 000-955 000]), and Pakistan (664 000 [498 000-847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7-378·4] per 100 000) and Mexico (325·1 [301·6-353·3] per 100 000), and was similar in Brazil (186·9 [172·2-199·8] per 100 000) and the USA (179·3 [170·7-187·5] per 100 000).The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic.BillMelinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.

Timely, accurate, and comprehensive estimates of SARS-CoV-2 daily infection rates, cumulative infections, the proportion of the population that has been infected at least once, and the effective reproductive number (RWe produced a comprehensive set of global and location-specific estimates of daily and cumulative SARS-CoV-2 infections through Nov 14, 2021, using data largely from Johns Hopkins University (Baltimore, MD, USA) and national databases for reported cases, hospital admissions, and reported deaths, as well as seroprevalence surveys identified through previous reviews, SeroTracker, and governmental organisations. We corrected these data for known biases such as lags in reporting, accounted for under-reporting of deaths by use of a statistical model of the proportion of excess mortality attributable to SARS-CoV-2, and adjusted seroprevalence surveys for waning antibody sensitivity, vaccinations, and reinfection from SARS-CoV-2 escape variants. We then created an empirical database of infection-detection ratios (IDRs), infection-hospitalisation ratios (IHRs), and infection-fatality ratios (IFRs). To estimate a complete time series for each location, we developed statistical models to predict the IDR, IHR, and IFR by location and day, testing a set of predictors justified through published systematic reviews. Next, we combined three series of estimates of daily infections (cases divided by IDR, hospitalisations divided by IHR, and deaths divided by IFR), into a more robust estimate of daily infections. We then used daily infections to estimate cumulative infections and the cumulative proportion of the population with one or more infections, and we then calculated posterior estimates of cumulative IDR, IHR, and IFR using cumulative infections and the corrected data on reported cases, hospitalisations, and deaths. Finally, we converted daily infections into a historical time series of RGlobal daily SARS-CoV-2 infections fluctuated between 3 million and 17 million new infections per day between April, 2020, and October, 2021, peaking in mid-April, 2021, primarily as a result of surges in India. Between the start of the pandemic and Nov 14, 2021, there were an estimated 3·80 billion (95% uncertainty interval 3·44-4·08) total SARS-CoV-2 infections and reinfections combined, and an estimated 3·39 billion (3·08-3·63) individuals, or 43·9% (39·9-46·9) of the global population, had been infected one or more times. 1·34 billion (1·20-1·49) of these infections occurred in south Asia, the highest among the seven super-regions, although the sub-Saharan Africa super-region had the highest infection rate (79·3 per 100 population [69·0-86·4]). The high-income super-region had the fewest infections (239 million [226-252]), and southeast Asia, east Asia, and Oceania had the lowest infection rate (13·0 per 100 population [8·4-17·7]). The cumulative proportion of the population ever infected varied greatly between countries and territories, with rates higher than 70% in 40 countries and lower than 20% in 39 countries. There was no discernible relationship between RCOVID-19 has already had a staggering impact on the world up to the beginning of the omicron (B.1.1.529) wave, with over 40% of the global population infected at least once by Nov 14, 2021. The vast differences in cumulative proportion of the population infected across locations could help policy makers identify the transmission-prevention strategies that have been most effective, as well as the populations at greatest risk for future infection. This information might also be useful for targeted transmission-prevention interventions, including vaccine prioritisation. Our statistical approach to estimating SARS-CoV-2 infection allows estimates to be updated and disseminated rapidly on the basis of newly available data, which has and will be crucially important for timely COVID-19 research, science, and policy responses.BillMelinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.

Background Before 2020, mental disorders were leading causes of the global health-related burden, with depressive and anxiety disorders being leading contributors to this burden. The emergence of the COVID-19 pandemic has created an environment where many determinants of poor mental health are exacerbated. The need for up-to-date information on the mental health impacts of COVID-19 in a way that informs health system responses is imperative. In this study, we aimed to quantify the impact of the COVID-19 pandemic on the prevalence and burden of major depressive disorder and anxiety disorders globally in 2020. Methods We conducted a systematic review of data reporting the prevalence of major depressive disorder and anxiety disorders during the COVID-19 pandemic and published between Jan 1, 2020, and Jan 29, 2021. We searched PubMed, Google Scholar, preprint servers, grey literature sources, and consulted experts. Eligible studies reported prevalence of depressive or anxiety disorders that were representative of the general population during the COVID-19 pandemic and had a pre-pandemic baseline. We used the assembled data in a meta-regression to estimate change in the prevalence of major depressive disorder and anxiety disorders between pre-pandemic and mid-pandemic (using periods as defined by each study) via COVID-19 impact indicators (human mobility, daily SARS-CoV-2 infection rate, and daily excess mortality rate). We then used this model to estimate the change from pre-pandemic prevalence (estimated using Disease Modelling Meta-Regression version 2.1 [known as DisMod-MR 2.1]) by age, sex, and location. We used final prevalence estimates and disability weights to estimate years lived with disability and disability-adjusted life-years (DALYs) for major depressive disorder and anxiety disorders. Findings We identified 5683 unique data sources, of which 48 met inclusion criteria (46 studies met criteria for major depressive disorder and 27 for anxiety disorders). Two COVID-19 impact indicators, specifically daily SARS-CoV-2 infection rates and reductions in human mobility, were associated with increased prevalence of major depressive disorder (regression coefficient [B] 0·9 [95% uncertainty interval 0·1 to 1·8; p=0·029] for human mobility, 18·1 [7·9 to 28·3; p=0·0005] for daily SARS-CoV-2 infection) and anxiety disorders (0·9 [0·1 to 1·7; p=0·022] and 13·8 [10·7 to 17·0; p

Background National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics. Methods Daily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates. Findings The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5·4% [4·0–7·9] of variation), GDP per capita (4·2% [1·8–6·6] of variation), and the proportion of infections attributable to seasonality (2·1% [95% uncertainty interval 1·7–2·7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46·7% [18·4–67·6] of variation), GDP per capita (3·1% [0·3–8·6] of variation), and national mean BMI (1·1% [0·2–2·6] of variation). 44·4% (29·2–61·7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as well as less government corruption, were also associated with higher COVID-19 vaccine coverage among middle-income and high-income countries where vaccine availability was more widespread, and lower corruption was associated with greater reductions in mobility. If these modelled associations were to be causal, an increase in trust of governments such that all countries had societies that attained at least the amount of trust in government or interpersonal trust measured in Denmark, which is in the 75th percentile across these spectrums, might have reduced global infections by 12·9% (5·7–17·8) for government trust and 40·3% (24·3–51·4) for interpersonal trust. Similarly, if all countries had a national BMI equal to or less than that of the 25th percentile, our analysis suggests global standardised IFR would be reduced by 11·1%. Interpretation Efforts to improve pandemic preparedness and response for the next pandemic might benefit from greater investment in risk communication and community engagement strategies to boost the confidence that individuals have in public health guidance. Our results suggest that increasing health promotion for key modifiable risks is associated with a reduction of fatalities in such a scenario. Funding Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

Context: Social distancing is an essential but economically painful measure to flatten the curve of emergent infectious diseases. As the novel coronavirus that causes COVID-19 spread throughout the United States in early 2020, the federal government left to the states the difficult and consequential decisions about when to cancel events, close schools and businesses, and issue stay-at-home orders.Methods: The authors present an original, detailed dataset of state-level social distancing policy responses to the epidemic; they then apply event history analysis to study the timing of implementation of five social distancing policies across all 50 states.Results: The most important predictor of when states adopted social distancing policies is political: all else equal, states led by Republican governors were slower to implement such policies during a critical window of early COVID-19 response.Conclusions: Continuing actions driven by partisanship rather than by public health expertise and scientific recommendations may exact greater tolls on health and broader society.

Environmental clubs have proliferated across sectors and issue areas. We examine the diffusion of the chemical industry’s Responsible Care® (RC) program. Much of the work on the diffusion of clubs has focused on the demand side: why firms join these clubs despite the costs of doing so. There is some work focusing on the supply side: why actors establish or create a new club. However, there is virtually no work examining why national-level industry associations decide to subscribe to an existing global environmental club in order to make it available to their members. Industry organizations in 17 lower and middle-income countries have joined RC, comprising 25 percent of RC members. We ask, in the context of developing countries, what motivates national associations to join RC? Drawing on an original dataset of RC global diffusion in 195 countries (1985–2017), we estimate a Cox proportional hazards model of the risk of joining RC. We find that RC adoption is more likely when a country exports chemicals to other countries that have joined RC (the California effect) and is unaffected by the total volume of its chemical trade. Thus, while exposure to global markets per se may not influence RC adoption, incentives change considerably when countries’ key importers signal their support for these environmental practices. This is because importing firms often realize that because they have joined Responsible Care, NGOs and stakeholders expect them to demand that their overseas suppliers adopt the same sort of environmental policies and work place safety practices. In addition, peer pressure and learning matter: RC adoption is more likely when countries in close physical vicinity (e.g., within 500 miles) have joined the club. Finally, domestic factors play a role as well: both the level of democracy and the size of the economy encourage national associations to join RC.

Because the American states operate under balanced budget requirements, increases in spending in one area typically entail equal and opposite budget cuts in other programs. The literature analysing the correlates of government spending by policy area has mostly ignored these trade-offs inherent to policymaking, failing to address one of the most politically interesting and important dimensions of fiscal policy. Borrowing from the statistical literature on compositional data, we present more appropriate and efficient methods that explicitly incorporate the budget constraint into models of spending by budget category. We apply these methods to eight categories of spending from the American states over the years 1984–2009 to reveal winners and losers in the scramble for government spending. Our findings show that partisan governments finance their distinct priorities by raiding spending items that the opposition prefers, while different political institutions, economic conditions and state demographics impose different trade-offs across the budget.

Summary To investigate whether Africa’s exports to China influence labor practices in Africa, we reconsider the debate over trade’s influence on regulatory standards in exporting countries. The first generation of trade–regulation scholars asked whether high levels of exports influenced regulatory standards of exporting countries, with inconclusive results. The second generation of scholarship focused not on how much a country exported but to whom it exported, identifying a “California Effect” by which firms and consumers in (mostly developed) importing countries projected their high regulatory standards on less developed export partners. Structural change—especially the rise of China as a major importer—poses a challenge to these optimistic findings. Drawing on insights from the analysis of compositional data, this paper introduces a third generation of trade–regulation research, which suggests examining not only with whom a country trades, but also how the composition of markets in a country’s export basket reshuffles over time. Specifically, we explore the possibility of a “Shanghai Effect” whereby African countries begin to reflect the lower labor standards of China, which has emerged as a major destination for their exports. We show that when a country increases exports to China, the net effect on domestic labor standards depends critically on the labor practices of other export destinations compositionally displaced by China exports. Our analysis of a panel of 49 African countries for the period 1985–2010 produces a small continent-wide estimate of China’s negative influence on African labor practices. In-sample simulation at the country level uncovers a moderate Shanghai Effect for a handful of countries only.

Background Haiti has one of the world's highest maternal mortality ratios. Comprehensive obstetric services could prevent many of these deaths, though most births in Haiti occur outside health facilities. Demand-side factors like a mother's socioeconomic status are understood to affect her access or choice to deliver in a health facility. However, analyses of the role of supply-side factors like health facility readiness have been constrained by limited data and methodological challenges. We sought to address these challenges and determine whether Haiti could increase rates of facility-based birth by improving facility readiness to provide delivery services. # Methods Our task was to characterize facility delivery readiness and link it to nearby births. We used birth data from the 2012 Haiti DHS and facility data from the 2013 Haiti SPA. Our outcome of interest was facility-based birth. Our predictor of interest was delivery readiness at the DHS sampling cluster level. We derived a novel likelihood function that used Kernel Density Estimation to estimate cluster-level readiness alongside the coefficients of a logistic regression. # Results We analyzed data from 389 facilities and 1,991 births. Rural facilities were less ready than urban facilities to provide delivery services. Women delivering in health facilities were younger, more educated, wealthier, less likely to live in rural areas, and had fewer previous children. Our model estimated that rural facilities (bandwidth (h)=12.28, standard error (SE)=0.16) spread their readiness over larger areas than urban facilities (h=7.14, SE=0.016). Cluster-level readiness was strongly associated with facility-based birth (adjusted log-odds=0.031; *P*=0.005), as was socioeconomic status (adjusted log-odds=0.78; *P*\

Objectives To describe changes in the organizational structure of state health-related departments/agencies between 1990 and 2009; to identify factors associated with key organizational structures; and to investigate their relationship with different resource allocations across health policy areas, as represented by state budgets. Design Original data collection on the organization of state health-related departments/agencies from 1990 to 2009. Analyses included descriptive statistics, logistic regression, and time-series regression modeling. Setting and participants All 50 states. Main outcomes measures Organizational structure of state government related to health in 4 areas (Medicaid, public health, mental health, human services); coupling of Medicaid and public health in the same agency; state budget changes in health policy areas, including Medicaid, public health, and hospitals. Results The housing of 2 or more health-related functions in the same unit was common, with 21 states combining public health and Medicaid at 1 or more points in time. Eighteen states (36%) reorganized their health agencies/departments during the study period. Controlling for numerous economic, social, and political factors, when the state agency responsible for public health is consolidated with Medicaid, the share of the state budget allocated to Medicaid declined significantly, while public health allocations were unchanged. However, consolidating Medicaid with other services did not impact state Medicaid spending. Conclusions Government organizational structure related to health varies greatly across states and is somewhat dynamic. When Medicaid and public health functions are consolidated in the same stage agency, public health does not "lose" in terms of its share of the state budget. However, this could change as Medicaid costs continue to grow and with the implementation the Patient Protection and Affordable Care Act of 2010.

Although many study the effects of different allocations of health policy authority, few ask why countries assign responsibility over different policies as they do. We test two broad theories: fiscal federalism, which predicts rational governments will concentrate information-intensive operations at lower levels, and redistributive and regulatory functions at higher levels; and “politicized federalism”, which suggests a combination of systematic and historically idiosyncratic political variables interfere with efficient allocation of authority. Drawing on the WHO Health in Transition country profiles, we present new data on the allocation of responsibility for key health care policy tasks (implementation, provision, finance, regulation, and framework legislation) and policy areas (primary, secondary and tertiary care, public health and pharmaceuticals) in the 27 EU member states and Switzerland. We use a Bayesian multinomial mixed logit model to analyze how different countries arrive at different allocations of authority over each task and area of health policy, and find the allocation of powers broadly follows fiscal federalism. Responsibility for pharmaceuticals, framework legislation, and most finance lodges at the highest levels of government, acute and primary care in the regions, and provision at the local and regional levels. Where allocation does not follow fiscal federalism, it appears to reflect ethnic divisions, the population of states and regions, the presence of mountainous terrain, and the timing of region creation.

Spectacular economic growth in China suggests the ruling Chinese Communist Party (CCP) has somehow gotten it right. A key hypothesis in both economics and political science is that the CCP's cadre evaluation system, combined with China's geography-based governing logic, has motivated local administrators to compete with one another to generate high growth. We raise a number of theoretical and empirical challenges to this claim. Using a new biographical database of Central Committee members, a previously overlooked feature of CCP reporting, and a novel Bayesian method that can estimate individual-level correlates of partially observed ranks, we find no evidence that strong growth performance was rewarded with higher party ranks at any of the postreform party congresses. Instead, factional ties with various top leaders, educational qualifications, and provincial revenue collection played substantial roles in elite ranking, suggesting that promotion systems served the immediate needs of the regime and its leaders, rather than encompassing goals such as economic growth.

What effects do interest groups have on the democratization and legitimacy of the European Union (EU)? Interest groups can democratize the EU only to the extent that they do not replicate inequalities. We use a newly constructed database to look for inequalities: Are the big organizations in Brussels the same as the ones in the EU member states? Are some member states' lobbies more active than others? And does the structure of EU lobbying create insiders and outsiders itself? We find representative biases in favor of powerful incumbents, groups from some member states and wellresourced groups.

We take this opportunity to comment on Herron and Shotts (2003; hereinafter HS) because of its interesting and productive ideas and because of the potential to affect the way a considerable body of practical research is conducted. This article, and the literature referenced therein, is based on the suggestions in three paragraphs in King (1997, pp. 289–290). Because these paragraphs were not summarized in HS, we thought they might be a useful place to start.

Since Herron and Shotts (2003a; hereinafter HS), Adolph and King (2003; hereinafter AK), and Herron and Shotts (2003b; hereinafter HS2), the four of us have iterated many more times, learned a great deal, and arrived at a consensus on this issue. This paper describes our joint recommendations for how to run second-stage ecological regressions, and provides detailed analyses to back up our claims. Our research applies to the problem of estimating how the unobserved parameters βb i and β i (e.g., the fraction of blacks and whites, respectively, who vote in precinct i) vary with observed exogenous variables Zi (e.g., campaign spending in precinct i), using two other observed variables, Xi (e.g., the proportion of people in the precinct who are black) and Ti (e.g., the proportion of people who vote), and using King’s (1997) ecological inference model (popularly known as EI). We offer three main conclusions. First, the best approach is to use the extended EI model for estimation (i.e., to include Zi in the model from the start and estimate all parameters jointly). Second, in most applications, the best way to use the extended model is not to look at the coefficients on Zi in EI’s systematic component, as some have done, but instead to condition on the precinct-level data in a way we describe later. Third, point estimates from weighted least squares (WLS) second-stage regressions are useful because they have substantively negligible bias in a diverse variety of situations (knowable in advance from the observed data), even if Zi is omitted from the first-stage ecological inference and even when a second-stage analysis based on least squares (LS) would be biased.

Most studies of the political economy of money focus on the laws protecting central banks from government interference; this book turns to the overlooked people who actually make monetary policy decisions. Using formal theory and statistical evidence from dozens of central banks across the developed and developing worlds, this book shows that monetary policy agents are not all the same. Molded by specific professional and sectoral backgrounds and driven by career concerns, central bankers with different career trajectories choose predictably different monetary policies. These differences undermine the widespread belief that central bank independence is a neutral solution for macroeconomic management. Instead, through careful selection and retention of central bankers, partisan governments can and do influence monetary policy - preserving a political trade-off between inflation and real economic performance even in an age of legally independent central banks.