IMPOSING REGULARITY CONDITIONS ON A SYSTEM OF COST AND FACTOR SHARE EQUATIONS

Systems of equations comprising cost functions and first-order derivative equations are often used to estimate characteristics of production technologies. Unfortunately, many estimated systems violate the regularity conditions implied by economic theory. Sampling theory methods can be used to impose these conditions globally, but these methods destroy the flexibility properties of most functional forms. We demonstrate how Bayesian methods can be used to maintain flexibility by imposing regularity conditions locally. The Bayesian approach is used to estimate a system of cost and share equations for the merino-woolgrowing sector. The effect of local imposition of monotonicity and concavity on the signs and magnitudes of elasticities is examined.Cost functions, Markov Chain Monte Carlo, inequality constraints, Demand and Price Analysis,

Imposing regularity conditions on a system of cost and factor share equations

Systems of equations comprising cost functions and first‐order derivative equations are often used to estimate characteristics of production technologies. Unfortunately, many estimated systems violate the regularity conditions implied by economic theory. Sampling theory methods can be used to impose these conditions globally, but these methods destroy the flexibility properties of most functional forms. We demonstrate how Bayesian methods can be used to maintain flexibility by imposing regularity conditions locally. The Bayesian approach is used to estimate a system of cost and share equations for the merino wool‐growing sector. The effect of local imposition of monotonicity and concavity on the signs and magnitudes of elasticities is examined.Resource /Energy Economics and Policy,

FAMILY SAVINGS OF THE WORKING POPULATION IN MINDANAO, PHILIPPINES

The study was conducted to understand the saving behavior of the working population in Mindanao using the Family Income and Expenditure Survey (FIES) datasets covering the periods 2003 to 2015. The study determined the factors which influence the families to save using Tobit regression and compared the levels of family income, expenditures, and savings of the regions of Mindanao. The study revealed that the real value of family savings increases as the family head gets older, but it eventually declines as the head nears retirement age. Tobit estimates revealed that in Mindanao, the age of the family head, household tenure, wages from agricultural and non-agricultural activities, income from entrepreneurial activities and cash assistance from abroad positively influence families to save; while sex of the family head, educational attainment, and the number of young dependents negatively influence the families to save. Moreover, the family size, marital status and geospatial location of the family heads have shown both positive and negative influences on family savings for different years. Meanwhile, the ANOVA results revealed that in 2015 there were significant variations between and among the income, expenditure, and savings of families in all regions of Mindanao. However, the Tukey-Kramer HSD test revealed that family savings in some regions in Mindanao have statistically the same means such as the Zamboanga Peninsula and CARAGA, Northern Mindanao and Davao Region, and SOCCKSARGEN and ARMM, while other regions vary significantly from each other. Overall, the ANOVA results revealed that for 2015 the Davao Region has the highest mean family income and expenditure, while Northern Mindanao recorded the highest mean family savings. The ARMM region showed the least value of mean income, mean savings and mean expenditures of families among the regions of Mindanao. JEL: C34, D14, D15, E21  Article visualizations

Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990-2021: findings from the Global Burden of Disease Study 2021

Background Anaemia is a major health problem worldwide. Global estimates of anaemia burden are crucial for developing appropriate interventions to meet current international targets for disease mitigation. We describe the prevalence, years lived with disability, and trends of anaemia and its underlying causes in 204 countries and territories. Methods We estimated population-level distributions of haemoglobin concentration by age and sex for each location from 1990 to 2021. We then calculated anaemia burden by severity and associated years lived with disability (YLDs). With data on prevalence of the causes of anaemia and associated cause-specific shifts in haemoglobin concentrations, we modelled the proportion of anaemia attributed to 37 underlying causes for all locations, years, and demographics in the Global Burden of Disease Study 2021. Findings In 2021, the global prevalence of anaemia across all ages was 24·3% (95% uncertainty interval [UI] 23·9–24·7), corresponding to 1·92 billion (1·89–1·95) prevalent cases, compared with a prevalence of 28·2% (27·8–28·5) and 1·50 billion (1·48–1·52) prevalent cases in 1990. Large variations were observed in anaemia burden by age, sex, and geography, with children younger than 5 years, women, and countries in sub-Saharan Africa and south Asia being particularly affected. Anaemia caused 52·0 million (35·1–75·1) YLDs in 2021, and the YLD rate due to anaemia declined with increasing Socio-demographic Index. The most common causes of anaemia YLDs in 2021 were dietary iron deficiency (cause-specific anaemia YLD rate per 100 000 population: 422·4 [95% UI 286·1–612·9]), haemoglobinopathies and haemolytic anaemias (89·0 [58·2–123·7]), and other neglected tropical diseases (36·3 [24·4–52·8]), collectively accounting for 84·7% (84·1–85·2) of anaemia YLDs. Interpretation Anaemia remains a substantial global health challenge, with persistent disparities according to age, sex, and geography. Estimates of cause-specific anaemia burden can be used to design locally relevant health interventions aimed at improving anaemia management and prevention. Funding Bill & Melinda Gates Foundation

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Imposing regularity conditions on a system of cost and cost-share equations: a Bayesian approach

Systems of equations comprising cost functions and first-order derivative equations are often used to estimate characteristics of production technologies. Unfortunately, many estimated systems violate the regularity conditions implied by economic theory. Sampling theory methods can be used to impose these conditions globally, but these methods destroy the flexibility properties of most functional forms. We demonstrate how Bayesian methods can be used to maintain flexibility by imposing regularity conditions locally. The Bayesian approach is used to estimate a system of cost and share equations for the merino wool-growing sector. The effect of local imposition of monotonicity and concavity on the signs and magnitudes of elasticities is examined

IMPOSING REGULARITY CONDITIONS ON A SYSTEM OF COST AND FACTOR SHARE EQUATIONS

Systems of equations comprising cost functions and first-order derivative equations are often used to estimate characteristics of production technologies. Unfortunately, many estimated systems violate the regularity conditions implied by economic theory. Sampling theory methods can be used to impose these conditions globally, but these methods destroy the flexibility properties of most functional forms. We demonstrate how Bayesian methods can be used to maintain flexibility by imposing regularity conditions locally. The Bayesian approach is used to estimate a system of cost and share equations for the merino-woolgrowing sector. The effect of local imposition of monotonicity and concavity on the signs and magnitudes of elasticities is examined

Imposing Regularity Conditions on a System of Cost and Cost-Share Equations: A Bayesian Approach

A problem with the use of flexible functional forms is that the estimated functions frequently violate the regularity conditions (eg, monotonicity, concavity) implied by economic theory. Sampling theory methods exist for imposing curvature conditions at all non-negative prices, but such methods may significantly reduce the flexibility properties of the functional forms. Bayesian methods can be used to maintain these flexibility properties, by imposing regularity condtions at a point, at several points, or over the region within the inferences will be drawn. We use this Bayesian approach to estimate a system of cost and input cost-share equationsfor the Australian merino-woolgrowing sector. The imposition of both monotonicity and concavity constraints at several sets of prices leads to sign reversals and significant changes in the magnitudes of a small number of estimated input-price elasticities

Imposing regularity conditions on a system of cost and factor share equations

Systems of equations comprising cost functions and first‐order derivative equations are often used to estimate characteristics of production technologies. Unfortunately, many estimated systems violate the regularity conditions implied by economic theory. Sampling theory methods can be used to impose these conditions globally, but these methods destroy the flexibility properties of most functional forms. We demonstrate how Bayesian methods can be used to maintain flexibility by imposing regularity conditions locally. The Bayesian approach is used to estimate a system of cost and share equations for the merino wool‐growing sector. The effect of local imposition of monotonicity and concavity on the signs and magnitudes of elasticities is examined